diff --git a/.github/workflows/deploy_gh-pages.yml b/.github/workflows/deploy_gh-pages.yml index 01b50e123..d928c3605 100644 --- a/.github/workflows/deploy_gh-pages.yml +++ b/.github/workflows/deploy_gh-pages.yml @@ -22,7 +22,7 @@ jobs: cache-dependency-path: ./docs/package-lock.json - name: Install dependencies - run: cd docs && npm install + run: cd docs && npm install --legacy-peer-deps - name: Build website run: cd docs && npm run build diff --git a/.github/workflows/docs_test.yml b/.github/workflows/docs_test.yml index e0adfda80..0298c30fa 100644 --- a/.github/workflows/docs_test.yml +++ b/.github/workflows/docs_test.yml @@ -40,7 +40,7 @@ jobs: - name: Install Node.js dependencies run: | cd docs - npm install + npm install --legacy-peer-deps if: ${{ steps.setup-node.outputs.cache-hit != 'true' }} - name: Build Docs diff --git a/.github/workflows/fetch_docs_notion.yml b/.github/workflows/fetch_docs_notion.yml new file mode 100644 index 000000000..56004d739 --- /dev/null +++ b/.github/workflows/fetch_docs_notion.yml @@ -0,0 +1,77 @@ +name: Fetch Docs from Notion + +on: + workflow_dispatch: + +env: + NODE_VERSION: "21" + +jobs: + fetch-docs: + name: Fetch Docs from Notion + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Setup Node.js + uses: actions/setup-node@v4 + id: setup-node + with: + node-version: ${{ env.NODE_VERSION }} + + - name: Configure git + run: | + git config --global user.name 'github-actions[bot]' + git config --global user.email 'github-actions[bot]@users.noreply.github.com' + + - name: Create new branch + run: | + git checkout -b update-docs-$(date +%Y%m%d%H%M%S) + + - name: Cache Node.js dependencies + uses: actions/cache@v4 + id: npm-cache + with: + path: ~/.npm + key: ${{ runner.os }}-node-${{ hashFiles('docs/package-lock.json') }} + restore-keys: | + ${{ runner.os }}-node- + + - name: Install Node.js dependencies + run: | + cd docs + npm install --legacy-peer-deps + if: ${{ steps.setup-node.outputs.cache-hit != 'true' }} + + - name: Fetch Docs from Notion + run: | + cd docs + npm run pull + env: + NOTION_TOKEN: ${{ secrets.NOTION_TOKEN }} + NOTION_DOCS_ROOT_PAGE_ID: ${{ secrets.NOTION_DOCS_ROOT_PAGE_ID }} + + - name: Commit changes + run: | + git add . + git commit -m "Update docs from Notion" + + - name: Push changes + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + git push --set-upstream origin $(git rev-parse --abbrev-ref HEAD) + + - name: Create Pull Request + id: create_pr + uses: peter-evans/create-pull-request@v5 + with: + token: ${{ secrets.GITHUB_TOKEN }} + commit-message: Update docs from Notion + branch: update-docs-$(date +%Y%m%d%H%M%S) + base: main + title: "docs: update docs from notion" + body: This PR updates the documentation from Notion. + labels: documentation diff --git a/docs/.gitignore b/docs/.gitignore new file mode 100644 index 000000000..5977fb006 --- /dev/null +++ b/docs/.gitignore @@ -0,0 +1,23 @@ +# Dependencies +/node_modules + +# Production +/build + +# Generated files +.docusaurus +.cache-loader + +# Misc +.DS_Store +.env.local +.env.development.local +.env.test.local +.env.production.local + +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +static +*.orig diff --git a/docs/src/css/custom.css b/docs/css/custom.css similarity index 100% rename from docs/src/css/custom.css rename to docs/css/custom.css diff --git a/docs/css/docu-notion-styles.css b/docs/css/docu-notion-styles.css new file mode 100644 index 000000000..6332fd236 --- /dev/null +++ b/docs/css/docu-notion-styles.css @@ -0,0 +1,60 @@ +/* This should be added to the docusaurus.config.js in order to show some notion things correctly. +See the option: --css-output-directory +See the docusaurus docs: https://docusaurus.io/docs/styling-layout +See the use in the docu-notion-sample-site: https://github.com/sillsdev/docu-notion-sample-site/blob/main/docusaurus.config.js +*/ + +/* Copied from + https://github1s.com/NotionX/react-notion-x/blob/master/packages/react-notion-x/src/styles.css#L934 + and + https://github1s.com/NotionX/react-notion-x/blob/master/packages/react-notion-x/src/styles.css#L1063 +*/ +.notion-column { + display: flex; + flex-direction: column; + padding-top: 12px; + padding-bottom: 12px; +} + +.notion-column > *:first-child { + margin-top: 0; + margin-left: 0; + margin-right: 0; +} + +.notion-column > *:last-child { + margin-left: 0; + margin-right: 0; + margin-bottom: 0; +} + +.notion-row { + display: flex; + overflow: hidden; + width: 100%; + max-width: 100%; +} + +@media (max-width: 640px) { + .notion-row { + flex-direction: column; + } + + .notion-row .notion-column { + width: 100% !important; + } + + .notion-row .notion-spacer { + display: none; + } +} + +.notion-spacer { + /* This matches the value in ColumnTransformer.ts */ + width: calc(min(32px, 4vw)); +} + +.notion-spacer:last-child { + display: none; +} +/* End copied from NotionX */ diff --git a/docs/css/gifplayer.css b/docs/css/gifplayer.css new file mode 100644 index 000000000..120b9b389 --- /dev/null +++ b/docs/css/gifplayer.css @@ -0,0 +1,36 @@ +.gif_player { + display: inline-block; + position: relative; + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; + -webkit-touch-callout: none; + -webkit-tap-highlight-color: transparent; } + .gif_player .play_button { + background-color: rgba(0, 0, 0, 0.5); + border: 2px dashed #fff; + border-radius: 50%; + box-shadow: 0 0 0 3px rgba(0, 0, 0, 0.5); + color: #fff; + cursor: pointer; + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; + font-size: 24px; + left: 50%; + opacity: 1; + padding: 14px 10px; + position: absolute; + top: 50%; + transform: translate(-50%, -50%) scale(1) rotate(0deg); + transition: transform 0.4s, opacity 0.4s; } + .gif_player .play_button:hover { + background-color: rgba(0, 0, 0, 0.7); + box-shadow: 0 0 0 3px rgba(0, 0, 0, 0.7); } + .gif_player .play_button::after { + content: "GIF"; } + .gif_player.playing .play_button { + transform: translate(-50%, -50%) scale(0) rotate(180deg); + opacity: 0.5; } + .gif_player img { + max-width: 100%; } + diff --git a/docs/docs/Components/1028644105.png b/docs/docs/Components/1028644105.png new file mode 100644 index 000000000..8ae649963 Binary files /dev/null and b/docs/docs/Components/1028644105.png differ diff --git a/docs/docs/Components/238089171.png b/docs/docs/Components/238089171.png new file mode 100644 index 000000000..cd1ab72ae Binary files /dev/null and b/docs/docs/Components/238089171.png differ diff --git a/docs/docs/Components/241280398.png b/docs/docs/Components/241280398.png new file mode 100644 index 000000000..27e83c969 Binary files /dev/null and b/docs/docs/Components/241280398.png differ diff --git a/docs/docs/Components/263391508.png b/docs/docs/Components/263391508.png new file mode 100644 index 000000000..1c15466c2 Binary files /dev/null and b/docs/docs/Components/263391508.png differ diff --git a/docs/docs/Components/565424296.png b/docs/docs/Components/565424296.png new file mode 100644 index 000000000..b3a3cb822 Binary files /dev/null and b/docs/docs/Components/565424296.png differ diff --git a/docs/docs/Components/938852908.png b/docs/docs/Components/938852908.png new file mode 100644 index 000000000..6e7bbde2f Binary files /dev/null and b/docs/docs/Components/938852908.png differ diff --git a/docs/static/img/inspect-output.png b/docs/docs/Components/987204819.png similarity index 100% rename from docs/static/img/inspect-output.png rename to docs/docs/Components/987204819.png diff --git a/docs/docs/Components/_category_.json b/docs/docs/Components/_category_.json new file mode 100644 index 000000000..fdcf11823 --- /dev/null +++ b/docs/docs/Components/_category_.json @@ -0,0 +1 @@ +{"position":5, "label":"Components"} \ No newline at end of file diff --git a/docs/docs/Components/components-custom-components.md b/docs/docs/Components/components-custom-components.md new file mode 100644 index 000000000..167cef9ec --- /dev/null +++ b/docs/docs/Components/components-custom-components.md @@ -0,0 +1,456 @@ +--- +title: Custom Components +sidebar_position: 8 +slug: /components-custom-components +--- + + + +Langflow components can be created from within the platform, allowing users to extend the platform's functionality using Python code. They encapsulate are designed to be independent units, reusable across different workflows. + + +These components can be easily connected within a language model pipeline, adding freedom and flexibility to what can be included in between user and AI messages. + + +![](./238089171.png) + + +Since Langflow operates with Python behind the scenes, you can implement any Python function within a Custom Component. This means you can leverage the power of libraries such as Pandas, Scikit-learn, Numpy, and thousands of packages to create components that handle data processing in unlimited ways. + + +Custom Components are not just about extending functionality; they also streamline the development process. By creating reusable and configurable components, you can enhance the capabilities of Langflow, making it a powerful tool for developing complex workflows. + + +### Key Characteristics: {#d3a151089a9e4584bd420461cd1432c6} + +1. **Modular and Reusable**: Designed as independent units, components encapsulate specific functionality, making them reusable across different projects and workflows. +2. **Integration with Python Libraries**: You can import libraries like Pandas, Scikit-learn, Numpy, etc., to build components that handle data processing, machine learning, numerical computations, and more. +3. **Flexible Inputs and Outputs**: While Langflow offers native input and output types, you can use any type as long as they are properly annotated in the output methods (e.g., `> list[int]`). +4. **Python-Powered**: Since Langflow operates with Python behind the scenes, any Python function can be implemented within a custom component. +5. **Enhanced Workflow**: Custom components serve as reusable building blocks, enabling you to create pre-processing visual blocks with ease and integrate them into your language model pipeline. + +### Why Use Custom Components? {#827a2b5acec94426a4a2106a8332622d} + +- **Customization**: Tailor the functionality to your specific needs by writing Python code that suits your workflow. +- **Flexibility**: Add any Python-based logic or processing step between user/AI messages, enhancing the flexibility of Langflow. +- **Efficiency**: Streamline your development process by creating reusable, configurable components that can be easily deployed. + +### How to Write Them {#2088ade519514bb3923cdf7f2ac2089a} + + +--- + + +Writing custom components in Langflow involves creating a Python class that defines the component's functionality, inputs, and outputs. The process involves a few key steps: + +1. **Define the Class**: Start by defining a Python class that inherits from `Component`. This class will encapsulate the functionality of your custom component. +2. **Specify Inputs and Outputs**: Use Langflow's input and output classes to define the inputs and outputs of your component. They should be declared as class attributes. +3. **Implement Output Methods**: Implement methods for each output, which contains the logic of your component. These methods can access input values using `self.` , return processed values and define what to be displayed in the component with the `self.status` attribute. +4. **Use Proper Annotations**: Ensure that output methods are properly annotated with their types. Langflow uses these annotations to validate and handle data correctly. + +Here's a basic structure of a custom component: + + +```python +from langflow.custom import Component +from langflow.inputs import StrInput, IntInput +from langflow.template import Output + +class MyCustomComponent(Component): + icon = "coffee" # check lucide.dev/icons or pass an emoji + + inputs = [ + StrInput( + name="input_text", + display_name="Input Text", + info="Text to be processed.", + ), + IntInput( + name="input_number", + display_name="Input Number", + info="Number to be processed.", + ), + ] + + outputs = [ + Output(display_name="Processed Text", name="processed_text", method="process_text"), + ] + + def process_text(self) -> str: + input_text = self.input_text + input_number = self.input_number + # Implement your logic here + processed_text = f"{input_text} processed with number {input_number}" + self.status = processed_text + return processed_text + + +``` + + +Paste that code into the Custom Component code snippet and click **Check & Save.** + + +![](./1028644105.png) + + +You should see something like the component below. Double click the name or description areas to edit them. + + +![](./241280398.png) + + +## Input Types {#3815589831f24ab792328ed233c8b00d} + + +--- + + +Langflow provides several higher-level input types to simplify the creation of custom components. These input types standardize how inputs are defined, validated, and used. Here’s a guide on how to use these inputs and their primary purposes: + + +### **HandleInput** {#fb06c48a326043ffa46badc1ab3ba467} + + +Represents an input that has a handle to a specific type (e.g., `BaseLanguageModel`, `BaseRetriever`, etc.). + +- **Usage:** Useful for connecting to specific component types in a flow. + +### **DataInput** {#0e1dcb768e38487180d720b0884a90f5} + + +Represents an input that receives a `Data` object. + +- **Usage:** Ideal for components that process or manipulate data objects. +- **Input Types:** `["Data"]` + +### **StrInput** {#4ec6e68ad9ab4cd194e8e607bc5b3411} + + +Represents a standard string input field. + +- **Usage:** Used for any text input where the user needs to provide a string. +- **Input Types:** `["Text"]` + +### **MessageInput** {#9292ac0105e14177af5eff2131b9c71b} + + +Represents an input field specifically for `Message` objects. + +- **Usage:** Used in components that handle or process messages. +- **Input Types:** `["Message"]` + +### **MessageTextInput** {#5511f5e32b944b4e973379a6bd5405e4} + + +Represents a text input for messages. + +- **Usage:** Suitable for components that need to extract text from message objects. +- **Input Types:** `["Message"]` + +### **MultilineInput** {#e6d8315b0fb44a2fb8c62c3f3184bbe9} + + +Represents a text field that supports multiple lines. + +- **Usage:** Ideal for longer text inputs where the user might need to write extended text. +- **Input Types:** `["Text"]` +- **Attributes:** `multiline=True` + +### **SecretStrInput** {#2283c13aa5f745b8b0009f7d40e59419} + + +Represents a password input field. + +- **Usage:** Used for sensitive text inputs where the input should be hidden (e.g., passwords, API keys). +- **Attributes:** `password=True` +- **Input Types:** Does not accept input types, meaning it has no input handles for previous nodes/components to connect to it. + +### **IntInput** {#612680db6578451daef695bd19827a56} + + +Represents an integer input field. + +- **Usage:** Used for numeric inputs where the value should be an integer. +- **Input Types:** `["Integer"]` + +### **FloatInput** {#a15e1fdae15b49fc9bfbf38f8bd7b203} + + +Represents a float input field. + +- **Usage:** Used for numeric inputs where the value should be a floating-point number. +- **Input Types:** `["Float"]` + +### **BoolInput** {#3083671e0e7f4390a03396485114be66} + + +Represents a boolean input field. + +- **Usage:** Used for true/false or yes/no type inputs. +- **Input Types:** `["Boolean"]` + +### **NestedDictInput** {#2866fc4018e743d8a45afde53f1e57be} + + +Represents an input field for nested dictionaries. + +- **Usage:** Used for more complex data structures where the input needs to be a dictionary. +- **Input Types:** `["NestedDict"]` + +### **DictInput** {#daa2c2398f694ec199b425e2ed4bcf93} + + +Represents an input field for dictionaries. + +- **Usage:** Suitable for inputs that require a dictionary format. +- **Input Types:** `["Dict"]` + +### **DropdownInput** {#14dcdef11bab4d3f8127eaf2e36a77b9} + + +Represents a dropdown input field. + +- **Usage:** Used where the user needs to select from a predefined list of options. +- **Attributes:** `options` to define the list of selectable options. +- **Input Types:** `["Text"]` + +### **FileInput** {#73e6377dc5f446f39517a558a1291410} + + +Represents a file input field. + +- **Usage:** Used to upload files. +- **Attributes:** `file_types` to specify the types of files that can be uploaded. +- **Input Types:** `["File"]` + +Here is an example of how these inputs can be defined in a custom component: + + +```python +from langflow.custom import Component +from langflow.inputs import StrInput, MultilineInput, SecretStrInput, IntInput, DropdownInput +from langflow.template import Output, Input + +class MyCustomComponent(Component): + display_name = "My Custom Component" + description = "An example of a custom component with various input types." + + inputs = [ + StrInput( + name="username", + display_name="Username", + info="Enter your username." + ), + SecretStrInput( + name="password", + display_name="Password", + info="Enter your password." + ), + MultilineInput( + name="description", + display_name="Description", + info="Enter a detailed description.", + ), + IntInput( + name="age", + display_name="Age", + info="Enter your age." + ), + DropdownInput( + name="gender", + display_name="Gender", + options=["Male", "Female", "Other"], + info="Select your gender." + ) + ] + + outputs = [ + Output(display_name="Result", name="result", method="process_inputs"), + ] + + def process_inputs(self): + # Your processing logic here + return "Processed" +``` + + +By defining inputs this way, Langflow can automatically handle the validation and display of these fields in the user interface, making it easier to create robust and user-friendly custom components. + + +All of the types detailed above derive from a general class that can also be accessed through the generic `Input` class. + + +### Generic Input {#278e2027493e45b68746af0a5b6c06f6} + + +--- + + +Langflow offers native input types, but you can use any type as long as they are properly annotated in the output methods (e.g., `-> list[int]`). + + +The `Input` class is highly customizable, allowing you to specify a wide range of attributes for each input field. It has several attributes that can be customized: + +- `field_type`: Specifies the type of field (e.g., `str`, `int`). Default is `str`. +- `required`: Boolean indicating if the field is required. Default is `False`. +- `placeholder`: Placeholder text for the input field. Default is an empty string. +- `is_list`: Boolean indicating if the field should accept a list of values. Default is `False`. +- `show`: Boolean indicating if the field should be shown. Default is `True`. +- `multiline`: Boolean indicating if the field should allow multi-line input. Default is `False`. +- `value`: Default value for the input field. Default is `None`. +- `file_types`: List of accepted file types (for file inputs). Default is an empty list. +- `file_path`: File path if the field is a file input. Default is `None`. +- `password`: Boolean indicating if the field is a password. Default is `False`. +- `options`: List of options for the field (for dropdowns). Default is `None`. +- `name`: Name of the input field. Default is `None`. +- `display_name`: Display name for the input field. Default is `None`. +- `advanced`: Boolean indicating if the field is an advanced parameter. Default is `False`. +- `input_types`: List of accepted input types. Default is `None`. +- `dynamic`: Boolean indicating if the field is dynamic. Default is `False`. +- `info`: Additional information or tooltip for the input field. Default is an empty string. +- `real_time_refresh`: Boolean indicating if the field should refresh in real-time. Default is `None`. +- `refresh_button`: Boolean indicating if the field should have a refresh button. Default is `None`. +- `refresh_button_text`: Text for the refresh button. Default is `None`. +- `range_spec`: Range specification for numeric fields. Default is `None`. +- `load_from_db`: Boolean indicating if the field should load from the database. Default is `False`. +- `title_case`: Boolean indicating if the display name should be in title case. Default is `True`. + +Below is an example of how to define inputs for a component using the `Input` class: + + +```python +from langflow.template import Input, Output +from langflow.custom import Component +from langflow.field_typing import Text + +class ExampleComponent(Component): + display_name = "Example Component" + description = "An example component demonstrating input fields." + + inputs = [ + Input( + name="input_text", + display_name="Input Text", + field_type="str", + required=True, + placeholder="Enter some text", + multiline=True, + info="This is a required text input.", + input_types=["Text"] + ), + Input( + name="max_length", + display_name="Max Length", + field_type="int", + required=False, + placeholder="Maximum length", + info="Enter the maximum length of the text.", + range_spec={"min": 0, "max": 1000}, + ), + Input( + name="options", + display_name="Options", + field_type="str", + is_list=True, + options=["Option 1", "Option 2", "Option 3"], + info="Select one or more options." + ), + ] + + outputs = [ + Output(display_name="Result", name="result", method="process_input"), + ] + + def process_input(self) -> Text: + # Process the inputs and generate output + return Text(value=f"Processed: {self.input_text}, Max Length: {self.max_length}, Options: {self.options}") + +# Define how to use the inputs and outputs +component = ExampleComponent() + + +``` + + +In this example: + +- The `input_text` input is a required multi-line text field. +- The `max_length` input is an optional integer field with a range specification. +- The `options` input is a list of strings with predefined options. + +These attributes allow for a high degree of customization, making it easy to create input fields that suit the needs of your specific component. + + +### Multiple Outputs {#6f225be8a142450aa19ee8e46a3b3c8c} + + +--- + + +In Langflow, custom components can have multiple outputs. Each output can be associated with a specific method in the component, allowing you to define distinct behaviors for each output path. This feature is particularly useful when you want to route data based on certain conditions or process it in multiple ways. + +1. **Definition of Outputs**: Each output is defined in the `outputs` list of the component. Each output is associated with a display name, an internal name, and a method that gets called to generate the output. +2. **Output Methods**: The methods associated with outputs are responsible for generating the data for that particular output. These methods are called when the component is executed, and each method can independently produce its result. + +Below is an example of a component with two outputs: + +- `process_data`: Processes the input text (e.g., converts it to uppercase) and returns it. +- `get_processing_function`: Returns the `process_data` method itself to be reused in composition. + +```python +from typing import Callable +from langflow.custom import Component +from langflow.inputs import StrInput +from langflow.template import Output +from langflow.field_typing import Text + +class DualOutputComponent(Component): + display_name = "Dual Output" + description = "Processes input text and returns both the result and the processing function." + icon = "double-arrow" + + inputs = [ + StrInput( + name="input_text", + display_name="Input Text", + info="The text input to be processed.", + ), + ] + + outputs = [ + Output(display_name="Processed Data", name="processed_data", method="process_data"), + Output(display_name="Processing Function", name="processing_function", method="get_processing_function"), + ] + + def process_data(self) -> Text: + # Process the input text (e.g., convert to uppercase) + processed = self.input_text.upper() + self.status = processed + return processed + + def get_processing_function(self) -> Callable[[], Text]: + # Return the processing function itself + return self.process_data +``` + + +This example shows how to define multiple outputs in a custom component. The first output returns the processed data, while the second output returns the processing function itself. + + +The `processing_function` output can be used in scenarios where the function itself is needed for further processing or dynamic flow control. Notice how both outputs are properly annotated with their respective types, ensuring clarity and type safety. + + +## Special Operations {#b1ef2d18e2694b93927ae9403d24b96b} + + +--- + + +Advanced methods and attributes offer additional control and functionality. Understanding how to leverage these can enhance your custom components' capabilities. + +- `self.inputs`: Access all defined inputs. Useful when an output method needs to interact with multiple inputs. +- `self.outputs`: Access all defined outputs. This is particularly useful if an output function needs to trigger another output function. +- `self.status`: Use this to update the component's status or intermediate results. It helps track the component's internal state or store temporary data. +- `self.graph.flow_id`: Retrieve the flow ID, useful for maintaining context or debugging. +- `self.stop("output_name")`: Use this method within an output function to prevent data from being sent through other components. This method stops next component execution and is particularly useful for specific operations where a component should stop from running based on specific conditions. diff --git a/docs/docs/Components/components-data.md b/docs/docs/Components/components-data.md new file mode 100644 index 000000000..96a0e88bb --- /dev/null +++ b/docs/docs/Components/components-data.md @@ -0,0 +1,89 @@ +--- +title: Data +sidebar_position: 3 +slug: /components-data +--- + + + +## API Request {#23da589293f74016a1f70d6d7c0fdc55} + + +--- + + +This component sends HTTP requests to the specified URLs. + + +Use this component to interact with external APIs or services and retrieve data. Ensure that the URLs are valid and that you configure the method, headers, body, and timeout correctly. + + +**Parameters:** + +- **URLs:** The URLs to target. +- **Method:** The HTTP method, such as GET or POST. +- **Headers:** The headers to include with the request. +- **Body:** The data to send with the request (for methods like POST, PATCH, PUT). +- **Timeout:** The maximum time to wait for a response. + +## Directory {#4fe56acaaac847029ace173dc793f8f4} + + +--- + + +This component recursively retrieves files from a specified directory. + + +Use this component to retrieve various file types, such as text or JSON files, from a directory. Make sure to provide the correct path and configure the other parameters as needed. + + +**Parameters:** + +- **Path:** The directory path. +- **Types:** The types of files to retrieve. Leave this blank to retrieve all file types. +- **Depth:** The level of directory depth to search. +- **Max Concurrency:** The maximum number of simultaneous file loading operations. +- **Load Hidden:** Set to true to include hidden files. +- **Recursive:** Set to true to enable recursive search. +- **Silent Errors:** Set to true to suppress exceptions on errors. +- **Use Multithreading:** Set to true to use multithreading in file loading. + +## File {#d5d4bb78ce0a473d8a3b6a296d3e8383} + + +--- + + +This component loads a file. + + +Use this component to load files, such as text or JSON files. Ensure you specify the correct path and configure other parameters as necessary. + + +**Parameters:** + +- **Path:** The file path. +- **Silent Errors:** Set to true to prevent exceptions on errors. + +## URL {#1cc513827a0942d6885b3a9168eabc97} + + +--- + + +This component retrieves content from specified URLs. + + +Ensure the URLs are valid and adjust other parameters as needed. **Parameters:** + +- **URLs:** The URLs to retrieve content from. + +## Create Data {#aac4cad0cd38426191c2e7516285877b} + + +--- + + +This component allows you to create a `Data` from a number of inputs. You can add as many key-value pairs as you want (as long as it is less than 15). Once you've picked that number you'll need to write the name of the Key and can pass `Text` values from other components to it. + diff --git a/docs/docs/components/embeddings.mdx b/docs/docs/Components/components-embedding-models.md similarity index 83% rename from docs/docs/components/embeddings.mdx rename to docs/docs/Components/components-embedding-models.md index 9a224b048..831396393 100644 --- a/docs/docs/components/embeddings.mdx +++ b/docs/docs/Components/components-embedding-models.md @@ -1,37 +1,45 @@ -import Admonition from "@theme/Admonition"; +--- +title: Embedding Models +sidebar_position: 6 +slug: /components-embedding-models +--- -# Embeddings - -This page may contain outdated information. It will be updated as soon as possible. - -## Amazon Bedrock Embeddings +## Amazon Bedrock Embeddings {#4ddcfde8c1664e358d3f16d718e944d8} + + +Used to load embedding models from [Amazon Bedrock](https://aws.amazon.com/bedrock/). -Used to load embedding models from [Amazon Bedrock](https://aws.amazon.com/bedrock/). | **Parameter** | **Type** | **Description** | **Default** | | -------------------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | ----------- | | `credentials_profile_name` | `str` | Name of the AWS credentials profile in ~/.aws/credentials or ~/.aws/config, which has access keys or role information. | | -| `model_id` | `str` | ID of the model to call, e.g., `amazon.titan-embed-text-v1`. This is equivalent to the `modelId` property in the `list-foundation-models` API. | | +| `model_id` | `str` | ID of the model to call, e.g., `amazon.titan-embed-text-v1`. This is equivalent to the `modelId` property in the `list-foundation-models` API. | | | `endpoint_url` | `str` | URL to set a specific service endpoint other than the default AWS endpoint. | | -| `region_name` | `str` | AWS region to use, e.g., `us-west-2`. Falls back to `AWS_DEFAULT_REGION` environment variable or region specified in ~/.aws/config if not provided. | | +| `region_name` | `str` | AWS region to use, e.g., `us-west-2`. Falls back to `AWS_DEFAULT_REGION` environment variable or region specified in ~/.aws/config if not provided. | | -## Astra vectorize -Used to generate server-side embeddings using [DataStax Astra](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html). +## Astra vectorize {#c1e6d1373824424ea130e052ba0f46af} + + +Used to generate server-side embeddings using [DataStax Astra](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html). + | **Parameter** | **Type** | **Description** | **Default** | -|--------------------|----------|-----------------------------------------------------------------------------------------------------------------------|-------------| +| ------------------ | -------- | --------------------------------------------------------------------------------------------------------------------- | ----------- | | `provider` | `str` | The embedding provider to use. | | | `model_name` | `str` | The embedding model to use. | | | `authentication` | `dict` | Authentication parameters. Use the Astra Portal to add the embedding provider integration to your Astra organization. | | | `provider_api_key` | `str` | An alternative to the Astra Authentication that let you use directly the API key of the provider. | | | `model_parameters` | `dict` | Additional model parameters. | | -## Cohere Embeddings -Used to load embedding models from [Cohere](https://cohere.com/). +## Cohere Embeddings {#0c5b7b8790da448fabd4c5ddba1fcbde} + + +Used to load embedding models from [Cohere](https://cohere.com/). + | **Parameter** | **Type** | **Description** | **Default** | | ---------------- | -------- | ------------------------------------------------------------------------- | -------------------- | @@ -39,21 +47,27 @@ Used to load embedding models from [Cohere](https://cohere.com/). | `model` | `str` | Language model used for embedding text documents and performing queries. | `embed-english-v2.0` | | `truncate` | `bool` | Whether to truncate the input text to fit within the model's constraints. | `False` | -## Azure OpenAI Embeddings + +## Azure OpenAI Embeddings {#8ffb790d5a6c484dab3fe6c777638a44} + Generate embeddings using Azure OpenAI models. + | **Parameter** | **Type** | **Description** | **Default** | | ----------------- | -------- | -------------------------------------------------------------------------------------------------- | ----------- | -| `Azure Endpoint` | `str` | Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/` | | +| `Azure Endpoint` | `str` | Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/` | | | `Deployment Name` | `str` | The name of the deployment. | | | `API Version` | `str` | The API version to use, options include various dates. | | | `API Key` | `str` | The API key to access the Azure OpenAI service. | | -## Hugging Face API Embeddings + +## Hugging Face API Embeddings {#8536e4ee907b48688e603ae9bf7822cb} + Generate embeddings using Hugging Face Inference API models. + | **Parameter** | **Type** | **Description** | **Default** | | --------------- | -------- | ----------------------------------------------------- | ------------------------ | | `API Key` | `str` | API key for accessing the Hugging Face Inference API. | | @@ -64,9 +78,12 @@ Generate embeddings using Hugging Face Inference API models. | `Model Kwargs` | `dict` | Additional arguments for the model. | | | `Multi Process` | `bool` | Whether to use multiple processes. | `False` | -## Hugging Face Embeddings -Used to load embedding models from [HuggingFace](https://huggingface.co). +## Hugging Face Embeddings {#b2b74732874743d3be6fdf8aae049e74} + + +Used to load embedding models from [HuggingFace](https://huggingface.co/). + | **Parameter** | **Type** | **Description** | **Default** | | --------------- | -------- | ---------------------------------------------- | ----------------------------------------- | @@ -76,9 +93,12 @@ Used to load embedding models from [HuggingFace](https://huggingface.co). | `Model Name` | `str` | Name of the HuggingFace model to use. | `sentence-transformers/all-mpnet-base-v2` | | `Multi Process` | `bool` | Whether to use multiple processes. | `False` | -## OpenAI Embeddings -Used to load embedding models from [OpenAI](https://openai.com/). +## OpenAI Embeddings {#af7630df05a245d1a632e1bf6db2a4c5} + + +Used to load embedding models from [OpenAI](https://openai.com/). + | **Parameter** | **Type** | **Description** | **Default** | | -------------------------- | ---------------- | ------------------------------------------------ | ------------------------ | @@ -105,19 +125,25 @@ Used to load embedding models from [OpenAI](https://openai.com/). | `TikToken Enable` | `bool` | Whether to enable TikToken. | `True` | | `TikToken Model Name` | `str` | Name of the TikToken model. | | -## Ollama Embeddings + +## Ollama Embeddings {#a26d2cb92e6d44669c2cfff71a5e9431} + Generate embeddings using Ollama models. + | **Parameter** | **Type** | **Description** | **Default** | | ------------------- | -------- | ---------------------------------------------------------------------------------------- | ------------------------ | | `Ollama Model` | `str` | Name of the Ollama model to use. | `llama2` | | `Ollama Base URL` | `str` | Base URL of the Ollama API. | `http://localhost:11434` | | `Model Temperature` | `float` | Temperature parameter for the model. Adjusts the randomness in the generated embeddings. | | -## VertexAI Embeddings -Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) [Embeddings API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings). +## VertexAI Embeddings {#707b38c23cb9413fbbaab1ae7b872311} + + +Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) [Embeddings API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings). + | **Parameter** | **Type** | **Description** | **Default** | | --------------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ------------- | @@ -128,7 +154,11 @@ Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) [Embedding | `project` | `str` | The default GCP project to use when making Vertex API calls. | | | `request_parallelism` | `int` | The amount of parallelism allowed for requests issued to VertexAI models. | `5` | | `temperature` | `float` | Tunes the degree of randomness in text generations. Should be a non-negative value. | `0` | -| `top_k` | `int` | How the model selects tokens for output, the next token is selected from the top `k` tokens. | `40` | -| `top_p` | `float` | Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top `p` value. | `0.95` | -| `tuned_model_name` | `str` | The name of a tuned model. If provided, `model_name` is ignored. | | -| `verbose` | `bool` | This parameter controls the level of detail in the output. When set to `True`, it prints internal states of the chain to help debug. | `False` | +| `top_k` | `int` | How the model selects tokens for output, the next token is selected from the top `k` tokens. | `40` | +| `top_p` | `float` | Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top `p` value. | `0.95` | +| `tuned_model_name` | `str` | The name of a tuned model. If provided, `model_name` is ignored. | | +| `verbose` | `bool` | This parameter controls the level of detail in the output. When set to `True`, it prints internal states of the chain to help debug. | `False` | + + +[Previous Vector Stores](/components-vector-stores) + diff --git a/docs/docs/Components/components-helpers.md b/docs/docs/Components/components-helpers.md new file mode 100644 index 000000000..402ccd516 --- /dev/null +++ b/docs/docs/Components/components-helpers.md @@ -0,0 +1,161 @@ +--- +title: Helpers +sidebar_position: 4 +slug: /components-helpers +--- + + + +## Chat memory {#304dc4a3bea74efb9068093ff18a56ad} + + +This component retrieves stored chat messages based on a specific session ID. + + +### Parameters {#e0af57d97f844ce99789958161d19767} + +- **Sender type:** Choose the sender type from options like "Machine", "User", or "Both". +- **Sender name:** (Optional) The name of the sender. +- **Number of messages:** Number of messages to retrieve. +- **Session ID:** The session ID of the chat history. +- **Order:** Choose the message order, either "Ascending" or "Descending". +- **Data template:** (Optional) Template to convert a record to text. If left empty, the system dynamically sets it to the record's text key. + +--- + + +### Combine text {#13443183e6054d0694d65f8df08833d5} + + +This component concatenates two text sources into a single text chunk using a specified delimiter. + + +### Parameters {#246676d119604fc5bf1be85fe93044aa} + +- **First text:** The first text input to concatenate. +- **Second text:** The second text input to concatenate. +- **Delimiter:** A string used to separate the two text inputs. Defaults to a space. + +--- + + +### Create record {#506f43345854473b8199631bf68a3b4a} + + +This component dynamically creates a record with a specified number of fields. + + +### Parameters {#08735e90bd10406695771bad8a95976a} + +- **Number of fields:** Number of fields to be added to the record. +- **Text key:** Key used as text. + +--- + + +### Custom component {#cda421d4bccb4e7db2e48615884ed753} + + +Use this component as a template to create your custom component. + + +### Parameters {#04f9eb5e6da4431593a5bee8831f2327} + +- **Parameter:** Describe the purpose of this parameter. + +INFO + + +Customize the `build_config` and `build` methods according to your requirements. + + +Learn more about creating custom components at [Custom Component](http://docs.langflow.org/components/custom). + + +--- + + +### Documents to Data {#53a6a99a54f0435e9209169cf7730c55} + + +Convert LangChain documents into Data. + + +### Parameters {#0eb5fce528774c2db4a3677973e75cf8} + +- **Documents:** Documents to be converted into Data. + +--- + + +### ID generator {#4a8fbfb95ebe44ee8718725546db5393} + + +Generates a unique ID. + + +### Parameters {#4629dd15594c47399c97d9511060e114} + +- **Value:** Unique ID generated. + +--- + + +### Message history {#6a1a60688641490197c6443df573960e} + + +Retrieves stored chat messages based on a specific session ID. + + +### Parameters {#31c7fc2a3e8c4f7c89f923e700f4ea34} + +- **Sender type:** Options for the sender type. +- **Sender name:** Sender name. +- **Number of messages:** Number of messages to retrieve. +- **Session ID:** Session ID of the chat history. +- **Order:** Order of the messages. + +--- + + +### Data to text {#f60ab5bbc0db4b27b427897eba97fe29} + + +Convert Data into plain text following a specified template. + + +### Parameters {#01b91376569149a49cfcfd9321323688} + +- **Data:** The Data to convert to text. +- **Template:** The template used for formatting the Data. It can contain keys like `{text}`, `{data}`, or any other key in the record. + +--- + + +### Split text {#210be0ae518d411695d6caafdd7700eb} + + +Split text into chunks of a specified length. + + +### Parameters {#04197fcd05e64e10b189de1171a32682} + +- **Texts:** Texts to split. +- **Separators:** Characters to split on. Defaults to a space. +- **Max chunk size:** The maximum length (in characters) of each chunk. +- **Chunk overlap:** The amount of character overlap between chunks. +- **Recursive:** Whether to split recursively. + +--- + + +### Update record {#d3b6116dfd8d4af080ad01bc8fd2b0b3} + + +Update a record with text-based key/value pairs, similar to updating a Python dictionary. + + +### Parameters {#c830224edc1d486aaaa5e2889f4f6689} + +- **Data:** The record to update. +- **New data:** The new data to update the record with. diff --git a/docs/docs/Components/components-io.md b/docs/docs/Components/components-io.md new file mode 100644 index 000000000..d36955d85 --- /dev/null +++ b/docs/docs/Components/components-io.md @@ -0,0 +1,135 @@ +--- +title: Inputs & Outputs +sidebar_position: 1 +slug: /components-io +--- + + + +Inputs and Outputs are a category of components that are used to define where data comes in and out of your flow. They also dynamically change the Playground and can be renamed to facilitate building and maintaining your flows. + + +## Inputs {#6b1421ec66994d5ebe9fcce000829328} + + +--- + + +Inputs are components used to define where data enters your flow. They can receive data from the user, a database, or any other source that can be converted to Text or Data. + + +The difference between Chat Input and other Input components is the output format, the number of configurable fields, and the way they are displayed in the Playground. + + +Chat Input components can output `Text` or `Data`. When you want to pass the sender name or sender to the next component, use the `Data` output. To pass only the message, use the `Text` output, useful when saving the message to a database or memory system like Zep. + + +You can find out more about Chat Input and other Inputs [here](/components-io). + + +### Chat Input {#2a5f02262f364f8fb75bcfa246e7bb26} + + +--- + + +This component collects user input from the chat. + + +**Parameters** + +- **Sender Type:** Specifies the sender type. Defaults to `User`. Options are `Machine` and `User`. +- **Sender Name:** Specifies the name of the sender. Defaults to `User`. +- **Message:** Specifies the message text. It is a multiline text input. +- **Session ID:** Specifies the session ID of the chat history. If provided, the message will be saved in the Message History. + +NOTE + + +If `As Data` is `true` and the `Message` is a `Data`, the data of the `Data` will be updated with the `Sender`, `Sender Name`, and `Session ID`. + + +One significant capability of the Chat Input component is its ability to transform the Playground into a chat window. This feature is particularly valuable for scenarios requiring user input to initiate or influence the flow. + + +### Text Input {#260aef3726834896b496b56cdefb6d4a} + + +--- + + +The **Text Input** component adds an **Input** field on the Playground. This enables you to define parameters while running and testing your flow. + + +**Parameters** + +- **Value:** Specifies the text input value. This is where the user inputs text data that will be passed to the next component in the sequence. If no value is provided, it defaults to an empty string. +- **Data Template:** Specifies how a `Data` should be converted into `Text`. + +The **Data Template** field is used to specify how a `Data` should be converted into `Text`. This is particularly useful when you want to extract specific information from a `Data` and pass it as text to the next component in the sequence. + + +For example, if you have a `Data` with the following structure: + + +`{ "name": "John Doe", "age": 30, "email": "johndoe@email.com"}` + + +A template with `Name: {name}, Age: {age}` will convert the `Data` into a text string of `Name: John Doe, Age: 30`. + + +If you pass more than one `Data`, the text will be concatenated with a new line separator. + + +## Outputs {#f62c5ad37a6f45a39b463c9b35ce7842} + + +--- + + +Outputs are components that are used to define where data comes out of your flow. They can be used to send data to the user, to the Playground, or to define how the data will be displayed in the Playground. + + +The Chat Output works similarly to the Chat Input but does not have a field that allows for written input. It is used as an Output definition and can be used to send data to the user. + + +You can find out more about it and the other Outputs [here](/components-io). + + +### Chat Output {#1edd49b72781432ea29d70acbda4e7e7} + + +--- + + +This component sends a message to the chat. + + +**Parameters** + +- **Sender Type:** Specifies the sender type. Default is `"Machine"`. Options are `"Machine"` and `"User"`. +- **Sender Name:** Specifies the sender's name. Default is `"AI"`. +- **Session ID:** Specifies the session ID of the chat history. If provided, messages are saved in the Message History. +- **Message:** Specifies the text of the message. + +NOTE + + +If `As Data` is `true` and the `Message` is a `Data`, the data in the `Data` is updated with the `Sender`, `Sender Name`, and `Session ID`. + + +### Text Output {#b607000bc0c5402db0433c1a7d734d01} + + +--- + + +This component displays text data to the user. It is useful when you want to show text without sending it to the chat. + + +**Parameters** + +- **Value:** Specifies the text data to be displayed. Defaults to an empty string. + +The `TextOutput` component provides a simple way to display text data. It allows textual data to be visible in the chat window during your interaction flow. + diff --git a/docs/docs/Components/components-models.md b/docs/docs/Components/components-models.md new file mode 100644 index 000000000..bf0b4ec9b --- /dev/null +++ b/docs/docs/Components/components-models.md @@ -0,0 +1,309 @@ +--- +title: Models +sidebar_position: 5 +slug: /components-models +--- + + + +## Amazon Bedrock {#3b8ceacef3424234814f95895a25bf43} + + +This component facilitates the generation of text using the LLM (Large Language Model) model from Amazon Bedrock. + + +**Params** + +- **Input Value:** Specifies the input text for text generation. +- **System Message (Optional):** A system message to pass to the model. +- **Model ID (Optional):** Specifies the model ID to be used for text generation. Defaults to `"anthropic.claude-instant-v1"`. Available options include: + - `"ai21.j2-grande-instruct"` + - `"ai21.j2-jumbo-instruct"` + - `"ai21.j2-mid"` + - `"ai21.j2-mid-v1"` + - `"ai21.j2-ultra"` + - `"ai21.j2-ultra-v1"` + - `"anthropic.claude-instant-v1"` + - `"anthropic.claude-v1"` + - `"anthropic.claude-v2"` + - `"cohere.command-text-v14"` +- **Credentials Profile Name (Optional):** Specifies the name of the credentials profile. +- **Region Name (Optional):** Specifies the region name. +- **Model Kwargs (Optional):** Additional keyword arguments for the model. +- **Endpoint URL (Optional):** Specifies the endpoint URL. +- **Streaming (Optional):** Specifies whether to stream the response from the model. Defaults to `False`. +- **Cache (Optional):** Specifies whether to cache the response. +- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to `False`. + +NOTE + + +Ensure that necessary credentials are provided to connect to the Amazon Bedrock API. If connection fails, a ValueError will be raised. + + +--- + + +## Anthropic {#a6ae46f98c4c4d389d44b8408bf151a1} + + +This component allows the generation of text using Anthropic Chat&Completion large language models. + + +**Params** + +- **Model Name:** Specifies the name of the Anthropic model to be used for text generation. Available options include (and not limited to): + - `"claude-2.1"` + - `"claude-2.0"` + - `"claude-instant-1.2"` + - `"claude-instant-1"` +- **Anthropic API Key:** Your Anthropic API key. +- **Max Tokens (Optional):** Specifies the maximum number of tokens to generate. Defaults to `256`. +- **Temperature (Optional):** Specifies the sampling temperature. Defaults to `0.7`. +- **API Endpoint (Optional):** Specifies the endpoint of the Anthropic API. Defaults to `"https://api.anthropic.com"`if not specified. +- **Input Value:** Specifies the input text for text generation. +- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to `False`. +- **System Message (Optional):** A system message to pass to the model. + +For detailed documentation and integration guides, please refer to the [Anthropic Component Documentation](https://python.langchain.com/docs/integrations/chat/anthropic). + + +--- + + +## Azure OpenAI {#7e3bff29ce714479b07feeb4445680cd} + + +This component allows the generation of text using the LLM (Large Language Model) model from Azure OpenAI. + + +**Params** + +- **Model Name:** Specifies the name of the Azure OpenAI model to be used for text generation. Available options include: + - `"gpt-35-turbo"` + - `"gpt-35-turbo-16k"` + - `"gpt-35-turbo-instruct"` + - `"gpt-4"` + - `"gpt-4-32k"` + - `"gpt-4-vision"` + - `"gpt-4o"` +- **Azure Endpoint:** Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/`. +- **Deployment Name:** Specifies the name of the deployment. +- **API Version:** Specifies the version of the Azure OpenAI API to be used. Available options include: + - `"2023-03-15-preview"` + - `"2023-05-15"` + - `"2023-06-01-preview"` + - `"2023-07-01-preview"` + - `"2023-08-01-preview"` + - `"2023-09-01-preview"` + - `"2023-12-01-preview"` +- **API Key:** Your Azure OpenAI API key. +- **Temperature (Optional):** Specifies the sampling temperature. Defaults to `0.7`. +- **Max Tokens (Optional):** Specifies the maximum number of tokens to generate. Defaults to `1000`. +- **Input Value:** Specifies the input text for text generation. +- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to `False`. +- **System Message (Optional):** A system message to pass to the model. + +For detailed documentation and integration guides, please refer to the [Azure OpenAI Component Documentation](https://python.langchain.com/docs/integrations/llms/azure_openai). + + +--- + + +## Cohere {#706396a33bf94894966c95571252d78b} + + +This component enables text generation using Cohere large language models. + + +**Params** + +- **Cohere API Key:** Your Cohere API key. +- **Max Tokens (Optional):** Specifies the maximum number of tokens to generate. Defaults to `256`. +- **Temperature (Optional):** Specifies the sampling temperature. Defaults to `0.75`. +- **Input Value:** Specifies the input text for text generation. +- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to `False`. +- **System Message (Optional):** A system message to pass to the model. + +--- + + +## Google Generative AI {#074d9623463449f99d41b44699800e8a} + + +This component enables text generation using Google Generative AI. + + +**Params** + +- **Google API Key:** Your Google API key to use for the Google Generative AI. +- **Model:** The name of the model to use. Supported examples are `"gemini-pro"` and `"gemini-pro-vision"`. +- **Max Output Tokens (Optional):** The maximum number of tokens to generate. +- **Temperature:** Run inference with this temperature. Must be in the closed interval [0.0, 1.0]. +- **Top K (Optional):** Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive. +- **Top P (Optional):** The maximum cumulative probability of tokens to consider when sampling. +- **N (Optional):** Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated. +- **Input Value:** The input to the model. +- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to `False`. +- **System Message (Optional):** A system message to pass to the model. + +--- + + +## Hugging Face API {#c1267b9a6b36487cb2ee127ce9b64dbb} + + +This component facilitates text generation using LLM models from the Hugging Face Inference API. + + +**Params** + +- **Endpoint URL:** The URL of the Hugging Face Inference API endpoint. Should be provided along with necessary authentication credentials. +- **Task:** Specifies the task for text generation. Options include `"text2text-generation"`, `"text-generation"`, and `"summarization"`. +- **API Token:** The API token required for authentication with the Hugging Face Hub. +- **Model Keyword Arguments (Optional):** Additional keyword arguments for the model. Should be provided as a Python dictionary. +- **Input Value:** The input text for text generation. +- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to `False`. +- **System Message (Optional):** A system message to pass to the model. + +--- + + +## LiteLLM Model {#9fb59dad3b294a05966320d39f483a50} + + +Generates text using the `LiteLLM` collection of large language models. + + +**Parameters** + +- **Model name:** The name of the model to use. For example, `gpt-3.5-turbo`. (Type: str) +- **API key:** The API key to use for accessing the provider's API. (Type: str, Optional) +- **Provider:** The provider of the API key. (Type: str, Choices: "OpenAI", "Azure", "Anthropic", "Replicate", "Cohere", "OpenRouter") +- **Temperature:** Controls the randomness of the text generation. (Type: float, Default: 0.7) +- **Model kwargs:** Additional keyword arguments for the model. (Type: Dict, Optional) +- **Top p:** Filter responses to keep the cumulative probability within the top p tokens. (Type: float, Optional) +- **Top k:** Filter responses to only include the top k tokens. (Type: int, Optional) +- **N:** Number of chat completions to generate for each prompt. (Type: int, Default: 1) +- **Max tokens:** The maximum number of tokens to generate for each chat completion. (Type: int, Default: 256) +- **Max retries:** Maximum number of retries for failed requests. (Type: int, Default: 6) +- **Verbose:** Whether to print verbose output. (Type: bool, Default: False) +- **Input:** The input prompt for text generation. (Type: str) +- **Stream:** Whether to stream the output. (Type: bool, Default: False) +- **System message:** System message to pass to the model. (Type: str, Optional) + +--- + + +## Ollama {#14e8e411d28d4711add53bfc3e52c6cd} + + +Generate text using Ollama Local LLMs. + + +**Parameters** + +- **Base URL:** Endpoint of the Ollama API. Defaults to '[http://localhost:11434](http://localhost:11434/)' if not specified. +- **Model Name:** The model name to use. Refer to [Ollama Library](https://ollama.ai/library) for more models. +- **Temperature:** Controls the creativity of model responses. (Default: 0.8) +- **Cache:** Enable or disable caching. (Default: False) +- **Format:** Specify the format of the output (e.g., json). (Advanced) +- **Metadata:** Metadata to add to the run trace. (Advanced) +- **Mirostat:** Enable/disable Mirostat sampling for controlling perplexity. (Default: Disabled) +- **Mirostat Eta:** Learning rate for Mirostat algorithm. (Default: None) (Advanced) +- **Mirostat Tau:** Controls the balance between coherence and diversity of the output. (Default: None) (Advanced) +- **Context Window Size:** Size of the context window for generating tokens. (Default: None) (Advanced) +- **Number of GPUs:** Number of GPUs to use for computation. (Default: None) (Advanced) +- **Number of Threads:** Number of threads to use during computation. (Default: None) (Advanced) +- **Repeat Last N:** How far back the model looks to prevent repetition. (Default: None) (Advanced) +- **Repeat Penalty:** Penalty for repetitions in generated text. (Default: None) (Advanced) +- **TFS Z:** Tail free sampling value. (Default: None) (Advanced) +- **Timeout:** Timeout for the request stream. (Default: None) (Advanced) +- **Top K:** Limits token selection to top K. (Default: None) (Advanced) +- **Top P:** Works together with top-k. (Default: None) (Advanced) +- **Verbose:** Whether to print out response text. +- **Tags:** Tags to add to the run trace. (Advanced) +- **Stop Tokens:** List of tokens to signal the model to stop generating text. (Advanced) +- **System:** System to use for generating text. (Advanced) +- **Template:** Template to use for generating text. (Advanced) +- **Input:** The input text. +- **Stream:** Whether to stream the response. +- **System Message:** System message to pass to the model. (Advanced) + +--- + + +## OpenAI {#fe6cd793446748eda6eaad72e30f70b3} + + +This component facilitates text generation using OpenAI's models. + + +**Params** + +- **Input Value:** The input text for text generation. +- **Max Tokens (Optional):** The maximum number of tokens to generate. Defaults to `256`. +- **Model Kwargs (Optional):** Additional keyword arguments for the model. Should be provided as a nested dictionary. +- **Model Name (Optional):** The name of the model to use. Defaults to `gpt-4-1106-preview`. Supported options include: `gpt-4-turbo-preview`, `gpt-4-0125-preview`, `gpt-4-1106-preview`, `gpt-4-vision-preview`, `gpt-3.5-turbo-0125`, `gpt-3.5-turbo-1106`. +- **OpenAI API Base (Optional):** The base URL of the OpenAI API. Defaults to `https://api.openai.com/v1`. +- **OpenAI API Key (Optional):** The API key for accessing the OpenAI API. +- **Temperature:** Controls the creativity of model responses. Defaults to `0.7`. +- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to `False`. +- **System Message (Optional):** System message to pass to the model. + +--- + + +## Qianfan {#6e4a6b2370ee4b9f8beb899e7cf9c8f6} + + +This component facilitates the generation of text using Baidu Qianfan chat models. + + +**Params** + +- **Model Name:** Specifies the name of the Qianfan chat model to be used for text generation. Available options include: + - `"ERNIE-Bot"` + - `"ERNIE-Bot-turbo"` + - `"BLOOMZ-7B"` + - `"Llama-2-7b-chat"` + - `"Llama-2-13b-chat"` + - `"Llama-2-70b-chat"` + - `"Qianfan-BLOOMZ-7B-compressed"` + - `"Qianfan-Chinese-Llama-2-7B"` + - `"ChatGLM2-6B-32K"` + - `"AquilaChat-7B"` +- **Qianfan Ak:** Your Baidu Qianfan access key, obtainable from [here](https://cloud.baidu.com/product/wenxinworkshop). +- **Qianfan Sk:** Your Baidu Qianfan secret key, obtainable from [here](https://cloud.baidu.com/product/wenxinworkshop). +- **Top p (Optional):** Model parameter. Specifies the top-p value. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to `0.8`. +- **Temperature (Optional):** Model parameter. Specifies the sampling temperature. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to `0.95`. +- **Penalty Score (Optional):** Model parameter. Specifies the penalty score. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to `1.0`. +- **Endpoint (Optional):** Endpoint of the Qianfan LLM, required if custom model is used. +- **Input Value:** Specifies the input text for text generation. +- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to `False`. +- **System Message (Optional):** A system message to pass to the model. + +--- + + +## Vertex AI {#86b7d539e17c436fb758c47ec3ffb084} + + +The `ChatVertexAI` is a component for generating text using Vertex AI Chat large language models API. + + +**Params** + +- **Credentials:** The JSON file containing the credentials for accessing the Vertex AI Chat API. +- **Project:** The name of the project associated with the Vertex AI Chat API. +- **Examples (Optional):** List of examples to provide context for text generation. +- **Location:** The location of the Vertex AI Chat API service. Defaults to `us-central1`. +- **Max Output Tokens:** The maximum number of tokens to generate. Defaults to `128`. +- **Model Name:** The name of the model to use. Defaults to `chat-bison`. +- **Temperature:** Controls the creativity of model responses. Defaults to `0.0`. +- **Input Value:** The input text for text generation. +- **Top K:** Limits token selection to top K. Defaults to `40`. +- **Top P:** Works together with top-k. Defaults to `0.95`. +- **Verbose:** Whether to print out response text. Defaults to `False`. +- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to `False`. +- **System Message (Optional):** System message to pass to the model. diff --git a/docs/docs/Components/components-prompts.md b/docs/docs/Components/components-prompts.md new file mode 100644 index 000000000..1fe95f4d1 --- /dev/null +++ b/docs/docs/Components/components-prompts.md @@ -0,0 +1,30 @@ +--- +title: Prompts +sidebar_position: 2 +slug: /components-prompts +--- + + + +A prompt is the input provided to a language model, consisting of multiple components and can be parameterized using prompt templates. A prompt template offers a reproducible method for generating prompts, enabling easy customization through input variables. + + +### Prompt {#c852d1761e6c46b19ce72e5f7c70958c} + + +This component creates a prompt template with dynamic variables. This is useful for structuring prompts and passing dynamic data to a language model. + + +**Parameters** + +- **Template:** The template for the prompt. This field allows you to create other fields dynamically by using curly brackets `{}`. For example, if you have a template like `Hello {name}, how are you?`, a new field called `name` will be created. Prompt variables can be created with any name inside curly brackets, e.g. `{variable_name}`. + +### PromptTemplate {#6e32412f062b42efbdf56857eafb3651} + + +The `PromptTemplate` component enables users to create prompts and define variables that control how the model is instructed. Users can input a set of variables which the template uses to generate the prompt when a conversation starts. + + +After defining a variable in the prompt template, it acts as its own component input. + +- **template:** The template used to format an individual request. diff --git a/docs/docs/Components/components-vector-stores.md b/docs/docs/Components/components-vector-stores.md new file mode 100644 index 000000000..b24546c84 --- /dev/null +++ b/docs/docs/Components/components-vector-stores.md @@ -0,0 +1,546 @@ +--- +title: Vector Stores +sidebar_position: 7 +slug: /components-vector-stores +--- + + + +### Astra DB {#453bcf5664154e37a920f1b602bd39da} + + +The `Astra DB` initializes a vector store using Astra DB from Data. It creates Astra DB-based vector indexes to efficiently store and retrieve documents. + + +**Parameters:** + +- **Input:** Documents or Data for input. +- **Embedding or Astra vectorize:** External or server-side model Astra DB uses. +- **Collection Name:** Name of the Astra DB collection. +- **Token:** Authentication token for Astra DB. +- **API Endpoint:** API endpoint for Astra DB. +- **Namespace:** Astra DB namespace. +- **Metric:** Metric used by Astra DB. +- **Batch Size:** Batch size for operations. +- **Bulk Insert Batch Concurrency:** Concurrency level for bulk inserts. +- **Bulk Insert Overwrite Concurrency:** Concurrency level for overwriting during bulk inserts. +- **Bulk Delete Concurrency:** Concurrency level for bulk deletions. +- **Setup Mode:** Setup mode for the vector store. +- **Pre Delete Collection:** Option to delete the collection before setup. +- **Metadata Indexing Include:** Fields to include in metadata indexing. +- **Metadata Indexing Exclude:** Fields to exclude from metadata indexing. +- **Collection Indexing Policy:** Indexing policy for the collection. + +NOTE + + +Ensure you configure the necessary Astra DB token and API endpoint before starting. + + +--- + + +### Astra DB Search {#26f25d1933a9459bad2d6725f87beb11} + + +`Astra DBSearch` searches an existing Astra DB vector store for documents similar to the input. It uses the `Astra DB`component's functionality for efficient retrieval. + + +**Parameters:** + +- **Search Type:** Type of search, such as Similarity or MMR. +- **Input Value:** Value to search for. +- **Embedding or Astra vectorize:** External or server-side model Astra DB uses. +- **Collection Name:** Name of the Astra DB collection. +- **Token:** Authentication token for Astra DB. +- **API Endpoint:** API endpoint for Astra DB. +- **Namespace:** Astra DB namespace. +- **Metric:** Metric used by Astra DB. +- **Batch Size:** Batch size for operations. +- **Bulk Insert Batch Concurrency:** Concurrency level for bulk inserts. +- **Bulk Insert Overwrite Concurrency:** Concurrency level for overwriting during bulk inserts. +- **Bulk Delete Concurrency:** Concurrency level for bulk deletions. +- **Setup Mode:** Setup mode for the vector store. +- **Pre Delete Collection:** Option to delete the collection before setup. +- **Metadata Indexing Include:** Fields to include in metadata indexing. +- **Metadata Indexing Exclude:** Fields to exclude from metadata indexing. +- **Collection Indexing Policy:** Indexing policy for the collection. + +--- + + +### Chroma {#74730795605143cba53e1f4c4f2ef5d6} + + +`Chroma` sets up a vector store using Chroma for efficient vector storage and retrieval within language processing workflows. + + +**Parameters:** + +- **Collection Name:** Name of the collection. +- **Persist Directory:** Directory to persist the Vector Store. +- **Server CORS Allow Origins (Optional):** CORS allow origins for the Chroma server. +- **Server Host (Optional):** Host for the Chroma server. +- **Server Port (Optional):** Port for the Chroma server. +- **Server gRPC Port (Optional):** gRPC port for the Chroma server. +- **Server SSL Enabled (Optional):** SSL configuration for the Chroma server. +- **Input:** Input data for creating the Vector Store. +- **Embedding:** Embeddings used for the Vector Store. + +For detailed documentation and integration guides, please refer to the [Chroma Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/chroma). + + +--- + + +### Chroma Search {#5718072a155441f3a443b944ad4d638f} + + +`ChromaSearch` searches a Chroma collection for documents similar to the input text. It leverages Chroma to ensure efficient document retrieval. + + +**Parameters:** + +- **Input:** Input text for search. +- **Search Type:** Type of search, such as Similarity or MMR. +- **Collection Name:** Name of the Chroma collection. +- **Index Directory:** Directory where the Chroma index is stored. +- **Embedding:** Embedding model used for vectorization. +- **Server CORS Allow Origins (Optional):** CORS allow origins for the Chroma server. +- **Server Host (Optional):** Host for the Chroma server. +- **Server Port (Optional):** Port for the Chroma server. +- **Server gRPC Port (Optional):** gRPC port for the Chroma server. +- **Server SSL Enabled (Optional):** SSL configuration for the Chroma server. + +--- + + +### Couchbase {#6900a79347164f35af27ae27f0d64a6d} + + +`Couchbase` builds a Couchbase vector store from Data, streamlining the storage and retrieval of documents. + + +**Parameters:** + +- **Embedding:** Model used by Couchbase. +- **Input:** Documents or Data. +- **Couchbase Cluster Connection String:** Cluster Connection string. +- **Couchbase Cluster Username:** Cluster Username. +- **Couchbase Cluster Password:** Cluster Password. +- **Bucket Name:** Bucket identifier in Couchbase. +- **Scope Name:** Scope identifier in Couchbase. +- **Collection Name:** Collection identifier in Couchbase. +- **Index Name:** Index identifier. + +For detailed documentation and integration guides, please refer to the [Couchbase Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/couchbase). + + +--- + + +### Couchbase Search {#c77bb09425a3426f9677d38d8237d9ba} + + +`CouchbaseSearch` leverages the Couchbase component to search for documents based on similarity metric. + + +**Parameters:** + +- **Input:** Search query. +- **Embedding:** Model used in the Vector Store. +- **Couchbase Cluster Connection String:** Cluster Connection string. +- **Couchbase Cluster Username:** Cluster Username. +- **Couchbase Cluster Password:** Cluster Password. +- **Bucket Name:** Bucket identifier. +- **Scope Name:** Scope identifier. +- **Collection Name:** Collection identifier in Couchbase. +- **Index Name:** Index identifier. + +--- + + +### FAISS {#5b3f4e6592a847b69e07df2f674a03f0} + + +The `FAISS` component manages document ingestion into a FAISS Vector Store, optimizing document indexing and retrieval. + + +**Parameters:** + +- **Embedding:** Model used for vectorizing inputs. +- **Input:** Documents to ingest. +- **Folder Path:** Save path for the FAISS index, relative to Langflow. + +For more details, see the [FAISS Component Documentation](https://faiss.ai/index.html). + + +--- + + +### FAISS Search {#81ff12d7205940a3b14e3ddf304630f8} + + +`FAISSSearch` searches a FAISS Vector Store for documents similar to a given input, using similarity metrics for efficient retrieval. + + +**Parameters:** + +- **Embedding:** Model used in the FAISS Vector Store. +- **Folder Path:** Path to load the FAISS index from, relative to Langflow. +- **Input:** Search query. +- **Index Name:** Index identifier. + +--- + + +### MongoDB Atlas {#eba8892f7a204b97ad1c353e82948149} + + +`MongoDBAtlas` builds a MongoDB Atlas-based vector store from Data, streamlining the storage and retrieval of documents. + + +**Parameters:** + +- **Embedding:** Model used by MongoDB Atlas. +- **Input:** Documents or Data. +- **Collection Name:** Collection identifier in MongoDB Atlas. +- **Database Name:** Database identifier. +- **Index Name:** Index identifier. +- **MongoDB Atlas Cluster URI:** Cluster URI. +- **Search Kwargs:** Additional search parameters. + +NOTE + + +Ensure pymongo is installed for using MongoDB Atlas Vector Store. + + +--- + + +### MongoDB Atlas Search {#686ba0e30a54438cbc7153b81ee4b1df} + + +`MongoDBAtlasSearch` leverages the MongoDBAtlas component to search for documents based on similarity metrics. + + +**Parameters:** + +- **Search Type:** Type of search, such as "Similarity" or "MMR". +- **Input:** Search query. +- **Embedding:** Model used in the Vector Store. +- **Collection Name:** Collection identifier. +- **Database Name:** Database identifier. +- **Index Name:** Index identifier. +- **MongoDB Atlas Cluster URI:** Cluster URI. +- **Search Kwargs:** Additional search parameters. + +--- + + +### PGVector {#7ceebdd84ab14f8e8589c13c58370e5b} + + +`PGVector` integrates a Vector Store within a PostgreSQL database, allowing efficient storage and retrieval of vectors. + + +**Parameters:** + +- **Input:** Value for the Vector Store. +- **Embedding:** Model used. +- **PostgreSQL Server Connection String:** Server URL. +- **Table:** Table name in the PostgreSQL database. + +For more details, see the [PGVector Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/pgvector). + + +NOTE + + +Ensure the PostgreSQL server is accessible and configured correctly. + + +--- + + +### PGVector Search {#196bf22ea2844bdbba971b5082750943} + + +`PGVectorSearch` extends `PGVector` to search for documents based on similarity metrics. + + +**Parameters:** + +- **Input:** Search query. +- **Embedding:** Model used. +- **PostgreSQL Server Connection String:** Server URL. +- **Table:** Table name. +- **Search Type:** Type of search, such as "Similarity" or "MMR". + +--- + + +### Pinecone {#67abbe3e27c34fb4bcb35926ce831727} + + +`Pinecone` constructs a Pinecone wrapper from Data, setting up Pinecone-based vector indexes for document storage and retrieval. + + +**Parameters:** + +- **Input:** Documents or Data. +- **Embedding:** Model used. +- **Index Name:** Index identifier. +- **Namespace:** Namespace used. +- **Pinecone API Key:** API key. +- **Pinecone Environment:** Environment settings. +- **Search Kwargs:** Additional search parameters. +- **Pool Threads:** Number of threads. + +NOTE + + +Ensure the Pinecone API key and environment are correctly configured. + + +--- + + +### Pinecone Search {#977944558cad4cf2ba332ea4f06bf485} + + +`PineconeSearch` searches a Pinecone Vector Store for documents similar to the input, using advanced similarity metrics. + + +**Parameters:** + +- **Search Type:** Type of search, such as "Similarity" or "MMR". +- **Input Value:** Search query. +- **Embedding:** Model used. +- **Index Name:** Index identifier. +- **Namespace:** Namespace used. +- **Pinecone API Key:** API key. +- **Pinecone Environment:** Environment settings. +- **Search Kwargs:** Additional search parameters. +- **Pool Threads:** Number of threads. + +--- + + +### Qdrant {#88df77f3044e4ac6980950835a919fb0} + + +`Qdrant` allows efficient similarity searches and retrieval operations, using a list of texts to construct a Qdrant wrapper. + + +**Parameters:** + +- **Input:** Documents or Data. +- **Embedding:** Model used. +- **API Key:** Qdrant API key. +- **Collection Name:** Collection identifier. +- **Advanced Settings:** Includes content payload key, distance function, gRPC port, host, HTTPS, location, metadata payload key, path, port, prefer gRPC, prefix, search kwargs, timeout, URL. + +--- + + +### Qdrant Search {#5ba5f8dca0f249d7ad00778f49901e6c} + + +`QdrantSearch` extends `Qdrant` to search for documents similar to the input based on advanced similarity metrics. + + +**Parameters:** + +- **Search Type:** Type of search, such as "Similarity" or "MMR". +- **Input Value:** Search query. +- **Embedding:** Model used. +- **API Key:** Qdrant API key. +- **Collection Name:** Collection identifier. +- **Advanced Settings:** Includes content payload key, distance function, gRPC port, host, HTTPS, location, metadata payload key, path, port, prefer gRPC, prefix, search kwargs, timeout, URL. + +--- + + +### Redis {#a0fb8a9d244a40eb8439d0f8c22a2562} + + +`Redis` manages a Vector Store in a Redis database, supporting efficient vector storage and retrieval. + + +**Parameters:** + +- **Index Name:** Default index name. +- **Input:** Data for building the Redis Vector Store. +- **Embedding:** Model used. +- **Schema:** Optional schema file (.yaml) for document structure. +- **Redis Server Connection String:** Server URL. +- **Redis Index:** Optional index name. + +For detailed documentation, refer to the [Redis Documentation](https://python.langchain.com/docs/integrations/vectorstores/redis). + + +NOTE + + +Ensure the Redis server URL and index name are configured correctly. Provide a schema if no documents are available. + + +--- + + +### Redis Search {#80aea4da515f490e979c8576099ee880} + + +`RedisSearch` searches a Redis Vector Store for documents similar to the input. + + +**Parameters:** + +- **Search Type:** Type of search, such as "Similarity" or "MMR". +- **Input Value:** Search query. +- **Index Name:** Default index name. +- **Embedding:** Model used. +- **Schema:** Optional schema file (.yaml) for document structure. +- **Redis Server Connection String:** Server URL. +- **Redis Index:** Optional index name. + +--- + + +### Supabase {#e86fb3cc507e4b5494f0a421f94e853b} + + +`Supabase` initializes a Supabase Vector Store from texts and embeddings, setting up an environment for efficient document retrieval. + + +**Parameters:** + +- **Input:** Documents or data. +- **Embedding:** Model used. +- **Query Name:** Optional query name. +- **Search Kwargs:** Advanced search parameters. +- **Supabase Service Key:** Service key. +- **Supabase URL:** Instance URL. +- **Table Name:** Optional table name. + +NOTE + + +Ensure the Supabase service key, URL, and table name are properly configured. + + +--- + + +### Supabase Search {#fd02d550b9b2457f91f2f4073656cb09} + + +`SupabaseSearch` searches a Supabase Vector Store for documents similar to the input. + + +**Parameters:** + +- **Search Type:** Type of search, such as "Similarity" or "MMR". +- **Input Value:** Search query. +- **Embedding:** Model used. +- **Query Name:** Optional query name. +- **Search Kwargs:** Advanced search parameters. +- **Supabase Service Key:** Service key. +- **Supabase URL:** Instance URL. +- **Table Name:** Optional table name. + +--- + + +### Vectara {#b4e05230b62a47c792a89c5511af97ac} + + +`Vectara` sets up a Vectara Vector Store from files or upserted data, optimizing document retrieval. + + +**Parameters:** + +- **Vectara Customer ID:** Customer ID. +- **Vectara Corpus ID:** Corpus ID. +- **Vectara API Key:** API key. +- **Files Url:** Optional URLs for file initialization. +- **Input:** Optional data for corpus upsert. + +For more information, consult the [Vectara Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/vectara). + + +NOTE + + +If inputs or files_url are provided, they will be processed accordingly. + + +--- + + +### Vectara Search {#31a47221c23f4fbba4a7465cf1d89eb0} + + +`VectaraSearch` searches a Vectara Vector Store for documents based on the provided input. + + +**Parameters:** + +- **Search Type:** Type of search, such as "Similarity" or "MMR". +- **Input Value:** Search query. +- **Vectara Customer ID:** Customer ID. +- **Vectara Corpus ID:** Corpus ID. +- **Vectara API Key:** API key. +- **Files Url:** Optional URLs for file initialization. + +--- + + +### Weaviate {#57c7969574b1418dbb079ac5fc8cd857} + + +`Weaviate` facilitates a Weaviate Vector Store setup, optimizing text and document indexing and retrieval. + + +**Parameters:** + +- **Weaviate URL:** Default instance URL. +- **Search By Text:** Indicates whether to search by text. +- **API Key:** Optional API key for authentication. +- **Index Name:** Optional index name. +- **Text Key:** Default text extraction key. +- **Input:** Document or record. +- **Embedding:** Model used. +- **Attributes:** Optional additional attributes. + +For more details, see the [Weaviate Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/weaviate). + + +NOTE + + +Ensure Weaviate instance is running and accessible. Verify API key, index name, text key, and attributes are set correctly. + + +--- + + +### Weaviate Search {#6d4e616dfd6143b28dc055bc1c40ecae} + + +`WeaviateSearch` searches a Weaviate Vector Store for documents similar to the input. + + +**Parameters:** + +- **Search Type:** Type of search, such as "Similarity" or "MMR". +- **Input Value:** Search query. +- **Weaviate URL:** Default instance URL. +- **Search By Text:** Indicates whether to search by text. +- **API Key:** Optional API key for authentication. +- **Index Name:** Optional index name. +- **Text Key:** Default text extraction key. +- **Embedding:** Model used. +- **Attributes:** Optional additional attributes. diff --git a/docs/docs/Components/components.md b/docs/docs/Components/components.md new file mode 100644 index 000000000..181bc242d --- /dev/null +++ b/docs/docs/Components/components.md @@ -0,0 +1,87 @@ +--- +title: Intro to Components +sidebar_position: 0 +slug: /components +--- + + + +## Component {#0323a728d8314767adb907b998036bb4} + + +A component is a single building block within a flow. It consists of inputs, outputs, and parameters that define their functionality. These elements provide a convenient and straightforward way to compose LLM-based applications. Learn more about components and how they work below. + + +During the flow creation process, you will notice handles (colored circles) attached to one or both sides of a component. These handles use distinct colors to indicate the types of inputs and outputs that can be interconnected. Hover over a handle to see connection details. + + +![](./565424296.png) + + +On the top right corner of the component, you'll find the a play button to run a component. Once it runs, a status icon appears and you can hover over that to visualize success or error messages. Start interacting with your AI by clicking the **Playground** at the bottom right of the workspace. + + +### Component Menu {#7e3f2f8ff5074b2fb3eee97c9cfaabe7} + + +Each component is unique, but they all have a menu bar at the top that looks something like this. + + +![](./938852908.png) + + +It consists of options such as: + +- **Code** — displays the component's Python code. You can modify the code and save it. +- **Advanced** — See and adjust all parameters of a component. +- **Freeze** — After a component runs, lock its previous output state to prevent it from re-running. + +Click **All** (the "..." button) to see all options. + + +### Output Preview {#ed7b3c34e0774b8a916b0e68821c9a7a} + + +Langflow includes an output visualizer for components that opens a pop-up screen. This allows you to easily inspect and monitor transmissions between components, providing instant feedback on your workflows. + + +![](./987204819.png) + + +### Advanced Settings {#b6430d4903df44f0ba4618a558c83d7b} + + +Langflow components can be edited by clicking the **Advanced Settings** button. + + +Hide parameters with the **Show** button to reduce complexity and keep the workspace clean and intuitive for experimentation. + + +You can also double-click a component's name and description to modify those. Component descriptions accept markdown syntax. + + +### Group Components {#c3f5ed818e3b40ceb6534dc358e1a5f2} + + +Multiple components can be grouped into a single component for reuse. This is useful when combining large flows into single components (like RAG with a vector database, for example) and saving space. + +1. Hold **Shift** and drag to select components. +2. Select **Group**. +3. The components merge into a single component. +4. Double-click the name and description to change them. +5. Save your grouped component to in the sidebar for later use! + +[group video here] + + +### Component Version {#887fd587589448dc8c27336d1c235b9b} + + +A component's state is stored in a database, while sidebar components are like starter templates. As soon as you drag a component from the sidebar to the workspace, the two components are no longer in parity. + + +The component will keep the version number it was initialized to the workspace with. Click the **Update Component** icon (exclamation mark) to bring the component up to the `latest` version. This will change the code of the component in place so you can validate that the component was updated by checking its Python code before and after updating it. + + +![](./263391508.png) + diff --git a/docs/static/img/sign-up.png b/docs/docs/Configuration/1009571828.png similarity index 100% rename from docs/static/img/sign-up.png rename to docs/docs/Configuration/1009571828.png diff --git a/docs/docs/Configuration/1125619904.png b/docs/docs/Configuration/1125619904.png new file mode 100644 index 000000000..48349ff89 Binary files /dev/null and b/docs/docs/Configuration/1125619904.png differ diff --git a/docs/static/img/my-collection.png b/docs/docs/Configuration/1289235516.png similarity index 100% rename from docs/static/img/my-collection.png rename to docs/docs/Configuration/1289235516.png diff --git a/docs/static/img/profile-settings.png b/docs/docs/Configuration/1813063533.png similarity index 100% rename from docs/static/img/profile-settings.png rename to docs/docs/Configuration/1813063533.png diff --git a/docs/docs/Configuration/1926471667.png b/docs/docs/Configuration/1926471667.png new file mode 100644 index 000000000..220e604fd Binary files /dev/null and b/docs/docs/Configuration/1926471667.png differ diff --git a/docs/static/img/admin-page.png b/docs/docs/Configuration/383358552.png similarity index 100% rename from docs/static/img/admin-page.png rename to docs/docs/Configuration/383358552.png diff --git a/docs/static/img/my-account.png b/docs/docs/Configuration/563306242.png similarity index 100% rename from docs/static/img/my-account.png rename to docs/docs/Configuration/563306242.png diff --git a/docs/static/img/api-key.png b/docs/docs/Configuration/596474918.png similarity index 100% rename from docs/static/img/api-key.png rename to docs/docs/Configuration/596474918.png diff --git a/docs/docs/Configuration/711485342.gif b/docs/docs/Configuration/711485342.gif new file mode 100644 index 000000000..5ba0f95b2 Binary files /dev/null and b/docs/docs/Configuration/711485342.gif differ diff --git a/docs/docs/Configuration/945175915.png b/docs/docs/Configuration/945175915.png new file mode 100644 index 000000000..a3b0883be Binary files /dev/null and b/docs/docs/Configuration/945175915.png differ diff --git a/docs/docs/Configuration/Folders.md b/docs/docs/Configuration/Folders.md new file mode 100644 index 000000000..be947f48e --- /dev/null +++ b/docs/docs/Configuration/Folders.md @@ -0,0 +1,96 @@ +--- +title: Folders +sidebar_position: 3 +slug: /365085a8-a90a-43f9-a779-f8769ec7eca1 +--- + + + +## **Collections and Projects** {#a259c11235144b18b3ae7a8265a2e6f5} + + +My Collection is a space in Langflow where users can manage, organize, and access their flows and components. Flows and components are displayed as individual cards that provide relevant information. + + +![](./1289235516.png) + +- **Folders**: Users can organize their projects into folders. Default folders include "My Projects" and the ability to create new folders. Hover over a folder to access options to download or delete it. +- **Search Bar** Enables users to quickly search through their flows and components. +- **Select All**: This feature allows users to select all projects displayed on the page for batch actions like moving, deleting, or exporting. + +Click on a flow card to open it in Langflow Workspace or use the **Playground Button** for direct access to execute and interact with the flow’s chatbot interface. + + +## Folders {#776a3866273f4efbbbb2febdfc1baa12} + + +Folders can help you keep your projects organized in Langflow. They help you manage and categorize your work efficiently, making it easier to find and access the resources you need. + + +![](./1926471667.png) + + +Multiple projects can be stored in **folders**. + + +Folders allow you to categorize flows and components into manageable groups. This makes it easier to find and access specific projects quickly. + + +**My Projects** is a default folder where all new projects and components are initially stored unless specified otherwise. Users can create custom folders to better organize their work according to specific needs. + + +Hovering over a folder in Langflow provides options to either remove or download the entire folder, allowing you to keep an offline copy or migrate projects between environments + + +Create new folders with the **New folder** button. One folder can store multiple projects (as the default My Projects folder does). + + +You can download folders of projects as a single JSON file, and upload files and flows to your folder. + + +Click the **Trash** icon to delete a folder. + + + +### How to Create Folders {#5ba5abe995c843e4a429e41413f9d539} + +1. **Navigate to the Home Screen:** + - Go to your Langflow Home Page (outside of projects). +2. **Create a New Folder:** + - Click on the "New Folder" button + + ![](./1125619904.png) + + - Double-click the new folder created to rename your folder appropriately to reflect its contents. + + ![](./945175915.png) + +3. **Move Files:** + - Drag and drop files into the corresponding folders and subfolders to keep everything organized. + + ![](./711485342.gif) + + +### Best Practices for Organizing Folders {#66f23f8e129a48598a7bb4565a508360} + +- **Categorize by Project:** Create a main folder for each project, then add projects for different aspects such as research, drafts, and final documents. +- **Use Descriptive Names:** Use clear and descriptive names for your folders to easily identify their contents at a glance. + +### Example Structure {#ebe6acad99c24d6f9aaabf18e4a17ff4} + + +Here's an example of how you might organize folders and subfolders for a Langflow project: + + +```text +Langflow +├── Research +│ ├── Articles Project +│ ├── Data Project +│ └── Notes Project +└── Documents + ├── RAG Project + └── Advanced RAG Project + +``` + diff --git a/docs/docs/Configuration/_category_.json b/docs/docs/Configuration/_category_.json new file mode 100644 index 000000000..27519db0b --- /dev/null +++ b/docs/docs/Configuration/_category_.json @@ -0,0 +1 @@ +{"position":8, "label":"Configuration"} \ No newline at end of file diff --git a/docs/docs/administration/api.mdx b/docs/docs/Configuration/configuration-api-keys.md similarity index 75% rename from docs/docs/administration/api.mdx rename to docs/docs/Configuration/configuration-api-keys.md index 737213cfd..402940622 100644 --- a/docs/docs/administration/api.mdx +++ b/docs/docs/Configuration/configuration-api-keys.md @@ -1,47 +1,53 @@ -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import Admonition from "@theme/Admonition"; +--- +title: API Keys +sidebar_position: 1 +slug: /configuration-api-keys +--- -# API Keys - + +:::caution + This page may contain outdated information. It will be updated as soon as possible. - + +::: + + + Langflow provides an API key functionality that allows users to access their individual components and flows without traditional login authentication. The API key is a user-specific token that can be included in the request header or query parameter to authenticate API calls. This documentation outlines how to generate, use, and manage API keys in Langflow. - - The default user and password are set using the LANGFLOW_SUPERUSER and - LANGFLOW_SUPERUSER_PASSWORD environment variables. -The default values are `langflow` and `langflow`, respectively. +:::caution - +The default user and password are set using the LANGFLOW_SUPERUSER and LANGFLOW_SUPERUSER_PASSWORD environment variables. The default values are langflow and langflow, respectively. + +::: + + + + +## Generate an API key {#c29986a69cad4cdbbe7537e383ea7207} -## Generate an API key Generate a user-specific token to use with Langflow. -### Generate an API key with the Langflow UI - +### Generate an API key with the Langflow UI {#3d90098ddd7c44b6836c0273acf57123} 1. Click on the "API Key" icon. + + ![](./596474918.png) + 2. Click on "Create new secret key". 3. Give it an optional name. 4. Click on "Create secret key". 5. Copy the API key and store it in a secure location. -### Generate an API key with the Langflow CLI +### Generate an API key with the Langflow CLI {#2368f62fc4b8477e8080c9c2d3659d76} -```bash + +```shell langflow api-key # or python -m langflow api-key @@ -55,31 +61,40 @@ python -m langflow api-key │ │ │ The API key has been copied to your clipboard. Cmd + V to paste it. │ ╰────────────────────────────── + ``` -## Use the Langflow API key + +## Use the Langflow API key {#ae787e4b0d3846aa9094fac75e0ac04f} + Include your API key in API requests to authenticate requests to Langflow. -### Use the `x-api-key` header + +### Use the `x-api-key` header {#70965b3ad24d467ca4f90e7c13a1f394} + Include the `x-api-key` in the HTTP header when making API requests: -```bash -curl -X POST \ - http://localhost:3000/api/v1/run/ \ - -H 'Content-Type: application/json'\ - -H 'x-api-key: '\ + +```shell +curl -X POST \\ + \\ + -H 'Content-Type: application/json'\\ + -H 'x-api-key: '\\ -d '{"inputs": {"text":""}, "tweaks": {}}' + ``` + With Python using `requests`: + ```python import requests from typing import Optional -BASE_API_URL = "http://localhost:3001/api/v1/process" +BASE_API_URL = "" FLOW_ID = "4441b773-0724-434e-9cee-19d995d8f2df" # You can tweak the flow by adding a tweaks dictionary # e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}} @@ -114,25 +129,32 @@ def run_flow(inputs: dict, inputs = {"text":""} api_key = "" print(run_flow(inputs, flow_id=FLOW_ID, tweaks=TWEAKS, apiKey=api_key)) + ``` -### Use the query parameter + +### Use the query parameter {#febb797f3bb5403b9f070afc0fa4f453} + Include the API key as a query parameter in the URL: -```bash -curl -X POST \ - http://localhost:3000/api/v1/process/?x-api-key= \ - -H 'Content-Type: application/json'\ + +```shell +curl -X POST \\ + ?x-api-key= \\ + -H 'Content-Type: application/json'\\ -d '{"inputs": {"text":""}, "tweaks": {}}' + ``` + With Python using `requests`: + ```python import requests -BASE_API_URL = "http://localhost:3001/api/v1/process" +BASE_API_URL = "" FLOW_ID = "4441b773-0724-434e-9cee-19d995d8f2df" # You can tweak the flow by adding a tweaks dictionary # e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}} @@ -167,17 +189,23 @@ def run_flow(inputs: dict, inputs = {"text":""} api_key = "" print(run_flow(inputs, flow_id=FLOW_ID, tweaks=TWEAKS, apiKey=api_key)) + ``` -## Security Considerations + +## Security Considerations {#1273eb69a61344d19827b30dba46dfd5} - **Visibility**: For security reasons, the API key cannot be retrieved again through the UI. - **Scope**: The key allows access only to the flows and components of the specific user to whom it was issued. -## Custom API endpoint +## Custom API endpoint {#da933a86690a4fdeac24024472caf8a9} -Under **Project Settings** > **Endpoint Name**, you can pick a custom name for the endpoint used to call your flow from the API. -## Revoke an API Key +Under **Project Settings** > **Endpoint Name**, you can pick a custom name for the endpoint used to call your flow from the API. + + +## Revoke an API Key {#f0ea41ea167845cea91bb5e8f90d9df0} + To revoke an API key, delete it from the UI. This action immediately invalidates the key and prevents it from being used again. + diff --git a/docs/docs/administration/login.mdx b/docs/docs/Configuration/configuration-authentication.md similarity index 61% rename from docs/docs/administration/login.mdx rename to docs/docs/Configuration/configuration-authentication.md index 65ed2c178..a439d55f9 100644 --- a/docs/docs/administration/login.mdx +++ b/docs/docs/Configuration/configuration-authentication.md @@ -1,18 +1,24 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; +--- +title: Authentication +sidebar_position: 0 +slug: /configuration-authentication +--- -# Sign Up and Sign In - -This page may contain outdated information. It will be updated as soon as possible. - + +## Sign Up and Sign In {#f480dac5d2094d75a433de0b8e195641} + + +> ⚠️ WARNING +> This page may contain outdated information. It will be updated as soon as possible. +> + The login functionality in Langflow serves to authenticate users and protect sensitive routes in the application. Starting from version 0.5, Langflow introduces an enhanced login mechanism that is governed by a few environment variables. This allows new secure features. -## Environment variables + +## Environment variables {#3ed7cae6f5324ba0ac14783cf2a6cc07} + The following environment variables are crucial in configuring the login settings: @@ -24,76 +30,93 @@ The following environment variables are crucial in configuring the login setting All of these variables can be passed to the CLI command _`langflow run`_ through the _`--env-file`_ option. For example: -```bash + +```shell langflow run --env-file .env + ``` - - It is critical not to expose these environment variables in your code - repository. Always set them securely in your deployment environment, for - example, using Docker secrets, Kubernetes ConfigMaps/Secrets, or dedicated - secure environment configuration systems like AWS Secrets Manager. - -### _`LANGFLOW_AUTO_LOGIN`_ +> ℹ︎ INFO +> It is critical not to expose these environment variables in your code repository. Always set them securely in your deployment environment, for example, using Docker secrets, Kubernetes ConfigMaps/Secrets, or dedicated secure environment configuration systems like AWS Secrets Manager. +> + + +### _`LANGFLOW_AUTO_LOGIN`_ {#8b10059e0fbc44f3bc8ce63fe7692e7e} + By default, this variable is set to `True`. When enabled (`True`), Langflow operates as it did in versions prior to 0.5—automatic login without requiring explicit user authentication. + To disable automatic login and enforce user authentication: -```bash + +```shell export LANGFLOW_AUTO_LOGIN=False ``` -### _`LANGFLOW_SUPERUSER`_ and _`LANGFLOW_SUPERUSER_PASSWORD`_ + +### _`LANGFLOW_SUPERUSER`_ and _`LANGFLOW_SUPERUSER_PASSWORD`_ {#a61a651a0fc7443a82cec93c07a14503} + These environment variables are only relevant when `LANGFLOW_AUTO_LOGIN` is set to `False`. They specify the username and password for the superuser, which is essential for administrative tasks. + To create a superuser manually: -```bash + +```shell export LANGFLOW_SUPERUSER=admin export LANGFLOW_SUPERUSER_PASSWORD=securepassword ``` + You can also use the CLI command `langflow superuser` to set up a superuser interactively. -### _`LANGFLOW_SECRET_KEY`_ + +### _`LANGFLOW_SECRET_KEY`_ {#977aea34e6174c58bd76107990d62a1f} + This environment variable holds a secret key used for encrypting the superuser's password. Make sure to set this to a secure, randomly generated string. -```bash + +```shell export LANGFLOW_SECRET_KEY=randomly_generated_secure_key + ``` -### _`LANGFLOW_NEW_USER_IS_ACTIVE`_ + +### _`LANGFLOW_NEW_USER_IS_ACTIVE`_ {#c8f5df9283be4e20be51e14518f5272e} + By default, this variable is set to `False`. When enabled (`True`), new users are automatically activated and can log in without requiring explicit activation by the superuser. -## Manage superusers with the CLI + +## Manage superusers with the CLI {#3b0c36a5cc0f4acc95c884d3de858d46} + Langflow provides a command-line utility for managing superusers: -```bash + +```shell langflow superuser ``` + This command prompts you to enter the username and password for the superuser, unless they are already set using environment variables. -## Sign in + +## Sign in {#736ebb8c854b4c268d5e748c119a08ea} + With _`LANGFLOW_AUTO_LOGIN`_ set to _`False`_, Langflow requires users to sign up before they can log in. The sign-up page is the default landing page when a user visits Langflow for the first time. - -## Profile settings +![](./1009571828.png) + + +## Profile settings {#dd5926e12471448d99bd6849d2149dc8} + Once signed in, you can change your profile settings by clicking on the profile icon in the top right corner of the Langflow dashboard. This opens a dropdown menu with the following options: @@ -101,34 +124,17 @@ Once signed in, you can change your profile settings by clicking on the profile - **Profile Settings**: Opens the profile settings page. - **Sign Out**: Logs the user out. - + ![](./563306242.png) + Select **Profile Settings** to change your password and your profile picture. - + +![](./1813063533.png) + Select **Admin Page** to manage users and groups as the superuser. - +![](./383358552.png) + diff --git a/docs/docs/deployment/backend-only.mdx b/docs/docs/Configuration/configuration-backend-only.md similarity index 59% rename from docs/docs/deployment/backend-only.mdx rename to docs/docs/Configuration/configuration-backend-only.md index 1fc16de2f..1c2cd38a3 100644 --- a/docs/docs/deployment/backend-only.mdx +++ b/docs/docs/Configuration/configuration-backend-only.md @@ -1,36 +1,41 @@ -import Admonition from "@theme/Admonition"; +--- +title: Backend-Only +sidebar_position: 4 +slug: /configuration-backend-only +--- -# Backend-only - -This page may contain outdated information. It will be updated as soon as possible. - + +> ⚠️ WARNING +> This page may contain outdated information. It will be updated as soon as possible. + You can run Langflow in `--backend-only` mode to expose your Langflow app as an API, without running the frontend UI. + Start langflow in backend-only mode with `python3 -m langflow run --backend-only`. + The terminal prints `Welcome to ⛓ Langflow`, and a blank window opens at `http://127.0.0.1:7864/all`. Langflow will now serve requests to its API without the frontend running. -## Prerequisites -- [Langflow installed](../getting-started/install-langflow) +## Prerequisites {#81dfa9407ed648889081b9d08b0e5cfe} -- [OpenAI API key](https://platform.openai.com) +- [Langflow installed](/getting-started-installation) +- [OpenAI API key](https://platform.openai.com/) +- [A Langflow flow created](/starter-projects-basic-prompting) -- [A Langflow flow created](../starter-projects/basic-prompting) - -## Download your flow's curl call +## Download your flow's curl call {#d2cf1b694e4741eca07fd9806516007b} 1. Click API. -2. Click **curl** > **Copy code** and save the code to your local machine. - It will look something like this: +2. Click **curl** > **Copy code** and save the code to your local machine. +It will look something like this: -```curl -curl -X POST \ - "http://127.0.0.1:7864/api/v1/run/ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef?stream=false" \ - -H 'Content-Type: application/json'\ +```text +curl -X POST \\ + "" \\ + -H 'Content-Type: application/json'\\ -d '{"input_value": "message", "output_type": "chat", "input_type": "chat", @@ -40,39 +45,46 @@ curl -X POST \ "ChatOutput-ktwdw": {}, "ChatInput-xXC4F": {} }}' + ``` + Note the flow ID of `ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef`. You can find this ID in the UI as well to ensure you're querying the right flow. -## Start Langflow in backend-only mode + +## Start Langflow in backend-only mode {#f0ba018daf3041c39c0d226dadf78d35} 1. Stop Langflow with Ctrl+C. 2. Start langflow in backend-only mode with `python3 -m langflow run --backend-only`. - The terminal prints `Welcome to ⛓ Langflow`, and a blank window opens at `http://127.0.0.1:7864/all`. - Langflow will now serve requests to its API. +The terminal prints `Welcome to ⛓ Langflow`, and a blank window opens at `http://127.0.0.1:7864/all`. +Langflow will now serve requests to its API. 3. Run the curl code you copied from the UI. - You should get a result like this: +You should get a result like this: -```bash +```shell {"session_id":"ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef:bf81d898868ac87e1b4edbd96c131c5dee801ea2971122cc91352d144a45b880","outputs":[{"inputs":{"input_value":"hi, are you there?"},"outputs":[{"results":{"result":"Arrr, ahoy matey! Aye, I be here. What be ye needin', me hearty?"},"artifacts":{"message":"Arrr, ahoy matey! Aye, I be here. What be ye needin', me hearty?","sender":"Machine","sender_name":"AI"},"messages":[{"message":"Arrr, ahoy matey! Aye, I be here. What be ye needin', me hearty?","sender":"Machine","sender_name":"AI","component_id":"ChatOutput-ktwdw"}],"component_display_name":"Chat Output","component_id":"ChatOutput-ktwdw","used_frozen_result":false}]}]}% + ``` + Again, note that the flow ID matches. Langflow is receiving your POST request, running the flow, and returning the result, all without running the frontend. Cool! -## Download your flow's Python API call + +## Download your flow's Python API call {#5923ff9dc40843c7a22a72fa6c66540c} + Instead of using curl, you can download your flow as a Python API call instead. 1. Click API. -2. Click **Python API** > **Copy code** and save the code to your local machine. - The code will look something like this: +2. Click **Python API** > **Copy code** and save the code to your local machine. +The code will look something like this: ```python import requests from typing import Optional -BASE_API_URL = "http://127.0.0.1:7864/api/v1/run" +BASE_API_URL = "" FLOW_ID = "ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef" # You can tweak the flow by adding a tweaks dictionary # e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}} @@ -83,47 +95,55 @@ def run_flow(message: str, input_type: str = "chat", tweaks: Optional[dict] = None, api_key: Optional[str] = None) -> dict: - """ - Run a flow with a given message and optional tweaks. + """Run a flow with a given message and optional tweaks. - :param message: The message to send to the flow - :param flow_id: The ID of the flow to run - :param tweaks: Optional tweaks to customize the flow - :return: The JSON response from the flow - """ - api_url = f"{BASE_API_URL}/{flow_id}" + :param message: The message to send to the flow + :param flow_id: The ID of the flow to run + :param tweaks: Optional tweaks to customize the flow + :return: The JSON response from the flow + """ + api_url = f"{BASE_API_URL}/{flow_id}" + payload = { + "input_value": message, + "output_type": output_type, + "input_type": input_type, + } + headers = None + if tweaks: + payload["tweaks"] = tweaks + if api_key: + headers = {"x-api-key": api_key} + response = requests.post(api_url, json=payload, headers=headers) + return response.json() - payload = { - "input_value": message, - "output_type": output_type, - "input_type": input_type, - } - headers = None - if tweaks: - payload["tweaks"] = tweaks - if api_key: - headers = {"x-api-key": api_key} - response = requests.post(api_url, json=payload, headers=headers) - return response.json() + # Setup any tweaks you want to apply to the flow -# Setup any tweaks you want to apply to the flow -message = "message" + message = "message" + + print(run_flow(message=message, flow_id=FLOW_ID)) -print(run_flow(message=message, flow_id=FLOW_ID)) ``` + 3. Run your Python app: -```python + +```shell python3 app.py ``` + The result is similar to the curl call: -```bash + +```json {'session_id': 'ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef:bf81d898868ac87e1b4edbd96c131c5dee801ea2971122cc91352d144a45b880', 'outputs': [{'inputs': {'input_value': 'message'}, 'outputs': [{'results': {'result': "Arrr matey! What be yer message for this ol' pirate? Speak up or walk the plank!"}, 'artifacts': {'message': "Arrr matey! What be yer message for this ol' pirate? Speak up or walk the plank!", 'sender': 'Machine', 'sender_name': 'AI'}, 'messages': [{'message': "Arrr matey! What be yer message for this ol' pirate? Speak up or walk the plank!", 'sender': 'Machine', 'sender_name': 'AI', 'component_id': 'ChatOutput-ktwdw'}], 'component_display_name': 'Chat Output', 'component_id': 'ChatOutput-ktwdw', 'used_frozen_result': False}]}]} + ``` + Your Python app POSTs to your Langflow server, and the server runs the flow and returns the result. -See [API](../administration/api) for more ways to interact with your headless Langflow server. + +See [API](https://www.notion.so/administration/api) for more ways to interact with your headless Langflow server. + diff --git a/docs/docs/administration/cli.mdx b/docs/docs/Configuration/configuration-cli.md similarity index 74% rename from docs/docs/administration/cli.mdx rename to docs/docs/Configuration/configuration-cli.md index 2a54c1fca..15f04528f 100644 --- a/docs/docs/administration/cli.mdx +++ b/docs/docs/Configuration/configuration-cli.md @@ -1,34 +1,44 @@ -import Admonition from "@theme/Admonition"; +--- +title: Command Line Interface (CLI) +sidebar_position: 2 +slug: /configuration-cli +--- -# Command Line Interface (CLI) - -This page may contain outdated information. It will be updated as soon as possible. - + +> ⚠️ WARNING +> This page may contain outdated information. It will be updated as soon as possible. +> + Langflow's Command Line Interface (CLI) is a powerful tool that allows you to interact with the Langflow server from the command line. The CLI provides a wide range of commands to help you shape Langflow to your needs. + The available commands are below. Navigate to their individual sections of this page to see the parameters. -- [langflow](#overview) -- [langflow api-key](#langflow-api-key) -- [langflow copy-db](#langflow-copy-db) -- [langflow migration](#langflow-migration) -- [langflow run](#langflow-run) -- [langflow superuser](#langflow-superuser) +- [langflow](/configuration-cli) +- [langflow api-key](/configuration-cli) +- [langflow copy-db](/configuration-cli) +- [langflow migration](/configuration-cli) +- [langflow run](/configuration-cli) +- [langflow superuser](/configuration-cli) + +## Overview {#c50e5530289349cf8ed7bee22ba2211a} -## Overview Running the CLI without any arguments displays a list of available options and commands. -```bash + +```shell langflow # or langflow --help # or python -m langflow + ``` + | Command | Description | | ----------- | ---------------------------------------------------------------------- | | `api-key` | Creates an API key for the default superuser if AUTO_LOGIN is enabled. | @@ -37,7 +47,9 @@ python -m langflow | `run` | Run the Langflow. | | `superuser` | Create a superuser. | -### Options + +### Options {#8a3b5b7ed55b4774ad6d533bb337ef47} + | Option | Description | | ---------------------- | -------------------------------------------------------------------------------- | @@ -45,11 +57,14 @@ python -m langflow | `--show-completion` | Show completion for the current shell, to copy it or customize the installation. | | `--help` | Show this message and exit. | -## langflow api-key + +## langflow api-key {#dbfc8c4c83474b83a38bdc7471bccf41} + Run the `api-key` command to create an API key for the default superuser if `LANGFLOW_AUTO_LOGIN` is set to `True`. -```bash + +```shell langflow api-key # or python -m langflow api-key @@ -63,36 +78,50 @@ python -m langflow api-key │ │ │ The API key has been copied to your clipboard. Cmd + V to paste it. │ ╰────────────────────────────── + ``` -### Options + +### Options {#ec2ef993dc984811b25838c8d8230b31} + | Option | Type | Description | | ----------- | ---- | ------------------------------------------------------------- | | --log-level | TEXT | Logging level. [env var: LANGFLOW_LOG_LEVEL] [default: error] | | --help | | Show this message and exit. | -## langflow copy-db + +## langflow copy-db {#729a13f4847545e5973d8f9c20f8833d} + Run the `copy-db` command to copy the cached `langflow.db` and `langflow-pre.db` database files to the current directory. + If the files exist in the cache directory, they will be copied to the same directory as `__main__.py`, which can be found with `which langflow`. -### Options + +### Options {#7b7e6bd02b3243218e1d666711854673} + None. -## langflow migration -Run or test migrations with the [Alembic](https://pypi.org/project/alembic/) database tool. +## langflow migration {#7027c1925a444119a7a8ea2bff4bd16d} -```bash + +Run or test migrations with the Alembic database tool. + + +```shell langflow migration # or python -m langflow migration + ``` -### Options + +### Options {#0b38fbe97bb34edeb7740a7db58433e9} + | Option | Description | | ------------------- | -------------------------------------------------------------------------------------------------------------------------- | @@ -100,56 +129,71 @@ python -m langflow migration | `--fix, --no-fix` | Fix migrations. This is a destructive operation, and should only be used if you know what you are doing. [default: no-fix] | | `--help` | Show this message and exit. | -## langflow run + +## langflow run {#fe050aa659cb4d33a560b859d54c94ea} + Run Langflow. -```bash + +```shell langflow run # or python -m langflow run + ``` -### Options -| Option | Description | -| ---------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `--help` | Displays all available options. | -| `--host` | Defines the host to bind the server to. Can be set using the `LANGFLOW_HOST` environment variable. The default is `127.0.0.1`. | -| `--workers` | Sets the number of worker processes. Can be set using the `LANGFLOW_WORKERS` environment variable. The default is `1`. | -| `--timeout` | Sets the worker timeout in seconds. The default is `60`. | -| `--port` | Sets the port to listen on. Can be set using the `LANGFLOW_PORT` environment variable. The default is `7860`. | -| `--env-file` | Specifies the path to the .env file containing environment variables. The default is `.env`. | -| `--log-level` | Defines the logging level. Can be set using the `LANGFLOW_LOG_LEVEL` environment variable. The default is `critical`. | -| `--components-path` | Specifies the path to the directory containing custom components. Can be set using the `LANGFLOW_COMPONENTS_PATH` environment variable. The default is `langflow/components`. | -| `--log-file` | Specifies the path to the log file. Can be set using the `LANGFLOW_LOG_FILE` environment variable. The default is `logs/langflow.log`. | -| `--cache` | Select the type of cache to use. Options are `InMemoryCache` and `SQLiteCache`. Can be set using the `LANGFLOW_LANGCHAIN_CACHE` environment variable. The default is `SQLiteCache`. | -| `--dev`/`--no-dev` | Toggles the development mode. The default is `no-dev`. | -| `--path` | Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using the `LANGFLOW_FRONTEND_PATH` environment variable. | -| `--open-browser`/`--no-open-browser` | Toggles the option to open the browser after starting the server. Can be set using the `LANGFLOW_OPEN_BROWSER` environment variable. The default is `open-browser`. | -| `--remove-api-keys`/`--no-remove-api-keys` | Toggles the option to remove API keys from the projects saved in the database. Can be set using the `LANGFLOW_REMOVE_API_KEYS` environment variable. The default is `no-remove-api-keys`. | -| `--install-completion [bash\|zsh\|fish\|powershell\|pwsh]` | Installs completion for the specified shell. | -| `--show-completion [bash\|zsh\|fish\|powershell\|pwsh]` | Shows completion for the specified shell, allowing you to copy it or customize the installation. | -| `--backend-only` | This parameter, with a default value of `False`, allows running only the backend server without the frontend. It can also be set using the `LANGFLOW_BACKEND_ONLY` environment variable. For more, see [Backend-only](../deployment/backend-only). | -| `--store` | This parameter, with a default value of `True`, enables the store features, use `--no-store` to deactivate it. It can be configured using the `LANGFLOW_STORE` environment variable. | +### Options {#4e811481ec9142f1b60309bb1ce5a2ce} + + +| Option | Description | +| ---------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--help` | Displays all available options. | +| `--host` | Defines the host to bind the server to. Can be set using the `LANGFLOW_HOST` environment variable. The default is `127.0.0.1`. | +| `--workers` | Sets the number of worker processes. Can be set using the `LANGFLOW_WORKERS` environment variable. The default is `1`. | +| `--timeout` | Sets the worker timeout in seconds. The default is `60`. | +| `--port` | Sets the port to listen on. Can be set using the `LANGFLOW_PORT` environment variable. The default is `7860`. | +| `--env-file` | Specifies the path to the .env file containing environment variables. The default is `.env`. | +| `--log-level` | Defines the logging level. Can be set using the `LANGFLOW_LOG_LEVEL` environment variable. The default is `critical`. | +| `--components-path` | Specifies the path to the directory containing custom components. Can be set using the `LANGFLOW_COMPONENTS_PATH` environment variable. The default is `langflow/components`. | +| `--log-file` | Specifies the path to the log file. Can be set using the `LANGFLOW_LOG_FILE` environment variable. The default is `logs/langflow.log`. | +| `--cache` | Select the type of cache to use. Options are `InMemoryCache` and `SQLiteCache`. Can be set using the `LANGFLOW_LANGCHAIN_CACHE` environment variable. The default is `SQLiteCache`. | +| `--dev`/`--no-dev` | Toggles the development mode. The default is `no-dev`. | +| `--path` | Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using the `LANGFLOW_FRONTEND_PATH` environment variable. | +| `--open-browser`/`--no-open-browser` | Toggles the option to open the browser after starting the server. Can be set using the `LANGFLOW_OPEN_BROWSER` environment variable. The default is `open-browser`. | +| `--remove-api-keys`/`--no-remove-api-keys` | Toggles the option to remove API keys from the projects saved in the database. Can be set using the `LANGFLOW_REMOVE_API_KEYS` environment variable. The default is `no-remove-api-keys`. | +| `--install-completion [bash\|zsh\|fish\|powershell\|pwsh]` | Installs completion for the specified shell. | +| `--show-completion [bash\|zsh\|fish\|powershell\|pwsh]` | Shows completion for the specified shell, allowing you to copy it or customize the installation. | +| `--backend-only` | This parameter, with a default value of `False`, allows running only the backend server without the frontend. It can also be set using the `LANGFLOW_BACKEND_ONLY` environment variable. | +| `--store` | This parameter, with a default value of `True`, enables the store features, use `--no-store` to deactivate it. It can be configured using the `LANGFLOW_STORE` environment variable. | + + +### CLI environment variables {#5868aaccfcc74e26968538ef4d07e756} -#### CLI environment variables You can configure many of the CLI options using environment variables. These can be exported in your operating system or added to a `.env` file and loaded using the `--env-file` option. + A sample `.env` file named `.env.example` is included with the project. Copy this file to a new file named `.env` and replace the example values with your actual settings. If you're setting values in both your OS and the `.env` file, the `.env` settings will take precedence. -## langflow superuser + +## langflow superuser {#5944233ce0c942878e928e1f2945d717} + Create a superuser for Langflow. -```bash + +```shell langflow superuser # or python -m langflow superuser + ``` -### Options + +### Options {#f333c5635ead4c3d95985467bb08cc8f} + | Option | Type | Description | | ------------- | ---- | ------------------------------------------------------------- | @@ -157,3 +201,4 @@ python -m langflow superuser | `--password` | TEXT | Password for the superuser. [default: None] [required] | | `--log-level` | TEXT | Logging level. [env var: LANGFLOW_LOG_LEVEL] [default: error] | | `--help` | | Show this message and exit. | + diff --git a/docs/docs/Contributing/683296796.png b/docs/docs/Contributing/683296796.png new file mode 100644 index 000000000..66965f8ed Binary files /dev/null and b/docs/docs/Contributing/683296796.png differ diff --git a/docs/docs/Contributing/_category_.json b/docs/docs/Contributing/_category_.json new file mode 100644 index 000000000..32da1de7c --- /dev/null +++ b/docs/docs/Contributing/_category_.json @@ -0,0 +1 @@ +{"position":10, "label":"Contributing"} \ No newline at end of file diff --git a/docs/docs/Contributing/contributing-community.md b/docs/docs/Contributing/contributing-community.md new file mode 100644 index 000000000..d3ad514af --- /dev/null +++ b/docs/docs/Contributing/contributing-community.md @@ -0,0 +1,55 @@ +--- +title: Community +sidebar_position: 3 +slug: /contributing-community +--- + + + +## 🤖 Join **Langflow** Discord server {#80011e0bda004e83a8012c7ec6eab29a} + + +Join us to ask questions and showcase your projects. + + +Let's bring together the building blocks of AI integration! + + +Langflow [Discord](https://discord.gg/EqksyE2EX9) server. + + +--- + + +## 🐦 Stay tuned for **Langflow** on Twitter {#6a17ba5905ad4f7aa5347af7854779f6} + + +Follow [@langflow_ai](https://twitter.com/langflow_ai) on **Twitter** to get the latest news about **Langflow**. + + +--- + + +## ⭐️ Star **Langflow** on GitHub {#c903a569934643799bf52b7d1b3514e1} + + +You can "star" **Langflow** in [GitHub](https://github.com/langflow-ai/langflow). + + +By adding a star, other users will be able to find it more easily and see that it has been already useful for others. + + +--- + + +## 👀 Watch the GitHub repository for releases {#d0a089ed717742308bd17430e5ae6309} + + +You can "watch" **Langflow** in [GitHub](https://github.com/langflow-ai/langflow). If you select "Watching" instead of "Releases only" you will receive notifications when someone creates a new issue or question. You can also specify that you only want to be notified about new issues, discussions, PRs, etc. so you can try and help them solve those questions. + + +--- + + +Thanks! 🚀 + diff --git a/docs/docs/Contributing/contributing-github-issues.md b/docs/docs/Contributing/contributing-github-issues.md new file mode 100644 index 000000000..be89264e6 --- /dev/null +++ b/docs/docs/Contributing/contributing-github-issues.md @@ -0,0 +1,21 @@ +--- +title: GitHub Issues +sidebar_position: 2 +slug: /contributing-github-issues +--- + + + +Our [issues](https://github.com/langflow-ai/langflow/issues) page is kept up to date with bugs, improvements, and feature requests. There is a taxonomy of labels to help with sorting and discovery of issues of interest. + + +If you're looking for help with your code, consider posting a question on the [GitHub Discussions board](https://github.com/langflow-ai/langflow/discussions). Please understand that we won't be able to provide individual support via email. We also believe that help is much more valuable if it's **shared publicly**, so that more people can benefit from it. + +- **Describing your issue:** Try to provide as many details as possible. What exactly goes wrong? _How_ is it failing? Is there an error? "XY doesn't work" usually isn't that helpful for tracking down problems. Always remember to include the code you ran and if possible, extract only the relevant parts and don't just dump your entire script. This will make it easier for us to reproduce the error. +- **Sharing long blocks of code or logs:** If you need to include long code, logs or tracebacks, you can wrap them in `
` and `
`. This [collapses the content](https://developer.mozilla.org/en/docs/Web/HTML/Element/details) so it only becomes visible on click, making the issue easier to read and follow. + +## Issue labels {#e19eae656c914ce7aedc4f55565cc0bc} + + +[See this page](https://github.com/langflow-ai/langflow/labels) for an overview of the system we use to tag our issues and pull requests. + diff --git a/docs/docs/Contributing/contributing-how-to-contribute.md b/docs/docs/Contributing/contributing-how-to-contribute.md new file mode 100644 index 000000000..685a5d440 --- /dev/null +++ b/docs/docs/Contributing/contributing-how-to-contribute.md @@ -0,0 +1,141 @@ +--- +title: How to contribute? +sidebar_position: 1 +slug: /contributing-how-to-contribute +--- + + + +👋 Hello there! We welcome contributions from developers of all levels to our open-source project on [GitHub](https://github.com/langflow-ai/langflow). If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible. + + +As an open-source project in a rapidly developing field, we are extremely open to contributions, whether in the form of a new feature, improved infra, or better documentation. + + +To contribute to this project, please follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow. + + +Please do not try to push directly to this repo unless you are a maintainer. + + +--- + + +## Local development {#0388cc3c758d434d994022863a6bafa9} + + +You can develop Langflow using docker compose, or locally. + + +We provide a `.vscode/launch.json` file for debugging the backend in VSCode, which is a lot faster than using docker compose. + + +Setting up hooks: + + +`make init` + + +This will install the pre-commit hooks, which will run `make format` on every commit. + + +It is advised to run `make lint` before pushing to the repository. + + +--- + + +## Run locally {#5225c2ef0cd6403c9f6c6bbd888115e0} + + +Langflow can run locally by cloning the repository and installing the dependencies. We recommend using a virtual environment to isolate the dependencies from your system. + + +Before you start, make sure you have the following installed: + +- Poetry (>=1.4) +- Node.js + +Then, in the root folder, install the dependencies and start the development server for the backend: + + +`make backend` + + +And the frontend: + + +`make frontend` + + +--- + + +## Docker compose {#b07f359414ff4220ac615afc364ee46e} + + +The following snippet will run the backend and frontend in separate containers. The frontend will be available at `localhost:3000` and the backend at `localhost:7860`. + + +`docker compose up --build# ormake dev build=1` + + +--- + + +## Documentation {#5f34bcaeccdc4489b0c5ee2c4a21354e} + + +The documentation is built using [Docusaurus](https://docusaurus.io/). To run the documentation locally, run the following commands: + + +`cd docsnpm installnpm run start` + + +The documentation will be available at `localhost:3000` and all the files are located in the `docs/docs` folder. Once you are done with your changes, you can create a Pull Request to the `main` branch. + + +## Submitting Components {#9676353bc4504551a4014dd572ac8be8} + + +--- + + +New components are added as objects of the [CustomComponent](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/interface/custom/custom_component/custom_component.py) class and any dependencies are added to the [pyproject.toml](https://github.com/langflow-ai/langflow/blob/dev/pyproject.toml#L27) file. + + +## Add an example component {#8caae106c853465d83183e7f5272e4d8} + + +You have a new document loader called **MyCustomDocumentLoader** and it would look awesome in Langflow. + +1. Write your loader as an object of the [CustomComponent](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/interface/custom/custom_component/custom_component.py) class. You'll create a new class, `MyCustomDocumentLoader`, that will inherit from `CustomComponent` and override the base class's methods. +2. Define optional attributes like `display_name`, `description`, and `documentation` to provide information about your custom component. +3. Implement the `build_config` method to define the configuration options for your custom component. +4. Implement the `build` method to define the logic for taking input parameters specified in the `build_config` method and returning the desired output. +5. Add the code to the [/components/documentloaders](https://github.com/langflow-ai/langflow/tree/dev/src/backend/base/langflow/components) folder. +6. Add the dependency to [/documentloaders/__init__.py](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/components/documentloaders/__init__.py) as `from .MyCustomDocumentLoader import MyCustomDocumentLoader`. +7. Add any new dependencies to the outer [pyproject.toml](https://github.com/langflow-ai/langflow/blob/dev/pyproject.toml#L27) file. +8. Submit documentation for your component. For this example, you'd submit documentation to the [loaders page](https://github.com/langflow-ai/langflow/blob/dev/docs/docs/components/loaders). +9. Submit your changes as a pull request. The Langflow team will have a look, suggest changes, and add your component to Langflow. + +## User Sharing {#34ac32e11f344eab892b94531a21d2c9} + + +You might want to share and test your custom component with others, but don't need it merged into the main source code. + + +If so, you can share your component on the Langflow store. + +1. [Register at the Langflow store](https://www.langflow.store/login/). +2. Undergo pre-validation before receiving an API key. +3. To deploy your amazing component directly to the Langflow store, without it being merged into the main source code, navigate to your flow, and then click **Share**. The share window appears: + +![](./683296796.png) + + +4. Choose whether you want to flow to be public or private. You can also **Export** your flow as a JSON file from this window. When you're ready to share the flow, click **Share Flow**. You should see a **Flow shared successfully** popup. + + +5. To confirm, navigate to the **Langflow Store** and filter results by **Created By Me**. You should see your new flow on the **Langflow Store**. + diff --git a/docs/docs/contributing/telemetry.mdx b/docs/docs/Contributing/contributing-telemetry.md similarity index 77% rename from docs/docs/contributing/telemetry.mdx rename to docs/docs/Contributing/contributing-telemetry.md index a0bdb5d2d..dc1957a54 100644 --- a/docs/docs/contributing/telemetry.mdx +++ b/docs/docs/Contributing/contributing-telemetry.md @@ -1,30 +1,38 @@ -import Admonition from "@theme/Admonition"; +--- +title: Telemetry +sidebar_position: 0 +slug: /contributing-telemetry +--- + -# Telemetry Our system uses anonymous telemetry to collect essential usage statistics to enhance functionality and user experience. This data helps us identify commonly used features and areas needing improvement, ensuring our development efforts align with what you need. - + +INFO + + We respect your privacy and are committed to protecting your data. We do not collect any personal information or sensitive data. All telemetry data is anonymized and used solely for improving Langflow. -You can opt-out of telemetry by setting the `LANGFLOW_DO_NOT_TRACK` or `DO_NOT_TRACK` environment variable to `true` before running Langflow. This will disable telemetry data collection. - +You can opt-out of telemetry by setting the `LANGFLOW_DO_NOT_TRACK` or `DO_NOT_TRACK` environment variable to `true` before running Langflow. This will disable telemetry data collection. -## Data Collected Includes: -### Run +## Data Collected Includes: {#1734ed50fb4a4a45aaa84185b44527ca} + + +### Run {#2d427dca4f0148ae867997f6789e8bfb} - **IsWebhook**: Indicates whether the operation was triggered via a webhook. - **Seconds**: Duration in seconds for how long the operation lasted, providing insights into performance. - **Success**: Boolean value indicating whether the operation was successful, helping identify potential errors or issues. - **ErrorMessage**: Provides error message details if the operation was unsuccessful, aiding in troubleshooting and enhancements. -### Shutdown +### Shutdown {#081e4bd4faec430fb05b657026d1a69c} - **Time Running**: Total runtime before shutdown, useful for understanding application lifecycle and optimizing uptime. -### Version +### Version {#dc09f6aba6c64c7b8dad3d86a7cba6d6} - **Version**: The specific version of Langflow used, which helps in tracking feature adoption and compatibility. - **Platform**: Operating system of the host machine, which aids in focusing our support for popular platforms like Windows, macOS, and Linux. @@ -34,18 +42,18 @@ You can opt-out of telemetry by setting the `LANGFLOW_DO_NOT_TRACK` or `DO_NOT_T - **CacheType**: Type of caching mechanism used, which impacts performance and efficiency. - **BackendOnly**: Boolean indicating whether you are running Langflow in a backend-only mode, useful for understanding deployment configurations. -### Playground +### Playground {#ae6c3859f612441db3c15a7155e9f920} - **Seconds**: Duration in seconds for playground execution, offering insights into performance during testing or experimental stages. - **ComponentCount**: Number of components used in the playground, which helps understand complexity and usage patterns. - **Success**: Success status of the playground operation, aiding in identifying the stability of experimental features. -### Component +### Component {#630728d6654c40a6b8901459a4bc3a4e} - **Name**: Identifies the component, providing data on which components are most utilized or prone to issues. - **Seconds**: Time taken by the component to execute, offering performance metrics. - **Success**: Whether the component operated successfully, which helps in quality control. - **ErrorMessage**: Details of any errors encountered, crucial for debugging and improvement. -This telemetry data is crucial for enhancing Langflow and ensuring that our development efforts align with your needs. -Your feedback and suggestions are invaluable in shaping the future of Langflow, and we appreciate your support in making Langflow better for everyone. \ No newline at end of file +This telemetry data is crucial for enhancing Langflow and ensuring that our development efforts align with your needs. Your feedback and suggestions are invaluable in shaping the future of Langflow, and we appreciate your support in making Langflow better for everyone. + diff --git a/docs/docs/Deployment/1098199232.png b/docs/docs/Deployment/1098199232.png new file mode 100644 index 000000000..884c6bf74 Binary files /dev/null and b/docs/docs/Deployment/1098199232.png differ diff --git a/docs/docs/Deployment/1861599636.png b/docs/docs/Deployment/1861599636.png new file mode 100644 index 000000000..934e77f85 Binary files /dev/null and b/docs/docs/Deployment/1861599636.png differ diff --git a/docs/docs/Deployment/1929176153.png b/docs/docs/Deployment/1929176153.png new file mode 100644 index 000000000..e46805ccd Binary files /dev/null and b/docs/docs/Deployment/1929176153.png differ diff --git a/docs/docs/Deployment/257215214.png b/docs/docs/Deployment/257215214.png new file mode 100644 index 000000000..d1adaa231 Binary files /dev/null and b/docs/docs/Deployment/257215214.png differ diff --git a/docs/docs/Deployment/_category_.json b/docs/docs/Deployment/_category_.json new file mode 100644 index 000000000..6d03dfaa4 --- /dev/null +++ b/docs/docs/Deployment/_category_.json @@ -0,0 +1 @@ +{"position":6, "label":"Deployment"} \ No newline at end of file diff --git a/docs/docs/Deployment/deployment-docker.md b/docs/docs/Deployment/deployment-docker.md new file mode 100644 index 000000000..44924b624 --- /dev/null +++ b/docs/docs/Deployment/deployment-docker.md @@ -0,0 +1,76 @@ +--- +title: Docker +sidebar_position: 2 +slug: /deployment-docker +--- + + + +This guide will help you get LangFlow up and running using Docker and Docker Compose. + + +## Prerequisites {#856bb2d98156402bbd1980365b98110c} + +- Docker +- Docker Compose + +## Clone repo and build Docker container {#ba89773aa8b8425b985bfe7ba91c35cc} + +1. Clone the LangFlow repository: + + `git clone https://github.com/langflow-ai/langflow.git` + +2. Navigate to the `docker_example` directory: + + `cd langflow/docker_example` + +3. Run the Docker Compose file: + + `docker compose up` + + +LangFlow will now be accessible at [http://localhost:7860/](http://localhost:7860/). + + +## Docker Compose configuration {#02226209cad24185a6ec5b69bd820d0f} + + +The Docker Compose configuration spins up two services: `langflow` and `postgres`. + + +### LangFlow service {#d749848451ea43bd86f6f096dc77e6e6} + + +The `langflow` service uses the `langflowai/langflow:latest` Docker image and exposes port 7860. It depends on the `postgres` service. + + +Environment variables: + +- `LANGFLOW_DATABASE_URL`: The connection string for the PostgreSQL database. +- `LANGFLOW_CONFIG_DIR`: The directory where LangFlow stores logs, file storage, monitor data, and secret keys. + +Volumes: + +- `langflow-data`: This volume is mapped to `/var/lib/langflow` in the container. + +### PostgreSQL service {#121140decbfe4997b12213bdd2c4da7e} + + +The `postgres` service uses the `postgres:16` Docker image and exposes port 5432. + + +Environment variables: + +- `POSTGRES_USER`: The username for the PostgreSQL database. +- `POSTGRES_PASSWORD`: The password for the PostgreSQL database. +- `POSTGRES_DB`: The name of the PostgreSQL database. + +Volumes: + +- `langflow-postgres`: This volume is mapped to `/var/lib/postgresql/data` in the container. + +## Switch to a specific LangFlow version {#2b3e191ea48f4feab89242433cf012d5} + + +If you want to use a specific version of LangFlow, you can modify the `image` field under the `langflow` service in the Docker Compose file. For example, to use version 1.0-alpha, change `langflowai/langflow:latest` to `langflowai/langflow:1.0-alpha`. + diff --git a/docs/docs/deployment/gcp-deployment.mdx b/docs/docs/Deployment/deployment-gcp.md similarity index 63% rename from docs/docs/deployment/gcp-deployment.mdx rename to docs/docs/Deployment/deployment-gcp.md index e2abace6b..574fae5e5 100644 --- a/docs/docs/deployment/gcp-deployment.mdx +++ b/docs/docs/Deployment/deployment-gcp.md @@ -1,39 +1,56 @@ -import Admonition from "@theme/Admonition"; +--- +title: GCP +sidebar_position: 3 +slug: /deployment-gcp +--- -# Deploy on Google Cloud Platform - -This page may contain outdated information. It will be updated as soon as possible. - -## Run Langflow from a New Google Cloud Project +# Deploy on Google Cloud Platform {#4ee01cda736c4f7396936409f23cdb52} + + +> Warning: This page may contain outdated information. It will be updated as soon as possible. + + +## Run Langflow from a New Google Cloud Project {#ce729796d7404ccdb627bee47d6a4399} + This guide will help you set up a Langflow development VM in a Google Cloud Platform project using Google Cloud Shell. -> Note: When Cloud Shell opens, be sure to select **Trust repo**. Some `gcloud` commands might not run in an ephemeral Cloud Shell environment. -## Standard VM +> Note: When Cloud Shell opens, be sure to select Trust repo. Some gcloud commands might not run in an ephemeral Cloud Shell environment. + + +## Standard VM {#245b47b450dd4159a5c56a5124bab84f} + + +[embed](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial.md) -[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial.md) This script sets up a Debian-based VM with the Langflow package, Nginx, and the necessary configurations to run the Langflow Dev environment. + --- -## Spot/Preemptible Instance -[![Open in Cloud Shell - Spot Instance](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial_spot.md) +## Spot/Preemptible Instance {#de9b8f7c71284cbb98e8137a3c44553d} + When running as a [spot (preemptible) instance](https://cloud.google.com/compute/docs/instances/preemptible), the code and VM will behave the same way as in a regular instance, executing the startup script to configure the environment, install necessary dependencies, and run the Langflow application. However, **due to the nature of spot instances, the VM may be terminated at any time if Google Cloud needs to reclaim the resources**. This makes spot instances suitable for fault-tolerant, stateless, or interruptible workloads that can handle unexpected terminations and restarts. + --- -## Pricing (approximate) -> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator) +## Pricing (approximate) {#2289f4ba9f544e6e9d4b915ef5aacd24} + + +> For a more accurate breakdown of costs, please use the GCP Pricing Calculator + | Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes | | ------------------ | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | -------------------------------------------------------------------------- | | 100 GB Disk | - | $10/month | - | $10/month | Disk cost remains the same for both regular and Spot/Preemptible VMs | | VM (n1-standard-4) | $0.15/hr | ~$108/month | ~$0.04/hr | ~$29/month | The VM cost can be significantly reduced using a Spot/Preemptible instance | | **Total** | **$0.15/hr** | **~$118/month** | **~$0.04/hr** | **~$39/month** | Total costs for running the VM and disk 24/7 for an entire month | + diff --git a/docs/docs/Deployment/deployment-hugging-face-spaces.md b/docs/docs/Deployment/deployment-hugging-face-spaces.md new file mode 100644 index 000000000..611cdd323 --- /dev/null +++ b/docs/docs/Deployment/deployment-hugging-face-spaces.md @@ -0,0 +1,26 @@ +--- +title: 🤗 Spaces +sidebar_position: 0 +slug: /deployment-hugging-face-spaces +--- + + + +# HuggingFace Spaces {#00f5b3a6818d496dbb18e1a6a910e57d} + + +--- + + +HuggingFace provides a great alternative for running Langflow in their Spaces environment. This means you can run Langflow in the cloud without any local installation required. Here's how you can get Langflow up and running on HuggingFace Spaces: + +1. **Access Langflow Space**: Open a Chromium-based browser and navigate to the [Langflow Space](https://huggingface.co/spaces/Langflow/Langflow?duplicate=true). This link directs you to a pre-configured environment for Langflow. +2. **Duplicate the Space**: You'll encounter an option to duplicate the Langflow space. This step involves a few simple decisions: + - **Naming Your Space**: Assign a unique name to your new Space. + - **Visibility Settings**: Choose between Public or Private visibility for your Space. + - After setting these parameters, click on **Duplicate Space** to initiate the setup. + +![](./257215214.png) + +1. **Complete Installation**: The duplication and setup process begins immediately after you click **Duplicate Space**. Once completed, you will be automatically redirected to the main page of your new Space. +2. **Start Exploring Langflow**: With the setup complete, Langflow is now ready for use in your Space and you can start exploring its features and capabilities right away! diff --git a/docs/docs/Deployment/deployment-kubernetes.md b/docs/docs/Deployment/deployment-kubernetes.md new file mode 100644 index 000000000..bb50a6fe4 --- /dev/null +++ b/docs/docs/Deployment/deployment-kubernetes.md @@ -0,0 +1,361 @@ +--- +title: Kubernetes +sidebar_position: 1 +slug: /deployment-kubernetes +--- + + + +This guide will help you get LangFlow up and running in Kubernetes cluster, including the following steps: + +- Install [LangFlow as IDE](/deployment-kubernetes) in a Kubernetes cluster (for development) +- Install [LangFlow as a standalone application](/deployment-kubernetes) in a Kubernetes cluster (for production runtime workloads) + +## LangFlow (IDE) {#cb60b2f34e70490faf231cb0fe1a4b42} + + +This solution is designed to provide a complete environment for developers to create, test, and debug their flows. It includes both the API and the UI. + + +### Prerequisites {#3efd3c63ff8849228c136f9252e504fd} + +- Kubernetes server +- kubectl +- Helm + +### Step 0. Prepare a Kubernetes cluster {#290b9624770a4c1ba2c889d384b7ef4c} + + +We use [Minikube](https://minikube.sigs.k8s.io/docs/start/) for this example, but you can use any Kubernetes cluster. + +1. Create a Kubernetes cluster on Minikube. + + ```text + minikube start + ``` + +2. Set `kubectl` to use Minikube. + + ```text + kubectl config use-context minikube + ``` + + +### Step 1. Install the LangFlow Helm chart {#b5c2a35144634a05a392f7e650929efe} + +1. Add the repository to Helm. + + ```text + helm repo add langflow + helm repo update + ``` + +2. Install LangFlow with the default options in the `langflow` namespace. + + ```text + helm install langflow-ide langflow/langflow-ide -n langflow --create-namespace + ``` + +3. Check the status of the pods + + ```text + kubectl get pods -n langflow + ``` + + + ```text + NAME READY STATUS RESTARTS AGE + langflow-0 1/1 Running 0 33s + langflow-frontend-5d9c558dbb-g7tc9 1/1 Running 0 38s + ``` + + +### Step 2. Access LangFlow {#34c71d04351949deb6c8ed7ffe30eafb} + + +Enable local port forwarding to access LangFlow from your local machine. + + +```text +kubectl port-forward -n langflow svc/langflow-langflow-runtime 7860:7860 +``` + + +Now you can access LangFlow at [http://localhost:7860/](http://localhost:7860/). + + +### LangFlow version {#645c6ef7984d4da0bcc4170bab0ff415} + + +To specify a different LangFlow version, you can set the `langflow.backend.image.tag` and `langflow.frontend.image.tag` values in the `values.yaml` file. + + +```yaml +langflow: + backend: + image: + tag: "1.0.0a59" + frontend: + image: + tag: "1.0.0a59" + +``` + + +### Storage {#6772c00af79147d293c821b4c6905d3b} + + +By default, the chart will use a SQLLite database stored in a local persistent disk. +If you want to use an external PostgreSQL database, you can set the `langflow.database` values in the `values.yaml` file. + + +```yaml +# Deploy postgresql. You can skip this section if you have an existing postgresql database. +postgresql: + enabled: true + fullnameOverride: "langflow-ide-postgresql-service" + auth: + username: "langflow" + password: "langflow-postgres" + database: "langflow-db" + +langflow: + backend: + externalDatabase: + enabled: true + driver: + value: "postgresql" + host: + value: "langflow-ide-postgresql-service" + port: + value: "5432" + database: + value: "langflow-db" + user: + value: "langflow" + password: + valueFrom: + secretKeyRef: + key: "password" + name: "langflow-ide-postgresql-service" + sqlite: + enabled: false + +``` + + +### Scaling {#e1d95ba6551742aa86958dc03b26129e} + + +You can scale the number of replicas for the LangFlow backend and frontend services by changing the `replicaCount` value in the `values.yaml` file. + + +```yaml +langflow: + backend: + replicaCount: 3 + frontend: + replicaCount: 3 + +``` + + +You can scale frontend and backend services independently. + + +To scale vertically (increase the resources for the pods), you can set the `resources` values in the `values.yaml` file. + + +```yaml +langflow: + backend: + resources: + requests: + memory: "2Gi" + cpu: "1000m" + frontend: + resources: + requests: + memory: "1Gi" + cpu: "1000m" + +``` + + +### Deploy on AWS EKS, Google GKE, or Azure AKS and other examples {#a8c3d4dc4e4f42f49b21189df5e2b851} + + +Visit the [LangFlow Helm Charts repository](https://github.com/langflow-ai/langflow-helm-charts) for more information. + + +## LangFlow (Runtime) {#49f2813ad2d3460081ad26a286a65e73} + + +The runtime chart is tailored for deploying applications in a production environment. It is focused on stability, performance, isolation, and security to ensure that applications run reliably and efficiently. + + +Using a dedicated deployment for a set of flows is fundamental in production environments to have granular resource control. + + +### Prerequisites {#3ad3a9389fff483ba8bd309189426a9d} + +- Kubernetes server +- kubectl +- Helm + +### Step 0. Prepare a Kubernetes cluster {#aaa764703ec44bd5ba64b5ef4599630b} + + +Follow the same steps as for the LangFlow IDE. + + +### Step 1. Install the LangFlow runtime Helm chart {#72a18aa8349c421186ba01d73a002531} + +1. Add the repository to Helm. + + ```shell + helm repo add langflow + helm repo update + + ``` + +2. Install the LangFlow app with the default options in the `langflow` namespace. +If you bundled the flow in a docker image, you can specify the image name in the `values.yaml` file or with the `-set` flag: +If you want to download the flow from a remote location, you can specify the URL in the `values.yaml` file or with the `-set` flag: + + ```shell + helm install my-langflow-app langflow/langflow-runtime -n langflow --create-namespace --set image.repository=myuser/langflow-just-chat --set image.tag=1.0.0 + + ``` + + + ```shell + helm install my-langflow-app langflow/langflow-runtime -n langflow --create-namespace --set downloadFlows.flows[0].url=https://raw.githubusercontent.com/langflow-ai/langflow/dev/src/backend/base/langflow/initial_setup/starter_projects/Basic%20Prompting%20(Hello%2C%20world!).json + + ``` + +3. Check the status of the pods. + + ```text + kubectl get pods -n langflow + + ``` + + +### Step 2. Access the LangFlow app API {#e13326fc07734e4aa86dfb75ccfa31f8} + + +Enable local port forwarding to access LangFlow from your local machine. + + +```text +kubectl port-forward -n langflow svc/langflow-my-langflow-app 7860:7860 + +``` + + +Now you can access the API at [http://localhost:7860/api/v1/flows](http://localhost:7860/api/v1/flows) and execute the flow: + + +```shell +id=$(curl -s | jq -r '.flows[0].id') +curl -X POST \\ + "" \\ + -H 'Content-Type: application/json'\\ + -d '{ + "input_value": "Hello!", + "output_type": "chat", + "input_type": "chat" + }' + +``` + + +### Storage {#09514d2b59064d37b685c7c0acecb861} + + +In this case, storage is not needed as our deployment is stateless. + + +### Log level and LangFlow configurations {#ecd97f0be96d4d1cabcc5b77a2d00980} + + +You can set the log level and other LangFlow configurations in the `values.yaml` file. + + +```yaml +env: + - name: LANGFLOW_LOG_LEVEL + value: "INFO" + +``` + + +### Configure secrets and variables {#b91929e92acf47c183ea4c9ba9d19514} + + +To inject secrets and LangFlow global variables, you can use the `secrets` and `env` sections in the `values.yaml` file. + + +Let's say your flow uses a global variable which is a secret; when you export the flow as JSON, it's recommended to not include it. +When importing the flow in the LangFlow runtime, you can set the global variable using the `env` section in the `values.yaml` file. +Assuming you have a global variable called `openai_key_var`, you can read it directly from a secret: + + +```yaml +env: + - name: openai_key_var + valueFrom: + secretKeyRef: + name: openai-key + key: openai-key + +``` + + +or directly from the values file (not recommended for secret values!): + + +```yaml +env: + - name: openai_key_var + value: "sk-...." + +``` + + +### Scaling {#359b9ea5302147ebbed3ab8aa49dae8d} + + +You can scale the number of replicas for the LangFlow app by changing the `replicaCount` value in the `values.yaml` file. + + +```yaml +replicaCount: 3 + +``` + + +To scale vertically (increase the resources for the pods), you can set the `resources` values in the `values.yaml` file. + + +```yaml +resources: + requests: + memory: "2Gi" + cpu: "1000m" + +``` + + +### Other examples {#8522b4276b51448e9f8f0c6efc731a7c} + + +Visit the LangFlow Helm Charts repository for more examples and configurations. + + +Use the default values file as reference for all the options available. + + +Visit the examples directory to learn more about different deployment options. + diff --git a/docs/docs/Deployment/deployment-railway.md b/docs/docs/Deployment/deployment-railway.md new file mode 100644 index 000000000..3ff02e073 --- /dev/null +++ b/docs/docs/Deployment/deployment-railway.md @@ -0,0 +1,27 @@ +--- +title: Railway +sidebar_position: 5 +slug: /deployment-railway +--- + + + +# Deploy on Railway {#a9a1ce4d39e74cc29aef4d30c6172d10} + + +Railway is a cloud infrastructure platform that enables developers to deploy and manage applications effortlessly. It provides an intuitive interface, seamless integrations, and powerful features like auto-deploy from GitHub, managed databases, and automatic scaling. + + +Deploying Langflow to Railway involves a few simple steps: + +1. **Click the Button Below**: Start by clicking the deployment button provided below. This will redirect you to the Railway platform. + + [https://railway.app/template/JMXEWp?referralCode=MnPSdg](https://railway.app/template/JMXEWp?referralCode=MnPSdg) + +2. **Deploy**: Proceed to deploy your Langflow instance. Click Deploy Now to deploy the instance. Railway will handle the rest, including setting up the infrastructure, deploying the Langflow instance, and starting the application. + + ![](./1098199232.png) + + +By following these steps, your Langflow instance will be successfully deployed on Railway. + diff --git a/docs/docs/Deployment/deployment-render.md b/docs/docs/Deployment/deployment-render.md new file mode 100644 index 000000000..92039157c --- /dev/null +++ b/docs/docs/Deployment/deployment-render.md @@ -0,0 +1,31 @@ +--- +title: Render +sidebar_position: 4 +slug: /deployment-render +--- + + + +# Deploy on Render {#20a959b7047e44e490cc129fd21895c0} + + +[Render.com](http://render.com/) is a unified cloud platform designed to make deploying web applications, APIs, and static sites easy. It provides a streamlined experience with powerful features like automatic SSL, managed databases, and auto-deploy from Git, making it a popular choice for developers looking to simplify their deployment workflows. + + +Deploying Langflow to Render is a straightforward process that can be completed in just a few steps: + +1. **Click the Button Below**: Start by clicking the deployment button provided below. This will redirect you to the Render platform. + + [https://render.com/deploy?repo=https://github.com/langflow-ai/langflow/tree/dev](https://render.com/deploy?repo=https%3A%2F%2Fgithub.com%2Flangflow-ai%2Flangflow%2Ftree%2Fdev) + +2. **Select the Default Configuration**: Once on the Render platform, you will be prompted to provide a blueprint name and to select the default configuration for Langflow. This configuration includes all the necessary settings and resources to run Langflow efficiently. You can change the branch of the repo to “main” or “dev” based on your preference. Click “Create New Resources” to proceed. + + ![](./1861599636.png) + +3. **Deploy**: After selecting the configuration, proceed to deploy your Langflow instance. You can keep the default Starter instance, or change it to another instance based on your specific needs. Click Apply to deploy the instance. Render will handle the rest, including setting up the database, deploying the Langflow instance, and starting the application. + + ![](./1929176153.png) + + +By following these steps, your Langflow instance will be successfully deployed on Render. Remember to review the pricing details on the Render platform to understand any costs involved. + diff --git a/docs/static/img/langflow_basic_howto.gif b/docs/docs/Getting-Started/1160086633.gif similarity index 100% rename from docs/static/img/langflow_basic_howto.gif rename to docs/docs/Getting-Started/1160086633.gif diff --git a/docs/static/img/quickstart.png b/docs/docs/Getting-Started/131952085.png similarity index 100% rename from docs/static/img/quickstart.png rename to docs/docs/Getting-Started/131952085.png diff --git a/docs/static/img/welcome-to-langflow.png b/docs/docs/Getting-Started/221680153.png similarity index 100% rename from docs/static/img/welcome-to-langflow.png rename to docs/docs/Getting-Started/221680153.png diff --git a/docs/docs/Getting-Started/602374500.png b/docs/docs/Getting-Started/602374500.png new file mode 100644 index 000000000..59b4faaa8 Binary files /dev/null and b/docs/docs/Getting-Started/602374500.png differ diff --git a/docs/docs/Getting-Started/_category_.json b/docs/docs/Getting-Started/_category_.json new file mode 100644 index 000000000..e0dda2c74 --- /dev/null +++ b/docs/docs/Getting-Started/_category_.json @@ -0,0 +1 @@ +{"position":1, "label":"Getting Started"} \ No newline at end of file diff --git a/docs/docs/Getting-Started/getting-started-common-installation-issues.md b/docs/docs/Getting-Started/getting-started-common-installation-issues.md new file mode 100644 index 000000000..067c8b234 --- /dev/null +++ b/docs/docs/Getting-Started/getting-started-common-installation-issues.md @@ -0,0 +1,53 @@ +--- +title: ❗️ Common Installation Issues +sidebar_position: 3 +slug: /getting-started-common-installation-issues +--- + + + +This is a list of possible issues that you may encounter when installing Langflow and how to solve them. + + +--- + + +```bash +> No module named 'langflow.__main__' +``` + +1. Run `python -m langflow run` instead of `langflow run`. +2. If that doesn't work, reinstall Langflow with `_python -m pip install langflow --pre -U`. +3. If that doesn't work, reinstall Langflow and its dependencies with `python -m pip install langflow --pre -U --force-reinstall`. + +When you try to run Langflow using the command `langflow run`, you may encounter the following error: + + +```bash +> langflow runTraceback (most recent call last): File ".../langflow", line 5, in from langflow.__main__ import mainModuleNotFoundError: No module named 'langflow.__main__' +``` + + +There are two possible reasons for this error: + +1. You've installed Langflow using `pip install langflow` but you already had a previous version of Langflow installed in your system. In this case, you might be running the wrong executable. To solve this issue, run the correct executable by running `python -m langflow run` instead of `langflow run`. If that doesn't work, try uninstalling and reinstalling Langflow with `python -m pip install langflow --pre -U`. +2. Some version conflicts might have occurred during the installation process. Run `python -m pip install langflow --pre -U --force-reinstall` to reinstall Langflow and its dependencies. + +```bash +> Something went wrong running migrations. Please, run 'langflow migration --fix' +``` + + +Clear the cache by deleting the contents of the cache folder. + + +This folder can be found at: + +- **Linux or WSL2 on Windows**: `home//.cache/langflow/` +- **MacOS**: `/Users//Library/Caches/langflow/` + +This error can occur during Langflow upgrades when the new version can't override `langflow-pre.db` in `.cache/langflow/`. Clearing the cache removes this file but will also erase your settings. + + +If you wish to retain your files, back them up before clearing the folder. + diff --git a/docs/docs/Getting-Started/getting-started-installation.md b/docs/docs/Getting-Started/getting-started-installation.md new file mode 100644 index 000000000..14ade9749 --- /dev/null +++ b/docs/docs/Getting-Started/getting-started-installation.md @@ -0,0 +1,85 @@ +--- +title: 📦 Installation +sidebar_position: 1 +slug: /getting-started-installation +--- + + + +## **DataStax Langflow** {#a3b4bd8dec5a49ebbfca4828492133e9} + + +--- + + +The easiest way to get started with Langflow is through the DataStax Cloud Service! + + + +**DataStax Langflow** is a hosted version of Langflow integrated with [AstraDB](https://www.datastax.com/products/datastax-astra). Be up and running in minutes with no installation or setup required. [Sign up for free](https://langflow.datastax.com/). + + +![](./602374500.png) + + +# **Install Langflow Locally** {#ef364ee864c545649d248113ad7d3038} + + +--- + + +:::caution + +Langflow **requires** Python version 3.10 or greater and [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/) to be installed on your system. + +::: + + + + +Install Langflow with pip: + + +```bash +python -m pip install langflow -U +``` + + +Install Langflow with pipx: + + +```bash +pipx install langflow --python python3.10 --fetch-missing-python +``` + + +Pipx can fetch the missing Python version for you with `--fetch-missing-python`, but you can also install the Python version manually. Use `--force-reinstall` to ensure you have the latest version of Langflow and its dependencies. + + +## Having a problem? {#86a16dad1d6e481cafb90efea2b9ff93} + + +If you encounter a problem, see [Common Installation Issues](/getting-started-common-installation-issues). + + +To get help in the Langflow CLI: + + +```bash +python -m langflow --help +``` + + +## ⛓️ Run Langflow {#d318c4d486b74f5383c45b4f6859dcaa} + +1. To run Langflow, enter the following command. + +```bash +python -m langflow run +``` + +1. Confirm that a local Langflow instance starts by visiting `http://127.0.0.1:7860` in a Chromium-based browser. + +![](./221680153.png) + +1. Continue on to the [Quickstart](/getting-started-quickstart). diff --git a/docs/docs/Getting-Started/getting-started-quickstart.md b/docs/docs/Getting-Started/getting-started-quickstart.md new file mode 100644 index 000000000..ec4d1e19e --- /dev/null +++ b/docs/docs/Getting-Started/getting-started-quickstart.md @@ -0,0 +1,71 @@ +--- +title: ⚡️ Quickstart +sidebar_position: 2 +slug: /getting-started-quickstart +--- + + + +## Prerequisites {#b5f154a3a1d242c7bdf57acf0a552732} + + +--- + +- [Python >=3.10](https://www.python.org/downloads/release/python-3100/) and [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/) +- [OpenAI API key](https://platform.openai.com/) +- [Langflow installed and running](/getting-started-installation) + +## Hello World - Basic Prompting {#67e7cd59d0fa43e3926bdc75134f7472} + + +Let's start with a Prompt component to instruct an OpenAI Model. + + +Prompts serve as the inputs to a large language model (LLM), acting as the interface between human instructions and computational tasks. By submitting natural language requests in a prompt to an LLM, you can obtain answers, generate text, and solve problems. + +1. From the Langflow dashboard, click **New Project**. +2. Select **Basic Prompting**. + +![](./131952085.png) + + +This flow allows you to chat with the **OpenAI** model by using a **Prompt** to send instructions. + + +Examine the **Prompt** component. The **Template** field instructs the LLM to `Answer the user as if you were a pirate.` This should be interesting... + + +To use the **OpenAI** component, you have two options for providing your OpenAI API Key: directly passing it to the component or creating an environment variable. For better security and manageability, creating an environment variable is recommended. Here's how to set it up: + + +In the **OpenAI API Key** field, click the **Globe** button to access environment variables, and then click **Add New Variable**. + +1. In the **Variable Name** field, enter `openai_api_key`. +2. In the **Value** field, paste your OpenAI API Key (`sk-...`). +3. Click **Save Variable**. + +By creating an environment variable, you keep your API key secure and make it easier to manage across different components or projects. + + +## Run the basic prompting flow {#27ac88f4721b42c9a9587326905b8df4} + +1. Click the **Playground** button. This where you can interact with your bot. +2. Type any message and press Enter. And... Ahoy! 🏴‍☠️ The bot responds in a piratical manner! + +## Modify the prompt for a different result {#5208b946024846169fe59ee206021a4f} + +1. To modify your prompt results, in the **Prompt** template, click the **Template** field. The **Edit Prompt** window opens. +2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Harold Abelson.` +3. Run the basic prompting flow again. The response will be markedly different. + +## Next steps {#63b6db6cb571489c86b3ae89051f1a4f} + + +Well done! You've built your first prompt in Langflow. 🎉 + + +By dragging Langflow components to your workspace, you can create all sorts of interesting behaviors. Here are a couple of examples: + +- [Memory Chatbot](https://docs.langflow.org/starter-projects/memory-chatbot) +- [Blog Writer](https://docs.langflow.org/starter-projects/blog-writer) +- [Document QA](https://docs.langflow.org/starter-projects/document-qa) diff --git a/docs/docs/Getting-Started/👋 Welcome-to-Langflow.md b/docs/docs/Getting-Started/👋 Welcome-to-Langflow.md new file mode 100644 index 000000000..6b57b2ad6 --- /dev/null +++ b/docs/docs/Getting-Started/👋 Welcome-to-Langflow.md @@ -0,0 +1,31 @@ +--- +title: 👋 Welcome to Langflow +sidebar_position: 0 +slug: / +--- + + + +## Introduction {#e12578e9f465459592d89dbe47a54460} + + +--- + + +Langflow is a new, visual framework for building multi-agent and RAG applications. It is open-source, Python-powered, fully customizable, LLM and vector store agnostic. + + +Its intuitive interface allows for easy manipulation of AI building blocks, enabling developers to quickly prototype and turn their ideas into powerful, real-world solutions. + + +![](./1160086633.gif) + + +## 🚀 First steps {#c1ccb5e315b040edaa8d9a67f4960d81} + +- [Install Langflow](/getting-started-installation) - Install and start a local Langflow server. +- [Quickstart](/getting-started-quickstart) - Create a flow and run it. +- [Langflow Workspace](/workspace) - Learn more about the Langflow Workspace. + +Learn more about the exciting changes in Langflow 1.0 in [A new chapter for Langflow](/whats-new-a-new-chapter-langflow). + diff --git a/docs/docs/Guides/1313358839.png b/docs/docs/Guides/1313358839.png new file mode 100644 index 000000000..d353d23fa Binary files /dev/null and b/docs/docs/Guides/1313358839.png differ diff --git a/docs/docs/Guides/1988919422.png b/docs/docs/Guides/1988919422.png new file mode 100644 index 000000000..fc738ffb8 Binary files /dev/null and b/docs/docs/Guides/1988919422.png differ diff --git a/docs/docs/Guides/207457678.png b/docs/docs/Guides/207457678.png new file mode 100644 index 000000000..94f3eb544 Binary files /dev/null and b/docs/docs/Guides/207457678.png differ diff --git a/docs/docs/Guides/403427222.png b/docs/docs/Guides/403427222.png new file mode 100644 index 000000000..0615c051e Binary files /dev/null and b/docs/docs/Guides/403427222.png differ diff --git a/docs/docs/Guides/948333764.png b/docs/docs/Guides/948333764.png new file mode 100644 index 000000000..7bdfa0705 Binary files /dev/null and b/docs/docs/Guides/948333764.png differ diff --git a/docs/docs/Guides/_category_.json b/docs/docs/Guides/_category_.json new file mode 100644 index 000000000..4b98c2824 --- /dev/null +++ b/docs/docs/Guides/_category_.json @@ -0,0 +1 @@ +{"position":3, "label":"Guides"} \ No newline at end of file diff --git a/docs/docs/Guides/guides-chat-memory.md b/docs/docs/Guides/guides-chat-memory.md new file mode 100644 index 000000000..c617ac8eb --- /dev/null +++ b/docs/docs/Guides/guides-chat-memory.md @@ -0,0 +1,61 @@ +--- +title: Chat Memory +sidebar_position: 1 +slug: /guides-chat-memory +--- + + + +Langflow allows every chat message to be stored, and a single flow can have multiple memory sessions. This enables you to create separate _memories_ for agents to store and recall information as needed. + + +In any project, as long as there are [**Chat**](/components-io) being used, memories are always being stored by default. These are messages from a user to the AI or vice-versa. + + +To see and access this history of messages, Langflow features a component called **Chat Memory**. It retrieves previous messages and outputs them in structured format or parsed. + + +![](./403427222.png) + + +To learn the basics about memory in Langflow, check out the [Memory Chatbot ](/starter-projects-memory-chatbot)starter example. + + +Memories can be visualized and managed directly from the **Playground**. You can edit and remove previous messages to inspect and validate the AI’s response behavior. You can remove or edit previous messages to get your models acting just right. + + +![](./1988919422.png) + + +Modifying these memories will influence the behavior of the chatbot responses, as long as an agent uses them. Here you have the ability to remove or edit previous messages, allowing them to manipulate and explore how these changes affect model responses. + + +![](./948333764.png) + + +## Session ID {#4ee86e27d1004e8288a72c633c323703} + + +--- + + +Chat conversations store messages categorized by a `Session ID`. A a single flow can host multiple session IDs, and different flows can also share the same one. + + +The **Chat Memory** component also retrieves message histories by `Session ID` which users can change in the advanced settings. + + +![](./207457678.png) + + + + + +By default, if the `Session ID` value is empty, it is set to match the the same value as the `Flow ID`. + + +You can also display all messages stored across every flow and session by going to **Settings** > **Messages**. + + +![](./1313358839.png) + diff --git a/docs/docs/Guides/guides-data-message.md b/docs/docs/Guides/guides-data-message.md new file mode 100644 index 000000000..be1eccd97 --- /dev/null +++ b/docs/docs/Guides/guides-data-message.md @@ -0,0 +1,89 @@ +--- +title: Data & Message +sidebar_position: 2 +slug: /guides-data-message +--- + + + +In Langflow, the `Data` and `Message` objects serve as structured, functional representations of data that enhance the capabilities and reliability of the platform. + + +### The Data Object {#e0d56e463d2f483bb1b5df09d88bf309} + + +The `Data` object is a Pydantic model that serves as a container for storing and manipulating data. It carries `data`—a dictionary that can be accessed as attributes—and uses `text_key` to specify which key in the dictionary should be considered the primary text content. + + +- **Main Attributes:** + - `text_key`: Specifies the key to retrieve the primary text data. + - `data`: A dictionary to store additional data. + - `default_value`: default value when the `text_key` is not present in the `data` dictionary. + +**Creating a Data Object:** + + +You can create a `Data` object by directly assigning key-value pairs to it. For example: + + +```python +from langflow.schema import Data + +# Creating a Data object with specified key-value pairs +data = Data(text="my_string", bar=3, foo="another_string") + +# Outputs: +print(data.text) # Outputs: "my_string" +print(data.bar) # Outputs: 3 +print(data.foo) # Outputs: "another_string" +``` + + +The `text_key` specifies which key in the `data` dictionary should be considered the primary text content. The `default_value` provides a fallback if the `text_key` is not present. + + +```python +# Creating a Data object with a specific text_key and default_value +data = Data(data={"title": "Hello, World!"}, text_key="content", default_value="No content available") + +# Accessing the primary text using text_key and default_value +print(data.get_text()) # Outputs: "No content available" because "content" key is not in the data dictionary + +# Accessing data keys by calling the attribute directly +print(data.title) # Outputs: "Hello, World!" because "title" key is in the data dictionary +``` + + +The `Data` object is also convenient for visualization of outputs, since the output preview has visual elements to inspect data as a table and its cells as pop ups for basic types. The idea is to create a unified way to work and visualize complex information in Langflow. + + +To receive `Data` objects in a component input, you can use the `DataInput` input type. + + +### The Message Object {#f4f17cad02a545068f407d515cbc2902} + + +The `Message` object extends the functionality of `Data` and includes additional attributes and methods for chat interactions. + +- **Main Attributes:** + - `text_key`: Key to retrieve the primary text data. + - `text`: The main text content of the message. + - `sender`: Identifier for the sender (e.g., "User" or "AI"). + - `sender_name`: Name of the sender. + - `files`: List of files associated with the message. + - `session_id`: Identifier for the chat session. + - `timestamp`: Timestamp when the message was created. + - `flow_id`: Identifier for the flow. + +The `Message` object can be used to send, store and manipulate chat messages within Langflow. You can create a `Message` object by directly assigning key-value pairs to it. For example: + + +```python +from langflow.schema.message import Message + +message = Message(text="Hello, AI!", sender="User", sender_name="John Doe") +``` + + +To receive `Message` objects in a component input, you can use the `MessageInput` input type or `MessageTextInput` when the goal is to extract just the `text` field of the `Message` object. + diff --git a/docs/docs/Guides/guides-new-to-llms.md b/docs/docs/Guides/guides-new-to-llms.md new file mode 100644 index 000000000..1b2be8af5 --- /dev/null +++ b/docs/docs/Guides/guides-new-to-llms.md @@ -0,0 +1,19 @@ +--- +title: 📚 New to LLMs? +sidebar_position: 0 +slug: /guides-new-to-llms +--- + + + +Large Language Models, or LLMs, are part of an exciting new world in computing. + + +We made Langflow for anyone to create with LLMs, and hope you'll feel comfortable installing Langflow and [getting started](/getting-started-quickstart). + + +If you want to learn the basics of LLMs, prompt engineering, and AI models, Langflow recommends [promptingguide.ai](https://promptingguide.ai/), an open-source repository of prompt engineering content maintained by AI experts. PromptingGuide offers content for [beginners](https://www.promptingguide.ai/introduction/basics) and [experts](https://www.promptingguide.ai/techniques/cot), as well as the latest [research papers](https://www.promptingguide.ai/papers) and [test results](https://www.promptingguide.ai/research) fueling AI's progress. + + +For in depth readings, we recommend [Awesome LLM Books](https://github.com/Hannibal046/Awesome-LLM?tab=readme-ov-file#llm-books), a curated list of resources for learning about LLMs and their applications. + diff --git a/docs/static/img/langsmith-trace.png b/docs/docs/Integrations/1641825585.png similarity index 100% rename from docs/static/img/langsmith-trace.png rename to docs/docs/Integrations/1641825585.png diff --git a/docs/static/img/langsmith-flow.png b/docs/docs/Integrations/965098683.png similarity index 100% rename from docs/static/img/langsmith-flow.png rename to docs/docs/Integrations/965098683.png diff --git a/docs/docs/Integrations/_category_.json b/docs/docs/Integrations/_category_.json new file mode 100644 index 000000000..6aed9c635 --- /dev/null +++ b/docs/docs/Integrations/_category_.json @@ -0,0 +1 @@ +{"position":9, "label":"Integrations"} \ No newline at end of file diff --git a/docs/docs/Integrations/integrations-langsmith.md b/docs/docs/Integrations/integrations-langsmith.md new file mode 100644 index 000000000..b79ec4163 --- /dev/null +++ b/docs/docs/Integrations/integrations-langsmith.md @@ -0,0 +1,32 @@ +--- +title: LangSmith +sidebar_position: 0 +slug: /integrations-langsmith +--- + + + +LangSmith is a full-lifecycle DevOps service from LangChain that provides monitoring and observability. To integrate with Langflow, just add your LangChain API key as a Langflow environment variable and you are good to go! + + +## Step-by-step Configuration {#b912579a43984f9a92921232b67c885d} + +1. Obtain your LangChain API key from [https://smith.langchain.com](https://smith.langchain.com/) +2. Add the following keys to Langflow .env file: + +`LANGCHAIN_API_KEY="your-api-key"LANGCHAIN_PROJECT="your-project-name"` + + +or export the environment variables in your terminal: + + +`export LANGCHAIN_API_KEY="your-api-key"export LANGCHAIN_PROJECT="your-project-name"` + +1. Restart Langflow using `langflow run --env-file .env` +2. Run any project and check the LangSmith dashboard for monitoring and observability. + +![](./965098683.png) + + +![](./1641825585.png) + diff --git a/docs/docs/Settings/418277339.png b/docs/docs/Settings/418277339.png new file mode 100644 index 000000000..9909932f9 Binary files /dev/null and b/docs/docs/Settings/418277339.png differ diff --git a/docs/docs/Settings/_category_.json b/docs/docs/Settings/_category_.json new file mode 100644 index 000000000..42ef60736 --- /dev/null +++ b/docs/docs/Settings/_category_.json @@ -0,0 +1 @@ +{"position":7, "label":"Settings"} \ No newline at end of file diff --git a/docs/docs/administration/global-env.mdx b/docs/docs/Settings/settings-global-variables.md similarity index 55% rename from docs/docs/administration/global-env.mdx rename to docs/docs/Settings/settings-global-variables.md index fa8f7ff5d..6d42451fb 100644 --- a/docs/docs/administration/global-env.mdx +++ b/docs/docs/Settings/settings-global-variables.md @@ -1,17 +1,20 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; +--- +title: Global Variables +sidebar_position: 0 +slug: /settings-global-variables +--- + import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; -# Global Variables +> +> ⚠️ WARNING +> This page may contain outdated information. It will be updated as soon as possible. +> - -This page may contain outdated information. It will be updated as soon as possible. - Global Variables are a useful feature of Langflow, allowing you to define reusable variables accessed from any Text field in your project. + **TL;DR** - Global Variables are reusable variables accessible from any Text field in your project. @@ -21,64 +24,65 @@ Global Variables are a useful feature of Langflow, allowing you to define reusab - All Credential Global Variables are encrypted and accessible only by you. - Set _`LANGFLOW_STORE_ENVIRONMENT_VARIABLES`_ to _`true`_ in your `.env` file to add all variables in _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ to your user's Global Variables. -## Create and Add a Global Variable +## Create and Add a Global Variable {#3543d5ef00eb453aa459b97ba85501e5} + To create and add a global variable, click the 🌐 button in a Text field, and then click **+ Add New Variable**. + Text fields are where you write text without opening a Text area, and are identified with the 🌐 icon. + For example, to create an environment variable for the **OpenAI** component: -1. In the **OpenAI API Key** text field, click the 🌐 button, then **Add New Variable**. -2. Enter `openai_api_key` in the **Variable Name** field. -3. Paste your OpenAI API Key (`sk-...`) in the **Value** field. -4. Select **Credential** for the **Type**. -5. Choose **OpenAI API Key** in the **Apply to Fields** field to apply this variable to all fields named **OpenAI API Key**. -6. Click **Save Variable**. +1. In the **OpenAI API Key** text field, click the 🌐 button, then **Add New Variable**. +2. Enter `openai_api_key` in the **Variable Name** field. +3. Paste your OpenAI API Key (`sk-...`) in the **Value** field. +4. Select **Credential** for the **Type**. +5. Choose **OpenAI API Key** in the **Apply to Fields** field to apply this variable to all fields named **OpenAI API Key**. +6. Click **Save Variable**. You now have a `openai_api_key` global environment variable for your Langflow project. Subsequently, clicking the 🌐 button in a Text field will display the new variable in the dropdown. - - You can also create global variables in **Settings** > **Variables and - Secrets**. - - +> 💡 TIP +> You can also create global variables in Settings > Variables and Secrets. +> -To view and manage your project's global environment variables, visit **Settings** > **Variables and Secrets**. -For more on variables in HuggingFace Spaces, see [Managing Secrets](https://huggingface.co/docs/hub/spaces-overview#managing-secrets). +![](./418277339.png) -{/* All variables are encrypted */} - - All Credential Global Variables are encrypted and accessible only by you. - +To view and manage your project's global environment variables, visit **Settings** > **Variables and Secrets**. + + +> ⚠️ WARNING +> All Credential Global Variables are encrypted and accessible only by you. +> + + +## Configure Environment Variables in your .env file {#76844a93dbbc4d1ba551ea1a4a89ccdd} -## Configure Environment Variables in your .env file Setting `LANGFLOW_STORE_ENVIRONMENT_VARIABLES` to `true` in your `.env` file (default) adds all variables in `LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT` to your user's Global Variables. + These variables are accessible like any other Global Variable. - - To prevent this behavior, set `LANGFLOW_STORE_ENVIRONMENT_VARIABLES` to - `false` in your `.env` file. - + +> 💡 TIP +> To prevent this behavior, set `LANGFLOW_STORE_ENVIRONMENT_VARIABLES` to `false` in your `.env` file. +> + You can specify variables to get from the environment by listing them in `LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`. + Specify variables as a comma-separated list (e.g., _`"VARIABLE1, VARIABLE2"`_) or a JSON-encoded string (e.g., _`'["VARIABLE1", "VARIABLE2"]'`_). -The default list of variables includes: + +The default list of variables includes the ones below and more: - ANTHROPIC_API_KEY - ASTRA_DB_API_ENDPOINT @@ -102,8 +106,5 @@ The default list of variables includes: - VECTARA_CORPUS_ID - VECTARA_API_KEY -
- -
+ + diff --git a/docs/docs/Settings/settings-project-general-settings.md b/docs/docs/Settings/settings-project-general-settings.md new file mode 100644 index 000000000..eee7d5d89 --- /dev/null +++ b/docs/docs/Settings/settings-project-general-settings.md @@ -0,0 +1,82 @@ +--- +title: Project & General Settings +sidebar_position: 1 +slug: /settings-project-general-settings +--- + + + +> ⚠️ WARNING +> This page may contain outdated information. It will be updated as soon as possible. + + +Change the **Project Settings** or **General Settings** for Langflow. + + +## Project Settings {#71e61e6544c94f808cd74b8cc012363d} + + +Click **Project Name** > **Settings** to view your **Project Settings**. + +- **Name** - the name of your project. +- **Description** - the description for your project. +Visible on the Langflow Store. +- **Endpoint name** - the custom endpoint name for your project's API endpoint. +To use the default value, leave this field blank. + +## General Settings {#1a0c451fd5e84feeb1d18c2886d642eb} + + +Select your **Profile Picture** > **Settings** to view your **General Settings**. + + +### Profile Picture {#8abfa80ed6c448b6977467679d43c275} + + +Select a profile picture. + + +### Store API Key {#6a12756beb0b42fd84bdf5ab5f10fffb} + + +Add your **Langflow Store** API key. To get a Store key, go to the [Langflow store](https://www.langflow.store/). + + +### Global Variables {#12aa7f28060447babc987bdf57fc065e} + + +Select **Add New** to add a key to Langflow. + + +Select the **trash icon** to delete a key. + + +For more information, see Global Variables. + + +### Langflow API {#0a08ffc3dd9042da9f9d2d49c9df0b6a} + + +Create a Langflow API key. + + +Click **Add New** > **Create Secret Key** and copy the key somewhere safe and accessible. + + +For more information, see Langflow API. + + +### Shortcuts {#6c9d705c9e7f466db496dbc6571c81d2} + + +A list of keyboard shortcuts for Langflow. + + +### Messages {#898425cf88b74c048c23e8e1e1d7c2bd} + + +Inspect, edit, and remove messages in your flow for testing and debugging purposes. + + +For more information, see the Playground. + diff --git a/docs/docs/Starter-Projects/1073956357.png b/docs/docs/Starter-Projects/1073956357.png new file mode 100644 index 000000000..835092f82 Binary files /dev/null and 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b/docs/docs/Starter-Projects/starter-projects-basic-prompting.md @@ -0,0 +1,60 @@ +--- +title: Basic Prompting +sidebar_position: 0 +slug: /starter-projects-basic-prompting +--- + + + +Prompts serve as the inputs to a large language model (LLM), acting as the interface between human instructions and computational tasks. + + +By submitting natural language requests in a prompt to an LLM, you can obtain answers, generate text, and solve problems. + + +This article demonstrates how to use Langflow's prompt tools to issue basic prompts to an LLM, and how various prompting strategies can affect your outcomes. + + +## Prerequisites {#20bd7bc51ce04e2fb4922c95f00870d3} + +- [Langflow installed and running](/getting-started-installation) +- [OpenAI API key created](https://platform.openai.com/) + +## Create the basic prompting project {#19d5305239c841548a695e2bf7839e7a} + +1. From the Langflow dashboard, click **New Project**. + +![](./1835734464.png) + +1. Select **Basic Prompting**. +2. The **Basic Prompting** flow is created. + +![](./487525520.png) + + +This flow allows you to chat with the **OpenAI** component through the **Prompt** component. + + +Examine the **Prompt** component. The **Template** field instructs the LLM to `Answer the user as if you were a pirate.` This should be interesting... + + +![](./690736575.png) + +1. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. + 1. In the **Variable Name** field, enter `openai_api_key`. + 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). + 3. Click **Save Variable**. + +![](./1390293355.png) + + +## Run {#ce52f8e6b491452a9dfb069feb962eed} + +1. Click the **Playground** button on the control panel (bottom right side of the workspace). This is where you can interact with your AI. +2. Type a message and press Enter. The bot should respond in a markedly piratical manner! + +## Modify the prompt for a different result {#3ab045fcbe774c8fb3adc528f9042ba0} + +1. To modify your prompt results, in the **Prompt** template, click the **Template** field. The **Edit Prompt** window opens. +2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Hermione Granger.` +3. Run the workflow again. The response will be markedly different. diff --git a/docs/docs/Starter-Projects/starter-projects-blog-writer.md b/docs/docs/Starter-Projects/starter-projects-blog-writer.md new file mode 100644 index 000000000..e37e0f199 --- /dev/null +++ b/docs/docs/Starter-Projects/starter-projects-blog-writer.md @@ -0,0 +1,53 @@ +--- +title: Blog Writer +sidebar_position: 1 +slug: /starter-projects-blog-writer +--- + + + +Build a blog writer with OpenAI that uses URLs for reference content. + + +## Prerequisites {#899268e6c12c49b59215373a38287507} + +- [Langflow installed and running](/getting-started-installation) +- [OpenAI API key created](https://platform.openai.com/) + +## Create the Blog Writer project {#0c1a9c65b7d640f693ec3aad963416ff} + +1. From the Langflow dashboard, click **New Project**. +2. Select **Blog Writer**. +3. A workspace for the **Blog Writer** is displayed. + +![](./282456806.png) + + +This flow creates a one-shot article generator with **Prompt**, **OpenAI**, and **Chat Output** components, augmented with reference content and instructions from the **URL** and **Instructions** components. + + +The **Template** field of the **Prompt** looks like this: + + +![](./257920618.png) + + +The `{instructions}` value is received from the **Instructions** component. One or more `{references}`  are received from a list of URLs. + +- **URL** extracts raw text and metadata from one or more web links. +- **Parse Data** converts the data coming from the **URL** component into plain text to feed a prompt. + +![](./25156979.png) + + +## Run the Blog Writer {#b93be7a567f5400293693b31b8d0f81a} + + +--- + +1. Click the **Playground** button. Here you can chat with the AI that has access to the **URL** content. +2. Click the **Lighting Bolt** icon to run it. +3. To write about something different, change the values in the **URL** component and adjust the instructions on the left side bar of the **Playground**. Try again and see what the LLM constructs. + +![](./447530731.png) + diff --git a/docs/docs/Starter-Projects/starter-projects-document-qa.md b/docs/docs/Starter-Projects/starter-projects-document-qa.md new file mode 100644 index 000000000..24c88fccd --- /dev/null +++ b/docs/docs/Starter-Projects/starter-projects-document-qa.md @@ -0,0 +1,39 @@ +--- +title: Document QA +sidebar_position: 2 +slug: /starter-projects-document-qa +--- + + + +Build a question-and-answer chatbot with a document loaded from local memory. + + +## Prerequisites {#6555c100a30e4a21954af25e2e05403a} + +- [Langflow installed and running](/getting-started-installation) +- [OpenAI API key created](https://platform.openai.com/) + +## Create the Document QA project {#204500104f024553aab2b633bb99f603} + +1. From the Langflow dashboard, click **New Project**. +2. Select **Document QA**. +3. The **Document QA** project is created. + +![](./727819216.png) + + +This flow is composed of a standard chatbot with the **Chat Input**, **Prompt**, **OpenAI**, and **Chat Output** components, but it also incorporates a **File** component, which loads a file from your local machine. **Parse Data** is used to convert the data from **File** into the **Prompt** component as `{Document}`. The **Prompt** component is instructed to answer questions based on the contents of `{Document}`. This gives the **OpenAI** component context it would not otherwise have access to. + + +![](./1140665127.png) + + +## Run the Document QA {#f58fcc2b9e594156a829b1772b6a7191} + +1. To select a document to load, in the **File** component, click the **Path** field. Select a local file, and then click **Open**. The file name appears in the field. + +![](./1073956357.png) + +1. Click the **Playground** button. Here you can chat with the AI that has access to your document's content. +2. Type in a question about the document content and press Enter. You should see a contextual response. diff --git a/docs/docs/Starter-Projects/starter-projects-memory-chatbot.md b/docs/docs/Starter-Projects/starter-projects-memory-chatbot.md new file mode 100644 index 000000000..e44492629 --- /dev/null +++ b/docs/docs/Starter-Projects/starter-projects-memory-chatbot.md @@ -0,0 +1,70 @@ +--- +title: Memory Chatbot +sidebar_position: 3 +slug: /starter-projects-memory-chatbot +--- + + + +This flow extends the [Basic Prompting](http://localhost:3000/starter-projects/basic-prompting) flow to include a chat memory. This makes the AI remember previous user inputs. + + +## Prerequisites {#a71d73e99b1543bbba827207503cf31f} + +- [Langflow installed and running](/getting-started-installation) +- [OpenAI API key created](https://platform.openai.com/) + +## Create the memory chatbot project {#70ce99381b7043a1b417a81e9ae74c72} + +1. From the Langflow dashboard, click **New Project**. +2. Select **Memory Chatbot**. +3. The **Memory Chatbot** flow is created . + +![](./1511598495.png) + + +This flow uses the same components as the Basic Prompting one, but extends it with a **Chat Memory** component. This component retrieves previous messages and sends them to the **Prompt** component to fill a part of the **Template** with context. + + +By clicking the template, you'll see the prompt editor like below: + + +![](./450254819.png) + + +This gives the **OpenAI** component a memory of previous chat messages. + +1. Don't forget to [set up your OpenAI API key](http://localhost:3000/starter-projects/basic-prompting#open-ai) + +## Run {#a110cad860584c98af1aead006035378} + +1. Open the Playground. +2. Type multiple questions. In the **Memories** tab, your queries are logged in order. Up to 100 queries are stored by default. Try telling the AI your name and asking `What is my name?` on a second message, or `What is the first subject I asked you about?` to validate that previous knowledge is taking effect. + +> +> 💡  Check and adjust advanced parameters by opening the Advanced Settings of the **Chat Memory** component. +> + + +![](./1079168789.png) + + +## Session ID {#4e68c3c0750942f98c45c1c45d7ffbbe} + + +`SessionID` is a unique identifier in Langflow that stores conversation sessions between the AI and a user. A `SessionID` is created when a conversation is initiated, and then associated with all subsequent messages during that session. + + +In the **Memory Chatbot** flow you created, the **Chat Memory** component references past interactions by **Session ID**. You can demonstrate this by modifying the **Session ID** value to switch between conversation histories. + +1. In the **Session ID** field of the **Chat Memory** and **Chat Input** components, add a **Session ID** value like `MySessionID`. +2. Now, once you send a new message the **Playground**, you should have a new memory created on the **Memories** tab. +3. Notice how your conversation is being stored in different memory sessions. + +> +> 💡  Every chat component in Langflow comes with a `SessionID`. It defaults to the flow ID. Explore how changing it affects what the AI remembers. +> + + +Learn more about memories in the [Chat Memory](/guides-chat-memory) section. + diff --git a/docs/docs/Starter-Projects/starter-projects-vector-store-rag.md b/docs/docs/Starter-Projects/starter-projects-vector-store-rag.md new file mode 100644 index 000000000..77ab951e8 --- /dev/null +++ b/docs/docs/Starter-Projects/starter-projects-vector-store-rag.md @@ -0,0 +1,80 @@ +--- +title: Vector Store RAG +sidebar_position: 4 +slug: /starter-projects-vector-store-rag +--- + + + +Retrieval Augmented Generation, or RAG, is a pattern for training LLMs on your data and querying it. + + +RAG is backed by a **vector store**, a vector database which stores embeddings of the ingested data. + + +This enables **vector search**, a more powerful and context-aware search. + + +We've chosen [Astra DB](https://astra.datastax.com/signup?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=create-a-free-astra-db-account) as the vector database for this starter project, but you can follow along with any of Langflow's vector database options. + + +## Prerequisites {#6aa2c6dff6894eccadc39d4903d79e66} + +- [Langflow installed and running](http://localhost:3000/getting-started/install-langflow) +- [OpenAI API key](https://platform.openai.com/) +- [An Astra DB vector database created](https://docs.datastax.com/en/astra-db-serverless/get-started/quickstart.html) with: + - Application Token + - API Endpoint + +![](./648489928.png) + + +## Create the vector store RAG project {#e3ed64193e5e448f81279e1d54ba43cf} + +1. From the Langflow dashboard, click **New Project**. +2. Select **Vector Store RAG**. +3. The **Vector Store RAG** project is created. + +![](./1946624394.png) + + +The vector store RAG flow is built of two separate flows. Ingestion and query. + + +The **ingestion** part (bottom of the screen) populates the vector store with data from a local file. It ingests data from a file (**File**), splits it into chunks (**Split Text**), indexes it in Astra DB (**Astra DB**), and computes embeddings for the chunks using an embedding model (**OpenAI Embeddings**). + + +> +> 💡  Embeddings are numerical vectors that represent data meaningfully. They enable efficient similarity searches in vector stores by placing similar items close together in the vector space, enhancing search and recommendation tasks. +> + + +This part creates a searchable index to be queried for contextual similarity. + + +The **query** part (top of the screen) allows users to retrieve embedded vector store data. Components: + +- **Chat Input** defines where to send the user input (coming from the Playground). +- **OpenAI Embeddings** is the model used to generate embeddings from the user input. +- **Astra DB** retrieves the most relevant chunks from the Astra DB database (here, used for search, not ingestion). +- **Parse Data** converts chunks coming from the **Astra DB** component into plain text to feed a prompt. +- **Prompt** takes in the user input and the retrieved chunks as text and builds a prompt for the model. +- **OpenAI** takes in the prompt to generate a response. +- **Chat Output** component displays the response in the Playground. +1. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. + 1. In the **Variable Name** field, enter `openai_api_key`. + 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). + 3. Click **Save Variable**. +1. To create environment variables for the **Astra DB** and **Astra DB Search** components: + 1. In the **Token** field, click the **Globe** button, and then click **Add New Variable**. + 2. In the **Variable Name** field, enter `astra_token`. + 3. In the **Value** field, paste your Astra application token (`AstraCS:WSnyFUhRxsrg…`). + 4. Click **Save Variable**. + 5. Repeat the above steps for the **API Endpoint** field, pasting your Astra API Endpoint instead (`https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com`). + 6. Add the global variable to both the **Astra DB** and **Astra DB Search** components. + +## Run the Vector Store RAG {#815a6536d2d548d987f0f4e375a58b15} + +1. Click the **Playground** button. Here you can chat with the AI that uses context from the database you created. +2. Type a message and press Enter. (Try something like "What topics do you know about?") +3. The bot will respond with a summary of the data you've embedded. diff --git a/docs/static/img/flow-composition.webp b/docs/docs/What’s-New/1019037178.webp similarity index 100% rename from docs/static/img/flow-composition.webp rename to docs/docs/What’s-New/1019037178.webp diff --git a/docs/static/img/multiple_outputs.webp b/docs/docs/What’s-New/1285484510.webp similarity index 100% rename from docs/static/img/multiple_outputs.webp rename to docs/docs/What’s-New/1285484510.webp diff --git a/docs/static/img/component-freeze.jpeg b/docs/docs/What’s-New/1528084074.jpg similarity index 100% rename from docs/static/img/component-freeze.jpeg rename to docs/docs/What’s-New/1528084074.jpg diff --git a/docs/static/img/playground.png b/docs/docs/What’s-New/17412613.png similarity index 100% rename from docs/static/img/playground.png rename to docs/docs/What’s-New/17412613.png diff --git a/docs/static/img/output-preview.gif b/docs/docs/What’s-New/196150825.gif similarity index 100% rename from docs/static/img/output-preview.gif rename to docs/docs/What’s-New/196150825.gif diff --git a/docs/static/img/multi-modal.png b/docs/docs/What’s-New/2087505425.png similarity index 100% rename from docs/static/img/multi-modal.png rename to docs/docs/What’s-New/2087505425.png diff --git a/docs/static/img/langflow-1-0.png b/docs/docs/What’s-New/225113267.png similarity index 100% rename from docs/static/img/langflow-1-0.png rename to docs/docs/What’s-New/225113267.png diff --git a/docs/static/img/logs-monitoring.webp b/docs/docs/What’s-New/543968297.webp similarity index 100% rename from docs/static/img/logs-monitoring.webp rename to docs/docs/What’s-New/543968297.webp diff --git a/docs/static/img/custom-endpoint.webp b/docs/docs/What’s-New/82097598.webp similarity index 100% rename from docs/static/img/custom-endpoint.webp rename to docs/docs/What’s-New/82097598.webp diff --git a/docs/docs/What’s-New/_category_.json b/docs/docs/What’s-New/_category_.json new file mode 100644 index 000000000..d014c875a --- /dev/null +++ b/docs/docs/What’s-New/_category_.json @@ -0,0 +1 @@ +{"position":0, "label":"What’s New?"} \ No newline at end of file diff --git a/docs/docs/whats-new/a-new-chapter-langflow.mdx b/docs/docs/What’s-New/whats-new-a-new-chapter-langflow.md similarity index 69% rename from docs/docs/whats-new/a-new-chapter-langflow.mdx rename to docs/docs/What’s-New/whats-new-a-new-chapter-langflow.md index 6e1356c61..7c78e1f2a 100644 --- a/docs/docs/whats-new/a-new-chapter-langflow.mdx +++ b/docs/docs/What’s-New/whats-new-a-new-chapter-langflow.md @@ -1,29 +1,40 @@ -import ZoomableImage from "/src/theme/ZoomableImage.js"; +--- +title: 1.0 - A new chapter for Langflow +sidebar_position: 0 +slug: /whats-new-a-new-chapter-langflow +--- -# 1.0 - A new chapter for Langflow - -## First things first +![](./225113267.png) + + +## First things first {#5add758564574950862f689198a4e085} + + +--- + **Thank you all for being part of the Langflow community**. The journey so far has been amazing, and we are thrilled to have you with us. + We have some exciting news to share with you. Langflow is evolving, and we want to tell you all about it! -## What's new? + +## What's new? {#ecad059e8f8249c598404c0b2bab36fa} + + +--- + In the past year, we learned a lot from the community and our users. We saw the potential of Langflow and the need for a visual, interactive platform for building conversational AI applications (and beyond). You thought us the importance of a platform that is easy to use, but also powerful and controllable, and that made clear to us how Langflow's transparency could be improved. + Below are some of the new features we included to make that happen! -### Same Component, Multiple Outputs + +### Same Component, Multiple Outputs {#85986f3ae303444fb69d9ea1baa2eca8} + Components can now have more than a single output, allowing for unique flexibility in creating complex flows. The game-changer is output routing — it allows for so many new capabilities it’s almost silly to describe! @@ -33,143 +44,131 @@ Components can now have more than a single output, allowing for unique flexibili 4. Enable the development of agent architectures from scratch; 5. Build an orchestrator that routes between agents. - +![](./1285484510.webp) -### Flow + Composition -One key change you'll notice is that projects now require you to define **Inputs** and **Outputs**. They allow you to define the structure of your conversation and how data flows through it. This change comes with a new way of visualizing your projects. +### Flow + Composition {#12d86f91b5af4abc9f8e885c5f828df4} + + +One key change you'll notice is that projects now require you to define **Inputs** and **Outputs**. They allow you to define the structure of your conversation and how data flows through it. This change comes with a new way of visualizing your projects. + Before 1.0 you would connect components to ultimately build one final component that was processed behind the scenes. Now, each step of the process is defined by you, is visible on the workspace, and can be monitored and controlled. -This makes it so that composition is now just **another way** of building in Langflow and **data flows through your project more transparently**. This means that the easy stuff is *really* easy and the complex parts are still possible! + +This makes it so that composition is now just **another way** of building in Langflow and **data flows through your project more transparently**. This means that the easy stuff is _really_ easy and the complex parts are still possible! - **Flow:** Data is processed by one component and then passed to the next component in line for immediate execution. - **Composition**: Allows components to not only forward data but also share states for modular building. For example, a flow can sequentially process text, and after a few steps, trigger an agent. It can access functions that wait to be called or to respond. This blend of flow and composition brings an unprecedented level of flexibility and control to data workflows in LLM-based apps and agents that use multiple models and APIs working together to achieve tasks. - -### Memory Management +![](./1019037178.webp) + + +### Memory Management {#a9e352272f4a44499f52fae65b663118} + Langflow 1.0 natively allows every chat message to be stored, and a single flow can have multiple memory sessions. This enables you to create multiple “memories” for agents to store and recall specific information as needed. + You can edit and remove previous messages to inspect and validate a model’s response behavior. Control, explore, and manage conversation histories to get your models acting just right. - -### Component Freeze 🥶 +![](./17412613.png) + + +### Component Freeze 🥶 {#4912d08da5464ff2aff595d6b26fd809} + Component output freezing is back in Langflow, and it’s cooler than ever! + Once a component runs, you can now lock its previous output state to prevent it from re-running. + Avoid spending extra tokens and remove repetition when output should be constant — plus it's great for debugging and prototyping! - -### Output Preview +![](./1528084074.jpg) + + +### Output Preview {#f56d4400b0214ef2ab1206add068dd99} + Each component now includes an output visualizer that opens a pop-up screen, allowing you to easily inspect and monitor transmissions between components. It provides instant feedback on your workflows, letting you see results as they are processed. 🔍 - -### Inputs and Outputs Handling +![](./196150825.gif) + + +### Inputs and Outputs Handling {#2112c11dc496480c9bd681f04f8533ea} + Inputs and outputs finally make more sense to us, and hopefully to you too. + We’re proposing Langflow-native types to keep things consistent, but not limited to use any Python type. For instance, a Chat Input component sends out what we call a Message object (text + metadata like date, time, and sender), but maybe you want to introduce external types from your favorite Python package? Go wild. Each native type will have their own visualization modes and will evolve according to new integrations added. -### Custom Endpoint Name + +### Custom Endpoint Name {#bfeb7018e8bd46bf9dbef43ce2d3692b} + Now you can pick a custom name for your endpoint used to call your flow from the API. - -### Logs & Monitoring +![](./82097598.webp) + + +### Logs & Monitoring {#c902ee43b4b24711b526352ae2451c48} + A new logs page has been added! Now, both component executions and message history from the chat can be inspected in an interactive table. This will make it easier to debug, inspect, and manage messages passing through components. - -### Folders 📁 +![](./543968297.webp) + + +### Folders 📁 {#d01237fd83e4467994fed6fa7a8ee4f4} + We introduced folders on the home page to help categorize flows and components. Create, remove and edit them to keep your work organized. -### Playground -By having a clear definition of Inputs and Outputs, we could build the experience around that, which led us to create the Playground. When building a project, testing and debugging are crucial. The Playground is an interface that changes dynamically based on the Inputs and Outputs you defined in your project. +### Playground {#27649918dcb64a0ebae83db767bfe2eb} + + +By having a clear definition of Inputs and Outputs, we could build the experience around that, which led us to create the Playground. + + +When building a project, testing and debugging are crucial. The Playground is an interface that changes dynamically based on the Inputs and Outputs you defined in your project. + For example, let's say you are building a simple RAG application. Generally, you have an Input, some references that come from a Vector Store Search, a Prompt, and the answer. Now, you could plug the output of your Prompt into a Text Output, rename that to "Prompt Result," and see the output of your Prompt in the Playground. We have many planned features for the Playground, and we're excited to see how you'll explore it! -### Multi-Modal + +### Multi-Modal {#b7753bc9d72f40c49c4074a8d0e51344} + Langflow is now multi-modal! It can now handle images and more soon! - + +![](./2087505425.png) + We’ve also improved project organization, global variables and overall settings, added Python 3.12 compatibility, keyboard shortcuts and a lot of new and fun experimental components! -## An Easier Start + +## An Easier Start {#c6bf434c0d35482bbf2ef0bceff902f0} + + +--- + The experience for first-time users is something we wanted to improve. For that we created a couple of Starter Projects. It's now much easier to start a new project, and you can choose from a list of starter projects to get you going. + For now, we have: - **Basic Prompting (Hello, World)**: Learn the basics of a Prompt Component. @@ -180,14 +179,21 @@ For now, we have: Please let us know what other starter projects you would like to see in the future! + +## What's Next? {#21004578890d4397bc291b43eb140640} + + --- -## What's Next? Langflow has gone through a big change, and we are excited to see how you use it and what you think of it. We plan to add more types of Input and Output like Image and Audio, and we also plan to add more Components to help you build more complex projects. + We are excited to see the community embracing Langflow as their number one AI builder and eagerly wait to see what new inspiring projects will come out of this release! A big thanks to everyone who's supporting or being part of this community in any way. ✨ + Sincerely, -**The Langflow Team 🚀** \ No newline at end of file + +**The Langflow Team 🚀** + diff --git a/docs/docs/Workspace/1109547993.png b/docs/docs/Workspace/1109547993.png new file mode 100644 index 000000000..5bc8db5c9 Binary files /dev/null and b/docs/docs/Workspace/1109547993.png differ diff --git a/docs/docs/Workspace/1224026366.png b/docs/docs/Workspace/1224026366.png new file mode 100644 index 000000000..ca271dd08 Binary files /dev/null and b/docs/docs/Workspace/1224026366.png differ diff --git a/docs/docs/Workspace/1769489314.png b/docs/docs/Workspace/1769489314.png new file mode 100644 index 000000000..dbc9493e0 Binary files /dev/null and b/docs/docs/Workspace/1769489314.png differ diff --git a/docs/docs/Workspace/1801518600.png b/docs/docs/Workspace/1801518600.png new file mode 100644 index 000000000..868b5d5e9 Binary files /dev/null and b/docs/docs/Workspace/1801518600.png differ diff --git a/docs/docs/Workspace/310382168.png b/docs/docs/Workspace/310382168.png new file mode 100644 index 000000000..9a29e3c7e Binary files /dev/null and b/docs/docs/Workspace/310382168.png differ diff --git a/docs/docs/Workspace/563382604.png b/docs/docs/Workspace/563382604.png new file mode 100644 index 000000000..bddaf128c Binary files /dev/null and b/docs/docs/Workspace/563382604.png differ diff --git a/docs/static/img/widget-code.png b/docs/docs/Workspace/566212295.png similarity index 99% rename from docs/static/img/widget-code.png rename to docs/docs/Workspace/566212295.png index 389945a58..f5e133b76 100644 Binary files a/docs/static/img/widget-code.png and b/docs/docs/Workspace/566212295.png differ diff --git a/docs/docs/Workspace/816620192.png b/docs/docs/Workspace/816620192.png new file mode 100644 index 000000000..6b34528c8 Binary files /dev/null and b/docs/docs/Workspace/816620192.png differ diff --git a/docs/docs/Workspace/_category_.json b/docs/docs/Workspace/_category_.json new file mode 100644 index 000000000..8e39087ba --- /dev/null +++ b/docs/docs/Workspace/_category_.json @@ -0,0 +1 @@ +{"position":4, "label":"Workspace"} \ No newline at end of file diff --git a/docs/docs/Workspace/workspace-api.md b/docs/docs/Workspace/workspace-api.md new file mode 100644 index 000000000..b86aa8e27 --- /dev/null +++ b/docs/docs/Workspace/workspace-api.md @@ -0,0 +1,172 @@ +--- +title: API +sidebar_position: 2 +slug: /workspace-api +--- + +import ReactPlayer from "react-player"; + +The **API** section presents code templates for integrating your flow into external applications. + + +[API IMAGE HERE] + + +### cURL {#4eb287a8424349c4b0b436a6703de5f3} + + +The **cURL** tab displays sample code for posting a query to your flow. Modify the `input_value` to change your input message. Copy the code and run it to post a query to your flow and get the result. + + +[cURL Image here] + + +### Python API {#fb7db14e6330418389562ef647aa2354} + + +The **Python API** tab displays code to interact with your flow using the Python HTTP requests library. + + +### Python Code {#7af87438549b4972907ac310a4193067} + + +The **Python Code** tab displays code to interact with your flow's `.json` file using the Langflow runtime. + + +### Tweaks {#5680600063724590ac2302b4ddeea867} + + +The **Tweaks** tab displays the available parameters for your flow. Modifying the parameters changes the code parameters across all windows. For example, changing the **Chat Input** component's `input_value` will change that value across all API calls. + + + + + +### Chat Widget HTML {#48f121a6cb3243979a341753da0c2700} + + +The **Chat Widget HTML** tab displays code that can be inserted in the `` of your HTML to interact with your flow. + + +The **Langflow Chat Widget** is a powerful web component that enables communication with a Langflow project. This widget allows for a chat interface embedding, allowing the integration of Langflow into web applications effortlessly. + + +Easily integrate the Langflow Chat Widget into your website or web application with just a few lines of JavaScript. + + +Engage your users with a user-friendly conversation, powered by Langflow's advanced language understanding capabilities. + + +Customize the appearance of the chat widget to match your application's design and branding. + + +Communicate with users in multiple languages, opening up your application to a global audience. + + +You can get the HTML code embedded with the chat by clicking the Code button at the Sidebar after building a flow. + + +[image] + + +Clicking the Chat Widget HTML tab, you'll get the code to be inserted. Read below to learn how to use it with HTML, React and Angular. + + +![](./566212295.png) + + +### Embed your flow into HTML {#6e84db2f2a0d451db6fa03c57e9bf9a4} + + +The Chat Widget can be embedded into any HTML page, inside a `` tag, as demonstrated in the video below. + + + + + +### Embed your flow with React {#fe5d3b1c42e74e4c84ebc9d1799b7665} + +1. To embed the Chat Widget using React, insert this `` + +1. Declare your Web Component and encapsulate it in a React component. + +`declare global { namespace JSX { interface IntrinsicElements { "langflow-chat": any; } }}export default function ChatWidget({ className }) { return (
);}` + +1. Finally, you can place the component anywhere in your code to display the Chat Widget. + +--- + + +### Embed your flow with Angular {#4fd87355b9aa409894acfbb9e1497980} + +1. To use the chat widget in Angular, first add this `` + +1. When you use a custom web component in an Angular template, the Angular compiler might show a warning when it doesn't recognize the custom elements by default. To suppress this warning, add `CUSTOM_ELEMENTS_SCHEMA` to the module's `@NgModule.schemas`. +2. Open the module file (it typically ends with _.module.ts_) where you'd add the `langflow-chat` web component. +3. Import `CUSTOM_ELEMENTS_SCHEMA` at the top of the file: + +`import { NgModule, CUSTOM_ELEMENTS_SCHEMA } from "@angular/core";` + +1. Add `CUSTOM_ELEMENTS_SCHEMA` to the 'schemas' array inside the '@NgModule' decorator: + +`@NgModule({ declarations: [ // ... Other components and directives ... ], imports: [ // ... Other imported modules ... ], schemas: [CUSTOM_ELEMENTS_SCHEMA], // Add the CUSTOM_ELEMENTS_SCHEMA here})export class YourModule {}` + +1. In your Angular project, find the component belonging to the module where `CUSTOM_ELEMENTS_SCHEMA` was added. Inside the template, add the `langflow-chat` tag to include the Chat Widget in your component's view: + +`` + + +INFO + +- `CUSTOM_ELEMENTS_SCHEMA` is a built-in schema that allows Angular to recognize custom elements. +- Adding `CUSTOM_ELEMENTS_SCHEMA` tells Angular to allow custom elements in your templates, and it will suppress the warning related to unknown elements like `langflow-chat`. +- Notice that you can only use the Chat Widget in components that are part of the module where you added `CUSTOM_ELEMENTS_SCHEMA`. + +--- + + +## Chat widget configuration {#5ede4bbbd2ac43e29c90f3edb43cba58} + + +Use the widget API to customize your Chat Widget: + + +:::caution + +Props with the type JSON need to be passed as stringified JSONs, with the format {"key":"value"}. + +::: + + + + +| Prop | Type | Required | Description | +| --------------------- | ------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| bot_message_style | JSON | No | Applies custom formatting to bot messages. | +| chat_input_field | String | Yes | Defines the type of the input field for chat messages. | +| chat_inputs | JSON | Yes | Determines the chat input elements and their respective values. | +| chat_output_key | String | No | Specifies which output to display if multiple outputs are available. | +| chat_position | String | No | Positions the chat window on the screen (options include: top-left, top-center, top-right, center-left, center-right, bottom-right, bottom-center, bottom-left). | +| chat_trigger_style | JSON | No | Styles the chat trigger button. | +| chat_window_style | JSON | No | Customizes the overall appearance of the chat window. | +| error_message_style | JSON | No | Sets the format for error messages within the chat window. | +| flow_id | String | Yes | Identifies the flow that the component is associated with. | +| height | Number | No | Sets the height of the chat window in pixels. | +| host_url | String | Yes | Specifies the URL of the host for chat component communication. | +| input_container_style | JSON | No | Applies styling to the container where chat messages are entered. | +| input_style | JSON | No | Sets the style for the chat input field. | +| online | Boolean | No | Toggles the online status of the chat component. | +| online_message | String | No | Sets a custom message to display when the chat component is online. | +| placeholder | String | No | Sets the placeholder text for the chat input field. | +| placeholder_sending | String | No | Sets the placeholder text to display while a message is being sent. | +| send_button_style | JSON | No | Sets the style for the send button in the chat window. | +| send_icon_style | JSON | No | Sets the style for the send icon in the chat window. | +| tweaks | JSON | No | Applies additional custom adjustments for the associated flow. | +| user_message_style | JSON | No | Determines the formatting for user messages in the chat window. | +| width | Number | No | Sets the width of the chat window in pixels. | +| window_title | String | No | Sets the title displayed in the chat window's header or title bar. | + diff --git a/docs/docs/Workspace/workspace-logs.md b/docs/docs/Workspace/workspace-logs.md new file mode 100644 index 000000000..a29d76fa3 --- /dev/null +++ b/docs/docs/Workspace/workspace-logs.md @@ -0,0 +1,16 @@ +--- +title: Logs +sidebar_position: 3 +slug: /workspace-logs +--- + + + +The **Logs** page provides a detailed record of all component executions within a workspace. It is designed to help you track actions, debug issues, and understand the flow of data through various components. + + +![](./816620192.png) + + +Double click the cells on that table for a detailed view. + diff --git a/docs/docs/Workspace/workspace-playground.md b/docs/docs/Workspace/workspace-playground.md new file mode 100644 index 000000000..6694ed5a9 --- /dev/null +++ b/docs/docs/Workspace/workspace-playground.md @@ -0,0 +1,45 @@ +--- +title: Playground +sidebar_position: 1 +slug: /workspace-playground +--- + +import ReactPlayer from "react-player"; + +The **Playground** is a dynamic interface designed for real-time interaction with AIs, allowing users to chat, access memories and monitor inputs and outputs. Here, users can directly prototype and their models, making adjustments and observing different outcomes. + + +--- + + +As long as you have an [Input or Output](/components-io) component working, you can open it up by clicking the **Playground** button. + + +![](./1109547993.png) + + +> +> 💡  Notice how the **Playground's** window arrangement changes depending on what components are being used. Langflow can be used for applications that go beyond chat-based interfaces. +> + + +You can also open a flow's **Playground** without entering its workspace. From **My Collections** or **Langflow Store**, click the **Playground** in one of the projects card. + + + + + +## Memory Management {#821a5ee2a8a44d35b49943cba630511c} + + +--- + + +Whenever you send a message from the **Playground** interface, under the **Memories** **Tab** you'll see a table of previous interactions for that session. + + +--- + + +Langflow allows every chat message to be stored, and a single flow can have multiple memory sessions. To learn more about how to use memories in Langflow, see [Chat Memory](/guides-chat-memory). + diff --git a/docs/docs/Workspace/workspace.md b/docs/docs/Workspace/workspace.md new file mode 100644 index 000000000..e58b5ce78 --- /dev/null +++ b/docs/docs/Workspace/workspace.md @@ -0,0 +1,81 @@ +--- +title: Workspace Overview +sidebar_position: 0 +slug: /workspace +--- + + + +## The Langflow Workspace {#5c8161f9bcc14bfca766685d15251d0c} + + +--- + + +The **Langflow Workspace** is where you assemble new flows and create AIs by connecting and running components. + + +![](./310382168.png) + + +### Sidebar {#9d571b1d46804a01bcd8fbb9a4677af4} + + +--- + + +Located on the left, this the sidebar includes several collapsible sections that categorize the different types of pre-built components available in Langflow. Use the search bar to locate components by name. + + +![](./1769489314.png) + + +### Canvas {#5b050d0f8406436a9201f4711f0063ae} + + +--- + + +The canvas is the main area in the center where you can drag and drop components to create workflows. + + +![](./1224026366.png) + + +Use canvas controls in the bottom left side for zooming in and out, resetting the view, and locking or unlocking the canvas. + + +### Top Navigation Bar {#603c9941be154e1eba4e7c9ec9554b92} + + +--- + + +In the top navigation bar, the dropdown menu labeled with the project name offers several management and customization options for the current flow in the Langflow Workspace. + + +![](./1801518600.png) + +- **New**: Create a new flow from scratch. +- **Settings**: Adjust settings specific to the current flow, such as its Name, Description, and Endpoint Name. +- **Logs**: View logs for the current project, including execution history, errors, and other runtime events. +- **Import**: Import a flow or component from a JSON file into the Workspace. +- **Export**: Export the current flow as a JSON file. +- **Undo (⌘Z)**: Revert the last action taken in the project. +- **Redo (⌘Y)**: Reapply a previously undone action. +- **Refresh All**: Refresh all components and delete cache. + +### Toolbar {#8038bcc315c34350a1f587e1ad0de59f} + + +--- + + +The toolbar at the bottom-right corner that provides options for executing, accessing the API, and sharing workflows. + + +![](./563382604.png) + +- **Playground**: Button that executes the current flow in the workspace. +- **API**: Provides API access details and integration options for the current flow. +- **Share**: Allows users to share their AI with others. diff --git a/docs/docs/administration/chat-widget.mdx b/docs/docs/administration/chat-widget.mdx deleted file mode 100644 index e4d744a40..000000000 --- a/docs/docs/administration/chat-widget.mdx +++ /dev/null @@ -1,212 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Chat Widget - - -This page may contain outdated information. It will be updated as soon as possible. - - -
- The Langflow Chat Widget is a powerful web component that enables - communication with a Langflow project. This widget allows for a chat interface - embedding, allowing the integration of Langflow into web applications - effortlessly. -
- -## Features - -🌟 **Seamless Integration:** Easily integrate the Langflow Chat Widget into your website or web application with just a few lines of JavaScript. - -🚀 **Interactive Chat Interface:** Engage your users with a user-friendly conversation, powered by Langflow's advanced language understanding capabilities. - -🎛️ **Customizable Styling:** Customize the appearance of the chat widget to match your application's design and branding. - -🌐 **Multilingual Support:** Communicate with users in multiple languages, opening up your application to a global audience. - ---- - -## Usage - -
- You can get the HTML code embedded with the chat by clicking the Code button - at the Sidebar after building a flow. -
- -{" "} - - - -
- Clicking the Chat Widget HTML tab, you'll get the code to be inserted. Read - below to learn how to use it with HTML, React and Angular. -
- -{" "} - - - ---- - -### Embed your flow into HTML - -The Chat Widget can be embedded into any HTML page, inside a _``_ tag, as demonstrated in the video below. - -
- -
- ---- - -### Embed your flow with React - -1. To embed the Chat Widget using React, insert this _` -``` - -2. Declare your Web Component and encapsulate it in a React component. - -```jsx -declare global { - namespace JSX { - interface IntrinsicElements { - "langflow-chat": any; - } - } -} - -export default function ChatWidget({ className }) { - return ( -
- -
- ); -} -``` - -3. Finally, you can place the component anywhere in your code to display the Chat Widget. - ---- - -### Embed your flow with Angular - -1. To use the chat widget in Angular, first add this _` -``` - -2. When you use a custom web component in an Angular template, the Angular compiler might show a warning when it doesn't recognize the custom elements by default. To suppress this warning, add _`CUSTOM_ELEMENTS_SCHEMA`_ to the module's _`@NgModule.schemas`_. -3. Open the module file (it typically ends with _.module.ts_) where you'd add the _`langflow-chat`_ web component. -4. Import _`CUSTOM_ELEMENTS_SCHEMA`_ at the top of the file: - -```ts -import { NgModule, CUSTOM_ELEMENTS_SCHEMA } from "@angular/core"; -``` - -5. Add _`CUSTOM_ELEMENTS_SCHEMA`_ to the 'schemas' array inside the '@NgModule' decorator: - -```ts -@NgModule({ - declarations: [ - // ... Other components and directives ... - ], - imports: [ - // ... Other imported modules ... - ], - schemas: [CUSTOM_ELEMENTS_SCHEMA], // Add the CUSTOM_ELEMENTS_SCHEMA here -}) -export class YourModule {} -``` - -6. In your Angular project, find the component belonging to the module where _`CUSTOM_ELEMENTS_SCHEMA`_ was added. Inside the template, add the _`langflow-chat`_ tag to include the Chat Widget in your component's view: - -```jsx - -``` - - -
    -
  • - _`CUSTOM_ELEMENTS_SCHEMA`_ is a built-in schema that allows Angular to - recognize custom elements. -
    • -
  • - Adding _`CUSTOM_ELEMENTS_SCHEMA`_ tells Angular to allow custom elements - in your templates, and it will suppress the warning related to unknown - elements like _`langflow-chat`_. -
    • -
  • - Notice that you can only use the Chat Widget in components that are part - of the module where you added _`CUSTOM_ELEMENTS_SCHEMA`_. -
    • -
- - ---- - -## Chat widget configuration - -Use the widget API to customize your Chat Widget: - - - Props with the type JSON need to be passed as Stringified JSONs, with the - format {"key":"value"}. - - -| Prop | Type | Required | Description | -| --------------------- | ------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| bot_message_style | JSON | No | Applies custom formatting to bot messages. | -| chat_input_field | String | Yes | Defines the type of the input field for chat messages. | -| chat_inputs | JSON | Yes | Determines the chat input elements and their respective values. | -| chat_output_key | String | No | Specifies which output to display if multiple outputs are available. | -| chat_position | String | No | Positions the chat window on the screen (options include: top-left, top-center, top-right, center-left, center-right, bottom-right, bottom-center, bottom-left). | -| chat_trigger_style | JSON | No | Styles the chat trigger button. | -| chat_window_style | JSON | No | Customizes the overall appearance of the chat window. | -| error_message_style | JSON | No | Sets the format for error messages within the chat window. | -| flow_id | String | Yes | Identifies the flow that the component is associated with. | -| height | Number | No | Sets the height of the chat window in pixels. | -| host_url | String | Yes | Specifies the URL of the host for chat component communication. | -| input_container_style | JSON | No | Applies styling to the container where chat messages are entered. | -| input_style | JSON | No | Sets the style for the chat input field. | -| online | Boolean | No | Toggles the online status of the chat component. | -| online_message | String | No | Sets a custom message to display when the chat component is online. | -| placeholder | String | No | Sets the placeholder text for the chat input field. | -| placeholder_sending | String | No | Sets the placeholder text to display while a message is being sent. | -| send_button_style | JSON | No | Sets the style for the send button in the chat window. | -| send_icon_style | JSON | No | Sets the style for the send icon in the chat window. | -| tweaks | JSON | No | Applies additional custom adjustments for the associated flow. | -| user_message_style | JSON | No | Determines the formatting for user messages in the chat window. | -| width | Number | No | Sets the width of the chat window in pixels. | -| window_title | String | No | Sets the title displayed in the chat window's header or title bar. | diff --git a/docs/docs/administration/collections-projects.mdx b/docs/docs/administration/collections-projects.mdx deleted file mode 100644 index f0ef70ee4..000000000 --- a/docs/docs/administration/collections-projects.mdx +++ /dev/null @@ -1,105 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Collections and Projects - - -This page may contain outdated information. It will be updated as soon as possible. - - -My Collection is a space in Langflow where users can manage, organize, and access their flows and components. -Flows and components are displayed as individual cards that provide relevant information. - - - -* **Folders**: Users can organize their projects into folders. Default folders include "My Projects" and the ability to create new folders. Hover over a folder to access options to download or delete it. - -* **Search Bar** Enables users to quickly search through their flows and components. - -* **Select All**: This feature allows users to select all projects displayed on the page for batch actions like moving, deleting, or exporting. - -Click on a flow card to open it in Langflow Workspace or use the **Playground Button** for direct access to execute and interact with the flow’s chatbot interface. - -## Collections - -Components created or imported by the user are also displayed in **My Collection** and can be directly removed from here. - -A collection is a snapshot of flows available in a database. - -Collections can be downloaded to local storage and uploaded for future use. - -
- -
- -## Project - -A **Project** can be a flow or a component. To view your saved projects, select **My Collection**. - -Your **Projects** are displayed. - -Click the **![Playground icon](/logos/botmessage.svg) Playground** button to run a flow from the **My Collection** screen. - -In the top left corner of the screen are options for **Download Collection**, **Upload Collection**, and **New Project**. - -Select **Download Collection** to save your project to your local machine. This downloads all flows and components as a `.json` file. - -Select **Upload Collection** to upload a flow or component `.json` file from your local machine. - -Select **New Project** to create a new project. In addition to a blank workspace, [starter projects](../starter-projects/basic-prompting) are also available. - -## Project options menu - -To see options for your project, in the upper left corner of the workspace, select the dropdown menu. - - - -* **New** - Start a new project. - -* **Duplicate** - Duplicate the current flow as a new project. - -* **Settings** - Modify the project's **Name** or **Description**. - -* **Import** - Upload a flow `.json` file from your local machine. - -* **Export** - Download your current project to your local machine as a `.json` file. - -* **Undo** or **Redo** - Undo or redo your last action. - -## Project folders - -Multiple projects can be stored in **folders**. - -Folders allow you to categorize flows and components into manageable groups. This makes it easier to find and access specific projects quickly. - -**My Projects** is a default folder where all new projects and components are initially stored unless specified otherwise. Users can create custom folders to better organize their work according to specific needs. - -Hovering over a folder in Langflow provides options to either remove or download the entire folder, allowing you to keep an offline copy or migrate projects between environments - -Create new folders with the **New folder** button. One folder can store multiple projects (as the default My Projects folder does). - -You can download folders of projects as a single JSON file, and upload files and flows to your folder. - -Click the **Trash** icon to delete a folder. - - - diff --git a/docs/docs/administration/logs.mdx b/docs/docs/administration/logs.mdx deleted file mode 100644 index 85c1497be..000000000 --- a/docs/docs/administration/logs.mdx +++ /dev/null @@ -1,28 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Logs - - -This page may contain outdated information. It will be updated as soon as possible. - - -To view logs, go to **Project Options** > **Logs**. - -The **Logs** page provides a detailed record of all component executions within a workspace. - -It is designed to help you track actions, debug issues, and understand the flow of data through various components. - -Each log entry includes an execution with source and target components, and displays the data and parameters passed from one component to another. The status of each execution is indicated and errors encountered are easily detected. - - \ No newline at end of file diff --git a/docs/docs/administration/memories.mdx b/docs/docs/administration/memories.mdx deleted file mode 100644 index 5946590ec..000000000 --- a/docs/docs/administration/memories.mdx +++ /dev/null @@ -1,38 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import Admonition from "@theme/Admonition"; - -# Chat Memory - - -This page may contain outdated information. It will be updated as soon as possible. - - -Langflow allows every chat message to be stored, and a single flow can have multiple memory sessions. This enables you to create multiple “memories” for agents to store and recall specific information as needed. You can edit and remove previous messages to inspect and validate a model’s response behavior. Control, explore, and manage conversation histories to get your models acting just right. - -The **Chat Memory** component retrieves message histories by session ID. Users can change the session ID in the advanced settings, with the default session ID set to match the flow ID. These memories are accessible and manageable directly from the Playground; modifications to them directly affect the behavior of chatbot responses. Users can remove or edit previous messages to manipulate and explore model responses further. - - - -By default, chat conversations store Message objects categorized by session ID. A a single flow can host multiple session IDs, and different flows can also share the same session ID. - -Memories can be visualized and managed directly from the Playground. Modifying these memories will influence the behavior of the chatbot responses, as long as an agent uses them. Here you have the ability to remove or edit previous messages, allowing them to manipulate and explore how these changes affect model responses. - - - -You can also display all messages stored across every flow in your workspace by going to **Settings** > **Messages**. \ No newline at end of file diff --git a/docs/docs/administration/playground.mdx b/docs/docs/administration/playground.mdx deleted file mode 100644 index 1ef59e08b..000000000 --- a/docs/docs/administration/playground.mdx +++ /dev/null @@ -1,69 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Playground - - -This page may contain outdated information. It will be updated as soon as possible. - - - - From the **My Collection** page, click the Playground button in one of your flow cards. - It will directly open up a window with that project's Playground, without even showing the flow (this also works for flows hosted on the Langflow Store!). - - -The **Playground** is a dynamic interface designed for real-time interaction with agents, allowing users to access and manage memories and monitor the inputs and outputs. Here, users can directly prototype and experiment with their configured components or AI models, making adjustments and observing different outcomes in real-time. - -It even works for flows hosted on the Langflow store! - -As long as you have a flow properly working, you can interact with it by clicking the Playground button. - -1. From your **Collections** page, click the **![Playground icon](/logos/botmessage.svg)Playground** in one of your flows. - The **Playground** window opens. - - - -2. Chat with your bot as you normally would, all without having to open the editor. - -
- -
- -## Playground I/O - -The Playground's window arrangement changes depending on what components are being used. - -Adding or removing any of the below components modifies your Playground so you can monitor the inputs and outputs. - -- Chat Input -- Text Input -- Chat Output -- Text Output -- Data Output -- Inspect Memory - -You can also select **Options** > **Logs** to see your flow's logs. - -For more information, see [Inputs and Outputs](../components/inputs-and-outputs). - -## Memory Management - -When you send a message, under **Memories**, you can view a table of previous interactions for this session. - -Langflow allows every chat message to be stored, and a single flow can have multiple memory sessions. This enables you to create multiple “memories” for agents to store and recall specific information as needed. - -You can edit and remove previous messages to inspect and validate a model’s response behavior. - -For more information, see [Memories](./memories). diff --git a/docs/docs/administration/prompt-customization.mdx b/docs/docs/administration/prompt-customization.mdx deleted file mode 100644 index 42036f88b..000000000 --- a/docs/docs/administration/prompt-customization.mdx +++ /dev/null @@ -1,78 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Prompt Customization - - -This page may contain outdated information. It will be updated as soon as possible. - - -The prompt template allows users to create prompts and define variables that provide control over instructing the model. - -{" "} - - - -Variables can be used to define instructions, questions, context, inputs, or examples for the model and can be created with any chosen name in curly brackets, e.g., `{variable_name}`. They act as placeholders for parts of the text that can be easily modified. - -{" "} - - - -Once inserted, these variables are immediately recognized as new fields in the prompt component. Here, you can define their values within the component itself or leave a field empty to be adjusted over the chat interface. - -{" "} - - - -You can also use documents or output parsers as prompt variables. By plugging them into prompt handles, they’ll disable and feed that input field. - -{" "} - - - -With this, users can interact with documents, webpages, or any other type of content directly from the prompt, which allows for seamless integration of external resources with the language model. - -If working with an interactive (chat-like) flow, remember to keep one of the input variables empty to behave as the chat input. - -{" "} - - diff --git a/docs/docs/administration/settings.mdx b/docs/docs/administration/settings.mdx deleted file mode 100644 index dba6da78b..000000000 --- a/docs/docs/administration/settings.mdx +++ /dev/null @@ -1,60 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Settings - - -This page may contain outdated information. It will be updated as soon as possible. - - -Change the **Project Settings** or **General Settings** for Langflow. - -## Project Settings - -Click **Project Name** > **Settings** to view your **Project Settings**. - -* **Name** - the name of your project. - -* **Description** - the description for your project. -Visible on the Langflow Store. - -* **Endpoint name** - the custom endpoint name for your project's API endpoint. -To use the default value, leave this field blank. - -## General Settings - -Select your **Profile Picture** > **Settings** to view your **General Settings**. - -### Profile Picture - -Select a profile picture. - -### Store API Key - -Add your **Langflow Store** API key. -To get a Store key, go to the [Langflow store](https://www.langflow.store/). - -### Global Variables - -Select **Add New** to add a key to Langflow. - -Select the **trash icon** to delete a key. - -For more information, see [Global Variables](./global-env). - -### Langflow API - -Create a Langflow API key. - -Click **Add New** > **Create Secret Key** and copy the key somewhere safe and accessible. - -For more information, see [Langflow API](./api). - -### Shortcuts - -A list of keyboard shortcuts for Langflow. - -### Messages - -Inspect, edit, and remove messages in your flow for testing and debugging purposes. - -For more information, see the [Playground](./playground). \ No newline at end of file diff --git a/docs/docs/components/agents.mdx b/docs/docs/components/agents.mdx deleted file mode 100644 index 4482c0637..000000000 --- a/docs/docs/components/agents.mdx +++ /dev/null @@ -1,88 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Agents - - -This page may contain outdated information. It will be updated as soon as possible. - - -Agents are components that use reasoning to make decisions and take actions, designed to autonomously perform tasks or provide services with some degree of agency. LLM chains can only perform hardcoded sequences of actions, while agents use LLMs to reason through which actions to take, and in which order. - ---- - -### AgentInitializer - -The `AgentInitializer` constructs a zero-shot agent from a language model (LLM) and additional tools. - -**Parameters**: - -- **LLM:** The language model used by the `AgentInitializer`. -- **Memory:** Enables memory functionality, allowing the agent to recall and use information from previous interactions. -- **Tools:** The tools available to the agent. -- **Agent:** Specifies the type of agent to instantiate. Currently supported types include `zero-shot-react-description`, `react-docstore`, `self-ask-with-search`, `conversational-react-description`, and `openai-functions`. - ---- - -### CSVAgent - -The `CSVAgent` interacts with CSV (Comma-Separated Values) files, commonly used to store tabular data. Each row in a CSV file represents a record, and each column represents a field. The CSV agent can read and write CSV files, process data, and perform tasks such as filtering, sorting, and aggregating. - -**Parameters**: - -- **LLM:** The language model used by the `CSVAgent`. -- **Path:** The file path to the CSV data. - ---- - -### JSONAgent - -The `JSONAgent` manages JSON (JavaScript Object Notation) data. This agent, like the CSVAgent, uses a language model (LLM) and a toolkit for JSON manipulation. It can explore a JSON blob to extract needed information, list keys, retrieve values, and navigate through the JSON structure. - -**Parameters**: - -- **LLM:** The language model used by the `JSONAgent`. -- **Toolkit:** The toolkit available to the agent. - ---- - -### SQLAgent - -The `SQLAgent` interacts with SQL databases, capable of querying, retrieving data, and executing SQL statements. It provides insights into the database structure, including tables and schemas, and can perform operations such as insertions, updates, and deletions. - -**Parameters**: - -- **LLM:** The language model used by the `SQLAgent`. -- **Database URI:** The connection URI for the SQL database. - ---- - -### VectorStoreAgent - -The `VectorStoreAgent` operates with a vector store, which is a data structure for storing and querying vector-based data representations. This agent can query the vector store to find information relevant to user inputs. - -**Parameters**: - -- **LLM:** The language model used by the `VectorStoreAgent`. -- **Vector Store Info:** The `VectorStoreInfo` used by the agent. - ---- - -### VectorStoreRouterAgent - -The `VectorStoreRouterAgent` is a custom agent that uses a vector store router. It is typically used to retrieve information from multiple vector stores connected through a `VectorStoreRouterToolkit`. - -**Parameters**: - -- **LLM:** The language model used by the `VectorStoreRouterAgent`. -- **Vector Store Router Toolkit:** The toolkit used by the agent. - ---- - -### ZeroShotAgent - -The `ZeroShotAgent` uses the ReAct framework to decide which tool to use based on the tool's description. It is the most general-purpose action agent, capable of determining the necessary actions and their sequence through an `LLMChain`. - -**Parameters**: - -- **Allowed Tools:** The tools accessible to the agent. -- **LLM Chain:** The LLM Chain used by the agent. diff --git a/docs/docs/components/chains.mdx b/docs/docs/components/chains.mdx deleted file mode 100644 index 3d4b60b6a..000000000 --- a/docs/docs/components/chains.mdx +++ /dev/null @@ -1,66 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import Admonition from "@theme/Admonition"; - -# Chains - - -This page may contain outdated information. It will be updated as soon as possible. - - -Chains, in the context of language models, refer to a series of calls made to a language model. This approach allows for using the output of one call as the input for another. Different chain types facilitate varying complexity levels, making them useful for creating pipelines and executing specific scenarios. - ---- - -## CombineDocsChain - -`CombineDocsChain` includes methods to combine or aggregate loaded documents for question-answering functionality. - -Acts as a proxy for LangChain’s [documents](https://python.langchain.com/docs/modules/chains/document/) chains produced by the `load_qa_chain` function. - -**Parameters**: - -- **LLM:** Language Model to use in the chain. -- **chain_type:** Type of chain to be used, each applying a different combination strategy: - - **stuff**: Most straightforward document chain. It takes a list of documents, inserts them all into a prompt, and passes that prompt to an LLM. Suitable for cases where documents are small and few. - - **map_reduce**: Applies an LLM to each document individually (the `Map` step), treating the output as a new document. It then combines these documents to get a single output (the `Reduce` step). Compression may occur to ensure documents fit in the final chain. - - **map_rerank**: Runs an initial prompt on each document to complete a task and score its certainty. Returns the highest-scoring response. - - **refine**: Iteratively updates its answer by looping over the input documents. Each document, along with the latest intermediate answer, is passed to an LLM to generate a new response. This method suits tasks requiring analysis of more documents than the model's context can handle, though it can be less effective for tasks requiring detailed cross-referencing or comprehensive information. - ---- - -## ConversationChain - -`ConversationChain` facilitates dynamic, interactive conversations with a language model, ideal for chatbots or virtual assistants. - -**Parameters**: - -- **LLM:** Language Model to use in the chain. -- **Memory:** Default memory store. -- **input_key:** Specifies the key under which user input is stored in the conversation memory, enabling the chain to process and generate responses. -- **output_key:** Specifies the key under which the generated response is stored, allowing retrieval of the response using this key. -- **verbose:** Controls the verbosity of the chain's output. Set to `True` to enable detailed internal state outputs, useful for debugging and understanding the chain's behavior. Defaults to `False`. - ---- - -## ConversationalRetrievalChain - -`ConversationalRetrievalChain` combines document search with question-answering capabilities, extracting information and providing answers. - -A retriever finds documents based on a query but doesn’t store them; it returns the documents matching the query. - -**Parameters**: - -- **LLM:** Language Model to use in the chain. -- **Memory:** Default memory store. -- **Retriever:** The retriever used to fetch relevant documents. -- **chain_type:** Type of chain to be used, each applying a different combination strategy: - - **stuff**: Inserts a list of documents into a prompt and passes it to an LLM. Suitable for cases where documents are small and few. - - **map_reduce**: Processes each document with an LLM separately, combines them for a single output. Compressions may occur to fit documents into the final chain. - - **map_rerank**: Scores responses based on certainty from each document, returns the highest. - - **refine**: Updates answers iteratively by looping through documents, passing each with intermediate answers to an LLM for a new response. This method is beneficial for tasks that involve extensive document analysis. -- **return_source_documents:** Specifies whether to include source documents used in the output. Useful for providing context or references to the user. Defaults to `True`. -- **verbose:** Controls verbosity of output. Set to `True` for detailed logs, useful for debugging. Defaults to `False`. - ---- diff --git a/docs/docs/components/custom.mdx b/docs/docs/components/custom.mdx deleted file mode 100644 index 10b45c9b7..000000000 --- a/docs/docs/components/custom.mdx +++ /dev/null @@ -1,227 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Custom Components - - -This page may contain outdated information. It will be updated as soon as possible. - - -Build custom components in Langflow for various data processing and transformation tasks. - -This guide provides a comprehensive overview of how to create custom components using Langflow. - -## Basic Structure of a Custom Component - -A custom component in Langflow typically includes the following parts: - -1. **Class Definition**: Inherits from the `Component` class. -2. **Component Metadata**: Defines display name, description, and icon. -3. **Inputs and Outputs**: Specifies the inputs and outputs for the component. -4. **Processing Logic**: Implements the logic for processing data within the component. - -A custom component in Python looks like this: - -```python -from langflow.custom import Component -from langflow.inputs import MessageTextInput, IntInput, BoolInput, DropdownInput, HandleInput -from langflow.template import Output -from langflow.schema import Data, Message -from typing import List, Optional - -class ExampleComponent(Component): - display_name = "Example Component" - description = "A template for creating custom components." - icon = "icon-name" - - inputs = [ - MessageTextInput( - name="input_text", - display_name="Input Text", - info="Text input for the component.", - ), - IntInput( - name="input_number", - display_name="Input Number", - info="Numeric input for the component.", - ), - BoolInput( - name="input_boolean", - display_name="Input Boolean", - info="Boolean input for the component.", - ), - DropdownInput( - name="input_choice", - display_name="Input Choice", - options=["Option1", "Option2", "Option3"], - info="Dropdown input for the component.", - ), - ] - - outputs = [ - Output(display_name="Output Data", name="output_data", method="process_data"), - ] - - def process_data(self) -> Data: - input_text = self.input_text - input_number = self.input_number - input_boolean = self.input_boolean - input_choice = self.input_choice - - # Implement your processing logic here - result = f"Processed: {input_text}, {input_number}, {input_boolean}, {input_choice}" - - self.status = result - return Data(data={"result": result}) - -``` - -## Create a Custom Component Step-by-Step - -1. Create a class that inherits from the `Component` class. - -```python -class ExampleComponent(Component): - # Class content -``` - -2. Define metadata such as `display_name`, `description`, and `icon`. - -```python -display_name = "Example Component" -description = "A template for creating custom components." -icon = "icon-name" -``` - -3. Define the inputs and outputs for the component using the `inputs` and `outputs` lists. - -**Inputs** can be of various types such as `TextInput`, `IntInput`, `BoolInput`, `DropdownInput`, etc. - -```python -inputs = [ - MessageTextInput( - name="input_text", - display_name="Input Text", - info="Text input for the component.", - ), - IntInput( - name="input_number", - display_name="Input Number", - info="Numeric input for the component.", - ), - BoolInput( - name="input_boolean", - display_name="Input Boolean", - info="Boolean input for the component.", - ), - DropdownInput( - name="input_choice", - display_name="Input Choice", - options=["Option1", "Option2", "Option3"], - info="Dropdown input for the component.", - ), -] - -``` - -**Outputs** define the output methods for the component. - -```python -outputs = [ - Output(display_name="Output Data", name="output_data", method="process_data"), -] -``` - -4. Implement the logic for processing data within the component. Define methods for processing data and returning results. - -```python -def process_data(self) -> Data: - input_text = self.input_text - input_number = self.input_number - input_boolean = self.input_boolean - input_choice = self.input_choice - - # Implement your processing logic here - result = f"Processed: {input_text}, {input_number}, {input_boolean}, {input_choice}" - - self.status = result - return Data(data={"result": result}) - -``` - -## Advanced Example: Create a Conditional Router Component - -This example demonstrates a more complex component that routes data based on a condition. - -Notice that this component has two outputs associated with the methods `true_response` and `false_response`. - -These methods trigger `self.stop` to block the transmission for the selected output, allowing for logic operations to be implemented visually. - -```python -from langflow.custom import Component -from langflow.inputs import MessageTextInput, DropdownInput, BoolInput -from langflow.template import Output -from langflow.field_typing import Text - -class ConditionalRouterComponent(Component): - display_name = "Conditional Router" - description = "Routes input based on a specified condition." - icon = "router" - - inputs = [ - MessageTextInput( - name="input_value", - display_name="Input Value", - info="Value to be evaluated.", - ), - MessageTextInput( - name="comparison_value", - display_name="Comparison Value", - info="Value to compare against.", - ), - DropdownInput( - name="operator", - display_name="Operator", - options=["equals", "not equals", "contains"], - info="Comparison operator.", - ), - ] - - outputs = [ - Output(display_name="True Output", name="true_output", method="true_response"), - Output(display_name="False Output", name="false_response", method="false_response"), - ] - - def evaluate_condition(self, input_value: str, comparison_value: str, operator: str) -> bool: - if operator == "equals": - return input_value == comparison_value - elif operator == "not equals": - return input_value != comparison_value - elif operator == "contains": - return comparison_value in input_value - return False - - def true_response(self) -> Text: - if self.evaluate_condition(self.input_value, self.comparison_value, self.operator): - self.stop("false_response") - return self.input_value - else: - self.stop("true_response") - return "" - - def false_response(self) -> Text: - if not self.evaluate_condition(self.input_value, self.comparison_value, self.operator): - self.stop("true_response") - return self.input_value - else: - self.stop("false_response") - return "" - -``` - -By following these steps and examples, you can create custom components in Langflow tailored to your specific needs. The modular structure of Custom Components allows for flexible and reusable components that can be easily integrated into your workflows. - ---- diff --git a/docs/docs/components/data.mdx b/docs/docs/components/data.mdx deleted file mode 100644 index 2c62b2664..000000000 --- a/docs/docs/components/data.mdx +++ /dev/null @@ -1,64 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Data - - -This page may contain outdated information. It will be updated as soon as possible. - - -## API Request - -This component sends HTTP requests to the specified URLs. - -Use this component to interact with external APIs or services and retrieve data. Ensure that the URLs are valid and that you configure the method, headers, body, and timeout correctly. - -**Parameters:** - -- **URLs:** The URLs to target. -- **Method:** The HTTP method, such as GET or POST. -- **Headers:** The headers to include with the request. -- **Body:** The data to send with the request (for methods like POST, PATCH, PUT). -- **Timeout:** The maximum time to wait for a response. - ---- - -## Directory - -This component recursively retrieves files from a specified directory. - -Use this component to retrieve various file types, such as text or JSON files, from a directory. Make sure to provide the correct path and configure the other parameters as needed. - -**Parameters:** - -- **Path:** The directory path. -- **Types:** The types of files to retrieve. Leave this blank to retrieve all file types. -- **Depth:** The level of directory depth to search. -- **Max Concurrency:** The maximum number of simultaneous file loading operations. -- **Load Hidden:** Set to true to include hidden files. -- **Recursive:** Set to true to enable recursive search. -- **Silent Errors:** Set to true to suppress exceptions on errors. -- **Use Multithreading:** Set to true to use multithreading in file loading. - ---- - -## File - -This component loads a file. - -Use this component to load files, such as text or JSON files. Ensure you specify the correct path and configure other parameters as necessary. - -**Parameters:** - -- **Path:** The file path. -- **Silent Errors:** Set to true to prevent exceptions on errors. - ---- - -## URL - -This component retrieves content from specified URLs. - -Ensure the URLs are valid and adjust other parameters as needed. -**Parameters:** - -- **URLs:** The URLs to retrieve content from. diff --git a/docs/docs/components/experimental.mdx b/docs/docs/components/experimental.mdx deleted file mode 100644 index 5abee146d..000000000 --- a/docs/docs/components/experimental.mdx +++ /dev/null @@ -1,275 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Experimental - - -This page may contain outdated information. It will be updated as soon as possible. - - -Components in the experimental phase are currently in beta. They have been initially developed and tested but haven't yet achieved a stable or fully supported status. We encourage users to explore these components, provide feedback, and report any issues encountered. - ---- - -## Clear Message History Component - -This component clears the message history for a specified session ID. - -**Beta:** This component is in beta. - -**Parameters** - -- **Session ID:** - - **Display Name:** Session ID - - **Info:** Clears the message history for this ID. - -**Usage** - -Provide the session ID to clear its message history. - ---- - -## Extract Key From Data - -This component extracts specified keys from a record. - -**Parameters** - -- **Data:** - - - **Display Name:** Data - - **Info:** The record from which to extract keys. - -- **Keys:** - - - **Display Name:** Keys - - **Info:** The keys to be extracted. - -- **Silent Errors:** - - **Display Name:** Silent Errors - - **Info:** Set to true to suppress errors. - - **Advanced:** True - -**Usage** - -Provide the record and specify the keys you want to extract. Optionally, enable silent errors for missing keys. - ---- - -## Flow as Tool - -This component turns a function running a flow into a Tool. - -**Parameters** - -- **Flow Name:** - - - **Display Name:** Flow Name - - **Info:** Select the flow to run. - - **Options:** List of available flows. - - **Real-time Refresh:** True - - **Refresh Button:** True - -- **Name:** - - - **Display Name:** Name - - **Description:** The tool's name. - -- **Description:** - - - **Display Name:** Description - - **Description:** Describes the tool. - -- **Return Direct:** - - **Display Name:** Return Direct - - **Description:** Returns the result directly. - - **Advanced:** True - -**Usage** - -Select a flow, name and describe the tool, and decide if you want to return the result directly. - ---- - -## Listen - -This component listens for a specified notification. - -**Parameters** - -- **Name:** - - **Display Name:** Name - - **Info:** The notification to listen for. - -**Usage** - -Specify the notification to listen for. - ---- - -## List Flows - -This component lists all available flows. - -**Usage** - -Call this component without parameters to list all flows. - ---- - -## Merge Data - -This component merges a list of Data. - -**Parameters** - -- **Data:** - - **Display Name:** Data - -**Usage** - -Provide the Data you want to merge. - ---- - -## Notify - -This component generates a notification. - -**Parameters** - -- **Name:** - - - **Display Name:** Name - - **Info:** The notification's name. - -- **Data:** - - - **Display Name:** Data - - **Info:** Optionally, a record to store in the notification. - -- **Append:** - - **Display Name:** Append - - **Info:** Set to true to append the record to the notification. - -**Usage** - -Specify the notification name, provide a record if necessary, and indicate whether to append it. - ---- - -## Run Flow - -This component runs a specified flow. - -**Parameters** - -- **Input Value:** - - - **Display Name:** Input Value - - **Multiline:** True - -- **Flow Name:** - - - **Display Name:** Flow Name - - **Info:** Select the flow to run. - - **Options:** List of available flows. - - **Refresh Button:** True - -- **Tweaks:** - - **Display Name:** Tweaks - - **Info:** Modifications to apply to the flow. - -**Usage** - -Provide the input value, select the flow, and apply any tweaks. - ---- - -## Runnable Executor - -This component executes a specified runnable. - -**Parameters** - -- **Input Key:** - - - **Display Name:** Input Key - - **Info:** The input key. - -- **Inputs:** - - - **Display Name:** Inputs - - **Info:** Inputs for the runnable. - -- **Runnable:** - - - **Display Name:** Runnable - - **Info:** The runnable to execute. - -- **Output Key:** - - **Display Name:** Output Key - - **Info:** The output key. - -**Usage** - -Specify the input key, provide inputs, select the runnable, and optionally define the output key. - ---- - -## SQL Executor - -This component executes an SQL query. - -**Parameters** - -- **Database URL:** - - - **Display Name:** Database URL - - **Info:** The database's URL. - -- **Include Columns:** - - - **Display Name:** Include Columns - - **Info:** Whether to include columns in the result. - -- **Passthrough:** - - - **Display Name:** Passthrough - - **Info:** Returns the query instead of raising an exception if an error occurs. - -- **Add Error:** - - **Display Name:** Add Error - - **Info:** Includes the error in the result. - -**Usage** - -Provide the SQL query, specify the database URL, and configure settings for columns, error handling, and passthrough. - ---- - -## SubFlow - -This component dynamically generates a tool from a flow. - -**Parameters** - -- **Input Value:** - - - **Display Name:** Input Value - - **Multiline:** True - -- **Flow Name:** - - - **Display Name:** Flow Name - - **Info:** Select the flow to run. - - **Options:** List of available flows. - - **Real Time Refresh:** True - - **Refresh Button:** True - -- **Tweaks:** - - **Display Name:** Tweaks - - **Info:** Modifications to apply to the flow. - -**Usage** - -Select a flow, apply any necessary tweaks, and generate a tool. diff --git a/docs/docs/components/helpers.mdx b/docs/docs/components/helpers.mdx deleted file mode 100644 index c30315a42..000000000 --- a/docs/docs/components/helpers.mdx +++ /dev/null @@ -1,132 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Helpers - - -This page may contain outdated information. It will be updated as soon as possible. - - -### Chat memory - -This component retrieves stored chat messages based on a specific session ID. - -#### Parameters - -- **Sender type:** Choose the sender type from options like "Machine", "User", or "Both". -- **Sender name:** (Optional) The name of the sender. -- **Number of messages:** Number of messages to retrieve. -- **Session ID:** The session ID of the chat history. -- **Order:** Choose the message order, either "Ascending" or "Descending". -- **Data template:** (Optional) Template to convert a record to text. If left empty, the system dynamically sets it to the record's text key. - ---- - -### Combine text - -This component concatenates two text sources into a single text chunk using a specified delimiter. - -#### Parameters - -- **First text:** The first text input to concatenate. -- **Second text:** The second text input to concatenate. -- **Delimiter:** A string used to separate the two text inputs. Defaults to a space. - ---- - -### Create record - -This component dynamically creates a record with a specified number of fields. - -#### Parameters - -- **Number of fields:** Number of fields to be added to the record. -- **Text key:** Key used as text. - ---- - -### Custom component - -Use this component as a template to create your custom component. - -#### Parameters - -- **Parameter:** Describe the purpose of this parameter. - - -

- Customize the build_config and build methods - according to your requirements. -

- - -Learn more about creating custom components at [Custom Component](http://docs.langflow.org/components/custom). - ---- - -### Documents to Data - -Convert LangChain documents into Data. - -#### Parameters - -- **Documents:** Documents to be converted into Data. - ---- - -### ID generator - -Generates a unique ID. - -#### Parameters - -- **Value:** Unique ID generated. - ---- - -### Message history - -Retrieves stored chat messages based on a specific session ID. - -#### Parameters - -- **Sender type:** Options for the sender type. -- **Sender name:** Sender name. -- **Number of messages:** Number of messages to retrieve. -- **Session ID:** Session ID of the chat history. -- **Order:** Order of the messages. - ---- - -### Data to text - -Convert Data into plain text following a specified template. - -#### Parameters - -- **Data:** The Data to convert to text. -- **Template:** The template used for formatting the Data. It can contain keys like `{text}`, `{data}`, or any other key in the record. - ---- - -### Split text - -Split text into chunks of a specified length. - -#### Parameters - -- **Texts:** Texts to split. -- **Separators:** Characters to split on. Defaults to a space. -- **Max chunk size:** The maximum length (in characters) of each chunk. -- **Chunk overlap:** The amount of character overlap between chunks. -- **Recursive:** Whether to split recursively. - ---- - -### Update record - -Update a record with text-based key/value pairs, similar to updating a Python dictionary. - -#### Parameters - -- **Data:** The record to update. -- **New data:** The new data to update the record with. diff --git a/docs/docs/components/inputs-and-outputs.mdx b/docs/docs/components/inputs-and-outputs.mdx deleted file mode 100644 index 8b78d8567..000000000 --- a/docs/docs/components/inputs-and-outputs.mdx +++ /dev/null @@ -1,162 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import useBaseUrl from "@docusaurus/useBaseUrl"; - -# Inputs and Outputs - - -This page may contain outdated information. It will be updated as soon as possible. - - -Inputs and Outputs are a category of components that are used to define where data comes in and out of your flow. -They also dynamically change the Playground and can be renamed to facilitate building and maintaining your flows. - -## Inputs - -Inputs are components used to define where data enters your flow. They can receive data from the user, a database, or any other source that can be converted to Text or Data. - -The difference between Chat Input and other Input components is the output format, the number of configurable fields, and the way they are displayed in the Playground. - -Chat Input components can output `Text` or `Data`. When you want to pass the sender name or sender to the next component, use the `Data` output. To pass only the message, use the `Text` output, useful when saving the message to a database or memory system like Zep. - -You can find out more about Chat Input and other Inputs [here](#chat-input). - -### Chat Input - -This component collects user input from the chat. - - - -**Parameters** - -- **Sender Type:** Specifies the sender type. Defaults to `User`. Options are `Machine` and `User`. -- **Sender Name:** Specifies the name of the sender. Defaults to `User`. -- **Message:** Specifies the message text. It is a multiline text input. -- **Session ID:** Specifies the session ID of the chat history. If provided, the message will be saved in the Message History. - - -

- If `As Data` is `true` and the `Message` is a `Data`, the data of the `Data` - will be updated with the `Sender`, `Sender Name`, and `Session ID`. -

- - -One significant capability of the Chat Input component is its ability to transform the Playground into a chat window. This feature is particularly valuable for scenarios requiring user input to initiate or influence the flow. - - - -### Text Input - -The **Text Input** component adds an **Input** field on the Playground. This enables you to define parameters while running and testing your flow. - - - -**Parameters** - -- **Value:** Specifies the text input value. This is where the user inputs text data that will be passed to the next component in the sequence. If no value is provided, it defaults to an empty string. -- **Data Template:** Specifies how a `Data` should be converted into `Text`. - -The **Data Template** field is used to specify how a `Data` should be converted into `Text`. This is particularly useful when you want to extract specific information from a `Data` and pass it as text to the next component in the sequence. - -For example, if you have a `Data` with the following structure: - -```json -{ - "name": "John Doe", - "age": 30, - "email": "johndoe@email.com" -} -``` - -A template with `Name: {name}, Age: {age}` will convert the `Data` into a text string of `Name: John Doe, Age: 30`. - -If you pass more than one `Data`, the text will be concatenated with a new line separator. - -## Outputs - -Outputs are components that are used to define where data comes out of your flow. They can be used to send data to the user, to the Playground, or to define how the data will be displayed in the Playground. - -The Chat Output works similarly to the Chat Input but does not have a field that allows for written input. It is used as an Output definition and can be used to send data to the user. - -You can find out more about it and the other Outputs [here](#chat-output). - -### Chat Output - -This component sends a message to the chat. - -**Parameters** - -- **Sender Type:** Specifies the sender type. Default is `"Machine"`. Options are `"Machine"` and `"User"`. - -- **Sender Name:** Specifies the sender's name. Default is `"AI"`. - -- **Session ID:** Specifies the session ID of the chat history. If provided, messages are saved in the Message History. - -- **Message:** Specifies the text of the message. - - -

- If `As Data` is `true` and the `Message` is a `Data`, the data in the `Data` - is updated with the `Sender`, `Sender Name`, and `Session ID`. -

- - -### Text Output - -This component displays text data to the user. It is useful when you want to show text without sending it to the chat. - -**Parameters** - -- **Value:** Specifies the text data to be displayed. Defaults to an empty string. - -The `TextOutput` component provides a simple way to display text data. It allows textual data to be visible in the chat window during your interaction flow. - -## Prompts - -A prompt is the input provided to a language model, consisting of multiple components and can be parameterized using prompt templates. A prompt template offers a reproducible method for generating prompts, enabling easy customization through input variables. - -### Prompt - -This component creates a prompt template with dynamic variables. This is useful for structuring prompts and passing dynamic data to a language model. - - - -**Parameters** - -- **Template:** The template for the prompt. This field allows you to create other fields dynamically by using curly brackets `{}`. For example, if you have a template like `Hello {name}, how are you?`, a new field called `name` will be created. Prompt variables can be created with any name inside curly brackets, e.g. `{variable_name}`. - -### PromptTemplate - -The `PromptTemplate` component enables users to create prompts and define variables that control how the model is instructed. Users can input a set of variables which the template uses to generate the prompt when a conversation starts. - -After defining a variable in the prompt template, it acts as its own component input. See [Prompt Customization](../administration/prompt-customization) for more details. - -- **template:** The template used to format an individual request. diff --git a/docs/docs/components/loaders.mdx b/docs/docs/components/loaders.mdx deleted file mode 100644 index 66c0c3a31..000000000 --- a/docs/docs/components/loaders.mdx +++ /dev/null @@ -1,14 +0,0 @@ -import Admonition from '@theme/Admonition'; - -# Loaders - - -This page may contain outdated information. It will be updated as soon as possible. - - - -

- We appreciate your understanding as we polish our documentation – it may contain some rough edges. Share your feedback or report issues to help us improve! 🛠️📝 -

- - diff --git a/docs/docs/components/memories.mdx b/docs/docs/components/memories.mdx deleted file mode 100644 index 3fdf61066..000000000 --- a/docs/docs/components/memories.mdx +++ /dev/null @@ -1,138 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Memories - - -This page may contain outdated information. It will be updated as soon as possible. - - - -

- Thanks for your patience as we improve our documentation—it might have some - rough edges. Share your feedback or report issues to help us enhance it! - 🛠️📝 -

- - -Memory is a concept in chat-based applications that allows the system to remember previous interactions. This capability helps maintain the context of the conversation and enables the system to understand new messages in light of past messages. - ---- - -### MessageHistory - -This component retrieves stored messages using various filters such as sender type, sender name, session ID, and the specific file path where messages are stored. It offers flexible retrieval of chat history, providing insights into past interactions. - -**Parameters** - -- **sender_type** (optional): Specifies the sender's type. Options include `"Machine"`, `"User"`, or `"Machine and User"`. Filters messages by the sender type. -- **sender_name** (optional): Specifies the sender's name. Filters messages by the sender's name. -- **session_id** (optional): Specifies the session ID of the chat history. Filters messages by session. -- **number_of_messages**: Specifies the number of messages to retrieve. Defaults to `5`. Determines the number of recent messages from the chat history to fetch. - - -

- The component retrieves messages based on the provided criteria, including - the specific file path for stored messages. If no specific criteria are - provided, it returns the most recent messages up to the specified limit. - This component can be used to review past interactions and analyze - conversation flows. -

- - -### ConversationBufferMemory - -The `ConversationBufferMemory` component stores the last few inputs and outputs of a conversation. - -**Parameters** - -- **input_key**: Specifies the key under which the user input will be stored in the conversation memory. -- **memory_key**: Specifies the prompt variable name where the memory will store and retrieve chat messages. Defaults to `chat_history`. -- **output_key**: Specifies the key under which the generated response will be stored. -- **return_messages**: Determines whether the history should be returned as a string or as a list of messages. The default is `False`. - ---- - -### ConversationBufferWindowMemory - -`ConversationBufferWindowMemory` is a variant of the `ConversationBufferMemory` that keeps only the last K interactions in memory. It's useful for maintaining a sliding window of recent interactions without letting the buffer get too large. - -**Parameters** - -- **input_key**: Specifies the keys in the memory object where input messages are stored. -- **memory_key**: Specifies the prompt variable name for storing and retrieving chat messages. Defaults to `chat_history`. -- **k**: Specifies the number of interactions or messages to be stored in the conversation buffer. -- **output_key**: Specifies the key under which the generated response will be stored. -- **return_messages**: Determines whether the history should be returned as a string or as a list of messages. The default is `False`. - ---- - -### ConversationEntityMemory - -The `ConversationEntityMemory` component uses a key-value store to manage entities mentioned in conversations. This structure enhances the storage and retrieval of information about specific entities. - -**Parameters** - -- **entity_store**: A structure that stores information about entities mentioned in a conversation. -- **LLM**: Specifies the language model used in the `ConversationEntityMemory`. -- **chat_history_key**: A unique identifier for the chat history data associated with a particular entity. This key helps organize and access chat history data for each entity within the memory. Defaults to `history`. -- **input_key**: Identifies where input messages are stored in the memory object, allowing for their retrieval and manipulation. -- **k**: Specifies the maximum number of entities that can be stored and retrieved from the memory. Defaults to `10`. -- **output_key**: Identifies the key under which the generated response is stored, enabling retrieval using this key. -- **return_messages**: Controls whether the history is returned as a string or as a list of messages. Defaults to `False`. - ---- - -### ConversationKGMemory - -The `ConversationKGMemory` utilizes a knowledge graph to enhance memory capabilities. It extracts entities and knowledge triplets from new messages, using previous messages as context. - -**Parameters** - -- **LLM**: Specifies the language model used in the `ConversationKGMemory`. -- **input_key**: Identifies where input messages are stored in the memory object, facilitating their retrieval and manipulation. -- **k**: Indicates the number of previous conversation turns stored in memory, allowing the model to utilize information from these turns. Defaults to `10`. -- **memory_key**: Specifies the prompt variable name where the memory stores and retrieves chat messages. Defaults to `chat_history`. -- **output_key**: Identifies the key under which the generated response - -is stored, enabling retrieval using this key. - -- **return_messages**: Controls whether the history is returned as a string or as a list of messages. Defaults to `False`. - ---- - -### ConversationSummaryMemory - -The `ConversationSummaryMemory` summarizes conversations over time, condensing information and storing it efficiently. It's particularly useful for long conversations. - -**Parameters** - -- **LLM**: Specifies the language model used in the `ConversationSummaryMemory`. -- **input_key**: Identifies where input messages are stored in the memory object, facilitating their retrieval and manipulation. -- **memory_key**: Specifies the prompt variable name where the memory stores and retrieves chat messages. Defaults to `chat_history`. -- **output_key**: Identifies the key under which the generated response is stored, enabling retrieval using this key. -- **return_messages**: Controls whether the history is returned as a string or as a list of messages. Defaults to `False`. - ---- - -### PostgresChatMessageHistory - -The `PostgresChatMessageHistory` component uses a PostgreSQL database to store and retrieve chat message history. - -**Parameters** - -- **connection_string**: Specifies the details needed to connect to the PostgreSQL database, including username, password, host, port, and database name. Defaults to `postgresql://postgres:mypassword@localhost/chat_history`. -- **session_id**: A unique identifier used to link chat message history with a specific session or conversation. -- **table_name**: The name of the PostgreSQL database table where chat message history is stored. Defaults to `message_store`. - ---- - -### VectorRetrieverMemory - -The `VectorRetrieverMemory` retrieves vectors based on queries, facilitating vector-based searches and retrievals. - -**Parameters** - -- **Retriever**: The tool used to fetch documents. -- **input_key**: Identifies where input messages are stored in the memory object, facilitating their retrieval and manipulation. -- **memory_key**: Specifies the prompt variable name where the memory stores and retrieves chat messages. Defaults to `chat_history`. -- **return_messages**: Controls whether the history is returned as a string or as a list of messages. Defaults to `False`. diff --git a/docs/docs/components/model_specs.mdx b/docs/docs/components/model_specs.mdx deleted file mode 100644 index d5835e9d7..000000000 --- a/docs/docs/components/model_specs.mdx +++ /dev/null @@ -1,144 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Large Language Models (LLMs) - - -This page may contain outdated information. It will be updated as soon as possible. - - -A Large Language Model (LLM) is a foundational component of Langflow. It provides a uniform interface for interacting with LLMs from various providers, including OpenAI, Cohere, and HuggingFace. Langflow extensively uses LLMs across its chains and agents, employing them to generate text based on specific prompts or inputs. - ---- - -## Anthropic - -This is a wrapper for Anthropic's large language models. Learn more at [Anthropic](https://www.anthropic.com). - -- **anthropic_api_key:** This key authenticates and authorizes access to the Anthropic API. -- **anthropic_api_url:** This URL connects to the Anthropic API. -- **temperature:** This parameter adjusts the randomness level in text generation. Set this to a non-negative number. - ---- - -## ChatAnthropic - -This is a wrapper for Anthropic's large language model designed for chat-based interactions. Learn more at [Anthropic](https://www.anthropic.com). - -- **anthropic_api_key:** This key authenticates and authorizes access to the Anthropic API. -- **anthropic_api_url:** This URL connects to the Anthropic API. -- **temperature:** This parameter adjusts the randomness level in text generation. Set this to a non-negative number. - ---- - -## CTransformers - -`CTransformers` provides access to Transformer models implemented in C/C++ using the [GGML](https://github.com/ggerganov/ggml) library. - - - Ensure the `ctransformers` Python package is installed. Discover more about - installation, supported models, and usage - [here](https://github.com/marella/ctransformers). - - -- **config:** This configuration is for the Transformer models. Check the default settings and possible configurations at [config](https://github.com/marella/ctransformers#config). - -```json -{ - "top_k": 40, - "top_p": 0.95, - "temperature": 0.8, - "repetition_penalty": 1.1, - "last_n_tokens": 64, - "seed": -1, - "max_new_tokens": 256, - "stop": null, - "stream": false, - "reset": true, - "batch_size": 8, - "threads": -1, - "context_length": -1, - "gpu_layers": 0 -} -``` - -- **model**: The file path, directory, or Hugging Face Hub model repository name. -- **model_file**: The specific model file name within the repository or directory. -- **model_type**: The type of transformer model used. For further information, visit [ctransformers](https://github.com/marella/ctransformers). - -## ChatOpenAI Component - -This component interfaces with [OpenAI's](https://openai.com) large language models, supporting a variety of tasks such as chatbots, generative question-answering, and summarization. - -- **max_tokens**: The maximum number of tokens to generate for each completion. Set to `-1` to generate as many tokens as possible, based on the model's context size. The default is `256`. -- **model_kwargs**: A dictionary containing any additional model parameters for undefined calls. -- **model_name**: Specifies the OpenAI chat model in use. -- **openai_api_base**: The base URL for accessing the OpenAI API. -- **openai_api_key**: The API key required for authentication with the OpenAI API. -- **temperature**: Adjusts the randomness level of the text generation. This should be a non-negative number, defaulting to `0.7`. - -## Cohere Component - -A wrapper for accessing [Cohere's](https://cohere.com) large language models. - -- **cohere_api_key**: The API key needed for Cohere service authentication. -- **max_tokens**: The limit on the number of tokens to generate per request, defaulting to `256`. -- **temperature**: Adjusts the randomness level in text generations. This should be a non-negative number, defaulting to `0.75`. - -## HuggingFaceHub Component - -A component facilitating access to models hosted on the [HuggingFace Hub](https://www.huggingface.co/models). - -- **huggingfacehub_api_token**: The token required for API authentication. -- **model_kwargs**: Parameters passed to the model. -- **repo_id**: Specifies the model repository, defaulting to `gpt2`. -- **task**: The specific task to execute with the model, returning either `generated_text` or `summary_text`. - -## LlamaCpp Component - -This component provides access to `llama.cpp` models, ensuring high performance and flexibility. - -- **echo**: Whether to echo the input prompt, defaulting to `False`. -- **f16_kv**: Indicates if half-precision should be used for the key/value cache, defaulting to `True`. -- **last_n_tokens_size**: The lookback size for applying repeat penalties, defaulting to `64`. -- **logits_all**: Whether to return logits for all tokens or just the last one, defaulting to `False`. -- **logprobs**: The number of log probabilities to return. If set to None, no probabilities are returned. -- **lora_base**: The path to the base Llama LoRA model. -- **lora_path**: The specific path to the Llama LoRA model. If set to None, no LoRA model is loaded. -- **max_tokens**: The maximum number of tokens to generate in one session, defaulting to `256`. -- **model_path**: The file path to the Llama model. -- **n_batch**: The number of tokens processed in parallel, defaulting to `8`. -- **n_ctx**: The context window size for tokens, defaulting to `512`. -- **repeat_penalty**: The penalty applied to repeated tokens, defaulting to `1.1`. -- **seed**: The seed for random number generation. If set to `-1`, a random seed is used. -- **stop**: A list of stop strings that terminate generation when encountered. -- **streaming**: Indicates whether to stream results token by token, defaulting to `True`. -- **suffix**: A suffix appended to generated text. If None, no suffix is appended. -- **tags**: Tags added to the execution trace for monitoring. -- **temperature**: The sampling temperature, defaulting to `0.8`. -- **top_k**: The top-k sampling setting, defaulting to `40`. -- **top_p**: The cumulative probability threshold for top-p sampling, defaulting to `0.95`. -- **use_mlock**: Forces the system to retain the model in RAM, defaulting to `False`. -- **use_mmap**: Indicates whether to maintain the model loaded in RAM, defaulting to `True`. -- **verbose**: Controls the verbosity of output details. When enabled, it provides insights into internal states to aid debugging and understanding, defaulting to `False`. -- **vocab_only**: Loads only the vocabulary without model weights, defaulting to `False`. - -## VertexAI Component - -This component integrates with [Google Vertex AI](https://cloud.google.com/vertex-ai) large language models to enhance AI capabilities. - -- **credentials**: Custom - -credentials used for API interactions. - -- **location**: The default location for API calls, defaulting to `us-central1`. -- **max_output_tokens**: Limits the output tokens per prompt, defaulting to `128`. -- **model_name**: The name of the Vertex AI model in use, defaulting to `text-bison`. -- **project**: The default Google Cloud Platform project for API calls. -- **request_parallelism**: The level of request parallelism for VertexAI model interactions, defaulting to `5`. -- **temperature**: Adjusts the randomness level in text generations, defaulting to `0`. -- **top_k**: The setting for selecting the top-k tokens for outputs. -- **top_p**: The threshold for summing probabilities of the most likely tokens, defaulting to `0.95`. -- **tuned_model_name**: Specifies a tuned model name, which overrides the default model name if provided. -- **verbose**: Controls the output verbosity to assist in debugging and understanding the operational details, defaulting to `False`. - ---- diff --git a/docs/docs/components/models.mdx b/docs/docs/components/models.mdx deleted file mode 100644 index 4d739b59e..000000000 --- a/docs/docs/components/models.mdx +++ /dev/null @@ -1,354 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Models - - -This page may contain outdated information. It will be updated as soon as possible. - - -## Amazon Bedrock - -This component facilitates the generation of text using the LLM (Large Language Model) model from Amazon Bedrock. - -**Params** - -- **Input Value:** Specifies the input text for text generation. - -- **System Message (Optional):** A system message to pass to the model. - -- **Model ID (Optional):** Specifies the model ID to be used for text generation. Defaults to _`"anthropic.claude-instant-v1"`_. Available options include: - - - _`"ai21.j2-grande-instruct"`_ - - _`"ai21.j2-jumbo-instruct"`_ - - _`"ai21.j2-mid"`_ - - _`"ai21.j2-mid-v1"`_ - - _`"ai21.j2-ultra"`_ - - _`"ai21.j2-ultra-v1"`_ - - _`"anthropic.claude-instant-v1"`_ - - _`"anthropic.claude-v1"`_ - - _`"anthropic.claude-v2"`_ - - _`"cohere.command-text-v14"`_ - -- **Credentials Profile Name (Optional):** Specifies the name of the credentials profile. - -- **Region Name (Optional):** Specifies the region name. - -- **Model Kwargs (Optional):** Additional keyword arguments for the model. - -- **Endpoint URL (Optional):** Specifies the endpoint URL. - -- **Streaming (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. - -- **Cache (Optional):** Specifies whether to cache the response. - -- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. - - -

- Ensure that necessary credentials are provided to connect to the Amazon - Bedrock API. If connection fails, a ValueError will be raised. -

- - ---- - -## Anthropic - -This component allows the generation of text using Anthropic Chat&Completion large language models. - -**Params** - -- **Model Name:** Specifies the name of the Anthropic model to be used for text generation. Available options include: - - - _`"claude-2.1"`_ - - _`"claude-2.0"`_ - - _`"claude-instant-1.2"`_ - - _`"claude-instant-1"`_ - -- **Anthropic API Key:** Your Anthropic API key. - -- **Max Tokens (Optional):** Specifies the maximum number of tokens to generate. Defaults to _`256`_. - -- **Temperature (Optional):** Specifies the sampling temperature. Defaults to _`0.7`_. - -- **API Endpoint (Optional):** Specifies the endpoint of the Anthropic API. Defaults to _`"https://api.anthropic.com"`_ if not specified. - -- **Input Value:** Specifies the input text for text generation. - -- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. - -- **System Message (Optional):** A system message to pass to the model. - -For detailed documentation and integration guides, please refer to the [Anthropic Component Documentation](https://python.langchain.com/docs/integrations/chat/anthropic). - ---- - -## Azure OpenAI - -This component allows the generation of text using the LLM (Large Language Model) model from Azure OpenAI. - -**Params** - -- **Model Name:** Specifies the name of the Azure OpenAI model to be used for text generation. Available options include: - - - _`"gpt-35-turbo"`_ - - _`"gpt-35-turbo-16k"`_ - - _`"gpt-35-turbo-instruct"`_ - - _`"gpt-4"`_ - - _`"gpt-4-32k"`_ - - _`"gpt-4-vision"`_ - -- **Azure Endpoint:** Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/`. - -- **Deployment Name:** Specifies the name of the deployment. - -- **API Version:** Specifies the version of the Azure OpenAI API to be used. Available options include: - - - _`"2023-03-15-preview"`_ - - _`"2023-05-15"`_ - - _`"2023-06-01-preview"`_ - - _`"2023-07-01-preview"`_ - - _`"2023-08-01-preview"`_ - - _`"2023-09-01-preview"`_ - - _`"2023-12-01-preview"`_ - -- **API Key:** Your Azure OpenAI API key. - -- **Temperature (Optional):** Specifies the sampling temperature. Defaults to _`0.7`_. - -- **Max Tokens (Optional):** Specifies the maximum number of tokens to generate. Defaults to _`1000`_. - -- **Input Value:** Specifies the input text for text generation. - -- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. - -- **System Message (Optional):** A system message to pass to the model. - -For detailed documentation and integration guides, please refer to the [Azure OpenAI Component Documentation](https://python.langchain.com/docs/integrations/llms/azure_openai). - ---- - -## Cohere - -This component enables text generation using Cohere large language models. - -**Params** - -- **Cohere API Key:** Your Cohere API key. - -- **Max Tokens (Optional):** Specifies the maximum number of tokens to generate. Defaults to _`256`_. - -- **Temperature (Optional):** Specifies the sampling temperature. Defaults to _`0.75`_. - -- **Input Value:** Specifies the input text for text generation. - -- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. - -- **System Message (Optional):** A system message to pass to the model. - ---- - -## Google Generative AI - -This component enables text generation using Google Generative AI. - -**Params** - -- **Google API Key:** Your Google API key to use for the Google Generative AI. - -- **Model:** The name of the model to use. Supported examples are _`"gemini-pro"`_ and _`"gemini-pro-vision"`_. - -- **Max Output Tokens (Optional):** The maximum number of tokens to generate. - -- **Temperature:** Run inference with this temperature. Must be in the closed interval [0.0, 1.0]. - -- **Top K (Optional):** Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive. - -- **Top P (Optional):** The maximum cumulative probability of tokens to consider when sampling. - -- **N (Optional):** Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated. - -- **Input Value:** The input to the model. - -- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. - -- **System Message (Optional):** A system message to pass to the model. - ---- - -## Hugging Face API - -This component facilitates text generation using LLM models from the Hugging Face Inference API. - -**Params** - -- **Endpoint URL:** The URL of the Hugging Face Inference API endpoint. Should be provided along with necessary authentication credentials. - -- **Task:** Specifies the task for text generation. Options include _`"text2text-generation"`_, _`"text-generation"`_, and _`"summarization"`_. - -- **API Token:** The API token required for authentication with the Hugging Face Hub. - -- **Model Keyword Arguments (Optional):** Additional keyword arguments for the model. Should be provided as a Python dictionary. - -- **Input Value:** The input text for text generation. - -- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. - -- **System Message (Optional):** A system message to pass to the model. - ---- - -## LiteLLM Model - -Generates text using the `LiteLLM` collection of large language models. - -**Parameters** - -- **Model name:** The name of the model to use. For example, `gpt-3.5-turbo`. (Type: str) -- **API key:** The API key to use for accessing the provider's API. (Type: str, Optional) -- **Provider:** The provider of the API key. (Type: str, Choices: "OpenAI", "Azure", "Anthropic", "Replicate", "Cohere", "OpenRouter") -- **Temperature:** Controls the randomness of the text generation. (Type: float, Default: 0.7) -- **Model kwargs:** Additional keyword arguments for the model. (Type: Dict, Optional) -- **Top p:** Filter responses to keep the cumulative probability within the top p tokens. (Type: float, Optional) -- **Top k:** Filter responses to only include the top k tokens. (Type: int, Optional) -- **N:** Number of chat completions to generate for each prompt. (Type: int, Default: 1) -- **Max tokens:** The maximum number of tokens to generate for each chat completion. (Type: int, Default: 256) -- **Max retries:** Maximum number of retries for failed requests. (Type: int, Default: 6) -- **Verbose:** Whether to print verbose output. (Type: bool, Default: False) -- **Input:** The input prompt for text generation. (Type: str) -- **Stream:** Whether to stream the output. (Type: bool, Default: False) -- **System message:** System message to pass to the model. (Type: str, Optional) - ---- - -## Ollama - -Generate text using Ollama Local LLMs. - -**Parameters** - -- **Base URL:** Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified. -- **Model Name:** The model name to use. Refer to [Ollama Library](https://ollama.ai/library) for more models. -- **Temperature:** Controls the creativity of model responses. (Default: 0.8) -- **Cache:** Enable or disable caching. (Default: False) -- **Format:** Specify the format of the output (e.g., json). (Advanced) -- **Metadata:** Metadata to add to the run trace. (Advanced) -- **Mirostat:** Enable/disable Mirostat sampling for controlling perplexity. (Default: Disabled) -- **Mirostat Eta:** Learning rate for Mirostat algorithm. (Default: None) (Advanced) -- **Mirostat Tau:** Controls the balance between coherence and diversity of the output. (Default: None) (Advanced) -- **Context Window Size:** Size of the context window for generating tokens. (Default: None) (Advanced) -- **Number of GPUs:** Number of GPUs to use for computation. (Default: None) (Advanced) -- **Number of Threads:** Number of threads to use during computation. (Default: None) (Advanced) -- **Repeat Last N:** How far back the model looks to prevent repetition. (Default: None) (Advanced) -- **Repeat Penalty:** Penalty for repetitions in generated text. (Default: None) (Advanced) -- **TFS Z:** Tail free sampling value. (Default: None) (Advanced) -- **Timeout:** Timeout for the request stream. (Default: None) (Advanced) -- **Top K:** Limits token selection to top K. (Default: None) (Advanced) -- **Top P:** Works together with top-k. (Default: None) (Advanced) -- **Verbose:** Whether to print out response text. -- **Tags:** Tags to add to the run trace. (Advanced) -- **Stop Tokens:** List of tokens to signal the model to stop generating text. (Advanced) -- **System:** System to use for generating text. (Advanced) -- **Template:** Template to use for generating text. (Advanced) -- **Input:** The input text. -- **Stream:** Whether to stream the response. -- **System Message:** System message to pass to the model. (Advanced) - ---- - -## OpenAI - -This component facilitates text generation using OpenAI's models. - -**Params** - -- **Input Value:** The input text for text generation. - -- **Max Tokens (Optional):** The maximum number of tokens to generate. Defaults to _`256`_. - -- **Model Kwargs (Optional):** Additional keyword arguments for the model. Should be provided as a nested dictionary. - -- **Model Name (Optional):** The name of the model to use. Defaults to _`gpt-4-1106-preview`_. Supported options include: _`gpt-4-turbo-preview`_, _`gpt-4-0125-preview`_, _`gpt-4-1106-preview`_, _`gpt-4-vision-preview`_, _`gpt-3.5-turbo-0125`_, _`gpt-3.5-turbo-1106`_. - -- **OpenAI API Base (Optional):** The base URL of the OpenAI API. Defaults to _`https://api.openai.com/v1`_. - -- **OpenAI API Key (Optional):** The API key for accessing the OpenAI API. - -- **Temperature:** Controls the creativity of model responses. Defaults to _`0.7`_. - -- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. - -- **System Message (Optional):** System message to pass to the model. - ---- - -## Qianfan - -This component facilitates the generation of text using Baidu Qianfan chat models. - -**Params** - -- **Model Name:** Specifies the name of the Qianfan chat model to be used for text generation. Available options include: - - - _`"ERNIE-Bot"`_ - - _`"ERNIE-Bot-turbo"`_ - - _`"BLOOMZ-7B"`_ - - _`"Llama-2-7b-chat"`_ - - _`"Llama-2-13b-chat"`_ - - _`"Llama-2-70b-chat"`_ - - _`"Qianfan-BLOOMZ-7B-compressed"`_ - - _`"Qianfan-Chinese-Llama-2-7B"`_ - - _`"ChatGLM2-6B-32K"`_ - - _`"AquilaChat-7B"`_ - -- **Qianfan Ak:** Your Baidu Qianfan access key, obtainable from [here](https://cloud.baidu.com/product/wenxinworkshop). - -- **Qianfan Sk:** Your Baidu Qianfan secret key, obtainable from [here](https://cloud.baidu.com/product/wenxinworkshop). - -- **Top p (Optional):** Model parameter. Specifies the top-p value. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to _`0.8`_. - -- **Temperature (Optional):** Model parameter. Specifies the sampling temperature. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to _`0.95`_. - -- **Penalty Score (Optional):** Model parameter. Specifies the penalty score. Only supported in ERNIE-Bot and ERNIE-Bot-turbo models. Defaults to _`1.0`_. - -- **Endpoint (Optional):** Endpoint of the Qianfan LLM, required if custom model is used. - -- **Input Value:** Specifies the input text for text generation. - -- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. - -- **System Message (Optional):** A system message to pass to the model. - ---- - -## Vertex AI - -The `ChatVertexAI` is a component for generating text using Vertex AI Chat large language models API. - -**Params** - -- **Credentials:** The JSON file containing the credentials for accessing the Vertex AI Chat API. - -- **Project:** The name of the project associated with the Vertex AI Chat API. - -- **Examples (Optional):** List of examples to provide context for text generation. - -- **Location:** The location of the Vertex AI Chat API service. Defaults to _`us-central1`_. - -- **Max Output Tokens:** The maximum number of tokens to generate. Defaults to _`128`_. - -- **Model Name:** The name of the model to use. Defaults to _`chat-bison`_. - -- **Temperature:** Controls the creativity of model responses. Defaults to _`0.0`_. - -- **Input Value:** The input text for text generation. - -- **Top K:** Limits token selection to top K. Defaults to _`40`_. - -- **Top P:** Works together with top-k. Defaults to _`0.95`_. - -- **Verbose:** Whether to print out response text. Defaults to _`False`_. - -- **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. - -- **System Message (Optional):** System message to pass to the model. diff --git a/docs/docs/components/retrievers.mdx b/docs/docs/components/retrievers.mdx deleted file mode 100644 index e13ed2d89..000000000 --- a/docs/docs/components/retrievers.mdx +++ /dev/null @@ -1,22 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Retrievers - - -This page may contain outdated information. It will be updated as soon as possible. - - -A retriever is an interface that returns documents in response to an unstructured query. It's broader than a vector store because it doesn't need to store documents; it only needs to retrieve them. - ---- - -## MultiQueryRetriever - -The `MultiQueryRetriever` automates generating multiple queries, retrieves relevant documents for each query, and aggregates the results. This method improves retrieval effectiveness and addresses the limitations of traditional distance-based methods. - -**Parameters** - -- **LLM:** Specifies the language model used in the `MultiQueryRetriever`. -- **Prompt:** Defines a schema for the LLM. -- **Retriever:** Identifies the retriever that fetches documents. -- **parser_key:** Specifies the key or attribute name of the parsed output for retrieval. By default, it's set to `lines`, meaning the output from the language model is split into separate lines of text. This allows the retriever to fetch documents relevant to each line of text. diff --git a/docs/docs/components/text-and-record.mdx b/docs/docs/components/text-and-record.mdx deleted file mode 100644 index ab3f9302f..000000000 --- a/docs/docs/components/text-and-record.mdx +++ /dev/null @@ -1,55 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Text and Data - - -This page may contain outdated information. It will be updated as soon as possible. - - -There are two main input and output types: `Text` and `Data`. - -`Text` is a simple string input and output type, while `Data` is a structure very similar to a dictionary in Python. It is a key-value pair data structure. - -We've created a few components to help you work with these types. Let's see how a few of them work. - -## Data To Text - -This is a component that takes in Data and outputs a `Text`. It does this using a template string and concatenating the values of the `Data`, one per line. - -If we have the following Data: - -```json -{ - "sender_name": "Alice", - "message": "Hello!" -} -{ - "sender_name": "John", - "message": "Hi!" -} -``` - -And the template string is: _`{sender_name}: {message}`_ - -The output is: - -``` -Alice: Hello! -John: Hi! -``` - -## Create Data - -This component allows you to create a `Data` from a number of inputs. You can add as many key-value pairs as you want (as long as it is less than 15). Once you've picked that number you'll need to write the name of the Key and can pass `Text` values from other components to it. - -## Documents To Data - -This component takes in a LangChain `Document` and outputs a `Data`. It does this by extracting the `page_content` and the `metadata` from the `Document` and adding them to the `Data` as text and data respectively. - -## Why is this useful? - -The idea was to create a unified way to work with complex data in Langflow and to make it easier to work with data that is not just a simple string. This way you can create more complex workflows and use the data in more ways. - -## What's next? - -We are planning to integrate an array of modalities to Langflow, such as images, audio, and video. This will allow you to create even more complex workflows and use cases. Stay tuned for more updates! 🚀 diff --git a/docs/docs/components/text-splitters.mdx b/docs/docs/components/text-splitters.mdx deleted file mode 100644 index b438d9c21..000000000 --- a/docs/docs/components/text-splitters.mdx +++ /dev/null @@ -1,46 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Text Splitters - - -This page may contain outdated information. It will be updated as soon as possible. - - -A text splitter is a tool that divides a document or text into smaller chunks or segments. This helps make large texts more manageable for analysis or processing. - ---- - -## CharacterTextSplitter - -The `CharacterTextSplitter` splits a long text into smaller chunks based on a specified character. It aims to keep paragraphs, sentences, and words intact as much as possible since these are semantically related elements of text. - -**Parameters** - -- **Documents:** The input documents to split. -- **chunk_overlap:** The number of characters that overlap between consecutive chunks. This setting ensures a smoother transition between chunks and prevents information loss. For example, with a `chunk_overlap` of 20 and a `chunk_size` of 100, each chunk will have the last 20 characters overlap with the next chunk's first 20 characters. The default is `200`. -- **chunk_size:** The maximum number of characters in each chunk. If the text exceeds the specified `chunk_size`, it will be divided into multiple chunks of equal size, with the possible exception of the last chunk, which may be smaller if fewer characters remain. The default is `1000`. -- **separator:** The character used to split the text into chunks. The default is `.`. - ---- - -## RecursiveCharacterTextSplitter - -The `RecursiveCharacterTextSplitter` functions similarly to the `CharacterTextSplitter` by trying to keep paragraphs, sentences, and words together. It also recursively splits the text into smaller chunks if the initial chunk size exceeds a specified threshold. - -**Parameters** - -- **Documents:** The input documents to split. -- **chunk_overlap:** The number of characters that overlap between consecutive chunks. -- **chunk_size:** The maximum number of characters in each chunk. -- **separators:** A list of characters used to split the text into chunks. The splitter first tries to split text using the first character in the `separators` list. If any chunk exceeds the maximum size, it proceeds to the next character in the list and continues splitting. The defaults are ["\n\n", "\n", " ", ""]. - -## LanguageRecursiveTextSplitter - -The `LanguageRecursiveTextSplitter` divides text into smaller chunks based on the programming language of the text. - -**Parameters** - -- **Documents:** The input documents to split. -- **chunk_overlap:** The number of characters that overlap between consecutive chunks. -- **chunk_size:** The maximum number of characters in each chunk. -- **separator_type:** This parameter allows splitting text across multiple programming languages such as Ruby, Python, Solidity, Java, and more. The default is `Python`. diff --git a/docs/docs/components/toolkits.mdx b/docs/docs/components/toolkits.mdx deleted file mode 100644 index f4b2004ba..000000000 --- a/docs/docs/components/toolkits.mdx +++ /dev/null @@ -1,15 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Toolkits - - -This page may contain outdated information. It will be updated as soon as possible. - - - -

- We appreciate your understanding as we polish our documentation - it may - contain some rough edges. Share your feedback or report issues to help us - improve! 🛠️📝 -

- diff --git a/docs/docs/components/tools.mdx b/docs/docs/components/tools.mdx deleted file mode 100644 index a7daae919..000000000 --- a/docs/docs/components/tools.mdx +++ /dev/null @@ -1,69 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Tools - - -This page may contain outdated information. It will be updated as soon as possible. - - -## SearchApi - -SearchApi offers a real-time search engine results API that returns structured JSON data, including answer boxes, knowledge graphs, organic results, and more. - -### Parameters - -- **Api Key:** A unique identifier required for authentication with real-time search engines, obtainable through the [SearchApi dashboard](https://www.searchapi.io/). -- **Engine:** Specifies the search engine used, such as Google, Google Scholar, Bing, YouTube, and YouTube transcripts. Refer to the [documentation](https://www.searchapi.io/docs/google) for a complete list of supported engines. -- **Parameters:** Allows the selection of various parameters recognized by SearchApi. Some parameters are mandatory while others are optional. - -### Output - -- **Document:** The JSON response from the request. - -## BingSearchRun - -Bing Search, a web search engine by Microsoft, provides search results for various content types like web pages, images, videos, and news articles. It combines algorithms and human editors to deliver these results. - -### Parameters - -- **Api Wrapper:** A BingSearchAPIWrapper component that processes the search URL and subscription key. - -## Calculator - -The calculator tool leverages an LLMMathChain to provide mathematical calculation capabilities, enabling the agent to perform computations as needed. - -### Parameters - -- **LLM:** The Language Model used for calculations. - -## GoogleSearchResults - -This is a wrapper around Google Search tailored for users who need precise control over the JSON data returned from the API. - -### Parameters - -- **Api Wrapper:** A GoogleSearchAPIWrapper equipped with a Google API key and CSE ID. - -## GoogleSearchRun - -This tool acts as a quick wrapper around Google Search, executing the search query and returning the snippet from the most relevant result. - -### Parameters - -- **Api Wrapper:** A GoogleSearchAPIWrapper equipped with a Google API key and CSE ID. - -## GoogleSerperRun - -A cost-effective Google Search API. - -### Parameters - -- **Api Wrapper:** A GoogleSerperAPIWrapper with the required API key and result keys. - -## InfoSQLDatabaseTool - -This tool retrieves metadata about SQL databases. It takes a comma-separated list of table names as input and outputs the schema and sample rows for those tables. - -### Parameters - -- **Db:** The SQL database to query. diff --git a/docs/docs/components/utilities.mdx b/docs/docs/components/utilities.mdx deleted file mode 100644 index e0e95aa78..000000000 --- a/docs/docs/components/utilities.mdx +++ /dev/null @@ -1,94 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Utilities - - -This page may contain outdated information. It will be updated as soon as possible. - - -Utilities are a set of actions that can be used to perform common tasks in a flow. They are available in the **Utilities** section in the sidebar. - ---- - -## GET request - -Make a GET request to the specified URL. - -**Parameters** - -- **URL:** The URL to make the request to. If there are multiple URLs, the request will be made to each URL in order. -- **Headers:** A dictionary of headers to send with the request. - -**Output** - -- **List of documents:** A list of documents containing the JSON response from each request. - ---- - -## POST request - -Make a POST request to the specified URL. - -**Parameters** - -- **URL:** The URL to make the request to. -- **Headers:** A dictionary of headers to send with the request. -- **Document:** The document containing a JSON object to send with the request. - -**Output** - -- **Document:** The JSON response from the request as a document. - ---- - -## Update request - -Make a PATCH or PUT request to the specified URL. - -**Parameters** - -- **URL:** The URL to make the request to. -- **Headers:** A dictionary of headers to send with the request. -- **Document:** The document containing a JSON object to send with the request. -- **Method:** The HTTP method to use for the request, either `PATCH` or `PUT`. - -**Output** - -- **Document:** The JSON response from the request as a document. - ---- - -## JSON document builder - -Build a document containing a JSON object using a key and another document page content. - -**Parameters** - -- **Key:** The key to use for the JSON object. -- **Document:** The document page to use for the JSON object. - -**Output** - -- **List of documents:** A list containing the document with the JSON object. - -## Unique ID generator - -Generates a unique identifier (UUID) for each instance it is invoked, providing a distinct and reliable identifier suitable for a variety of applications. - -**Parameters** - -- **Value:** This field displays the generated unique identifier (UUID). The UUID is dynamically generated for each instance of the component, ensuring uniqueness across different uses. - -**Output** - -- Returns a unique identifier (UUID) as a string. This UUID is generated using Python's `uuid` module, ensuring that each identifier is unique and can be used as a reliable reference in your application. - - - The Unique ID Generator is crucial for scenarios requiring distinct - identifiers, such as session management, transaction tracking, or any context - where different instances or entities must be uniquely identified. The - generated UUID is provided as a hexadecimal string, offering a high level of - uniqueness and security for identification purposes. - - -For additional information and examples, please consult the [Langflow Components Custom Documentation](http://docs.langflow.org/components/custom). diff --git a/docs/docs/components/vector-stores.mdx b/docs/docs/components/vector-stores.mdx deleted file mode 100644 index b5ac66992..000000000 --- a/docs/docs/components/vector-stores.mdx +++ /dev/null @@ -1,485 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Vector Stores - - -This page may contain outdated information. It will be updated as soon as possible. - - -### Astra DB - -The `Astra DB` initializes a vector store using Astra DB from Data. It creates Astra DB-based vector indexes to efficiently store and retrieve documents. - -**Parameters:** - -- **Input:** Documents or Data for input. -- **Embedding or Astra vectorize:** External or server-side model Astra DB uses. -- **Collection Name:** Name of the Astra DB collection. -- **Token:** Authentication token for Astra DB. -- **API Endpoint:** API endpoint for Astra DB. -- **Namespace:** Astra DB namespace. -- **Metric:** Metric used by Astra DB. -- **Batch Size:** Batch size for operations. -- **Bulk Insert Batch Concurrency:** Concurrency level for bulk inserts. -- **Bulk Insert Overwrite Concurrency:** Concurrency level for overwriting during bulk inserts. -- **Bulk Delete Concurrency:** Concurrency level for bulk deletions. -- **Setup Mode:** Setup mode for the vector store. -- **Pre Delete Collection:** Option to delete the collection before setup. -- **Metadata Indexing Include:** Fields to include in metadata indexing. -- **Metadata Indexing Exclude:** Fields to exclude from metadata indexing. -- **Collection Indexing Policy:** Indexing policy for the collection. - - - Ensure you configure the necessary Astra DB token and API endpoint before - starting. - - ---- - -### Astra DB Search - -`Astra DBSearch` searches an existing Astra DB vector store for documents similar to the input. It uses the `Astra DB` component's functionality for efficient retrieval. - -**Parameters:** - -- **Search Type:** Type of search, such as Similarity or MMR. -- **Input Value:** Value to search for. -- **Embedding or Astra vectorize:** External or server-side model Astra DB uses. -- **Collection Name:** Name of the Astra DB collection. -- **Token:** Authentication token for Astra DB. -- **API Endpoint:** API endpoint for Astra DB. -- **Namespace:** Astra DB namespace. -- **Metric:** Metric used by Astra DB. -- **Batch Size:** Batch size for operations. -- **Bulk Insert Batch Concurrency:** Concurrency level for bulk inserts. -- **Bulk Insert Overwrite Concurrency:** Concurrency level for overwriting during bulk inserts. -- **Bulk Delete Concurrency:** Concurrency level for bulk deletions. -- **Setup Mode:** Setup mode for the vector store. -- **Pre Delete Collection:** Option to delete the collection before setup. -- **Metadata Indexing Include:** Fields to include in metadata indexing. -- **Metadata Indexing Exclude:** Fields to exclude from metadata indexing. -- **Collection Indexing Policy:** Indexing policy for the collection. - ---- - -### Chroma - -`Chroma` sets up a vector store using Chroma for efficient vector storage and retrieval within language processing workflows. - -**Parameters:** - -- **Collection Name:** Name of the collection. -- **Persist Directory:** Directory to persist the Vector Store. -- **Server CORS Allow Origins (Optional):** CORS allow origins for the Chroma server. -- **Server Host (Optional):** Host for the Chroma server. -- **Server Port (Optional):** Port for the Chroma server. -- **Server gRPC Port (Optional):** gRPC port for the Chroma server. -- **Server SSL Enabled (Optional):** SSL configuration for the Chroma server. -- **Input:** Input data for creating the Vector Store. -- **Embedding:** Embeddings used for the Vector Store. - -For detailed documentation and integration guides, please refer to the [Chroma Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/chroma). - ---- - -### Chroma Search - -`ChromaSearch` searches a Chroma collection for documents similar to the input text. It leverages Chroma to ensure efficient document retrieval. - -**Parameters:** - -- **Input:** Input text for search. -- **Search Type:** Type of search, such as Similarity or MMR. -- **Collection Name:** Name of the Chroma collection. -- **Index Directory:** Directory where the Chroma index is stored. -- **Embedding:** Embedding model used for vectorization. -- **Server CORS Allow Origins (Optional):** CORS allow origins for the Chroma server. -- **Server Host (Optional):** Host for the Chroma server. -- **Server Port (Optional):** Port for the Chroma server. -- **Server gRPC Port (Optional):** gRPC port for the Chroma server. -- **Server SSL Enabled (Optional):** SSL configuration for the Chroma server. - ---- - -### Couchbase - -`Couchbase` builds a Couchbase vector store from Data, streamlining the storage and retrieval of documents. - -**Parameters:** - -- **Embedding:** Model used by Couchbase. -- **Input:** Documents or Data. -- **Couchbase Cluster Connection String:** Cluster Connection string. -- **Couchbase Cluster Username:** Cluster Username. -- **Couchbase Cluster Password:** Cluster Password. -- **Bucket Name:** Bucket identifier in Couchbase. -- **Scope Name:** Scope identifier in Couchbase. -- **Collection Name:** Collection identifier in Couchbase. -- **Index Name:** Index identifier. - -For detailed documentation and integration guides, please refer to the [Couchbase Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/couchbase). - ---- - -### Couchbase Search - -`CouchbaseSearch` leverages the Couchbase component to search for documents based on similarity metric. - -**Parameters:** - -- **Input:** Search query. -- **Embedding:** Model used in the Vector Store. -- **Couchbase Cluster Connection String:** Cluster Connection string. -- **Couchbase Cluster Username:** Cluster Username. -- **Couchbase Cluster Password:** Cluster Password. -- **Bucket Name:** Bucket identifier. -- **Scope Name:** Scope identifier. -- **Collection Name:** Collection identifier in Couchbase. -- **Index Name:** Index identifier. - ---- - -### FAISS - -The `FAISS` component manages document ingestion into a FAISS Vector Store, optimizing document indexing and retrieval. - -**Parameters:** - -- **Embedding:** Model used for vectorizing inputs. -- **Input:** Documents to ingest. -- **Folder Path:** Save path for the FAISS index, relative to Langflow. -- **Index Name:** Index identifier. - -For more details, see the [FAISS Component Documentation](https://faiss.ai/index.html). - ---- - -### FAISS Search - -`FAISSSearch` searches a FAISS Vector Store for documents similar to a given input, using similarity metrics for efficient retrieval. - -**Parameters:** - -- **Embedding:** Model used in the FAISS Vector Store. -- **Folder Path:** Path to load the FAISS index from, relative to Langflow. -- **Input:** Search query. -- **Index Name:** Index identifier. - ---- - -### Milvus - -`Milvus` builds a vector store based on Milvus or Zilliz Cloud service to support document ingestion and search. - -**Parameters:** - -- **Input:** Documents or Data. -- **Embedding:** Model used in Milvus vector store. -- **Collection Name:** Collection identifier in Milvus. -- **Connection URI:** URI used to connect Milvus or Zilliz Cloud service. - ---- - -### Milvus Search - -`MilvusSearch` searches a Milvus Vector Store for documents similar to the input, using advanced similarity metrics. - -**Parameters:** - -- **Input Value:** Search query. -- **Connection URI:** URI used to connect Milvus or Zilliz Cloud service. -- **Embedding:** Model used in Milvus vector store. -- **Collection Name:** Collection identifier in Milvus. -- **Number of Results:** Number of search results to return. - ---- - -### MongoDB Atlas - -`MongoDBAtlas` builds a MongoDB Atlas-based vector store from Data, streamlining the storage and retrieval of documents. - -**Parameters:** - -- **Embedding:** Model used by MongoDB Atlas. -- **Input:** Documents or Data. -- **Collection Name:** Collection identifier in MongoDB Atlas. -- **Database Name:** Database identifier. -- **Index Name:** Index identifier. -- **MongoDB Atlas Cluster URI:** Cluster URI. -- **Search Kwargs:** Additional search parameters. - - - Ensure pymongo is installed for using MongoDB Atlas Vector Store. - - ---- - -### MongoDB Atlas Search - -`MongoDBAtlasSearch` leverages the MongoDBAtlas component to search for documents based on similarity metrics. - -**Parameters:** - -- **Search Type:** Type of search, such as "Similarity" or "MMR". -- **Input:** Search query. -- **Embedding:** Model used in the Vector Store. -- **Collection Name:** Collection identifier. -- **Database Name:** Database identifier. -- **Index Name:** Index identifier. -- **MongoDB Atlas Cluster URI:** Cluster URI. -- **Search Kwargs:** Additional search parameters. - ---- - -### PGVector - -`PGVector` integrates a Vector Store within a PostgreSQL database, allowing efficient storage and retrieval of vectors. - -**Parameters:** - -- **Input:** Value for the Vector Store. -- **Embedding:** Model used. -- **PostgreSQL Server Connection String:** Server URL. -- **Table:** Table name in the PostgreSQL database. - -For more details, see the [PGVector Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/pgvector). - - - Ensure the PostgreSQL server is accessible and configured correctly. - - ---- - -### PGVector Search - -`PGVectorSearch` extends `PGVector` to search for documents based on similarity metrics. - -**Parameters:** - -- **Input:** Search query. -- **Embedding:** Model used. -- **PostgreSQL Server Connection String:** Server URL. -- **Table:** Table name. -- **Search Type:** Type of search, such as "Similarity" or "MMR". - ---- - -### Pinecone - -`Pinecone` constructs a Pinecone wrapper from Data, setting up Pinecone-based vector indexes for document storage and retrieval. - -**Parameters:** - -- **Input:** Documents or Data. -- **Embedding:** Model used. -- **Index Name:** Index identifier. -- **Namespace:** Namespace used. -- **Pinecone API Key:** API key. -- **Pinecone Environment:** Environment settings. -- **Search Kwargs:** Additional search parameters. -- **Pool Threads:** Number of threads. - - - Ensure the Pinecone API key and environment are correctly configured. - - ---- - -### Pinecone Search - -`PineconeSearch` searches a Pinecone Vector Store for documents similar to the input, using advanced similarity metrics. - -**Parameters:** - -- **Search Type:** Type of search, such as "Similarity" or "MMR". -- **Input Value:** Search query. -- **Embedding:** Model used. -- **Index Name:** Index identifier. -- **Namespace:** Namespace used. -- **Pinecone API Key:** API key. -- **Pinecone Environment:** Environment settings. -- **Search Kwargs:** Additional search parameters. -- **Pool Threads:** Number of threads. - ---- - -### Qdrant - -`Qdrant` allows efficient similarity searches and retrieval operations, using a list of texts to construct a Qdrant wrapper. - -**Parameters:** - -- **Input:** Documents or Data. -- **Embedding:** Model used. -- **API Key:** Qdrant API key. -- **Collection Name:** Collection identifier. -- **Advanced Settings:** Includes content payload key, distance function, gRPC port, host, HTTPS, location, metadata payload key, path, port, prefer gRPC, prefix, search kwargs, timeout, URL. - ---- - -### Qdrant Search - -`QdrantSearch` extends `Qdrant` to search for documents similar to the input based on advanced similarity metrics. - -**Parameters:** - -- **Search Type:** Type of search, such as "Similarity" or "MMR". -- **Input Value:** Search query. -- **Embedding:** Model used. -- **API Key:** Qdrant API key. -- **Collection Name:** Collection identifier. -- **Advanced Settings:** Includes content payload key, distance function, gRPC port, host, HTTPS, location, metadata payload key, path, port, prefer gRPC, prefix, search kwargs, timeout, URL. - ---- - -### Redis - -`Redis` manages a Vector Store in a Redis database, supporting efficient vector storage and retrieval. - -**Parameters:** - -- **Index Name:** Default index name. -- **Input:** Data for building the Redis Vector Store. -- **Embedding:** Model used. -- **Schema:** Optional schema file (.yaml) for document structure. -- **Redis Server Connection String:** Server URL. -- **Redis Index:** Optional index name. - -For detailed documentation, refer to the [Redis Documentation](https://python.langchain.com/docs/integrations/vectorstores/redis). - - - Ensure the Redis server URL and index name are configured correctly. Provide a - schema if no documents are available. - - ---- - -### Redis Search - -`RedisSearch` searches a Redis Vector Store for documents similar to the input. - -**Parameters:** - -- **Search Type:** Type of search, such as "Similarity" or "MMR". -- **Input Value:** Search query. -- **Index Name:** Default index name. -- **Embedding:** Model used. -- **Schema:** Optional schema file (.yaml) for document structure. -- **Redis Server Connection String:** Server URL. -- **Redis Index:** Optional index name. - ---- - -### Supabase - -`Supabase` initializes a Supabase Vector Store from texts and embeddings, setting up an environment for efficient document retrieval. - -**Parameters:** - -- **Input:** Documents or data. -- **Embedding:** Model used. -- **Query Name:** Optional query name. -- **Search Kwargs:** Advanced search parameters. -- **Supabase Service Key:** Service key. -- **Supabase URL:** Instance URL. -- **Table Name:** Optional table name. - - - Ensure the Supabase service key, URL, and table name are properly configured. - - ---- - -### Supabase Search - -`SupabaseSearch` searches a Supabase Vector Store for documents similar to the input. - -**Parameters:** - -- **Search Type:** Type of search, such as "Similarity" or "MMR". -- **Input Value:** Search query. -- **Embedding:** Model used. -- **Query Name:** Optional query name. -- **Search Kwargs:** Advanced search parameters. -- **Supabase Service Key:** Service key. -- **Supabase URL:** Instance URL. -- **Table Name:** Optional table name. - ---- - -### Vectara - -`Vectara` sets up a Vectara Vector Store from files or upserted data, optimizing document retrieval. - -**Parameters:** - -- **Vectara Customer ID:** Customer ID. -- **Vectara Corpus ID:** Corpus ID. -- **Vectara API Key:** API key. -- **Files Url:** Optional URLs for file initialization. -- **Input:** Optional data for corpus upsert. - -For more information, consult the [Vectara Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/vectara). - - - If inputs or files_url are provided, they will be processed accordingly. - - ---- - -### Vectara Search - -`VectaraSearch` searches a Vectara Vector Store for documents based on the provided input. - -**Parameters:** - -- **Search Type:** Type of search, such as "Similarity" or "MMR". -- **Input Value:** Search query. -- **Vectara Customer ID:** Customer ID. -- **Vectara Corpus ID:** Corpus ID. -- **Vectara API Key:** API key. -- **Files Url:** Optional URLs for file initialization. - ---- - -### Weaviate - -`Weaviate` facilitates a Weaviate Vector Store setup, optimizing text and document indexing and retrieval. - -**Parameters:** - -- **Weaviate URL:** Default instance URL. -- **Search By Text:** Indicates whether to search by text. -- **API Key:** Optional API key for authentication. -- **Index Name:** Optional index name. -- **Text Key:** Default text extraction key. -- **Input:** Document or record. -- **Embedding:** Model used. -- **Attributes:** Optional additional attributes. - -For more details, see the [Weaviate Component Documentation](https://python.langchain.com/docs/integrations/vectorstores/weaviate). - - - Ensure Weaviate instance is running and accessible. Verify API key, index - name, text key, and attributes are set correctly. - - ---- - -### Weaviate Search - -`WeaviateSearch` searches a Weaviate Vector Store for documents similar to the input. - -**Parameters:** - -- **Search Type:** Type of search, such as "Similarity" or "MMR". -- **Input Value:** Search query. -- **Weaviate URL:** Default instance URL. -- **Search By Text:** Indicates whether to search by text. -- **API Key:** Optional API key for authentication. -- **Index Name:** Optional index name. -- **Text Key:** Default text extraction key. -- **Embedding:** Model used. -- **Attributes:** Optional additional attributes. - ---- diff --git a/docs/docs/contributing/community.mdx b/docs/docs/contributing/community.mdx deleted file mode 100644 index 964abf277..000000000 --- a/docs/docs/contributing/community.mdx +++ /dev/null @@ -1,43 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Community - - -This page may contain outdated information. It will be updated as soon as possible. - - -## 🤖 Join **Langflow** Discord server - -Join us to ask questions and showcase your projects. - -Let's bring together the building blocks of AI integration! - -Langflow [Discord](https://discord.gg/EqksyE2EX9) server. - ---- - -## 🐦 Stay tuned for **Langflow** on Twitter - -Follow [@langflow_ai](https://twitter.com/langflow_ai) on **Twitter** to get the latest news about **Langflow**. - ---- - -## ⭐️ Star **Langflow** on GitHub - -You can "star" **Langflow** in [GitHub](https://github.com/langflow-ai/langflow). - -By adding a star, other users will be able to find it more easily and see that it has been already useful for others. - ---- - -## 👀 Watch the GitHub repository for releases - -You can "watch" **Langflow** in [GitHub](https://github.com/langflow-ai/langflow). - -If you select "Watching" instead of "Releases only" you will receive notifications when someone creates a new issue or question. You can also specify that you only want to be notified about new issues, discussions, PRs, etc. - -Then you can try and help them solve those questions. - ---- - -Thanks! 🚀 diff --git a/docs/docs/contributing/contribute-component.mdx b/docs/docs/contributing/contribute-component.mdx deleted file mode 100644 index 4ad8072f3..000000000 --- a/docs/docs/contributing/contribute-component.mdx +++ /dev/null @@ -1,50 +0,0 @@ -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import Admonition from "@theme/Admonition"; - -# How to Contribute Components? - - -This page may contain outdated information. It will be updated as soon as possible. - - -New components are added as objects of the [CustomComponent](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/interface/custom/custom_component/custom_component.py) class and any dependencies are added to the [pyproject.toml](https://github.com/langflow-ai/langflow/blob/dev/pyproject.toml#L27) file. - -## Add an example component - -You have a new document loader called **MyCustomDocumentLoader** and it would look awesome in Langflow. - -1. Write your loader as an object of the [CustomComponent](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/interface/custom/custom_component/custom_component.py) class. You'll create a new class, `MyCustomDocumentLoader`, that will inherit from `CustomComponent` and override the base class's methods. -2. Define optional attributes like `display_name`, `description`, and `documentation` to provide information about your custom component. -3. Implement the `build_config` method to define the configuration options for your custom component. -4. Implement the `build` method to define the logic for taking input parameters specified in the `build_config` method and returning the desired output. -5. Add the code to the [/components/documentloaders](https://github.com/langflow-ai/langflow/tree/dev/src/backend/base/langflow/components) folder. -6. Add the dependency to [/documentloaders/\_\_init\_\_.py](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/components/documentloaders/__init__.py) as `from .MyCustomDocumentLoader import MyCustomDocumentLoader`. -7. Add any new dependencies to the outer [pyproject.toml](https://github.com/langflow-ai/langflow/blob/dev/pyproject.toml#L27) file. -8. Submit documentation for your component. For this example, you'd submit documentation to the [loaders page](https://github.com/langflow-ai/langflow/blob/dev/docs/docs/components/loaders). -9. Submit your changes as a pull request. The Langflow team will have a look, suggest changes, and add your component to Langflow. - -## User Sharing - -You might want to share and test your custom component with others, but don't need it merged into the main source code. - -If so, you can share your component on the Langflow store. - -1. [Register at the Langflow store](https://www.langflow.store/login/). -2. Undergo pre-validation before receiving an API key. -3. To deploy your amazing component directly to the Langflow store, without it being merged into the main source code, navigate to your flow, and then click **Share**. - The share window appears: - - - -5. Choose whether you want to flow to be public or private. - You can also **Export** your flow as a JSON file from this window. - When you're ready to share the flow, click **Share Flow**. - You should see a **Flow shared successfully** popup. -6. To confirm, navigate to the **Langflow Store** and filter results by **Created By Me**. You should see your new flow on the **Langflow Store**. diff --git a/docs/docs/contributing/github-issues.mdx b/docs/docs/contributing/github-issues.mdx deleted file mode 100644 index 84d049849..000000000 --- a/docs/docs/contributing/github-issues.mdx +++ /dev/null @@ -1,32 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# GitHub Issues - - -This page may contain outdated information. It will be updated as soon as possible. - - -Our [issues](https://github.com/langflow-ai/langflow/issues) page is kept up to date -with bugs, improvements, and feature requests. There is a taxonomy of labels to help -with sorting and discovery of issues of interest. - -If you're looking for help with your code, consider posting a question on the -[GitHub Discussions board](https://github.com/langflow-ai/langflow/discussions). Please -understand that we won't be able to provide individual support via email. We -also believe that help is much more valuable if it's **shared publicly**, -so that more people can benefit from it. - -- **Describing your issue:** Try to provide as many details as possible. What - exactly goes wrong? _How_ is it failing? Is there an error? - "XY doesn't work" usually isn't that helpful for tracking down problems. Always - remember to include the code you ran and if possible, extract only the relevant - parts and don't just dump your entire script. This will make it easier for us to - reproduce the error. - -- **Sharing long blocks of code or logs:** If you need to include long code, - logs or tracebacks, you can wrap them in `
` and `
`. This - [collapses the content](https://developer.mozilla.org/en/docs/Web/HTML/Element/details) so it only becomes visible on click, making the issue easier to read and follow. - -## Issue labels - -[See this page](https://github.com/langflow-ai/langflow/labels) for an overview of the system we use to tag our issues and pull requests. diff --git a/docs/docs/contributing/how-contribute.mdx b/docs/docs/contributing/how-contribute.mdx deleted file mode 100644 index 6776be0b1..000000000 --- a/docs/docs/contributing/how-contribute.mdx +++ /dev/null @@ -1,84 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# How to Contribute? - - -This page may contain outdated information. It will be updated as soon as possible. - - -👋 Hello there! We welcome contributions from developers of all levels to our open-source project on [GitHub](https://github.com/langflow-ai/langflow). If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible. - -As an open-source project in a rapidly developing field, we are extremely open -to contributions, whether in the form of a new feature, improved infra, or better documentation. - -To contribute to this project, please follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow. - -Please do not try to push directly to this repo unless you are a maintainer. - ---- - -## Local development - -You can develop Langflow using docker compose, or locally. - -We provide a `.vscode/launch.json` file for debugging the backend in VSCode, which is a lot faster than using docker compose. - -Setting up hooks: - -```bash -make init -``` - -This will install the pre-commit hooks, which will run `make format` on every commit. - -It is advised to run `make lint` before pushing to the repository. - ---- - -## Run locally - -Langflow can run locally by cloning the repository and installing the dependencies. We recommend using a virtual environment to isolate the dependencies from your system. - -Before you start, make sure you have the following installed: - -- Poetry (>=1.4) -- Node.js - -Then, in the root folder, install the dependencies and start the development server for the backend: - -```bash -make backend -``` - -And the frontend: - -```bash -make frontend -``` - ---- - -## Docker compose - -The following snippet will run the backend and frontend in separate containers. The frontend will be available at `localhost:3000` and the backend at `localhost:7860`. - -```bash -docker compose up --build -# or -make dev build=1 -``` - ---- - -## Documentation - -The documentation is built using [Docusaurus](https://docusaurus.io/). To run the documentation locally, run the following commands: - -```bash -cd docs -npm install -npm run start -``` - -The documentation will be available at `localhost:3000` and all the files are located in the `docs/docs` folder. -Once you are done with your changes, you can create a Pull Request to the `main` branch. diff --git a/docs/docs/deployment/docker.mdx b/docs/docs/deployment/docker.mdx deleted file mode 100644 index e734d6210..000000000 --- a/docs/docs/deployment/docker.mdx +++ /dev/null @@ -1,71 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Docker - - -This page may contain outdated information. It will be updated as soon as possible. - - -This guide will help you get LangFlow up and running using Docker and Docker Compose. - -## Prerequisites - -- Docker -- Docker Compose - -## Clone repo and build Docker container - -1. Clone the LangFlow repository: - - ```sh - git clone https://github.com/langflow-ai/langflow.git - ``` - -2. Navigate to the `docker_example` directory: - - ```sh - cd langflow/docker_example - ``` - -3. Run the Docker Compose file: - - ```sh - docker compose up - ``` - -LangFlow will now be accessible at [http://localhost:7860/](http://localhost:7860/). - -## Docker Compose configuration - -The Docker Compose configuration spins up two services: `langflow` and `postgres`. - -### LangFlow service - -The `langflow` service uses the `langflowai/langflow:latest` Docker image and exposes port 7860. It depends on the `postgres` service. - -Environment variables: - -- `LANGFLOW_DATABASE_URL`: The connection string for the PostgreSQL database. -- `LANGFLOW_CONFIG_DIR`: The directory where LangFlow stores logs, file storage, monitor data, and secret keys. - -Volumes: - -- `langflow-data`: This volume is mapped to `/var/lib/langflow` in the container. - -### PostgreSQL service - -The `postgres` service uses the `postgres:16` Docker image and exposes port 5432. - -Environment variables: - -- `POSTGRES_USER`: The username for the PostgreSQL database. -- `POSTGRES_PASSWORD`: The password for the PostgreSQL database. -- `POSTGRES_DB`: The name of the PostgreSQL database. - -Volumes: - -- `langflow-postgres`: This volume is mapped to `/var/lib/postgresql/data` in the container. - -## Switch to a specific LangFlow version - -If you want to use a specific version of LangFlow, you can modify the `image` field under the `langflow` service in the Docker Compose file. For example, to use version 1.0-alpha, change `langflowai/langflow:latest` to `langflowai/langflow:1.0-alpha`. diff --git a/docs/docs/deployment/kubernetes.md b/docs/docs/deployment/kubernetes.md deleted file mode 100644 index 8648354e2..000000000 --- a/docs/docs/deployment/kubernetes.md +++ /dev/null @@ -1,315 +0,0 @@ -# Kubernetes - -This guide will help you get LangFlow up and running in Kubernetes cluster, including the following steps: - -- Install [LangFlow as IDE](#langflow-ide) in a Kubernetes cluster (for development) -- Install [LangFlow as a standalone application](#langflow-runtime) in a Kubernetes cluster (for production runtime workloads) - -## LangFlow (IDE) - -This solution is designed to provide a complete environment for developers to create, test, and debug their flows. It includes both the API and the UI. - -### Prerequisites - -- Kubernetes server -- kubectl -- Helm - -### Step 0. Prepare a Kubernetes cluster - -We use [Minikube](https://minikube.sigs.k8s.io/docs/start/) for this example, but you can use any Kubernetes cluster. - -1. Create a Kubernetes cluster on Minikube. - ```shell - minikube start - ``` -2. Set `kubectl` to use Minikube. - ```shell - kubectl config use-context minikube - ``` - -### Step 1. Install the LangFlow Helm chart - -1. Add the repository to Helm. - ```shell - helm repo add langflow https://langflow-ai.github.io/langflow-helm-charts - helm repo update - ``` -2. Install LangFlow with the default options in the `langflow` namespace. - ```shell - helm install langflow-ide langflow/langflow-ide -n langflow --create-namespace - ``` -3. Check the status of the pods - ```shell - kubectl get pods -n langflow - ``` - ``` - NAME READY STATUS RESTARTS AGE - langflow-0 1/1 Running 0 33s - langflow-frontend-5d9c558dbb-g7tc9 1/1 Running 0 38s - ``` - -### Step 2. Access LangFlow - -Enable local port forwarding to access LangFlow from your local machine. - -```shell -kubectl port-forward -n langflow svc/langflow-langflow-runtime 7860:7860 -``` - -Now you can access LangFlow at [http://localhost:7860/](http://localhost:7860/). - -### LangFlow version - -To specify a different LangFlow version, you can set the `langflow.backend.image.tag` and `langflow.frontend.image.tag` values in the `values.yaml` file. - -```yaml -langflow: - backend: - image: - tag: "1.0.0a59" - frontend: - image: - tag: "1.0.0a59" -``` - -### Storage - -By default, the chart will use a SQLLite database stored in a local persistent disk. -If you want to use an external PostgreSQL database, you can set the `langflow.database` values in the `values.yaml` file. - -```yaml -# Deploy postgresql. You can skip this section if you have an existing postgresql database. -postgresql: - enabled: true - fullnameOverride: "langflow-ide-postgresql-service" - auth: - username: "langflow" - password: "langflow-postgres" - database: "langflow-db" - -langflow: - backend: - externalDatabase: - enabled: true - driver: - value: "postgresql" - host: - value: "langflow-ide-postgresql-service" - port: - value: "5432" - database: - value: "langflow-db" - user: - value: "langflow" - password: - valueFrom: - secretKeyRef: - key: "password" - name: "langflow-ide-postgresql-service" - sqlite: - enabled: false -``` - -### Scaling - -You can scale the number of replicas for the LangFlow backend and frontend services by changing the `replicaCount` value in the `values.yaml` file. - -```yaml -langflow: - backend: - replicaCount: 3 - frontend: - replicaCount: 3 -``` - -You can scale frontend and backend services independently. - -To scale vertically (increase the resources for the pods), you can set the `resources` values in the `values.yaml` file. - -```yaml -langflow: - backend: - resources: - requests: - memory: "2Gi" - cpu: "1000m" - frontend: - resources: - requests: - memory: "1Gi" - cpu: "1000m" -``` - -### Deploy on AWS EKS, Google GKE, or Azure AKS and other examples - -Visit the [LangFlow Helm Charts repository](https://github.com/langflow-ai/langflow-helm-charts) for more examples and configurations. - -Use the [default values file](https://github.com/langflow-ai/langflow-helm-charts/blob/main/charts/langflow-ide/values.yaml) as reference for all the options available. - -Visit the [examples directory](https://github.com/langflow-ai/langflow-helm-charts/tree/main/examples/langflow-ide) to learn more about different deployment options. - -## LangFlow (Runtime) - -The runtime chart is tailored for deploying applications in a production environment. It is focused on stability, performance, isolation and security to ensure that applications run reliably and efficiently. - -Using a dedicated deployment for a set of flows is fundamental in production environments in order to have a granular resource control. - -## Import a flow - -There are two ways to import a flow (or multiple flows) and deploy it with the LangFlow runtime Helm chart: - -1. **From a remote location**: You can reference a flow stored in a remote location, such as a URL or a Git repository by customizing the `values.yaml` file in the section `downloadFlows`: - - ```yaml - downloadFlows: - flows: - - url: https://raw.githubusercontent.com/langflow-ai/langflow/dev/src/backend/base/langflow/initial_setup/starter_projects/Basic%20Prompting%20(Hello%2C%20world!).json - ``` - - When the LangFlow runtime starts, it will download the flow from the specified URL and run it. - The flow UUID to use to call the API endpoints is the same as the one in the JSON file under the `id` field. - You can also specify a `endpoint_name` field to give a friendly name to the flow. - -2. **Packaging the flow as docker image**: You can add a flow from to a docker image based on Langflow runtime and refer to it in the chart. - - First you need a base Dockerfile to get the langflow image and add your local flows: - - ```Dockerfile - FROM langflowai/langflow-backend:latest - RUN mkdir /app/flows - COPY ./*json /app/flows/. - ``` - - Then you can build the image and push it to DockerHub (or any registry you prefer): - - ```bash - # Create the Dockerfile - echo """FROM langflowai/langflow-backend:latest - RUN mkdir /app/flows - ENV LANGFLOW_LOAD_FLOWS_PATH=/app/flows - COPY ./*json /app/flows/.""" > Dockerfile - # Download the flows - wget https://raw.githubusercontent.com/langflow-ai/langflow/dev/src/backend/base/langflow/initial_setup/starter_projects/Basic%20Prompting%20(Hello%2C%20world!).json - # Build the docker image locally - docker build -t myuser/langflow-just-chat:1.0.0 -f Dockerfile . - # Push the image to DockerHub - docker push myuser/langflow-just-chat:1.0.0 - ``` - -### Prerequisites - -- Kubernetes server -- kubectl -- Helm - -### Step 0. Prepare a Kubernetes cluster - -Follow the same steps as for the LangFlow IDE. - -### Step 1. Install the LangFlow runtime Helm chart - -1. Add the repository to Helm. - ```shell - helm repo add langflow https://langflow-ai.github.io/langflow-helm-charts - helm repo update - ``` -2. Install the LangFlow app with the default options in the `langflow` namespace. - If you bundled the flow in a docker image, you can specify the image name in the `values.yaml` file or with the `--set` flag: - ```shell - helm install my-langflow-app langflow/langflow-runtime -n langflow --create-namespace --set image.repository=myuser/langflow-just-chat --set image.tag=1.0.0 - ``` - If you want to download the flow from a remote location, you can specify the URL in the `values.yaml` file or with the `--set` flag: - ```shell - helm install my-langflow-app langflow/langflow-runtime -n langflow --create-namespace --set downloadFlows.flows[0].url=https://raw.githubusercontent.com/langflow-ai/langflow/dev/src/backend/base/langflow/initial_setup/starter_projects/Basic%20Prompting%20(Hello%2C%20world!).json - ``` - 3. Check the status of the pods - ```shell - kubectl get pods -n langflow - ``` - -### Step 2. Access the LangFlow app API - -Enable local port forwarding to access LangFlow from your local machine. - -```shell -kubectl port-forward -n langflow svc/langflow-my-langflow-app 7860:7860 -``` - -Now you can access the API at [http://localhost:7860/api/v1/flows](http://localhost:7860/api/v1/flows) and execute the flow: - -```bash -id=$(curl -s http://localhost:7860/api/v1/flows | jq -r '.flows[0].id') -curl -X POST \ - "http://localhost:7860/api/v1/run/$id?stream=false" \ - -H 'Content-Type: application/json'\ - -d '{ - "input_value": "Hello!", - "output_type": "chat", - "input_type": "chat" - }' -``` - -### Storage - -In this case, the storage is not needed as our deployment is stateless. - -### Log level and LangFlow configurations - -You can set the log level and other LangFlow configurations in the `values.yaml` file. - -```yaml -env: - - name: LANGFLOW_LOG_LEVEL - value: "INFO" -``` - -### Configure secrets and variables - -In order to inject secrets and LangFlow global variables, you can use the `secrets` and `env` sections in the `values.yaml` file. - -Let's say your flow uses a global variable which is a secret; when you export the flow as JSON, it's recommended to not include it. -When importing the flow in the LangFlow runtime, you can set the global variable using the `env` section in the `values.yaml` file. -Assuming you have a global variable called `openai_key_var`, you can read it directly from a secret: - -```yaml -env: - - name: openai_key_var - valueFrom: - secretKeyRef: - name: openai-key - key: openai-key -``` - -or directly from the values file (not recommended for secret values!): - -```yaml -env: - - name: openai_key_var - value: "sk-...." -``` - -### Scaling - -You can scale the number of replicas for the LangFlow app by changing the `replicaCount` value in the `values.yaml` file. - -```yaml -replicaCount: 3 -``` - -To scale vertically (increase the resources for the pods), you can set the `resources` values in the `values.yaml` file. - -```yaml -resources: - requests: - memory: "2Gi" - cpu: "1000m" -``` - -### Other examples - -Visit the [LangFlow Helm Charts repository](https://github.com/langflow-ai/langflow-helm-charts) for more examples and configurations. - -Use the [default values file](https://github.com/langflow-ai/langflow-helm-charts/blob/main/charts/langflow-runtime/values.yaml) as reference for all the options available. - -Visit the [examples directory](https://github.com/langflow-ai/langflow-helm-charts/tree/main/examples/langflow-runtime) to learn more about different deployment options. diff --git a/docs/docs/examples/chat-memory.mdx b/docs/docs/examples/chat-memory.mdx deleted file mode 100644 index 4fa2775aa..000000000 --- a/docs/docs/examples/chat-memory.mdx +++ /dev/null @@ -1,21 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Chat Memory - - -This page may contain outdated information. It will be updated as soon as possible. - - -The **Chat Memory** component restores previous messages given a Session ID, which can be any string. - -This component is available under the **Helpers** tab of the Langflow sidebar. - -
- -
diff --git a/docs/docs/examples/combine-text.mdx b/docs/docs/examples/combine-text.mdx deleted file mode 100644 index 10c2e8900..000000000 --- a/docs/docs/examples/combine-text.mdx +++ /dev/null @@ -1,25 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Combine Text - - -This page may contain outdated information. It will be updated as soon as possible. - - -With LLM pipelines, combining text from different sources may be as important as splitting text. - -The **Combine Text** component concatenates two text inputs into a single chunk using a specified delimiter, such as whitespace or a newline. - -Also, check out **Combine Texts (Unsorted)** as a similar alternative. - -This component is available under the **Helpers** tab of the Langflow sidebar. - -
- -
diff --git a/docs/docs/examples/create-record.mdx b/docs/docs/examples/create-record.mdx deleted file mode 100644 index 9e681699c..000000000 --- a/docs/docs/examples/create-record.mdx +++ /dev/null @@ -1,21 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Create Data - - -This page may contain outdated information. It will be updated as soon as possible. - - -In Langflow, a `Data` has a structure very similar to a Python dictionary. It is a key-value pair data structure. - -The **Create Data** component allows you to dynamically create a `Data` from a specified number of inputs. You can add as many key-value pairs as you want (as long as it is less than 15 😅). Once you've chosen the number of `Data`, add keys and fill up values, or pass on values from other components to the component using the input handles. - -
- -
diff --git a/docs/docs/examples/pass.mdx b/docs/docs/examples/pass.mdx deleted file mode 100644 index 5f75adac1..000000000 --- a/docs/docs/examples/pass.mdx +++ /dev/null @@ -1,21 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Pass - - -This page may contain outdated information. It will be updated as soon as possible. - - -Sometimes all you need to do is… nothing! - -The **Pass** component enables you to ignore one input and move forward with another one. This is super helpful to swap routes for A/B testing! - -
- -
diff --git a/docs/docs/examples/store-message.mdx b/docs/docs/examples/store-message.mdx deleted file mode 100644 index df2cb3c39..000000000 --- a/docs/docs/examples/store-message.mdx +++ /dev/null @@ -1,21 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Store Message - - -This page may contain outdated information. It will be updated as soon as possible. - - -The **Store Message** component allows you to save information under a specified Session ID and sender type. - -The **Message History** component can then be used to retrieve stored messages. - -
- -
diff --git a/docs/docs/examples/sub-flow.mdx b/docs/docs/examples/sub-flow.mdx deleted file mode 100644 index 31cc7448e..000000000 --- a/docs/docs/examples/sub-flow.mdx +++ /dev/null @@ -1,19 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Sub Flow - - -This page may contain outdated information. It will be updated as soon as possible. - - -The **Sub Flow** component enables a user to select a previously built flow and dynamically generate a component out of it. - -
- -
diff --git a/docs/docs/examples/text-operator.mdx b/docs/docs/examples/text-operator.mdx deleted file mode 100644 index a5fd07e08..000000000 --- a/docs/docs/examples/text-operator.mdx +++ /dev/null @@ -1,19 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Text Operator - - -This page may contain outdated information. It will be updated as soon as possible. - - -The **Text Operator** component simplifies logic. It evaluates the results from another component (for example, if the input text exactly equals `Tuna`) and runs another component based on the results. Basically, the text operator is an if/else component for your flow. - -
- -
diff --git a/docs/docs/getting-started/install-langflow.mdx b/docs/docs/getting-started/install-langflow.mdx deleted file mode 100644 index 58bd2fca0..000000000 --- a/docs/docs/getting-started/install-langflow.mdx +++ /dev/null @@ -1,82 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import Admonition from "@theme/Admonition"; - -# 📦 Install Langflow - - - Langflow **requires** Python version 3.10 or greater and - [pip](https://pypi.org/project/pip/) or - [pipx](https://pipx.pypa.io/stable/installation/) to be installed on your - system. - - -Install Langflow with pip: - -```bash -python -m pip install langflow -U -``` - -Install Langflow with pipx: - -```bash -pipx install langflow --python python3.10 --fetch-missing-python -``` - -Pipx can fetch the missing Python version for you with `--fetch-missing-python`, but you can also install the Python version manually. Use `--force-reinstall` to ensure you have the latest version of Langflow and its dependencies. - -## Having a problem? - -If you encounter a problem, see [Common Installation Issues](/getting-started/possible-installation-issues). - -To get help in the Langflow CLI: - -```bash -python -m langflow --help -``` - -## ⛓️ Run Langflow - -1. To run Langflow, enter the following command. - -```bash -python -m langflow run -``` - -2. Confirm that a local Langflow instance starts by visiting `http://127.0.0.1:7860` in a Chromium-based browser. - - - -3. Continue on to the [Quickstart](./quickstart). - -## HuggingFace Spaces - -HuggingFace provides a great alternative for running Langflow in their Spaces environment. This means you can run Langflow in the cloud without any local installation required. Here's how you can get Langflow up and running on HuggingFace Spaces: - -1. **Access Langflow Space**: Open a Chromium-based browser and navigate to the [Langflow Space](https://huggingface.co/spaces/Langflow/Langflow?duplicate=true). This link directs you to a pre-configured environment for Langflow. - -2. **Duplicate the Space**: You'll encounter an option to duplicate the Langflow space. This step involves a few simple decisions: - - **Naming Your Space**: Assign a unique name to your new Space. - - **Visibility Settings**: Choose between Public or Private visibility for your Space. - - After setting these parameters, click on **Duplicate Space** to initiate the setup. - - - -3. **Complete Installation**: The duplication and setup process begins immediately after you click **Duplicate Space**. Once completed, you will be automatically redirected to the main page of your new Space. - -4. **Start Exploring Langflow**: With the setup complete, Langflow is now ready for use in your Space and you can start exploring its features and capabilities right away! diff --git a/docs/docs/getting-started/new-to-llms.mdx b/docs/docs/getting-started/new-to-llms.mdx deleted file mode 100644 index c6a73e96d..000000000 --- a/docs/docs/getting-started/new-to-llms.mdx +++ /dev/null @@ -1,12 +0,0 @@ -# 📚 New to LLMs? - -Large Language Models, or LLMs, are part of an exciting new world in computing. - -We made Langflow for anyone to create with LLMs, and hope you'll feel comfortable installing Langflow and [getting started](./quickstart). - -If you want to learn the basics of LLMs, prompt engineering, and AI models, Langflow recommends [promptingguide.ai](https://promptingguide.ai), an open-source repository of prompt engineering content maintained by AI experts. -PromptingGuide offers content for [beginners](https://www.promptingguide.ai/introduction/basics) and [experts](https://www.promptingguide.ai/techniques/cot), as well as the latest [research papers](https://www.promptingguide.ai/papers) and [test results](https://www.promptingguide.ai/research) fueling AI's progress. - -For an in depth readings, we recommend [Awesome LLM Books](https://github.com/Hannibal046/Awesome-LLM?tab=readme-ov-file#llm-books), a curated list of resources for learning about LLMs and their applications. - -{/* Wherever you are on your AI journey, it's helpful to keep Prompting Guide open in a tab. */} diff --git a/docs/docs/getting-started/possible-installation-issues.mdx b/docs/docs/getting-started/possible-installation-issues.mdx deleted file mode 100644 index 0d4de5175..000000000 --- a/docs/docs/getting-started/possible-installation-issues.mdx +++ /dev/null @@ -1,43 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# ❗️ Common Installation Issues - -This is a list of possible issues that you may encounter when installing Langflow and how to solve them. - -## _`No module named 'langflow.__main__'`_ - -1. Run _`python -m langflow run`_ instead of _`langflow run`_. -2. If that doesn't work, reinstall Langflow with _`_python -m pip install langflow --pre -U`_. -3. If that doesn't work, reinstall Langflow and its dependencies with _`python -m pip install langflow --pre -U --force-reinstall`_. - -When you try to run Langflow using the command `langflow run`, you may encounter the following error: - -```bash -> langflow run -Traceback (most recent call last): - File ".../langflow", line 5, in - from langflow.__main__ import main -ModuleNotFoundError: No module named 'langflow.__main__' -``` - -There are two possible reasons for this error: - -1. You've installed Langflow using _`pip install langflow`_ but you already had a previous version of Langflow installed in your system. - In this case, you might be running the wrong executable. - To solve this issue, run the correct executable by running _`python -m langflow run`_ instead of _`langflow run`_. - If that doesn't work, try uninstalling and reinstalling Langflow with _`python -m pip install langflow --pre -U`_. -2. Some version conflicts might have occurred during the installation process. - Run _`python -m pip install langflow --pre -U --force-reinstall`_ to reinstall Langflow and its dependencies. - -## _`Something went wrong running migrations. Please, run 'langflow migration --fix'`_ - -Clear the cache by deleting the contents of the cache folder. - -This folder can be found at: - -- **Linux or WSL2 on Windows**: `home//.cache/langflow/` -- **MacOS**: `/Users//Library/Caches/langflow/` - -This error can occur during Langflow upgrades when the new version can't override `langflow-pre.db` in `.cache/langflow/`. Clearing the cache removes this file but will also erase your settings. - -If you wish to retain your files, back them up before clearing the folder. diff --git a/docs/docs/getting-started/quickstart.mdx b/docs/docs/getting-started/quickstart.mdx deleted file mode 100644 index 7d4f15573..000000000 --- a/docs/docs/getting-started/quickstart.mdx +++ /dev/null @@ -1,79 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# ⚡️ Quickstart - -This guide demonstrates how to build a basic flow and modify the prompt for different outcomes. - -## Prerequisites - -- [Python >=3.10](https://www.python.org/downloads/release/python-3100/) and [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/) - -- [Langflow installed and running](./install-langflow) - -- [OpenAI API key](https://platform.openai.com) - -## Hello World - Basic Prompting - -Let's start with a Prompt component to instruct an OpenAI Model. - -Prompts serve as the inputs to a large language model (LLM), acting as the interface between human instructions and computational tasks. By submitting natural language requests in a prompt to an LLM, you can obtain answers, generate text, and solve problems. - -
- -1. From the Langflow dashboard, click **New Project**. -2. Select **Basic Prompting**. - -
- - - -This flow allows you to chat with the **OpenAI** model by using a **Prompt** to send instructions. - -Examine the **Prompt** component. The **Template** field instructs the LLM to `Answer the user as if you were a pirate.` -This should be interesting... - -To use the **OpenAI** component, you have two options for providing your OpenAI API Key: directly passing it to the component or creating an environment variable. For better security and manageability, creating an environment variable is recommended. Here's how to set it up: - -In the **OpenAI API Key** field, click the **Globe** button to access environment variables, and then click **Add New Variable**. - -1. In the **Variable Name** field, enter `openai_api_key`. -2. In the **Value** field, paste your OpenAI API Key (`sk-...`). -3. Click **Save Variable**. - -By creating an environment variable, you keep your API key secure and make it easier to manage across different components or projects. - -## Run the basic prompting flow - -1. Click the **Playground** button. This where you can interact with your bot. -2. Type any message and press Enter. - And... Ahoy! 🏴‍☠️ - The bot responds in a piratical manner! - -## Modify the prompt for a different result - -1. To modify your prompt results, in the **Prompt** template, click the **Template** field. - The **Edit Prompt** window opens. -2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Harold Abelson.` -3. Run the basic prompting flow again. - The response will be markedly different. - -## Next steps - -Well done! You've built your first prompt in Langflow. 🎉 - -By dragging Langflow components to your workspace, you can create all sorts of interesting behaviors. Here are a couple of examples: - -- [Memory Chatbot](/starter-projects/memory-chatbot) -- [Blog Writer](/starter-projects/blog-writer) -- [Document QA](/starter-projects/document-qa) diff --git a/docs/docs/getting-started/workspace.mdx b/docs/docs/getting-started/workspace.mdx deleted file mode 100644 index 374faca74..000000000 --- a/docs/docs/getting-started/workspace.mdx +++ /dev/null @@ -1,275 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# 🎨 Langflow Workspace - -## The Langflow Workspace Interface - -The **Langflow Workspace** is where you assemble new flows and create AIs by connecting and running components. To get started, click on **New Project**. You can either build a flow from scratch (Blank Flow) or choose from pre-built starter examples. - -### Sidebar Menu - -Located on the left, this menu includes several collapsible sections that categorize the different types of pre-built components available in Langflow. - -### Workspace Area - -The large central area where users can visually assemble and connect components to create flows. - -## Top Navigation Bar - -- **Project Options**: Indicates the name of the current project or document. Clicking here allows users to rename the project or access project settings. -- **My Collection**: Provides access to saved or user-defined collections of components or flows. -- **Store**: Opens the component store where users can browse and add new components to their workspace. - -In the top navigation bar, the dropdown menu labeled with the project name offers several management and customization options for the current flow in the Langflow Workspace. - - - -- **New**: Create a new flow from scratch. - -- **Settings**: Adjust settings specific to the current flow, such as its Name, Description, and Endpoint Name. - -- **Logs**: View logs for the current project, including execution history, errors, and other runtime events. - -- **Import**: Import a flow or component from a JSON file into the Workspace. - -- **Export**: Export the current flow as a JSON file. - -- **Undo (⌘Z)**: Revert the last action taken in the project. - -- **Redo (⌘Y)**: Reapply a previously undone action. - -- **Refresh All**: Refresh all components and delete cache. - -## Control Panel - -- **Playground**: Button that executes the current flow in the workspace. -- **API**: Provides API access details and integration options for the current flow. -- **Share**: Allows users to share their AI with others. - -## Flows & Components - -A [flow](#flow) is a pipeline of components connected together in the Langflow Workspace. - -A [component](#component) is a single building block within a flow. It has inputs, outputs, and parameters that define its functionality. - -For example, the **OpenAI Model** is a **component** of the **Basic Prompting** flow. - -## Flow - -A **Flow** is a sequence of components that are connected to perform a series of operations or tasks. Each flow is essentially a pipeline where data functions travel through various components, each modifying or analyzing the data according to its specific functionality. - -For example, the [Basic Prompting](../starter-projects/basic-prompting) flow is a pipeline of four components: - - - -{" "} - -In this flow, the **OpenAI Model** receives input (left side) and produces output (right side) - in this case, receiving input from the **Chat Input** and **Prompt** components and passing the output to the **Chat Output** component. - -## Component - -Components are the building blocks of flows. They consist of inputs, outputs, and parameters that define their functionality. These elements provide a convenient and straightforward way to compose LLM-based applications. Learn more about components and how they work below. - -
- During the flow creation process, you will notice handles (colored circles) - attached to one or both sides of a component. These handles use distinct - colors to indicate the types of inputs and outputs that can be interconnected. - Hover over a handle to see connection details. -
- -{" "} - - - -{/*
*/} - -On the top right corner of the component, you'll find the a play button to run a component. Once it runs, a status icon appears and you can hover over that to visualize success or error messages. - -Start interacting with your AI by clicking the **![Playground icon](/logos/botmessage.svg)Playground** at the bottom right of the workspace. - -### Output Preview - -Langflow includes an output visualizer for components that opens a pop-up screen. This allows you to easily inspect and monitor transmissions between components, providing instant feedback on your workflows. - - - -### Component Menu - -Each component is a little unique, but they will all have a menu bar on top that looks something like this. - - - -It consists of options such as: - -**Code** - displays component's Python code. You can modify the code and save it. - -**Advanced** - See and adjust all parameters of a component. - -**Freeze** - After a component runs, lock its previous output state to prevent it from re-running. - -Click **All** (the "..." button) to see all options. - -
- -
- -### Advanced Settings - -Langflow components can be edited by clicking the **Advanced Settings** button. - -
- -
- -Hide parameters with the **SHOW** button to reduce complexity and keep the workspace clean and intuitive for experimentation. - -You can also double-click a component's name and description to modify those. Component descriptions accept markdown syntax. - -### Group Components - -Multiple components can be grouped into a single component for reuse. This is useful when combining large flows into single components (like RAG with a vector database, for example) and saving space. - -1. Hold **Shift** and drag to select components. -2. Select **Group**. -3. The components merge into a single component. -4. Double click name and description to change them. -5. Save your grouped component to in the sidebar for later use! - -### Update component version - -A component's state is stored in a database, while sidebar components are like starter templates. - -As soon as you drag a component from the sidebar to the workspace, the two components are no longer in parity. - -The component will keep the version number it was initilized to the workspace with. Click the **Update Component** icon (exclamation mark) to bring the component up to the `latest` version. This will change the code of the component in place so you can validate that the component was updated by checking its Python code before and after updating it. - - - -## Playground - -Run your AI by clicking the **![Playground icon](/logos/botmessage.svg)Playground** button. - -For more, see [Playground](../administration/playground). - -## API - -{/* rsn */} -The **API** session presents code templates for integrating your flow into external applications. - -{/* Modify parameters in the **Tweaks** window, click **Copy Code** or **Download** buttons, and paste your code where you want to use it. */} - - - -### cURL - -The **cURL** tab displays sample code for posting a query to your flow. -Modify the `input_value` to change your input message. -Copy the code and run it to post a query to your flow and get the result. - -```bash -curl -X POST \ - http://127.0.0.1:7863/api/v1/run/f2eefd80-bb91-4190-9279-0d6ffafeaac4\?stream\=false \ - -H 'Content-Type: application/json'\ - -d '{"input_value": "is anybody there?", - "output_type": "chat", - "input_type": "chat", - "tweaks": { - "Prompt-uxBqP": {}, - "OpenAIModel-k39HS": {}, - "ChatOutput-njtka": {}, - "ChatInput-P3fgL": {} -}}' -``` - -Result: - -``` -{"session_id":"f2eefd80-bb91-4190-9279-0d6ffafeaac4:53856a772b8e1cfcb3dd2e71576b5215399e95bae318d3c02101c81b7c252da3","outputs":[{"inputs":{"input_value":"is anybody there?"},"outputs":[{"results":{"result":"Arrr, me hearties! Aye, this be Captain [Your Name] speakin'. What be ye needin', matey?"},"artifacts":{"message":"Arrr, me hearties! Aye, this be Captain [Your Name] speakin'. What be ye needin', matey?","sender":"Machine","sender_name":"AI"},"messages":[{"message":"Arrr, me hearties! Aye, this be Captain [Your Name] speakin'. What be ye needin', matey?","sender":"Machine","sender_name":"AI","component_id":"ChatOutput-njtka"}],"component_display_name":"Chat Output","component_id":"ChatOutput-njtka"}]}]}% -``` - -### Python API - -The **Python API** tab displays code to interact with your flow using the Python HTTP requests library. - -### Python Code - -The **Python Code** tab displays code to interact with your flow's `.json` file using the Langflow runtime. - -### Chat Widget HTML - -The **Chat Widget HTML** tab displays code that can be inserted in the `` of your HTML to interact with your flow. -For more, see the [Chat widget documentation](../administration/chat-widget). - -### Tweaks - -The **Tweaks** tab displays the available parameters for your flow. -Modifying the parameters changes the code parameters across all windows. -For example, changing the **Chat Input** component's `input_value` will change that value across all API calls. - -
- -
diff --git a/docs/docs/index.mdx b/docs/docs/index.mdx deleted file mode 100644 index 64946d6ad..000000000 --- a/docs/docs/index.mdx +++ /dev/null @@ -1,37 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import Admonition from "@theme/Admonition"; - -# 👋 Welcome to Langflow - -Langflow is a new, visual framework for building multi-agent and RAG applications. It is open-source, Python-powered, fully customizable, LLM and vector store agnostic. - -Its intuitive interface allows for easy manipulation of AI building blocks, enabling developers to quickly prototype and turn their ideas into powerful, real-world solutions. - -{" "} -{/* rsn */} - - - -## 🚀 First steps - -- [Install Langflow](/getting-started/install-langflow) - Install and start a local Langflow server. - -- [Quickstart](/getting-started/quickstart) - Create a flow and run it. - -- [Langflow Workspace](/getting-started/workspace) - Learn more about the Langflow Workspace. - -## Learn more about Langflow 1.0 - -Learn more about the exciting changes in Langflow 1.0, and how to migrate your existing Langflow projects. - -- [A new chapter for Langflow](/whats-new/a-new-chapter-langflow) -- [Migration guides](/migration/migrating-to-one-point-zero) diff --git a/docs/docs/integrations/crewai/agent.md b/docs/docs/integrations/crewai/agent.md deleted file mode 100644 index 6a46eaf50..000000000 --- a/docs/docs/integrations/crewai/agent.md +++ /dev/null @@ -1,138 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# Agent Component - -The `CrewAIAgent` component represents an agent of CrewAI. It provides a convenient way to integrate CrewAI agent data into your Langflow workflows. - -[CrewAI Reference](https://docs.crewai.com/how-to/LLM-Connections/) - -The `CrewAIAgent` component enables you to: - -- Define the role, goal, and backstory of the agent -- Specify tools and language models for the agent -- Configure advanced settings such as memory, verbosity, and delegation - -## Component Usage - -To use the `CrewAIAgent` component in a Langflow flow, follow these steps: - -1. Add the `CrewAIAgent` component to your flow. -2. Configure the component by providing the required inputs such as role, goal, and backstory. -3. Connect the component to other nodes in your flow as needed. - -## Component Python code - -```python -from langflow.custom import Component -from langflow.io import MessageTextInput, Output -from langflow.schema import Data -from langflow.io import BoolInput, DictInput, DropdownInput, MessageTextInput, HandleInput -from crewai import Agent - - -class CrewAIAgent(Component): - display_name = "CrewAIAgent" - description = "Represents an agent of CrewAI." - documentation: str = "https://docs.crewai.com/how-to/LLM-Connections/" - icon = "CrewAI" - - inputs = [ - MessageTextInput(name="role", display_name="Role", info="The role of the agent."), - MessageTextInput(name="goal", display_name="Goal", info="The objective of the agent."), - MessageTextInput(name="backstory", display_name="Backstory", info="The backstory of the agent."), - HandleInput( - name="tools", - display_name="Tools", - input_types=["Tool"], - is_list=True, - info="Tools at agents disposal", - value=[], - ), - HandleInput( - name="llm", - display_name="Language Model", - info="Language model that will run the agent.", - input_types=["LanguageModel"], - ), - BoolInput( - name="memory", - display_name="Memory", - info="Whether the agent should have memory or not", - advanced=True, - value=True, - ), - BoolInput( - name="verbose", - display_name="Verbose", - advanced=True, - value=False, - ), - BoolInput( - name="allow_delegation", - display_name="Allow Delegation", - info="Whether the agent is allowed to delegate tasks to other agents.", - value=True, - ), - DictInput( - name="kwargs", - display_name="kwargs", - info="kwargs of agent.", - is_list=True, - advanced=True, - ), - ] - - outputs = [ - Output(display_name="Output", name="output", method="build_output"), - ] - - - def build_output(self) -> Agent: - kwargs = self.kwargs if self.kwargs else {} - agent = Agent( - role=self.role, - goal=self.goal, - backstory=self.backstory, - llm=self.llm, - verbose=self.verbose, - memory=self.memory, - tools=self.tools if self.tools else [], - allow_delegation=self.allow_delegation, - **kwargs - ) - self.status = agent - return agent - -``` - -## Example Usage - -Here's an example of how to use the `CrewAIAgent` component in a Langflow flow, connecting the `OpenAI` component's output to the CrewAIAgent component. - - - -## Best Practices - -When using the `CrewAIAgent` component, consider the following best practices: - -- Ensure that you have configured the agent's role, goal, and backstory appropriately. - -The `CrewAIAgent` component provides a seamless way to integrate CrewAI agent data into your Langflow workflows. By leveraging this component, you can easily define and utilize agent information from CrewAI, enhancing the capabilities of your Langflow applications. Feel free to explore and experiment with the `CrewAIAgent` component to unlock new possibilities in your Langflow projects! - -## Troubleshooting - -If you encounter any issues while using the `CrewAIAgent` component, consider the following: - -- Double-check that your inputs such as role, goal, and backstory are correctly configured. -- Verify that you have installed the necessary dependencies for the component to function properly. -- Check the CrewAI documentation for any updates or changes that may affect the component's functionality. diff --git a/docs/docs/integrations/crewai/crew.md b/docs/docs/integrations/crewai/crew.md deleted file mode 100644 index 56328d1dd..000000000 --- a/docs/docs/integrations/crewai/crew.md +++ /dev/null @@ -1,104 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# Crew Component - -The `CrewAICrew` component represents a group of agents in CrewAI. It defines how agents collaborate and the tasks they perform, integrating seamlessly into your Langflow workflows. - -[CrewAI Reference](https://docs.crewai.com/how-to/LLM-Connections/) - -The `CrewAICrew` component enables you to: - -- Define tasks and assign agents -- Specify the topic and collaboration process -- Configure advanced settings such as verbosity, memory, cache usage, and maximum RPM - -## Component Usage - -To use the `CrewAICrew` component in a Langflow flow, follow these steps: - -1. Add the `CrewAICrew` component to your flow. -2. Configure the component by providing the required inputs such as tasks, agents, and topic. -3. Connect the component to other nodes in your flow as needed. - -## Component Python code - -```python -from langflow.custom import Component -from crewai import Crew, Task, Agent, Process -from typing import List, Optional -from langflow.field_typing import Text -from langflow.io import NestedDictInput, DropdownInput, MessageTextInput, HandleInput, IntInput, BoolInput -from langflow.schema.message import Message - -class CrewAICrew(Component): - display_name: str = "CrewAICrew" - description: str = "Represents a group of agents, defining how they should collaborate and the tasks they should perform." - documentation: str = "https://docs.crewai.com/how-to/LLM-Connections/" - icon = "CrewAI" - - inputs = [ - HandleInput(name="tasks", display_name="Tasks", input_types=["Task"], is_list=True), - HandleInput(name="agents", display_name="Agents", input_types=["Agent"], is_list=True), - MessageTextInput(name="topic", display_name="Topic"), - IntInput(name="verbose", display_name="Verbose", value=0, advanced=True), - BoolInput(name="memory", display_name="Memory", value=False, advanced=True), - BoolInput(name="use_cache", display_name="Cache", value=True, advanced=True), - IntInput(name="max_rpm", display_name="Max RPM", value=100, advanced=True), - DropdownInput(name="process", display_name="Process", value=Process.sequential, options=[Process.sequential, Process.hierarchical]), - BoolInput(name="share_crew", display_name="Share Crew", value=False, advanced=True), - NestedDictInput(name="input", display_name="Input", value={"topic": ""}, is_list=True) - ] - - outputs = [ - Output(display_name="Output", name="output", method="build_output"), - ] - - async def build_output(self) -> Message: - if not self.agents or not self.tasks: - raise ValueError("No agents or tasks have been added.") - - response = Crew( - agents=self.agents, - tasks=self.tasks, - process=self.process, - verbose=self.verbose, - memory=self.memory, - cache=self.use_cache, - max_rpm=self.max_rpm, - share_crew=self.share_crew, - ) - message = await response.kickoff_async(inputs=self.input) - self.status = message - return message -``` - -Example Usage -Here's an example of how you can use the `CrewAICrew` component in a Langflow flow, connecting the CrewAIAgent component to the `CrewAITask`, `CrewAIAgent`, and `Chat Input`component, and then passing the output Chat Output component: - - - -## Best Practices - -When using the `CrewAICrew` component, consider the following best practices: - -Clearly define the tasks and assign the appropriate agents. -Configure the collaboration process and advanced settings according to your needs. -The `CrewAICrew` component provides a streamlined way to manage groups of agents and their tasks within your Langflow workflows. By leveraging this component, you can effectively organize and automate agent collaboration, enhancing the efficiency of your Langflow projects. Feel free to explore and experiment with the `CrewAICrew` component to unlock new possibilities in your Langflow projects! - -## Troubleshooting - -If you encounter any issues while using the `CrewAICrew` component, consider the following: - -- Double-check that your inputs such as tasks and agents are correctly configured. -- Verify that you have installed the necessary dependencies for the component to function properly. -- Check the CrewAI documentation for any updates or changes that may affect the component's functionality. diff --git a/docs/docs/integrations/crewai/intro.md b/docs/docs/integrations/crewai/intro.md deleted file mode 100644 index c2707e039..000000000 --- a/docs/docs/integrations/crewai/intro.md +++ /dev/null @@ -1,41 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# Introduction to CrewAI in Langflow - -The CrewAI integration in Langflow enables seamless connectivity with CrewAI components, facilitating automation and improving productivity. - - - -#### Download CrewAI Components Bundle - -### Key Features of CrewAI Integration in Langflow - -- **Manage Agents**: Define and manage a group of agents, specifying their tasks and collaboration methods. -- **Task Automation**: Automate task creation and management using CrewAI's capabilities. -- **Process Management**: Define and manage processes for task execution and agent collaboration. - -### Potential Use Cases for CrewAI Integration in Langflow - -- **Task Automation**: Automate task creation and management using CrewAI's AI capabilities. -- **Agent Collaboration**: Define and manage how agents should collaborate on tasks. -- **Process Management**: Automate and manage processes for task execution and agent collaboration. - -### Getting Started with CrewAI Integration in Langflow - -1. **Learn about CrewAI Components**: Follow the guide [Understanding CrewAI Components](./setup) to learn how to use CrewAI components effectively. -2. **Configure CrewAI Components**: Provide the necessary parameters to configure the CrewAI components in your Langflow flows. -3. **Connect Components**: Integrate CrewAI components with other Langflow components to build your workflow. -4. **Test and Refine**: Ensure your Langflow flow operates as intended by testing and refining it. -5. **Deploy and Run**: Deploy your Langflow flow to automate CrewAI-related tasks and processes. - -The CrewAI integration in Langflow offers a powerful toolset for automation and productivity enhancement. Whether managing tasks, defining agent collaboration, or managing processes, Langflow and CrewAI provide robust solutions for streamlining workflows. diff --git a/docs/docs/integrations/crewai/setup.md b/docs/docs/integrations/crewai/setup.md deleted file mode 100644 index 43e07ebcd..000000000 --- a/docs/docs/integrations/crewai/setup.md +++ /dev/null @@ -1,27 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Using CrewAI Components in Langflow - -Langflow provides the following CrewAI components: - -- **[CrewAIAgent](./agent)**: Represents an agent of CrewAI. -- **[CrewAITask](./task)**: Each task must have a description, an expected output, and an agent responsible for execution. -- **[CrewAICrew](./crew)**: Represents a group of agents, defining how they should collaborate and the tasks they should perform. - -Refer to the individual component documentation for more details on how to use each component in your Langflow flows. - -## Components Compatibility - -- **CrewAIAgent**: - - Compatible with CrewAITask and CrewAICrew components. -- **CrewAITask**: - - Compatible with CrewAIAgent and CrewAICrew components. -- **CrewAICrew**: - - Compatible with CrewAIAgent and CrewAITask components. - -## Additional Resources - -- [CrewAI API Documentation](https://docs.crewai.com/how-to/Creating-a-Crew-and-kick-it-off/) -- [CrewAI Examples](https://github.com/joaomdmoura/crewAI-examples/tree/main) - -If you encounter any issues or have questions, please reach out to our support team or consult the Langflow community forums. diff --git a/docs/docs/integrations/crewai/task.md b/docs/docs/integrations/crewai/task.md deleted file mode 100644 index 47418575e..000000000 --- a/docs/docs/integrations/crewai/task.md +++ /dev/null @@ -1,116 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# Task Component - -The `CrewAITask` component represents a task in CrewAI. It allows you to define tasks with detailed descriptions, expected outputs, and the responsible agent, integrating seamlessly into your Langflow workflows. - -[CrewAI Reference](https://docs.crewai.com/how-to/LLM-Connections/) - -The `CrewAITask` component enables you to: - -- Define the task's description and expected output -- Assign an agent responsible for executing the task -- Specify tools and resources for task execution -- Configure advanced settings such as asynchronous execution - -## Component Usage - -To use the `CrewAITask` component in a Langflow flow, follow these steps: - -1. Add the `CrewAITask` component to your flow. -2. Configure the component by providing the required inputs such as description and expected output. -3. Connect the component to other nodes in your flow as needed. - -## Component Python code - -```python -from langflow.custom import Component -from langflow.io import BoolInput, DictInput, DropdownInput, MessageTextInput, HandleInput -from crewai import Task, Agent - -class CrewAITask(Component): - display_name: str = "CrewAITask" - description: str = "Each task must have a description, an expected output and an agent responsible for execution." - documentation: str = "https://docs.crewai.com/how-to/LLM-Connections/" - icon = "CrewAI" - - inputs = [ - MessageTextInput( - name="description", - display_name="Description", - info="Descriptive text detailing task's purpose and execution.", - ), - MessageTextInput( - name="expected_output", - display_name="Expected Output", - info="Clear definition of expected task outcome.", - ), - HandleInput( - name="tools", - display_name="Tools", - input_types=["Tool"], - is_list=True, - info="List of tools/resources limited for task execution.", - ), - HandleInput( - name="agent", - display_name="Agent", - input_types=["Agent"], - info="Agent responsible for task execution. Represents entity performing task.", - ), - BoolInput( - name="async_execution", - display_name="Async Execution", - value=False, - advanced=True, - info="Boolean flag indicating asynchronous task execution.", - ), - ] - - outputs = [ - Output(display_name="Task", name="task_output", method="build_task"), - ] - - def build_task(self) -> Task: - task = Task( - description=self.description, - expected_output=self.expected_output, - tools=self.tools if self.tools else [], - async_execution=self.async_execution, - agent=self.agent - ) - self.status = task - return task -``` - -## Example Usage - -Here's an example of how you can use the CrewAITask component in a Langflow flow, connecting the CrewAIAgent component to the CrewAITask component, and then passing the outputs to the CrewAICrew component: - - - -## Best Practices - -When using the CrewAITask component, consider the following best practices: - -Ensure that you have defined the task's description and expected output clearly. -Assign the appropriate agent for task execution. -The CrewAITask component provides a streamlined way to define and manage tasks within your Langflow workflows. By leveraging this component, you can effectively organize and automate task execution, enhancing the efficiency of your Langflow projects. Feel free to explore and experiment with the CrewAITask component to unlock new possibilities in your Langflow projects! - -## Troubleshooting - -If you encounter any issues while using the CrewAITask component, consider the following: - -Double-check that your inputs such as description and expected output are correctly configured. -Verify that you have installed the necessary dependencies for the component to function properly. -Check the CrewAI documentation for any updates or changes that may affect the component's functionality. diff --git a/docs/docs/integrations/langsmith/intro.mdx b/docs/docs/integrations/langsmith/intro.mdx deleted file mode 100644 index 68f28a891..000000000 --- a/docs/docs/integrations/langsmith/intro.mdx +++ /dev/null @@ -1,46 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# LangSmith - -LangSmith is a full-lifecycle DevOps service from LangChain that provides monitoring and observability. To integrate with Langflow, just add your LangChain API key as a Langflow environment variable and you are good to go! - -## Step-by-step Configuration - -1. Obtain your LangChain API key from https://smith.langchain.com -2. Add the following keys to Langflow .env file: - -```bash -LANGCHAIN_API_KEY="your-api-key" -LANGCHAIN_PROJECT="your-project-name" -``` - -or export the environment variables in your terminal: - -```bash -export LANGCHAIN_API_KEY="your-api-key" -export LANGCHAIN_PROJECT="your-project-name" -``` - -3. Restart Langflow using `langflow run --env-file .env` -4. Run any project and check the LangSmith dashboard for monitoring and observability. - - - - diff --git a/docs/docs/integrations/notion/add-content-to-page.md b/docs/docs/integrations/notion/add-content-to-page.md deleted file mode 100644 index 197046922..000000000 --- a/docs/docs/integrations/notion/add-content-to-page.md +++ /dev/null @@ -1,135 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# Add Content To Page - - -This page may contain outdated information. It will be updated as soon as possible. - - -The `AddContentToPage` component converts markdown text to Notion blocks and appends them to a Notion page. - -[Notion Reference](https://developers.notion.com/reference/patch-block-children) - -The `AddContentToPage` component enables you to: - -- Convert markdown text to Notion blocks. -- Append the converted blocks to a specified Notion page. -- Seamlessly integrate Notion content creation into Langflow workflows. - -## Component Usage - -To use the `AddContentToPage` component in a Langflow flow: - -1. **Add the `AddContentToPage` component** to your flow. -2. **Configure the component** by providing: - - `markdown_text`: The markdown text to convert. - - `block_id`: The ID of the Notion page/block to append the content. - - `notion_secret`: The Notion integration token for authentication. -3. **Connect the component** to other nodes in your flow as needed. -4. **Run the flow** to convert the markdown text and append it to the specified Notion page. - -## Component Python Code - -```python -import json -from typing import Optional - -import requests -from langflow.custom import CustomComponent - - -class NotionPageCreator(CustomComponent): - display_name = "Create Page [Notion]" - description = "A component for creating Notion pages." - documentation: str = "https://docs.langflow.org/integrations/notion/add-content-to-page" - icon = "NotionDirectoryLoader" - - def build_config(self): - return { - "database_id": { - "display_name": "Database ID", - "field_type": "str", - "info": "The ID of the Notion database.", - }, - "notion_secret": { - "display_name": "Notion Secret", - "field_type": "str", - "info": "The Notion integration token.", - "password": True, - }, - "properties": { - "display_name": "Properties", - "field_type": "str", - "info": "The properties of the new page. Depending on your database setup, this can change. E.G: {'Task name': {'id': 'title', 'type': 'title', 'title': [{'type': 'text', 'text': {'content': 'Send Notion Components to LF', 'link': null}}]}}", - }, - } - - def build( - self, - database_id: str, - notion_secret: str, - properties: str = '{"Task name": {"id": "title", "type": "title", "title": [{"type": "text", "text": {"content": "Send Notion Components to LF", "link": null}}]}}', - ) -> str: - if not database_id or not properties: - raise ValueError("Invalid input. Please provide 'database_id' and 'properties'.") - - headers = { - "Authorization": f"Bearer {notion_secret}", - "Content-Type": "application/json", - "Notion-Version": "2022-06-28", - } - - data = { - "parent": {"database_id": database_id}, - "properties": json.loads(properties), - } - - response = requests.post("https://api.notion.com/v1/pages", headers=headers, json=data) - - if response.status_code == 200: - page_id = response.json()["id"] - self.status = f"Successfully created Notion page with ID: {page_id}\n {str(response.json())}" - return response.json() - else: - error_message = f"Failed to create Notion page. Status code: {response.status_code}, Error: {response.text}" - self.status = error_message - raise Exception(error_message) -``` - -## Example Usage - -Example of using the `AddContentToPage` component in a Langflow flow using Markdown as input: - - - -In this example, the `AddContentToPage` component connects to a `MarkdownLoader` component to provide the markdown text input. The converted Notion blocks are appended to the specified Notion page using the provided `block_id` and `notion_secret`. - -## Best Practices - -When using the `AddContentToPage` component: - -- Ensure markdown text is well-formatted. -- Verify the `block_id` corresponds to the right Notion page/block. -- Keep your Notion integration token secure. -- Test with sample markdown text before production use. - -The `AddContentToPage` component is a powerful tool for integrating Notion content creation into Langflow workflows, facilitating easy conversion of markdown text to Notion blocks and appending them to specific pages. - -## Troubleshooting - -If you encounter any issues while using the `AddContentToPage` component, consider the following: - -- Verify the Notion integration token’s validity and permissions. -- Check the Notion API documentation for updates. -- Ensure markdown text is properly formatted. -- Double-check the `block_id` for correctness. diff --git a/docs/docs/integrations/notion/intro.md b/docs/docs/integrations/notion/intro.md deleted file mode 100644 index 77f09e5b6..000000000 --- a/docs/docs/integrations/notion/intro.md +++ /dev/null @@ -1,47 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# Introduction to Notion in Langflow - - -This page may contain outdated information. It will be updated as soon as possible. - - -The Notion integration in Langflow enables seamless connectivity with Notion databases, pages, and users, facilitating automation and improving productivity. - - - -#### Download Notion Components Bundle - -### Key Features of Notion Integration in Langflow - -- **List Pages**: Retrieve a list of pages from a Notion database and access data stored in your Notion workspace. -- **List Database Properties**: Obtain insights into the properties of a Notion database, allowing for easy understanding of its structure and metadata. -- **Add Page Content**: Programmatically add new content to a Notion page, simplifying the creation and updating of pages. -- **List Users**: Retrieve a list of users with access to a Notion workspace, aiding in user management and collaboration. -- **Update Property**: Update the value of a specific property in a Notion page, enabling easy modification and maintenance of Notion data. - -### Potential Use Cases for Notion Integration in Langflow - -- **Task Automation**: Automate task creation in Notion using Langflow's AI capabilities. Describe the required tasks, and they will be automatically created and updated in Notion. -- **Context Extraction from Meetings**: Leverage AI to analyze meeting contexts, extract key points, and update the relevant Notion pages automatically. -- **Content Creation**: Utilize AI to generate ideas, suggest templates, and populate Notion pages with relevant data, enhancing content management efficiency. - -### Getting Started with Notion Integration in Langflow - -1. **Set Up Notion Integration**: Follow the guide [Setting up a Notion App](./setup) to set up a Notion integration in your workspace. -2. **Configure Notion Components**: Provide the necessary authentication details and parameters to configure the Notion components in your Langflow flows. -3. **Connect Components**: Integrate Notion components with other Langflow components to build your workflow. -4. **Test and Refine**: Ensure your Langflow flow operates as intended by testing and refining it. -5. **Deploy and Run**: Deploy your Langflow flow to automate Notion-related tasks and processes. - -The Notion integration in Langflow offers a powerful toolset for automation and productivity enhancement. Whether managing tasks, extracting meeting insights, or creating content, Langflow and Notion provide robust solutions for streamlining workflows. diff --git a/docs/docs/integrations/notion/list-database-properties.md b/docs/docs/integrations/notion/list-database-properties.md deleted file mode 100644 index c72c52c27..000000000 --- a/docs/docs/integrations/notion/list-database-properties.md +++ /dev/null @@ -1,121 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# Database Properties - - -This page may contain outdated information. It will be updated as soon as possible. - - -The `NotionDatabaseProperties` component retrieves properties of a Notion database. It provides a convenient way to integrate Notion database information into your Langflow workflows. - -[Notion Reference](https://developers.notion.com/reference/post-database-query) - - -The `NotionDatabaseProperties` component enables you to: -- Retrieve properties of a Notion database -- Access the retrieved properties in your Langflow flows -- Integrate Notion database information seamlessly into your workflows - - -## Component Usage - -To use the `NotionDatabaseProperties` component in a Langflow flow, follow these steps: - -1. Add the `NotionDatabaseProperties` component to your flow. -2. Configure the component by providing the required inputs: - - `database_id`: The ID of the Notion database you want to retrieve properties from. - - `notion_secret`: The Notion integration token for authentication. -3. Connect the output of the `NotionDatabaseProperties` component to other components in your flow as needed. - -## Component Python code - -```python -import requests -from typing import Dict - -from langflow import CustomComponent -from langflow.schema import Data - - -class NotionDatabaseProperties(CustomComponent): - display_name = "List Database Properties [Notion]" - description = "Retrieve properties of a Notion database." - documentation: str = "https://docs.langflow.org/integrations/notion/list-database-properties" - icon = "NotionDirectoryLoader" - - def build_config(self): - return { - "database_id": { - "display_name": "Database ID", - "field_type": "str", - "info": "The ID of the Notion database.", - }, - "notion_secret": { - "display_name": "Notion Secret", - "field_type": "str", - "info": "The Notion integration token.", - "password": True, - }, - } - - def build( - self, - database_id: str, - notion_secret: str, - ) -> Data: - url = f"https://api.notion.com/v1/databases/{database_id}" - headers = { - "Authorization": f"Bearer {notion_secret}", - "Notion-Version": "2022-06-28", # Use the latest supported version - } - - response = requests.get(url, headers=headers) - response.raise_for_status() - - data = response.json() - properties = data.get("properties", {}) - - record = Data(text=str(response.json()), data=properties) - self.status = f"Retrieved {len(properties)} properties from the Notion database.\n {record.text}" - return record -``` - -## Example Usage - - -Here's an example of how you can use the `NotionDatabaseProperties` component in a Langflow flow: - - - -In this example, the `NotionDatabaseProperties` component retrieves the properties of a Notion database, and the retrieved properties are then used as input for subsequent components in the flow. - - -## Best Practices - -When using the `NotionDatabaseProperties` component, consider the following best practices: - -- Ensure that you have a valid Notion integration token with the necessary permissions to access the desired database. -- Double-check the database ID to avoid retrieving properties from the wrong database. -- Handle potential errors gracefully by checking the response status and providing appropriate error messages. - -The `NotionDatabaseProperties` component simplifies the process of retrieving properties from a Notion database and integrating them into your Langflow workflows. By leveraging this component, you can easily access and utilize Notion database information in your flows, enabling powerful integrations and automations. - -Feel free to explore the capabilities of the `NotionDatabaseProperties` component and experiment with different use cases to enhance your Langflow workflows! - -## Troubleshooting - -If you encounter any issues while using the `NotionDatabaseProperties` component, consider the following: - -- Verify that the Notion integration token is valid and has the required permissions. -- Check the database ID to ensure it matches the intended Notion database. -- Inspect the response from the Notion API for any error messages or status codes that may indicate the cause of the issue. diff --git a/docs/docs/integrations/notion/list-pages.md b/docs/docs/integrations/notion/list-pages.md deleted file mode 100644 index a232a594f..000000000 --- a/docs/docs/integrations/notion/list-pages.md +++ /dev/null @@ -1,183 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# List Pages - - -This page may contain outdated information. It will be updated as soon as possible. - - -The `NotionListPages` component queries a Notion database with filtering and sorting. It provides a convenient way to integrate Notion database querying capabilities into your Langflow workflows. - -[Notion Reference](https://developers.notion.com/reference/post-database-query) - - - The `NotionListPages` component enables you to: - -- Query a Notion database with custom filters and sorting options -- Retrieve specific pages from a Notion database based on the provided criteria -- Integrate Notion database data seamlessly into your Langflow workflows - - - -## Component Usage - -To use the `NotionListPages -` component in a Langflow flow, follow these steps: - -1. **Add the `NotionListPages -` component to your flow.** -2. **Configure the component by providing the required parameters:** - - `notion_secret`: The Notion integration token for authentication. - - `database_id`: The ID of the Notion database you want to query. - - `query_payload`: A JSON string containing the filters and sorting options for the query. -3. **Connect the `NotionListPages -` component to other components in your flow as needed.** - -## Component Python code - -```python -import requests -import json -from typing import Dict, Any, List -from langflow.custom import CustomComponent -from langflow.schema import Data - -class NotionListPages(CustomComponent): - display_name = "List Pages [Notion]" - description = ( - "Query a Notion database with filtering and sorting. " - "The input should be a JSON string containing the 'filter' and 'sorts' objects. " - "Example input:\n" - '{"filter": {"property": "Status", "select": {"equals": "Done"}}, "sorts": [{"timestamp": "created_time", "direction": "descending"}]}' - ) - documentation: str = "https://docs.langflow.org/integrations/notion/list-pages" - icon = "NotionDirectoryLoader" - - field_order = [ - "notion_secret", - "database_id", - "query_payload", - ] - - def build_config(self): - return { - "notion_secret": { - "display_name": "Notion Secret", - "field_type": "str", - "info": "The Notion integration token.", - "password": True, - }, - "database_id": { - "display_name": "Database ID", - "field_type": "str", - "info": "The ID of the Notion database to query.", - }, - "query_payload": { - "display_name": "Database query", - "field_type": "str", - "info": "A JSON string containing the filters that will be used for querying the database. EG: {'filter': {'property': 'Status', 'status': {'equals': 'In progress'}}}", - }, - } - - def build( - self, - notion_secret: str, - database_id: str, - query_payload: str = "{}", - ) -> List[Data]: - try: - query_data = json.loads(query_payload) - filter_obj = query_data.get("filter") - sorts = query_data.get("sorts", []) - - url = f"https://api.notion.com/v1/databases/{database_id}/query" - headers = { - "Authorization": f"Bearer {notion_secret}", - "Content-Type": "application/json", - "Notion-Version": "2022-06-28", - } - - data = { - "sorts": sorts, - } - - if filter_obj: - data["filter"] = filter_obj - - response = requests.post(url, headers=headers, json=data) - response.raise_for_status() - - results = response.json() - data = [] - combined_text = f"Pages found: {len(results['results'])}\n\n" - for page in results['results']: - page_data = { - 'id': page['id'], - 'url': page['url'], - 'created_time': page['created_time'], - 'last_edited_time': page['last_edited_time'], - 'properties': page['properties'], - } - - text = ( - f"id: {page['id']}\n" - f"url: {page['url']}\n" - f"created_time: {page['created_time']}\n" - f"last_edited_time: {page['last_edited_time']}\n" - f"properties: {json.dumps(page['properties'], indent=2)}\n\n" - ) - - combined_text += text - data.append(Data(text=text, data=page_data)) - - self.status = combined_text.strip() - return data - - except Exception as e: - self.status = f"An error occurred: {str(e)}" - return [Data(text=self.status, data=[])] -``` - - - -## Example Usage - -Here's an example of how you can use the `NotionListPages` component in a Langflow flow and passing to the Prompt component: - - - -In this example, the `NotionListPages` component is used to retrieve specific pages from a Notion database based on the provided filters and sorting options. The retrieved data can then be processed further in the subsequent components of the flow. - - -## Best Practices - -When using the `NotionListPages -` component, consider the following best practices: - -- Ensure that you have a valid Notion integration token with the necessary permissions to query the desired database. -- Construct the `query_payload` JSON string carefully, following the Notion API documentation for filtering and sorting options. - -The `NotionListPages -` component provides a powerful way to integrate Notion database querying capabilities into your Langflow workflows. By leveraging this component, you can easily retrieve specific pages from a Notion database based on custom filters and sorting options, enabling you to build more dynamic and data-driven flows. - -We encourage you to explore the capabilities of the `NotionListPages -` component further and experiment with different querying scenarios to unlock the full potential of integrating Notion databases into your Langflow workflows. - -## Troubleshooting - -If you encounter any issues while using the `NotionListPages` component, consider the following: - -- Double-check that the `notion_secret` and `database_id` are correct and valid. -- Verify that the `query_payload` JSON string is properly formatted and contains valid filtering and sorting options. -- Check the Notion API documentation for any updates or changes that may affect the component's functionality. diff --git a/docs/docs/integrations/notion/list-users.md b/docs/docs/integrations/notion/list-users.md deleted file mode 100644 index c3cd6696b..000000000 --- a/docs/docs/integrations/notion/list-users.md +++ /dev/null @@ -1,126 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# User List - - -This page may contain outdated information. It will be updated as soon as possible. - - -The `NotionUserList` component retrieves users from Notion. It provides a convenient way to integrate Notion user data into your Langflow workflows. - -[Notion Reference](https://developers.notion.com/reference/get-users) - -The `NotionUserList` component enables you to: - -- Retrieve user data from Notion -- Access user information such as ID, type, name, and avatar URL -- Integrate Notion user data seamlessly into your Langflow workflows - -## Component Usage - -To use the `NotionUserList` component in a Langflow flow, follow these steps: - -1. Add the `NotionUserList` component to your flow. -2. Configure the component by providing the required Notion secret token. -3. Connect the component to other nodes in your flow as needed. - -## Component Python code - -```python -import requests -from typing import List - -from langflow import CustomComponent -from langflow.schema import Data - - -class NotionUserList(CustomComponent): - display_name = "List Users [Notion]" - description = "Retrieve users from Notion." - documentation: str = "https://docs.langflow.org/integrations/notion/list-users" - icon = "NotionDirectoryLoader" - - def build_config(self): - return { - "notion_secret": { - "display_name": "Notion Secret", - "field_type": "str", - "info": "The Notion integration token.", - "password": True, - }, - } - - def build( - self, - notion_secret: str, - ) -> List[Data]: - url = "https://api.notion.com/v1/users" - headers = { - "Authorization": f"Bearer {notion_secret}", - "Notion-Version": "2022-06-28", - } - - response = requests.get(url, headers=headers) - response.raise_for_status() - - data = response.json() - results = data['results'] - - data = [] - for user in results: - id = user['id'] - type = user['type'] - name = user.get('name', '') - avatar_url = user.get('avatar_url', '') - - data_dict = { - "id": id, - "type": type, - "name": name, - "avatar_url": avatar_url, - } - - output = "User:\n" - for key, value in data_dict.items(): - output += f"{key.replace('_', ' ').title()}: {value}\n" - output += "________________________\n" - - record = Data(text=output, data=data_dict) - data.append(record) - - self.status = "\n".join(record.text for record in data) - return data -``` - -## Example Usage - -Here's an example of how you can use the `NotionUserList` component in a Langflow flow and passing the outputs to the Prompt component: - - - -## Best Practices - -When using the `NotionUserList` component, consider the following best practices: - -- Ensure that you have a valid Notion integration token with the necessary permissions to retrieve user data. -- Handle the retrieved user data securely and in compliance with Notion's API usage guidelines. - -The `NotionUserList` component provides a seamless way to integrate Notion user data into your Langflow workflows. By leveraging this component, you can easily retrieve and utilize user information from Notion, enhancing the capabilities of your Langflow applications. Feel free to explore and experiment with the `NotionUserList` component to unlock new possibilities in your Langflow projects! - -## Troubleshooting - -If you encounter any issues while using the `NotionUserList` component, consider the following: - -- Double-check that your Notion integration token is valid and has the required permissions. -- Verify that you have installed the necessary dependencies (`requests`) for the component to function properly. -- Check the Notion API documentation for any updates or changes that may affect the component's functionality. diff --git a/docs/docs/integrations/notion/page-content-viewer.md b/docs/docs/integrations/notion/page-content-viewer.md deleted file mode 100644 index df3ebdf60..000000000 --- a/docs/docs/integrations/notion/page-content-viewer.md +++ /dev/null @@ -1,146 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# Page Content - - -This page may contain outdated information. It will be updated as soon as possible. - - -The `NotionPageContent` component retrieves the content of a Notion page as plain text. It provides a convenient way to integrate Notion page content into your Langflow workflows. - -[Notion Reference](https://developers.notion.com/reference/get-page) - - - -The `NotionPageContent` component enables you to: - -- Retrieve the content of a Notion page as plain text -- Extract text from various block types, including paragraphs, headings, lists, and more -- Integrate Notion page content seamlessly into your Langflow workflows - - - -## Component Usage - -To use the `NotionPageContent` component in a Langflow flow, follow these steps: - -1. Add the `NotionPageContent` component to your flow. -2. Configure the component by providing the required inputs: - - `page_id`: The ID of the Notion page you want to retrieve. - - `notion_secret`: Your Notion integration token for authentication. -3. Connect the output of the `NotionPageContent` component to other components in your flow as needed. - -## Component Python code - -```python -import requests -from typing import Dict, Any - -from langflow import CustomComponent -from langflow.schema import Data - - -class NotionPageContent(CustomComponent): - display_name = "Page Content Viewer [Notion]" - description = "Retrieve the content of a Notion page as plain text." - documentation: str = "https://docs.langflow.org/integrations/notion/page-content-viewer" - icon = "NotionDirectoryLoader" - - def build_config(self): - return { - "page_id": { - "display_name": "Page ID", - "field_type": "str", - "info": "The ID of the Notion page to retrieve.", - }, - "notion_secret": { - "display_name": "Notion Secret", - "field_type": "str", - "info": "The Notion integration token.", - "password": True, - }, - } - - def build( - self, - page_id: str, - notion_secret: str, - ) -> Data: - blocks_url = f"https://api.notion.com/v1/blocks/{page_id}/children?page_size=100" - headers = { - "Authorization": f"Bearer {notion_secret}", - "Notion-Version": "2022-06-28", # Use the latest supported version - } - - # Retrieve the child blocks - blocks_response = requests.get(blocks_url, headers=headers) - blocks_response.raise_for_status() - blocks_data = blocks_response.json() - - # Parse the blocks and extract the content as plain text - content = self.parse_blocks(blocks_data["results"]) - - self.status = content - return Data(data={"content": content}, text=content) - - def parse_blocks(self, blocks: list) -> str: - content = "" - for block in blocks: - block_type = block["type"] - if block_type in ["paragraph", "heading_1", "heading_2", "heading_3", "quote"]: - content += self.parse_rich_text(block[block_type]["rich_text"]) + "\n\n" - elif block_type in ["bulleted_list_item", "numbered_list_item"]: - content += self.parse_rich_text(block[block_type]["rich_text"]) + "\n" - elif block_type == "to_do": - content += self.parse_rich_text(block["to_do"]["rich_text"]) + "\n" - elif block_type == "code": - content += self.parse_rich_text(block["code"]["rich_text"]) + "\n\n" - elif block_type == "image": - content += f"[Image: {block['image']['external']['url']}]\n\n" - elif block_type == "divider": - content += "---\n\n" - return content.strip() - - def parse_rich_text(self, rich_text: list) -> str: - text = "" - for segment in rich_text: - text += segment["plain_text"] - return text -``` - -## Example Usage - - - -Here's an example of how you can use the `NotionPageContent` component in a Langflow flow: - - - - -## Best Practices - -When using the `NotionPageContent` component, consider the following best practices: - -- Ensure that you have the necessary permissions to access the Notion page you want to retrieve. -- Keep your Notion integration token secure and avoid sharing it publicly. -- Be mindful of the content you retrieve and ensure that it aligns with your intended use case. - -The `NotionPageContent` component provides a seamless way to integrate Notion page content into your Langflow workflows. By leveraging this component, you can easily retrieve and process the content of Notion pages, enabling you to build powerful and dynamic applications. Explore the capabilities of the `NotionPageContent` component and unlock new possibilities in your Langflow projects! - -## Troubleshooting - -If you encounter any issues while using the `NotionPageContent` component, consider the following: - -- Double-check that you have provided the correct Notion page ID. -- Verify that your Notion integration token is valid and has the necessary permissions. -- Check the Notion API documentation for any updates or changes that may affect the component's functionality. diff --git a/docs/docs/integrations/notion/page-create.md b/docs/docs/integrations/notion/page-create.md deleted file mode 100644 index f2e7534d1..000000000 --- a/docs/docs/integrations/notion/page-create.md +++ /dev/null @@ -1,135 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# Page Create - - -This page may contain outdated information. It will be updated as soon as possible. - - -The `NotionPageCreator` component creates pages in a Notion database. It provides a convenient way to integrate Notion page creation into your Langflow workflows. - -[Notion Reference](https://developers.notion.com/reference/patch-block-children) - - -The `NotionPageCreator` component enables you to: -- Create new pages in a specified Notion database -- Set custom properties for the created pages -- Retrieve the ID and URL of the newly created pages - - -## Component Usage - -To use the `NotionPageCreator` component in a Langflow flow, follow these steps: - -1. Add the `NotionPageCreator` component to your flow. -2. Configure the component by providing the required inputs: - - `database_id`: The ID of the Notion database where the pages will be created. - - `notion_secret`: The Notion integration token for authentication. - - `properties`: The properties of the new page, specified as a JSON string. -3. Connect the component to other components in your flow as needed. -4. Run the flow to create Notion pages based on the configured inputs. - -## Component Python Code - -```python -import json -from typing import Optional - -import requests -from langflow.custom import CustomComponent - - -class NotionPageCreator(CustomComponent): - display_name = "Create Page [Notion]" - description = "A component for creating Notion pages." - documentation: str = "https://docs.langflow.org/integrations/notion/page-create" - icon = "NotionDirectoryLoader" - - def build_config(self): - return { - "database_id": { - "display_name": "Database ID", - "field_type": "str", - "info": "The ID of the Notion database.", - }, - "notion_secret": { - "display_name": "Notion Secret", - "field_type": "str", - "info": "The Notion integration token.", - "password": True, - }, - "properties": { - "display_name": "Properties", - "field_type": "str", - "info": "The properties of the new page. Depending on your database setup, this can change. E.G: {'Task name': {'id': 'title', 'type': 'title', 'title': [{'type': 'text', 'text': {'content': 'Send Notion Components to LF', 'link': null}}]}}", - }, - } - - def build( - self, - database_id: str, - notion_secret: str, - properties: str = '{"Task name": {"id": "title", "type": "title", "title": [{"type": "text", "text": {"content": "Send Notion Components to LF", "link": null}}]}}', - ) -> str: - if not database_id or not properties: - raise ValueError("Invalid input. Please provide 'database_id' and 'properties'.") - - headers = { - "Authorization": f"Bearer {notion_secret}", - "Content-Type": "application/json", - "Notion-Version": "2022-06-28", - } - - data = { - "parent": {"database_id": database_id}, - "properties": json.loads(properties), - } - - response = requests.post("https://api.notion.com/v1/pages", headers=headers, json=data) - - if response.status_code == 200: - page_id = response.json()["id"] - self.status = f"Successfully created Notion page with ID: {page_id}\n {str(response.json())}" - return response.json() - else: - error_message = f"Failed to create Notion page. Status code: {response.status_code}, Error: {response.text}" - self.status = error_message - raise Exception(error_message) -``` - -## Example Usage - - -Here's an example of how to use the `NotionPageCreator` component in a Langflow flow: - - - - -## Best Practices - -When using the `NotionPageCreator` component, consider the following best practices: - -- Ensure that you have a valid Notion integration token with the necessary permissions to create pages in the specified database. -- Properly format the `properties` input as a JSON string, matching the structure and field types of your Notion database. -- Handle any errors or exceptions that may occur during the page creation process and provide appropriate error messages. -- To avoid the hassle of messing with JSON, we recommend using the LLM to create the JSON for you as input. - -The `NotionPageCreator` component simplifies the process of creating pages in a Notion database directly from your Langflow workflows. By leveraging this component, you can seamlessly integrate Notion page creation functionality into your automated processes, saving time and effort. Feel free to explore the capabilities of the `NotionPageCreator` component and adapt it to suit your specific requirements. - -## Troubleshooting - -If you encounter any issues while using the `NotionPageCreator` component, consider the following: - -- Double-check that the `database_id` and `notion_secret` inputs are correct and valid. -- Verify that the `properties` input is properly formatted as a JSON string and matches the structure of your Notion database. -- Check the Notion API documentation for any updates or changes that may affect the component's functionality. diff --git a/docs/docs/integrations/notion/page-update.md b/docs/docs/integrations/notion/page-update.md deleted file mode 100644 index 3f9709f0a..000000000 --- a/docs/docs/integrations/notion/page-update.md +++ /dev/null @@ -1,142 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# Page Update - - -This page may contain outdated information. It will be updated as soon as possible. - - -The `NotionPageUpdate` component updates the properties of a Notion page. It provides a convenient way to integrate updating Notion page properties into your Langflow workflows. - -[Notion Reference](https://developers.notion.com/reference/patch-page) - -## Component Usage - -To use the `NotionPageUpdate` component in your Langflow flow: - -1. Drag and drop the `NotionPageUpdate` component onto the workspace. -2. Double-click the component to open its configuration. -3. Provide the required parameters as defined in the component's `build_config` method. -4. Connect the component to other nodes in your flow as needed. - -## Component Python Code - -```python -import json -import requests -from typing import Dict, Any - -from langflow import CustomComponent -from langflow.schema import Data - - -class NotionPageUpdate(CustomComponent): - display_name = "Update Page Property [Notion]" - description = "Update the properties of a Notion page." - documentation: str = "https://docs.langflow.org/integrations/notion/page-update" - icon = "NotionDirectoryLoader" - - def build_config(self): - return { - "page_id": { - "display_name": "Page ID", - "field_type": "str", - "info": "The ID of the Notion page to update.", - }, - "properties": { - "display_name": "Properties", - "field_type": "str", - "info": "The properties to update on the page (as a JSON string).", - "multiline": True, - }, - "notion_secret": { - "display_name": "Notion Secret", - "field_type": "str", - "info": "The Notion integration token.", - "password": True, - }, - } - - def build( - self, - page_id: str, - properties: str, - notion_secret: str, - ) -> Data: - url = f"https://api.notion.com/v1/pages/{page_id}" - headers = { - "Authorization": f"Bearer {notion_secret}", - "Content-Type": "application/json", - "Notion-Version": "2022-06-28", # Use the latest supported version - } - - try: - parsed_properties = json.loads(properties) - except json.JSONDecodeError as e: - raise ValueError("Invalid JSON format for properties") from e - - data = { - "properties": parsed_properties - } - - response = requests.patch(url, headers=headers, json=data) - response.raise_for_status() - - updated_page = response.json() - - output = "Updated page properties:\n" - for prop_name, prop_value in updated_page["properties"].items(): - output += f"{prop_name}: {prop_value}\n" - - self.status = output - return Data(data=updated_page) -``` - -Let's break down the key parts of this component: - -- The `build_config` method defines the configuration fields for the component. It specifies the required parameters and their properties, such as display names, field types, and any additional information or validation. - -- The `build` method contains the main logic of the component. It takes the configured parameters as input and performs the necessary operations to update the properties of a Notion page. - -- The component interacts with the Notion API to update the page properties. It constructs the API URL, headers, and request data based on the provided parameters. - -- The processed data is returned as a `Data` object, which can be connected to other components in the Langflow flow. The `Data` object contains the updated page data. - -- The component also stores the updated page properties in the `status` attribute for logging and debugging purposes. - -## Example Usage - - -Here's an example of how to use the `NotionPageUpdate` component in a Langflow flow using: - - - - -## Best Practices - -When using the `NotionPageUpdate` component, consider the following best practices: - -- Ensure that you have a valid Notion integration token with the necessary permissions to update page properties. -- Handle edge cases and error scenarios gracefully, such as invalid JSON format for properties or API request failures. -- We recommend using an LLM to generate the inputs for this component, to allow flexibility - -By leveraging the `NotionPageUpdate` component in Langflow, you can easily integrate updating Notion page properties into your language model workflows and build powerful applications that extend Langflow's capabilities. - -## Troubleshooting - -If you encounter any issues while using the `NotionPageUpdate` component, consider the following: - -- Double-check that you have correctly configured the component with the required parameters, including the page ID, properties JSON, and Notion integration token. -- Verify that your Notion integration token has the necessary permissions to update page properties. -- Check the Langflow logs for any error messages or exceptions related to the component, such as invalid JSON format or API request failures. -- Consult the [Notion API Documentation](https://developers.notion.com/reference/patch-page) for specific troubleshooting steps or common issues related to updating page properties. diff --git a/docs/docs/integrations/notion/search.md b/docs/docs/integrations/notion/search.md deleted file mode 100644 index f7092fdb9..000000000 --- a/docs/docs/integrations/notion/search.md +++ /dev/null @@ -1,188 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# Notion Search - - -This page may contain outdated information. It will be updated as soon as possible. - - -The `NotionSearch` component is designed to search all pages and databases that have been shared with an integration in Notion. It provides a convenient way to integrate Notion search capabilities into your Langflow workflows. - -[Notion Reference](https://developers.notion.com/reference/search) - - - The `NotionSearch` component enables you to: - -- Search for pages and databases in Notion that have been shared with an integration -- Filter the search results based on object type (pages or databases) -- Sort the search results in ascending or descending order based on the last edited time - - - -## Component Usage - -To use the `NotionSearch` component in a Langflow flow, follow these steps: - -1. **Add the `NotionSearch` component to your flow.** -2. **Configure the component by providing the required parameters:** - - `notion_secret`: The Notion integration token for authentication. - - `query`: The text to search for in page and database titles. - - `filter_value`: The type of objects to include in the search results (pages or databases). - - `sort_direction`: The direction to sort the search results (ascending or descending). -3. **Connect the `NotionSearch` component to other components in your flow as needed.** - -## Component Python Code - -```python -import requests -from typing import Dict, Any, List -from langflow.custom import CustomComponent -from langflow.schema import Data - -class NotionSearch(CustomComponent): - display_name = "Search Notion" - description = ( - "Searches all pages and databases that have been shared with an integration." - ) - documentation: str = "https://docs.langflow.org/integrations/notion/search" - icon = "NotionDirectoryLoader" - - field_order = [ - "notion_secret", - "query", - "filter_value", - "sort_direction", - ] - - def build_config(self): - return { - "notion_secret": { - "display_name": "Notion Secret", - "field_type": "str", - "info": "The Notion integration token.", - "password": True, - }, - "query": { - "display_name": "Search Query", - "field_type": "str", - "info": "The text that the API compares page and database titles against.", - }, - "filter_value": { - "display_name": "Filter Type", - "field_type": "str", - "info": "Limits the results to either only pages or only databases.", - "options": ["page", "database"], - "default_value": "page", - }, - "sort_direction": { - "display_name": "Sort Direction", - "field_type": "str", - "info": "The direction to sort the results.", - "options": ["ascending", "descending"], - "default_value": "descending", - }, - } - - def build( - self, - notion_secret: str, - query: str = "", - filter_value: str = "page", - sort_direction: str = "descending", - ) -> List[Data]: - try: - url = "https://api.notion.com/v1/search" - headers = { - "Authorization": f"Bearer {notion_secret}", - "Content-Type": "application/json", - "Notion-Version": "2022-06-28", - } - - data = { - "query": query, - "filter": { - "value": filter_value, - "property": "object" - }, - "sort":{ - "direction": sort_direction, - "timestamp": "last_edited_time" - } - } - - response = requests.post(url, headers=headers, json=data) - response.raise_for_status() - - results = response.json() - data = [] - combined_text = f"Results found: {len(results['results'])}\n\n" - for result in results['results']: - result_data = { - 'id': result['id'], - 'type': result['object'], - 'last_edited_time': result['last_edited_time'], - } - - if result['object'] == 'page': - result_data['title_or_url'] = result['url'] - text = f"id: {result['id']}\ntitle_or_url: {result['url']}\n" - elif result['object'] == 'database': - if 'title' in result and isinstance(result['title'], list) and len(result['title']) > 0: - result_data['title_or_url'] = result['title'][0]['plain_text'] - text = f"id: {result['id']}\ntitle_or_url: {result['title'][0]['plain_text']}\n" - else: - result_data['title_or_url'] = "N/A" - text = f"id: {result['id']}\ntitle_or_url: N/A\n" - - text += f"type: {result['object']}\nlast_edited_time: {result['last_edited_time']}\n\n" - combined_text += text - data.append(Data(text=text, data=result_data)) - - self.status = combined_text - return data - - except Exception as e: - self.status = f"An error occurred: {str(e)}" - return [Data(text=self.status, data=[])] -``` - -## Example Usage - - -Here's an example of how you can use the `NotionSearch` component in a Langflow flow: - - - -In this example, the `NotionSearch` component is used to search for pages and databases in Notion based on the provided query and filter criteria. The retrieved data can then be processed further in the subsequent components of the flow. - - -## Best Practices - -When using the `NotionSearch` component, consider these best practices: - -- Ensure you have a valid Notion integration token with the necessary permissions to search for pages and databases. -- Provide a meaningful search query to narrow down the results to the desired pages or databases. -- Choose the appropriate filter type (`page` or `database`) based on your search requirements. -- Consider the sorting direction (`ascending` or `descending`) to organize the search results effectively. - -The `NotionSearch` component provides a powerful way to integrate Notion search capabilities into your Langflow workflows. By leveraging this component, you can easily search for pages and databases in Notion based on custom queries and filters, enabling you to build more dynamic and data-driven flows. - -We encourage you to explore the capabilities of the `NotionSearch` component further and experiment with different search scenarios to unlock the full potential of integrating Notion search into your Langflow workflows. - -## Troubleshooting - -If you encounter any issues while using the `NotionSearch` component, consider the following: - -- Double-check that the `notion_secret` is correct and valid. -- Verify that the Notion integration has the necessary permissions to access the desired pages and databases. -- Check the Notion API documentation for any updates or changes that may affect the component's functionality. diff --git a/docs/docs/integrations/notion/setup.md b/docs/docs/integrations/notion/setup.md deleted file mode 100644 index 4ff81dd30..000000000 --- a/docs/docs/integrations/notion/setup.md +++ /dev/null @@ -1,82 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Setting up a Notion App - - -This page may contain outdated information. It will be updated as soon as possible. - - -To use Notion components in Langflow, you first need to create a Notion integration and configure it with the necessary capabilities. This guide will walk you through the process of setting up a Notion integration and granting it access to your Notion databases. - -## Prerequisites - -- A Notion account with access to the workspace where you want to use the integration. -- Admin permissions in the Notion workspace to create and manage integrations. - -## Step 1: Create a Notion Integration - -1. Go to the [Notion Integrations](https://www.notion.com/my-integrations) page. -2. Click on the "New integration" button. -3. Give your integration a name and select the workspace where you want to use it. -4. Click "Submit" to create the integration. - - -When creating the integration, make sure to enable the necessary capabilities based on your requirements. Refer to the [Notion Integration Capabilities](https://developers.notion.com/reference/capabilities) documentation for more information on each capability. - - -## Step 2: Configure Integration Capabilities - -After creating the integration, you need to configure its capabilities to define what actions it can perform and what data it can access. - -1. In the integration settings page, go to the **Capabilities** tab. -2. Enable the required capabilities for your integration. For example: - - If your integration needs to read data from Notion, enable the "Read content" capability. - - If your integration needs to create new content in Notion, enable the "Insert content" capability. - - If your integration needs to update existing content in Notion, enable the "Update content" capability. -3. Configure the user information access level based on your integration's requirements. -4. Save the changes. - -## Step 3: Obtain Integration Token - -To authenticate your integration with Notion, you need to obtain an integration token. - -1. In the integration settings page, go to the "Secrets" tab. -2. Copy the "Internal Integration Token" value. This token will be used to authenticate your integration with Notion. - - -Your integration token is a sensitive piece of information. Make sure to keep it secure and never share it publicly. Store it safely in your Langflow configuration or environment variables. - - -## Step 4: Grant Integration Access to Notion Databases - -For your integration to interact with Notion databases, you need to grant it access to the specific databases it will be working with. - -1. Open the Notion database that you want your integration to access. -2. Click on the "Share" button in the top-right corner of the page. -3. In the "Invite" section, select your integration from the list. -4. Click "Invite" to grant the integration access to the database. - - -If your database contains references to other databases, you need to grant the integration access to those referenced databases as well. Repeat step 4 for each referenced database to ensure your integration has the necessary access. - - -## Using Notion Components in Langflow - -Once you have set up your Notion integration and granted it access to the required databases, you can start using the Notion components in Langflow. - -Langflow provides the following Notion components: - -- **List Pages**: Retrieves a list of pages from a Notion database. -- **List Database Properties**: Retrieves the properties of a Notion database. -- **Add Page Content**: Adds content to a Notion page. -- **List Users**: Retrieves a list of users with access to a Notion workspace. -- **Update Property**: Updates the value of a property in a Notion page. - -Refer to the individual component documentation for more details on how to use each component in your Langflow flows. - -## Additional Resources - -- [Notion API Documentation](https://developers.notion.com/docs/getting-started) -- [Notion Integration Capabilities](https://developers.notion.com/reference/capabilities) - -If you encounter any issues or have questions, please reach out to our support team or consult the Langflow community forums. diff --git a/docs/docs/migration/compatibility.mdx b/docs/docs/migration/compatibility.mdx deleted file mode 100644 index 268214c4b..000000000 --- a/docs/docs/migration/compatibility.mdx +++ /dev/null @@ -1,56 +0,0 @@ -import Admonition from "@theme/Admonition"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; - -# Compatibility with Previous Versions - - -This page may contain outdated information. It will be updated as soon as possible. - - -## TLDR; - -- You'll need to add a few components to your flow to make it compatible with the new version of Langflow. -- Add a Runnable Executor, connect it to the last component (a Chain or an Agent) in your flow, and connect a Chat Input and a Chat Output to the Runnable Executor. This should work _most of the time_. -- You might also need to update the Chain or Agent component to the latest version. -- Most Components will work as they are, but you'll need to add an Input and an Output to your flow. -- You can use the Runnable Executor to run a LangChain runnable (which is the output of many components before 1.0) -- We need your feedback on this, so please let us know how it goes and what you think. - -## Introduction - -Langflow now works best with a flow that has an Input and an Output and that is mostly what you'll need to add to your existing flows. - -Hopefully, you'll find that even though you still can work with your current flows, updating all your components to the new version of Langflow will be worth it. - -We've tried to make it as easy as possible for you to adapt your existing flows to work seamlessly in the new version of Langflow. - -## How to Adapt Your Existing Flows - -The steps to take are few but not always simple. Here's how you can adapt your existing flows to work seamlessly in the new version of Langflow: - - -

- While this should work most of the time, it might not work for all flows. - You might need to update the Chain or Agent component to the latest version. - Please let us know if you encounter any issues. -

- - -1. **Check if your flow ends with a Chain or Agent component**. - - If it does not, it _should_ work as it is because it probably was not a chat flow. -2. **Add a Runnable Executor**. - - Add a Runnable Executor to the end of your flow. - - Connect the last component (a Chain or an Agent) in your flow to the Runnable Executor. -3. **Add a Chat Input and a Chat Output**. - - Add a Chat Input and a Chat Output to your flow. - - Connect the Chat Input to the Runnable Executor. - - Connect the Chat Output to the Runnable Executor. - - diff --git a/docs/docs/migration/migrating-to-one-point-zero.mdx b/docs/docs/migration/migrating-to-one-point-zero.mdx deleted file mode 100644 index 2fd1fe935..000000000 --- a/docs/docs/migration/migrating-to-one-point-zero.mdx +++ /dev/null @@ -1,131 +0,0 @@ -import Admonition from "@theme/Admonition"; - -# Migrating to Langflow 1.0: A Guide - - -This page may contain outdated information. It will be updated as soon as possible. - - -Langflow 1.0 is a significant update that brings many exciting changes and improvements to the platform. -This guide will walk you through the key improvements and help you migrate your existing projects to the new version. - -If you have any questions or need assistance during the migration process, please don't hesitate to reach out to in our [Discord](https://discord.gg/wZSWQaukgJ) or [GitHub](https://github.com/langflow-ai/langflow/issues) community. - -We have a special channel in our Discord server dedicated to Langflow 1.0 migration, where you can ask questions, share your experiences, and get help from the community. - -## TLDR; - -- Inputs and Outputs of Components have changed -- We've surfaced steps that were previously run in the background -- Continued support for LangChain and new support for multiple frameworks -- Redesigned sidebar and customizable interaction panel -- New Native Categories and Components -- Improved user experience with Text and Data modes -- CustomComponent for all components -- Compatibility with previous versions using Runnable Executor -- Multiple flows in the workspace -- Improved component status -- Ability to connect Output components to any other Component -- Rename and edit component descriptions -- Pass tweaks and inputs in the API using Display Name -- Global Variables for Text Fields -- Experimental components like SubFlow and Flow as Tool -- Experimental State Management system with Notify and Listen components - -## Inputs and Outputs of Components - -Langflow 1.0 introduces adds the concept of Inputs and Outputs to flows, allowing a clear definition of the data flow between components. Discover how to use Inputs and Outputs to pass data between components and create more dynamic flows. - -[Learn more about Inputs and Outputs](../components/inputs-and-outputs) - -## To Compose or Not to Compose: The Choice is Yours - -Even though composition is still possible in Langflow 1.0, the new standard is getting data moving through the flow. This allows for more flexibility and control over the data flow in your projects. - -[See our example components](../examples/create-record) for examples of interweaving LangChain components with our Core components. - -## Continued Support for LangChain and Multiple Frameworks - -Langflow 1.0 continues to support LangChain while also introducing support for multiple frameworks. This is another important boon that adding the paradigm of data flow brings to the table. Find out how to leverage the power of different frameworks in your projects. - -[Learn more about compatibility and updating existing flows](./compatibility) - -## Sidebar Redesign and Customizable Playground - -We've expanded on the chat experience by creating a customizable interaction panel that allows you to design a panel that fits your needs and interact with it. The sidebar has also been redesigned to provide a more intuitive and user-friendly experience. Explore the new sidebar and interaction panel features to enhance your workflow. - -[Learn more about the Playground](../administration/playground) - -## New Native Categories and Components - -Langflow 1.0 introduces many new native categories, including Inputs, Outputs, Helpers, Experimental, Models, and more. Discover the new components available, such as Chat Input, Prompt, Files, API Request, and others. - -[Learn more about new components](../components/inputs-and-outputs) - -## New Way of Using Langflow: Text and Data (and more to come) - -With the introduction of Text and Data types connections between Components are more intuitive and easier to understand. This is the first step in a series of improvements to the way you interact with Langflow. Learn how to use Text, and Data and how they help you build better flows. - -[Learn more about Text and Record](../components/text-and-record) - -## CustomComponent for All Components - -Almost all components in Langflow 1.0 are now CustomComponents, allowing you to check and modify the code of each component. Discover how to leverage this feature to customize your components to your specific needs. - -[Learn more about CustomComponents](../components/custom) - -## Compatibility with Previous Versions - -To use flows built in previous versions of Langflow, you can utilize the experimental component Runnable Executor along with an Input and Output. **We'd love your feedback on this**. Learn how to adapt your existing flows to work seamlessly in the new version of Langflow. - -[Learn more about Compatibility with Previous Versions](./compatibility) - -## Multiple Flows in the Workspace - -Langflow 1.0 allows you to have more than one flow in the workspace and run them separately. Discover how to create and manage multiple flows within a single project. - -**Guide coming soon** - -## Improved Component Status - -Each component now displays its status more clearly, allowing you to quickly identify any issues or errors. Explore how to use the new component status feature to troubleshoot and optimize your flows. - -[Learn more about Component Status](../getting-started/workspace#component) - -## Connecting Output Components - -You can now connect Output components to any other component (that has a Text output), providing a better understanding of the data flow. Explore the possibilities of connecting Output components and how it enhances your flow's functionality. - -[Learn more about Inputs and Outputs](../components/inputs-and-outputs) - -## Renaming and Editing Component Descriptions - -Langflow 1.0 allows you to rename and edit the description of each component, making it easier to understand and interact with the flow. Learn how to customize your component names and descriptions for improved clarity. - -[Learn more about Component Descriptions](../getting-started/workspace#flows--components) - -## Passing Tweaks and Inputs in the API - -Things got a whole lot easier. You can now pass tweaks and inputs in the API by referencing the Display Name of the component. Discover how to leverage this feature to dynamically control your flow's behavior. - -[Learn more about Tweaks and API inputs](../getting-started/workspace#tweaks) - -## Global Variables for Text Fields - -Global Variables can be used in any Text Field across your projects. Learn how to define and utilize Global Variables to streamline your workflow. - -[Learn more about Global Variables](../administration/global-env) - -## Experimental Components - -Explore the experimental components available in Langflow 1.0, such as SubFlow, which allows you to load a flow as a component dynamically, and Flow as Tool, which enables you to use a flow as a tool for an Agent. - -[Learn more about Experimental Components](../components/experimental) - -## Experimental State Management System - -We are experimenting with a State Management system for flows that allow components to trigger other components and pass messages between them using the Notify and Listen components. Discover how to leverage this system to create more dynamic and interactive flows. - -**Guide coming soon** - -We hope this guide helps you navigate the changes and improvements in Langflow 1.0. If you have any questions or need further assistance, please don't hesitate to reach out to us in our [Discord](https://discord.gg/wZSWQaukgJ). diff --git a/docs/docs/starter-projects/basic-prompting.mdx b/docs/docs/starter-projects/basic-prompting.mdx deleted file mode 100644 index dc5fc8c0f..000000000 --- a/docs/docs/starter-projects/basic-prompting.mdx +++ /dev/null @@ -1,62 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Basic Prompting - - -This page may contain outdated information. It will be updated as soon as possible. - - -Prompts serve as the inputs to a large language model (LLM), acting as the interface between human instructions and computational tasks. - -By submitting natural language requests in a prompt to an LLM, you can obtain answers, generate text, and solve problems. - -This article demonstrates how to use Langflow's prompt tools to issue basic prompts to an LLM, and how various prompting strategies can affect your outcomes. - -## Prerequisites - -- [Langflow installed and running](../getting-started/install-langflow) - -- [OpenAI API key created](https://platform.openai.com) - -## Create the basic prompting project - -1. From the Langflow dashboard, click **New Project**. -2. Select **Basic Prompting**. -3. The **Basic Prompting** flow is created. - - - -This flow allows you to chat with the **OpenAI** component via a **Prompt** component. -Examine the **Prompt** component. The **Template** field instructs the LLM to `Answer the user as if you were a pirate.` -This should be interesting... - -4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. - 1. In the **Variable Name** field, enter `openai_api_key`. - 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). - 3. Click **Save Variable**. - -## Run the basic prompting flow - -1. Click the **Run** button. - The **Interaction Panel** opens, where you can converse with your bot. -2. Type a message and press Enter. - The bot responds in a markedly piratical manner! - -## Modify the prompt for a different result - -1. To modify your prompt results, in the **Prompt** template, click the **Template** field. - The **Edit Prompt** window opens. -2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Harold Abelson.` -3. Run the basic prompting flow again. - The response will be markedly different. diff --git a/docs/docs/starter-projects/blog-writer.mdx b/docs/docs/starter-projects/blog-writer.mdx deleted file mode 100644 index 1fb1a52d0..000000000 --- a/docs/docs/starter-projects/blog-writer.mdx +++ /dev/null @@ -1,75 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Blog Writer - - -This page may contain outdated information. It will be updated as soon as possible. - - -Build a blog writer with OpenAI that uses URLs for reference content. - -## Prerequisites - -- [Langflow installed and running](../getting-started/install-langflow) - -- [OpenAI API key created](https://platform.openai.com) - -## Create the Blog Writer project - -1. From the Langflow dashboard, click **New Project**. -2. Select **Blog Writer**. -3. The **Blog Writer** flow is created. - - - -This flow creates a one-shot prompt flow with **Prompt**, **OpenAI**, and **Chat Output** components, and augments the flow with reference content and instructions from the **URL** and **Instructions** components. - -The **Prompt** component's default **Template** field looks like this: - -```bash -Reference 1: - -{reference_1} - ---- - -Reference 2: - -{reference_2} - ---- - -{instructions} - -Blog: - -``` - -The `{instructions}` value is received from the **Value** field of the **Instructions** component. -The `reference_1` and `reference_2` values are received from the **URL** fields of the **URL** components. - -4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. - 1. In the **Variable Name** field, enter `openai_api_key`. - 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). - 3. Click **Save Variable**. - -## Run the Blog Writer flow - -1. Click the **Run** button. - The **Interaction Panel** opens, where you can run your one-shot flow. -2. Click the **Lighting Bolt** icon to run your flow. -3. The **OpenAI** component constructs a blog post with the **URL** items as context. - The default **URL** values are for web pages at `promptingguide.ai`, so your blog post will be about prompting LLMs. - -To write about something different, change the values in the **URL** components, and see what the LLM constructs. diff --git a/docs/docs/starter-projects/document-qa.mdx b/docs/docs/starter-projects/document-qa.mdx deleted file mode 100644 index 25f5d1234..000000000 --- a/docs/docs/starter-projects/document-qa.mdx +++ /dev/null @@ -1,67 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Document QA - - -This page may contain outdated information. It will be updated as soon as possible. - - -Build a question-and-answer chatbot with a document loaded from local memory. - -## Prerequisites - -- [Langflow installed and running](../getting-started/install-langflow) - -- [OpenAI API key created](https://platform.openai.com) - -## Create the Document QA project - -1. From the Langflow dashboard, click **New Project**. -2. Select **Document QA**. -3. The **Document QA** flow is created. - - - -This flow creates a basic chatbot with the **Chat Input**, **Prompt**, **OpenAI**, and **Chat Output** components. -This chatbot is augmented with the **Files** component, which loads a file from your local machine into the **Prompt** component as `{Document}`. -The **Prompt** component is instructed to answer questions based on the contents of `{Document}`. -Including a file with the prompt gives the **OpenAI** component context it may not otherwise have access to. - -4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. - - 1. In the **Variable Name** field, enter `openai_api_key`. - 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). - 3. Click **Save Variable**. - -5. To select a document to load, in the **Files** component, click within the **Path** field. - 1. Select a local file, and then click **Open**. - 2. The file name appears in the field. - - The file must be of an extension type listed - [here](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/base/data/utils.py#L13). - - -## Run the Document QA flow - -1. Click the **Run** button. - The **Interaction Panel** opens, where you can converse with your bot. -2. Type a message and press Enter. - For this example, we loaded an error log `.txt` file and asked, "What went wrong?" - The bot responded: - -``` -The issue occurred during the execution of migrations in the application. Specifically, an error was raised by the Alembic library, indicating that new upgrade operations were detected that had not been accounted for in the existing migration scripts. The operation in question involved modifying the nullable property of a column (apikey, created_at) in the database, with details about the existing type (DATETIME()), existing server default, and other properties. -``` - -This result indicates that the bot received the loaded document and understood the context surrounding the vague question. It also correctly identified the issue in the error log, and followed up with appropriate troubleshooting suggestions. Nice! diff --git a/docs/docs/starter-projects/memory-chatbot.mdx b/docs/docs/starter-projects/memory-chatbot.mdx deleted file mode 100644 index a620dd714..000000000 --- a/docs/docs/starter-projects/memory-chatbot.mdx +++ /dev/null @@ -1,81 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Memory Chatbot - - -This page may contain outdated information. It will be updated as soon as possible. - - -This flow extends the [basic prompting flow](./basic-prompting) to include chat memory for unique SessionIDs. - -## Prerequisites - -- [Langflow installed and running](../getting-started/install-langflow) - -- [OpenAI API key created](https://platform.openai.com) - -## Create the memory chatbot project - -1. From the Langflow dashboard, click **New Project**. -2. Select **Memory Chatbot**. -3. The **Memory Chatbot** flow is created. - - - -This flow creates a basic chatbot with the **Chat Input**, **Prompt**, and **OpenAI** components. -This chatbot is augmented with the **Chat Memory** component, which stores messages submitted via **Chat Input** and prepends them to subsequent prompts to OpenAI via `{context}`. -The **Chat History** component gives the **OpenAI** component a memory of previous questions. - -4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. - 1. In the **Variable Name** field, enter `openai_api_key`. - 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). - 3. Click **Save Variable**. - -## Run the memory chatbot flow - -1. Click the **Run** button. - The **Interaction Panel** opens, where you can converse with your bot. -2. Type a message and press Enter. - The bot will respond according to the template in the **Prompt** component. -3. Type more questions. In the **Outputs** log, your queries are logged in order. Up to 5 queries are stored by default. Try asking `What is the first subject I asked you about?` to see where the LLM's memory disappears. - -## Modify the Session ID field to have multiple conversations - -`SessionID` is a unique identifier in Langchain for a conversation session between a chatbot and a client. -A `SessionID` is created when a conversation is initiated, and then associated with all subsequent messages during that session. - -In the **Memory Chatbot** flow you created, the **Chat Memory** component references past interactions with **Chat Input** by **Session ID**. -You can demonstrate this by modifying the **Session ID** value to switch between conversation histories. - -1. In the **Session ID** field of the **Chat Memory** and **Chat Input** components, change the **Session ID** value from `MySessionID` to `AnotherSessionID`. -2. Click the **Run** button to run your flow. - In the **Interaction Panel**, you will have a new conversation. (You may need to clear the cache with the **Eraser** button). -3. Type a few questions to your bot. -4. In the **Session ID** field of the **Chat Memory** and **Chat Input** components, change the **Session ID** value back to `MySessionID`. -5. Run your flow. - The **Outputs** log of the **Interaction Panel** displays the history from your initial chat with `MySessionID`. - -## Store Session ID as a Langflow variable - -To store **Session ID** as a Langflow variable, in the **Session ID** field, click the **Globe** button, and then click **Add New Variable**. - -1. In the **Variable Name** field, enter a name like `customer_chat_emea`. -2. In the **Value** field, enter a value like `1B5EBD79-6E9C-4533-B2C8-7E4FF29E983B`. -3. Click **Save Variable**. -4. Apply this variable to **Chat Input**. diff --git a/docs/docs/starter-projects/vector-store-rag.mdx b/docs/docs/starter-projects/vector-store-rag.mdx deleted file mode 100644 index 6fb514d9a..000000000 --- a/docs/docs/starter-projects/vector-store-rag.mdx +++ /dev/null @@ -1,106 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Vector Store RAG - - -This page may contain outdated information. It will be updated as soon as possible. - - -Retrieval Augmented Generation, or RAG, is a pattern for training LLMs on your data and querying it. - -RAG is backed by a **vector store**, a vector database which stores embeddings of the ingested data. - -This enables **vector search**, a more powerful and context-aware search. - -We've chosen [Astra DB](https://astra.datastax.com/signup?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=create-a-free-astra-db-account) as the vector database for this starter project, but you can follow along with any of Langflow's vector database options. - -## Prerequisites - -- [Langflow installed and running](../getting-started/install-langflow) - -- [OpenAI API key](https://platform.openai.com) - -- [An Astra DB vector database created](https://docs.datastax.com/en/astra-db-serverless/get-started/quickstart.html) with: - - Application token (`AstraCS:WSnyFUhRxsrg…​`) - - API endpoint (`https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com`) - -## Create the vector store RAG project - -1. From the Langflow dashboard, click **New Project**. -2. Select **Vector Store RAG**. -3. The **Vector Store RAG** flow is created. - - - -The vector store RAG flow is built of two separate flows. - -The **ingestion** flow (bottom of the screen) populates the vector store with data from a local file. -It ingests data from a file (**File**), splits it into chunks (**Recursive Character Text Splitter**), indexes it in Astra DB (**Astra DB**), and computes embeddings for the chunks (**OpenAI Embeddings**). -This forms a "brain" for the query flow. - -The **query** flow (top of the screen) allows users to chat with the embedded vector store data. It's a little more complex: - -- **Chat Input** component defines where to put the user input coming from the Playground. -- **OpenAI Embeddings** component generates embeddings from the user input. -- **Astra DB Search** component retrieves the most relevant Data from the Astra DB database. -- **Text Output** component turns the Data into Text by concatenating them and also displays it in the Playground. -- **Prompt** component takes in the user input and the retrieved Data as text and builds a prompt for the OpenAI model. -- **OpenAI** component generates a response to the prompt. -- **Chat Output** component displays the response in the Playground. - -4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. - - 1. In the **Variable Name** field, enter `openai_api_key`. - 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). - 3. Click **Save Variable**. - -5. To create environment variables for the **Astra DB** and **Astra DB Search** components: - 1. In the **Token** field, click the **Globe** button, and then click **Add New Variable**. - 2. In the **Variable Name** field, enter `astra_token`. - 3. In the **Value** field, paste your Astra application token (`AstraCS:WSnyFUhRxsrg…​`). - 4. Click **Save Variable**. - 5. Repeat the above steps for the **API Endpoint** field, pasting your Astra API Endpoint instead (`https://ASTRA_DB_ID-ASTRA_DB_REGION.apps.astra.datastax.com`). - 6. Add the global variable to both the **Astra DB** and **Astra DB Search** components. - -## Run the vector store RAG flow - -1. Click the **Playground** button. - The **Playground** opens, where you can chat with your data. -2. Type a message and press Enter. (Try something like "What topics do you know about?") -3. The bot will respond with a summary of the data you've embedded. - -For example, we embedded a PDF of an engine maintenance manual and asked, "How do I change the oil?" -The bot responds: - -``` -To change the oil in the engine, follow these steps: - -Make sure the engine is turned off and cool before starting. - -Locate the oil drain plug on the bottom of the engine. - -Place a drain pan underneath the oil drain plug to catch the old oil... -``` - -We can also get more specific: - -``` -User -What size wrench should I use to remove the oil drain cap? - -AI -You should use a 3/8 inch wrench to remove the oil drain cap. -``` - -This is the size the engine manual lists as well. This confirms our flow works, because the query returns the unique knowledge we embedded from the Astra vector store. diff --git a/docs/docs/tutorials/chatprompttemplate_guide.mdx b/docs/docs/tutorials/chatprompttemplate_guide.mdx deleted file mode 100644 index 9fd6ca1ea..000000000 --- a/docs/docs/tutorials/chatprompttemplate_guide.mdx +++ /dev/null @@ -1,82 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Build Chatbots with the System Message Component - - -This page may contain outdated information. It will be updated as soon as possible. - - -## Overview - -In this guide, we will modify the "Basic Chat with Prompt and History" example, integrating the ChatPromptTemplate with the SystemMessagePromptTemplate and HumanMessagePromptTemplate components. By following these steps, you'll be able to build a personalized chatbot that can interpret and respond based on user-defined System messages. - -## Interactive Guide - - - -## Step-by-Step Instructions - -1. Navigate to the "Community Examples" section. - -2. Locate the "Basic Chat with Prompt and History" example, and click on "Fork Example". - -3. Once in the editor, find the "PromptTemplate" component and remove it. - -4. Now, add these three components: ChatPromptTemplate, SystemMessagePromptTemplate, and HumanMessagePromptTemplate. - -> **Note:** Remember to set the model to gpt-3.5-turbo-0613 or the most up-to-date version. The latest models have improved capabilities to comprehend System messages. - -5. Open the "Prompt" field on the SystemMessagePromptTemplate component. - -6. Enter the text: _`You are a {role} that {behavior}.`_ -7. Save your changes by clicking on "Check & Save". - -8. Define the 'role' variable by typing "obedient assistant". - -9. Next, navigate to the HumanMessagePromptTemplate and open the "Prompt" field. - -10. Here, simply enter `{input}`. - -11. Save these changes by clicking on "Check & Save". - -12. Now, you should see your flow populated with the variables you defined. - -13. In the Memory component, set the 'Input Key' to "input". - -> **Tip:** When using a Memory component with multiple variables, it's crucial to specify which variable should be used to generate the conversation history. - -14. Click on the "Build" button to implement your changes. - -15. Open the chat interface to test your modifications. - -16. You should now be able to see and use the defined variables in the chat interface. - -17. Click on 'role' to examine the variable you established in the workspace. - -18. Now, let's define the 'behavior' variable. - -19. Enter the text: "writes the word 'Langflow' at the end of every sentence." - -20. Test your chatbot by typing "How can you help me?" - -21. If everything was set up correctly, your chatbot should respond appropriately, following the defined behavior. - -22. Congratulations! You have successfully customized and built your chatbot. - -By following these instructions, you have created a dynamic chatbot capable of understanding and responding based on custom system messages, enhancing the user experience and interaction. Enjoy your personalized assistant! diff --git a/docs/docs/tutorials/custom_components.mdx b/docs/docs/tutorials/custom_components.mdx deleted file mode 100644 index 6996bea72..000000000 --- a/docs/docs/tutorials/custom_components.mdx +++ /dev/null @@ -1,408 +0,0 @@ ---- -description: Custom Components -hide_table_of_contents: true ---- - -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import Admonition from "@theme/Admonition"; - -# Custom Components - - -This page may contain outdated information. It will be updated as soon as possible. - - -In Langflow, a Custom Component is a special component type that allows users to extend the platform's functionality by creating their own reusable and configurable components. - -A Custom Component is created from a user-defined Python script that uses the _`CustomComponent`_ class provided by the Langflow library. These components can be as simple as a basic function that takes and returns a string or as complex as a combination of multiple sub-components and API calls. - -Let's take a look at the basic rules and features. Then we'll go over an example. - -**TL;DR** - -- Create a class that inherits from _`CustomComponent`_ and contains a _`build`_ method. -- Use arguments with [Type Annotations (or Type Hints)](https://docs.python.org/3/library/typing.html) of the _`build`_ method to create component fields. -- Use the _`build_config`_ method to customize how these fields look and behave. -- Set up a folder with your components to load them up in Langflow's sidebar. - -Here is an example: - -
- - -```python -from langflow.custom import CustomComponent -from langchain.schema import Document - -class DocumentProcessor(CustomComponent): - display_name = "Document Processor" - description = "This component processes a document" - - def build_config(self) -> dict: - options = ["Uppercase", "Lowercase", "Titlecase"] - return { - "function": {"options": options, - "value": options[0]}} - - def build(self, document: Document, function: str) -> Document: - if isinstance(document, list): - document = document[0] - page_content = document.page_content - if function == "Uppercase": - page_content = page_content.upper() - elif function == "Lowercase": - page_content = page_content.lower() - elif function == "Titlecase": - page_content = page_content.title() - self.repr_value = f"Result of {function} function: {page_content}" - return Document(page_content=page_content) -``` - - - - - -
- ---- - -## Rules - -The Python script for every Custom Component should follow a set of rules. Let's go over them one by one: - - - -### Rule 1 - -The script must contain a **single class** that inherits from _`CustomComponent`_. - -```python -from langflow.custom import CustomComponent -from langchain.schema import Document - -class MyComponent(CustomComponent): - display_name = "Custom Component" - description = "This is a custom component" - - def build_config(self) -> dict: - ... - - def build(self, document: Document, function: str) -> Document: - ... -``` - ---- - -### Rule 2 - -This class requires a _`build`_ method used to run the component and define its fields. - -```python -from langflow.custom import CustomComponent -from langchain.schema import Document - -class MyComponent(CustomComponent): - display_name = "Custom Component" - description = "This is a custom component" - - def build_config(self) -> dict: - ... - - # focus - # mark - def build(self) -> Document: - ... -``` - ---- - -The [Return Type Annotation](https://docs.python.org/3/library/typing.html) of the _`build`_ method defines the component type (e.g., Chain, BaseLanguageModel, or basic Python types). Check out all supported types in the [component reference](../components/custom). - -```python -from langflow.custom import CustomComponent -from langchain.schema import Document - -class MyComponent(CustomComponent): - display_name = "Custom Component" - description = "This is a custom component" - - def build_config(self) -> dict: - ... - - # focus[20:31] - # mark - def build(self) -> Document: - ... -``` - ---- - -```python -from langflow.custom import CustomComponent -from langchain.schema import Document - -class MyComponent(CustomComponent): - display_name = "Custom Component" - description = "This is a custom component" - - def build_config(self) -> dict: - ... - - def build(self) -> Document: - ... -``` - -### Rule 3 - -The class can have a [_`build_config`_](focus://8) method, which defines configuration fields for the component. The [_`build_config`_](focus://8) method should always return a dictionary with specific keys representing the field names and their corresponding configurations. It must follow the format described below: - -- Top-level keys are field names. -- Their values are also of type _`dict`_. They specify the behavior of the generated fields. - -Check out the [component reference](../components/custom) for more details on the available field configurations. - ---- - -```python -from langflow.custom import CustomComponent -from langchain.schema import Document - -class MyComponent(CustomComponent): - display_name = "Custom Component" - description = "This is a custom component" - - def build_config(self) -> dict: - ... - - def build(self) -> Document: - ... -``` - -## Example - -Let's create a custom component that processes a document (_`langchain.schema.Document`_) using a simple function. - ---- - -### Pick a display name - -To start, let's choose a name for our component by adding a _`display_name`_ attribute. This name will appear on the workspace. The name of the class is not relevant, but let's call it _`DocumentProcessor`_. - -```python -from langflow.custom import CustomComponent -from langchain.schema import Document - -# focus -class DocumentProcessor(CustomComponent): - # focus - display_name = "Document Processor" - description = "This is a custom component" - - def build_config(self) -> dict: - ... - - def build(self) -> Document: - ... -``` - ---- - -### Write a description - -We can also write a description for it using a _`description`_ attribute. - -```python -from langflow.custom import CustomComponent -from langchain.schema import Document - -class DocumentProcessor(CustomComponent): - display_name = "Document Processor" - description = "This component processes a document" - - def build_config(self) -> dict: - ... - - def build(self) -> Document: - ... -``` - ---- - -```python -from langflow.custom import CustomComponent -from langchain.schema import Document - -class DocumentProcessor(CustomComponent): - display_name = "Document Processor" - description = "This component processes a document" - - def build_config(self) -> dict: - ... - - def build(self, document: Document, function: str) -> Document: - if isinstance(document, list): - document = document[0] - page_content = document.page_content - if function == "Uppercase": - page_content = page_content.upper() - elif function == "Lowercase": - page_content = page_content.lower() - elif function == "Titlecase": - page_content = page_content.title() - self.repr_value = f"Result of {function} function: {page_content}" - return Document(page_content=page_content) -``` - -### Add the build method - -Here, the build method takes two input parameters: _`document`_, representing the input document to be processed, and _`function`_, a string representing the selected text transformation to be applied (either "Uppercase," "Lowercase," or "Titlecase"). The method processes the text content of the input Document based on the selected function. - -The attribute _`repr_value`_ is used to display the result of the component on the workspace. It is optional and can be used to display any string value. - -The return type is _`Document`_. - ---- - -### Customize the component fields - -The _`build_config`_ method is here defined to customize the component fields. - -- _`options`_ determines that the field will be a dropdown menu. The list values and field type must be _`str`_. -- _`value`_ is the default value of the field. -- _`display_name`_ is the name of the field to be displayed. - -```python -from langflow.custom import CustomComponent -from langchain.schema import Document - -class DocumentProcessor(CustomComponent): - display_name = "Document Processor" - description = "This component processes a document" - - def build_config(self) -> dict: - options = ["Uppercase", "Lowercase", "Titlecase"] - return { - "function": {"options": options, - "value": options[0], - "display_name": "Function" - }, - "document": {"display_name": "Document"} - } - - def build(self, document: Document, function: str) -> Document: - if isinstance(document, list): - document = document[0] - page_content = document.page_content - if function == "Uppercase": - page_content = page_content.upper() - elif function == "Lowercase": - page_content = page_content.lower() - elif function == "Titlecase": - page_content = page_content.title() - self.repr_value = f"Result of {function} function: {page_content}" - return Document(page_content=page_content) -``` - - - -All done! This is what our script and brand-new custom component look like: - -
- - - - - -
- ---- - -## Load Custom Components - -For advanced customization, Langflow offers the option to create and load custom components outside of the standard interface. This process involves creating the desired components using a text editor and loading them using the Langflow CLI. - -### Folder Structure - -Create a folder that follows the same structural conventions as the [config.yaml](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/config.yaml) file. Inside this main directory, use a `custom_components` subdirectory for your custom components. - -Inside `custom_components`, you can create a Python file for each component. Similarly, any custom agents should be housed in an `agents` subdirectory. - -If you use a subdirectory name that is not in our config.yaml file, your component will appear in an `Other` category in the sidebar. - -Your structure should look something like this: - -``` -. -└── custom_components - ├── document_processor.py - └── ... -└── agents - └── ... -└── my_agents <-- Other category - └── ... -``` - -### Load Custom Components - -The recommended way to load custom components is to set the _`LANGFLOW_COMPONENTS_PATH`_ environment variable to the path of your custom components directory. Then, run the Langflow CLI as usual. - -```bash -export LANGFLOW_COMPONENTS_PATH='["/path/to/components"]' -langflow run -``` - -Alternatively, you can specify the path to your custom components using the _`--components-path`_ argument when running the Langflow CLI, as shown below: - -```bash -langflow run --components-path /path/to/components -``` - -Langflow will attempt to load all of the components found in the specified directory. If a component fails to load due to errors in the component's code, Langflow will print an error message to the console but will continue loading the rest of the components. - -### Interact with Custom Components - -Once your custom components have been loaded successfully, they will appear in Langflow's sidebar. From there, you can add them to your Langflow workspace for use. However, please note that components with errors will not be available for addition to the workspace. Always ensure your code is error-free before attempting to load components. - -Remember, creating custom components allows you to extend the functionality of Langflow to better suit your unique needs. Happy coding! diff --git a/docs/docs/tutorials/loading_document.mdx b/docs/docs/tutorials/loading_document.mdx deleted file mode 100644 index 592332eee..000000000 --- a/docs/docs/tutorials/loading_document.mdx +++ /dev/null @@ -1,69 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import ReactPlayer from "react-player"; -import Admonition from "@theme/Admonition"; - -# Integrate Documents with Prompt Variables - - -This page may contain outdated information. It will be updated as soon as possible. - - -## Overview - -This guide takes you through the process of augmenting the "Basic Chat with Prompt and History" example. You'll learn how to embed documents as context into the PromptTemplate component utilizing a WebBaseLoader. - -## Interactive Guide - - - -## Step-by-Step Instructions - -1. Start by navigating to the "Community Examples" section. - -2. Find the "Basic Chat with Prompt and History" example and click on "Fork Example". - -3. In the editor, open the "Template" field. - -4. Here, introduce the `{context}` variable, placing it somewhere before the "Current conversation:" text. - -5. Once done, save your changes by clicking on "Check & Save". - -6. Next, open the search bar and type "web". - -7. Drag and drop a WebBaseLoader (or any other loader of your choice) onto the workspace. - -8. Connect this loader to the `{context}` variable that we just added. - -9. In the "Web Page" field, enter "https://docs.langflow.org/how-upload-examples". - -10. Now, click on "ConversationBufferMemory". - -11. In the "Input Key" field, enter "text" to define the Chat variable. - -> **Tip:** When defining more than one variable and using a Memory component, it's crucial to specify which variable should be used to create the conversation history. - -12. Click on the "Build" button to implement your changes. - -13. Open the chat interface to test your modifications. - -14. Try asking something like, "How do I upload examples?" - -15. Click on "Display Prompt" to view your template. - -16. Now, you can see what the model used as a basis to generate its response. - -By following these instructions, you have successfully loaded a document into a PromptTemplate variable, allowing for more enriched and context-aware chat responses. This customization enhances user interaction by integrating relevant document content into the chat flow. diff --git a/docs/docs/tutorials/rag-with-astradb.mdx b/docs/docs/tutorials/rag-with-astradb.mdx deleted file mode 100644 index 0aa0dafe8..000000000 --- a/docs/docs/tutorials/rag-with-astradb.mdx +++ /dev/null @@ -1,192 +0,0 @@ -import ThemedImage from "@theme/ThemedImage"; -import useBaseUrl from "@docusaurus/useBaseUrl"; -import ZoomableImage from "/src/theme/ZoomableImage.js"; -import Admonition from "@theme/Admonition"; - -# 🌟 RAG with Astra DB - - -This page may contain outdated information. It will be updated as soon as possible. - - -This guide will walk you through how to build a RAG (Retrieval Augmented Generation) application using **Astra DB** and **Langflow**. - -[Astra DB](https://www.datastax.com/products/datastax-astra?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=astradb) is a cloud-native database built on Apache Cassandra that is optimized for the cloud. It is a fully managed database-as-a-service that simplifies operations and reduces costs. Astra DB is built on the same technology that powers the largest Cassandra deployments in the world. - -In this guide, we will use Astra DB as a vector store to store and retrieve the documents that will be used by the RAG application to generate responses. - - - This guide assumes that you have Langflow up and running. If you are new to - Langflow, you can check out the [Getting - Started](../getting-started/install-langflow) guide. - - -TLDR; - -- [Create a free Astra DB account](https://astra.datastax.com/signup?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=create-a-free-astra-db-account) -- Create a new database, get a **Token** and the **API Endpoint** -- Start Langflow and click on the **New Project** button and look for Vector Store RAG. This will create a new project with the necessary components -- Import the project into Langflow by dropping it on the Workspace or My Collection page -- Update the **Token** and **API Endpoint** in the **Astra DB** components -- Update the OpenAI API key in the **OpenAI** components -- Run the ingestion flow which is the one that uses the **Astra DB** component -- Click on the ⚡ _Run_ button and start interacting with your RAG application - -# First things first - -## Create an Astra DB Database - -To get started, you will need to [create an Astra DB database](https://astra.datastax.com/signup?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=create-an-astradb-database). - -Once you have created an account, you will be taken to the Astra DB dashboard. Click on the **Create Database** button. - - - -Now you will need to configure your database. Choose the **Serverless (Vector)** deployment type, and pick a Database name, provider and region. - -After you have configured your database, click on the **Create Database** button. - - - -Once your database is initialized, to the right of the page, you will see the _Database Details_ section which contains a button for you to copy the **API Endpoint** and another to generate a **Token**. - - - -Now we are all set to start building our RAG application using Astra DB and Langflow. - -## (Optional) Duplicate the Langflow 1.0 HuggingFace Space - -If you haven't already, now is the time to launch Langflow. To make things easier, you can duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) which sets up a Langflow instance just for you. - -## Open the Vector Store RAG Project in Langflow - -Run Langflow and open the UI. - -In the Langflow dashboard, click the **New Project** button and select the **Vector Store RAG** project. This will open a starter project with the necessary components to run a RAG application using Astra DB. - -This project consists of two flows. The simpler one is the **Ingestion Flow** which is responsible for ingesting the documents into the Astra DB database. - -Your first step should be to understand what each flow does and how they interact with each other. - -The ingestion flow consists of: - -- **Files** component that uploads a text file to Langflow -- **Recursive Character Text Splitter** component that splits the text into smaller chunks -- **OpenAIEmbeddings** component that generates embeddings for the text chunks -- **Astra DB** component that stores the text chunks in the Astra DB database - - - -Now, let's update the **Astra DB** and **Astra DB Search** components with the **Token** and **API Endpoint** that we generated earlier, and the OpenAI Embeddings components with your OpenAI API key. - - - -And run it! This will ingest the Text data from your file into the Astra DB database. - - - -Now, on to the **RAG Flow**. This flow is responsible for generating responses to your queries. It will define all of the steps from getting the User's input to generating a response and displaying it in the Playground. - -The RAG flow is a bit more complex. It consists of: - -- **Chat Input** component that defines where to put the user input coming from the Playground -- **OpenAI Embeddings** component that generates embeddings from the user input -- **Astra DB Search** component that retrieves the most relevant Data from the Astra DB database -- **Text Output** component that turns the Data into Text by concatenating them and also displays it in the Playground - - One interesting point you'll see here is that this component is named `Extracted Chunks`, and that is how it will appear in the Playground -- **Prompt** component that takes in the user input and the retrieved Data as text and builds a prompt for the OpenAI model -- **OpenAI** component that generates a response to the prompt -- **Chat Output** component that displays the response in the Playground - - - -To run it all we have to do is click on the **![Playground icon](/logos/botmessage.svg)Playground** button and start interacting with your RAG application. - - - -This opens the Playground where you can chat with your data. - -Because this flow has a **Chat Input** and a **Text Output** component, the Panel displays a chat input at the bottom and the Extracted Chunks section on the left. - - - -Once we interact with it we get a response and the Extracted Chunks section is updated with the retrieved Data. - - - -And that's it! You have successfully ran a RAG application using Astra DB and Langflow. - -# Conclusion - -In this guide, we have learned how to run a RAG application using Astra DB and Langflow. -We have seen how to create an Astra DB database, import the Astra DB RAG Flows project into Langflow, and run the ingestion and RAG flows. diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js index e42512b91..785576264 100644 --- a/docs/docusaurus.config.js +++ b/docs/docusaurus.config.js @@ -1,9 +1,12 @@ -const lightCodeTheme = require("prism-react-renderer/themes/github"); +// @ts-check +// Note: type annotations allow type checking and IDEs autocompletion +const lightCodeTheme = require("prism-react-renderer/themes/github"); +const darkCodeTheme = require("prism-react-renderer/themes/dracula"); const { remarkCodeHike } = require("@code-hike/mdx"); -// With JSDoc @type annotations, IDEs can provide config autocompletion -/** @type {import('@docusaurus/types').DocusaurusConfig} */ -module.exports = { + +/** @type {import('@docusaurus/types').Config} */ +const config = { title: "Langflow Documentation", tagline: "Langflow is a GUI for LangChain, designed with react-flow", favicon: "img/favicon.ico", @@ -22,12 +25,16 @@ module.exports = { defaultLocale: "en", locales: ["en"], }, + presets: [ [ - "@docusaurus/preset-classic", + "classic", /** @type {import('@docusaurus/preset-classic').Options} */ ({ docs: { + routeBasePath: "/", // Serve the docs at the site's root + sidebarPath: undefined, // `undefined` to create a fully autogenerated sidebar. + sidebarCollapsed: false, beforeDefaultRemarkPlugins: [ [ remarkCodeHike, @@ -38,19 +45,21 @@ module.exports = { }, ], ], - routeBasePath: "/", - sidebarPath: require.resolve("./sidebars.js"), - path: "docs", - // sidebarPath: 'sidebars.js', }, gtag: { trackingID: "G-XHC7G628ZP", anonymizeIP: true, }, + blog: false, theme: { customCss: [ require.resolve("@code-hike/mdx/styles.css"), - require.resolve("./src/css/custom.css"), + require.resolve("./css/custom.css"), + require.resolve("./css/docu-notion-styles.css"), + require.resolve( + "./css/gifplayer.css" + //"./node_modules/react-gif-player/dist/gifplayer.css" // this gave a big red compile warning which is seaming unrelated " Replace Autoprefixer browsers option to Browserslist config..." + ), ], }, }), @@ -72,10 +81,16 @@ module.exports = { }; }, ], - themes: ["mdx-v2"], + markdown: { format: "detect" }, themeConfig: /** @type {import('@docusaurus/preset-classic').ThemeConfig} */ ({ + sitemap: { + // https://www.sitemaps.org/protocol.html#xmlTagDefinitions + changefreq: "weekly", + priority: 0.5, + ignorePatterns: [], + }, navbar: { hideOnScroll: true, title: "Langflow", @@ -88,7 +103,6 @@ module.exports = { { position: "right", href: "https://github.com/langflow-ai/langflow", - position: "right", className: "header-github-link", target: "_blank", rel: null, @@ -96,7 +110,6 @@ module.exports = { { position: "right", href: "https://twitter.com/langflow_ai", - position: "right", className: "header-twitter-link", target: "_blank", rel: null, @@ -104,17 +117,12 @@ module.exports = { { position: "right", href: "https://discord.gg/EqksyE2EX9", - position: "right", className: "header-discord-link", target: "_blank", rel: null, }, ], }, - tableOfContents: { - minHeadingLevel: 2, - maxHeadingLevel: 5, - }, colorMode: { defaultMode: "light", /* Allow users to chose light or dark mode. */ @@ -122,6 +130,10 @@ module.exports = { /* Respect user preferences, such as low light mode in the evening */ respectPrefersColorScheme: true, }, + prism: { + theme: lightCodeTheme, + darkTheme: darkCodeTheme, + }, zoom: { selector: ".markdown :not(a) > img:not(.no-zoom)", background: { @@ -129,9 +141,6 @@ module.exports = { }, config: {}, }, - // prism: { - // theme: require("prism-react-renderer/themes/dracula"), - // }, docs: { sidebar: { hideable: true, @@ -139,3 +148,5 @@ module.exports = { }, }), }; + +module.exports = config; diff --git a/docs/i18n/es/docusaurus-plugin-content-docs/current/Examples/527257662.png b/docs/i18n/es/docusaurus-plugin-content-docs/current/Examples/527257662.png new file mode 100644 index 000000000..0c8d3e5fe Binary files /dev/null and b/docs/i18n/es/docusaurus-plugin-content-docs/current/Examples/527257662.png differ diff --git a/docs/i18n/fr/docusaurus-plugin-content-docs/current/Examples/527257662.png b/docs/i18n/fr/docusaurus-plugin-content-docs/current/Examples/527257662.png new file mode 100644 index 000000000..3e02e55c1 Binary files /dev/null and b/docs/i18n/fr/docusaurus-plugin-content-docs/current/Examples/527257662.png differ diff --git a/docs/index.d.ts b/docs/index.d.ts index 68fbc7ed5..686a5df6a 100644 --- a/docs/index.d.ts +++ b/docs/index.d.ts @@ -1,10 +1,10 @@ -declare module '*.module.scss' { - const classes: { readonly [key: string]: string }; - export default classes; - } - - declare module '@theme/*'; - - declare module '@components/*'; - - declare module '@docusaurus/*'; \ No newline at end of file +declare module "*.module.scss" { + const classes: { readonly [key: string]: string }; + export default classes; +} + +declare module "@theme/*"; + +declare module "@components/*"; + +declare module "@docusaurus/*"; diff --git a/docs/package-lock.json b/docs/package-lock.json index 9de415c12..937d1f6f8 100644 --- a/docs/package-lock.json +++ b/docs/package-lock.json @@ -8,55 +8,36 @@ "name": "langflow-docs", "version": "0.0.0", "dependencies": { - "@babel/preset-react": "^7.22.3", "@code-hike/mdx": "^0.9.0", "@docusaurus/core": "^3.2.0", - "@docusaurus/plugin-google-gtag": "^3.2.0", - "@docusaurus/plugin-ideal-image": "^3.2.0", "@docusaurus/preset-classic": "^3.2.0", - "@docusaurus/theme-classic": "^3.2.0", - "@docusaurus/theme-search-algolia": "^3.2.0", - "@mdx-js/react": "^2.3.0", - "@mendable/search": "^0.0.154", - "@pbe/react-yandex-maps": "^1.2.4", - "@prismicio/client": "^7.0.1", - "@uiball/loaders": "^1.2.6", - "autoprefixer": "^10.4.14", + "@easyops-cn/docusaurus-search-local": "^0.44.3", + "@mdx-js/react": "^3.0.1", + "@mendable/search": "^0.0.206", + "@sillsdev/docu-notion": "^0.15.0", "clsx": "^1.2.1", - "docusaurus-plugin-image-zoom": "^0.1.4", - "docusaurus-theme-mdx-v2": "^0.1.2", - "jquery": "^3.7.0", - "medium-zoom": "^1.0.8", - "node-fetch": "^3.3.1", - "path-browserify": "^1.0.1", - "postcss": "^8.4.31", + "docusaurus-node-polyfills": "^1.0.0", + "docusaurus-plugin-image-zoom": "^2.0.0", "prism-react-renderer": "^1.3.5", "react": "^18.2.0", "react-dom": "^18.2.0", - "react-images": "^0.6.7", - "react-medium-image-zoom": "^5.1.6", - "react-player": "^2.12.0", - "react-transition-group": "^4.4.5", - "remark-parse": "^10.0.2", - "swizzle": "^1.1.0", - "tailwindcss": "^3.3.2" + "react-gif-player": "^0.4.2", + "react-player": "^2.10.1", + "rimraf": "^4.1.2", + "tailwindcss": "^3.4.4" }, "devDependencies": { "@docusaurus/module-type-aliases": "^3.2.0", - "css-loader": "^6.8.1", - "docusaurus-node-polyfills": "^1.0.0", - "node-sass": "^9.0.0", - "sass": "^1.62.1", - "ts-loader": "^9.4.3" - }, - "engines": { - "node": ">=16.14" + "@tsconfig/docusaurus": "^2.0.3", + "cross-var": "^1.1.0", + "typescript": "^5.2.2" } }, "node_modules/@algolia/autocomplete-core": { "version": "1.9.3", "resolved": "https://registry.npmjs.org/@algolia/autocomplete-core/-/autocomplete-core-1.9.3.tgz", "integrity": "sha512-009HdfugtGCdC4JdXUbVJClA0q0zh24yyePn+KUGk3rP7j8FEe/m5Yo/z65gn6nP/cM39PxpzqKrL7A6fP6PPw==", + "license": "MIT", "dependencies": { "@algolia/autocomplete-plugin-algolia-insights": "1.9.3", "@algolia/autocomplete-shared": "1.9.3" @@ -66,6 +47,7 @@ "version": "1.9.3", "resolved": "https://registry.npmjs.org/@algolia/autocomplete-plugin-algolia-insights/-/autocomplete-plugin-algolia-insights-1.9.3.tgz", "integrity": "sha512-a/yTUkcO/Vyy+JffmAnTWbr4/90cLzw+CC3bRbhnULr/EM0fGNvM13oQQ14f2moLMcVDyAx/leczLlAOovhSZg==", + "license": "MIT", "dependencies": { 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"dependencies": { "@babel/helper-plugin-utils": "^7.8.0" }, @@ -887,6 +1045,7 @@ "version": "7.8.3", "resolved": "https://registry.npmjs.org/@babel/plugin-syntax-optional-catch-binding/-/plugin-syntax-optional-catch-binding-7.8.3.tgz", "integrity": "sha512-6VPD0Pc1lpTqw0aKoeRTMiB+kWhAoT24PA+ksWSBrFtl5SIRVpZlwN3NNPQjehA2E/91FV3RjLWoVTglWcSV3Q==", + "license": "MIT", "dependencies": { "@babel/helper-plugin-utils": "^7.8.0" }, @@ -898,6 +1057,7 @@ "version": "7.8.3", "resolved": "https://registry.npmjs.org/@babel/plugin-syntax-optional-chaining/-/plugin-syntax-optional-chaining-7.8.3.tgz", "integrity": "sha512-KoK9ErH1MBlCPxV0VANkXW2/dw4vlbGDrFgz8bmUsBGYkFRcbRwMh6cIJubdPrkxRwuGdtCk0v/wPTKbQgBjkg==", + "license": "MIT", "dependencies": { "@babel/helper-plugin-utils": "^7.8.0" }, @@ -909,6 +1069,7 @@ "version": "7.14.5", "resolved": "https://registry.npmjs.org/@babel/plugin-syntax-private-property-in-object/-/plugin-syntax-private-property-in-object-7.14.5.tgz", "integrity": 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"@babel/helper-environment-visitor": "^7.24.7", "@babel/helper-function-name": "^7.24.7", - "@babel/helper-plugin-utils": "^7.24.7", + "@babel/helper-plugin-utils": "^7.24.8", "@babel/helper-replace-supers": "^7.24.7", "@babel/helper-split-export-declaration": "^7.24.7", "globals": "^11.1.0" @@ -1093,6 +1265,7 @@ "version": "7.24.7", "resolved": "https://registry.npmjs.org/@babel/plugin-transform-computed-properties/-/plugin-transform-computed-properties-7.24.7.tgz", "integrity": "sha512-25cS7v+707Gu6Ds2oY6tCkUwsJ9YIDbggd9+cu9jzzDgiNq7hR/8dkzxWfKWnTic26vsI3EsCXNd4iEB6e8esQ==", + "license": "MIT", "dependencies": { "@babel/helper-plugin-utils": "^7.24.7", "@babel/template": "^7.24.7" @@ -1105,11 +1278,12 @@ } }, "node_modules/@babel/plugin-transform-destructuring": { - "version": "7.24.7", - "resolved": "https://registry.npmjs.org/@babel/plugin-transform-destructuring/-/plugin-transform-destructuring-7.24.7.tgz", - "integrity": 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"@babel/plugin-syntax-numeric-separator": "^7.10.4" @@ -1407,6 +1600,7 @@ "version": "7.24.7", "resolved": "https://registry.npmjs.org/@babel/plugin-transform-object-rest-spread/-/plugin-transform-object-rest-spread-7.24.7.tgz", "integrity": "sha512-4QrHAr0aXQCEFni2q4DqKLD31n2DL+RxcwnNjDFkSG0eNQ/xCavnRkfCUjsyqGC2OviNJvZOF/mQqZBw7i2C5Q==", + "license": "MIT", "dependencies": { "@babel/helper-compilation-targets": "^7.24.7", "@babel/helper-plugin-utils": "^7.24.7", @@ -1424,6 +1618,7 @@ "version": "7.24.7", "resolved": "https://registry.npmjs.org/@babel/plugin-transform-object-super/-/plugin-transform-object-super-7.24.7.tgz", "integrity": "sha512-A/vVLwN6lBrMFmMDmPPz0jnE6ZGx7Jq7d6sT/Ev4H65RER6pZ+kczlf1DthF5N0qaPHBsI7UXiE8Zy66nmAovg==", + "license": "MIT", "dependencies": { "@babel/helper-plugin-utils": "^7.24.7", "@babel/helper-replace-supers": "^7.24.7" @@ -1439,6 +1634,7 @@ "version": "7.24.7", "resolved": 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"dependencies": { "@babel/helper-create-class-features-plugin": "^7.24.7", "@babel/helper-plugin-utils": "^7.24.7" @@ -1499,6 +1698,7 @@ "version": "7.24.7", "resolved": "https://registry.npmjs.org/@babel/plugin-transform-private-property-in-object/-/plugin-transform-private-property-in-object-7.24.7.tgz", "integrity": "sha512-9z76mxwnwFxMyxZWEgdgECQglF2Q7cFLm0kMf8pGwt+GSJsY0cONKj/UuO4bOH0w/uAel3ekS4ra5CEAyJRmDA==", + "license": "MIT", "dependencies": { "@babel/helper-annotate-as-pure": "^7.24.7", "@babel/helper-create-class-features-plugin": "^7.24.7", @@ -1516,6 +1716,7 @@ "version": "7.24.7", "resolved": "https://registry.npmjs.org/@babel/plugin-transform-property-literals/-/plugin-transform-property-literals-7.24.7.tgz", "integrity": "sha512-EMi4MLQSHfd2nrCqQEWxFdha2gBCqU4ZcCng4WBGZ5CJL4bBRW0ptdqqDdeirGZcpALazVVNJqRmsO8/+oNCBA==", + "license": "MIT", "dependencies": { "@babel/helper-plugin-utils": "^7.24.7" }, @@ -1530,6 +1731,7 @@ "version": "7.24.7", "resolved": "https://registry.npmjs.org/@babel/plugin-transform-react-constant-elements/-/plugin-transform-react-constant-elements-7.24.7.tgz", "integrity": "sha512-7LidzZfUXyfZ8/buRW6qIIHBY8wAZ1OrY9c/wTr8YhZ6vMPo+Uc/CVFLYY1spZrEQlD4w5u8wjqk5NQ3OVqQKA==", + "license": "MIT", "dependencies": { "@babel/helper-plugin-utils": "^7.24.7" }, @@ -1544,6 +1746,7 @@ "version": "7.24.7", "resolved": "https://registry.npmjs.org/@babel/plugin-transform-react-display-name/-/plugin-transform-react-display-name-7.24.7.tgz", "integrity": "sha512-H/Snz9PFxKsS1JLI4dJLtnJgCJRoo0AUm3chP6NYr+9En1JMKloheEiLIhlp5MDVznWo+H3AAC1Mc8lmUEpsgg==", + "license": "MIT", "dependencies": { "@babel/helper-plugin-utils": "^7.24.7" }, @@ -1558,6 +1761,7 @@ "version": "7.24.7", "resolved": "https://registry.npmjs.org/@babel/plugin-transform-react-jsx/-/plugin-transform-react-jsx-7.24.7.tgz", "integrity": "sha512-+Dj06GDZEFRYvclU6k4bme55GKBEWUmByM/eoKuqg4zTNQHiApWRhQph5fxQB2wAEFvRzL1tOEj1RJ19wJrhoA==", + "license": "MIT", "dependencies": { "@babel/helper-annotate-as-pure": "^7.24.7", "@babel/helper-module-imports": "^7.24.7", @@ -1576,6 +1780,7 @@ "version": "7.24.7", "resolved": "https://registry.npmjs.org/@babel/plugin-transform-react-jsx-development/-/plugin-transform-react-jsx-development-7.24.7.tgz", "integrity": "sha512-QG9EnzoGn+Qar7rxuW+ZOsbWOt56FvvI93xInqsZDC5fsekx1AlIO4KIJ5M+D0p0SqSH156EpmZyXq630B8OlQ==", + "license": "MIT", "dependencies": { "@babel/plugin-transform-react-jsx": "^7.24.7" }, @@ -1590,6 +1795,7 @@ "version": "7.24.7", "resolved": "https://registry.npmjs.org/@babel/plugin-transform-react-pure-annotations/-/plugin-transform-react-pure-annotations-7.24.7.tgz", "integrity": "sha512-PLgBVk3fzbmEjBJ/u8kFzOqS9tUeDjiaWud/rRym/yjCo/M9cASPlnrd2ZmmZpQT40fOOrvR8jh+n8jikrOhNA==", + "license": "MIT", "dependencies": { "@babel/helper-annotate-as-pure": "^7.24.7", "@babel/helper-plugin-utils": "^7.24.7" @@ -1605,6 +1811,7 @@ "version": "7.24.7", "resolved": 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"@babel/helper-module-imports": "^7.24.7", "@babel/helper-plugin-utils": "^7.24.7", @@ -1649,10 +1858,20 @@ "@babel/core": "^7.0.0-0" } }, + "node_modules/@babel/plugin-transform-runtime/node_modules/semver": { + "version": "6.3.1", + "resolved": "https://registry.npmjs.org/semver/-/semver-6.3.1.tgz", + "integrity": "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==", + "license": "ISC", + "bin": { + "semver": "bin/semver.js" + } + }, "node_modules/@babel/plugin-transform-shorthand-properties": { "version": "7.24.7", "resolved": "https://registry.npmjs.org/@babel/plugin-transform-shorthand-properties/-/plugin-transform-shorthand-properties-7.24.7.tgz", "integrity": "sha512-KsDsevZMDsigzbA09+vacnLpmPH4aWjcZjXdyFKGzpplxhbeB4wYtury3vglQkg6KM/xEPKt73eCjPPf1PgXBA==", + "license": "MIT", "dependencies": { "@babel/helper-plugin-utils": "^7.24.7" }, @@ -1667,6 +1886,7 @@ "version": "7.24.7", "resolved": 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"license": "MIT", "dependencies": { "@babel/helper-plugin-utils": "^7.24.7" }, @@ -1707,11 +1929,12 @@ } }, "node_modules/@babel/plugin-transform-typeof-symbol": { - "version": "7.24.7", - "resolved": "https://registry.npmjs.org/@babel/plugin-transform-typeof-symbol/-/plugin-transform-typeof-symbol-7.24.7.tgz", - "integrity": "sha512-VtR8hDy7YLB7+Pet9IarXjg/zgCMSF+1mNS/EQEiEaUPoFXCVsHG64SIxcaaI2zJgRiv+YmgaQESUfWAdbjzgg==", + "version": "7.24.8", + "resolved": "https://registry.npmjs.org/@babel/plugin-transform-typeof-symbol/-/plugin-transform-typeof-symbol-7.24.8.tgz", + "integrity": "sha512-adNTUpDCVnmAE58VEqKlAA6ZBlNkMnWD0ZcW76lyNFN3MJniyGFZfNwERVk8Ap56MCnXztmDr19T4mPTztcuaw==", + "license": "MIT", "dependencies": { - "@babel/helper-plugin-utils": "^7.24.7" + "@babel/helper-plugin-utils": "^7.24.8" }, "engines": { "node": ">=6.9.0" @@ -1721,13 +1944,14 @@ } }, "node_modules/@babel/plugin-transform-typescript": { - "version": "7.24.7", - "resolved": 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"dependencies": { "devlop": "^1.0.0", "micromark-core-commonmark": "^2.0.0", @@ -14197,6 +12601,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-character": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14216,6 +12621,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14234,12 +12640,14 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-extension-gfm-strikethrough": { - "version": "2.0.0", - "resolved": "https://registry.npmjs.org/micromark-extension-gfm-strikethrough/-/micromark-extension-gfm-strikethrough-2.0.0.tgz", - "integrity": "sha512-c3BR1ClMp5fxxmwP6AoOY2fXO9U8uFMKs4ADD66ahLTNcwzSCyRVU4k7LPV5Nxo/VJiR4TdzxRQY2v3qIUceCw==", + "version": "2.1.0", + "resolved": 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"license": "MIT", "dependencies": { "micromark-util-types": "^2.0.0" }, @@ -14350,9 +12764,10 @@ } }, "node_modules/micromark-extension-gfm-task-list-item": { - "version": "2.0.1", - "resolved": "https://registry.npmjs.org/micromark-extension-gfm-task-list-item/-/micromark-extension-gfm-task-list-item-2.0.1.tgz", - "integrity": "sha512-cY5PzGcnULaN5O7T+cOzfMoHjBW7j+T9D2sucA5d/KbsBTPcYdebm9zUd9zzdgJGCwahV+/W78Z3nbulBYVbTw==", + "version": "2.1.0", + "resolved": "https://registry.npmjs.org/micromark-extension-gfm-task-list-item/-/micromark-extension-gfm-task-list-item-2.1.0.tgz", + "integrity": "sha512-qIBZhqxqI6fjLDYFTBIa4eivDMnP+OZqsNwmQ3xNLE4Cxwc+zfQEfbs6tzAo2Hjq+bh6q5F+Z8/cksrLFYWQQw==", + "license": "MIT", "dependencies": { "devlop": "^1.0.0", "micromark-factory-space": "^2.0.0", @@ -14379,6 +12794,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-character": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14398,6 +12814,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14416,7 +12833,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-extension-mdx-expression": { "version": "3.0.0", @@ -14432,6 +12850,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "@types/estree": "^1.0.0", "devlop": "^1.0.0", @@ -14457,6 +12876,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-character": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14476,6 +12896,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14494,12 +12915,14 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-extension-mdx-jsx": { "version": "3.0.0", "resolved": "https://registry.npmjs.org/micromark-extension-mdx-jsx/-/micromark-extension-mdx-jsx-3.0.0.tgz", "integrity": "sha512-uvhhss8OGuzR4/N17L1JwvmJIpPhAd8oByMawEKx6NVdBCbesjH4t+vjEp3ZXft9DwvlKSD07fCeI44/N0Vf2w==", + "license": "MIT", "dependencies": { "@types/acorn": "^4.0.0", "@types/estree": "^1.0.0", @@ -14531,6 +12954,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-character": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14550,6 +12974,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14568,12 +12993,14 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-extension-mdx-md": { "version": "2.0.0", "resolved": 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@@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14653,7 +13083,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-factory-destination": { "version": "2.0.0", @@ -14669,6 +13100,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-character": "^2.0.0", "micromark-util-symbol": "^2.0.0", @@ -14689,6 +13121,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14707,7 +13140,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-factory-label": { "version": "2.0.0", @@ -14723,6 +13157,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "devlop": "^1.0.0", "micromark-util-character": "^2.0.0", @@ -14744,6 +13179,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14762,7 +13198,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-factory-mdx-expression": { "version": "2.0.1", @@ -14778,6 +13215,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "@types/estree": "^1.0.0", "devlop": "^1.0.0", @@ -14803,6 +13241,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14821,7 +13260,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-factory-space": { "version": "1.1.0", @@ -14837,6 +13277,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-character": "^1.0.0", "micromark-util-types": "^1.0.0" @@ -14855,7 +13296,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-factory-title": { "version": "2.0.0", @@ -14871,6 +13313,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-factory-space": "^2.0.0", "micromark-util-character": "^2.0.0", @@ -14892,6 +13335,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-character": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14911,6 +13355,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14929,7 +13374,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-factory-whitespace": { "version": "2.0.0", @@ -14945,6 +13391,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-factory-space": "^2.0.0", "micromark-util-character": "^2.0.0", @@ -14966,6 +13413,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-character": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -14985,6 +13433,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -15003,7 +13452,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-util-character": { "version": "1.2.0", @@ -15019,6 +13469,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^1.0.0", "micromark-util-types": "^1.0.0" @@ -15037,7 +13488,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-util-chunked": { "version": "2.0.0", @@ -15053,6 +13505,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0" } @@ -15070,7 +13523,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-util-classify-character": { "version": "2.0.0", @@ -15086,6 +13540,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-character": "^2.0.0", "micromark-util-symbol": "^2.0.0", @@ -15106,6 +13561,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -15124,7 +13580,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-util-combine-extensions": { "version": "2.0.0", @@ -15140,6 +13597,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-chunked": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -15159,6 +13617,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0" } @@ -15176,7 +13635,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-util-decode-string": { "version": "2.0.0", @@ -15192,6 +13652,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "decode-named-character-reference": "^1.0.0", "micromark-util-character": "^2.0.0", @@ -15213,6 +13674,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -15231,7 +13693,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-util-encode": { "version": "2.0.0", @@ -15246,7 +13709,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-util-events-to-acorn": { "version": "2.0.2", @@ -15262,6 +13726,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "@types/acorn": "^4.0.0", "@types/estree": "^1.0.0", @@ -15286,7 +13751,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-util-html-tag-name": { "version": "2.0.0", @@ -15301,7 +13767,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-util-normalize-identifier": { "version": "2.0.0", @@ -15317,6 +13784,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0" } @@ -15334,7 +13802,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-util-resolve-all": { "version": "2.0.0", @@ -15350,6 +13819,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-types": "^2.0.0" } @@ -15368,6 +13838,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-character": "^2.0.0", "micromark-util-encode": "^2.0.0", @@ -15388,6 +13859,7 @@ "url": "https://opencollective.com/unified" } ], + "license": "MIT", "dependencies": { "micromark-util-symbol": "^2.0.0", "micromark-util-types": "^2.0.0" @@ -15406,7 +13878,8 @@ "type": "OpenCollective", "url": "https://opencollective.com/unified" } - ] + ], + "license": "MIT" }, "node_modules/micromark-util-subtokenize": { "version": 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"resolved": "https://registry.npmjs.org/yargs/-/yargs-17.7.2.tgz", - "integrity": "sha512-7dSzzRQ++CKnNI/krKnYRV7JKKPUXMEh61soaHKg9mrWEhzFWhFnxPxGl+69cD1Ou63C13NUPCnmIcrvqCuM6w==", - "dev": true, - "dependencies": { - "cliui": "^8.0.1", - "escalade": "^3.1.1", - "get-caller-file": "^2.0.5", - "require-directory": "^2.1.1", - "string-width": "^4.2.3", - "y18n": "^5.0.5", - "yargs-parser": "^21.1.1" - }, + "node_modules/yn": { + "version": "3.1.1", + "resolved": "https://registry.npmjs.org/yn/-/yn-3.1.1.tgz", + "integrity": "sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q==", + "license": "MIT", "engines": { - "node": ">=12" - } - }, - "node_modules/yargs-parser": { - "version": "20.2.9", - "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.9.tgz", - "integrity": "sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==", - "dev": true, - "engines": { - "node": ">=10" - } - }, - "node_modules/yargs/node_modules/emoji-regex": { - "version": "8.0.0", - "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz", - "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==", - "dev": true - }, - "node_modules/yargs/node_modules/string-width": { - "version": "4.2.3", - "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz", - "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==", - "dev": true, - "dependencies": { - "emoji-regex": "^8.0.0", - "is-fullwidth-code-point": "^3.0.0", - "strip-ansi": "^6.0.1" - }, - "engines": { - "node": ">=8" - } - }, - "node_modules/yargs/node_modules/yargs-parser": { - "version": "21.1.1", - "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-21.1.1.tgz", - "integrity": "sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==", - "dev": true, - "engines": { - "node": ">=12" + "node": ">=6" } }, "node_modules/yocto-queue": { - "version": "1.0.0", - "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-1.0.0.tgz", - "integrity": "sha512-9bnSc/HEW2uRy67wc+T8UwauLuPJVn28jb+GtJY16iiKWyvmYJRXVT4UamsAEGQfPohgr2q4Tq0sQbQlxTfi1g==", + "version": "1.1.1", + "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-1.1.1.tgz", + "integrity": "sha512-b4JR1PFR10y1mKjhHY9LaGo6tmrgjit7hxVIeAmyMw3jegXR4dhYqLaQF5zMXZxY7tLpMyJeLjr1C4rLmkVe8g==", + "license": "MIT", "engines": { "node": ">=12.20" }, @@ -23219,10 +20204,11 @@ "version": "2.0.4", "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz", "integrity": "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==", + "license": "MIT", "funding": { "type": "github", "url": "https://github.com/sponsors/wooorm" } } } -} \ No newline at end of file +} diff --git a/docs/package.json b/docs/package.json index 87f3d3d71..e4e3cb581 100644 --- a/docs/package.json +++ b/docs/package.json @@ -1,59 +1,39 @@ { "name": "langflow-docs", "version": "0.0.0", - "private": true, + "private": false, "scripts": { - "docusaurus": "docusaurus", + "clear-docs": "rimraf ./docs/", + "pull": "npm run clear-docs && npx @sillsdev/docu-notion -n $NOTION_TOKEN -r $NOTION_DOCS_ROOT_PAGE_ID", "start": "docusaurus start", "build": "docusaurus build", - "swizzle": "docusaurus swizzle", "deploy": "docusaurus deploy", - "clear": "docusaurus clear", - "serve": "docusaurus serve", - "write-translations": "docusaurus write-translations", - "write-heading-ids": "docusaurus write-heading-ids" + "serve": "docusaurus serve" }, "dependencies": { - "@babel/preset-react": "^7.22.3", "@code-hike/mdx": "^0.9.0", "@docusaurus/core": "^3.2.0", - "@docusaurus/plugin-google-gtag": "^3.2.0", - "@docusaurus/plugin-ideal-image": "^3.2.0", "@docusaurus/preset-classic": "^3.2.0", - "@docusaurus/theme-classic": "^3.2.0", - "@docusaurus/theme-search-algolia": "^3.2.0", - "@mdx-js/react": "^2.3.0", - "@mendable/search": "^0.0.154", - "@pbe/react-yandex-maps": "^1.2.4", - "@prismicio/client": "^7.0.1", - "@uiball/loaders": "^1.2.6", - "autoprefixer": "^10.4.14", + "@easyops-cn/docusaurus-search-local": "^0.44.3", + "@mdx-js/react": "^3.0.1", + "@mendable/search": "^0.0.206", + "@sillsdev/docu-notion": "^0.15.0", "clsx": "^1.2.1", - "docusaurus-plugin-image-zoom": "^0.1.4", - "docusaurus-theme-mdx-v2": "^0.1.2", - "jquery": "^3.7.0", - "medium-zoom": "^1.0.8", - "node-fetch": "^3.3.1", - "path-browserify": "^1.0.1", - "postcss": "^8.4.31", + "docusaurus-node-polyfills": "^1.0.0", + "docusaurus-plugin-image-zoom": "^2.0.0", "prism-react-renderer": "^1.3.5", "react": "^18.2.0", "react-dom": "^18.2.0", - "react-images": "^0.6.7", - "react-medium-image-zoom": "^5.1.6", - "react-player": "^2.12.0", - "react-transition-group": "^4.4.5", - "remark-parse": "^10.0.2", - "swizzle": "^1.1.0", - "tailwindcss": "^3.3.2" + "react-gif-player": "^0.4.2", + "react-player": "^2.10.1", + "rimraf": "^4.1.2", + "tailwindcss": "^3.4.4" }, "devDependencies": { "@docusaurus/module-type-aliases": "^3.2.0", - "css-loader": "^6.8.1", - "docusaurus-node-polyfills": "^1.0.0", - "node-sass": "^9.0.0", - "sass": "^1.62.1", - "ts-loader": "^9.4.3" + "@tsconfig/docusaurus": "^2.0.3", + "cross-var": "^1.1.0", + "typescript": "^5.2.2" }, "browserslist": { "production": [ @@ -67,7 +47,9 @@ "last 1 safari version" ] }, - "engines": { - "node": ">=16.14" + "packageManager": "yarn@1.22.19", + "volta": { + "node": "18.18.0", + "yarn": "1.22.19" } } diff --git a/docs/plugins/index.js b/docs/plugins/index.js deleted file mode 100644 index 844f2be07..000000000 --- a/docs/plugins/index.js +++ /dev/null @@ -1,18 +0,0 @@ -module.exports = function(context, options) { - return { - name: 'loaders', - configureWebpack(config, isServer) { - return { - module: { - rules: [ - { - test: /\.(gif|png|jpe?g|svg)$/i, - exclude: /\.(mdx?)$/i, - use: ['file-loader', { loader: 'image-webpack-loader' }], - }, - ], - }, - }; - }, - }; - }; \ No newline at end of file diff --git a/docs/sidebars.js b/docs/sidebars.js index 54bf6c042..fd342f2cd 100644 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -1,185 +1,31 @@ -module.exports = { - docs: [ +/** + * Creating a sidebar enables you to: + - create an ordered group of docs + - render a sidebar for each doc of that group + - provide next/previous navigation + + The sidebars can be generated from the filesystem, or explicitly defined here. + + Create as many sidebars as you want. + */ + +// @ts-check + +/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */ +const sidebars = { + // By default, Docusaurus generates a sidebar from the docs folder structure + tutorialSidebar: [{type: 'autogenerated', dirName: '.'}], + + // But you can create a sidebar manually + /* + tutorialSidebar: [ { - type: "category", - label: "What's New?", - collapsed: false, - items: ["whats-new/a-new-chapter-langflow"], - }, - { - type: "category", - label: "Getting Started", - collapsed: false, - items: [ - "index", - "getting-started/install-langflow", - "getting-started/quickstart", - "getting-started/workspace", - "getting-started/possible-installation-issues", - "getting-started/new-to-llms", - ], - }, - { - type: "category", - label: "Starter Projects", - collapsed: false, - items: [ - "starter-projects/basic-prompting", - "starter-projects/blog-writer", - "starter-projects/document-qa", - "starter-projects/memory-chatbot", - "starter-projects/vector-store-rag", - ], - }, - { - type: "category", - label: "Administration", - collapsed: false, - items: [ - "administration/api", - "administration/login", - "administration/cli", - "administration/playground", - "administration/memories", - "administration/logs", - "administration/collections-projects", - "administration/settings", - "administration/global-env", - "administration/chat-widget", - ], - }, - { - type: "category", - label: "Components", - collapsed: false, - items: [ - "components/inputs-and-outputs", - "components/text-and-record", - "components/data", - "components/models", - "components/helpers", - "components/vector-stores", - "components/embeddings", - "components/custom", - ], - }, - // RSN - Check if we need this - // { - // type: "category", - // label: "Extended Components", - // collapsed: true, - // items: [ - // "components/agents", - // "components/chains", - // "components/experimental", - // "components/utilities", - // "components/model_specs", - // "components/retrievers", - // "components/text-splitters", - // "components/toolkits", - // "components/tools", - // ], - // }, - // RSN - Check if we need this - // { - // type: "category", - // label: "Example Components", - // collapsed: true, - // items: [ - // "examples/chat-memory", - // "examples/combine-text", - // "examples/create-record", - // "examples/pass", - // "examples/store-message", - // "examples/sub-flow", - // "examples/text-operator", - // ], - // }, - // RSN - Check if we need this - // { - // type: "category", - // label: "Migration", - // collapsed: false, - // items: [ - // "migration/migrating-to-one-point-zero", - // "migration/compatibility", - // ], - // }, - { - type: "category", - label: "Tutorials", - collapsed: true, - items: [ - "tutorials/chatprompttemplate_guide", - "tutorials/custom_components", - "tutorials/loading_document", - "tutorials/rag-with-astradb", - ], - }, - { - type: "category", - label: "Deployment", - collapsed: true, - items: [ - "deployment/docker", - "deployment/backend-only", - "deployment/kubernetes", - "deployment/gcp-deployment", - ], - }, - { - type: "category", - label: "Contributing", - collapsed: false, - items: [ - "contributing/telemetry", - "contributing/how-contribute", - "contributing/github-issues", - "contributing/community", - "contributing/contribute-component", - ], - }, - { - type: "category", - label: "Integrations", - collapsed: false, - items: [ - "integrations/langsmith/intro", - { - type: "category", - label: "Notion", - items: [ - "integrations/notion/intro", - "integrations/notion/setup", - "integrations/notion/search", - "integrations/notion/list-database-properties", - "integrations/notion/list-pages", - "integrations/notion/list-users", - "integrations/notion/page-create", - "integrations/notion/add-content-to-page", - "integrations/notion/page-update", - "integrations/notion/page-content-viewer", - ], - }, - // { - // type: "category", - // label: "CrewAI", - // items: [ - // "integrations/crewai/intro", - // "integrations/crewai/setup", - // "integrations/crewai/agent", - // "integrations/crewai/task", - // "integrations/crewai/crew", - // ], - // }, - // { - // type: "category", - // label: "LangSmith", - // items: [ - // , - // ], - // }, - ], + type: 'category', + label: 'Tutorial', + items: ['hello'], }, ], + */ }; + +module.exports = sidebars; diff --git a/docs/spell_add.sh b/docs/spell_add.sh deleted file mode 100644 index 69b86322c..000000000 --- a/docs/spell_add.sh +++ /dev/null @@ -1,4 +0,0 @@ -for line in $(cat spell_check_results.txt); do - echo "Adding $line to cspell.config.yaml" - echo " - $line" >> cspell.config.yaml -done \ No newline at end of file diff --git a/docs/spell_check.sh b/docs/spell_check.sh deleted file mode 100644 index bec28ca11..000000000 --- a/docs/spell_check.sh +++ /dev/null @@ -1,26 +0,0 @@ -# Using cspell, we'll loop over each subdirectory inside ./docs and check every mdx file for spelling errors. -# If there is an error, we'll write the word to an output file - -# prep -if [ -f spell_check_results.txt ]; then - rm spell_check_results.txt -fi -cd docs - -# first check, over the mdx files in the root directory -find . -maxdepth 1 -type f -name "*.mdx" -exec cspell --words-only {} \; >> ../output.txt - -# loop over each subdirectory and any directories inside -for dir in */; do - find $dir -type d -exec cspell --words-only {}/*.mdx \; >> ../output.txt -done - -# loop over each line in the output file and prune duplications -cd ../ -awk '!a[$0]++' output.txt > spell_check_results.txt -rm output.txt - -# check the number of lines in spell_check_results.txt -lines=$(wc -l < spell_check_results.txt) - -echo "There are $lines spelling errors or unknown words." \ No newline at end of file diff --git a/docs/src/pages/index.module.css b/docs/src/pages/index.module.css deleted file mode 100644 index 9f71a5da7..000000000 --- a/docs/src/pages/index.module.css +++ /dev/null @@ -1,23 +0,0 @@ -/** - * CSS files with the .module.css suffix will be treated as CSS modules - * and scoped locally. - */ - -.heroBanner { - padding: 4rem 0; - text-align: center; - position: relative; - overflow: hidden; -} - -@media screen and (max-width: 996px) { - .heroBanner { - padding: 2rem; - } -} - -.buttons { - display: flex; - align-items: center; - justify-content: center; -} diff --git a/docs/src/pages/markdown-page.md b/docs/src/pages/markdown-page.md deleted file mode 100644 index 9756c5b66..000000000 --- a/docs/src/pages/markdown-page.md +++ /dev/null @@ -1,7 +0,0 @@ ---- -title: Markdown page example ---- - -# Markdown page example - -You don't need React to write simple standalone pages. diff --git a/docs/static/CNAME b/docs/static/CNAME deleted file mode 100644 index ab1d0c849..000000000 --- a/docs/static/CNAME +++ /dev/null @@ -1 +0,0 @@ -docs.langflow.org \ No newline at end of file diff --git a/docs/static/data/AstraDB-RAG-Flows.json b/docs/static/data/AstraDB-RAG-Flows.json deleted file mode 100644 index 88c4a22a0..000000000 --- a/docs/static/data/AstraDB-RAG-Flows.json +++ /dev/null @@ -1,3142 +0,0 @@ -{ - "id": "152a031a-a41a-4df1-a161-19800f686776", - "data": { - "nodes": [ - { - "data": { - "description": "Get chat inputs from the Playground.", - "display_name": "Chat Input", - "edited": false, - "id": "ChatInput-8ZRjI", - "node": { - "base_classes": [ - "Message" - ], - "beta": false, - "conditional_paths": [], - "custom_fields": {}, - "description": "Get chat inputs from the Playground.", - "display_name": "Chat Input", - "documentation": "", - "edited": true, - "field_order": [ - "input_value", - "sender", - "sender_name", - "session_id", - "files" - ], - "frozen": false, - "icon": "ChatInput", - "output_types": [], - "outputs": [ - { - "cache": true, - "display_name": "Message", - "hidden": false, - "method": "message_response", - "name": "message", - "selected": "Message", - "types": [ - "Message" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "template": { - "_type": "Component", - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Text\",\n value=\"\",\n info=\"Message to be passed as input.\",\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=\"User\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n FileInput(\n name=\"files\",\n display_name=\"Files\",\n file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n info=\"Files to be sent with the message.\",\n advanced=True,\n is_list=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n ]\n\n def message_response(self) -> Message:\n message = Message(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=self.session_id,\n files=self.files,\n )\n if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n self.store_message(message)\n self.message.value = message\n\n self.status = message\n return message\n" - }, - "files": { - "advanced": true, - "display_name": "Files", - "dynamic": false, - "fileTypes": [ - "txt", - "md", - "mdx", - "csv", - "json", - "yaml", - "yml", - "xml", - "html", - "htm", - "pdf", - "docx", - "py", - "sh", - "sql", - "js", - "ts", - "tsx", - "jpg", - "jpeg", - "png", - "bmp", - "image" - ], - "file_path": "", - "info": "Files to be sent with the message.", - "list": true, - "name": "files", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_metadata": true, - "type": "file", - "value": "" - }, - "input_value": { - "advanced": false, - "display_name": "Text", - "dynamic": false, - "info": "Message to be passed as input.", - "input_types": [ - "Message" - ], - "list": false, - "load_from_db": false, - "multiline": true, - "name": "input_value", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - }, - "sender": { - "advanced": true, - "display_name": "Sender Type", - "dynamic": false, - "info": "Type of sender.", - "name": "sender", - "options": [ - "Machine", - "User" - ], - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_metadata": true, - "type": "str", - "value": "User" - }, - "sender_name": { - "advanced": true, - "display_name": "Sender Name", - "dynamic": false, - "info": "Name of the sender.", - "input_types": [ - "Message" - ], - "list": false, - "load_from_db": false, - "name": "sender_name", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "User" - }, - "session_id": { - "advanced": true, - "display_name": "Session ID", - "dynamic": false, - "info": "Session ID for the message.", - "input_types": [ - "Message" - ], - "list": false, - "load_from_db": false, - "name": "session_id", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - } - } - }, - "type": "ChatInput" - }, - "dragging": false, - "height": 309, - "id": "ChatInput-8ZRjI", - "position": { - "x": 682.002772470747, - "y": 253.67030039648512 - }, - "positionAbsolute": { - "x": 682.002772470747, - "y": 253.67030039648512 - }, - "selected": false, - "type": "genericNode", - "width": 384 - }, - { - "data": { - "id": "OpenAIEmbeddings-hNOwh", - "node": { - "base_classes": [ - "Embeddings" - ], - "beta": false, - "custom_fields": { - "allowed_special": null, - "chunk_size": null, - "client": null, - "default_headers": null, - "default_query": null, - "deployment": null, - "disallowed_special": null, - "embedding_ctx_length": null, - "max_retries": null, - "model": null, - "model_kwargs": null, - "openai_api_base": null, - "openai_api_key": null, - "openai_api_type": null, - "openai_api_version": null, - "openai_organization": null, - "openai_proxy": null, - "request_timeout": null, - "show_progress_bar": null, - "skip_empty": null, - "tiktoken_enable": null, - "tiktoken_model_name": null - }, - "description": "Generate embeddings using OpenAI models.", - "display_name": "OpenAI Embeddings", - "documentation": "", - "field_formatters": {}, - "field_order": [], - "frozen": false, - "icon": "OpenAI", - "output_types": [], - "outputs": [ - { - "cache": true, - "display_name": "Embeddings", - "hidden": false, - "method": "build_embeddings", - "name": "embeddings", - "selected": "Embeddings", - "types": [ - "Embeddings" - ], - "value": "__UNDEFINED__" - } - ], - "template": { - "_type": "Component", - "chunk_size": { - "advanced": true, - "display_name": "Chunk Size", - "dynamic": false, - "info": "", - "list": false, - "name": "chunk_size", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": 1000 - }, - "client": { - "advanced": true, - "display_name": "Client", - "dynamic": false, - "info": "", - "input_types": [ - "Message", - "str" - ], - "list": false, - "load_from_db": false, - "name": "client", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.base.embeddings.model import LCEmbeddingsModel\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, SecretStrInput, MessageTextInput\n\n\nclass OpenAIEmbeddingsComponent(LCEmbeddingsModel):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n icon = \"OpenAI\"\n inputs = [\n DictInput(\n name=\"default_headers\",\n display_name=\"Default Headers\",\n advanced=True,\n info=\"Default headers to use for the API request.\",\n ),\n DictInput(\n name=\"default_query\",\n display_name=\"Default Query\",\n advanced=True,\n info=\"Default query parameters to use for the API request.\",\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n MessageTextInput(name=\"client\", display_name=\"Client\", advanced=True),\n MessageTextInput(name=\"deployment\", display_name=\"Deployment\", advanced=True),\n IntInput(name=\"embedding_ctx_length\", display_name=\"Embedding Context Length\", advanced=True, value=1536),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n DropdownInput(\n name=\"model\",\n display_name=\"Model\",\n advanced=False,\n options=[\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n value=\"text-embedding-3-small\",\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n SecretStrInput(name=\"openai_api_base\", display_name=\"OpenAI API Base\", advanced=True),\n SecretStrInput(name=\"openai_api_key\", display_name=\"OpenAI API Key\"),\n SecretStrInput(name=\"openai_api_type\", display_name=\"OpenAI API Type\", advanced=True),\n MessageTextInput(name=\"openai_api_version\", display_name=\"OpenAI API Version\", advanced=True),\n MessageTextInput(\n name=\"openai_organization\",\n display_name=\"OpenAI Organization\",\n advanced=True,\n ),\n MessageTextInput(name=\"openai_proxy\", display_name=\"OpenAI Proxy\", advanced=True),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n BoolInput(name=\"skip_empty\", display_name=\"Skip Empty\", advanced=True),\n MessageTextInput(\n name=\"tiktoken_model_name\",\n display_name=\"TikToken Model Name\",\n advanced=True,\n ),\n BoolInput(\n name=\"tiktoken_enable\",\n display_name=\"TikToken Enable\",\n advanced=True,\n value=True,\n info=\"If False, you must have transformers installed.\",\n ),\n ]\n\n def build_embeddings(self) -> Embeddings:\n return OpenAIEmbeddings(\n tiktoken_enabled=self.tiktoken_enable,\n default_headers=self.default_headers,\n default_query=self.default_query,\n allowed_special=\"all\",\n disallowed_special=\"all\",\n chunk_size=self.chunk_size,\n deployment=self.deployment,\n embedding_ctx_length=self.embedding_ctx_length,\n max_retries=self.max_retries,\n model=self.model,\n model_kwargs=self.model_kwargs,\n base_url=self.openai_api_base,\n api_key=self.openai_api_key,\n openai_api_type=self.openai_api_type,\n api_version=self.openai_api_version,\n organization=self.openai_organization,\n openai_proxy=self.openai_proxy,\n timeout=self.request_timeout or None,\n show_progress_bar=self.show_progress_bar,\n skip_empty=self.skip_empty,\n tiktoken_model_name=self.tiktoken_model_name,\n )\n" - }, - "default_headers": { - "advanced": true, - "display_name": "Default Headers", - "dynamic": false, - "info": "Default headers to use for the API request.", - "list": false, - "name": "default_headers", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "dict", - "value": {} - }, - "default_query": { - "advanced": true, - "display_name": "Default Query", - "dynamic": false, - "info": "Default query parameters to use for the API request.", - "list": false, - "name": "default_query", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "dict", - "value": {} - }, - "deployment": { - "advanced": true, - "display_name": "Deployment", - "dynamic": false, - "info": "", - "input_types": [ - "Message", - "str" - ], - "list": false, - "load_from_db": false, - "name": "deployment", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "embedding_ctx_length": { - "advanced": true, - "display_name": "Embedding Context Length", - "dynamic": false, - "info": "", - "list": false, - "name": "embedding_ctx_length", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": 1536 - }, - "max_retries": { - "advanced": true, - "display_name": "Max Retries", - "dynamic": false, - "info": "", - "list": false, - "name": "max_retries", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": 3 - }, - "model": { - "advanced": false, - "display_name": "Model", - "dynamic": false, - "info": "", - "name": "model", - "options": [ - "text-embedding-3-small", - "text-embedding-3-large", - "text-embedding-ada-002" - ], - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "text-embedding-3-small" - }, - "model_kwargs": { - "advanced": true, - "display_name": "Model Kwargs", - "dynamic": false, - "info": "", - "list": false, - "name": "model_kwargs", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "dict", - "value": {} - }, - "openai_api_base": { - "advanced": true, - "display_name": "OpenAI API Base", - "dynamic": false, - "info": "", - "input_types": [], - "load_from_db": true, - "name": "openai_api_base", - "password": true, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "openai_api_key": { - "advanced": false, - "display_name": "OpenAI API Key", - "dynamic": false, - "info": "", - "input_types": [], - "load_from_db": false, - "name": "openai_api_key", - "password": true, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "openai_api_type": { - "advanced": true, - "display_name": "OpenAI API Type", - "dynamic": false, - "info": "", - "input_types": [], - "load_from_db": true, - "name": "openai_api_type", - "password": true, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "openai_api_version": { - "advanced": true, - "display_name": "OpenAI API Version", - "dynamic": false, - "info": "", - "input_types": [ - "Message", - "str" - ], - "list": false, - "load_from_db": false, - "name": "openai_api_version", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "openai_organization": { - "advanced": true, - "display_name": "OpenAI Organization", - "dynamic": false, - "info": "", - "input_types": [ - "Message", - "str" - ], - "list": false, - "load_from_db": false, - "name": "openai_organization", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "openai_proxy": { - "advanced": true, - "display_name": "OpenAI Proxy", - "dynamic": false, - "info": "", - "input_types": [ - "Message", - "str" - ], - "list": false, - "load_from_db": false, - "name": "openai_proxy", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "request_timeout": { - "advanced": true, - "display_name": "Request Timeout", - "dynamic": false, - "info": "", - "list": false, - "name": "request_timeout", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "float", - "value": "" - }, - "show_progress_bar": { - "advanced": true, - "display_name": "Show Progress Bar", - "dynamic": false, - "info": "", - "list": false, - "name": "show_progress_bar", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "bool", - "value": false - }, - "skip_empty": { - "advanced": true, - "display_name": "Skip Empty", - "dynamic": false, - "info": "", - "list": false, - "name": "skip_empty", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "bool", - "value": false - }, - "tiktoken_enable": { - "advanced": true, - "display_name": "TikToken Enable", - "dynamic": false, - "info": "If False, you must have transformers installed.", - "list": false, - "name": "tiktoken_enable", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "bool", - "value": true - }, - "tiktoken_model_name": { - "advanced": true, - "display_name": "TikToken Model Name", - "dynamic": false, - "info": "", - "input_types": [ - "Message", - "str" - ], - "list": false, - "load_from_db": false, - "name": "tiktoken_model_name", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - } - } - }, - "type": "OpenAIEmbeddings" - }, - "dragging": false, - "height": 393, - "id": "OpenAIEmbeddings-hNOwh", - "position": { - "x": 672.1192980997866, - "y": 786.6985113716086 - }, - "positionAbsolute": { - "x": 672.1192980997866, - "y": 786.6985113716086 - }, - "selected": false, - "type": "genericNode", - "width": 384 - }, - { - "data": { - "description": "Generates text using OpenAI LLMs.", - "display_name": "OpenAI", - "edited": false, - "id": "OpenAIModel-euVNy", - "node": { - "base_classes": [ - "LanguageModel", - "Message" - ], - "beta": false, - "conditional_paths": [], - "custom_fields": {}, - "description": "Generates text using OpenAI LLMs.", - "display_name": "OpenAI", - "documentation": "", - "edited": true, - "field_order": [ - "input_value", - "max_tokens", - "model_kwargs", - "output_schema", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "stream", - "system_message", - "seed" - ], - "frozen": false, - "icon": "OpenAI", - "output_types": [], - "outputs": [ - { - "cache": true, - "display_name": "Text", - "hidden": false, - "method": "text_response", - "name": "text_output", - "selected": "Message", - "types": [ - "Message" - ], - "value": "__UNDEFINED__" - }, - { - "cache": true, - "display_name": "Language Model", - "method": "build_model", - "name": "model_output", - "selected": "LanguageModel", - "types": [ - "LanguageModel" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "template": { - "_type": "Component", - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageInput,\n SecretStrInput,\n StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n inputs = [\n MessageInput(name=\"input_value\", display_name=\"Input\"),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"openai_api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n StrInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"System message to pass to the model.\",\n advanced=True,\n ),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n ]\n\n def build_model(self) -> LanguageModel:\n # self.output_schea is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.openai_api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict)\n seed = self.seed\n model_kwargs[\"seed\"] = seed\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature or 0.1,\n )\n if json_mode:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n\n return output\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return\n" - }, - "input_value": { - "advanced": false, - "display_name": "Input", - "dynamic": false, - "info": "", - "input_types": [ - "Message" - ], - "list": false, - "load_from_db": false, - "name": "input_value", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "max_tokens": { - "advanced": true, - "display_name": "Max Tokens", - "dynamic": false, - "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.", - "list": false, - "name": "max_tokens", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": "" - }, - "model_kwargs": { - "advanced": true, - "display_name": "Model Kwargs", - "dynamic": false, - "info": "", - "list": false, - "name": "model_kwargs", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "dict", - "value": {} - }, - "model_name": { - "advanced": false, - "display_name": "Model Name", - "dynamic": false, - "info": "", - "name": "model_name", - "options": [ - "gpt-4o", - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-3.5-turbo-0125" - ], - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "gpt-4-turbo" - }, - "openai_api_base": { - "advanced": true, - "display_name": "OpenAI API Base", - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.", - "list": false, - "load_from_db": false, - "name": "openai_api_base", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "openai_api_key": { - "advanced": false, - "display_name": "OpenAI API Key", - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "input_types": [], - "load_from_db": false, - "name": "openai_api_key", - "password": true, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "output_schema": { - "advanced": true, - "display_name": "Schema", - "dynamic": false, - "info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.", - "list": true, - "name": "output_schema", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "dict", - "value": {} - }, - "seed": { - "advanced": true, - "display_name": "Seed", - "dynamic": false, - "info": "The seed controls the reproducibility of the job.", - "list": false, - "name": "seed", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": 1 - }, - "stream": { - "advanced": true, - "display_name": "Stream", - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "list": false, - "name": "stream", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "bool", - "value": false - }, - "system_message": { - "advanced": true, - "display_name": "System Message", - "dynamic": false, - "info": "System message to pass to the model.", - "list": false, - "load_from_db": false, - "name": "system_message", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "temperature": { - "advanced": false, - "display_name": "Temperature", - "dynamic": false, - "info": "", - "list": false, - "name": "temperature", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "float", - "value": 0.1 - } - } - }, - "type": "OpenAIModel" - }, - "dragging": false, - "height": 623, - "id": "OpenAIModel-euVNy", - "position": { - "x": 3243.967394111999, - "y": 392.861541437184 - }, - "positionAbsolute": { - "x": 3243.967394111999, - "y": 392.861541437184 - }, - "selected": false, - "type": "genericNode", - "width": 384 - }, - { - "data": { - "description": "Display a chat message in the Playground.", - "display_name": "Chat Output", - "edited": false, - "id": "ChatOutput-1eddV", - "node": { - "base_classes": [ - "Message" - ], - "beta": false, - "conditional_paths": [], - "custom_fields": {}, - "description": "Display a chat message in the Playground.", - "display_name": "Chat Output", - "documentation": "", - "edited": true, - "field_order": [ - "input_value", - "sender", - "sender_name", - "session_id", - "data_template" - ], - "frozen": false, - "icon": "ChatOutput", - "output_types": [], - "outputs": [ - { - "cache": true, - "display_name": "Message", - "method": "message_response", - "name": "message", - "selected": "Message", - "types": [ - "Message" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "template": { - "_type": "Component", - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n inputs = [\n MessageTextInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Message to be passed as output.\",\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n ),\n MessageTextInput(\n name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n ]\n\n def message_response(self) -> Message:\n message = Message(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=self.session_id,\n )\n if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n self.store_message(message)\n self.message.value = message\n\n self.status = message\n return message\n" - }, - "data_template": { - "advanced": true, - "display_name": "Data Template", - "dynamic": false, - "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.", - "input_types": [ - "Message" - ], - "list": false, - "load_from_db": false, - "name": "data_template", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "{text}" - }, - "input_value": { - "advanced": false, - "display_name": "Text", - "dynamic": false, - "info": "Message to be passed as output.", - "input_types": [ - "Message" - ], - "list": false, - "load_from_db": false, - "name": "input_value", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - }, - "sender": { - "advanced": true, - "display_name": "Sender Type", - "dynamic": false, - "info": "Type of sender.", - "name": "sender", - "options": [ - "Machine", - "User" - ], - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_metadata": true, - "type": "str", - "value": "Machine" - }, - "sender_name": { - "advanced": true, - "display_name": "Sender Name", - "dynamic": false, - "info": "Name of the sender.", - "input_types": [ - "Message" - ], - "list": false, - "load_from_db": false, - "name": "sender_name", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "AI" - }, - "session_id": { - "advanced": true, - "display_name": "Session ID", - "dynamic": false, - "info": "Session ID for the message.", - "input_types": [ - "Message" - ], - "list": false, - "load_from_db": false, - "name": "session_id", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "" - } - } - }, - "type": "ChatOutput" - }, - "dragging": false, - "height": 309, - "id": "ChatOutput-1eddV", - "position": { - "x": 3788.786948642587, - "y": 608.4077159222614 - }, - "positionAbsolute": { - "x": 3788.786948642587, - "y": 608.4077159222614 - }, - "selected": false, - "type": "genericNode", - "width": 384 - }, - { - "data": { - "id": "File-p2YBf", - "node": { - "base_classes": [ - "Record" - ], - "beta": false, - "custom_fields": { - "path": null, - "silent_errors": null - }, - "description": "A generic file loader.", - "display_name": "File", - "documentation": "", - "field_formatters": {}, - "field_order": [], - "frozen": false, - "icon": "file-text", - "output_types": [], - "outputs": [ - { - "cache": true, - "display_name": "Data", - "hidden": false, - "method": "load_file", - "name": "data", - "selected": "Data", - "types": [ - "Data" - ], - "value": "__UNDEFINED__" - } - ], - "template": { - "_type": "Component", - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from pathlib import Path\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, FileInput, Output\nfrom langflow.schema import Data\n\n\nclass FileComponent(Component):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n inputs = [\n FileInput(\n name=\"path\",\n display_name=\"Path\",\n file_types=TEXT_FILE_TYPES,\n info=f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n ),\n BoolInput(\n name=\"silent_errors\",\n display_name=\"Silent Errors\",\n advanced=True,\n info=\"If true, errors will not raise an exception.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\n ]\n\n def load_file(self) -> Data:\n if not self.path:\n raise ValueError(\"Please, upload a file to use this component.\")\n resolved_path = self.resolve_path(self.path)\n silent_errors = self.silent_errors\n\n extension = Path(resolved_path).suffix[1:].lower()\n\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n\n data = parse_text_file_to_data(resolved_path, silent_errors)\n self.status = data if data else \"No data\"\n return data or Data()\n" - }, - "path": { - "advanced": false, - "display_name": "Path", - "dynamic": false, - "fileTypes": [ - "txt", - "md", - "mdx", - "csv", - "json", - "yaml", - "yml", - "xml", - "html", - "htm", - "pdf", - "docx", - "py", - "sh", - "sql", - "js", - "ts", - "tsx" - ], - "file_path": "bba1609b-3af2-431d-a884-322cc253c69d/flatland.pdf", - "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx", - "list": false, - "name": "path", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "file", - "value": "" - }, - "silent_errors": { - "advanced": true, - "display_name": "Silent Errors", - "dynamic": false, - "info": "If true, errors will not raise an exception.", - "list": false, - "name": "silent_errors", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "bool", - "value": false - } - } - }, - "type": "File" - }, - "dragging": false, - "height": 301, - "id": "File-p2YBf", - "position": { - "x": 1435.8917804347734, - "y": 1603.546667861399 - }, - "positionAbsolute": { - "x": 1435.8917804347734, - "y": 1603.546667861399 - }, - "selected": false, - "type": "genericNode", - "width": 384 - }, - { - "data": { - "description": "Implementation of Vector Store using Astra DB with search capabilities", - "display_name": "Astra DB Vector Store", - "id": "AstraDB-p6135", - "node": { - "base_classes": [ - "Data" - ], - "beta": false, - "conditional_paths": [], - "custom_fields": {}, - "description": "Implementation of Vector Store using Astra DB with search capabilities", - "display_name": "Astra DB Vector Store", - "documentation": "https://python.langchain.com/docs/integrations/vectorstores/astradb", - "edited": false, - "field_order": [ - "collection_name", - "token", - "api_endpoint", - "vector_store_inputs", - "embedding", - "namespace", - "metric", - "batch_size", - "bulk_insert_batch_concurrency", - "bulk_insert_overwrite_concurrency", - "bulk_delete_concurrency", - "setup_mode", - "pre_delete_collection", - "metadata_indexing_include", - "metadata_indexing_exclude", - "collection_indexing_policy", - "add_to_vector_store", - "search_input", - "search_type", - "number_of_results" - ], - "frozen": false, - "icon": "AstraDB", - "output_types": [], - "outputs": [ - { - "cache": true, - "display_name": "Retriever", - "method": "build_base_retriever", - "name": "base_retriever", - "selected": "Data", - "types": [ - "Data" - ], - "value": "__UNDEFINED__" - }, - { - "cache": true, - "display_name": "Search Results", - "hidden": false, - "method": "search_documents", - "name": "search_results", - "selected": "Data", - "types": [ - "Data" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "template": { - "_type": "Component", - "add_to_vector_store": { - "advanced": false, - "display_name": "Add to Vector Store", - "dynamic": false, - "info": "If true, the Vector Store Inputs will be added to the Vector Store.", - "list": false, - "name": "add_to_vector_store", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "bool", - "value": false - }, - "api_endpoint": { - "advanced": false, - "display_name": "API Endpoint", - "dynamic": false, - "info": "API endpoint URL for the Astra DB service.", - "input_types": [], - "load_from_db": false, - "name": "api_endpoint", - "password": true, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "batch_size": { - "advanced": true, - "display_name": "Batch Size", - "dynamic": false, - "info": "Optional number of data to process in a single batch.", - "list": false, - "name": "batch_size", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": "" - }, - "bulk_delete_concurrency": { - "advanced": true, - "display_name": "Bulk Delete Concurrency", - "dynamic": false, - "info": "Optional concurrency level for bulk delete operations.", - "list": false, - "name": "bulk_delete_concurrency", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": "" - }, - "bulk_insert_batch_concurrency": { - "advanced": true, - "display_name": "Bulk Insert Batch Concurrency", - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations.", - "list": false, - "name": "bulk_insert_batch_concurrency", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": "" - }, - "bulk_insert_overwrite_concurrency": { - "advanced": true, - "display_name": "Bulk Insert Overwrite Concurrency", - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations that overwrite existing data.", - "list": false, - "name": "bulk_insert_overwrite_concurrency", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": "" - }, - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from loguru import logger\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent\nfrom langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput\nfrom langflow.schema import Data\n\n\nclass AstraVectorStoreComponent(LCVectorStoreComponent):\n display_name: str = \"Astra DB Vector Store\"\n description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n documentation: str = \"https://python.langchain.com/docs/integrations/vectorstores/astradb\"\n icon: str = \"AstraDB\"\n\n inputs = [\n StrInput(\n name=\"collection_name\",\n display_name=\"Collection Name\",\n info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n ),\n SecretStrInput(\n name=\"token\",\n display_name=\"Astra DB Application Token\",\n info=\"Authentication token for accessing Astra DB.\",\n value=\"ASTRA_DB_APPLICATION_TOKEN\",\n ),\n SecretStrInput(\n name=\"api_endpoint\",\n display_name=\"API Endpoint\",\n info=\"API endpoint URL for the Astra DB service.\",\n value=\"ASTRA_DB_API_ENDPOINT\",\n ),\n HandleInput(\n name=\"vector_store_inputs\",\n display_name=\"Vector Store Inputs\",\n input_types=[\"Document\", \"Data\"],\n is_list=True,\n ),\n HandleInput(\n name=\"embedding\",\n display_name=\"Embedding\",\n input_types=[\"Embeddings\"],\n ),\n StrInput(\n name=\"namespace\",\n display_name=\"Namespace\",\n info=\"Optional namespace within Astra DB to use for the collection.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"metric\",\n display_name=\"Metric\",\n info=\"Optional distance metric for vector comparisons in the vector store.\",\n options=[\"cosine\", \"dot_product\", \"euclidean\"],\n advanced=True,\n ),\n IntInput(\n name=\"batch_size\",\n display_name=\"Batch Size\",\n info=\"Optional number of data to process in a single batch.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_batch_concurrency\",\n display_name=\"Bulk Insert Batch Concurrency\",\n info=\"Optional concurrency level for bulk insert operations.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_overwrite_concurrency\",\n display_name=\"Bulk Insert Overwrite Concurrency\",\n info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_delete_concurrency\",\n display_name=\"Bulk Delete Concurrency\",\n info=\"Optional concurrency level for bulk delete operations.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"setup_mode\",\n display_name=\"Setup Mode\",\n info=\"Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.\",\n options=[\"Sync\", \"Async\", \"Off\"],\n advanced=True,\n value=\"Sync\",\n ),\n BoolInput(\n name=\"pre_delete_collection\",\n display_name=\"Pre Delete Collection\",\n info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_include\",\n display_name=\"Metadata Indexing Include\",\n info=\"Optional list of metadata fields to include in the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_exclude\",\n display_name=\"Metadata Indexing Exclude\",\n info=\"Optional list of metadata fields to exclude from the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"collection_indexing_policy\",\n display_name=\"Collection Indexing Policy\",\n info=\"Optional dictionary defining the indexing policy for the collection.\",\n advanced=True,\n ),\n BoolInput(\n name=\"add_to_vector_store\",\n display_name=\"Add to Vector Store\",\n info=\"If true, the Vector Store Inputs will be added to the Vector Store.\",\n ),\n MultilineInput(\n name=\"search_input\",\n display_name=\"Search Input\",\n ),\n DropdownInput(\n name=\"search_type\",\n display_name=\"Search Type\",\n options=[\"Similarity\", \"MMR\"],\n value=\"Similarity\",\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n advanced=True,\n value=4,\n ),\n ]\n\n def build_vector_store(self):\n try:\n from langchain_astradb import AstraDBVectorStore\n from langchain_astradb.utils.astradb import SetupMode\n except ImportError:\n raise ImportError(\n \"Could not import langchain Astra DB integration package. \"\n \"Please install it with `pip install langchain-astradb`.\"\n )\n\n try:\n if not self.setup_mode:\n self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n setup_mode_value = SetupMode[self.setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {self.setup_mode}\")\n\n vector_store_kwargs = {\n \"embedding\": self.embedding,\n \"collection_name\": self.collection_name,\n \"token\": self.token,\n \"api_endpoint\": self.api_endpoint,\n \"namespace\": self.namespace or None,\n \"metric\": self.metric or None,\n \"batch_size\": self.batch_size or None,\n \"bulk_insert_batch_concurrency\": self.bulk_insert_batch_concurrency or None,\n \"bulk_insert_overwrite_concurrency\": self.bulk_insert_overwrite_concurrency or None,\n \"bulk_delete_concurrency\": self.bulk_delete_concurrency or None,\n \"setup_mode\": setup_mode_value,\n \"pre_delete_collection\": self.pre_delete_collection or False,\n }\n\n if self.metadata_indexing_include:\n vector_store_kwargs[\"metadata_indexing_include\"] = self.metadata_indexing_include\n elif self.metadata_indexing_exclude:\n vector_store_kwargs[\"metadata_indexing_exclude\"] = self.metadata_indexing_exclude\n elif self.collection_indexing_policy:\n vector_store_kwargs[\"collection_indexing_policy\"] = self.collection_indexing_policy\n\n try:\n vector_store = AstraDBVectorStore(**vector_store_kwargs)\n except Exception as e:\n raise ValueError(f\"Error initializing AstraDBVectorStore: {str(e)}\") from e\n\n if self.add_to_vector_store:\n self._add_documents_to_vector_store(vector_store)\n\n return vector_store\n\n def build_base_retriever(self):\n vector_store = self.build_vector_store()\n self.status = self._astradb_collection_to_data(vector_store.collection)\n return vector_store\n\n def _add_documents_to_vector_store(self, vector_store):\n documents = []\n for _input in self.vector_store_inputs or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n raise ValueError(\"Vector Store Inputs must be Data objects.\")\n\n if documents and self.embedding is not None:\n logger.debug(f\"Adding {len(documents)} documents to the Vector Store.\")\n try:\n vector_store.add_documents(documents)\n except Exception as e:\n raise ValueError(f\"Error adding documents to AstraDBVectorStore: {str(e)}\") from e\n else:\n logger.debug(\"No documents to add to the Vector Store.\")\n\n def search_documents(self):\n vector_store = self.build_vector_store()\n\n logger.debug(f\"Search input: {self.search_input}\")\n logger.debug(f\"Search type: {self.search_type}\")\n logger.debug(f\"Number of results: {self.number_of_results}\")\n\n if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n try:\n if self.search_type == \"Similarity\":\n docs = vector_store.similarity_search(\n query=self.search_input,\n k=self.number_of_results,\n )\n elif self.search_type == \"MMR\":\n docs = vector_store.max_marginal_relevance_search(\n query=self.search_input,\n k=self.number_of_results,\n )\n else:\n raise ValueError(f\"Invalid search type: {self.search_type}\")\n except Exception as e:\n raise ValueError(f\"Error performing search in AstraDBVectorStore: {str(e)}\") from e\n\n logger.debug(f\"Retrieved documents: {len(docs)}\")\n\n data = [Data.from_document(doc) for doc in docs]\n logger.debug(f\"Converted documents to data: {len(data)}\")\n self.status = data\n return data\n else:\n logger.debug(\"No search input provided. Skipping search.\")\n return []\n\n def _astradb_collection_to_data(self, collection):\n data = []\n data_dict = collection.find()\n if data_dict and \"data\" in data_dict:\n data_dict = data_dict[\"data\"].get(\"documents\", [])\n\n for item in data_dict:\n data.append(Data(content=item[\"content\"]))\n return data\n" - }, - "collection_indexing_policy": { - "advanced": true, - "display_name": "Collection Indexing Policy", - "dynamic": false, - "info": "Optional dictionary defining the indexing policy for the collection.", - "list": false, - "load_from_db": false, - "name": "collection_indexing_policy", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "collection_name": { - "advanced": false, - "display_name": "Collection Name", - "dynamic": false, - "info": "The name of the collection within Astra DB where the vectors will be stored.", - "list": false, - "load_from_db": false, - "name": "collection_name", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "langflow" - }, - "embedding": { - "advanced": false, - "display_name": "Embedding", - "dynamic": false, - "info": "", - "input_types": [ - "Embeddings" - ], - "list": false, - "name": "embedding", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "other", - "value": "" - }, - "metadata_indexing_exclude": { - "advanced": true, - "display_name": "Metadata Indexing Exclude", - "dynamic": false, - "info": "Optional list of metadata fields to exclude from the indexing.", - "list": false, - "load_from_db": false, - "name": "metadata_indexing_exclude", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "metadata_indexing_include": { - "advanced": true, - "display_name": "Metadata Indexing Include", - "dynamic": false, - "info": "Optional list of metadata fields to include in the indexing.", - "list": false, - "load_from_db": false, - "name": "metadata_indexing_include", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "metric": { - "advanced": true, - "display_name": "Metric", - "dynamic": false, - "info": "Optional distance metric for vector comparisons in the vector store.", - "name": "metric", - "options": [ - "cosine", - "dot_product", - "euclidean" - ], - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "namespace": { - "advanced": true, - "display_name": "Namespace", - "dynamic": false, - "info": "Optional namespace within Astra DB to use for the collection.", - "list": false, - "load_from_db": false, - "name": "namespace", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "number_of_results": { - "advanced": true, - "display_name": "Number of Results", - "dynamic": false, - "info": "Number of results to return.", - "list": false, - "name": "number_of_results", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": 4 - }, - "pre_delete_collection": { - "advanced": true, - "display_name": "Pre Delete Collection", - "dynamic": false, - "info": "Boolean flag to determine whether to delete the collection before creating a new one.", - "list": false, - "name": "pre_delete_collection", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "bool", - "value": false - }, - "search_input": { - "advanced": false, - "display_name": "Search Input", - "dynamic": false, - "info": "", - "input_types": [ - "Message", - "str" - ], - "list": false, - "load_from_db": false, - "multiline": true, - "name": "search_input", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "search_type": { - "advanced": false, - "display_name": "Search Type", - "dynamic": false, - "info": "", - "name": "search_type", - "options": [ - "Similarity", - "MMR" - ], - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "Similarity" - }, - "setup_mode": { - "advanced": true, - "display_name": "Setup Mode", - "dynamic": false, - "info": "Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.", - "name": "setup_mode", - "options": [ - "Sync", - "Async", - "Off" - ], - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "Sync" - }, - "token": { - "advanced": false, - "display_name": "Astra DB Application Token", - "dynamic": false, - "info": "Authentication token for accessing Astra DB.", - "input_types": [], - "load_from_db": false, - "name": "token", - "password": true, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "vector_store_inputs": { - "advanced": false, - "display_name": "Vector Store Inputs", - "dynamic": false, - "info": "", - "input_types": [ - "Document", - "Data" - ], - "list": true, - "name": "vector_store_inputs", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "other", - "value": "" - } - } - }, - "type": "AstraDB" - }, - "dragging": false, - "height": 917, - "id": "AstraDB-p6135", - "position": { - "x": 1298.4611042465333, - "y": 160.7181472642742 - }, - "positionAbsolute": { - "x": 1298.4611042465333, - "y": 160.7181472642742 - }, - "selected": false, - "type": "genericNode", - "width": 384 - }, - { - "data": { - "id": "ParseData-9DrmC", - "node": { - "base_classes": [ - "Message" - ], - "beta": false, - "conditional_paths": [], - "custom_fields": {}, - "description": "Convert Data into plain text following a specified template.", - "display_name": "Parse Data", - "documentation": "", - "field_order": [ - "data", - "template", - "sep" - ], - "frozen": false, - "icon": "braces", - "output_types": [], - "outputs": [ - { - "cache": true, - "display_name": "Text", - "hidden": false, - "method": "parse_data", - "name": "text", - "selected": "Message", - "types": [ - "Message" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "template": { - "_type": "Component", - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n display_name = \"Parse Data\"\n description = \"Convert Data into plain text following a specified template.\"\n icon = \"braces\"\n\n inputs = [\n DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n value=\"{text}\",\n ),\n StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n ]\n\n outputs = [\n Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n ]\n\n def parse_data(self) -> Message:\n data = self.data if isinstance(self.data, list) else [self.data]\n template = self.template\n\n result_string = data_to_text(template, data, sep=self.sep)\n self.status = result_string\n return Message(text=result_string)\n" - }, - "data": { - "advanced": false, - "display_name": "Data", - "dynamic": false, - "info": "The data to convert to text.", - "input_types": [ - "Data" - ], - "list": false, - "name": "data", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "other", - "value": "" - }, - "sep": { - "advanced": true, - "display_name": "Separator", - "dynamic": false, - "info": "", - "list": false, - "load_from_db": false, - "name": "sep", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "---" - }, - "template": { - "advanced": false, - "display_name": "Template", - "dynamic": false, - "info": "The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.", - "input_types": [ - "Message", - "str" - ], - "list": false, - "load_from_db": false, - "multiline": true, - "name": "template", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "{text}" - } - } - }, - "type": "ParseData" - }, - "dragging": false, - "height": 385, - "id": "ParseData-9DrmC", - "position": { - "x": 1911.4866480237615, - "y": 566.903831987901 - }, - "positionAbsolute": { - "x": 1911.4866480237615, - "y": 566.903831987901 - }, - "selected": false, - "type": "genericNode", - "width": 384 - }, - { - "data": { - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt", - "id": "Prompt-EEXgw", - "node": { - "base_classes": [ - "Message" - ], - "beta": false, - "conditional_paths": [], - "custom_fields": { - "template": [ - "context", - "question" - ] - }, - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt", - "documentation": "", - "edited": false, - "error": null, - "field_order": [ - "template" - ], - "frozen": false, - "full_path": null, - "icon": "prompts", - "is_composition": null, - "is_input": null, - "is_output": null, - "name": "", - "output_types": [], - "outputs": [ - { - "cache": true, - "display_name": "Prompt Message", - "hidden": false, - "method": "build_prompt", - "name": "prompt", - "selected": "Message", - "types": [ - "Message" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "template": { - "_type": "Component", - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n trace_type = \"prompt\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n ]\n\n async def build_prompt(\n self,\n ) -> Message:\n prompt = await Message.from_template_and_variables(**self._attributes)\n self.status = prompt.text\n return prompt\n\n def post_code_processing(self, new_build_config: dict, current_build_config: dict):\n \"\"\"\n This function is called after the code validation is done.\n \"\"\"\n frontend_node = super().post_code_processing(new_build_config, current_build_config)\n template = frontend_node[\"template\"][\"template\"][\"value\"]\n _ = process_prompt_template(\n template=template,\n name=\"template\",\n custom_fields=frontend_node[\"custom_fields\"],\n frontend_node_template=frontend_node[\"template\"],\n )\n # Now that template is updated, we need to grab any values that were set in the current_build_config\n # and update the frontend_node with those values\n update_template_values(frontend_template=frontend_node, raw_template=current_build_config[\"template\"])\n return frontend_node\n" - }, - "context": { - "advanced": false, - "display_name": "context", - "dynamic": false, - "field_type": "str", - "fileTypes": [], - "file_path": "", - "info": "", - "input_types": [ - "Message", - "Text" - ], - "list": false, - "load_from_db": false, - "multiline": true, - "name": "context", - "password": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "question": { - "advanced": false, - "display_name": "question", - "dynamic": false, - "field_type": "str", - "fileTypes": [], - "file_path": "", - "info": "", - "input_types": [ - "Message", - "Text" - ], - "list": false, - "load_from_db": false, - "multiline": true, - "name": "question", - "password": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "template": { - "advanced": false, - "display_name": "Template", - "dynamic": false, - "info": "", - "list": false, - "name": "template", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_input": true, - "type": "prompt", - "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: " - } - } - }, - "type": "Prompt" - }, - "dragging": false, - "height": 517, - "id": "Prompt-EEXgw", - "position": { - "x": 2537.8054430938064, - "y": 442.35183727527414 - }, - "positionAbsolute": { - "x": 2537.8054430938064, - "y": 442.35183727527414 - }, - "selected": false, - "type": "genericNode", - "width": 384 - }, - { - "data": { - "id": "SplitText-qI0jS", - "node": { - "base_classes": [ - "Data" - ], - "beta": false, - "conditional_paths": [], - "custom_fields": {}, - "description": "Split text into chunks based on specified criteria.", - "display_name": "Split Text", - "documentation": "", - "edited": false, - "field_order": [ - "data_inputs", - "chunk_overlap", - "chunk_size", - "separator" - ], - "frozen": false, - "icon": "scissors-line-dashed", - "output_types": [], - "outputs": [ - { - "cache": true, - "display_name": "Chunks", - "hidden": false, - "method": "split_text", - "name": "chunks", - "selected": "Data", - "types": [ - "Data" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "template": { - "_type": "Component", - "chunk_overlap": { - "advanced": false, - "display_name": "Chunk Overlap", - "dynamic": false, - "info": "Number of characters to overlap between chunks.", - "list": false, - "name": "chunk_overlap", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_metadata": true, - "type": "int", - "value": 200 - }, - "chunk_size": { - "advanced": false, - "display_name": "Chunk Size", - "dynamic": false, - "info": "The maximum number of characters in each chunk.", - "list": false, - "name": "chunk_size", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_metadata": true, - "type": "int", - "value": 1000 - }, - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from typing import List\n\nfrom langchain_text_splitters import CharacterTextSplitter\n\nfrom langflow.custom import Component\nfrom langflow.io import HandleInput, IntInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.utils.util import unescape_string\n\n\nclass SplitTextComponent(Component):\n display_name: str = \"Split Text\"\n description: str = \"Split text into chunks based on specified criteria.\"\n icon = \"scissors-line-dashed\"\n\n inputs = [\n HandleInput(\n name=\"data_inputs\",\n display_name=\"Data Inputs\",\n info=\"The data to split.\",\n input_types=[\"Data\"],\n is_list=True,\n ),\n IntInput(\n name=\"chunk_overlap\",\n display_name=\"Chunk Overlap\",\n info=\"Number of characters to overlap between chunks.\",\n value=200,\n ),\n IntInput(\n name=\"chunk_size\",\n display_name=\"Chunk Size\",\n info=\"The maximum number of characters in each chunk.\",\n value=1000,\n ),\n MessageTextInput(\n name=\"separator\",\n display_name=\"Separator\",\n info=\"The character to split on. Defaults to newline.\",\n value=\"\\n\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Chunks\", name=\"chunks\", method=\"split_text\"),\n ]\n\n def _docs_to_data(self, docs):\n data = []\n for doc in docs:\n data.append(Data(text=doc.page_content, data=doc.metadata))\n return data\n\n def split_text(self) -> List[Data]:\n separator = unescape_string(self.separator)\n\n documents = []\n for _input in self.data_inputs:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n\n splitter = CharacterTextSplitter(\n chunk_overlap=self.chunk_overlap,\n chunk_size=self.chunk_size,\n separator=separator,\n )\n docs = splitter.split_documents(documents)\n data = self._docs_to_data(docs)\n self.status = data\n return data\n" - }, - "data_inputs": { - "advanced": false, - "display_name": "Data Inputs", - "dynamic": false, - "info": "The data to split.", - "input_types": [ - "Data" - ], - "list": true, - "name": "data_inputs", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_metadata": true, - "type": "other", - "value": "" - }, - "separator": { - "advanced": false, - "display_name": "Separator", - "dynamic": false, - "info": "The character to split on. Defaults to newline.", - "input_types": [ - "Message" - ], - "list": false, - "load_from_db": false, - "name": "separator", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_input": true, - "trace_as_metadata": true, - "type": "str", - "value": "\n" - } - } - }, - "type": "SplitText" - }, - "dragging": false, - "height": 557, - "id": "SplitText-qI0jS", - "position": { - "x": 1992.5159478772907, - "y": 1190.8288727494814 - }, - "positionAbsolute": { - "x": 1992.5159478772907, - "y": 1190.8288727494814 - }, - "selected": false, - "type": "genericNode", - "width": 384 - }, - { - "id": "AstraDB-AX2Xz", - "type": "genericNode", - "position": { - "x": 2773.0562333179937, - "y": 1160.0660495763536 - }, - "data": { - "description": "Implementation of Vector Store using Astra DB with search capabilities", - "display_name": "Astra DB Vector Store", - "id": "AstraDB-AX2Xz", - "node": { - "base_classes": [ - "Data" - ], - "beta": false, - "conditional_paths": [], - "custom_fields": {}, - "description": "Implementation of Vector Store using Astra DB with search capabilities", - "display_name": "Astra DB Vector Store", - "documentation": "https://python.langchain.com/docs/integrations/vectorstores/astradb", - "edited": false, - "field_order": [ - "collection_name", - "token", - "api_endpoint", - "vector_store_inputs", - "embedding", - "namespace", - "metric", - "batch_size", - "bulk_insert_batch_concurrency", - "bulk_insert_overwrite_concurrency", - "bulk_delete_concurrency", - "setup_mode", - "pre_delete_collection", - "metadata_indexing_include", - "metadata_indexing_exclude", - "collection_indexing_policy", - "add_to_vector_store", - "search_input", - "search_type", - "number_of_results" - ], - "frozen": false, - "icon": "AstraDB", - "output_types": [], - "outputs": [ - { - "cache": true, - "display_name": "Retriever", - "method": "build_base_retriever", - "name": "base_retriever", - "selected": "Data", - "types": [ - "Data" - ], - "value": "__UNDEFINED__" - }, - { - "cache": true, - "display_name": "Search Results", - "hidden": false, - "method": "search_documents", - "name": "search_results", - "selected": "Data", - "types": [ - "Data" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "template": { - "_type": "Component", - "add_to_vector_store": { - "advanced": false, - "display_name": "Add to Vector Store", - "dynamic": false, - "info": "If true, the Vector Store Inputs will be added to the Vector Store.", - "list": false, - "name": "add_to_vector_store", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "bool", - "value": true - }, - "api_endpoint": { - "advanced": false, - "display_name": "API Endpoint", - "dynamic": false, - "info": "API endpoint URL for the Astra DB service.", - "input_types": [], - "load_from_db": false, - "name": "api_endpoint", - "password": true, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "batch_size": { - "advanced": true, - "display_name": "Batch Size", - "dynamic": false, - "info": "Optional number of data to process in a single batch.", - "list": false, - "name": "batch_size", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": "" - }, - "bulk_delete_concurrency": { - "advanced": true, - "display_name": "Bulk Delete Concurrency", - "dynamic": false, - "info": "Optional concurrency level for bulk delete operations.", - "list": false, - "name": "bulk_delete_concurrency", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": "" - }, - "bulk_insert_batch_concurrency": { - "advanced": true, - "display_name": "Bulk Insert Batch Concurrency", - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations.", - "list": false, - "name": "bulk_insert_batch_concurrency", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": "" - }, - "bulk_insert_overwrite_concurrency": { - "advanced": true, - "display_name": "Bulk Insert Overwrite Concurrency", - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations that overwrite existing data.", - "list": false, - "name": "bulk_insert_overwrite_concurrency", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": "" - }, - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from loguru import logger\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent\nfrom langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput\nfrom langflow.schema import Data\n\n\nclass AstraVectorStoreComponent(LCVectorStoreComponent):\n display_name: str = \"Astra DB Vector Store\"\n description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n documentation: str = \"https://python.langchain.com/docs/integrations/vectorstores/astradb\"\n icon: str = \"AstraDB\"\n\n inputs = [\n StrInput(\n name=\"collection_name\",\n display_name=\"Collection Name\",\n info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n ),\n SecretStrInput(\n name=\"token\",\n display_name=\"Astra DB Application Token\",\n info=\"Authentication token for accessing Astra DB.\",\n value=\"ASTRA_DB_APPLICATION_TOKEN\",\n ),\n SecretStrInput(\n name=\"api_endpoint\",\n display_name=\"API Endpoint\",\n info=\"API endpoint URL for the Astra DB service.\",\n value=\"ASTRA_DB_API_ENDPOINT\",\n ),\n HandleInput(\n name=\"vector_store_inputs\",\n display_name=\"Vector Store Inputs\",\n input_types=[\"Document\", \"Data\"],\n is_list=True,\n ),\n HandleInput(\n name=\"embedding\",\n display_name=\"Embedding\",\n input_types=[\"Embeddings\"],\n ),\n StrInput(\n name=\"namespace\",\n display_name=\"Namespace\",\n info=\"Optional namespace within Astra DB to use for the collection.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"metric\",\n display_name=\"Metric\",\n info=\"Optional distance metric for vector comparisons in the vector store.\",\n options=[\"cosine\", \"dot_product\", \"euclidean\"],\n advanced=True,\n ),\n IntInput(\n name=\"batch_size\",\n display_name=\"Batch Size\",\n info=\"Optional number of data to process in a single batch.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_batch_concurrency\",\n display_name=\"Bulk Insert Batch Concurrency\",\n info=\"Optional concurrency level for bulk insert operations.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_overwrite_concurrency\",\n display_name=\"Bulk Insert Overwrite Concurrency\",\n info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_delete_concurrency\",\n display_name=\"Bulk Delete Concurrency\",\n info=\"Optional concurrency level for bulk delete operations.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"setup_mode\",\n display_name=\"Setup Mode\",\n info=\"Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.\",\n options=[\"Sync\", \"Async\", \"Off\"],\n advanced=True,\n value=\"Sync\",\n ),\n BoolInput(\n name=\"pre_delete_collection\",\n display_name=\"Pre Delete Collection\",\n info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_include\",\n display_name=\"Metadata Indexing Include\",\n info=\"Optional list of metadata fields to include in the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_exclude\",\n display_name=\"Metadata Indexing Exclude\",\n info=\"Optional list of metadata fields to exclude from the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"collection_indexing_policy\",\n display_name=\"Collection Indexing Policy\",\n info=\"Optional dictionary defining the indexing policy for the collection.\",\n advanced=True,\n ),\n BoolInput(\n name=\"add_to_vector_store\",\n display_name=\"Add to Vector Store\",\n info=\"If true, the Vector Store Inputs will be added to the Vector Store.\",\n ),\n MultilineInput(\n name=\"search_input\",\n display_name=\"Search Input\",\n ),\n DropdownInput(\n name=\"search_type\",\n display_name=\"Search Type\",\n options=[\"Similarity\", \"MMR\"],\n value=\"Similarity\",\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n advanced=True,\n value=4,\n ),\n ]\n\n def build_vector_store(self):\n try:\n from langchain_astradb import AstraDBVectorStore\n from langchain_astradb.utils.astradb import SetupMode\n except ImportError:\n raise ImportError(\n \"Could not import langchain Astra DB integration package. \"\n \"Please install it with `pip install langchain-astradb`.\"\n )\n\n try:\n if not self.setup_mode:\n self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n setup_mode_value = SetupMode[self.setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {self.setup_mode}\")\n\n vector_store_kwargs = {\n \"embedding\": self.embedding,\n \"collection_name\": self.collection_name,\n \"token\": self.token,\n \"api_endpoint\": self.api_endpoint,\n \"namespace\": self.namespace or None,\n \"metric\": self.metric or None,\n \"batch_size\": self.batch_size or None,\n \"bulk_insert_batch_concurrency\": self.bulk_insert_batch_concurrency or None,\n \"bulk_insert_overwrite_concurrency\": self.bulk_insert_overwrite_concurrency or None,\n \"bulk_delete_concurrency\": self.bulk_delete_concurrency or None,\n \"setup_mode\": setup_mode_value,\n \"pre_delete_collection\": self.pre_delete_collection or False,\n }\n\n if self.metadata_indexing_include:\n vector_store_kwargs[\"metadata_indexing_include\"] = self.metadata_indexing_include\n elif self.metadata_indexing_exclude:\n vector_store_kwargs[\"metadata_indexing_exclude\"] = self.metadata_indexing_exclude\n elif self.collection_indexing_policy:\n vector_store_kwargs[\"collection_indexing_policy\"] = self.collection_indexing_policy\n\n try:\n vector_store = AstraDBVectorStore(**vector_store_kwargs)\n except Exception as e:\n raise ValueError(f\"Error initializing AstraDBVectorStore: {str(e)}\") from e\n\n if self.add_to_vector_store:\n self._add_documents_to_vector_store(vector_store)\n\n return vector_store\n\n def build_base_retriever(self):\n vector_store = self.build_vector_store()\n self.status = self._astradb_collection_to_data(vector_store.collection)\n return vector_store\n\n def _add_documents_to_vector_store(self, vector_store):\n documents = []\n for _input in self.vector_store_inputs or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n raise ValueError(\"Vector Store Inputs must be Data objects.\")\n\n if documents and self.embedding is not None:\n logger.debug(f\"Adding {len(documents)} documents to the Vector Store.\")\n try:\n vector_store.add_documents(documents)\n except Exception as e:\n raise ValueError(f\"Error adding documents to AstraDBVectorStore: {str(e)}\") from e\n else:\n logger.debug(\"No documents to add to the Vector Store.\")\n\n def search_documents(self):\n vector_store = self.build_vector_store()\n\n logger.debug(f\"Search input: {self.search_input}\")\n logger.debug(f\"Search type: {self.search_type}\")\n logger.debug(f\"Number of results: {self.number_of_results}\")\n\n if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n try:\n if self.search_type == \"Similarity\":\n docs = vector_store.similarity_search(\n query=self.search_input,\n k=self.number_of_results,\n )\n elif self.search_type == \"MMR\":\n docs = vector_store.max_marginal_relevance_search(\n query=self.search_input,\n k=self.number_of_results,\n )\n else:\n raise ValueError(f\"Invalid search type: {self.search_type}\")\n except Exception as e:\n raise ValueError(f\"Error performing search in AstraDBVectorStore: {str(e)}\") from e\n\n logger.debug(f\"Retrieved documents: {len(docs)}\")\n\n data = [Data.from_document(doc) for doc in docs]\n logger.debug(f\"Converted documents to data: {len(data)}\")\n self.status = data\n return data\n else:\n logger.debug(\"No search input provided. Skipping search.\")\n return []\n\n def _astradb_collection_to_data(self, collection):\n data = []\n data_dict = collection.find()\n if data_dict and \"data\" in data_dict:\n data_dict = data_dict[\"data\"].get(\"documents\", [])\n\n for item in data_dict:\n data.append(Data(content=item[\"content\"]))\n return data\n" - }, - "collection_indexing_policy": { - "advanced": true, - "display_name": "Collection Indexing Policy", - "dynamic": false, - "info": "Optional dictionary defining the indexing policy for the collection.", - "list": false, - "load_from_db": false, - "name": "collection_indexing_policy", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "collection_name": { - "advanced": false, - "display_name": "Collection Name", - "dynamic": false, - "info": "The name of the collection within Astra DB where the vectors will be stored.", - "list": false, - "load_from_db": false, - "name": "collection_name", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "langflow" - }, - "embedding": { - "advanced": false, - "display_name": "Embedding", - "dynamic": false, - "info": "", - "input_types": [ - "Embeddings" - ], - "list": false, - "name": "embedding", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "other", - "value": "" - }, - "metadata_indexing_exclude": { - "advanced": true, - "display_name": "Metadata Indexing Exclude", - "dynamic": false, - "info": "Optional list of metadata fields to exclude from the indexing.", - "list": false, - "load_from_db": false, - "name": "metadata_indexing_exclude", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "metadata_indexing_include": { - "advanced": true, - "display_name": "Metadata Indexing Include", - "dynamic": false, - "info": "Optional list of metadata fields to include in the indexing.", - "list": false, - "load_from_db": false, - "name": "metadata_indexing_include", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "metric": { - "advanced": true, - "display_name": "Metric", - "dynamic": false, - "info": "Optional distance metric for vector comparisons in the vector store.", - "name": "metric", - "options": [ - "cosine", - "dot_product", - "euclidean" - ], - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "namespace": { - "advanced": true, - "display_name": "Namespace", - "dynamic": false, - "info": "Optional namespace within Astra DB to use for the collection.", - "list": false, - "load_from_db": false, - "name": "namespace", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "number_of_results": { - "advanced": true, - "display_name": "Number of Results", - "dynamic": false, - "info": "Number of results to return.", - "list": false, - "name": "number_of_results", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": 4 - }, - "pre_delete_collection": { - "advanced": true, - "display_name": "Pre Delete Collection", - "dynamic": false, - "info": "Boolean flag to determine whether to delete the collection before creating a new one.", - "list": false, - "name": "pre_delete_collection", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "bool", - "value": false - }, - "search_input": { - "advanced": false, - "display_name": "Search Input", - "dynamic": false, - "info": "", - "input_types": [ - "Message", - "str" - ], - "list": false, - "load_from_db": false, - "multiline": true, - "name": "search_input", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "search_type": { - "advanced": false, - "display_name": "Search Type", - "dynamic": false, - "info": "", - "name": "search_type", - "options": [ - "Similarity", - "MMR" - ], - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "Similarity" - }, - "setup_mode": { - "advanced": true, - "display_name": "Setup Mode", - "dynamic": false, - "info": "Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.", - "name": "setup_mode", - "options": [ - "Sync", - "Async", - "Off" - ], - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "Sync" - }, - "token": { - "advanced": false, - "display_name": "Astra DB Application Token", - "dynamic": false, - "info": "Authentication token for accessing Astra DB.", - "input_types": [], - "load_from_db": false, - "name": "token", - "password": true, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "vector_store_inputs": { - "advanced": false, - "display_name": "Vector Store Inputs", - "dynamic": false, - "info": "", - "input_types": [ - "Document", - "Data" - ], - "list": true, - "name": "vector_store_inputs", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "other", - "value": "" - } - } - }, - "type": "AstraDB" - }, - "selected": false, - "width": 384, - "height": 917, - "positionAbsolute": { - "x": 2773.0562333179937, - "y": 1160.0660495763536 - }, - "dragging": false - }, - { - "id": "OpenAIEmbeddings-PQc6x", - "type": "genericNode", - "position": { - "x": 1992.590633037642, - "y": 1850.2707576021312 - }, - "data": { - "id": "OpenAIEmbeddings-PQc6x", - "node": { - "base_classes": [ - "Embeddings" - ], - "beta": false, - "custom_fields": { - "allowed_special": null, - "chunk_size": null, - "client": null, - "default_headers": null, - "default_query": null, - "deployment": null, - "disallowed_special": null, - "embedding_ctx_length": null, - "max_retries": null, - "model": null, - "model_kwargs": null, - "openai_api_base": null, - "openai_api_key": null, - "openai_api_type": null, - "openai_api_version": null, - "openai_organization": null, - "openai_proxy": null, - "request_timeout": null, - "show_progress_bar": null, - "skip_empty": null, - "tiktoken_enable": null, - "tiktoken_model_name": null - }, - "description": "Generate embeddings using OpenAI models.", - "display_name": "OpenAI Embeddings", - "documentation": "", - "field_formatters": {}, - "field_order": [], - "frozen": false, - "icon": "OpenAI", - "output_types": [], - "outputs": [ - { - "cache": true, - "display_name": "Embeddings", - "hidden": false, - "method": "build_embeddings", - "name": "embeddings", - "selected": "Embeddings", - "types": [ - "Embeddings" - ], - "value": "__UNDEFINED__" - } - ], - "template": { - "_type": "Component", - "chunk_size": { - "advanced": true, - "display_name": "Chunk Size", - "dynamic": false, - "info": "", - "list": false, - "name": "chunk_size", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": 1000 - }, - "client": { - "advanced": true, - "display_name": "Client", - "dynamic": false, - "info": "", - "input_types": [ - "Message", - "str" - ], - "list": false, - "load_from_db": false, - "name": "client", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.base.embeddings.model import LCEmbeddingsModel\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, SecretStrInput, MessageTextInput\n\n\nclass OpenAIEmbeddingsComponent(LCEmbeddingsModel):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n icon = \"OpenAI\"\n inputs = [\n DictInput(\n name=\"default_headers\",\n display_name=\"Default Headers\",\n advanced=True,\n info=\"Default headers to use for the API request.\",\n ),\n DictInput(\n name=\"default_query\",\n display_name=\"Default Query\",\n advanced=True,\n info=\"Default query parameters to use for the API request.\",\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n MessageTextInput(name=\"client\", display_name=\"Client\", advanced=True),\n MessageTextInput(name=\"deployment\", display_name=\"Deployment\", advanced=True),\n IntInput(name=\"embedding_ctx_length\", display_name=\"Embedding Context Length\", advanced=True, value=1536),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n DropdownInput(\n name=\"model\",\n display_name=\"Model\",\n advanced=False,\n options=[\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n value=\"text-embedding-3-small\",\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n SecretStrInput(name=\"openai_api_base\", display_name=\"OpenAI API Base\", advanced=True),\n SecretStrInput(name=\"openai_api_key\", display_name=\"OpenAI API Key\"),\n SecretStrInput(name=\"openai_api_type\", display_name=\"OpenAI API Type\", advanced=True),\n MessageTextInput(name=\"openai_api_version\", display_name=\"OpenAI API Version\", advanced=True),\n MessageTextInput(\n name=\"openai_organization\",\n display_name=\"OpenAI Organization\",\n advanced=True,\n ),\n MessageTextInput(name=\"openai_proxy\", display_name=\"OpenAI Proxy\", advanced=True),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n BoolInput(name=\"skip_empty\", display_name=\"Skip Empty\", advanced=True),\n MessageTextInput(\n name=\"tiktoken_model_name\",\n display_name=\"TikToken Model Name\",\n advanced=True,\n ),\n BoolInput(\n name=\"tiktoken_enable\",\n display_name=\"TikToken Enable\",\n advanced=True,\n value=True,\n info=\"If False, you must have transformers installed.\",\n ),\n ]\n\n def build_embeddings(self) -> Embeddings:\n return OpenAIEmbeddings(\n tiktoken_enabled=self.tiktoken_enable,\n default_headers=self.default_headers,\n default_query=self.default_query,\n allowed_special=\"all\",\n disallowed_special=\"all\",\n chunk_size=self.chunk_size,\n deployment=self.deployment,\n embedding_ctx_length=self.embedding_ctx_length,\n max_retries=self.max_retries,\n model=self.model,\n model_kwargs=self.model_kwargs,\n base_url=self.openai_api_base,\n api_key=self.openai_api_key,\n openai_api_type=self.openai_api_type,\n api_version=self.openai_api_version,\n organization=self.openai_organization,\n openai_proxy=self.openai_proxy,\n timeout=self.request_timeout or None,\n show_progress_bar=self.show_progress_bar,\n skip_empty=self.skip_empty,\n tiktoken_model_name=self.tiktoken_model_name,\n )\n" - }, - "default_headers": { - "advanced": true, - "display_name": "Default Headers", - "dynamic": false, - "info": "Default headers to use for the API request.", - "list": false, - "name": "default_headers", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "dict", - "value": {} - }, - "default_query": { - "advanced": true, - "display_name": "Default Query", - "dynamic": false, - "info": "Default query parameters to use for the API request.", - "list": false, - "name": "default_query", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "dict", - "value": {} - }, - "deployment": { - "advanced": true, - "display_name": "Deployment", - "dynamic": false, - "info": "", - "input_types": [ - "Message", - "str" - ], - "list": false, - "load_from_db": false, - "name": "deployment", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "embedding_ctx_length": { - "advanced": true, - "display_name": "Embedding Context Length", - "dynamic": false, - "info": "", - "list": false, - "name": "embedding_ctx_length", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": 1536 - }, - "max_retries": { - "advanced": true, - "display_name": "Max Retries", - "dynamic": false, - "info": "", - "list": false, - "name": "max_retries", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "int", - "value": 3 - }, - "model": { - "advanced": false, - "display_name": "Model", - "dynamic": false, - "info": "", - "name": "model", - "options": [ - "text-embedding-3-small", - "text-embedding-3-large", - "text-embedding-ada-002" - ], - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "text-embedding-3-small" - }, - "model_kwargs": { - "advanced": true, - "display_name": "Model Kwargs", - "dynamic": false, - "info": "", - "list": false, - "name": "model_kwargs", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "dict", - "value": {} - }, - "openai_api_base": { - "advanced": true, - "display_name": "OpenAI API Base", - "dynamic": false, - "info": "", - "input_types": [], - "load_from_db": true, - "name": "openai_api_base", - "password": true, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "openai_api_key": { - "advanced": false, - "display_name": "OpenAI API Key", - "dynamic": false, - "info": "", - "input_types": [], - "load_from_db": false, - "name": "openai_api_key", - "password": true, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "openai_api_type": { - "advanced": true, - "display_name": "OpenAI API Type", - "dynamic": false, - "info": "", - "input_types": [], - "load_from_db": true, - "name": "openai_api_type", - "password": true, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "openai_api_version": { - "advanced": true, - "display_name": "OpenAI API Version", - "dynamic": false, - "info": "", - "input_types": [ - "Message", - "str" - ], - "list": false, - "load_from_db": false, - "name": "openai_api_version", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "openai_organization": { - "advanced": true, - "display_name": "OpenAI Organization", - "dynamic": false, - "info": "", - "input_types": [ - "Message", - "str" - ], - "list": false, - "load_from_db": false, - "name": "openai_organization", - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "type": "str", - "value": "" - }, - "openai_proxy": { - "advanced": true, - "display_name": "OpenAI Proxy", - "dynamic": false, - "info": "", - "input_types": [ - "Message", - "str" - ], - "list": false, - 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"selected": false - }, - { - "source": "OpenAIEmbeddings-PQc6x", - "sourceHandle": "{œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-PQc6xœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}", - "target": "AstraDB-AX2Xz", - "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œAstraDB-AX2Xzœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}", - "data": { - "targetHandle": { - "fieldName": "embedding", - "id": "AstraDB-AX2Xz", - "inputTypes": [ - "Embeddings" - ], - "type": "other" - }, - "sourceHandle": { - "dataType": "OpenAIEmbeddings", - "id": "OpenAIEmbeddings-PQc6x", - "name": "embeddings", - "output_types": [ - "Embeddings" - ] - } - }, - "id": "reactflow__edge-OpenAIEmbeddings-PQc6x{œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-PQc6xœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}-AstraDB-AX2Xz{œfieldNameœ:œembeddingœ,œidœ:œAstraDB-AX2Xzœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}", - "selected": false - } - ], - "viewport": { - "x": -173.2239134973895, - "y": 90.45548562382544, - "zoom": 0.32281188532359306 - } - }, - "description": "Visit https://docs.langflow.org/tutorials/rag-with-astradb for a detailed guide of this project.\nThis project give you both Ingestion and RAG in a single file. You'll need to visit https://astra.datastax.com/ to create an Astra DB instance, your Token and grab an API Endpoint.\nRunning this project requires you to add a file in the Files component, then define a Collection Name and click on the Play icon on the Astra DB component. \n\nAfter the ingestion ends you are ready to click on the Run button at the lower left corner and start asking questions about your data.", - "name": "Vector Store RAG", - "last_tested_version": "1.0.0rc1", - "endpoint_name": null, - "is_component": false -} \ No newline at end of file diff --git a/docs/static/data/organizations-100.csv b/docs/static/data/organizations-100.csv deleted file mode 100644 index a111992d7..000000000 --- a/docs/static/data/organizations-100.csv +++ /dev/null @@ -1,101 +0,0 @@ -Index,Organization Id,Name,Website,Country,Description,Founded,Industry,Number of employees -1,FAB0d41d5b5d22c,Ferrell LLC,https://price.net/,Papua New Guinea,Horizontal empowering knowledgebase,1990,Plastics,3498 -2,6A7EdDEA9FaDC52,"Mckinney, Riley and Day",http://www.hall-buchanan.info/,Finland,User-centric system-worthy leverage,2015,Glass / Ceramics / Concrete,4952 -3,0bFED1ADAE4bcC1,Hester Ltd,http://sullivan-reed.com/,China,Switchable scalable moratorium,1971,Public Safety,5287 -4,2bFC1Be8a4ce42f,Holder-Sellers,https://becker.com/,Turkmenistan,De-engineered systemic artificial intelligence,2004,Automotive,921 -5,9eE8A6a4Eb96C24,Mayer Group,http://www.brewer.com/,Mauritius,Synchronized needs-based challenge,1991,Transportation,7870 -6,cC757116fe1C085,Henry-Thompson,http://morse.net/,Bahamas,Face-to-face well-modulated customer loyalty,1992,Primary / Secondary Education,4914 -7,219233e8aFF1BC3,Hansen-Everett,https://www.kidd.org/,Pakistan,Seamless disintermediate collaboration,2018,Publishing Industry,7832 -8,ccc93DCF81a31CD,Mcintosh-Mora,https://www.brooks.com/,Heard Island and McDonald Islands,Centralized attitude-oriented capability,1970,Import / Export,4389 -9,0B4F93aA06ED03e,Carr Inc,http://ross.com/,Kuwait,Distributed impactful customer loyalty,1996,Plastics,8167 -10,738b5aDe6B1C6A5,Gaines Inc,http://sandoval-hooper.com/,Uzbekistan,Multi-lateral scalable protocol,1997,Outsourcing / Offshoring,9698 -11,AE61b8Ffebbc476,Kidd Group,http://www.lyons.com/,Bouvet Island (Bouvetoya),Proactive foreground paradigm,2001,Primary / Secondary Education,7473 -12,eb3B7D06cCdD609,Crane-Clarke,https://www.sandoval.com/,Denmark,Front-line clear-thinking encryption,2014,Food / Beverages,9011 -13,8D0c29189C9798B,"Keller, Campos and Black",https://www.garner.info/,Liberia,Ameliorated directional emulation,2020,Museums / Institutions,2862 -14,D2c91cc03CA394c,Glover-Pope,http://www.silva.biz/,United Arab Emirates,Persevering contextually-based approach,2013,Medical Practice,9079 -15,C8AC1eaf9C036F4,Pacheco-Spears,https://aguilar.com/,Sweden,Secured logistical synergy,1984,Maritime,769 -16,b5D10A14f7a8AfE,Hodge-Ayers,http://www.archer-elliott.com/,Honduras,Future-proofed radical implementation,1990,Facilities Services,8508 -17,68139b5C4De03B4,"Bowers, Guerra and Krause",http://www.carrillo-nicholson.com/,Uganda,De-engineered transitional strategy,1972,Primary / Secondary Education,6986 -18,5c2EffEfdba2BdF,Mckenzie-Melton,http://montoya-thompson.com/,Hong Kong,Reverse-engineered heuristic alliance,1998,Investment Management / Hedge Fund / Private Equity,4589 -19,ba179F19F7925f5,Branch-Mann,http://www.lozano.com/,Botswana,Adaptive intangible frame,1999,Architecture / Planning,7961 -20,c1Ce9B350BAc66b,Weiss and Sons,https://barrett.com/,Korea,Sharable optimal functionalities,2011,Plastics,5984 -21,8de40AC4e6EaCa4,"Velez, Payne and Coffey",http://burton.com/,Luxembourg,Mandatory coherent synergy,1986,Wholesale,5010 -22,Aad86a4F0385F2d,Harrell LLC,http://www.frey-rosario.com/,Guadeloupe,Reverse-engineered mission-critical moratorium,2018,Construction,2185 -23,22aC3FFd64fD703,"Eaton, Reynolds and Vargas",http://www.freeman.biz/,Monaco,Self-enabling multi-tasking process improvement,2014,Luxury Goods / Jewelry,8987 -24,5Ec4C272bCf085c,Robbins-Cummings,http://donaldson-wilkins.com/,Belgium,Organic non-volatile hierarchy,1991,Pharmaceuticals,5038 -25,5fDBeA8BB91a000,Jenkins Inc,http://www.kirk.biz/,South Africa,Front-line systematic help-desk,2002,Insurance,1215 -26,dFfD6a6F9AC2d9C,"Greene, Benjamin and Novak",http://www.kent.net/,Romania,Centralized leadingedge moratorium,2012,Museums / Institutions,4941 -27,4B217cC5a0674C5,"Dickson, Richmond and Clay",http://everett.com/,Czech Republic,Team-oriented tangible complexity,1980,Real Estate / Mortgage,3122 -28,88b1f1cDcf59a37,Prince-David,http://thompson.com/,Christmas Island,Virtual holistic methodology,1970,Banking / Mortgage,1046 -29,f9F7bBCAEeC360F,Ayala LLC,http://www.zhang.com/,Philippines,Open-source zero administration hierarchy,2021,Legal Services,7664 -30,7Cb3AeFcE4Ba31e,Rivas Group,https://hebert.org/,Australia,Open-architected well-modulated capacity,1998,Logistics / Procurement,4155 -31,ccBcC32adcbc530,"Sloan, Mays and Whitehead",http://lawson.com/,Chad,Face-to-face high-level conglomeration,1997,Civil Engineering,365 -32,f5afd686b3d05F5,"Durham, Allen and Barnes",http://chan-stafford.org/,Zimbabwe,Synergistic web-enabled framework,1993,Mechanical or Industrial Engineering,6135 -33,38C6cfC5074Fa5e,Fritz-Franklin,http://www.lambert.com/,Nepal,Automated 4thgeneration website,1972,Hospitality,4516 -34,5Cd7efccCcba38f,Burch-Ewing,http://cline.net/,Taiwan,User-centric 4thgeneration system engine,1981,Venture Capital / VC,7443 -35,9E6Acb51e3F9d6F,"Glass, Barrera and Turner",https://dunlap.com/,Kyrgyz Republic,Multi-channeled 3rdgeneration open system,2020,Utilities,2610 -36,4D4d7E18321eaeC,Pineda-Cox,http://aguilar.org/,Bolivia,Fundamental asynchronous capability,2010,Human Resources / HR,1312 -37,485f5d06B938F2b,"Baker, Mccann and Macdonald",http://www.anderson-barker.com/,Kenya,Cross-group user-facing focus group,2013,Legislative Office,1638 -38,19E3a5Bf6dBDc4F,Cuevas-Moss,https://dodson-castaneda.net/,Guatemala,Extended human-resource intranet,1994,Music,9995 -39,6883A965c7b68F7,Hahn PLC,http://newman.com/,Belarus,Organic logistical leverage,2012,Electrical / Electronic Manufacturing,3715 -40,AC5B7AA74Aa4A2E,"Valentine, Ferguson and Kramer",http://stuart.net/,Jersey,Centralized secondary time-frame,1997,Non - Profit / Volunteering,3585 -41,decab0D5027CA6a,Arroyo Inc,https://www.turner.com/,Grenada,Managed demand-driven website,2006,Writing / Editing,9067 -42,dF084FbBb613eea,Walls LLC,http://www.reese-vasquez.biz/,Cape Verde,Self-enabling fresh-thinking installation,1989,Investment Management / Hedge Fund / Private Equity,1678 -43,A2D89Ab9bCcAd4e,"Mitchell, Warren and Schneider",https://fox.biz/,Trinidad and Tobago,Enhanced intangible time-frame,2021,Capital Markets / Hedge Fund / Private Equity,3816 -44,77aDc905434a49f,Prince PLC,https://www.watts.com/,Sweden,Profit-focused coherent installation,2016,Individual / Family Services,7645 -45,235fdEFE2cfDa5F,Brock-Blackwell,http://www.small.com/,Benin,Secured foreground emulation,1986,Online Publishing,7034 -46,1eD64cFe986BBbE,Walton-Barnett,https://ashley-schaefer.com/,Western Sahara,Right-sized clear-thinking flexibility,2001,Luxury Goods / Jewelry,1746 -47,CbBbFcdd0eaE2cF,Bartlett-Arroyo,https://cruz.com/,Northern Mariana Islands,Realigned didactic function,1976,Civic / Social Organization,3987 -48,49aECbDaE6aBD53,"Wallace, Madden and Morris",http://www.blevins-fernandez.biz/,Germany,Persistent real-time customer loyalty,2016,Pharmaceuticals,9443 -49,7b3fe6e7E72bFa4,Berg-Sparks,https://cisneros-love.com/,Canada,Stand-alone static implementation,1974,Arts / Crafts,2073 -50,c6DedA82A8aef7E,Gonzales Ltd,http://bird.com/,Tonga,Managed human-resource policy,1988,Consumer Goods,9069 -51,7D9FBF85cdC3871,Lawson and Sons,https://www.wong.com/,French Southern Territories,Compatible analyzing intranet,2021,Arts / Crafts,3527 -52,7dd18Fb7cB07b65,"Mcguire, Mcconnell and Olsen",https://melton-briggs.com/,Korea,Profound client-server frame,1988,Printing,8445 -53,EF5B55FadccB8Fe,Charles-Phillips,https://bowman.com/,Cote d'Ivoire,Monitored client-server implementation,2012,Mental Health Care,3450 -54,f8D4B99e11fAF5D,Odom Ltd,https://www.humphrey-hess.com/,Cote d'Ivoire,Advanced static process improvement,2012,Management Consulting,1825 -55,e24D21BFd3bF1E5,Richard PLC,https://holden-coleman.net/,Mayotte,Object-based optimizing model,1971,Broadcast Media,4942 -56,B9BdfEB6D3Ca44E,Sampson Ltd,https://blevins.com/,Cayman Islands,Intuitive local adapter,2005,Farming,1418 -57,2a74D6f3D3B268e,"Cherry, Le and Callahan",https://waller-delacruz.biz/,Nigeria,Universal human-resource collaboration,2017,Entertainment / Movie Production,7202 -58,Bf3F3f62c8aBC33,Cherry PLC,https://www.avila.info/,Marshall Islands,Persistent tertiary website,1980,Plastics,8245 -59,aeBe26B80a7a23c,Melton-Nichols,https://kennedy.com/,Palau,User-friendly clear-thinking productivity,2021,Legislative Office,8741 -60,aAeb29ad43886C6,Potter-Walsh,http://thomas-french.org/,Turkey,Optional non-volatile open system,2008,Human Resources / HR,6923 -61,bD1bc6bB6d1FeD3,Freeman-Chen,https://mathis.com/,Timor-Leste,Phased next generation adapter,1973,International Trade / Development,346 -62,EB9f456e8b7022a,Soto Group,https://norris.info/,Vietnam,Enterprise-wide executive installation,1988,Business Supplies / Equipment,9097 -63,Dfef38C51D8DAe3,"Poole, Cruz and Whitney",https://reed.info/,Reunion,Balanced analyzing groupware,1978,Marketing / Advertising / Sales,2992 -64,055ffEfB2Dd95B0,Riley Ltd,http://wiley.com/,Brazil,Optional exuding superstructure,1986,Textiles,9315 -65,cBfe4dbAE1699da,"Erickson, Andrews and Bailey",https://www.hobbs-grant.com/,Eritrea,Vision-oriented secondary project,2014,Consumer Electronics,7829 -66,fdFbecbadcdCdf1,"Wilkinson, Charles and Arroyo",http://hunter-mcfarland.com/,United States Virgin Islands,Assimilated 24/7 archive,1996,Building Materials,602 -67,5DCb8A5a5ca03c0,Floyd Ltd,http://www.whitney.com/,Falkland Islands (Malvinas),Function-based fault-tolerant concept,2017,Public Relations / PR,2911 -68,ce57DCbcFD6d618,Newman-Galloway,https://www.scott.com/,Luxembourg,Enhanced foreground collaboration,1987,Information Technology / IT,3934 -69,5aaD187dc929371,Frazier-Butler,https://www.daugherty-farley.info/,Northern Mariana Islands,Persistent interactive circuit,1972,Outsourcing / Offshoring,5130 -70,902D7Ac8b6d476b,Newton Inc,https://www.richmond-manning.info/,Netherlands Antilles,Fundamental stable info-mediaries,1976,Military Industry,563 -71,32BB9Ff4d939788,Duffy-Levy,https://www.potter.com/,Guernsey,Diverse exuding installation,1982,Wireless,6146 -72,adcB0afbE58bAe3,Wagner LLC,https://decker-esparza.com/,Uruguay,Reactive attitude-oriented toolset,1987,International Affairs,6874 -73,dfcA1c84AdB61Ac,Mccall-Holmes,http://www.dean.com/,Benin,Object-based value-added database,2009,Legal Services,696 -74,208044AC2fe52F3,Massey LLC,https://frazier.biz/,Suriname,Configurable zero administration Graphical User Interface,1986,Accounting,5004 -75,f3C365f0c1A0623,Hicks LLC,http://alvarez.biz/,Pakistan,Quality-focused client-server Graphical User Interface,1970,Computer Software / Engineering,8480 -76,ec5Bdd3CBAfaB93,"Cole, Russell and Avery",http://www.blankenship.com/,Mongolia,De-engineered fault-tolerant challenge,2000,Law Enforcement,7012 -77,DDB19Be7eeB56B4,Cummings-Rojas,https://simon-pearson.com/,Svalbard & Jan Mayen Islands,User-centric modular customer loyalty,2012,Financial Services,7529 -78,dd6CA3d0bc3cAfc,"Beasley, Greene and Mahoney",http://www.petersen-lawrence.com/,Togo,Extended content-based methodology,1976,Religious Institutions,869 -79,A0B9d56e61070e3,"Beasley, Sims and Allison",http://burke.info/,Latvia,Secured zero tolerance hub,1972,Facilities Services,6182 -80,cBa7EFe5D05Adaf,Crawford-Rivera,https://black-ramirez.org/,Cuba,Persevering exuding budgetary management,1999,Online Publishing,7805 -81,Ea3f6D52Ec73563,Montes-Hensley,https://krueger.org/,Liechtenstein,Multi-tiered secondary productivity,2009,Printing,8433 -82,bC0CEd48A8000E0,Velazquez-Odom,https://stokes.com/,Djibouti,Streamlined 6thgeneration function,2002,Alternative Dispute Resolution,4044 -83,c89b9b59BC4baa1,Eaton-Morales,https://www.reeves-graham.com/,Micronesia,Customer-focused explicit frame,1990,Capital Markets / Hedge Fund / Private Equity,7013 -84,FEC51bce8421a7b,"Roberson, Pennington and Palmer",http://www.keith-fisher.com/,Cameroon,Adaptive bi-directional hierarchy,1993,Telecommunications,5571 -85,e0E8e27eAc9CAd5,"George, Russo and Guerra",https://drake.com/,Sweden,Centralized non-volatile capability,1989,Military Industry,2880 -86,B97a6CF9bf5983C,Davila Inc,https://mcconnell.info/,Cocos (Keeling) Islands,Profit-focused dedicated frame,2017,Consumer Electronics,2215 -87,a0a6f9b3DbcBEb5,Mays-Preston,http://www.browning-key.com/,Mali,User-centric heuristic focus group,2006,Military Industry,5786 -88,8cC1bDa330a5871,Pineda-Morton,https://www.carr.com/,United States Virgin Islands,Grass-roots methodical info-mediaries,1991,Printing,6168 -89,ED889CB2FE9cbd3,Huang and Sons,https://www.bolton.com/,Eritrea,Re-contextualized dynamic hierarchy,1981,Semiconductors,7484 -90,F4Dc1417BC6cb8f,Gilbert-Simon,https://www.bradford.biz/,Burundi,Grass-roots radical parallelism,1973,Newspapers / Journalism,1927 -91,7ABc3c7ecA03B34,Sampson-Griffith,http://hendricks.org/,Benin,Multi-layered composite paradigm,1972,Textiles,3881 -92,4e0719FBE38e0aB,Miles-Dominguez,http://www.turner.com/,Gibraltar,Organized empowering forecast,1996,Civic / Social Organization,897 -93,dEbDAAeDfaed00A,Rowe and Sons,https://www.simpson.org/,El Salvador,Balanced multimedia knowledgebase,1978,Facilities Services,8172 -94,61BDeCfeFD0cEF5,"Valenzuela, Holmes and Rowland",https://www.dorsey.net/,Taiwan,Persistent tertiary focus group,1999,Transportation,1483 -95,4e91eD25f486110,"Best, Wade and Shepard",https://zimmerman.com/,Zimbabwe,Innovative background definition,1991,Gambling / Casinos,4873 -96,0a0bfFbBbB8eC7c,Holmes Group,https://mcdowell.org/,Ethiopia,Right-sized zero tolerance focus group,1975,Photography,2988 -97,BA6Cd9Dae2Efd62,Good Ltd,http://duffy.com/,Anguilla,Reverse-engineered composite moratorium,1971,Consumer Services,4292 -98,E7df80C60Abd7f9,Clements-Espinoza,http://www.flowers.net/,Falkland Islands (Malvinas),Progressive modular hub,1991,Broadcast Media,236 -99,AFc285dbE2fEd24,Mendez Inc,https://www.burke.net/,Kyrgyz Republic,User-friendly exuding migration,1993,Education Management,339 -100,e9eB5A60Cef8354,Watkins-Kaiser,http://www.herring.com/,Togo,Synergistic background access,2009,Financial Services,2785 diff --git a/docs/static/img/Logspace.png b/docs/static/img/Logspace.png deleted file mode 100644 index 20ee9c5e9..000000000 Binary files a/docs/static/img/Logspace.png and /dev/null differ diff --git a/docs/static/img/add-component-to-store.png 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dark:stroke-gray-200"},{"source":"OpenAI-TakZY","sourceHandle":"OpenAI|OpenAI-TakZY|BaseLLM|BaseLanguageModel|Serializable|BaseOpenAI|OpenAI","target":"VectorStoreAgent-XlOGy","targetHandle":"BaseLanguageModel|llm|VectorStoreAgent-XlOGy","style":{"stroke":"inherit"},"animated":false,"id":"reactflow__edge-OpenAI-TakZYOpenAI|OpenAI-TakZY|BaseLLM|BaseLanguageModel|Serializable|BaseOpenAI|OpenAI-VectorStoreAgent-XlOGyBaseLanguageModel|llm|VectorStoreAgent-XlOGy","className":"stroke-gray-900 dark:stroke-gray-200"}],"viewport":{"x":89.45939216133138,"y":-68.45007135281094,"zoom":0.6912939873253691}},"id":"66d2e95e-44ac-4975-8105-6537534656d6"} \ No newline at end of file diff --git a/docs/static/json_files/CrewAI_Components_bundle.json b/docs/static/json_files/CrewAI_Components_bundle.json deleted file mode 100644 index 1d3654977..000000000 --- a/docs/static/json_files/CrewAI_Components_bundle.json +++ /dev/null @@ -1,764 +0,0 @@ -{ - "id": "8e7f4928-36e5-4348-bec2-166e24801b47", - "data": { - "nodes": [ - { - "id": "CustomComponent-j27OA", - "type": "genericNode", - "position": { "x": 257.58598820460725, "y": -171.98904954008663 }, - "data": { - "type": "CustomComponent", - "node": { - "template": { - "_type": "Component", - "llm": { - "trace_as_metadata": true, - "list": false, - "required": false, - "placeholder": "", - "show": true, - "value": "", - "name": "llm", - "display_name": "Language Model", - "advanced": false, - "input_types": ["LanguageModel"], - "dynamic": false, - "info": "Language model that will run the agent.", - "title_case": false, - "type": "other" - }, - "tools": { - "trace_as_metadata": true, - "list": true, - "required": false, - "placeholder": "", - "show": true, - "value": [], - "name": "tools", - "display_name": "Tools", - "advanced": false, - "input_types": ["Tool"], - "dynamic": false, - "info": "Tools at agents disposal", - "title_case": false, - "type": "other" - }, - "allow_delegation": { - "trace_as_metadata": true, - "list": false, - "required": false, - "placeholder": "", - "show": true, - "value": true, - "name": "allow_delegation", - "display_name": "Allow Delegation", - "advanced": false, - "dynamic": false, - "info": "Whether the agent is allowed to delegate tasks to other agents.", - "title_case": false, - "type": "bool" - }, - "backstory": { - "trace_as_input": true, - "trace_as_metadata": true, - "load_from_db": false, - "list": false, - "required": true, - "placeholder": "", - "show": true, - "value": "", - "name": "backstory", - "display_name": "Backstory", - "advanced": false, - "input_types": ["Message"], - "dynamic": false, - "info": "The backstory of the agent.", - "title_case": false, - "type": "str" - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "# from langflow.field_typing import Data\nfrom langflow.custom import Component\nfrom langflow.io import MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.io import BoolInput, IntInput, DictInput, DropdownInput, MessageTextInput, HandleInput\nfrom crewai import Agent\n\n\nclass CrewAIAgent(Component):\n display_name = \"CrewAIAgent\"\n description = \"Represents an agent of CrewAI.\"\n documentation: str = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n MessageTextInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\", required=True),\n MessageTextInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\", required=True),\n MessageTextInput(name=\"backstory\", display_name=\"Backstory\", info=\"The backstory of the agent.\", required=True),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agents disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\", \n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=False,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=True,\n ),\n DictInput(\n name=\"kwargs\",\n display_name=\"kwargs\",\n info=\"kwargs of agent.\",\n is_list=True,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Output\", name=\"output\", method=\"build_output\"),\n ]\n \n\n def build_output(self) -> Agent:\n kwargs = self.kwargs if self.kwargs else {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools if self.tools else [],\n allow_delegation=self.allow_delegation,\n **kwargs\n )\n self.status = agent\n return agent\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "goal": { - "trace_as_input": true, - "trace_as_metadata": true, - "load_from_db": false, - "list": false, - "required": true, - "placeholder": "", - "show": true, - "value": "", - "name": "goal", - "display_name": "Goal", - "advanced": false, - "input_types": ["Message"], - "dynamic": false, - "info": "The objective of the agent.", - "title_case": false, - "type": "str" - }, - "kwargs": { - "trace_as_input": true, - "list": true, - "required": false, - "placeholder": "", - "show": true, - "value": {}, - "name": "kwargs", - "display_name": "kwargs", - "advanced": true, - "dynamic": false, - "info": "kwargs of agent.", - "title_case": false, - "type": "dict" - }, - "memory": { - "trace_as_metadata": true, - "list": false, - "required": false, - "placeholder": "", - "show": true, - "value": true, - "name": "memory", - "display_name": "Memory", - "advanced": true, - "dynamic": false, - "info": "Whether the agent should have memory or not", - "title_case": false, - "type": "bool" - }, - "role": { - "trace_as_input": true, - "trace_as_metadata": true, - "load_from_db": false, - "list": false, - "required": true, - "placeholder": "", - "show": true, - "value": "", - "name": "role", - "display_name": "Role", - "advanced": false, - "input_types": ["Message"], - "dynamic": false, - "info": "The role of the agent.", - "title_case": false, - "type": "str" - }, - "verbose": { - "trace_as_metadata": true, - "list": false, - "required": false, - "placeholder": "", - "show": true, - "value": false, - "name": "verbose", - "display_name": "Verbose", - "advanced": true, - "dynamic": false, - "info": "", - "title_case": false, - "type": "bool" - } - }, - "description": "Represents an agent of CrewAI.", - "icon": "CrewAI", - "base_classes": ["Agent"], - "display_name": "CrewAIAgent", - "documentation": "https://docs.crewai.com/how-to/LLM-Connections/", - "custom_fields": {}, - "output_types": [], - "pinned": false, - "conditional_paths": [], - "frozen": false, - "outputs": [ - { - "types": ["Agent"], - "selected": "Agent", - "name": "output", - "display_name": "Output", - "method": "build_output", - "value": "__UNDEFINED__", - "cache": true - } - ], - "field_order": [ - "role", - "goal", - "backstory", - "tools", - "llm", - "memory", - "verbose", - "allow_delegation", - "kwargs" - ], - "beta": false, - "edited": true - }, - "id": "CustomComponent-j27OA", - "description": "Represents an agent of CrewAI.", - "display_name": "CrewAIAgent" - }, - "selected": false, - "width": 384, - "height": 668, - "positionAbsolute": { - "x": 257.58598820460725, - "y": -171.98904954008663 - }, - "dragging": false - }, - { - "id": "CustomComponent-4UaNS", - "type": "genericNode", - "position": { "x": 677.4952370910181, "y": -169.34324846822471 }, - "data": { - "type": "CustomComponent", - "node": { - "template": { - "_type": "Component", - "agent": { - "trace_as_metadata": true, - "list": false, - "required": false, - "placeholder": "", - "show": true, - "value": "", - "name": "agent", - "display_name": "Agent", - "advanced": false, - "input_types": ["Agent"], - "dynamic": false, - "info": "Agent responsible for task execution. Represents entity performing task.", - "title_case": false, - "type": "other" - }, - "tools": { - "trace_as_metadata": true, - "list": true, - "required": false, - "placeholder": "", - "show": true, - "value": "", - "name": "tools", - "display_name": "Tools", - "advanced": false, - "input_types": ["Tool"], - "dynamic": false, - "info": "List of tools/resources limited for task execution.", - "title_case": false, - "type": "other" - }, - "async_execution": { - "trace_as_metadata": true, - "list": false, - "required": false, - "placeholder": "", - "show": true, - "value": true, - "name": "async_execution", - "display_name": "Async Execution", - "advanced": true, - "dynamic": false, - "info": "Boolean flag indicating asynchronous task execution.", - "title_case": false, - "type": "bool" - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, DropdownInput, MessageTextInput, HandleInput\nfrom crewai import Task, Agent\n\nclass CrewAITask(Component):\n display_name: str = \"CrewAITask\"\n description: str = \"Each task must have a description, an expected output and an agent responsible for execution.\"\n documentation: str = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n \n inputs = [\n MessageTextInput(\n name=\"description\",\n display_name=\"Description\",\n info=\"Descriptive text detailing task's purpose and execution.\",\n required=True,\n ),\n MessageTextInput(\n name=\"expected_output\",\n display_name=\"Expected Output\",\n info=\"Clear definition of expected task outcome.\",\n required=True,\n ),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"List of tools/resources limited for task execution.\",\n ),\n HandleInput(\n name=\"agent\",\n display_name=\"Agent\",\n input_types=[\"Agent\"],\n info=\"Agent responsible for task execution. Represents entity performing task.\",\n ),\n BoolInput(\n name=\"async_execution\",\n display_name=\"Async Execution\",\n value=False,\n advanced=True,\n info=\"Boolean flag indicating asynchronous task execution.\",\n ),\n ]\n \n outputs = [\n # Output(display_name=\"Agent\", name=\"agent_output\", method=\"agent_response\"),\n Output(display_name=\"Task\", name=\"task_output\", method=\"build_task\"),\n ]\n\n def agent_response(self) -> Agent:\n output = self.agent\n self.status = output\n return output\n\n def build_task(self) -> Task:\n task = Task(\n description=self.description,\n expected_output=self.expected_output,\n tools=self.tools if self.tools else [],\n async_execution=self.async_execution,\n agent=self.agent\n )\n self.status = task\n return task\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "description": { - "trace_as_input": true, - "trace_as_metadata": true, - "load_from_db": false, - "list": false, - "required": true, - "placeholder": "", - "show": true, - "value": "", - "name": "description", - "display_name": "Description", - "advanced": false, - "input_types": ["Message"], - "dynamic": false, - "info": "Descriptive text detailing task's purpose and execution.", - "title_case": false, - "type": "str" - }, - "expected_output": { - "trace_as_input": true, - "trace_as_metadata": true, - "load_from_db": false, - "list": false, - "required": true, - "placeholder": "", - "show": true, - "value": "", - "name": "expected_output", - "display_name": "Expected Output", - "advanced": false, - "input_types": ["Message"], - "dynamic": false, - "info": "Clear definition of expected task outcome.", - "title_case": false, - "type": "str" - } - }, - "description": "Each task must have a description, an expected output and an agent responsible for execution.", - "icon": "CrewAI", - "base_classes": ["Task"], - "display_name": "CrewAITask", - "documentation": "https://docs.crewai.com/how-to/LLM-Connections/", - "custom_fields": {}, - "output_types": [], - "pinned": false, - "conditional_paths": [], - "frozen": false, - "outputs": [ - { - "types": ["Task"], - "selected": "Task", - "name": "task_output", - "display_name": "Task", - "method": "build_task", - "value": "__UNDEFINED__", - "cache": true - } - ], - "field_order": [ - "description", - "expected_output", - "tools", - "agent", - "async_execution" - ], - "beta": false, - "edited": true - }, - "id": "CustomComponent-4UaNS", - "description": "Each task must have a description, an expected output and an agent responsible for execution.", - "display_name": "CrewAITask" - }, - "selected": false, - "width": 384, - "height": 526, - "dragging": false, - "positionAbsolute": { "x": 677.4952370910181, "y": -169.34324846822471 } - }, - { - "id": "CustomComponent-OiXk9", - "type": "genericNode", - "position": { "x": 1096.3000687375263, "y": -163.76595453399347 }, - "data": { - "type": "CustomComponent", - "node": { - "template": { - "_type": "Component", - "agents": { - "trace_as_metadata": true, - "list": true, - "required": true, - "placeholder": "", - "show": true, - "value": "", - "name": "agents", - "display_name": "Agents", - "advanced": false, - "input_types": ["Agent"], - "dynamic": false, - "info": "", - "title_case": false, - "type": "other" - }, - "tasks": { - "trace_as_metadata": true, - "list": true, - "required": true, - "placeholder": "", - "show": true, - "value": "", - "name": "tasks", - "display_name": "Tasks", - "advanced": false, - "input_types": ["Task"], - "dynamic": false, - "info": "", - "title_case": false, - "type": "other" - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langflow.custom import Component\r\nfrom crewai import Crew, Task, Agent, Process\r\nfrom typing import List, Optional\r\nfrom langflow.field_typing import Text\r\nfrom langflow.io import NestedDictInput, DropdownInput, MessageTextInput, HandleInput\r\nfrom langflow.schema.message import Message\r\n\r\nclass CrewAICrew(Component):\r\n display_name: str = \"CrewAICrew\"\r\n description: str = \"Represents a group of agents, defining how they should collaborate and the tasks they should perform.\"\r\n documentation: str = \"https://docs.crewai.com/how-to/LLM-Connections/\"\r\n icon = \"CrewAI\"\r\n\r\n inputs = [\r\n HandleInput(name=\"agents\", display_name=\"Agents\", input_types=[\"Agent\"], is_list=True, required=True),\r\n HandleInput(name=\"tasks\", display_name=\"Tasks\", input_types=[\"Task\"], is_list=True, required=True),\r\n MessageTextInput(name=\"topic\", display_name=\"Topic\", value = \"\"),\r\n IntInput(name=\"verbose\", display_name=\"Verbose\", value=0, advanced=True),\r\n BoolInput(name=\"memory\", display_name=\"Memory\", value=False, advanced=True),\r\n BoolInput(name=\"use_cache\", display_name=\"Cache\", value=True, advanced=True),\r\n IntInput(name=\"max_rpm\", display_name=\"Max RPM\", value=100, advanced=True),\r\n DropdownInput(name=\"process\", display_name=\"Process\", value=Process.sequential, options=[Process.sequential,Process.hierarchical]),\r\n BoolInput(name=\"share_crew\", display_name=\"Share Crew\", value=False, advanced=True),\r\n NestedDictInput(name=\"input\", display_name=\"Input\", value={\"topic\": \"\"}, is_list=True),\r\n ]\r\n \r\n outputs = [\r\n Output(display_name=\"Output\", name=\"output\", method=\"build_output\"),\r\n ]\r\n\r\n async def build_output(self) -> Message:\r\n if not self.agents or not self.tasks:\r\n raise ValueError(\"No agents or tasks have been added.\")\r\n with open(\"dir.txt\",\"w\") as f:\r\n f.write(str(dir(Crew)))\r\n response = Crew(\r\n agents=self.agents,\r\n tasks=self.tasks,\r\n process=Process.sequential,\r\n verbose=self.verbose,\r\n memory=self.memory,\r\n cache=self.use_cache,\r\n max_rpm=self.max_rpm,\r\n share_crew=self.share_crew,\r\n )\r\n message = await response.kickoff_async(inputs=self.input)\r\n self.status = message\r\n return message\r\n\r\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input": { - "trace_as_input": true, - "trace_as_metadata": true, - "list": true, - "required": false, - "placeholder": "", - "show": true, - "value": { "topic": "" }, - "name": "input", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "title_case": false, - "type": "NestedDict" - }, - "max_rpm": { - "trace_as_metadata": true, - "list": false, - "required": false, - "placeholder": "", - "show": true, - "value": 100, - "name": "max_rpm", - "display_name": "Max RPM", - "advanced": true, - "dynamic": false, - "info": "", - "title_case": false, - "type": "int" - }, - "memory": { - "trace_as_metadata": true, - "list": false, - "required": false, - "placeholder": "", - "show": true, - "value": false, - "name": "memory", - "display_name": "Memory", - "advanced": true, - "dynamic": false, - "info": "", - "title_case": false, - "type": "bool" - }, - "process": { - "trace_as_metadata": true, - "options": ["sequential", "hierarchical"], - "required": false, - "placeholder": "", - "show": true, - "value": "sequential", - "name": "process", - "display_name": "Process", - "advanced": false, - "dynamic": false, - "info": "", - "title_case": false, - "type": "str" - }, - "share_crew": { - "trace_as_metadata": true, - "list": false, - "required": false, - "placeholder": "", - "show": true, - "value": true, - "name": "share_crew", - "display_name": "Share Crew", - "advanced": true, - "dynamic": false, - "info": "", - "title_case": false, - "type": "bool" - }, - "topic": { - "trace_as_input": true, - "trace_as_metadata": true, - "load_from_db": false, - "list": false, - "required": false, - "placeholder": "", - "show": true, - "value": "", - "name": "topic", - "display_name": "Topic", - "advanced": false, - "input_types": ["Message"], - "dynamic": false, - "info": "", - "title_case": false, - "type": "str" - }, - "use_cache": { - "trace_as_metadata": true, - "list": false, - "required": false, - "placeholder": "", - "show": true, - "value": true, - "name": "use_cache", - "display_name": "Cache", - "advanced": true, - "dynamic": false, - "info": "", - "title_case": false, - "type": "bool" - }, - "verbose": { - "trace_as_metadata": true, - "list": false, - "required": false, - "placeholder": "", - "show": true, - "value": "0", - "name": "verbose", - "display_name": "Verbose", - "advanced": true, - "dynamic": false, - "info": "", - "title_case": false, - "type": "int" - } - }, - "description": "Represents a group of agents, defining how they should collaborate and the tasks they should perform.", - "icon": "CrewAI", - "base_classes": ["Message"], - "display_name": "CrewAICrew", - "documentation": "https://docs.crewai.com/how-to/LLM-Connections/", - "custom_fields": {}, - "output_types": [], - "pinned": false, - "conditional_paths": [], - "frozen": false, - "outputs": [ - { - "types": ["Message"], - "selected": "Message", - "name": "output", - "display_name": "Output", - "method": "build_output", - "value": "__UNDEFINED__", - "cache": true - } - ], - "field_order": [ - "agents", - "tasks", - "topic", - "verbose", - "memory", - "use_cache", - "max_rpm", - "process", - "share_crew", - "input" - ], - "beta": false, - "edited": true - }, - "id": "CustomComponent-OiXk9", - "description": "Represents a group of agents, defining how they should collaborate and the tasks they should perform.", - "display_name": "CrewAICrew" - }, - "selected": false, - "width": 384, - "height": 631, - "positionAbsolute": { - "x": 1096.3000687375263, - "y": -163.76595453399347 - }, - "dragging": false - }, - { - "id": "CustomComponent-ZGe4u", - "type": "genericNode", - "position": { "x": -181.09156441161912, "y": -164.86510111633868 }, - "data": { - "type": "CustomComponent", - "node": { - "template": { - "_type": "Component", - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "# from langflow.field_typing import Data\nfrom langflow.custom import Component\nfrom langflow.schema import Data\nfrom langchain.agents import Tool\nfrom langflow.io import MessageTextInput, Output, SecretStrInput\nfrom crewai_tools import GithubSearchTool\n\n\nclass CrewAIGithubSearchTool(Component):\n display_name = \"CrewAIGithubSearchTool\"\n description = \"Search a github repo's content\"\n icon = \"CrewAI\"\n\n inputs = [\n MessageTextInput(name=\"github_repo\", display_name=\"Github Repo\", info=\"Fill it if you want to filter by specific github repo\"),\n SecretStrInput(name=\"gh_token\", display_name=\"Github Token\"),\n MessageTextInput(\n name=\"content_types\",\n display_name=\"Content Types\",\n value=[],\n info=\"Specifies the types of content to include in your search, options=['code', 'repo', 'pr', 'issue']\",\n is_list=True\n ),\n DictInput(\n name=\"kwargs\",\n display_name=\"kwargs\",\n info=\"kwargs of agent.\",\n is_list=True,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Output\", name=\"output\", method=\"build_output\"),\n ]\n \n\n def build_output(self) -> Tool:\n if self.content_types is None or not all([(item in ['code', 'repo', 'pr', 'issue']) for item in self.content_types]):\n raise Exception(\"Each content_type must be one of: ['code', 'repo', 'pr', 'issue']\")\n kwargs = self.kwargs if self.kwargs else {}\n tool = GithubSearchTool(\n github_repo=self.github_repo,\n content_types=self.content_types,\n **kwargs\n ).to_langchain()\n self.status = tool\n return tool\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "content_types": { - "trace_as_input": true, - "trace_as_metadata": true, - "load_from_db": false, - "list": true, - "required": false, - "placeholder": "", - "show": true, - "value": ["repo"], - "name": "content_types", - "display_name": "Content Types", - "advanced": false, - "input_types": ["Message"], - "dynamic": false, - "info": "Specifies the types of content to include in your search, options=['code', 'repo', 'pr', 'issue']", - "title_case": false, - "type": "str" - }, - "gh_token": { - "load_from_db": false, - "required": false, - "placeholder": "", - "show": true, - "value": "", - "name": "gh_token", - "display_name": "Github Token", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "", - "title_case": false, - "password": true, - "type": "str" - }, - "github_repo": { - "trace_as_input": true, - "trace_as_metadata": true, - "load_from_db": false, - "list": false, - "required": false, - "placeholder": 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dark:stroke-gray-200"}],"viewport":{"x":71.96454009007664,"y":737.5513351951806,"zoom":0.5221361634575208}},"id":"832a40f6-1c40-41f1-bd4a-83c37a35adfa"} \ No newline at end of file diff --git a/docs/static/json_files/Notion_Components_bundle.json b/docs/static/json_files/Notion_Components_bundle.json deleted file mode 100644 index 5e632ad9c..000000000 --- a/docs/static/json_files/Notion_Components_bundle.json +++ /dev/null @@ -1,881 +0,0 @@ -{ - "id": "7cd51434-9767-450f-8742-27857367f8c2", - "data": { - "nodes": [ - { - "id": "RecordsToText-Q69g5", - "type": "genericNode", - "position": { "x": -2671.5528488127866, "y": -963.4266471378126 }, - "data": { - "type": "RecordsToText", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "import requests\r\nfrom typing import List\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\n\r\nclass NotionUserList(CustomComponent):\r\n display_name = \"List Users [Notion]\"\r\n description = \"Retrieve users from Notion.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/list-users\"\r\n icon = \"NotionDirectoryLoader\"\r\n \r\n def build_config(self):\r\n return {\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n notion_secret: str,\r\n ) -> List[Record]:\r\n url = \"https://api.notion.com/v1/users\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Notion-Version\": \"2022-06-28\",\r\n }\r\n\r\n response = requests.get(url, headers=headers)\r\n response.raise_for_status()\r\n\r\n data = response.json()\r\n results = data['results']\r\n\r\n records = []\r\n for user in results:\r\n id = user['id']\r\n type = user['type']\r\n name = user.get('name', '')\r\n avatar_url = user.get('avatar_url', '')\r\n\r\n record_data = {\r\n \"id\": id,\r\n \"type\": type,\r\n \"name\": name,\r\n \"avatar_url\": avatar_url,\r\n }\r\n\r\n output = \"User:\\n\"\r\n for key, value in record_data.items():\r\n output += f\"{key.replace('_', ' ').title()}: {value}\\n\"\r\n output += \"________________________\\n\"\r\n\r\n record = Record(text=output, data=record_data)\r\n records.append(record)\r\n\r\n self.status = \"\\n\".join(record.text for record in records)\r\n return records", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "notion_secret": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "notion_secret", - "display_name": "Notion Secret", - "advanced": false, - "dynamic": false, - "info": "The Notion integration token.", - "load_from_db": false, - "title_case": false, - "input_types": ["Text"], - "value": "" - }, - "_type": "CustomComponent" - }, - "description": "Retrieve users from Notion.", - "icon": "NotionDirectoryLoader", - "base_classes": ["Record"], - "display_name": "List Users [Notion] ", - "documentation": "https://docs.langflow.org/integrations/notion/list-users", - "custom_fields": { "notion_secret": null }, - "output_types": ["Record"], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "RecordsToText-Q69g5", - "description": "Retrieve users from Notion.", - "display_name": "List Users [Notion] " - }, - "selected": false, - "width": 384, - "height": 289, - "dragging": false, - "positionAbsolute": { - "x": -2671.5528488127866, - "y": -963.4266471378126 - } - }, - { - "id": "CustomComponent-PU0K5", - "type": "genericNode", - "position": { "x": -3077.2269116193215, "y": -960.9450220159636 }, - "data": { - "type": "CustomComponent", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "import json\r\nfrom typing import Optional\r\n\r\nimport requests\r\nfrom langflow.custom import CustomComponent\r\n\r\n\r\nclass NotionPageCreator(CustomComponent):\r\n display_name = \"Create Page [Notion]\"\r\n description = \"A component for creating Notion pages.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/page-create\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n def build_config(self):\r\n return {\r\n \"database_id\": {\r\n \"display_name\": \"Database ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion database.\",\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n \"properties\": {\r\n \"display_name\": \"Properties\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The properties of the new page. Depending on your database setup, this can change. E.G: {'Task name': {'id': 'title', 'type': 'title', 'title': [{'type': 'text', 'text': {'content': 'Send Notion Components to LF', 'link': null}}]}}\",\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n database_id: str,\r\n notion_secret: str,\r\n properties: str = '{\"Task name\": {\"id\": \"title\", \"type\": \"title\", \"title\": [{\"type\": \"text\", \"text\": {\"content\": \"Send Notion Components to LF\", \"link\": null}}]}}',\r\n ) -> str:\r\n if not database_id or not properties:\r\n raise ValueError(\"Invalid input. Please provide 'database_id' and 'properties'.\")\r\n\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Content-Type\": \"application/json\",\r\n \"Notion-Version\": \"2022-06-28\",\r\n }\r\n\r\n data = {\r\n \"parent\": {\"database_id\": database_id},\r\n \"properties\": json.loads(properties),\r\n }\r\n\r\n response = requests.post(\"https://api.notion.com/v1/pages\", headers=headers, json=data)\r\n\r\n if response.status_code == 200:\r\n page_id = response.json()[\"id\"]\r\n self.status = f\"Successfully created Notion page with ID: {page_id}\\n {str(response.json())}\"\r\n return response.json()\r\n else:\r\n error_message = f\"Failed to create Notion page. Status code: {response.status_code}, Error: {response.text}\"\r\n self.status = error_message\r\n raise Exception(error_message)", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "database_id": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "database_id", - "display_name": "Database ID", - "advanced": false, - "dynamic": false, - "info": "The ID of the Notion database.", - "load_from_db": false, - "title_case": false, - "input_types": ["Text"] - }, - "notion_secret": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "notion_secret", - "display_name": "Notion Secret", - "advanced": false, - "dynamic": false, - "info": "The Notion integration token.", - "load_from_db": false, - "title_case": false, - "input_types": ["Text"], - "value": "" - }, - "properties": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "{\"Task name\": {\"id\": \"title\", \"type\": \"title\", \"title\": [{\"type\": \"text\", \"text\": {\"content\": \"Send Notion Components to LF\", \"link\": null}}]}}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "properties", - "display_name": "Properties", - "advanced": false, - "dynamic": false, - "info": "The properties of the new page. Depending on your database setup, this can change. E.G: {'Task name': {'id': 'title', 'type': 'title', 'title': [{'type': 'text', 'text': {'content': 'Send Notion Components to LF', 'link': null}}]}}", - "load_from_db": false, - "title_case": false, - "input_types": ["Text"] - }, - "_type": "CustomComponent" - }, - "description": "A component for creating Notion pages.", - "icon": "NotionDirectoryLoader", - "base_classes": ["object", "str", "Text"], - "display_name": "Create Page [Notion] ", - "documentation": "https://docs.langflow.org/integrations/notion/page-create", - "custom_fields": { - "database_id": null, - "notion_secret": null, - "properties": null - }, - "output_types": ["Text"], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "CustomComponent-PU0K5", - "description": "A component for creating Notion pages.", - "display_name": "Create Page [Notion] " - }, - "selected": false, - "width": 384, - "height": 477, - "positionAbsolute": { - "x": -3077.2269116193215, - "y": -960.9450220159636 - }, - "dragging": false - }, - { - "id": "CustomComponent-YODla", - "type": "genericNode", - "position": { "x": -3485.297183150799, "y": -362.8525892356713 }, - "data": { - "type": "CustomComponent", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "import requests\r\nfrom typing import Dict\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\n\r\nclass NotionDatabaseProperties(CustomComponent):\r\n display_name = \"List Database Properties [Notion]\"\r\n description = \"Retrieve properties of a Notion database.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/list-database-properties\"\r\n icon = \"NotionDirectoryLoader\"\r\n \r\n def build_config(self):\r\n return {\r\n \"database_id\": {\r\n \"display_name\": \"Database ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion database.\",\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n database_id: str,\r\n notion_secret: str,\r\n ) -> Record:\r\n url = f\"https://api.notion.com/v1/databases/{database_id}\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Notion-Version\": \"2022-06-28\", # Use the latest supported version\r\n }\r\n\r\n response = requests.get(url, headers=headers)\r\n response.raise_for_status()\r\n\r\n data = response.json()\r\n properties = data.get(\"properties\", {})\r\n\r\n record = Record(text=str(response.json()), data=properties)\r\n self.status = f\"Retrieved {len(properties)} properties from the Notion database.\\n {record.text}\"\r\n return record", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "database_id": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "database_id", - "display_name": "Database ID", - "advanced": false, - "dynamic": false, - "info": "The ID of the Notion database.", - "load_from_db": true, - "title_case": false, - "input_types": ["Text"], - "value": "NOTION_NMSTX_DB_ID" - }, - "notion_secret": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "notion_secret", - "display_name": "Notion Secret", - "advanced": false, - "dynamic": false, - "info": "The Notion integration token.", - "load_from_db": true, - "title_case": false, - "input_types": ["Text"], - "value": "" - }, - "_type": "CustomComponent" - }, - "description": "Retrieve properties of a Notion database.", - "icon": "NotionDirectoryLoader", - "base_classes": ["Record"], - "display_name": "List Database Properties [Notion] ", - "documentation": "https://docs.langflow.org/integrations/notion/list-database-properties", - "custom_fields": { "database_id": null, "notion_secret": null }, - "output_types": ["Record"], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "CustomComponent-YODla", - "description": "Retrieve properties of a Notion database.", - "display_name": "List Database Properties [Notion] " - }, - "selected": true, - "width": 384, - "height": 383, - "dragging": false, - "positionAbsolute": { "x": -3485.297183150799, "y": -362.8525892356713 } - }, - { - "id": "CustomComponent-wHlSz", - "type": "genericNode", - "position": { "x": -2668.7714642455403, "y": -657.2376228212606 }, - "data": { - "type": "CustomComponent", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "import json\r\nimport requests\r\nfrom typing import Dict, Any\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\n\r\nclass NotionPageUpdate(CustomComponent):\r\n display_name = \"Update Page Property [Notion]\"\r\n description = \"Update the properties of a Notion page.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/page-update\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n def build_config(self):\r\n return {\r\n \"page_id\": {\r\n \"display_name\": \"Page ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion page to update.\",\r\n },\r\n \"properties\": {\r\n \"display_name\": \"Properties\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The properties to update on the page (as a JSON string).\",\r\n \"multiline\": True,\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n page_id: str,\r\n properties: str,\r\n notion_secret: str,\r\n ) -> Record:\r\n url = f\"https://api.notion.com/v1/pages/{page_id}\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Content-Type\": \"application/json\",\r\n \"Notion-Version\": \"2022-06-28\", # Use the latest supported version\r\n }\r\n\r\n try:\r\n parsed_properties = json.loads(properties)\r\n except json.JSONDecodeError as e:\r\n raise ValueError(\"Invalid JSON format for properties\") from e\r\n\r\n data = {\r\n \"properties\": parsed_properties\r\n }\r\n\r\n response = requests.patch(url, headers=headers, json=data)\r\n response.raise_for_status()\r\n\r\n updated_page = response.json()\r\n\r\n output = \"Updated page properties:\\n\"\r\n for prop_name, prop_value in updated_page[\"properties\"].items():\r\n output += f\"{prop_name}: {prop_value}\\n\"\r\n\r\n self.status = output\r\n return Record(data=updated_page)", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "notion_secret": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "notion_secret", - "display_name": "Notion Secret", - "advanced": false, - "dynamic": false, - "info": "The Notion integration token.", - "load_from_db": true, - "title_case": false, - "input_types": ["Text"], - "value": "" - }, - "page_id": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "page_id", - "display_name": "Page ID", - "advanced": false, - "dynamic": false, - "info": "The ID of the Notion page to update.", - "load_from_db": false, - "title_case": false, - "input_types": ["Text"] - }, - "properties": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "properties", - "display_name": "Properties", - "advanced": false, - "dynamic": false, - "info": "The properties to update on the page (as a JSON string).", - "load_from_db": false, - "title_case": false, - "input_types": ["Text"], - "value": "{ \"title\": [ { \"text\": { \"content\": \"Test Page\" } } ] }" - }, - "_type": "CustomComponent" - }, - "description": "Update the properties of a Notion page.", - "icon": "NotionDirectoryLoader", - "base_classes": ["Record"], - "display_name": "Update Page Property [Notion]", - "documentation": "https://docs.langflow.org/integrations/notion/page-update", - "custom_fields": { - "page_id": null, - "properties": null, - "notion_secret": null - }, - "output_types": ["Record"], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "CustomComponent-wHlSz", - "description": "Update the properties of a Notion page.", - "display_name": "Update Page Property [Notion]" - }, - "selected": false, - "width": 384, - "height": 477, - "dragging": false, - "positionAbsolute": { - "x": -2668.7714642455403, - "y": -657.2376228212606 - } - }, - { - "id": "CustomComponent-oelYw", - "type": "genericNode", - "position": { "x": -2253.1007124701327, "y": -448.47240118604134 }, - "data": { - "type": "CustomComponent", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "import requests\r\nfrom typing import Dict, Any\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\n\r\nclass NotionPageContent(CustomComponent):\r\n display_name = \"Page Content Viewer [Notion]\"\r\n description = \"Retrieve the content of a Notion page as plain text.\"\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/page-content-viewer\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n def build_config(self):\r\n return {\r\n \"page_id\": {\r\n \"display_name\": \"Page ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion page to retrieve.\",\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n page_id: str,\r\n notion_secret: str,\r\n ) -> Record:\r\n blocks_url = f\"https://api.notion.com/v1/blocks/{page_id}/children?page_size=100\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Notion-Version\": \"2022-06-28\", # Use the latest supported version\r\n }\r\n\r\n # Retrieve the child blocks\r\n blocks_response = requests.get(blocks_url, headers=headers)\r\n blocks_response.raise_for_status()\r\n blocks_data = blocks_response.json()\r\n\r\n # Parse the blocks and extract the content as plain text\r\n content = self.parse_blocks(blocks_data[\"results\"])\r\n\r\n self.status = content\r\n return Record(data={\"content\": content}, text=content)\r\n\r\n def parse_blocks(self, blocks: list) -> str:\r\n content = \"\"\r\n for block in blocks:\r\n block_type = block[\"type\"]\r\n if block_type in [\"paragraph\", \"heading_1\", \"heading_2\", \"heading_3\", \"quote\"]:\r\n content += self.parse_rich_text(block[block_type][\"rich_text\"]) + \"\\n\\n\"\r\n elif block_type in [\"bulleted_list_item\", \"numbered_list_item\"]:\r\n content += self.parse_rich_text(block[block_type][\"rich_text\"]) + \"\\n\"\r\n elif block_type == \"to_do\":\r\n content += self.parse_rich_text(block[\"to_do\"][\"rich_text\"]) + \"\\n\"\r\n elif block_type == \"code\":\r\n content += self.parse_rich_text(block[\"code\"][\"rich_text\"]) + \"\\n\\n\"\r\n elif block_type == \"image\":\r\n content += f\"[Image: {block['image']['external']['url']}]\\n\\n\"\r\n elif block_type == \"divider\":\r\n content += \"---\\n\\n\"\r\n return content.strip()\r\n\r\n def parse_rich_text(self, rich_text: list) -> str:\r\n text = \"\"\r\n for segment in rich_text:\r\n text += segment[\"plain_text\"]\r\n return text", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "notion_secret": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "notion_secret", - "display_name": "Notion Secret", - "advanced": false, - "dynamic": false, - "info": "The Notion integration token.", - "load_from_db": true, - "title_case": false, - "input_types": ["Text"], - "value": "" - }, - "page_id": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "page_id", - "display_name": "Page ID", - "advanced": false, - "dynamic": false, - "info": "The ID of the Notion page to retrieve.", - "load_from_db": false, - "title_case": false, - "input_types": ["Text"] - }, - "_type": "CustomComponent" - }, - "description": "Retrieve the content of a Notion page as plain text.", - "icon": "NotionDirectoryLoader", - "base_classes": ["Record"], - "display_name": "Page Content Viewer [Notion] ", - "documentation": "https://docs.langflow.org/integrations/notion/page-content-viewer", - "custom_fields": { "page_id": null, "notion_secret": null }, - "output_types": ["Record"], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "CustomComponent-oelYw", - "description": "Retrieve the content of a Notion page as plain text.", - "display_name": "Page Content Viewer [Notion] " - }, - "selected": false, - "width": 384, - "height": 383, - "positionAbsolute": { - "x": -2253.1007124701327, - "y": -448.47240118604134 - }, - "dragging": false - }, - { - "id": "CustomComponent-Pn52w", - "type": "genericNode", - "position": { "x": -3070.9222948695096, "y": -472.4537855763852 }, - "data": { - "type": "CustomComponent", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "import requests\r\nimport json\r\nfrom typing import Dict, Any, List\r\nfrom langflow.custom import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\nclass NotionListPages(CustomComponent):\r\n display_name = \"List Pages [Notion]\"\r\n description = (\r\n \"Query a Notion database with filtering and sorting. \"\r\n \"The input should be a JSON string containing the 'filter' and 'sorts' objects. \"\r\n \"Example input:\\n\"\r\n '{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}'\r\n )\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/list-pages\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n field_order = [\r\n \"notion_secret\",\r\n \"database_id\",\r\n \"query_payload\",\r\n ]\r\n\r\n def build_config(self):\r\n return {\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n \"database_id\": {\r\n \"display_name\": \"Database ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the Notion database to query.\",\r\n },\r\n \"query_payload\": {\r\n \"display_name\": \"Database query\",\r\n \"field_type\": \"str\",\r\n \"info\": \"A JSON string containing the filters that will be used for querying the database. EG: {'filter': {'property': 'Status', 'status': {'equals': 'In progress'}}}\",\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n notion_secret: str,\r\n database_id: str,\r\n query_payload: str = \"{}\",\r\n ) -> List[Record]:\r\n try:\r\n query_data = json.loads(query_payload)\r\n filter_obj = query_data.get(\"filter\")\r\n sorts = query_data.get(\"sorts\", [])\r\n\r\n url = f\"https://api.notion.com/v1/databases/{database_id}/query\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Content-Type\": \"application/json\",\r\n \"Notion-Version\": \"2022-06-28\",\r\n }\r\n\r\n data = {\r\n \"sorts\": sorts,\r\n }\r\n\r\n if filter_obj:\r\n data[\"filter\"] = filter_obj\r\n\r\n response = requests.post(url, headers=headers, json=data)\r\n response.raise_for_status()\r\n\r\n results = response.json()\r\n records = []\r\n combined_text = f\"Pages found: {len(results['results'])}\\n\\n\"\r\n for page in results['results']:\r\n page_data = {\r\n 'id': page['id'],\r\n 'url': page['url'],\r\n 'created_time': page['created_time'],\r\n 'last_edited_time': page['last_edited_time'],\r\n 'properties': page['properties'],\r\n }\r\n\r\n text = (\r\n f\"id: {page['id']}\\n\"\r\n f\"url: {page['url']}\\n\"\r\n f\"created_time: {page['created_time']}\\n\"\r\n f\"last_edited_time: {page['last_edited_time']}\\n\"\r\n f\"properties: {json.dumps(page['properties'], indent=2)}\\n\\n\"\r\n )\r\n\r\n combined_text += text\r\n records.append(Record(text=text, data=page_data))\r\n \r\n self.status = combined_text.strip()\r\n return records\r\n\r\n except Exception as e:\r\n self.status = f\"An error occurred: {str(e)}\"\r\n return [Record(text=self.status, data=[])]", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "database_id": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "database_id", - "display_name": "Database ID", - "advanced": false, - "dynamic": false, - "info": "The ID of the Notion database to query.", - "load_from_db": true, - "title_case": false, - "input_types": ["Text"], - "value": "NOTION_NMSTX_DB_ID" - }, - "notion_secret": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "notion_secret", - "display_name": "Notion Secret", - "advanced": false, - "dynamic": false, - "info": "The Notion integration token.", - "load_from_db": true, - "title_case": false, - "input_types": ["Text"], - "value": "" - }, - "query_payload": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "query_payload", - "display_name": "Database query", - "advanced": false, - "dynamic": false, - "info": "A JSON string containing the filters that will be used for querying the database. EG: {'filter': {'property': 'Status', 'status': {'equals': 'In progress'}}}", - "load_from_db": false, - "title_case": false, - "input_types": ["Text"] - }, - "_type": "CustomComponent" - }, - "description": "Query a Notion database with filtering and sorting. The input should be a JSON string containing the 'filter' and 'sorts' objects. Example input:\n{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}", - "icon": "NotionDirectoryLoader", - "base_classes": ["Record"], - "display_name": "List Pages [Notion] ", - "documentation": "https://docs.langflow.org/integrations/notion/list-pages", - "custom_fields": { - "notion_secret": null, - "database_id": null, - "query_payload": null - }, - "output_types": ["Record"], - "field_formatters": {}, - "frozen": false, - "field_order": ["notion_secret", "database_id", "query_payload"], - "beta": false - }, - "id": "CustomComponent-Pn52w", - "description": "Query a Notion database with filtering and sorting. The input should be a JSON string containing the 'filter' and 'sorts' objects. Example input:\n{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}", - "display_name": "List Pages [Notion] " - }, - "selected": false, - "width": 384, - "height": 517, - "positionAbsolute": { - "x": -3070.9222948695096, - "y": -472.4537855763852 - }, - "dragging": false - }, - { - "id": "CustomComponent-I8Dec", - "type": "genericNode", - "position": { "x": -2256.686402636563, "y": -963.4541117792749 }, - "data": { - "type": "CustomComponent", - "node": { - "template": { - "block_id": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "block_id", - "display_name": "Page/Block ID", - "advanced": false, - "dynamic": false, - "info": "The ID of the page/block to add the content.", - "load_from_db": false, - "title_case": false, - "input_types": ["Text"] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "import json\r\nfrom typing import List, Dict, Any\r\nfrom markdown import markdown\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\n\r\nfrom langflow import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\nclass AddContentToPage(CustomComponent):\r\n display_name = \"Add Content to Page [Notion]\"\r\n description = \"Convert markdown text to Notion blocks and append them to a Notion page.\"\r\n documentation: str = \"https://developers.notion.com/reference/patch-block-children\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n def build_config(self):\r\n return {\r\n \"markdown_text\": {\r\n \"display_name\": \"Markdown Text\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The markdown text to convert to Notion blocks.\",\r\n \"multiline\": True,\r\n },\r\n \"block_id\": {\r\n \"display_name\": \"Page/Block ID\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The ID of the page/block to add the content.\",\r\n },\r\n \"notion_secret\": {\r\n \"display_name\": \"Notion Secret\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The Notion integration token.\",\r\n \"password\": True,\r\n },\r\n }\r\n\r\n def build(self, markdown_text: str, block_id: str, notion_secret: str) -> Record:\r\n html_text = markdown(markdown_text)\r\n soup = BeautifulSoup(html_text, 'html.parser')\r\n blocks = self.process_node(soup)\r\n\r\n url = f\"https://api.notion.com/v1/blocks/{block_id}/children\"\r\n headers = {\r\n \"Authorization\": f\"Bearer {notion_secret}\",\r\n \"Content-Type\": \"application/json\",\r\n \"Notion-Version\": \"2022-06-28\",\r\n }\r\n\r\n data = {\r\n \"children\": blocks,\r\n }\r\n\r\n response = requests.patch(url, headers=headers, json=data)\r\n self.status = str(response.json())\r\n response.raise_for_status()\r\n\r\n result = response.json()\r\n self.status = f\"Appended {len(blocks)} blocks to page with ID: {block_id}\"\r\n return Record(data=result, text=json.dumps(result))\r\n\r\n def process_node(self, node):\r\n blocks = []\r\n if isinstance(node, str):\r\n text = node.strip()\r\n if text:\r\n if text.startswith('#'):\r\n heading_level = text.count('#', 0, 6)\r\n heading_text = text[heading_level:].strip()\r\n if heading_level == 1:\r\n blocks.append(self.create_block('heading_1', heading_text))\r\n elif heading_level == 2:\r\n blocks.append(self.create_block('heading_2', heading_text))\r\n elif heading_level == 3:\r\n blocks.append(self.create_block('heading_3', heading_text))\r\n else:\r\n blocks.append(self.create_block('paragraph', text))\r\n elif node.name == 'h1':\r\n blocks.append(self.create_block('heading_1', node.get_text(strip=True)))\r\n elif node.name == 'h2':\r\n blocks.append(self.create_block('heading_2', node.get_text(strip=True)))\r\n elif node.name == 'h3':\r\n blocks.append(self.create_block('heading_3', node.get_text(strip=True)))\r\n elif node.name == 'p':\r\n code_node = node.find('code')\r\n if code_node:\r\n code_text = code_node.get_text()\r\n language, code = self.extract_language_and_code(code_text)\r\n blocks.append(self.create_block('code', code, language=language))\r\n elif self.is_table(str(node)):\r\n blocks.extend(self.process_table(node))\r\n else:\r\n blocks.append(self.create_block('paragraph', node.get_text(strip=True)))\r\n elif node.name == 'ul':\r\n blocks.extend(self.process_list(node, 'bulleted_list_item'))\r\n elif node.name == 'ol':\r\n blocks.extend(self.process_list(node, 'numbered_list_item'))\r\n elif node.name == 'blockquote':\r\n blocks.append(self.create_block('quote', node.get_text(strip=True)))\r\n elif node.name == 'hr':\r\n blocks.append(self.create_block('divider', ''))\r\n elif node.name == 'img':\r\n blocks.append(self.create_block('image', '', image_url=node.get('src')))\r\n elif node.name == 'a':\r\n blocks.append(self.create_block('bookmark', node.get_text(strip=True), link_url=node.get('href')))\r\n elif node.name == 'table':\r\n blocks.extend(self.process_table(node))\r\n\r\n for child in node.children:\r\n if isinstance(child, str):\r\n continue\r\n blocks.extend(self.process_node(child))\r\n\r\n return blocks\r\n\r\n def extract_language_and_code(self, code_text):\r\n lines = code_text.split('\\n')\r\n language = lines[0].strip()\r\n code = '\\n'.join(lines[1:]).strip()\r\n return language, code\r\n\r\n def is_code_block(self, text):\r\n return text.startswith('```')\r\n\r\n def extract_code_block(self, text):\r\n lines = text.split('\\n')\r\n language = lines[0].strip('`').strip()\r\n code = '\\n'.join(lines[1:]).strip('`').strip()\r\n return language, code\r\n \r\n def is_table(self, text):\r\n rows = text.split('\\n')\r\n if len(rows) < 2:\r\n return False\r\n\r\n has_separator = False\r\n for i, row in enumerate(rows):\r\n if '|' in row:\r\n cells = [cell.strip() for cell in row.split('|')]\r\n cells = [cell for cell in cells if cell] # Remove empty cells\r\n if i == 1 and all(set(cell) <= set('-|') for cell in cells):\r\n has_separator = True\r\n elif not cells:\r\n return False\r\n\r\n return has_separator and len(rows) >= 3\r\n\r\n def process_list(self, node, list_type):\r\n blocks = []\r\n for item in node.find_all('li'):\r\n item_text = item.get_text(strip=True)\r\n checked = item_text.startswith('[x]')\r\n is_checklist = item_text.startswith('[ ]') or checked\r\n\r\n if is_checklist:\r\n item_text = item_text.replace('[x]', '').replace('[ ]', '').strip()\r\n blocks.append(self.create_block('to_do', item_text, checked=checked))\r\n else:\r\n blocks.append(self.create_block(list_type, item_text))\r\n return blocks\r\n\r\n def process_table(self, node):\r\n blocks = []\r\n header_row = node.find('thead').find('tr') if node.find('thead') else None\r\n body_rows = node.find('tbody').find_all('tr') if node.find('tbody') else []\r\n\r\n if header_row or body_rows:\r\n table_width = max(len(header_row.find_all(['th', 'td'])) if header_row else 0,\r\n max(len(row.find_all(['th', 'td'])) for row in body_rows))\r\n\r\n table_block = self.create_block('table', '', table_width=table_width, has_column_header=bool(header_row))\r\n blocks.append(table_block)\r\n\r\n if header_row:\r\n header_cells = [cell.get_text(strip=True) for cell in header_row.find_all(['th', 'td'])]\r\n header_row_block = self.create_block('table_row', header_cells)\r\n blocks.append(header_row_block)\r\n\r\n for row in body_rows:\r\n cells = [cell.get_text(strip=True) for cell in row.find_all(['th', 'td'])]\r\n row_block = self.create_block('table_row', cells)\r\n blocks.append(row_block)\r\n\r\n return blocks\r\n \r\n def create_block(self, block_type: str, content: str, **kwargs) -> Dict[str, Any]:\r\n block = {\r\n \"object\": \"block\",\r\n \"type\": block_type,\r\n block_type: {},\r\n }\r\n\r\n if block_type in [\"paragraph\", \"heading_1\", \"heading_2\", \"heading_3\", \"bulleted_list_item\", \"numbered_list_item\", \"quote\"]:\r\n block[block_type][\"rich_text\"] = [\r\n {\r\n \"type\": \"text\",\r\n \"text\": {\r\n \"content\": content,\r\n },\r\n }\r\n ]\r\n elif block_type == 'to_do':\r\n block[block_type][\"rich_text\"] = [\r\n {\r\n \"type\": \"text\",\r\n \"text\": {\r\n \"content\": content,\r\n },\r\n }\r\n ]\r\n block[block_type]['checked'] = kwargs.get('checked', False)\r\n elif block_type == 'code':\r\n block[block_type]['rich_text'] = [\r\n {\r\n \"type\": \"text\",\r\n \"text\": {\r\n \"content\": content,\r\n },\r\n }\r\n ]\r\n block[block_type]['language'] = kwargs.get('language', 'plain text')\r\n elif block_type == 'image':\r\n block[block_type] = {\r\n \"type\": \"external\",\r\n \"external\": {\r\n \"url\": kwargs.get('image_url', '')\r\n }\r\n }\r\n elif block_type == 'divider':\r\n pass\r\n elif block_type == 'bookmark':\r\n block[block_type]['url'] = kwargs.get('link_url', '')\r\n elif block_type == 'table':\r\n block[block_type]['table_width'] = kwargs.get('table_width', 0)\r\n block[block_type]['has_column_header'] = kwargs.get('has_column_header', False)\r\n block[block_type]['has_row_header'] = kwargs.get('has_row_header', False)\r\n elif block_type == 'table_row':\r\n block[block_type]['cells'] = [[{'type': 'text', 'text': {'content': cell}} for cell in content]]\r\n\r\n return block", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "markdown_text": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "markdown_text", - "display_name": "Markdown Text", - "advanced": false, - "dynamic": false, - "info": "The markdown text to convert to Notion blocks.", - "load_from_db": false, - "title_case": false, - "input_types": ["Text"], - "value": "# Heading 1\n\n## Heading 2\n\n### Heading 3\n\nThis is a regular paragraph.\n\nHere's another paragraph with an image:\n![Image](https://example.com/image.jpg)\n\n## Checklist\n- [x] Completed task\n- [ ] Incomplete task\n- [x] Another completed task\n\n## Numbered List\n1. First item\n2. Second item\n3. Third item\n\n## Bulleted List\n- Item 1\n- Item 2\n- Item 3\n\n## Code Block\n```python\ndef hello_world():\n print(\"Hello, World!\")\n```\n\n## Quote\n> This is a blockquote.\n> It can span multiple lines.\n\n## Horizontal Rule\n---\n\n\n## Link\n[Notion API Documentation](https://developers.notion.com)\n\n" - }, - "notion_secret": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "notion_secret", - "display_name": "Notion Secret", - "advanced": false, - "dynamic": false, - "info": "The Notion integration token.", - "load_from_db": true, - "title_case": false, - "input_types": ["Text"], - "value": "" - }, - "_type": "CustomComponent" - }, - "description": "Convert markdown text to Notion blocks and append them to a Notion page.", - "icon": "NotionDirectoryLoader", - "base_classes": ["Record"], - "display_name": "Add Content to Page [Notion] ", - "documentation": "https://developers.notion.com/reference/patch-block-children", - 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"fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "filter_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "database", - "fileTypes": [], - "file_path": "", - "password": false, - "options": ["page", "database"], - "name": "filter_value", - "display_name": "Filter Type", - "advanced": false, - "dynamic": false, - "info": "Limits the results to either only pages or only databases.", - "load_from_db": false, - "title_case": false, - "input_types": ["Text"] - }, - "notion_secret": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "notion_secret", - "display_name": "Notion Secret", - "advanced": false, - "dynamic": false, - "info": "The Notion integration token.", - "load_from_db": true, - "title_case": false, - "input_types": ["Text"], - "value": "" - }, - "query": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "query", - "display_name": "Search Query", - "advanced": false, - "dynamic": false, - "info": "The text that the API compares page and database titles against.", - "load_from_db": false, - "title_case": false, - "input_types": ["Text"] - }, - "sort_direction": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "descending", - "fileTypes": [], - "file_path": "", - "password": false, - "options": ["ascending", "descending"], - "name": "sort_direction", - "display_name": "Sort Direction", - "advanced": false, - "dynamic": false, - "info": "The direction to sort the results.", - "load_from_db": false, - "title_case": false, - 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"ChatOpenAI", - "documentation": "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai", - "custom_fields": {}, - "output_types": [], - "field_formatters": {}, - "beta": false - }, - "id": "ChatOpenAI-4Mfuz" - }, - "selected": false, - "width": 384, - "height": 649, - "positionAbsolute": { - "x": -2243.8068684913856, - "y": -2026.350019258601 - }, - "dragging": false - }, - { - "id": "CharacterTextSplitter-WVMFU", - "type": "genericNode", - "position": { - "x": -2661.4749778477553, - "y": -1608.9437055023366 - }, - "data": { - "type": "CharacterTextSplitter", - "node": { - "template": { - "documents": { - "type": "Document", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "documents", - "advanced": false, - "dynamic": false, - "info": "" - }, - "chunk_overlap": { - "type": "int", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 200, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_overlap", - "display_name": "Chunk Overlap", - "advanced": false, - "dynamic": false, - "info": "" - }, - "chunk_size": { - "type": "int", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "2000", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_size", - "display_name": "Chunk Size", - "advanced": false, - "dynamic": false, - "info": "" - }, - "separator": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "\"", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "separator", - "display_name": "Separator", - "advanced": false, - "dynamic": false, - "info": "" - }, - "_type": "CharacterTextSplitter" - }, - "description": "Splitting text that looks at characters.", - "base_classes": [ - "Document" - ], - "display_name": "CharacterTextSplitter", - "documentation": "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter", - "custom_fields": {}, - "output_types": [ - "Document" - ], - "field_formatters": {}, - "beta": false - }, - "id": "CharacterTextSplitter-WVMFU" - }, - "selected": false, - "width": 384, - "height": 501, - "positionAbsolute": { - "x": -2661.4749778477553, - "y": -1608.9437055023366 - }, - "dragging": false - }, - { - "id": "Chroma-OtYDg", - "type": "genericNode", - "position": { - "x": -2194.2051907050227, - "y": -1370.1637632208287 - }, - "data": { - "type": "Chroma", - "node": { - "template": { - "documents": { - "type": "Document", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "documents", - "display_name": "Documents", - "advanced": false, - "dynamic": false, - "info": "" - }, - "embedding": { - "type": "Embeddings", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding", - "display_name": "Embedding", - "advanced": false, - "dynamic": false, - "info": "" - }, - "chroma_server_cors_allow_origins": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chroma_server_cors_allow_origins", - "display_name": "Server CORS Allow Origins", - "advanced": true, - "dynamic": false, - "info": "" - }, - "chroma_server_grpc_port": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chroma_server_grpc_port", - "display_name": "Server gRPC Port", - "advanced": true, - "dynamic": false, - "info": "" - }, - "chroma_server_host": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chroma_server_host", - "display_name": "Server Host", - "advanced": true, - "dynamic": false, - "info": "" - }, - "chroma_server_port": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chroma_server_port", - "display_name": "Server Port", - "advanced": true, - "dynamic": false, - "info": "" - }, - "chroma_server_ssl_enabled": { - "type": "bool", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chroma_server_ssl_enabled", - "display_name": "Server SSL Enabled", - "advanced": true, - "dynamic": false, - "info": "" - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import List, Optional, Union\n\nimport chromadb # type: ignore\nfrom langchain.embeddings.base import Embeddings\nfrom langchain.schema import BaseRetriever, Document\nfrom langchain.vectorstores import Chroma\nfrom langchain.vectorstores.base import VectorStore\n\nfrom langflow.custom import CustomComponent\n\n\nclass ChromaComponent(CustomComponent):\n \"\"\"\n A custom component for implementing a Vector Store using Chroma.\n \"\"\"\n\n display_name: str = \"Chroma\"\n description: str = \"Implementation of Vector Store using Chroma\"\n documentation = \"https://python.langchain.com/docs/integrations/vectorstores/chroma\"\n beta: bool = True\n\n def build_config(self):\n \"\"\"\n Builds the configuration for the component.\n\n Returns:\n - dict: A dictionary containing the configuration options for the component.\n \"\"\"\n return {\n \"collection_name\": {\"display_name\": \"Collection Name\", \"value\": \"langflow\"},\n \"persist\": {\"display_name\": \"Persist\"},\n \"persist_directory\": {\"display_name\": \"Persist Directory\"},\n \"code\": {\"show\": False, \"display_name\": \"Code\"},\n \"documents\": {\"display_name\": \"Documents\", \"is_list\": True},\n \"embedding\": {\"display_name\": \"Embedding\"},\n \"chroma_server_cors_allow_origins\": {\n \"display_name\": \"Server CORS Allow Origins\",\n \"advanced\": True,\n },\n \"chroma_server_host\": {\"display_name\": \"Server Host\", \"advanced\": True},\n \"chroma_server_port\": {\"display_name\": \"Server Port\", \"advanced\": True},\n \"chroma_server_grpc_port\": {\n \"display_name\": \"Server gRPC Port\",\n \"advanced\": True,\n },\n \"chroma_server_ssl_enabled\": {\n \"display_name\": \"Server SSL Enabled\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n collection_name: str,\n persist: bool,\n embedding: Embeddings,\n chroma_server_ssl_enabled: bool,\n persist_directory: Optional[str] = None,\n documents: Optional[List[Document]] = None,\n chroma_server_cors_allow_origins: Optional[str] = None,\n chroma_server_host: Optional[str] = None,\n chroma_server_port: Optional[int] = None,\n chroma_server_grpc_port: Optional[int] = None,\n ) -> Union[VectorStore, BaseRetriever]:\n \"\"\"\n Builds the Vector Store or BaseRetriever object.\n\n Args:\n - collection_name (str): The name of the collection.\n - persist_directory (Optional[str]): The directory to persist the Vector Store to.\n - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.\n - persist (bool): Whether to persist the Vector Store or not.\n - embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.\n - documents (Optional[Document]): The documents to use for the Vector Store.\n - chroma_server_cors_allow_origins (Optional[str]): The CORS allow origins for the Chroma server.\n - chroma_server_host (Optional[str]): The host for the Chroma server.\n - chroma_server_port (Optional[int]): The port for the Chroma server.\n - chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.\n\n Returns:\n - Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.\n \"\"\"\n\n # Chroma settings\n chroma_settings = None\n\n if chroma_server_host is not None:\n chroma_settings = chromadb.config.Settings(\n chroma_server_cors_allow_origins=chroma_server_cors_allow_origins or None,\n chroma_server_host=chroma_server_host,\n chroma_server_port=chroma_server_port or None,\n chroma_server_grpc_port=chroma_server_grpc_port or None,\n chroma_server_ssl_enabled=chroma_server_ssl_enabled,\n )\n\n # If documents, then we need to create a Chroma instance using .from_documents\n if documents is not None and embedding is not None:\n if len(documents) == 0:\n raise ValueError(\"If documents are provided, there must be at least one document.\")\n return Chroma.from_documents(\n documents=documents, # type: ignore\n persist_directory=persist_directory if persist else None,\n collection_name=collection_name,\n embedding=embedding,\n client_settings=chroma_settings,\n )\n\n return Chroma(persist_directory=persist_directory, client_settings=chroma_settings)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": false, - "dynamic": true, - "info": "" - }, - "collection_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "video", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_name", - "display_name": "Collection Name", - "advanced": false, - "dynamic": false, - "info": "" - }, - "persist": { - "type": "bool", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "persist", - "display_name": "Persist", - "advanced": false, - "dynamic": false, - "info": "" - }, - "persist_directory": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "persist_directory", - "display_name": "Persist Directory", - "advanced": false, - "dynamic": false, - "info": "" - }, - "_type": "CustomComponent" - }, - "description": "Implementation of Vector Store using Chroma", - "base_classes": [ - "VectorStore", - "BaseRetriever" - ], - "display_name": "Chroma", - "documentation": "https://python.langchain.com/docs/integrations/vectorstores/chroma", - "custom_fields": { - "chroma_server_cors_allow_origins": null, - "chroma_server_grpc_port": null, - "chroma_server_host": null, - "chroma_server_port": null, - "chroma_server_ssl_enabled": null, - "collection_name": null, - "documents": null, - "embedding": null, - "persist": null, - "persist_directory": null - }, - "output_types": [ - "Chroma" - ], - "field_formatters": {}, - "beta": true - }, - "id": "Chroma-OtYDg" - }, - "selected": false, - "width": 384, - "height": 625, - "positionAbsolute": { - "x": -2194.2051907050227, - "y": -1370.1637632208287 - }, - "dragging": false - }, - { - "id": "OpenAIEmbeddings-9yqtI", - "type": "genericNode", - "position": { - "x": -2653.011009324626, - "y": -1103.8414515074774 - }, - "data": { - "type": "OpenAIEmbeddings", - "node": { - "template": { - "allowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": [], - "fileTypes": [], - "password": false, - "name": "allowed_special", - "advanced": true, - "dynamic": false, - "info": "" - }, - "async_client": { - "type": "Any", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "password": false, - "name": "async_client", - "advanced": true, - "dynamic": false, - "info": "" - }, - "chunk_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 1000, - "fileTypes": [], - "password": false, - "name": "chunk_size", - "advanced": true, - "dynamic": false, - "info": "" - }, - "client": { - "type": "Any", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "password": false, - "name": "client", - "advanced": true, - "dynamic": false, - "info": "" - }, - "default_headers": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "password": false, - "name": "default_headers", - "advanced": true, - "dynamic": false, - "info": "" - }, - "default_query": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "password": false, - "name": "default_query", - "advanced": true, - "dynamic": false, - "info": "" - }, - "deployment": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "password": false, - "name": "deployment", - "advanced": true, - "dynamic": false, - "info": "" - }, - "disallowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "all", - "fileTypes": [], - "password": false, - "name": "disallowed_special", - "advanced": true, - "dynamic": false, - "info": "" - }, - "embedding_ctx_length": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 8191, - "fileTypes": [], - "password": false, - "name": "embedding_ctx_length", - "advanced": true, - "dynamic": false, - "info": "" - }, - "headers": { - "type": "Any", - "required": false, - "placeholder": "", - "list": false, - "show": false, - "multiline": true, - "value": "{\"Authorization\": \"Bearer \"}", - "fileTypes": [], - "password": false, - "name": "headers", - "advanced": true, - "dynamic": false, - "info": "" - }, - "http_client": { - "type": "Any", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "password": false, - "name": "http_client", - "advanced": true, - "dynamic": false, - "info": "" - }, - "max_retries": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 2, - "fileTypes": [], - "password": false, - "name": "max_retries", - "advanced": true, - "dynamic": false, - "info": "" - }, - "model": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "password": false, - "name": "model", - "advanced": true, - "dynamic": false, - "info": "" - }, - "model_kwargs": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "password": false, - "name": "model_kwargs", - "advanced": true, - "dynamic": false, - "info": "" - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "password": true, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "", - "value": "" - }, - "openai_api_key": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "" - }, - "openai_api_type": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "password": true, - "name": "openai_api_type", - "display_name": "OpenAI API Type", - "advanced": true, - "dynamic": false, - "info": "", - "value": "" - }, - "openai_api_version": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "password": true, - "name": "openai_api_version", - "display_name": "OpenAI API Version", - "advanced": true, - "dynamic": false, - "info": "", - "value": "" - }, - "openai_organization": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "password": false, - "name": "openai_organization", - "display_name": "OpenAI Organization", - "advanced": true, - "dynamic": false, - "info": "" - }, - "openai_proxy": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "password": false, - "name": "openai_proxy", - "display_name": "OpenAI Proxy", - "advanced": true, - "dynamic": false, - "info": "" - }, - "request_timeout": { - "type": "float", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "password": false, - "name": "request_timeout", - "advanced": true, - "dynamic": false, - "info": "", - "rangeSpec": { - "min": -1, - "max": 1, - "step": 0.1 - } - }, - "retry_max_seconds": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 20, - "fileTypes": [], - "password": false, - "name": "retry_max_seconds", - "advanced": true, - "dynamic": false, - "info": "" - }, - "retry_min_seconds": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 4, - "fileTypes": [], - "password": false, - "name": "retry_min_seconds", - "advanced": true, - "dynamic": false, - "info": "" - }, - "show_progress_bar": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "password": false, - "name": "show_progress_bar", - "advanced": true, - "dynamic": false, - "info": "" - }, - "skip_empty": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "password": false, - "name": "skip_empty", - "advanced": true, - "dynamic": false, - "info": "" - }, - "tiktoken_enabled": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "password": true, - "name": "tiktoken_enabled", - "advanced": false, - "dynamic": false, - "info": "" - }, - "tiktoken_model_name": { 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