- Read the [Custom Component Guidelines](../administration/custom-component) for detailed information on custom components.
+ Read the [Custom Component Guidelines](../administration/custom-component) for
+ detailed information on custom components.
Custom components let you extend Langflow by creating reusable and configurable components from a Python script.
@@ -31,57 +32,60 @@ This class is the foundation for creating custom components. It allows users to
The following types are supported in the build method:
-| Supported Types |
-| --------------------------------------------------------- |
-| _`str`_, _`int`_, _`float`_, _`bool`_, _`list`_, _`dict`_ |
-| _`langflow.field_typing.NestedDict`_ |
-| _`langflow.field_typing.Prompt`_ |
-| _`langchain.chains.base.Chain`_ |
-| _`langchain.PromptTemplate`_ |
+| Supported Types |
+| ----------------------------------------------------------------- |
+| _`str`_, _`int`_, _`float`_, _`bool`_, _`list`_, _`dict`_ |
+| _`langflow.field_typing.NestedDict`_ |
+| _`langflow.field_typing.Prompt`_ |
+| _`langchain.chains.base.Chain`_ |
+| _`langchain.PromptTemplate`_ |
| _`from langchain.schema.language_model import BaseLanguageModel`_ |
-| _`langchain.Tool`_ |
-| _`langchain.document_loaders.base.BaseLoader`_ |
-| _`langchain.schema.Document`_ |
-| _`langchain.text_splitters.TextSplitter`_ |
-| _`langchain.vectorstores.base.VectorStore`_ |
-| _`langchain.embeddings.base.Embeddings`_ |
-| _`langchain.schema.BaseRetriever`_ |
+| _`langchain.Tool`_ |
+| _`langchain.document_loaders.base.BaseLoader`_ |
+| _`langchain.schema.Document`_ |
+| _`langchain.text_splitters.TextSplitter`_ |
+| _`langchain.vectorstores.base.VectorStore`_ |
+| _`langchain.embeddings.base.Embeddings`_ |
+| _`langchain.schema.BaseRetriever`_ |
The difference between _`dict`_ and _`langflow.field_typing.NestedDict`_ is that one adds a simple key-value pair field, while the other opens a more robust dictionary editor.
- Use the `Prompt` type by adding **kwargs to the build method.
- If you want to add the values of the variables to the template you defined, format the `PromptTemplate` inside the `CustomComponent` class.
+ Use the `Prompt` type by adding **kwargs to the build method. If you want to
+ add the values of the variables to the template you defined, format the
+ `PromptTemplate` inside the `CustomComponent` class.
- Use base Python types without a handle by default. To add handles, use the `input_types` key in the `build_config` method.
+ Use base Python types without a handle by default. To add handles, use the
+ `input_types` key in the `build_config` method.
**build_config:** Defines the configuration fields of the component. This method returns a dictionary where each key represents a field name and each value defines the field's behavior.
Supported keys for configuring fields:
-| Key | Description |
-| --------------------- | --------------------------------------------------- |
-| `is_list` | Boolean indicating if the field can hold multiple values. |
-| `options` | Dropdown menu options. |
-| `multiline` | Boolean indicating if a field allows multiline input. |
-| `input_types` | Allows connection handles for string fields. |
-| `display_name` | Field name displayed in the UI. |
-| `advanced` | Hides the field in the default UI view. |
-| `password` | Masks input, useful for sensitive data. |
-| `required` | Overrides the default behavior to make a field mandatory. |
-| `info` | Tooltip for the field. |
-| `file_types` | Accepted file types, useful for file fields. |
-| `range_spec` | Defines valid ranges for float fields. |
-| `title_case` | Boolean that controls field name capitalization. |
-| `refresh_button` | Adds a refresh button that updates field values. |
-| `real_time_refresh` | Updates the configuration as field values change. |
-| `field_type` | Automatically set based on the build method's type hint. |
+| Key | Description |
+| ------------------- | --------------------------------------------------------- |
+| `is_list` | Boolean indicating if the field can hold multiple values. |
+| `options` | Dropdown menu options. |
+| `multiline` | Boolean indicating if a field allows multiline input. |
+| `input_types` | Allows connection handles for string fields. |
+| `display_name` | Field name displayed in the UI. |
+| `advanced` | Hides the field in the default UI view. |
+| `password` | Masks input, useful for sensitive data. |
+| `required` | Overrides the default behavior to make a field mandatory. |
+| `info` | Tooltip for the field. |
+| `file_types` | Accepted file types, useful for file fields. |
+| `range_spec` | Defines valid ranges for float fields. |
+| `title_case` | Boolean that controls field name capitalization. |
+| `refresh_button` | Adds a refresh button that updates field values. |
+| `real_time_refresh` | Updates the configuration as field values change. |
+| `field_type` | Automatically set based on the build method's type hint. |
- Use the `update_build_config` method to dynamically update configurations based on field values.
+ Use the `update_build_config` method to dynamically update configurations
+ based on field values.
## Additional methods and attributes
@@ -99,4 +103,3 @@ The `CustomComponent` class also provides helpful methods for specific tasks (e.
- `status`: Shows values from the `build` method, useful for debugging.
- `field_order`: Controls the display order of fields.
- `icon`: Sets the canvas display icon.
-
diff --git a/docs/docs/components/inputs-and-outputs.mdx b/docs/docs/components/inputs-and-outputs.mdx
new file mode 100644
index 000000000..2a624221a
--- /dev/null
+++ b/docs/docs/components/inputs-and-outputs.mdx
@@ -0,0 +1,161 @@
+import Admonition from "@theme/Admonition";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+
+# Inputs and Outputs
+
+TL;DR: 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 Record.
+
+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 `Record`. When you want to pass the sender name or sender to the next component, use the `Record` 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 Record` is `true` and the `Message` is a `Record`, the data of the
+ `Record` 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.
+- **Record Template:** Specifies how a `Record` should be converted into `Text`.
+
+The **Record Template** field is used to specify how a `Record` should be converted into `Text`. This is particularly useful when you want to extract specific information from a `Record` and pass it as text to the next component in the sequence.
+
+For example, if you have a `Record` with the following structure:
+
+```json
+{
+ "name": "John Doe",
+ "age": 30,
+ "email": "johndoe@email.com"
+}
+```
+
+A template with `Name: {name}, Age: {age}` will convert the `Record` into a text string of `Name: John Doe, Age: 30`.
+
+If you pass more than one `Record`, 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 Record` is `true` and the `Message` is a `Record`, the data in the
+ `Record` 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/inputs.mdx b/docs/docs/components/inputs.mdx
deleted file mode 100644
index 854f7fee3..000000000
--- a/docs/docs/components/inputs.mdx
+++ /dev/null
@@ -1,99 +0,0 @@
-import Admonition from '@theme/Admonition';
-import ZoomableImage from "/src/theme/ZoomableImage.js";
-
-# Inputs
-
-## Chat Input
-
-This component obtains 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 Record` is `true` and the `Message` is a `Record`, the data
- of the `Record` 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.
-
-
-
----
-
-## 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}`.
-
-
-
----
-
-## 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.
-- **Record Template:** Specifies how a `Record` should be converted into `Text`.
-
-The **Record Template** field is used to specify how a `Record` should be converted into `Text`. This is particularly useful when you want to extract specific information from a `Record` and pass it as text to the next component in the sequence.
-
-For example, if you have a `Record` with the following structure:
-
-```json
-{
- "name": "John Doe",
- "age": 30,
- "email": "johndoe@email.com"
-}
-```
-
-A template with `Name: {name}, Age: {age}` will convert the `Record` into a text string of `Name: John Doe, Age: 30`.
-
-If you pass more than one `Record`, the text will be concatenated with a new line separator.
-
-
-
diff --git a/docs/docs/components/outputs.mdx b/docs/docs/components/outputs.mdx
deleted file mode 100644
index a8947e60e..000000000
--- a/docs/docs/components/outputs.mdx
+++ /dev/null
@@ -1,34 +0,0 @@
-import Admonition from '@theme/Admonition';
-
-# Outputs
-
-## 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 Record` is `true` and the `Message` is a `Record`, the data in the `Record` 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.
diff --git a/docs/docs/components/prompts.mdx b/docs/docs/components/prompts.mdx
deleted file mode 100644
index 19fdedf11..000000000
--- a/docs/docs/components/prompts.mdx
+++ /dev/null
@@ -1,25 +0,0 @@
-import Admonition from "@theme/Admonition";
-
-# Prompts
-
-
-
- Thank you for your patience as we refine our documentation. It may
- still have some areas under development. Please share your feedback or report any issues to help us improve!
-
-
-
-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.
-
----
-
-### 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/text-and-record.mdx b/docs/docs/components/text-and-record.mdx
new file mode 100644
index 000000000..24c16e4aa
--- /dev/null
+++ b/docs/docs/components/text-and-record.mdx
@@ -0,0 +1,49 @@
+# Text and Record
+
+In Langflow 1.0, we added two main input and output types: `Text` and `Record`.
+
+`Text` is a simple string input and output type, while `Record` 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.
+
+## Records To Text
+
+This is a component that takes in Records and outputs a `Text`. It does this using a template string and concatenating the values of the `Record`, one per line.
+
+If we have the following Records:
+
+```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 Record
+
+This component allows you to create a `Record` 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 Records
+
+This component takes in a LangChain `Document` and outputs a `Record`. It does this by extracting the `page_content` and the `metadata` from the `Document` and adding them to the `Record` 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/vector-stores.mdx b/docs/docs/components/vector-stores.mdx
index 7e21f1021..6072abe29 100644
--- a/docs/docs/components/vector-stores.mdx
+++ b/docs/docs/components/vector-stores.mdx
@@ -1,6 +1,6 @@
import Admonition from "@theme/Admonition";
-# Vector Stores Documentation
+# Vector Stores
### Astra DB
diff --git a/docs/docs/examples/chat-memory.mdx b/docs/docs/examples/chat-memory.mdx
index d9b7d2e20..88dbbca2b 100644
--- a/docs/docs/examples/chat-memory.mdx
+++ b/docs/docs/examples/chat-memory.mdx
@@ -14,4 +14,4 @@ This component is available under the **Helpers** tab of the Langflow preview.
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
-
During the flow creation process, you will notice handles (colored circles)
attached to one or both sides of a component. These handles represent the
- availability to connect to other components. Hover over a handle to see connection details.
+ availability to connect to other components. Hover over a handle to see
+ connection details.
@@ -85,6 +86,7 @@ Build the flow by clicking the **Playgr
Once the validation is complete, the status of each validated component should turn green ().
To debug, hover over the component status to see the outputs.
+
---
@@ -196,6 +198,7 @@ curl -X POST \
```
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"}]}]}%
```
@@ -231,9 +234,10 @@ A collection is a snapshot of flows available in a database.
Collections can be downloaded to local storage and uploaded for future use.
-
-
+
+
## Project
@@ -276,9 +280,3 @@ To see options for your project, in the upper left corner of the canvas, select
**Export** - Download your current project to your local machine as a `.json` file.
**Undo** or **Redo** - Undo or redo your last action.
-
-
-
-
-
-
diff --git a/docs/docs/getting-started/flows-components-collections.mdx b/docs/docs/getting-started/flows-components-collections.mdx
index 586f08192..335fb5c12 100644
--- a/docs/docs/getting-started/flows-components-collections.mdx
+++ b/docs/docs/getting-started/flows-components-collections.mdx
@@ -1,7 +1,7 @@
-import ThemedImage from '@theme/ThemedImage';
-import useBaseUrl from '@docusaurus/useBaseUrl';
-import ZoomableImage from '/src/theme/ZoomableImage.js';
-import ReactPlayer from 'react-player';
+import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import ReactPlayer from "react-player";
# 🖥️ Flows, components, collections, and projects
@@ -17,10 +17,4 @@ A [project](#project) can be a component or a flow. Projects are saved as part o
For example, the **OpenAI LLM** is a **component** of the **Basic prompting** flow, and the **flow** is stored in a **collection**.
-
-
## Component
-
-
-
-
diff --git a/docs/docs/getting-started/install-langflow.mdx b/docs/docs/getting-started/install-langflow.mdx
index 836645a1e..4beb5e362 100644
--- a/docs/docs/getting-started/install-langflow.mdx
+++ b/docs/docs/getting-started/install-langflow.mdx
@@ -6,33 +6,40 @@ import Admonition from "@theme/Admonition";
# 📦 Install Langflow
- Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true), to create your own Langflow workspace in minutes.
+ Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
+ using this
+ link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true),
+ to create your own Langflow workspace in minutes.
Langflow requires [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/) 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.
+Pipx can fetch the missing Python version for you with `--fetch-missing-python`, but you can also install the Python version manually.
## Install Langflow pre-release
To install a pre-release version of Langflow:
pip:
+
```bash
python -m pip install langflow --pre --force-reinstall
```
pipx:
+
```bash
pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre --force-reinstall"
```
@@ -52,11 +59,13 @@ 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.
+
```bash
│ Welcome to ⛓ Langflow │
│ │
@@ -83,4 +92,4 @@ You'll be presented with the following screen:
style={{ width: "100%", margin: "20px auto" }}
/>
-Name your Space, define the visibility (Public or Private), and click on **Duplicate Space** to start the installation process. When installation is finished, you'll be redirected to the Space's main page to start using Langflow right away!
\ No newline at end of file
+Name your Space, define the visibility (Public or Private), and click on **Duplicate Space** to start the installation process. When installation is finished, you'll be redirected to the Space's main page to start using Langflow right away!
diff --git a/docs/docs/getting-started/quickstart.mdx b/docs/docs/getting-started/quickstart.mdx
index ef7d373a6..3f02db27f 100644
--- a/docs/docs/getting-started/quickstart.mdx
+++ b/docs/docs/getting-started/quickstart.mdx
@@ -10,12 +10,15 @@ This guide demonstrates how to build a basic prompt flow and modify that prompt
## Prerequisites
-* [Langflow installed and running](./install-langflow.mdx)
+- [Langflow installed and running](./install-langflow.mdx)
-* [OpenAI API key](https://platform.openai.com)
+- [OpenAI API key](https://platform.openai.com)
- Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes.
+ Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
+ using this
+ link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
+ to create your own Langflow workspace in minutes.
## Hello World - Basic Prompting
@@ -44,25 +47,25 @@ Examine the **Prompt** component. The **Template** field instructs the LLM to `A
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**.
+ 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 chat with your bot.
+ The **Interaction Panel** opens, where you can chat with your bot.
2. Type a message and press Enter.
-And... Ahoy! 🏴☠️
-The bot responds in a piratical manner!
+ 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.
+ 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.
+ The response will be markedly different.
## Next steps
@@ -72,8 +75,6 @@ By adding Langflow components to your flow, you can create all sorts of interest
Here are a couple of examples:
-* [Memory chatbot](/starter-projects/memory-chatbot.mdx)
-* [Blog writer](/starter-projects/blog-writer.mdx)
-* [Document QA](/starter-projects/document-qa.mdx)
-
-
+- [Memory chatbot](/starter-projects/memory-chatbot.mdx)
+- [Blog writer](/starter-projects/blog-writer.mdx)
+- [Document QA](/starter-projects/document-qa.mdx)
diff --git a/docs/docs/index.mdx b/docs/docs/index.mdx
index 54960502b..e762142f0 100644
--- a/docs/docs/index.mdx
+++ b/docs/docs/index.mdx
@@ -29,7 +29,10 @@ Its intuitive interface allows for easy manipulation of AI building blocks, enab
- [Langflow Canvas](/getting-started/canvas) - Learn more about the Langflow canvas.
- Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes.
+ Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
+ using this
+ link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
+ to create your own Langflow workspace in minutes.
## Learn more about Langflow 1.0
diff --git a/docs/docs/integrations/notion/add-content-to-page.md b/docs/docs/integrations/notion/add-content-to-page.md
index 83b395fd0..ace43e103 100644
--- a/docs/docs/integrations/notion/add-content-to-page.md
+++ b/docs/docs/integrations/notion/add-content-to-page.md
@@ -9,14 +9,11 @@ The `AddContentToPage` component converts markdown text to Notion blocks and app
[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
@@ -100,23 +97,19 @@ class NotionPageCreator(CustomComponent):
## 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:
@@ -131,8 +124,8 @@ The `AddContentToPage` component is a powerful tool for integrating Notion conte
## 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
index 11b8b7823..293038d4f 100644
--- a/docs/docs/integrations/notion/intro.md
+++ b/docs/docs/integrations/notion/intro.md
@@ -8,12 +8,12 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
The Notion integration in Langflow enables seamless connectivity with Notion databases, pages, and users, facilitating automation and improving productivity.
####
Download Notion Components Bundle
diff --git a/docs/docs/integrations/notion/list-database-properties.md b/docs/docs/integrations/notion/list-database-properties.md
index 830ea3324..c41159893 100644
--- a/docs/docs/integrations/notion/list-database-properties.md
+++ b/docs/docs/integrations/notion/list-database-properties.md
@@ -41,7 +41,7 @@ class NotionDatabaseProperties(CustomComponent):
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": {
@@ -80,6 +80,7 @@ class NotionDatabaseProperties(CustomComponent):
```
## Example Usage
+
Here's an example of how you can use the `NotionDatabaseProperties` component in a Langflow flow:
@@ -110,6 +111,7 @@ Feel free to explore the capabilities of the `NotionDatabaseProperties` componen
## 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.
\ No newline at end of file
+- 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
index 3e219870e..ea1b04950 100644
--- a/docs/docs/integrations/notion/list-pages.md
+++ b/docs/docs/integrations/notion/list-pages.md
@@ -140,16 +140,17 @@ class NotionListPages(CustomComponent):
## 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.
@@ -157,7 +158,7 @@ In this example, the `NotionListPages` component is used to retrieve specific pa
## Best Practices
- When using the `NotionListPages
+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.
@@ -171,7 +172,7 @@ We encourage you to explore the capabilities of the `NotionListPages
## Troubleshooting
- If you encounter any issues while using the `NotionListPages` component, consider the following:
+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.
diff --git a/docs/docs/integrations/notion/list-users.md b/docs/docs/integrations/notion/list-users.md
index 90761239a..a85642214 100644
--- a/docs/docs/integrations/notion/list-users.md
+++ b/docs/docs/integrations/notion/list-users.md
@@ -9,13 +9,11 @@ The `NotionUserList` component retrieves users from Notion. It provides a conven
[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
@@ -94,34 +92,31 @@ class NotionUserList(CustomComponent):
```
## 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:
+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:
+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.
\ No newline at end of file
+- 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
index a38c05fd0..f4eeba052 100644
--- a/docs/docs/integrations/notion/page-content-viewer.md
+++ b/docs/docs/integrations/notion/page-content-viewer.md
@@ -11,7 +11,7 @@ The `NotionPageContent` component retrieves the content of a Notion page as plai
- The `NotionPageContent` component enables you to:
+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
@@ -114,18 +114,18 @@ class NotionPageContent(CustomComponent):
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:
+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.
@@ -135,7 +135,7 @@ The `NotionPageContent` component provides a seamless way to integrate Notion pa
## Troubleshooting
- If you encounter any issues while using the `NotionPageContent` component, consider the following:
+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.
diff --git a/docs/docs/integrations/notion/page-create.md b/docs/docs/integrations/notion/page-create.md
index 0269096b9..f942f257b 100644
--- a/docs/docs/integrations/notion/page-create.md
+++ b/docs/docs/integrations/notion/page-create.md
@@ -97,16 +97,17 @@ class NotionPageCreator(CustomComponent):
```
## Example Usage
+
Here's an example of how to use the `NotionPageCreator` component in a Langflow flow:
@@ -124,6 +125,7 @@ The `NotionPageCreator` component simplifies the process of creating pages in a
## 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.
\ No newline at end of file
+- 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
index 3389f64d3..0370a2b3a 100644
--- a/docs/docs/integrations/notion/page-update.md
+++ b/docs/docs/integrations/notion/page-update.md
@@ -109,12 +109,12 @@ Let's break down the key parts of this component:
Here's an example of how to use the `NotionPageUpdate` component in a Langflow flow using:
@@ -128,7 +128,6 @@ When using the `NotionPageUpdate` component, consider the following best practic
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:
diff --git a/docs/docs/integrations/notion/search.md b/docs/docs/integrations/notion/search.md
index 3ff7472dc..a972bffc0 100644
--- a/docs/docs/integrations/notion/search.md
+++ b/docs/docs/integrations/notion/search.md
@@ -146,16 +146,17 @@ class NotionSearch(CustomComponent):
```
## 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.
diff --git a/docs/docs/integrations/notion/setup.md b/docs/docs/integrations/notion/setup.md
index 9511d9c81..72bb8f3b4 100644
--- a/docs/docs/integrations/notion/setup.md
+++ b/docs/docs/integrations/notion/setup.md
@@ -76,4 +76,3 @@ Refer to the individual component documentation for more details on how to use e
- [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/api.mdx b/docs/docs/migration/api.mdx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/docs/docs/migration/component-status-and-data-passing.mdx b/docs/docs/migration/component-status-and-data-passing.mdx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/docs/docs/migration/connecting-output-components.mdx b/docs/docs/migration/connecting-output-components.mdx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/docs/docs/migration/custom-component.mdx b/docs/docs/migration/custom-component.mdx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/docs/docs/migration/experimental-components.mdx b/docs/docs/migration/experimental-components.mdx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/docs/docs/migration/flow-of-data.mdx b/docs/docs/migration/flow-of-data.mdx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/docs/docs/migration/global-variables.mdx b/docs/docs/migration/global-variables.mdx
deleted file mode 100644
index 616fa3621..000000000
--- a/docs/docs/migration/global-variables.mdx
+++ /dev/null
@@ -1,118 +0,0 @@
-import ZoomableImage from "/src/theme/ZoomableImage.js";
-import Admonition from "@theme/Admonition";
-
-# Global Variables
-
-## TLDR;
-
-- Global Variables are reusable variables that can be accessed from any Text field in your project.
-- To create a Global Variable, click on the 🌐 button in a Text field and then **+ Add New Variable**.
-- Define the **Name**, **Type**, and **Value** of the variable.
-- Click on **Save Variable** to create the variable.
-- All Credential Global Variables are encrypted and cannot be accessed by anyone but 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.
-
-Global Variables are a really useful feature of Langflow.
-They allow you to define reusable variables that can be accessed from any Text field in your project.
-
-The first thing you need to do is find a **Text field** in a Component, so let's talk about what a Text field is.
-
-## Text Fields
-
-Text fields are the fields in a Component where you can write text but that does not allow you to open a Text Area.
-
-The easiest way to find fields that are Text fields, though, is to look for fields that have a 🌐 button.
-
-
-
-## Creating a Global Variable
-
-To create a Global Variable, you need to click on the 🌐 button in a Text field and that will open a dropdown showing your currently available variables and at the end of it **+ Add New Variable**.
-
-
-
-Click on **+ Add New Variable** and a window will open where you can define your new Global Variable.
-
-In it, you can define the **Name** of the variable, the optional **Type** of the variable, and the **Value** of the variable.
-
-The **Name** is the name that you will use to refer to the variable in your Text fields.
-
-The **Type** is optional for now but will be used in the future to allow for more advanced features.
-
-The **Value** is the value that the variable will have.
-{/* say that all variables are encrypted */}
-
-
- All Credential Global Variables are encrypted and cannot be accessed by anyone
- but you.
-
-
-
-
-After you have defined your variable, click on **Save Variable** and your variable will be created.
-
-After that, once you click on the 🌐 button in a Text field, you will see your new variable in the dropdown.
-
-## Environment Variables
-
-If you set _`LANGFLOW_STORE_ENVIRONMENT_VARIABLES`_ to _`true`_ (which is the default value) in your `.env` file, all variables in _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ will be added to your user's Global Variables.
-
-All of these variables can be used in your project as any other Global Variable.
-
-
- You can set _`LANGFLOW_STORE_ENVIRONMENT_VARIABLES`_ to _`false`_ in your
- `.env` file to prevent this behavior.
-
-
-You can also set _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ to a list of variables that you want to get from the environment.
-
-The default list at the moment is:
-
-- ANTHROPIC_API_KEY
-- ASTRA_DB_API_ENDPOINT
-- ASTRA_DB_APPLICATION_TOKEN
-- AZURE_OPENAI_API_KEY
-- AZURE_OPENAI_API_DEPLOYMENT_NAME
-- AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME
-- AZURE_OPENAI_API_INSTANCE_NAME
-- AZURE_OPENAI_API_VERSION
-- COHERE_API_KEY
-- GOOGLE_API_KEY
-- GROQ_API_KEY
-- HUGGINGFACEHUB_API_TOKEN
-- OPENAI_API_KEY
-- PINECONE_API_KEY
-- SEARCHAPI_API_KEY
-- SERPAPI_API_KEY
-- UPSTASH_VECTOR_REST_URL
-- UPSTASH_VECTOR_REST_TOKEN
-- VECTARA_CUSTOMER_ID
-- VECTARA_CORPUS_ID
-- VECTARA_API_KEY
-
-
- Set _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ as a comma-separated list
- of variables (e.g. _`"VARIABLE1, VARIABLE2"`_) or as a JSON-encoded string
- (e.g. _`'["VARIABLE1", "VARIABLE2"]'`_).
-
diff --git a/docs/docs/migration/inputs-and-outputs.mdx b/docs/docs/migration/inputs-and-outputs.mdx
deleted file mode 100644
index 1e1745347..000000000
--- a/docs/docs/migration/inputs-and-outputs.mdx
+++ /dev/null
@@ -1,36 +0,0 @@
-# Inputs and Outputs
-
-TL;DR: 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 make it easier to build and maintain your flows.
-
-## Introduction
-
-Langflow 1.0 introduces new categories of components called Inputs and Outputs. They are used to make it easier to understand and interact with your flows.
-
-Let's start with what they have in common:
-
-- Components in these categories connect to components that have Text or Record inputs or outputs. Some can connect to both but you have to pick what type of data you want to output or input.
-- They can be renamed to help you identify them more easily in the Playground and while using the API.
-- They dynamically change the Playground to make it easier to understand and interact with your flows.
-
-Native Langflow Components were created to be powerful tools that work around Langflow's features. They are designed to be easy to use and understand, and to help you build your flows faster.
-
-Let's dive into Inputs and Outputs.
-
-## Inputs
-
-Inputs are components that are used to define where data comes into your flow. They can be used to receive data from the user, from a database, or from any other source that can be converted to Text or Record.
-
-The difference between Chat Input and other Input components is the format of the output, the number of configurable fields, and the way they are displayed in the Playground.
-
-Chat Input components can output Text or Record. When you want to pass the sender name, or sender to the next component, you can use the Record output, and when you want to pass the message only you can use the Text output. This is useful when saving the message to a database or a memory system like Zep.
-
-You can find out more about it and the other Inputs [here](../components/inputs).
-
-## 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](../components/outputs).
diff --git a/docs/docs/migration/migrating-to-one-point-zero.mdx b/docs/docs/migration/migrating-to-one-point-zero.mdx
index 827f0e118..973393606 100644
--- a/docs/docs/migration/migrating-to-one-point-zero.mdx
+++ b/docs/docs/migration/migrating-to-one-point-zero.mdx
@@ -41,7 +41,7 @@ We have a special channel in our Discord server dedicated to Langflow 1.0 migrat
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 of Components](../migration/inputs-and-outputs)
+[Learn more about Inputs and Outputs of Components](../components/inputs-and-outputs)
## To Compose or Not to Compose: the choice is yours
@@ -71,7 +71,7 @@ Langflow 1.0 introduces many new native categories, including Inputs, Outputs, H
With the introduction of Text and Record 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 Record and how they help you build better flows.
-[Learn more about Text and Record](../migration/text-and-record)
+[Learn more about Text and Record](../components/text-and-record)
## CustomComponent for All Components
@@ -119,7 +119,7 @@ Things got a whole lot easier. You can now pass tweaks and inputs in the API by
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](../migration/global-variables)
+[Learn more about Global Variables](../administration/global-env.mdx)
## Experimental Components
diff --git a/docs/docs/migration/multiple-flows.mdx b/docs/docs/migration/multiple-flows.mdx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/docs/docs/migration/new-categories-and-components.mdx b/docs/docs/migration/new-categories-and-components.mdx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/docs/docs/migration/passing-tweaks-and-inputs.mdx b/docs/docs/migration/passing-tweaks-and-inputs.mdx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/docs/docs/migration/possible-installation-issues.mdx b/docs/docs/migration/possible-installation-issues.mdx
index 2590d0b8a..a012a1c09 100644
--- a/docs/docs/migration/possible-installation-issues.mdx
+++ b/docs/docs/migration/possible-installation-issues.mdx
@@ -25,11 +25,11 @@ 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`_.
+ 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.
+ 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'`_
@@ -45,4 +45,3 @@ There are two possible reasons for this error:
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/migration/renaming-and-editing-components.mdx b/docs/docs/migration/renaming-and-editing-components.mdx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/docs/docs/migration/sidebar-and-interaction-panel.mdx b/docs/docs/migration/sidebar-and-interaction-panel.mdx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/docs/docs/migration/state-management.mdx b/docs/docs/migration/state-management.mdx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/docs/docs/migration/supported-frameworks.mdx b/docs/docs/migration/supported-frameworks.mdx
deleted file mode 100644
index e69de29bb..000000000
diff --git a/docs/docs/migration/text-and-record.mdx b/docs/docs/migration/text-and-record.mdx
deleted file mode 100644
index cdfb26b6c..000000000
--- a/docs/docs/migration/text-and-record.mdx
+++ /dev/null
@@ -1,45 +0,0 @@
-# Text and Record
-
-In Langflow 1.0 we added two main input and output types: Text and Record. Text is a simple string input and output type, while Record 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.
-
-### Records To Text
-
-This is a Component that takes in Records and outputs a Text. It does this using a template string and concatenating the values of the Record, one per line.
-
-If we have the following Records:
-
-```json
-{
- "sender_name": "Alice",
- "message": "Hello!"
-}
-{
- "sender_name": "John",
- "message": "Hi!"
-}
-```
-
-And the template string is: _`{sender_name}: {message}`_
-
-```
-Alice: Hello!
-John: Hi!
-```
-
-### Create Record
-
-This Component allows you to create a Record 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 Records
-
-This Component takes in a [LangChain](https://langchain.com) Document and outputs a Record. It does this by extracting the _`page_content`_ and the _`metadata`_ from the Document and adding them to the Record 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/starter-projects/basic-prompting.mdx b/docs/docs/starter-projects/basic-prompting.mdx
index 6fb7391e2..26b054bcc 100644
--- a/docs/docs/starter-projects/basic-prompting.mdx
+++ b/docs/docs/starter-projects/basic-prompting.mdx
@@ -14,12 +14,15 @@ This article demonstrates how to use Langflow's prompt tools to issue basic prom
## Prerequisites
-* [Langflow installed and running](../getting-started/install-langflow.mdx)
+- [Langflow installed and running](../getting-started/install-langflow.mdx)
-* [OpenAI API key created](https://platform.openai.com)
+- [OpenAI API key created](https://platform.openai.com)
- Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes.
+ Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
+ using this
+ link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
+ to create your own Langflow workspace in minutes.
## Create the basic prompting project
@@ -42,25 +45,21 @@ Examine the **Prompt** component. The **Template** field instructs the LLM to `A
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**.
+ 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.
+ 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!
+ 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.
+ 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.
-
-
-
-
+ The response will be markedly different.
diff --git a/docs/docs/starter-projects/blog-writer.mdx b/docs/docs/starter-projects/blog-writer.mdx
index 0e8047fd6..9380bf114 100644
--- a/docs/docs/starter-projects/blog-writer.mdx
+++ b/docs/docs/starter-projects/blog-writer.mdx
@@ -10,12 +10,15 @@ Build a blog writer with OpenAI that uses URLs for reference content.
## Prerequisites
-* [Langflow installed and running](../getting-started/install-langflow.mdx)
+- [Langflow installed and running](../getting-started/install-langflow.mdx)
-* [OpenAI API key created](https://platform.openai.com)
+- [OpenAI API key created](https://platform.openai.com)
- Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes.
+ Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
+ using this
+ link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
+ to create your own Langflow workspace in minutes.
## Create the Blog Writer project
@@ -36,6 +39,7 @@ Build a blog writer with OpenAI that uses URLs for reference content.
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:
@@ -59,16 +63,16 @@ The `{instructions}` value is received from the **Value** field of the **Instruc
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**.
+ 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.
+ 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.
+ 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.
\ No newline at end of file
+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
index 5e5377355..ddbcd901a 100644
--- a/docs/docs/starter-projects/document-qa.mdx
+++ b/docs/docs/starter-projects/document-qa.mdx
@@ -10,12 +10,15 @@ Build a question-and-answer chatbot with a document loaded from local memory.
## Prerequisites
-* [Langflow installed and running](../getting-started/install-langflow.mdx)
+- [Langflow installed and running](../getting-started/install-langflow.mdx)
-* [OpenAI API key created](https://platform.openai.com)
+- [OpenAI API key created](https://platform.openai.com)
- Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes.
+ Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
+ using this
+ link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
+ to create your own Langflow workspace in minutes.
## Create the Document QA project
@@ -39,24 +42,27 @@ The **Prompt** component is instructed to answer questions based on the contents
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**.
+
+ 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).
-
+ 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.
+ 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:
+ 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.
```
diff --git a/docs/docs/starter-projects/memory-chatbot.mdx b/docs/docs/starter-projects/memory-chatbot.mdx
index 86c64d368..8e38ca3e0 100644
--- a/docs/docs/starter-projects/memory-chatbot.mdx
+++ b/docs/docs/starter-projects/memory-chatbot.mdx
@@ -10,12 +10,15 @@ This flow extends the [basic prompting flow](./basic-prompting.mdx) to include c
## Prerequisites
-* [Langflow installed and running](../getting-started/install-langflow.mdx)
+- [Langflow installed and running](../getting-started/install-langflow.mdx)
-* [OpenAI API key created](https://platform.openai.com)
+- [OpenAI API key created](https://platform.openai.com)
- Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes.
+ Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
+ using this
+ link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
+ to create your own Langflow workspace in minutes.
## Create the memory chatbot project
@@ -43,16 +46,16 @@ This chatbot is augmented with the **Chat Memory** component, which stores messa
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**.
+ 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.
+ 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.
+ 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
@@ -65,11 +68,11 @@ You can demonstrate this by modifying the **Session ID** value to switch between
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).
+ 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`.
+ The **Outputs** log of the **Interaction Panel** displays the history from your initial chat with `MySessionID`.
## Store Session ID as a Langflow variable
@@ -79,4 +82,3 @@ To store **Session ID** as a Langflow variable, in the **Session ID** field, cli
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
index ddb0a1d46..d0054e6c4 100644
--- a/docs/docs/starter-projects/vector-store-rag.mdx
+++ b/docs/docs/starter-projects/vector-store-rag.mdx
@@ -17,16 +17,19 @@ We've chosen [Astra DB](https://astra.datastax.com/signup?utm_source=langflow-pr
## Prerequisites
- Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) to create your own Langflow workspace in minutes.
+ Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
+ using this
+ link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true)
+ to create your own Langflow workspace in minutes.
-* [Langflow installed and running](../getting-started/install-langflow.mdx)
+- [Langflow installed and running](../getting-started/install-langflow.mdx)
-* [OpenAI API key](https://platform.openai.com)
+- [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`)
+- [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
@@ -49,38 +52,40 @@ The **ingestion** flow (bottom of the screen) populates the vector store with da
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:
+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 Records from the Astra DB database.
-* **Text Output** component turns the Records into Text by concatenating them and also displays it in the Playground.
-* **Prompt** component takes in the user input and the retrieved Records 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.
+- **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 Records from the Astra DB database.
+- **Text Output** component turns the Records into Text by concatenating them and also displays it in the Playground.
+- **Prompt** component takes in the user input and the retrieved Records 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**.
-4. 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.
+ 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.
+ 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:
@@ -102,7 +107,3 @@ 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/whats-new/a-new-chapter-langflow.mdx b/docs/docs/whats-new/a-new-chapter-langflow.mdx
index 3ff74ffb2..bdc0f178b 100644
--- a/docs/docs/whats-new/a-new-chapter-langflow.mdx
+++ b/docs/docs/whats-new/a-new-chapter-langflow.mdx
@@ -41,7 +41,7 @@ By having a clear definition of Inputs and Outputs, we could build the experienc
When building a project testing and debugging is crucial. The Playground is a tool 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](../components/outputs#Text-Output), rename that to "Prompt Result" and see the output of your Prompt in the Playground.
+Now, you could plug the output of your Prompt into a [Text Output](../components/inputs-and-outputs), rename that to "Prompt Result" and see the output of your Prompt in the Playground.
{/* Add image here of the described above */}
diff --git a/docs/sidebars.js b/docs/sidebars.js
index 26e128247..6a9b3580a 100644
--- a/docs/sidebars.js
+++ b/docs/sidebars.js
@@ -49,8 +49,8 @@ module.exports = {
label: "Core Components",
collapsed: false,
items: [
- "components/inputs",
- "components/outputs",
+ "components/inputs-and-outputs",
+ "components/text-and-record",
"components/data",
"components/models",
"components/helpers",
@@ -91,15 +91,12 @@ module.exports = {
},
{
type: "category",
- label: "Migration Guides",
+ label: "Migration",
collapsed: false,
items: [
"migration/possible-installation-issues",
"migration/migrating-to-one-point-zero",
- "migration/inputs-and-outputs",
- "migration/text-and-record",
"migration/compatibility",
- "migration/global-variables",
],
},
{
diff --git a/docs/static/data/AstraDB-RAG-Flows.json b/docs/static/data/AstraDB-RAG-Flows.json
index 10dafa85f..d8bd23eb2 100644
--- a/docs/static/data/AstraDB-RAG-Flows.json
+++ b/docs/static/data/AstraDB-RAG-Flows.json
@@ -1,3403 +1,3147 @@
{
- "id": "51e2b78a-199b-4054-9f32-e288eef6924c",
- "data": {
- "nodes": [
- {
- "id": "ChatInput-yxMKE",
- "type": "genericNode",
- "position": {
- "x": 1195.5276981160775,
- "y": 209.421875
- },
- "data": {
- "type": "ChatInput",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "input_value": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Message",
- "advanced": false,
- "input_types": [],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "value": "what is a line"
- },
- "return_record": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "return_record",
- "display_name": "Return Record",
- "advanced": true,
- "dynamic": false,
- "info": "Return the message as a record containing the sender, sender_name, and session_id.",
- "load_from_db": false,
- "title_case": false
- },
- "sender": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
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- },
- "id": "ChatInput-yxMKE"
- },
- "selected": false,
- "width": 384,
- "height": 383
+ "id": "51e2b78a-199b-4054-9f32-e288eef6924c",
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+ "nodes": [
+ {
+ "id": "ChatInput-yxMKE",
+ "type": "genericNode",
+ "position": {
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+ "y": 209.421875
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+ "required": true,
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+ "list": false,
+ "show": true,
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+ "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "input_value": {
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+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
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+ "advanced": false,
+ "input_types": [],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "value": "what is a line"
+ },
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+ "show": true,
+ "multiline": false,
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+ "advanced": true,
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+ "info": "Return the message as a record containing the sender, sender_name, and session_id.",
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+ "title_case": false
+ },
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+ "input_types": ["Text"]
+ },
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+ "multiline": false,
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+ "advanced": false,
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+ "load_from_db": false,
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+ "input_types": ["Text"]
+ },
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+ "title_case": false,
+ "input_types": ["Text"]
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+ "_type": "CustomComponent"
},
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- "type": "genericNode",
- "position": {
- "x": 2322.600672827879,
- "y": 604.9467307442569
- },
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- "fileTypes": [],
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- },
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+ "description": "Get chat inputs from the Playground.",
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+ "display_name": "Chat Input",
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+ "custom_fields": {
+ "sender": null,
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- "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"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 },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=openai_api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n",
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- "display_name": "OpenAI Proxy",
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- "input_types": [
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- },
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- "load_from_db": false,
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- "file_path": "",
- "password": false,
- "name": "skip_empty",
- "display_name": "Skip Empty",
- "advanced": true,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "tiktoken_enable": {
- "type": "bool",
- "required": false,
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- "list": false,
- "show": true,
- "multiline": false,
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- "fileTypes": [],
- "file_path": "",
- "password": false,
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- "display_name": "TikToken Enable",
- "advanced": true,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
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- "tiktoken_model_name": {
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- "input_types": [
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- "_type": "CustomComponent"
- },
- "description": "Generate embeddings using OpenAI models.",
- "base_classes": [
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- "display_name": "OpenAI Embeddings",
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- "field_formatters": {},
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- },
- "id": "OpenAIEmbeddings-ZlOk1"
- },
- "selected": false,
- "width": 384,
- "height": 383,
- "dragging": false
+ "output_types": ["Text", "Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "ChatInput-yxMKE"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 383
+ },
+ {
+ "id": "TextOutput-BDknO",
+ "type": "genericNode",
+ "position": {
+ "x": 2322.600672827879,
+ "y": 604.9467307442569
+ },
+ "data": {
+ "type": "TextOutput",
+ "node": {
+ "template": {
+ "input_value": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Value",
+ "advanced": false,
+ "input_types": ["Record", "Text"],
+ "dynamic": false,
+ "info": "Text or Record to be passed as output.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "record_template": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "{text}",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "record_template",
+ "display_name": "Record Template",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
},
- {
- "id": "OpenAIModel-EjXlN",
- "type": "genericNode",
- "position": {
- "x": 3410.117202077183,
- "y": 431.2038048137648
- },
- "data": {
- "type": "OpenAIModel",
- "node": {
- "template": {
- "input_value": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Input",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
- "fileTypes": [],
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- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
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- },
- "max_tokens": {
- "type": "int",
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- "list": false,
- "show": true,
- "multiline": false,
- "value": 256,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "max_tokens",
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- "advanced": true,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
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- "model_kwargs": {
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- "password": false,
- "name": "model_kwargs",
- "display_name": "Model Kwargs",
- "advanced": true,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "model_name": {
- "type": "str",
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- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "gpt-3.5-turbo",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "gpt-4-turbo-preview",
- "gpt-3.5-turbo",
- "gpt-4-0125-preview",
- "gpt-4-1106-preview",
- "gpt-4-vision-preview",
- "gpt-3.5-turbo-0125",
- "gpt-3.5-turbo-1106"
- ],
- "name": "model_name",
- "display_name": "Model Name",
- "advanced": false,
- "dynamic": false,
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- "load_from_db": false,
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- "input_types": [
- "Text"
- ]
- },
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- "required": false,
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- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "openai_api_base",
- "display_name": "OpenAI API Base",
- "advanced": true,
- "dynamic": false,
- "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "openai_api_key": {
- "type": "str",
- "required": true,
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- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": true,
- "name": "openai_api_key",
- "display_name": "OpenAI API Key",
- "advanced": false,
- "dynamic": false,
- "info": "The OpenAI API Key to use for the OpenAI model.",
- "load_from_db": true,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "OPENAI_API_KEY"
- },
- "stream": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "stream",
- "display_name": "Stream",
- "advanced": true,
- "dynamic": false,
- "info": "Stream the response from the model. Streaming works only in Chat.",
- "load_from_db": false,
- "title_case": false
- },
- "system_message": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
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- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "system_message",
- "display_name": "System Message",
- "advanced": true,
- "dynamic": false,
- "info": "System message to pass to the model.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "temperature": {
- "type": "float",
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- "_type": "CustomComponent"
- },
- "description": "Generates text using OpenAI LLMs.",
- "icon": "OpenAI",
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- "frozen": false,
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- "model_kwargs",
- "model_name",
- "openai_api_base",
- "openai_api_key",
- "temperature",
- "input_value",
- "system_message",
- "stream"
- ],
- "beta": false
- },
- "id": "OpenAIModel-EjXlN"
- },
- "selected": true,
- "width": 384,
- "height": 563,
- "positionAbsolute": {
- "x": 3410.117202077183,
- "y": 431.2038048137648
- },
- "dragging": false
+ "description": "Display a text output in the Playground.",
+ "icon": "type",
+ "base_classes": ["object", "Text", "str"],
+ "display_name": "Extracted Chunks",
+ "documentation": "",
+ "custom_fields": {
+ "input_value": null,
+ "record_template": null
},
- {
- "id": "Prompt-xeI6K",
- "type": "genericNode",
- "position": {
- "x": 2969.0261961391298,
- "y": 442.1613649809069
+ "output_types": ["Text"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "TextOutput-BDknO"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 289,
+ "positionAbsolute": {
+ "x": 2322.600672827879,
+ "y": 604.9467307442569
+ },
+ "dragging": false
+ },
+ {
+ "id": "OpenAIEmbeddings-ZlOk1",
+ "type": "genericNode",
+ "position": {
+ "x": 1183.667250865064,
+ "y": 687.3171828430261
+ },
+ "data": {
+ "type": "OpenAIEmbeddings",
+ "node": {
+ "template": {
+ "allowed_special": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": [],
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "allowed_special",
+ "display_name": "Allowed Special",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "chunk_size": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 1000,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "chunk_size",
+ "display_name": "Chunk Size",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "client": {
+ "type": "Any",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "client",
+ "display_name": "Client",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"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 },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=openai_api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
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+ "deployment": {
+ "type": "str",
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+ "show": true,
+ "multiline": false,
+ "value": "text-embedding-ada-002",
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+ "input_types": ["Text"]
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+ "multiline": false,
+ "value": ["all"],
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+ "advanced": true,
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+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "embedding_ctx_length": {
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+ "input_types": ["Text"],
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},
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- "node": {
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- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
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- "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: ",
- "fileTypes": [],
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- "description": "Create a prompt template with dynamic variables.",
- "icon": "prompts",
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- "beta": false,
- "error": null
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- "id": "Prompt-xeI6K",
- "description": "Create a prompt template with dynamic variables.",
- "display_name": "Prompt"
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- "selected": false,
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- "height": 477,
- "positionAbsolute": {
- "x": 2969.0261961391298,
- "y": 442.1613649809069
- },
- "dragging": false
+ "load_from_db": false,
+ "title_case": false
+ },
+ "show_progress_bar": {
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+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "show_progress_bar",
+ "display_name": "Show Progress Bar",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "skip_empty": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "skip_empty",
+ "display_name": "Skip Empty",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "tiktoken_enable": {
+ "type": "bool",
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+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "tiktoken_enable",
+ "display_name": "TikToken Enable",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "tiktoken_model_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "tiktoken_model_name",
+ "display_name": "TikToken Model Name",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
},
- {
- "id": "ChatOutput-Q39I8",
- "type": "genericNode",
- "position": {
- "x": 3887.2073667611485,
- "y": 588.4801225794856
- },
- "data": {
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- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
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- "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template,\n )\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
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- "info": "",
- "load_from_db": false,
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- "input_value": {
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- "show": true,
- "multiline": true,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Message",
- "advanced": false,
- "input_types": [
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "record_template": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "{text}",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "record_template",
- "display_name": "Record Template",
- "advanced": true,
- "dynamic": false,
- "info": "In case of Message being a Record, this template will be used to convert it to text.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "return_record": {
- "type": "bool",
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- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "return_record",
- "display_name": "Return Record",
- "advanced": true,
- "dynamic": false,
- "info": "Return the message as a record containing the sender, sender_name, and session_id.",
- "load_from_db": false,
- "title_case": false
- },
- "sender": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
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- "multiline": false,
- "value": "Machine",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "Machine",
- "User"
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- "name": "sender",
- "display_name": "Sender Type",
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- "name": "session_id",
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- "info": "If provided, the message will be stored in the memory.",
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- "input_types": [
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- "_type": "CustomComponent"
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- "height": 383,
- "positionAbsolute": {
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+ "description": "Generate embeddings using OpenAI models.",
+ "base_classes": ["Embeddings"],
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+ "custom_fields": {
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},
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- "id": "File-t0a6a",
- "type": "genericNode",
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- "x": 2257.233450682836,
- "y": 1747.5389618367233
+ "output_types": ["Embeddings"],
+ "field_formatters": {},
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+ "field_order": [],
+ "beta": false
+ },
+ "id": "OpenAIEmbeddings-ZlOk1"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 383,
+ "dragging": false
+ },
+ {
+ "id": "OpenAIModel-EjXlN",
+ "type": "genericNode",
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+ "x": 3410.117202077183,
+ "y": 431.2038048137648
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+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "max_tokens": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 256,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "max_tokens",
+ "display_name": "Max Tokens",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_kwargs": {
+ "type": "NestedDict",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": {},
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "model_kwargs",
+ "display_name": "Model Kwargs",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_name": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "gpt-3.5-turbo",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": [
+ "gpt-4-turbo-preview",
+ "gpt-3.5-turbo",
+ "gpt-4-0125-preview",
+ "gpt-4-1106-preview",
+ "gpt-4-vision-preview",
+ "gpt-3.5-turbo-0125",
+ "gpt-3.5-turbo-1106"
+ ],
+ "name": "model_name",
+ "display_name": "Model Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_base": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "openai_api_base",
+ "display_name": "OpenAI API Base",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_key": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": true,
+ "name": "openai_api_key",
+ "display_name": "OpenAI API Key",
+ "advanced": false,
+ "dynamic": false,
+ "info": "The OpenAI API Key to use for the OpenAI model.",
+ "load_from_db": true,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": "OPENAI_API_KEY"
+ },
+ "stream": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "stream",
+ "display_name": "Stream",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Stream the response from the model. Streaming works only in Chat.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "system_message": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "system_message",
+ "display_name": "System Message",
+ "advanced": true,
+ "dynamic": false,
+ "info": "System message to pass to the model.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "temperature": {
+ "type": "float",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 0.1,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "temperature",
+ "display_name": "Temperature",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "rangeSpec": {
+ "step_type": "float",
+ "min": -1,
+ "max": 1,
+ "step": 0.1
},
- "data": {
- "type": "File",
- "node": {
- "template": {
- "path": {
- "type": "file",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [
- ".txt",
- ".md",
- ".mdx",
- ".csv",
- ".json",
- ".yaml",
- ".yml",
- ".xml",
- ".html",
- ".htm",
- ".pdf",
- ".docx"
- ],
- "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf",
- "password": false,
- "name": "path",
- "display_name": "Path",
- "advanced": false,
- "dynamic": false,
- "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx",
- "load_from_db": false,
- "title_case": false,
- "value": ""
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\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 record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "silent_errors": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "silent_errors",
- "display_name": "Silent Errors",
- "advanced": true,
- "dynamic": false,
- "info": "If true, errors will not raise an exception.",
- "load_from_db": false,
- "title_case": false
- },
- "_type": "CustomComponent"
- },
- "description": "A generic file loader.",
- "icon": "file-text",
- "base_classes": [
- "Record"
- ],
- "display_name": "File",
- "documentation": "",
- "custom_fields": {
- "path": null,
- "silent_errors": null
- },
- "output_types": [
- "Record"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "File-t0a6a"
- },
- "selected": false,
- "width": 384,
- "height": 281,
- "positionAbsolute": {
- "x": 2257.233450682836,
- "y": 1747.5389618367233
- },
- "dragging": false
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent"
},
- {
- "id": "RecursiveCharacterTextSplitter-tR9QM",
- "type": "genericNode",
- "position": {
- "x": 2791.013514133929,
- "y": 1462.9588953494142
- },
- "data": {
- "type": "RecursiveCharacterTextSplitter",
- "node": {
- "template": {
- "inputs": {
- "type": "Document",
- "required": true,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "inputs",
- "display_name": "Input",
- "advanced": false,
- "input_types": [
- "Document",
- "Record"
- ],
- "dynamic": false,
- "info": "The texts to split.",
- "load_from_db": false,
- "title_case": false
- },
- "chunk_overlap": {
- "type": "int",
- "required": false,
- "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": "The amount of overlap between chunks.",
- "load_from_db": false,
- "title_case": false
- },
- "chunk_size": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": 1000,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "chunk_size",
- "display_name": "Chunk Size",
- "advanced": false,
- "dynamic": false,
- "info": "The maximum length of each chunk.",
- "load_from_db": false,
- "title_case": false
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional\n\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain_core.documents import Document\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "separators": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "separators",
- "display_name": "Separators",
- "advanced": false,
- "dynamic": false,
- "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": [
- ""
- ]
- },
- "_type": "CustomComponent"
- },
- "description": "Split text into chunks of a specified length.",
- "base_classes": [
- "Record"
- ],
- "display_name": "Recursive Character Text Splitter",
- "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter",
- "custom_fields": {
- "inputs": null,
- "separators": null,
- "chunk_size": null,
- "chunk_overlap": null
- },
- "output_types": [
- "Record"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "RecursiveCharacterTextSplitter-tR9QM"
- },
- "selected": false,
- "width": 384,
- "height": 501,
- "positionAbsolute": {
- "x": 2791.013514133929,
- "y": 1462.9588953494142
- },
- "dragging": false
+ "description": "Generates text using OpenAI LLMs.",
+ "icon": "OpenAI",
+ "base_classes": ["object", "Text", "str"],
+ "display_name": "OpenAI",
+ "documentation": "",
+ "custom_fields": {
+ "input_value": null,
+ "openai_api_key": null,
+ "temperature": null,
+ "model_name": null,
+ "max_tokens": null,
+ "model_kwargs": null,
+ "openai_api_base": null,
+ "stream": null,
+ "system_message": null
},
- {
- "id": "AstraDBSearch-41nRz",
- "type": "genericNode",
- "position": {
- "x": 1723.976434815103,
- "y": 277.03317407245913
- },
- "data": {
- "type": "AstraDBSearch",
- "node": {
- "template": {
- "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": "Embedding to use",
- "load_from_db": false,
- "title_case": false
- },
- "input_value": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Input Value",
- "advanced": false,
- "dynamic": false,
- "info": "Input value to search",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "api_endpoint": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "api_endpoint",
- "display_name": "API Endpoint",
- "advanced": false,
- "dynamic": false,
- "info": "API endpoint URL for the Astra DB service.",
- "load_from_db": true,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "ASTRA_DB_API_ENDPOINT"
- },
- "batch_size": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "batch_size",
- "display_name": "Batch Size",
- "advanced": true,
- "dynamic": false,
- "info": "Optional number of records to process in a single batch.",
- "load_from_db": false,
- "title_case": false
- },
- "bulk_delete_concurrency": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "bulk_delete_concurrency",
- "display_name": "Bulk Delete Concurrency",
- "advanced": true,
- "dynamic": false,
- "info": "Optional concurrency level for bulk delete operations.",
- "load_from_db": false,
- "title_case": false
- },
- "bulk_insert_batch_concurrency": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "bulk_insert_batch_concurrency",
- "display_name": "Bulk Insert Batch Concurrency",
- "advanced": true,
- "dynamic": false,
- "info": "Optional concurrency level for bulk insert operations.",
- "load_from_db": false,
- "title_case": false
- },
- "bulk_insert_overwrite_concurrency": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "bulk_insert_overwrite_concurrency",
- "display_name": "Bulk Insert Overwrite Concurrency",
- "advanced": true,
- "dynamic": false,
- "info": "Optional concurrency level for bulk insert operations that overwrite existing records.",
- "load_from_db": false,
- "title_case": false
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n display_name = \"Astra DB Search\"\n description = \"Searches an existing Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n def build_config(self):\n return {\n \"search_type\": {\n \"display_name\": \"Search Type\",\n \"options\": [\"Similarity\", \"MMR\"],\n },\n \"input_value\": {\n \"display_name\": \"Input Value\",\n \"info\": \"Input value to search\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"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 \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"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 \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"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 \"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 \"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 \"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 \"number_of_results\": {\n \"display_name\": \"Number of Results\",\n \"info\": \"Number of results to return.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n collection_name: str,\n input_value: Text,\n token: str,\n api_endpoint: str,\n search_type: str = \"Similarity\",\n number_of_results: int = 4,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> List[Record]:\n vector_store = AstraDBVectorStoreComponent().build(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n try:\n return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n except KeyError as e:\n if \"content\" in str(e):\n raise ValueError(\n \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'.\"\n )\n else:\n raise e\n",
- "fileTypes": [],
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- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "collection_indexing_policy",
- "display_name": "Collection Indexing Policy",
- "advanced": true,
- "dynamic": false,
- "info": "Optional dictionary defining the indexing policy for the collection.",
- "load_from_db": false,
- "title_case": false
- },
- "collection_name": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "collection_name",
- "display_name": "Collection Name",
- "advanced": false,
- "dynamic": false,
- "info": "The name of the collection within Astra DB where the vectors will be stored.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "langflow"
- },
- "metadata_indexing_exclude": {
- "type": "str",
- "required": false,
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- "list": true,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "metadata_indexing_exclude",
- "display_name": "Metadata Indexing Exclude",
- "advanced": true,
- "dynamic": false,
- "info": "Optional list of metadata fields to exclude from the indexing.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "metadata_indexing_include": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "metadata_indexing_include",
- "display_name": "Metadata Indexing Include",
- "advanced": true,
- "dynamic": false,
- "info": "Optional list of metadata fields to include in the indexing.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "metric": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "metric",
- "display_name": "Metric",
- "advanced": true,
- "dynamic": false,
- "info": "Optional distance metric for vector comparisons in the vector store.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "namespace": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "namespace",
- "display_name": "Namespace",
- "advanced": true,
- "dynamic": false,
- "info": "Optional namespace within Astra DB to use for the collection.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "number_of_results": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": 4,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "number_of_results",
- "display_name": "Number of Results",
- "advanced": true,
- "dynamic": false,
- "info": "Number of results to return.",
- "load_from_db": false,
- "title_case": false
- },
- "pre_delete_collection": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "pre_delete_collection",
- "display_name": "Pre Delete Collection",
- "advanced": true,
- "dynamic": false,
- "info": "Boolean flag to determine whether to delete the collection before creating a new one.",
- "load_from_db": false,
- "title_case": false
- },
- "search_type": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "Similarity",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "Similarity",
- "MMR"
- ],
- "name": "search_type",
- "display_name": "Search Type",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "setup_mode": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "Sync",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "Sync",
- "Async",
- "Off"
- ],
- "name": "setup_mode",
- "display_name": "Setup Mode",
- "advanced": true,
- "dynamic": false,
- "info": "Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "token": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": true,
- "name": "token",
- "display_name": "Token",
- "advanced": false,
- "dynamic": false,
- "info": "Authentication token for accessing Astra DB.",
- "load_from_db": true,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "ASTRA_DB_APPLICATION_TOKEN"
- },
- "_type": "CustomComponent"
- },
- "description": "Searches an existing Astra DB Vector Store.",
- "icon": "AstraDB",
- "base_classes": [
- "Record"
- ],
- "display_name": "Astra DB Search",
- "documentation": "",
- "custom_fields": {
- "embedding": null,
- "collection_name": null,
- "input_value": null,
- "token": null,
- "api_endpoint": null,
- "search_type": null,
- "number_of_results": null,
- "namespace": null,
- "metric": null,
- "batch_size": null,
- "bulk_insert_batch_concurrency": null,
- "bulk_insert_overwrite_concurrency": null,
- "bulk_delete_concurrency": null,
- "setup_mode": null,
- "pre_delete_collection": null,
- "metadata_indexing_include": null,
- "metadata_indexing_exclude": null,
- "collection_indexing_policy": null
- },
- "output_types": [
- "Record"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [
- "token",
- "api_endpoint",
- "collection_name",
- "input_value",
- "embedding"
- ],
- "beta": false
- },
- "id": "AstraDBSearch-41nRz"
- },
- "selected": false,
- "width": 384,
- "height": 713,
- "dragging": false,
- "positionAbsolute": {
- "x": 1723.976434815103,
- "y": 277.03317407245913
- }
+ "output_types": ["Text"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [
+ "max_tokens",
+ "model_kwargs",
+ "model_name",
+ "openai_api_base",
+ "openai_api_key",
+ "temperature",
+ "input_value",
+ "system_message",
+ "stream"
+ ],
+ "beta": false
+ },
+ "id": "OpenAIModel-EjXlN"
+ },
+ "selected": true,
+ "width": 384,
+ "height": 563,
+ "positionAbsolute": {
+ "x": 3410.117202077183,
+ "y": 431.2038048137648
+ },
+ "dragging": false
+ },
+ {
+ "id": "Prompt-xeI6K",
+ "type": "genericNode",
+ "position": {
+ "x": 2969.0261961391298,
+ "y": 442.1613649809069
+ },
+ "data": {
+ "type": "Prompt",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "template": {
+ "type": "prompt",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: ",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "template",
+ "display_name": "Template",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent",
+ "context": {
+ "field_type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "context",
+ "display_name": "context",
+ "advanced": false,
+ "input_types": [
+ "Document",
+ "BaseOutputParser",
+ "Record",
+ "Text"
+ ],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "type": "str"
+ },
+ "question": {
+ "field_type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "question",
+ "display_name": "question",
+ "advanced": false,
+ "input_types": [
+ "Document",
+ "BaseOutputParser",
+ "Record",
+ "Text"
+ ],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "type": "str"
+ }
},
- {
- "id": "AstraDB-eUCSS",
- "type": "genericNode",
- "position": {
- "x": 3372.04958055989,
- "y": 1611.0742035495277
- },
- "data": {
- "type": "AstraDB",
- "node": {
- "template": {
- "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": "Embedding to use",
- "load_from_db": false,
- "title_case": false
- },
- "inputs": {
- "type": "Record",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "inputs",
- "display_name": "Inputs",
- "advanced": false,
- "dynamic": false,
- "info": "Optional list of records to be processed and stored in the vector store.",
- "load_from_db": false,
- "title_case": false
- },
- "api_endpoint": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "api_endpoint",
- "display_name": "API Endpoint",
- "advanced": false,
- "dynamic": false,
- "info": "API endpoint URL for the Astra DB service.",
- "load_from_db": true,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "ASTRA_DB_API_ENDPOINT"
- },
- "batch_size": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "batch_size",
- "display_name": "Batch Size",
- "advanced": true,
- "dynamic": false,
- "info": "Optional number of records to process in a single batch.",
- "load_from_db": false,
- "title_case": false
- },
- "bulk_delete_concurrency": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "bulk_delete_concurrency",
- "display_name": "Bulk Delete Concurrency",
- "advanced": true,
- "dynamic": false,
- "info": "Optional concurrency level for bulk delete operations.",
- "load_from_db": false,
- "title_case": false
- },
- "bulk_insert_batch_concurrency": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "bulk_insert_batch_concurrency",
- "display_name": "Bulk Insert Batch Concurrency",
- "advanced": true,
- "dynamic": false,
- "info": "Optional concurrency level for bulk insert operations.",
- "load_from_db": false,
- "title_case": false
- },
- "bulk_insert_overwrite_concurrency": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "bulk_insert_overwrite_concurrency",
- "display_name": "Bulk Insert Overwrite Concurrency",
- "advanced": true,
- "dynamic": false,
- "info": "Optional concurrency level for bulk insert operations that overwrite existing records.",
- "load_from_db": false,
- "title_case": false
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import List, Optional\n\nfrom langchain_astradb import AstraDBVectorStore\nfrom langchain_astradb.utils.astradb import SetupMode\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, VectorStore\nfrom langflow.schema import Record\n\n\nclass AstraDBVectorStoreComponent(CustomComponent):\n display_name = \"Astra DB\"\n description = \"Builds or loads an Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"inputs\", \"embedding\"]\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Inputs\",\n \"info\": \"Optional list of records to be processed and stored in the vector store.\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"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 \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"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 \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"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 \"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 \"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 \"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 }\n\n def build(\n self,\n embedding: Embeddings,\n token: str,\n api_endpoint: str,\n collection_name: str,\n inputs: Optional[List[Record]] = None,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Async\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: 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vector_store = AstraDBVectorStore(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n\n return vector_store\n",
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+ "display_name": "Prompt"
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+ "height": 477,
+ "positionAbsolute": {
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+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template,\n )\n",
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+ "fileTypes": [],
+ "file_path": "",
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+ "multiline": true,
+ "value": "{text}",
+ "fileTypes": [],
+ "file_path": "",
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+ "display_name": "Record Template",
+ "advanced": true,
+ "dynamic": false,
+ "info": "In case of Message being a Record, this template will be used to convert it to text.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "return_record": {
+ "type": "bool",
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+ "multiline": false,
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+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "return_record",
+ "display_name": "Return Record",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Return the message as a record containing the sender, sender_name, and session_id.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "sender": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "Machine",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": ["Machine", "User"],
+ "name": "sender",
+ "display_name": "Sender Type",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "sender_name": {
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+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "AI",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "sender_name",
+ "display_name": "Sender Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
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+ "show": true,
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+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "session_id",
+ "display_name": "Session ID",
+ "advanced": true,
+ "dynamic": false,
+ "info": "If provided, the message will be stored in the memory.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
},
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+ "description": "Display a chat message in the Playground.",
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- "style": {
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- },
- "className": "stroke-gray-900 stroke-connection",
- "selected": false
+ "output_types": ["Text", "Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "ChatOutput-Q39I8"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 383,
+ "positionAbsolute": {
+ "x": 3887.2073667611485,
+ "y": 588.4801225794856
+ },
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+ {
+ "id": "File-t0a6a",
+ "type": "genericNode",
+ "position": {
+ "x": 2257.233450682836,
+ "y": 1747.5389618367233
+ },
+ "data": {
+ "type": "File",
+ "node": {
+ "template": {
+ "path": {
+ "type": "file",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [
+ ".txt",
+ ".md",
+ ".mdx",
+ ".csv",
+ ".json",
+ ".yaml",
+ ".yml",
+ ".xml",
+ ".html",
+ ".htm",
+ ".pdf",
+ ".docx"
+ ],
+ "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf",
+ "password": false,
+ "name": "path",
+ "display_name": "Path",
+ "advanced": false,
+ "dynamic": false,
+ "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx",
+ "load_from_db": false,
+ "title_case": false,
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+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\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 record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "silent_errors": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "silent_errors",
+ "display_name": "Silent Errors",
+ "advanced": true,
+ "dynamic": false,
+ "info": "If true, errors will not raise an exception.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent"
},
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+ "description": "A generic file loader.",
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+ "custom_fields": {
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- "selected": false
+ "output_types": ["Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "File-t0a6a"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 281,
+ "positionAbsolute": {
+ "x": 2257.233450682836,
+ "y": 1747.5389618367233
+ },
+ "dragging": false
+ },
+ {
+ "id": "RecursiveCharacterTextSplitter-tR9QM",
+ "type": "genericNode",
+ "position": {
+ "x": 2791.013514133929,
+ "y": 1462.9588953494142
+ },
+ "data": {
+ "type": "RecursiveCharacterTextSplitter",
+ "node": {
+ "template": {
+ "inputs": {
+ "type": "Document",
+ "required": true,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "inputs",
+ "display_name": "Input",
+ "advanced": false,
+ "input_types": ["Document", "Record"],
+ "dynamic": false,
+ "info": "The texts to split.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "chunk_overlap": {
+ "type": "int",
+ "required": false,
+ "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": "The amount of overlap between chunks.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "chunk_size": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 1000,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "chunk_size",
+ "display_name": "Chunk Size",
+ "advanced": false,
+ "dynamic": false,
+ "info": "The maximum length of each chunk.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\n\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain_core.documents import Document\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "separators": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "separators",
+ "display_name": "Separators",
+ "advanced": false,
+ "dynamic": false,
+ "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": [""]
+ },
+ "_type": "CustomComponent"
},
- {
- "source": "OpenAIEmbeddings-ZlOk1",
- "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-ZlOk1\u0153}",
- "target": "AstraDBSearch-41nRz",
- "targetHandle": "{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}",
- "data": {
- "targetHandle": {
- "fieldName": "embedding",
- "id": "AstraDBSearch-41nRz",
- "inputTypes": null,
- "type": "Embeddings"
- },
- "sourceHandle": {
- "baseClasses": [
- "Embeddings"
- ],
- "dataType": "OpenAIEmbeddings",
- "id": "OpenAIEmbeddings-ZlOk1"
- }
- },
- "style": {
- "stroke": "#555"
- },
- "className": "stroke-gray-900 stroke-connection",
- "id": "reactflow__edge-OpenAIEmbeddings-ZlOk1{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-ZlOk1\u0153}-AstraDBSearch-41nRz{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}"
+ "description": "Split text into chunks of a specified length.",
+ "base_classes": ["Record"],
+ "display_name": "Recursive Character Text Splitter",
+ "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter",
+ "custom_fields": {
+ "inputs": null,
+ "separators": null,
+ "chunk_size": null,
+ "chunk_overlap": null
},
- {
- "source": "ChatInput-yxMKE",
- "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}",
- "target": "AstraDBSearch-41nRz",
- "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
- "data": {
- "targetHandle": {
- "fieldName": "input_value",
- "id": "AstraDBSearch-41nRz",
- "inputTypes": [
- "Text"
- ],
- "type": "str"
- },
- "sourceHandle": {
- "baseClasses": [
- "Text",
- "str",
- "object",
- "Record"
- ],
- "dataType": "ChatInput",
- "id": "ChatInput-yxMKE"
- }
- },
- "style": {
- "stroke": "#555"
- },
- "className": "stroke-gray-900 stroke-connection",
- "id": "reactflow__edge-ChatInput-yxMKE{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}-AstraDBSearch-41nRz{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}"
+ "output_types": ["Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "RecursiveCharacterTextSplitter-tR9QM"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 501,
+ "positionAbsolute": {
+ "x": 2791.013514133929,
+ "y": 1462.9588953494142
+ },
+ "dragging": false
+ },
+ {
+ "id": "AstraDBSearch-41nRz",
+ "type": "genericNode",
+ "position": {
+ "x": 1723.976434815103,
+ "y": 277.03317407245913
+ },
+ "data": {
+ "type": "AstraDBSearch",
+ "node": {
+ "template": {
+ "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": "Embedding to use",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "input_value": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Input Value",
+ "advanced": false,
+ "dynamic": false,
+ "info": "Input value to search",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "api_endpoint": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "api_endpoint",
+ "display_name": "API Endpoint",
+ "advanced": false,
+ "dynamic": false,
+ "info": "API endpoint URL for the Astra DB service.",
+ "load_from_db": true,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": "ASTRA_DB_API_ENDPOINT"
+ },
+ "batch_size": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "batch_size",
+ "display_name": "Batch Size",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional number of records to process in a single batch.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "bulk_delete_concurrency": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "bulk_delete_concurrency",
+ "display_name": "Bulk Delete Concurrency",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional concurrency level for bulk delete operations.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "bulk_insert_batch_concurrency": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "bulk_insert_batch_concurrency",
+ "display_name": "Bulk Insert Batch Concurrency",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional concurrency level for bulk insert operations.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "bulk_insert_overwrite_concurrency": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "bulk_insert_overwrite_concurrency",
+ "display_name": "Bulk Insert Overwrite Concurrency",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional concurrency level for bulk insert operations that overwrite existing records.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n display_name = \"Astra DB Search\"\n description = \"Searches an existing Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n def build_config(self):\n return {\n \"search_type\": {\n \"display_name\": \"Search Type\",\n \"options\": [\"Similarity\", \"MMR\"],\n },\n \"input_value\": {\n \"display_name\": \"Input Value\",\n \"info\": \"Input value to search\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"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 \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"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 \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"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 \"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 \"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 \"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 \"number_of_results\": {\n \"display_name\": \"Number of Results\",\n \"info\": \"Number of results to return.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n collection_name: str,\n input_value: Text,\n token: str,\n api_endpoint: str,\n search_type: str = \"Similarity\",\n number_of_results: int = 4,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> List[Record]:\n vector_store = AstraDBVectorStoreComponent().build(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n try:\n return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n except KeyError as e:\n if \"content\" in str(e):\n raise ValueError(\n \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'.\"\n )\n else:\n raise e\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "collection_indexing_policy": {
+ "type": "dict",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "collection_indexing_policy",
+ "display_name": "Collection Indexing Policy",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional dictionary defining the indexing policy for the collection.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "collection_name": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "collection_name",
+ "display_name": "Collection Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "The name of the collection within Astra DB where the vectors will be stored.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": "langflow"
+ },
+ "metadata_indexing_exclude": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "metadata_indexing_exclude",
+ "display_name": "Metadata Indexing Exclude",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional list of metadata fields to exclude from the indexing.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "metadata_indexing_include": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "metadata_indexing_include",
+ "display_name": "Metadata Indexing Include",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional list of metadata fields to include in the indexing.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "metric": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "metric",
+ "display_name": "Metric",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional distance metric for vector comparisons in the vector store.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "namespace": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "namespace",
+ "display_name": "Namespace",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional namespace within Astra DB to use for the collection.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "number_of_results": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 4,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "number_of_results",
+ "display_name": "Number of Results",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Number of results to return.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "pre_delete_collection": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "pre_delete_collection",
+ "display_name": "Pre Delete Collection",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Boolean flag to determine whether to delete the collection before creating a new one.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "search_type": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "Similarity",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": ["Similarity", "MMR"],
+ "name": "search_type",
+ "display_name": "Search Type",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "setup_mode": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "Sync",
+ "fileTypes": [],
+ "file_path": "",
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Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"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 \"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 \"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 \"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 }\n\n def build(\n self,\n embedding: Embeddings,\n token: str,\n api_endpoint: str,\n collection_name: str,\n inputs: Optional[List[Record]] = None,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Async\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> VectorStore:\n try:\n setup_mode_value = SetupMode[setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {setup_mode}\")\n if inputs:\n documents = [_input.to_lc_document() for _input in inputs]\n\n vector_store = AstraDBVectorStore.from_documents(\n documents=documents,\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n else:\n vector_store = AstraDBVectorStore(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n\n return vector_store\n",
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+ "baseClasses": ["object", "Text", "str"],
+ "dataType": "OpenAIModel",
+ "id": "OpenAIModel-EjXlN"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "selected": false
+ },
+ {
+ "source": "File-t0a6a",
+ "target": "RecursiveCharacterTextSplitter-tR9QM",
+ "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-t0a6a\u0153}",
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+ "id": "reactflow__edge-File-t0a6a{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-t0a6a\u0153}-RecursiveCharacterTextSplitter-tR9QM{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153Record\u0153],\u0153type\u0153:\u0153Document\u0153}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "inputs",
+ "id": "RecursiveCharacterTextSplitter-tR9QM",
+ "inputTypes": ["Document", "Record"],
+ "type": "Document"
+ },
+ "sourceHandle": {
+ "baseClasses": ["Record"],
+ "dataType": "File",
+ "id": "File-t0a6a"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "selected": false
+ },
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+ "target": "AstraDBSearch-41nRz",
+ "targetHandle": "{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "embedding",
+ "id": "AstraDBSearch-41nRz",
+ "inputTypes": null,
+ "type": "Embeddings"
+ },
+ "sourceHandle": {
+ "baseClasses": ["Embeddings"],
+ "dataType": "OpenAIEmbeddings",
+ "id": "OpenAIEmbeddings-ZlOk1"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-OpenAIEmbeddings-ZlOk1{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-ZlOk1\u0153}-AstraDBSearch-41nRz{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}"
+ },
+ {
+ "source": "ChatInput-yxMKE",
+ "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}",
+ "target": "AstraDBSearch-41nRz",
+ "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "AstraDBSearch-41nRz",
+ "inputTypes": ["Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["Text", "str", "object", "Record"],
+ "dataType": "ChatInput",
+ "id": "ChatInput-yxMKE"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-ChatInput-yxMKE{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}-AstraDBSearch-41nRz{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}"
+ },
+ {
+ "source": "RecursiveCharacterTextSplitter-tR9QM",
+ "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153RecursiveCharacterTextSplitter\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153}",
+ "target": "AstraDB-eUCSS",
+ "targetHandle": "{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Record\u0153}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "inputs",
+ "id": "AstraDB-eUCSS",
+ "inputTypes": null,
+ "type": "Record"
+ },
+ "sourceHandle": {
+ "baseClasses": ["Record"],
+ "dataType": "RecursiveCharacterTextSplitter",
+ "id": "RecursiveCharacterTextSplitter-tR9QM"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-RecursiveCharacterTextSplitter-tR9QM{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153RecursiveCharacterTextSplitter\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153}-AstraDB-eUCSS{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Record\u0153}",
+ "selected": false
+ },
+ {
+ "source": "OpenAIEmbeddings-9TPjc",
+ "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-9TPjc\u0153}",
+ "target": "AstraDB-eUCSS",
+ "targetHandle": "{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "embedding",
+ "id": "AstraDB-eUCSS",
+ "inputTypes": null,
+ "type": "Embeddings"
+ },
+ "sourceHandle": {
+ "baseClasses": ["Embeddings"],
+ "dataType": "OpenAIEmbeddings",
+ "id": "OpenAIEmbeddings-9TPjc"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-OpenAIEmbeddings-9TPjc{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-9TPjc\u0153}-AstraDB-eUCSS{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDB-eUCSS\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}",
+ "selected": false
+ },
+ {
+ "source": "AstraDBSearch-41nRz",
+ "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153AstraDBSearch\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153}",
+ "target": "TextOutput-BDknO",
+ "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
+ "data": {
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+ "inputTypes": ["Record", "Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["Record"],
+ "dataType": "AstraDBSearch",
+ "id": "AstraDBSearch-41nRz"
+ }
+ },
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+ },
+ "className": "stroke-gray-900 stroke-connection",
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+ }
+ ],
+ "viewport": {
+ "x": -259.6782520315529,
+ "y": 90.3428735006047,
+ "zoom": 0.2687057134854984
+ }
+ },
+ "description": "Visit https://pre-release.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": "1.0.0a0",
+ "is_component": false
}
diff --git a/docs/static/json_files/Notion_Components_bundle.json b/docs/static/json_files/Notion_Components_bundle.json
index 21181187c..5e632ad9c 100644
--- a/docs/static/json_files/Notion_Components_bundle.json
+++ b/docs/static/json_files/Notion_Components_bundle.json
@@ -1 +1,881 @@
-{"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. 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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\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","custom_fields":{"markdown_text":null,"block_id":null,"notion_secret":null},"output_types":["Record"],"field_formatters":{},"frozen":false,"field_order":[],"beta":false,"official":false},"id":"CustomComponent-I8Dec"},"selected":false,"width":384,"height":497,"positionAbsolute":{"x":-2256.686402636563,"y":-963.4541117792749},"dragging":false},{"id":"CustomComponent-ZcsA9","type":"genericNode","position":{"x":-3488.029350341937,"y":-965.3756250644985},"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, List\r\nfrom langflow.custom import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\nclass NotionSearch(CustomComponent):\r\n display_name = \"Search Notion\"\r\n description = (\r\n \"Searches all pages and databases that have been shared with an integration.\"\r\n )\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/search\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n field_order = [\r\n \"notion_secret\",\r\n \"query\",\r\n \"filter_value\",\r\n \"sort_direction\",\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 \"query\": {\r\n \"display_name\": \"Search Query\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The text that the API compares page and database titles against.\",\r\n },\r\n \"filter_value\": {\r\n \"display_name\": \"Filter Type\",\r\n \"field_type\": \"str\",\r\n \"info\": \"Limits the results to either only pages or only databases.\",\r\n \"options\": [\"page\", \"database\"],\r\n \"default_value\": \"page\",\r\n },\r\n \"sort_direction\": {\r\n \"display_name\": \"Sort Direction\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The direction to sort the results.\",\r\n \"options\": [\"ascending\", \"descending\"],\r\n \"default_value\": \"descending\",\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n notion_secret: str,\r\n query: str = \"\",\r\n filter_value: str = \"page\",\r\n sort_direction: str = \"descending\",\r\n ) -> List[Record]:\r\n try:\r\n url = \"https://api.notion.com/v1/search\"\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 \"query\": query,\r\n \"filter\": {\r\n \"value\": filter_value,\r\n \"property\": \"object\"\r\n },\r\n \"sort\":{\r\n \"direction\": sort_direction,\r\n \"timestamp\": \"last_edited_time\"\r\n }\r\n }\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\"Results found: {len(results['results'])}\\n\\n\"\r\n for result in results['results']:\r\n result_data = {\r\n 'id': result['id'],\r\n 'type': result['object'],\r\n 'last_edited_time': result['last_edited_time'],\r\n }\r\n \r\n if result['object'] == 'page':\r\n result_data['title_or_url'] = result['url']\r\n text = f\"id: {result['id']}\\ntitle_or_url: {result['url']}\\n\"\r\n elif result['object'] == 'database':\r\n if 'title' in result and isinstance(result['title'], list) and len(result['title']) > 0:\r\n result_data['title_or_url'] = result['title'][0]['plain_text']\r\n text = f\"id: {result['id']}\\ntitle_or_url: {result['title'][0]['plain_text']}\\n\"\r\n else:\r\n result_data['title_or_url'] = \"N/A\"\r\n text = f\"id: {result['id']}\\ntitle_or_url: N/A\\n\"\r\n\r\n text += f\"type: {result['object']}\\nlast_edited_time: {result['last_edited_time']}\\n\\n\"\r\n combined_text += text\r\n records.append(Record(text=text, data=result_data))\r\n \r\n self.status = combined_text\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},"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,"input_types":["Text"]},"_type":"CustomComponent"},"description":"Searches all pages and databases that have been shared with an integration.","icon":"NotionDirectoryLoader","base_classes":["Record"],"display_name":"Search [Notion]","documentation":"https://docs.langflow.org/integrations/notion/search","custom_fields":{"notion_secret":null,"query":null,"filter_value":null,"sort_direction":null},"output_types":["Record"],"field_formatters":{},"frozen":false,"field_order":["notion_secret","query","filter_value","sort_direction"],"beta":false},"id":"CustomComponent-ZcsA9","description":"Searches all pages and databases that have been shared with an integration.","display_name":"Search [Notion]"},"selected":false,"width":384,"height":591,"positionAbsolute":{"x":-3488.029350341937,"y":-965.3756250644985},"dragging":false}],"edges":[],"viewport":{"x":2623.378922967084,"y":696.8541079344027,"zoom":0.5981384177708997}},"description":"A Bundle containing Notion components for Page and Database manipulation. You can list pages, users databases, update properties, create new pages and add content to Notion Pages.","name":"Notion - Components","last_tested_version":"1.0.0a36","is_component":false}
\ No newline at end of file
+{
+ "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\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",
+ "custom_fields": {
+ "markdown_text": null,
+ "block_id": null,
+ "notion_secret": null
+ },
+ "output_types": ["Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false,
+ "official": false
+ },
+ "id": "CustomComponent-I8Dec"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 497,
+ "positionAbsolute": {
+ "x": -2256.686402636563,
+ "y": -963.4541117792749
+ },
+ "dragging": false
+ },
+ {
+ "id": "CustomComponent-ZcsA9",
+ "type": "genericNode",
+ "position": { "x": -3488.029350341937, "y": -965.3756250644985 },
+ "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, List\r\nfrom langflow.custom import CustomComponent\r\nfrom langflow.schema import Record\r\n\r\nclass NotionSearch(CustomComponent):\r\n display_name = \"Search Notion\"\r\n description = (\r\n \"Searches all pages and databases that have been shared with an integration.\"\r\n )\r\n documentation: str = \"https://docs.langflow.org/integrations/notion/search\"\r\n icon = \"NotionDirectoryLoader\"\r\n\r\n field_order = [\r\n \"notion_secret\",\r\n \"query\",\r\n \"filter_value\",\r\n \"sort_direction\",\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 \"query\": {\r\n \"display_name\": \"Search Query\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The text that the API compares page and database titles against.\",\r\n },\r\n \"filter_value\": {\r\n \"display_name\": \"Filter Type\",\r\n \"field_type\": \"str\",\r\n \"info\": \"Limits the results to either only pages or only databases.\",\r\n \"options\": [\"page\", \"database\"],\r\n \"default_value\": \"page\",\r\n },\r\n \"sort_direction\": {\r\n \"display_name\": \"Sort Direction\",\r\n \"field_type\": \"str\",\r\n \"info\": \"The direction to sort the results.\",\r\n \"options\": [\"ascending\", \"descending\"],\r\n \"default_value\": \"descending\",\r\n },\r\n }\r\n\r\n def build(\r\n self,\r\n notion_secret: str,\r\n query: str = \"\",\r\n filter_value: str = \"page\",\r\n sort_direction: str = \"descending\",\r\n ) -> List[Record]:\r\n try:\r\n url = \"https://api.notion.com/v1/search\"\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 \"query\": query,\r\n \"filter\": {\r\n \"value\": filter_value,\r\n \"property\": \"object\"\r\n },\r\n \"sort\":{\r\n \"direction\": sort_direction,\r\n \"timestamp\": \"last_edited_time\"\r\n }\r\n }\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\"Results found: {len(results['results'])}\\n\\n\"\r\n for result in results['results']:\r\n result_data = {\r\n 'id': result['id'],\r\n 'type': result['object'],\r\n 'last_edited_time': result['last_edited_time'],\r\n }\r\n \r\n if result['object'] == 'page':\r\n result_data['title_or_url'] = result['url']\r\n text = f\"id: {result['id']}\\ntitle_or_url: {result['url']}\\n\"\r\n elif result['object'] == 'database':\r\n if 'title' in result and isinstance(result['title'], list) and len(result['title']) > 0:\r\n result_data['title_or_url'] = result['title'][0]['plain_text']\r\n text = f\"id: {result['id']}\\ntitle_or_url: {result['title'][0]['plain_text']}\\n\"\r\n else:\r\n result_data['title_or_url'] = \"N/A\"\r\n text = f\"id: {result['id']}\\ntitle_or_url: N/A\\n\"\r\n\r\n text += f\"type: {result['object']}\\nlast_edited_time: {result['last_edited_time']}\\n\\n\"\r\n combined_text += text\r\n records.append(Record(text=text, data=result_data))\r\n \r\n self.status = combined_text\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
+ },
+ "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,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
+ },
+ "description": "Searches all pages and databases that have been shared with an integration.",
+ "icon": "NotionDirectoryLoader",
+ "base_classes": ["Record"],
+ "display_name": "Search [Notion]",
+ "documentation": "https://docs.langflow.org/integrations/notion/search",
+ "custom_fields": {
+ "notion_secret": null,
+ "query": null,
+ "filter_value": null,
+ "sort_direction": null
+ },
+ "output_types": ["Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [
+ "notion_secret",
+ "query",
+ "filter_value",
+ "sort_direction"
+ ],
+ "beta": false
+ },
+ "id": "CustomComponent-ZcsA9",
+ "description": "Searches all pages and databases that have been shared with an integration.",
+ "display_name": "Search [Notion]"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 591,
+ "positionAbsolute": {
+ "x": -3488.029350341937,
+ "y": -965.3756250644985
+ },
+ "dragging": false
+ }
+ ],
+ "edges": [],
+ "viewport": {
+ "x": 2623.378922967084,
+ "y": 696.8541079344027,
+ "zoom": 0.5981384177708997
+ }
+ },
+ "description": "A Bundle containing Notion components for Page and Database manipulation. You can list pages, users databases, update properties, create new pages and add content to Notion Pages.",
+ "name": "Notion - Components",
+ "last_tested_version": "1.0.0a36",
+ "is_component": false
+}
diff --git a/poetry.lock b/poetry.lock
index e0978b558..b7b0e66a1 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -471,17 +471,17 @@ files = [
[[package]]
name = "boto3"
-version = "1.34.117"
+version = "1.34.116"
description = "The AWS SDK for Python"
optional = false
python-versions = ">=3.8"
files = [
- {file = "boto3-1.34.117-py3-none-any.whl", hash = "sha256:1506589e30566bbb2f4997b60968ff7d4ef8a998836c31eedd36437ac3b7408a"},
- {file = "boto3-1.34.117.tar.gz", hash = "sha256:c8a383b904d6faaf7eed0c06e31b423db128e4c09ce7bd2afc39d1cd07030a51"},
+ {file = "boto3-1.34.116-py3-none-any.whl", hash = "sha256:e7f5ab2d1f1b90971a2b9369760c2c6bae49dae98c084a5c3f5c78e3968ace15"},
+ {file = "boto3-1.34.116.tar.gz", hash = "sha256:53cb8aeb405afa1cd2b25421e27a951aeb568026675dec020587861fac96ac87"},
]
[package.dependencies]
-botocore = ">=1.34.117,<1.35.0"
+botocore = ">=1.34.116,<1.35.0"
jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.10.0,<0.11.0"
@@ -490,13 +490,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]]
name = "botocore"
-version = "1.34.117"
+version = "1.34.116"
description = "Low-level, data-driven core of boto 3."
optional = false
python-versions = ">=3.8"
files = [
- {file = "botocore-1.34.117-py3-none-any.whl", hash = "sha256:26a431997f882bcdd1e835f44c24b2a1752b1c4e5183c2ce62999ce95d518d6c"},
- {file = "botocore-1.34.117.tar.gz", hash = "sha256:4637ca42e6c51aebc4d9a2d92f97bf4bdb042e3f7985ff31a659a11e4c170e73"},
+ {file = "botocore-1.34.116-py3-none-any.whl", hash = "sha256:ec4d42c816e9b2d87a2439ad277e7dda16a4a614ef6839cf66f4c1a58afa547c"},
+ {file = "botocore-1.34.116.tar.gz", hash = "sha256:269cae7ba99081519a9f87d7298e238d9e68ba94eb4f8ddfa906224c34cb8b6c"},
]
[package.dependencies]
@@ -2882,13 +2882,13 @@ grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
[[package]]
name = "gotrue"
-version = "2.4.3"
+version = "2.4.2"
description = "Python Client Library for Supabase Auth"
optional = false
python-versions = "<4.0,>=3.8"
files = [
- {file = "gotrue-2.4.3-py3-none-any.whl", hash = "sha256:1e1679890c6a7bbfb10999b817e134d593dec4f7ff2ea05d48a78c2f384355ed"},
- {file = "gotrue-2.4.3.tar.gz", hash = "sha256:f93953654408aa35230317c1670ae1435e01ee5a461f2bfbc541fafd59b99c6b"},
+ {file = "gotrue-2.4.2-py3-none-any.whl", hash = "sha256:64cd40933d1f0a5d5cc4f4bd93bc51d730b94812447b6600f774790a4901e455"},
+ {file = "gotrue-2.4.2.tar.gz", hash = "sha256:e100745161f1c58dd05b9c1ef8bcd4cd78cdfb38d8d2c253ade63143a3dc6aeb"},
]
[package.dependencies]
@@ -4058,19 +4058,19 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
[[package]]
name = "langchain-anthropic"
-version = "0.1.15"
+version = "0.1.13"
description = "An integration package connecting AnthropicMessages and LangChain"
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
- {file = "langchain_anthropic-0.1.15-py3-none-any.whl", hash = "sha256:7cceea526f473e4d514f39295dc128eec57da628a4bbb54850d11dda7aa959fc"},
- {file = "langchain_anthropic-0.1.15.tar.gz", hash = "sha256:c5c3c6eaccb11ed99a63886e50873ac21eaf8e9441e0f75c7ae7cd8cdef65155"},
+ {file = "langchain_anthropic-0.1.13-py3-none-any.whl", hash = "sha256:121f6f480da7685c239573d98322adb94fe486d40651ac341637f65da36881de"},
+ {file = "langchain_anthropic-0.1.13.tar.gz", hash = "sha256:32e7ac51e1874c47e1a20493e75f5bfc88b0ffeaf5f1aed6091547e1ae44bb85"},
]
[package.dependencies]
-anthropic = ">=0.28.0,<1"
+anthropic = ">=0.26.0,<1"
defusedxml = ">=0.7.1,<0.8.0"
-langchain-core = ">=0.2.2rc1,<0.3"
+langchain-core = ">=0.1.43,<0.3"
[[package]]
name = "langchain-astradb"
@@ -4322,7 +4322,7 @@ types-requests = ">=2.31.0.2,<3.0.0.0"
[[package]]
name = "langflow-base"
-version = "0.0.55"
+version = "0.0.54"
description = "A Python package with a built-in web application"
optional = false
python-versions = ">=3.10,<3.13"
@@ -4365,7 +4365,7 @@ rich = "^13.7.0"
sqlmodel = "^0.0.18"
typer = "^0.12.0"
uncurl = "^0.0.11"
-uvicorn = "^0.30.0"
+uvicorn = "^0.29.0"
websockets = "*"
[package.extras]
@@ -4419,13 +4419,13 @@ requests = ">=2,<3"
[[package]]
name = "litellm"
-version = "1.40.0"
+version = "1.39.5"
description = "Library to easily interface with LLM API providers"
optional = false
python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
files = [
- {file = "litellm-1.40.0-py3-none-any.whl", hash = "sha256:c3055767ae144585699fdb07b3ad678e66738c2eff19abd7761c8fe22d6e636f"},
- {file = "litellm-1.40.0.tar.gz", hash = "sha256:12b4c0ad850ede5aebdb2f48e3a8e898efb25df5bc915ff89929ad963cb92f54"},
+ {file = "litellm-1.39.5-py3-none-any.whl", hash = "sha256:1e8dd43c5d257fa8d7a0039b20aed7aeed4463d53608d1ba4ac233f1967a5330"},
+ {file = "litellm-1.39.5.tar.gz", hash = "sha256:8f4ea9fe21d67890e81a578e12c30b4172260ff35971dc7c3edf7eb69167d3be"},
]
[package.dependencies]
@@ -6182,13 +6182,13 @@ tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "p
[[package]]
name = "postgrest"
-version = "0.16.7"
+version = "0.16.4"
description = "PostgREST client for Python. This library provides an ORM interface to PostgREST."
optional = false
python-versions = "<4.0,>=3.8"
files = [
- {file = "postgrest-0.16.7-py3-none-any.whl", hash = "sha256:0727dbdbc4e00fd98244596812e7dba6261019f3ec09138be291479a76d9fe99"},
- {file = "postgrest-0.16.7.tar.gz", hash = "sha256:779e20bd34f3c14e1e99129407612f85d7bb1d80b5cee2f54e50194b4488c658"},
+ {file = "postgrest-0.16.4-py3-none-any.whl", hash = "sha256:304425381eb38e31018832a524943d7d1f07687be80c3c7397d8ae69ca56cb88"},
+ {file = "postgrest-0.16.4.tar.gz", hash = "sha256:e16973155be1464101d18a51cc060707cd177b918f4b01ea8afa51746ca870ef"},
]
[package.dependencies]
@@ -6834,17 +6834,17 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
[[package]]
name = "pydantic-settings"
-version = "2.3.0"
+version = "2.2.1"
description = "Settings management using Pydantic"
optional = false
python-versions = ">=3.8"
files = [
- {file = "pydantic_settings-2.3.0-py3-none-any.whl", hash = "sha256:26eeed27370a9c5e3f64e4a7d6602573cbedf05ed940f1d5b11c3f178427af7a"},
- {file = "pydantic_settings-2.3.0.tar.gz", hash = "sha256:78db28855a71503cfe47f39500a1dece523c640afd5280edb5c5c9c9cfa534c9"},
+ {file = "pydantic_settings-2.2.1-py3-none-any.whl", hash = "sha256:0235391d26db4d2190cb9b31051c4b46882d28a51533f97440867f012d4da091"},
+ {file = "pydantic_settings-2.2.1.tar.gz", hash = "sha256:00b9f6a5e95553590434c0fa01ead0b216c3e10bc54ae02e37f359948643c5ed"},
]
[package.dependencies]
-pydantic = ">=2.7.0"
+pydantic = ">=2.3.0"
python-dotenv = ">=0.21.0"
[package.extras]
@@ -7763,28 +7763,28 @@ pyasn1 = ">=0.1.3"
[[package]]
name = "ruff"
-version = "0.4.7"
+version = "0.4.6"
description = "An extremely fast Python linter and code formatter, written in Rust."
optional = false
python-versions = ">=3.7"
files = [
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- {file = "ruff-0.4.7.tar.gz", hash = "sha256:2331d2b051dc77a289a653fcc6a42cce357087c5975738157cd966590b18b5e1"},
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+ {file = "ruff-0.4.6-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3a6a0a4f4b5f54fff7c860010ab3dd81425445e37d35701a965c0248819dde7a"},
+ {file = "ruff-0.4.6-py3-none-win32.whl", hash = "sha256:9018bf59b3aa8ad4fba2b1dc0299a6e4e60a4c3bc62bbeaea222679865453062"},
+ {file = "ruff-0.4.6-py3-none-win_amd64.whl", hash = "sha256:a769ae07ac74ff1a019d6bd529426427c3e30d75bdf1e08bb3d46ac8f417326a"},
+ {file = "ruff-0.4.6-py3-none-win_arm64.whl", hash = "sha256:735a16407a1a8f58e4c5b913ad6102722e80b562dd17acb88887685ff6f20cf6"},
+ {file = "ruff-0.4.6.tar.gz", hash = "sha256:a797a87da50603f71e6d0765282098245aca6e3b94b7c17473115167d8dfb0b7"},
]
[[package]]
@@ -9013,13 +9013,13 @@ types-pyOpenSSL = "*"
[[package]]
name = "types-requests"
-version = "2.32.0.20240602"
+version = "2.32.0.20240523"
description = "Typing stubs for requests"
optional = false
python-versions = ">=3.8"
files = [
- {file = "types-requests-2.32.0.20240602.tar.gz", hash = "sha256:3f98d7bbd0dd94ebd10ff43a7fbe20c3b8528acace6d8efafef0b6a184793f06"},
- {file = "types_requests-2.32.0.20240602-py3-none-any.whl", hash = "sha256:ed3946063ea9fbc6b5fc0c44fa279188bae42d582cb63760be6cb4b9d06c3de8"},
+ {file = "types-requests-2.32.0.20240523.tar.gz", hash = "sha256:26b8a6de32d9f561192b9942b41c0ab2d8010df5677ca8aa146289d11d505f57"},
+ {file = "types_requests-2.32.0.20240523-py3-none-any.whl", hash = "sha256:f19ed0e2daa74302069bbbbf9e82902854ffa780bc790742a810a9aaa52f65ec"},
]
[package.dependencies]
@@ -9038,13 +9038,13 @@ files = [
[[package]]
name = "typing-extensions"
-version = "4.12.1"
+version = "4.12.0"
description = "Backported and Experimental Type Hints for Python 3.8+"
optional = false
python-versions = ">=3.8"
files = [
- {file = "typing_extensions-4.12.1-py3-none-any.whl", hash = "sha256:6024b58b69089e5a89c347397254e35f1bf02a907728ec7fee9bf0fe837d203a"},
- {file = "typing_extensions-4.12.1.tar.gz", hash = "sha256:915f5e35ff76f56588223f15fdd5938f9a1cf9195c0de25130c627e4d597f6d1"},
+ {file = "typing_extensions-4.12.0-py3-none-any.whl", hash = "sha256:b349c66bea9016ac22978d800cfff206d5f9816951f12a7d0ec5578b0a819594"},
+ {file = "typing_extensions-4.12.0.tar.gz", hash = "sha256:8cbcdc8606ebcb0d95453ad7dc5065e6237b6aa230a31e81d0f440c30fed5fd8"},
]
[[package]]
@@ -9230,13 +9230,13 @@ files = [
[[package]]
name = "uvicorn"
-version = "0.30.1"
+version = "0.29.0"
description = "The lightning-fast ASGI server."
optional = false
python-versions = ">=3.8"
files = [
- {file = "uvicorn-0.30.1-py3-none-any.whl", hash = "sha256:cd17daa7f3b9d7a24de3617820e634d0933b69eed8e33a516071174427238c81"},
- {file = "uvicorn-0.30.1.tar.gz", hash = "sha256:d46cd8e0fd80240baffbcd9ec1012a712938754afcf81bce56c024c1656aece8"},
+ {file = "uvicorn-0.29.0-py3-none-any.whl", hash = "sha256:2c2aac7ff4f4365c206fd773a39bf4ebd1047c238f8b8268ad996829323473de"},
+ {file = "uvicorn-0.29.0.tar.gz", hash = "sha256:6a69214c0b6a087462412670b3ef21224fa48cae0e452b5883e8e8bdfdd11dd0"},
]
[package.dependencies]
@@ -9300,13 +9300,13 @@ test = ["Cython (>=0.29.36,<0.30.0)", "aiohttp (==3.9.0b0)", "aiohttp (>=3.8.1)"
[[package]]
name = "validators"
-version = "0.28.3"
+version = "0.28.1"
description = "Python Data Validation for Humans™"
optional = false
python-versions = ">=3.8"
files = [
- {file = "validators-0.28.3-py3-none-any.whl", hash = "sha256:53cafa854f13850156259d9cc479b864ee901f6a96e6b109e6fc33f98f37d99f"},
- {file = "validators-0.28.3.tar.gz", hash = "sha256:c6c79840bcde9ba77b19f6218f7738188115e27830cbaff43264bc4ed24c429d"},
+ {file = "validators-0.28.1-py3-none-any.whl", hash = "sha256:890c98789ad884037f059af6ea915ec2d667129d509180c2c590b8009a4c4219"},
+ {file = "validators-0.28.1.tar.gz", hash = "sha256:5ac88e7916c3405f0ce38ac2ac82a477fcf4d90dbbeddd04c8193171fc17f7dc"},
]
[[package]]
@@ -9454,13 +9454,13 @@ files = [
[[package]]
name = "weaviate-client"
-version = "4.6.4"
+version = "4.6.3"
description = "A python native Weaviate client"
optional = false
python-versions = ">=3.8"
files = [
- {file = "weaviate_client-4.6.4-py3-none-any.whl", hash = "sha256:19b76fb923a5f0b6fcb7471ef3cd990d2791ede71731e53429e1066a9dbf2af2"},
- {file = "weaviate_client-4.6.4.tar.gz", hash = "sha256:5378db8a33bf1d48adff3f9efa572d9fb04eaeb36444817cab56f1ba3c595500"},
+ {file = "weaviate_client-4.6.3-py3-none-any.whl", hash = "sha256:b2921f9aea84a4eccb1c75d55dd2857a87241e5536540fb96ffdf4737ed4fe8a"},
+ {file = "weaviate_client-4.6.3.tar.gz", hash = "sha256:a6e638f746f91c310fe6680cffa77949718f17d8b40b966f7037028cacfd94e0"},
]
[package.dependencies]
@@ -9471,7 +9471,7 @@ grpcio-tools = ">=1.57.0,<2.0.0"
httpx = ">=0.25.0,<=0.27.0"
pydantic = ">=2.5.0,<3.0.0"
requests = ">=2.30.0,<3.0.0"
-validators = "0.28.3"
+validators = "0.28.1"
[[package]]
name = "websocket-client"
diff --git a/pyproject.toml b/pyproject.toml
index 8c7f6c237..8ff91de1c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "langflow"
-version = "1.0.0a44"
+version = "1.0.0a43"
description = "A Python package with a built-in web application"
authors = ["Langflow "]
maintainers = [
@@ -140,7 +140,7 @@ testpaths = ["tests", "integration"]
console_output_style = "progress"
filterwarnings = ["ignore::DeprecationWarning"]
log_cli = true
-markers = ["async_test", "api_key_required"]
+markers = ["async_test"]
[tool.ruff]
diff --git a/scripts/factory_restart_space.py b/scripts/factory_restart_space.py
index e9972e8cb..07a25d0de 100644
--- a/scripts/factory_restart_space.py
+++ b/scripts/factory_restart_space.py
@@ -1,4 +1,4 @@
-import os
+import argparse
from huggingface_hub import HfApi, list_models
from rich import print
@@ -6,11 +6,27 @@ from rich import print
# Use root method
models = list_models()
+args = argparse.ArgumentParser(description="Restart a space in the Hugging Face Hub.")
+args.add_argument("--space", type=str, help="The space to restart.")
+args.add_argument("--token", type=str, help="The Hugging Face API token.")
+
+parsed_args = args.parse_args()
+
+space = parsed_args.space
+
+if not space:
+ print("Please provide a space to restart.")
+ exit()
+
+if not parsed_args.token:
+ print("Please provide an API token.")
+ exit()
+
# Or configure a HfApi client
hf_api = HfApi(
endpoint="https://huggingface.co", # Can be a Private Hub endpoint.
- token=os.getenv("HUGGINFACE_API_TOKEN"),
+ token=parsed_args.token,
)
-space_runtime = hf_api.restart_space("Langflow/Langflow-Preview", factory_reboot=True)
+space_runtime = hf_api.restart_space(space, factory_reboot=True)
print(space_runtime)
diff --git a/scripts/gcp/GCP_DEPLOYMENT.md b/scripts/gcp/GCP_DEPLOYMENT.md
index 9f17e550b..a848d3d2b 100644
--- a/scripts/gcp/GCP_DEPLOYMENT.md
+++ b/scripts/gcp/GCP_DEPLOYMENT.md
@@ -20,8 +20,7 @@ When running as a [spot (preemptible) instance](https://cloud.google.com/compute
## Pricing (approximate)
-> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator)
->
+> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator) >
| Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes |
| ------------------ | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | -------------------------------------------------------------------------- |
diff --git a/src/backend/base/langflow/__main__.py b/src/backend/base/langflow/__main__.py
index 4162629dd..343188336 100644
--- a/src/backend/base/langflow/__main__.py
+++ b/src/backend/base/langflow/__main__.py
@@ -121,7 +121,7 @@ def run(
),
):
"""
- Run the Langflow.
+ Run Langflow.
"""
configure(log_level=log_level, log_file=log_file)
diff --git a/src/backend/base/langflow/api/v1/flows.py b/src/backend/base/langflow/api/v1/flows.py
index 36030a12d..c1ccf68db 100644
--- a/src/backend/base/langflow/api/v1/flows.py
+++ b/src/backend/base/langflow/api/v1/flows.py
@@ -9,7 +9,7 @@ from loguru import logger
from sqlmodel import Session, col, select
from langflow.api.utils import remove_api_keys, validate_is_component
-from langflow.api.v1.schemas import FlowListCreate, FlowListIds, FlowListRead
+from langflow.api.v1.schemas import FlowListCreate, FlowListRead
from langflow.initial_setup.setup import STARTER_FOLDER_NAME
from langflow.services.auth.utils import get_current_active_user
from langflow.services.database.models.flow import Flow, FlowCreate, FlowRead, FlowUpdate
@@ -258,9 +258,9 @@ async def download_file(
return FlowListRead(flows=flows)
-@router.post("/multiple_delete/")
+@router.delete("/")
async def delete_multiple_flows(
- flow_ids: FlowListIds, user: User = Depends(get_current_active_user), db: Session = Depends(get_session)
+ flow_ids: List[UUID], user: User = Depends(get_current_active_user), db: Session = Depends(get_session)
):
"""
Delete multiple flows by their IDs.
@@ -274,9 +274,7 @@ async def delete_multiple_flows(
"""
try:
- deleted_flows = db.exec(
- select(Flow).where(col(Flow.id).in_(flow_ids.flow_ids)).where(Flow.user_id == user.id)
- ).all()
+ deleted_flows = db.exec(select(Flow).where(col(Flow.id).in_(flow_ids)).where(Flow.user_id == user.id)).all()
for flow in deleted_flows:
db.delete(flow)
db.commit()
diff --git a/src/backend/base/langflow/api/v1/monitor.py b/src/backend/base/langflow/api/v1/monitor.py
index 05fee6f03..e419ed5bf 100644
--- a/src/backend/base/langflow/api/v1/monitor.py
+++ b/src/backend/base/langflow/api/v1/monitor.py
@@ -1,9 +1,10 @@
from typing import List, Optional
-
+from uuid import UUID
from fastapi import APIRouter, Depends, HTTPException, Query
from langflow.services.deps import get_monitor_service
from langflow.services.monitor.schema import (
+ MessageModelRequest,
MessageModelResponse,
TransactionModelResponse,
VertexBuildMapModel,
@@ -66,6 +67,44 @@ async def get_messages(
raise HTTPException(status_code=500, detail=str(e))
+@router.delete("/messages", status_code=204)
+async def delete_messages(
+ message_ids: List[int],
+ monitor_service: MonitorService = Depends(get_monitor_service),
+):
+ try:
+ monitor_service.delete_messages(message_ids=message_ids)
+ except Exception as e:
+ raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post("/messages/{message_id}", response_model=MessageModelResponse)
+async def update_message(
+ message_id: str,
+ message: MessageModelRequest,
+ monitor_service: MonitorService = Depends(get_monitor_service),
+):
+ try:
+ message_dict = message.model_dump(exclude_none=True)
+ message_dict.pop("index", None)
+ monitor_service.update_message(message_id=message_id, **message_dict)
+ return MessageModelResponse(index=message_id, **message_dict)
+
+ except Exception as e:
+ raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.delete("/messages/session/{session_id}", status_code=204)
+async def delete_messages_session(
+ session_id: str,
+ monitor_service: MonitorService = Depends(get_monitor_service),
+):
+ try:
+ monitor_service.delete_messages_session(session_id=session_id)
+ except Exception as e:
+ raise HTTPException(status_code=500, detail=str(e))
+
+
@router.get("/transactions", response_model=List[TransactionModelResponse])
async def get_transactions(
source: Optional[str] = Query(None),
diff --git a/src/backend/base/langflow/base/curl/parse.py b/src/backend/base/langflow/base/curl/parse.py
index c86638306..892abdde2 100644
--- a/src/backend/base/langflow/base/curl/parse.py
+++ b/src/backend/base/langflow/base/curl/parse.py
@@ -15,10 +15,25 @@ import shlex
from collections import OrderedDict, namedtuple
from http.cookies import SimpleCookie
-from uncurl.api import parser # type: ignore
-
-parser.add_argument("-x", "--proxy", default={})
-parser.add_argument("-U", "--proxy-user", default="")
+ParsedArgs = namedtuple(
+ "ParsedContext",
+ [
+ "command",
+ "url",
+ "data",
+ "data_binary",
+ "method",
+ "headers",
+ "compressed",
+ "insecure",
+ "user",
+ "include",
+ "silent",
+ "proxy",
+ "proxy_user",
+ "cookies",
+ ],
+)
ParsedContext = namedtuple("ParsedContext", ["method", "url", "data", "headers", "cookies", "verify", "auth", "proxy"])
@@ -27,24 +42,89 @@ def normalize_newlines(multiline_text):
return multiline_text.replace(" \\\n", " ")
+def parse_curl_command(curl_command):
+ tokens = shlex.split(normalize_newlines(curl_command))
+ tokens = [token for token in tokens if token and token != " "]
+ if "curl" not in tokens[0]:
+ raise ValueError("Invalid curl command")
+ args_template = {
+ "command": None,
+ "url": None,
+ "data": None,
+ "data_binary": None,
+ "method": "get",
+ "headers": [],
+ "compressed": False,
+ "insecure": False,
+ "user": (),
+ "include": False,
+ "silent": False,
+ "proxy": None,
+ "proxy_user": None,
+ "cookies": {},
+ }
+ args = args_template.copy()
+
+ i = 0
+ while i < len(tokens):
+ token = tokens[i]
+ if token == "-X":
+ i += 1
+ args["method"] = tokens[i].lower()
+ elif token in ("-d", "--data"):
+ i += 1
+ args["data"] = tokens[i]
+ args["method"] = "post"
+ elif token in ("-b", "--data-binary", "--data-raw"):
+ i += 1
+ args["data_binary"] = tokens[i]
+ args["method"] = "post"
+ elif token in ("-H", "--header"):
+ i += 1
+ args["headers"].append(tokens[i])
+ elif token == "--compressed":
+ args["compressed"] = True
+ elif token in ("-k", "--insecure"):
+ args["insecure"] = True
+ elif token in ("-u", "--user"):
+ i += 1
+ args["user"] = tuple(tokens[i].split(":"))
+ elif token in ("-I", "--include"):
+ args["include"] = True
+ elif token in ("-s", "--silent"):
+ args["silent"] = True
+ elif token in ("-x", "--proxy"):
+ i += 1
+ args["proxy"] = tokens[i]
+ elif token in ("-U", "--proxy-user"):
+ i += 1
+ args["proxy_user"] = tokens[i]
+ elif not token.startswith("-"):
+ if args["command"] is None:
+ args["command"] = token
+ else:
+ args["url"] = token
+ i += 1
+
+ return ParsedArgs(**args)
+
+
def parse_context(curl_command):
method = "get"
- tokens = shlex.split(normalize_newlines(curl_command))
- tokens = [token for token in tokens if token and token != " "]
- parsed_args = parser.parse_args(tokens)
+ parsed_args: ParsedArgs = parse_curl_command(curl_command)
post_data = parsed_args.data or parsed_args.data_binary
if post_data:
method = "post"
- if parsed_args.X:
- method = parsed_args.X.lower()
+ if parsed_args.method:
+ method = parsed_args.method.lower()
cookie_dict = OrderedDict()
quoted_headers = OrderedDict()
- for curl_header in parsed_args.header:
+ for curl_header in parsed_args.headers:
if curl_header.startswith(":"):
occurrence = [m.start() for m in re.finditer(":", curl_header)]
header_key, header_value = curl_header[: occurrence[1]], curl_header[occurrence[1] + 1 :]
diff --git a/src/backend/base/langflow/base/data/utils.py b/src/backend/base/langflow/base/data/utils.py
index 7026d4968..2aaf3b23d 100644
--- a/src/backend/base/langflow/base/data/utils.py
+++ b/src/backend/base/langflow/base/data/utils.py
@@ -3,7 +3,7 @@ import xml.etree.ElementTree as ET
from concurrent import futures
from pathlib import Path
from typing import Callable, List, Optional, Text
-
+import chardet
import yaml
from langflow.schema.schema import Record
@@ -89,7 +89,12 @@ def retrieve_file_paths(
def read_text_file(file_path: str) -> str:
- with open(file_path, "r") as f:
+ with open(file_path, "rb") as f:
+ raw_data = f.read()
+ result = chardet.detect(raw_data)
+ encoding = result['encoding']
+
+ with open(file_path, "r", encoding=encoding) as f:
return f.read()
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json
index bdc6da29d..38e01111b 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json
@@ -1,886 +1,800 @@
{
- "id": "c091a57f-43a7-4a5e-b352-035ae8d8379c",
- "data": {
- "nodes": [
- {
- "id": "Prompt-uxBqP",
- "type": "genericNode",
- "position": {
- "x": 53.588791333410654,
- "y": -107.07318910019967
- },
- "data": {
- "type": "Prompt",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "template": {
- "type": "prompt",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: ",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "template",
- "display_name": "Template",
- "advanced": false,
- "input_types": [
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "_type": "CustomComponent",
- "user_input": {
- "field_type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "user_input",
- "display_name": "user_input",
- "advanced": false,
- "input_types": [
- "Document",
- "BaseOutputParser",
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "type": "str"
- }
- },
- "description": "Create a prompt template with dynamic variables.",
- "icon": "prompts",
- "is_input": null,
- "is_output": null,
- "is_composition": null,
- "base_classes": [
- "object",
- "str",
- "Text"
- ],
- "name": "",
- "display_name": "Prompt",
- "documentation": "",
- "custom_fields": {
- "template": [
- "user_input"
- ]
- },
- "output_types": [
- "Text"
- ],
- "full_path": null,
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false,
- "error": null
- },
- "id": "Prompt-uxBqP",
- "description": "Create a prompt template with dynamic variables.",
- "display_name": "Prompt"
- },
- "selected": true,
- "width": 384,
- "height": 383,
- "dragging": false,
- "positionAbsolute": {
- "x": 53.588791333410654,
- "y": -107.07318910019967
- }
+ "id": "c091a57f-43a7-4a5e-b352-035ae8d8379c",
+ "data": {
+ "nodes": [
+ {
+ "id": "Prompt-uxBqP",
+ "type": "genericNode",
+ "position": {
+ "x": 53.588791333410654,
+ "y": -107.07318910019967
+ },
+ "data": {
+ "type": "Prompt",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "template": {
+ "type": "prompt",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: ",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "template",
+ "display_name": "Template",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent",
+ "user_input": {
+ "field_type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "user_input",
+ "display_name": "user_input",
+ "advanced": false,
+ "input_types": [
+ "Document",
+ "BaseOutputParser",
+ "Record",
+ "Text"
+ ],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "type": "str"
+ }
},
- {
- "id": "OpenAIModel-k39HS",
- "type": "genericNode",
- "position": {
- "x": 634.8148772766217,
- "y": 27.035057029045305
- },
- "data": {
- "type": "OpenAIModel",
- "node": {
- "template": {
- "input_value": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Input",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional\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 NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"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 \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "max_tokens": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": 256,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "max_tokens",
- "display_name": "Max Tokens",
- "advanced": true,
- "dynamic": false,
- "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
- "load_from_db": false,
- "title_case": false
- },
- "model_kwargs": {
- "type": "NestedDict",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": {},
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "model_kwargs",
- "display_name": "Model Kwargs",
- "advanced": true,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "model_name": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "gpt-3.5-turbo",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "gpt-4o",
- "gpt-4-turbo",
- "gpt-4-turbo-preview",
- "gpt-3.5-turbo",
- "gpt-3.5-turbo-0125"
- ],
- "name": "model_name",
- "display_name": "Model Name",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "openai_api_base": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "openai_api_base",
- "display_name": "OpenAI API Base",
- "advanced": true,
- "dynamic": false,
- "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "openai_api_key": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": true,
- "name": "openai_api_key",
- "display_name": "OpenAI API Key",
- "advanced": false,
- "dynamic": false,
- "info": "The OpenAI API Key to use for the OpenAI model.",
- "load_from_db": true,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "OPENAI_API_KEY"
- },
- "stream": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": true,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "stream",
- "display_name": "Stream",
- "advanced": true,
- "dynamic": false,
- "info": "Stream the response from the model. Streaming works only in Chat.",
- "load_from_db": false,
- "title_case": false
- },
- "system_message": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "system_message",
- "display_name": "System Message",
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+ "value": "from typing import Optional\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 NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"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 \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
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+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "max_tokens": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 256,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "max_tokens",
+ "display_name": "Max Tokens",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_kwargs": {
+ "type": "NestedDict",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": {},
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "model_kwargs",
+ "display_name": "Model Kwargs",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "gpt-3.5-turbo",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": [
+ "gpt-4o",
+ "gpt-4-turbo",
+ "gpt-4-turbo-preview",
+ "gpt-3.5-turbo",
+ "gpt-3.5-turbo-0125"
+ ],
+ "name": "model_name",
+ "display_name": "Model Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_base": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "openai_api_base",
+ "display_name": "OpenAI API Base",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_key": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": true,
+ "name": "openai_api_key",
+ "display_name": "OpenAI API Key",
+ "advanced": false,
+ "dynamic": false,
+ "info": "The OpenAI API Key to use for the OpenAI model.",
+ "load_from_db": true,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": "OPENAI_API_KEY"
+ },
+ "stream": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "stream",
+ "display_name": "Stream",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Stream the response from the model. Streaming works only in Chat.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "system_message": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "system_message",
+ "display_name": "System Message",
+ "advanced": true,
+ "dynamic": false,
+ "info": "System message to pass to the model.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "temperature": {
+ "type": "float",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 0.1,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "temperature",
+ "display_name": "Temperature",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "rangeSpec": {
+ "step_type": "float",
+ "min": -1,
+ "max": 1,
+ "step": 0.1
+ },
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent"
+ },
+ "description": "Generates text using OpenAI LLMs.",
+ "icon": "OpenAI",
+ "base_classes": ["object", "Text", "str"],
+ "display_name": "OpenAI",
+ "documentation": "",
+ "custom_fields": {
+ "input_value": null,
+ "openai_api_key": null,
+ "temperature": null,
+ "model_name": null,
+ "max_tokens": null,
+ "model_kwargs": null,
+ "openai_api_base": null,
+ "stream": null,
+ "system_message": null
+ },
+ "output_types": ["Text"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [
+ "max_tokens",
+ "model_kwargs",
+ "model_name",
+ "openai_api_base",
+ "openai_api_key",
+ "temperature",
+ "input_value",
+ "system_message",
+ "stream"
+ ],
+ "beta": false
+ },
+ "id": "OpenAIModel-k39HS",
+ "description": "Generates text using OpenAI LLMs.",
+ "display_name": "OpenAI"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 563,
+ "positionAbsolute": {
+ "x": 634.8148772766217,
+ "y": 27.035057029045305
+ },
+ "dragging": false
+ },
+ {
+ "id": "ChatOutput-njtka",
+ "type": "genericNode",
+ "position": {
+ "x": 1193.250417197867,
+ "y": 71.88476890163852
+ },
+ "data": {
+ "type": "ChatOutput",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "input_value": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Message",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "record_template": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "{text}",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "record_template",
+ "display_name": "Record Template",
+ "advanced": true,
+ "dynamic": false,
+ "info": "In case of Message being a Record, this template will be used to convert it to text.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "return_record": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "return_record",
+ "display_name": "Return Record",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Return the message as a record containing the sender, sender_name, and session_id.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "sender": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "Machine",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": ["Machine", "User"],
+ "name": "sender",
+ "display_name": "Sender Type",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "sender_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "AI",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "sender_name",
+ "display_name": "Sender Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "session_id": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "session_id",
+ "display_name": "Session ID",
+ "advanced": true,
+ "dynamic": false,
+ "info": "If provided, the message will be stored in the memory.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
+ },
+ "description": "Display a chat message in the Playground.",
+ "icon": "ChatOutput",
+ "base_classes": ["Record", "Text", "str", "object"],
+ "display_name": "Chat Output",
+ "documentation": "",
+ "custom_fields": {
+ "sender": null,
+ "sender_name": null,
+ "input_value": null,
+ "session_id": null,
+ "return_record": null,
+ "record_template": null
+ },
+ "output_types": ["Text", "Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "ChatOutput-njtka"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 383,
+ "positionAbsolute": {
+ "x": 1193.250417197867,
+ "y": 71.88476890163852
+ },
+ "dragging": false
+ },
+ {
+ "id": "ChatInput-P3fgL",
+ "type": "genericNode",
+ "position": {
+ "x": -495.2223093083827,
+ "y": -232.56998443685862
+ },
+ "data": {
+ "type": "ChatInput",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "input_value": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Message",
+ "advanced": false,
+ "input_types": [],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "value": "hi"
+ },
+ "return_record": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "return_record",
+ "display_name": "Return Record",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Return the message as a record containing the sender, sender_name, and session_id.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "sender": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "User",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": ["Machine", "User"],
+ "name": "sender",
+ "display_name": "Sender Type",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "sender_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "User",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "sender_name",
+ "display_name": "Sender Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "session_id": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "session_id",
+ "display_name": "Session ID",
+ "advanced": true,
+ "dynamic": false,
+ "info": "If provided, the message will be stored in the memory.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
+ },
+ "description": "Get chat inputs from the Playground.",
+ "icon": "ChatInput",
+ "base_classes": ["object", "Record", "str", "Text"],
+ "display_name": "Chat Input",
+ "documentation": "",
+ "custom_fields": {
+ "sender": null,
+ "sender_name": null,
+ "input_value": null,
+ "session_id": null,
+ "return_record": null
+ },
+ "output_types": ["Text", "Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "ChatInput-P3fgL"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 375,
+ "positionAbsolute": {
+ "x": -495.2223093083827,
+ "y": -232.56998443685862
+ },
+ "dragging": false
+ }
+ ],
+ "edges": [
+ {
+ "source": "OpenAIModel-k39HS",
+ "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}",
+ "target": "ChatOutput-njtka",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "ChatOutput-njtka",
+ "inputTypes": ["Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["object", "Text", "str"],
+ "dataType": "OpenAIModel",
+ "id": "OpenAIModel-k39HS"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-OpenAIModel-k39HS{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}-ChatOutput-njtka{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "Prompt-uxBqP",
+ "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}",
+ "target": "OpenAIModel-k39HS",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "OpenAIModel-k39HS",
+ "inputTypes": ["Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["object", "str", "Text"],
+ "dataType": "Prompt",
+ "id": "Prompt-uxBqP"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-Prompt-uxBqP{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}-OpenAIModel-k39HS{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "ChatInput-P3fgL",
+ "sourceHandle": "{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}",
+ "target": "Prompt-uxBqP",
+ "targetHandle": "{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "user_input",
+ "id": "Prompt-uxBqP",
+ "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["object", "Record", "str", "Text"],
+ "dataType": "ChatInput",
+ "id": "ChatInput-P3fgL"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-ChatInput-P3fgL{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}-Prompt-uxBqP{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}"
+ }
+ ],
+ "viewport": {
+ "x": 260.58251815500563,
+ "y": 318.2261172111936,
+ "zoom": 0.43514115784696294
+ }
+ },
+ "description": "This flow will get you experimenting with the basics of the UI, the Chat and the Prompt component. \n\nTry changing the Template in it to see how the model behaves. \nYou can change it to this and a Text Input into the `type_of_person` variable : \"Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: \" ",
+ "name": "Basic Prompting (Hello, World)",
+ "last_tested_version": "1.0.0a4",
+ "is_component": false
}
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json
index bd6013ad5..fcdef4056 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json
@@ -1,1096 +1,987 @@
{
- "id": "6ad5559d-fb66-4fdc-8f98-96f4ac12799d",
- "data": {
- "nodes": [
- {
- "id": "Prompt-Rse03",
- "type": "genericNode",
- "position": {
- "x": 1331.381712783371,
- "y": 535.0279854229713
- },
- "data": {
- "type": "Prompt",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "template": {
- "type": "prompt",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "Reference 1:\n\n{reference_1}\n\n---\n\nReference 2:\n\n{reference_2}\n\n---\n\n{instructions}\n\nBlog: \n\n\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "template",
- "display_name": "Template",
- "advanced": false,
- "input_types": [
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "_type": "CustomComponent",
- "reference_1": {
- "field_type": "str",
- "required": false,
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- "name": "reference_1",
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- "advanced": false,
- "input_types": [
- "Document",
- "BaseOutputParser",
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "type": "str"
- },
- "reference_2": {
- "field_type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "reference_2",
- "display_name": "reference_2",
- "advanced": false,
- "input_types": [
- "Document",
- "BaseOutputParser",
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "type": "str"
- },
- "instructions": {
- "field_type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "instructions",
- "display_name": "instructions",
- "advanced": false,
- "input_types": [
- "Document",
- "BaseOutputParser",
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "type": "str"
- }
- },
- "description": "Create a prompt template with dynamic variables.",
- "icon": "prompts",
- "is_input": null,
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- "base_classes": [
- "object",
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- "str"
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- "name": "",
- "display_name": "Prompt",
- "documentation": "",
- "custom_fields": {
- "template": [
- "reference_1",
- "reference_2",
- "instructions"
- ]
- },
- "output_types": [
- "Text"
- ],
- "full_path": null,
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false,
- "error": null
- },
- "id": "Prompt-Rse03",
- "description": "Create a prompt template with dynamic variables.",
- "display_name": "Prompt"
- },
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- "height": 571,
- "dragging": false,
- "positionAbsolute": {
- "x": 1331.381712783371,
- "y": 535.0279854229713
- }
+ "id": "6ad5559d-fb66-4fdc-8f98-96f4ac12799d",
+ "data": {
+ "nodes": [
+ {
+ "id": "Prompt-Rse03",
+ "type": "genericNode",
+ "position": {
+ "x": 1331.381712783371,
+ "y": 535.0279854229713
+ },
+ "data": {
+ "type": "Prompt",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "template": {
+ "type": "prompt",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "Reference 1:\n\n{reference_1}\n\n---\n\nReference 2:\n\n{reference_2}\n\n---\n\n{instructions}\n\nBlog: \n\n\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "template",
+ "display_name": "Template",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent",
+ "reference_1": {
+ "field_type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "reference_1",
+ "display_name": "reference_1",
+ "advanced": false,
+ "input_types": [
+ "Document",
+ "BaseOutputParser",
+ "Record",
+ "Text"
+ ],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "type": "str"
+ },
+ "reference_2": {
+ "field_type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "reference_2",
+ "display_name": "reference_2",
+ "advanced": false,
+ "input_types": [
+ "Document",
+ "BaseOutputParser",
+ "Record",
+ "Text"
+ ],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "type": "str"
+ },
+ "instructions": {
+ "field_type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "instructions",
+ "display_name": "instructions",
+ "advanced": false,
+ "input_types": [
+ "Document",
+ "BaseOutputParser",
+ "Record",
+ "Text"
+ ],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "type": "str"
+ }
},
- {
- "id": "URL-HYPkR",
- "type": "genericNode",
- "position": {
- "x": 568.2971412887712,
- "y": 700.9983368007821
- },
- "data": {
- "type": "URL",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Any, Dict\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass URLComponent(CustomComponent):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"urls\": {\"display_name\": \"URL\"},\n }\n\n def build(\n self,\n urls: list[str],\n ) -> list[Record]:\n loader = WebBaseLoader(web_paths=urls)\n docs = loader.load()\n records = self.to_records(docs)\n self.status = records\n return records\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "urls": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "urls",
- "display_name": "URL",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": [
- "https://www.promptingguide.ai/techniques/prompt_chaining"
- ]
- },
- "_type": "CustomComponent"
- },
- "description": "Fetch content from one or more URLs.",
- "icon": "layout-template",
- "base_classes": [
- "Record"
- ],
- "display_name": "URL",
- "documentation": "",
- "custom_fields": {
- "urls": null
- },
- "output_types": [
- "Record"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "URL-HYPkR"
- },
- "selected": false,
- "width": 384,
- "height": 281,
- "positionAbsolute": {
- "x": 568.2971412887712,
- "y": 700.9983368007821
- },
- "dragging": false
+ "description": "Create a prompt template with dynamic variables.",
+ "icon": "prompts",
+ "is_input": null,
+ "is_output": null,
+ "is_composition": null,
+ "base_classes": ["object", "Text", "str"],
+ "name": "",
+ "display_name": "Prompt",
+ "documentation": "",
+ "custom_fields": {
+ "template": ["reference_1", "reference_2", "instructions"]
},
- {
- "id": "ChatOutput-JPlxl",
- "type": "genericNode",
- "position": {
- "x": 2503.8617424688505,
- "y": 789.3005578928434
- },
- "data": {
- "type": "ChatOutput",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "input_value": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Message",
- "advanced": false,
- "input_types": [
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "record_template": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "{text}",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "record_template",
- "display_name": "Record Template",
- "advanced": true,
- "dynamic": false,
- "info": "In case of Message being a Record, this template will be used to convert it to text.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "return_record": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "return_record",
- "display_name": "Return Record",
- "advanced": true,
- "dynamic": false,
- "info": "Return the message as a record containing the sender, sender_name, and session_id.",
- "load_from_db": false,
- "title_case": false
- },
- "sender": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "Machine",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "Machine",
- "User"
- ],
- "name": "sender",
- "display_name": "Sender Type",
- "advanced": true,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "sender_name": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "AI",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "sender_name",
- "display_name": "Sender Name",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "session_id": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "session_id",
- "display_name": "Session ID",
- "advanced": true,
- "dynamic": false,
- "info": "If provided, the message will be stored in the memory.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "_type": "CustomComponent"
- },
- "description": "Display a chat message in the Playground.",
- "icon": "ChatOutput",
- "base_classes": [
- "Text",
- "Record",
- "object",
- "str"
- ],
- "display_name": "Chat Output",
- "documentation": "",
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- "sender_name": null,
- "input_value": null,
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- "output_types": [
- "Text",
- "Record"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "ChatOutput-JPlxl"
- },
- "selected": false,
- "width": 384,
- "height": 383
- },
- {
- "id": "OpenAIModel-gi29P",
- "type": "genericNode",
- "position": {
- "x": 1917.7089968570963,
- "y": 575.9186499244129
- },
- "data": {
- "type": "OpenAIModel",
- "node": {
- "template": {
- "input_value": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Input",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional\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 NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"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 \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "max_tokens": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "1024",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "max_tokens",
- "display_name": "Max Tokens",
- "advanced": true,
- "dynamic": false,
- "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
- "load_from_db": false,
- "title_case": false
- },
- "model_kwargs": {
- "type": "NestedDict",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": {},
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "model_kwargs",
- "display_name": "Model Kwargs",
- "advanced": true,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "model_name": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "gpt-3.5-turbo-0125",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "gpt-4o",
- "gpt-4-turbo",
- "gpt-4-turbo-preview",
- "gpt-3.5-turbo",
- "gpt-3.5-turbo-0125"
- ],
- "name": "model_name",
- "display_name": "Model Name",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "openai_api_base": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "openai_api_base",
- "display_name": "OpenAI API Base",
- "advanced": true,
- "dynamic": false,
- "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "openai_api_key": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": true,
- "name": "openai_api_key",
- "display_name": "OpenAI API Key",
- "advanced": false,
- "dynamic": false,
- "info": "The OpenAI API Key to use for the OpenAI model.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "OPENAI_API_KEY"
- },
- "stream": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": true,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "stream",
- "display_name": "Stream",
- "advanced": true,
- "dynamic": false,
- "info": "Stream the response from the model. Streaming works only in Chat.",
- "load_from_db": false,
- "title_case": false
- },
- "system_message": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "system_message",
- "display_name": "System Message",
- "advanced": true,
- "dynamic": false,
- "info": "System message to pass to the model.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "temperature": {
- "type": "float",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "0.1",
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- "fileTypes": [],
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- "name": "Blog Writer",
- "last_tested_version": "1.0.0a0",
- "is_component": false
+ },
+ {
+ "id": "URL-HYPkR",
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+ "value": [
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+ "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
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+ "dynamic": true,
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+ "show": true,
+ "multiline": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Message",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "record_template": {
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+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "{text}",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "record_template",
+ "display_name": "Record Template",
+ "advanced": true,
+ "dynamic": false,
+ "info": "In case of Message being a Record, this template will be used to convert it to text.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "return_record": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "return_record",
+ "display_name": "Return Record",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Return the message as a record containing the sender, sender_name, and session_id.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "sender": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "Machine",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": ["Machine", "User"],
+ "name": "sender",
+ "display_name": "Sender Type",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "sender_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "AI",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "sender_name",
+ "display_name": "Sender Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "session_id": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "session_id",
+ "display_name": "Session ID",
+ "advanced": true,
+ "dynamic": false,
+ "info": "If provided, the message will be stored in the memory.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
+ },
+ "description": "Display a chat message in the Playground.",
+ "icon": "ChatOutput",
+ "base_classes": ["Text", "Record", "object", "str"],
+ "display_name": "Chat Output",
+ "documentation": "",
+ "custom_fields": {
+ "sender": null,
+ "sender_name": null,
+ "input_value": null,
+ "session_id": null,
+ "return_record": null,
+ "record_template": null
+ },
+ "output_types": ["Text", "Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "ChatOutput-JPlxl"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 383
+ },
+ {
+ "id": "OpenAIModel-gi29P",
+ "type": "genericNode",
+ "position": {
+ "x": 1917.7089968570963,
+ "y": 575.9186499244129
+ },
+ "data": {
+ "type": "OpenAIModel",
+ "node": {
+ "template": {
+ "input_value": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Input",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\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 NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"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 \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
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+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "max_tokens": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "1024",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "max_tokens",
+ "display_name": "Max Tokens",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_kwargs": {
+ "type": "NestedDict",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": {},
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "model_kwargs",
+ "display_name": "Model Kwargs",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "gpt-3.5-turbo-0125",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": [
+ "gpt-4o",
+ "gpt-4-turbo",
+ "gpt-4-turbo-preview",
+ "gpt-3.5-turbo",
+ "gpt-3.5-turbo-0125"
+ ],
+ "name": "model_name",
+ "display_name": "Model Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_base": {
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+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "openai_api_base",
+ "display_name": "OpenAI API Base",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_key": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": true,
+ "name": "openai_api_key",
+ "display_name": "OpenAI API Key",
+ "advanced": false,
+ "dynamic": false,
+ "info": "The OpenAI API Key to use for the OpenAI model.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": "OPENAI_API_KEY"
+ },
+ "stream": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "stream",
+ "display_name": "Stream",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Stream the response from the model. Streaming works only in Chat.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "system_message": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "system_message",
+ "display_name": "System Message",
+ "advanced": true,
+ "dynamic": false,
+ "info": "System message to pass to the model.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "temperature": {
+ "type": "float",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "0.1",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "temperature",
+ "display_name": "Temperature",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "rangeSpec": {
+ "step_type": "float",
+ "min": -1,
+ "max": 1,
+ "step": 0.1
+ },
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent"
+ },
+ "description": "Generates text using OpenAI LLMs.",
+ "icon": "OpenAI",
+ "base_classes": ["str", "Text", "object"],
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+ "max_tokens",
+ "model_kwargs",
+ "model_name",
+ "openai_api_base",
+ "openai_api_key",
+ "temperature",
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+ "stream"
+ ],
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+ },
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+ },
+ "selected": false,
+ "width": 384,
+ "height": 563,
+ "positionAbsolute": {
+ "x": 1917.7089968570963,
+ "y": 575.9186499244129
+ },
+ "dragging": false
+ },
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+ "position": {
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+ "y": 336.41463436122086
+ },
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+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Any, Dict\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass URLComponent(CustomComponent):\n display_name = \"URL\"\n description = \"Fetch content from one or more URLs.\"\n icon = \"layout-template\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"urls\": {\"display_name\": \"URL\"},\n }\n\n def build(\n self,\n urls: list[str],\n ) -> list[Record]:\n loader = WebBaseLoader(web_paths=urls)\n docs = loader.load()\n records = self.to_records(docs)\n self.status = records\n return records\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "urls": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "urls",
+ "display_name": "URL",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": ["https://www.promptingguide.ai/introduction/basics"]
+ },
+ "_type": "CustomComponent"
+ },
+ "description": "Fetch content from one or more URLs.",
+ "icon": "layout-template",
+ "base_classes": ["Record"],
+ "display_name": "URL",
+ "documentation": "",
+ "custom_fields": {
+ "urls": null
+ },
+ "output_types": ["Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "URL-2cX90"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 281,
+ "positionAbsolute": {
+ "x": 573.961301764604,
+ "y": 336.41463436122086
+ },
+ "dragging": false
+ },
+ {
+ "id": "TextInput-og8Or",
+ "type": "genericNode",
+ "position": {
+ "x": 569.9387927203336,
+ "y": 1095.3352160671316
+ },
+ "data": {
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+ "node": {
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+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[str] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "input_value": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "Use the references above for style to write a new blog/tutorial about prompt engineering techniques. Suggest non-covered topics.",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Value",
+ "advanced": false,
+ "input_types": ["Record", "Text"],
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+ "info": "Text or Record to be passed as input.",
+ "load_from_db": false,
+ "title_case": false
+ },
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+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "record_template",
+ "display_name": "Record Template",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
+ },
+ "description": "Get text inputs from the Playground.",
+ "icon": "type",
+ "base_classes": ["object", "Text", "str"],
+ "display_name": "Instructions",
+ "documentation": "",
+ "custom_fields": {
+ "input_value": null,
+ "record_template": null
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+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "TextInput-og8Or"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 289,
+ "positionAbsolute": {
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+ "dataType": "URL",
+ "id": "URL-HYPkR"
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+ "className": "stroke-gray-900 stroke-connection",
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+ }
+ },
+ "description": "This flow can be used to create a blog post following instructions from the user, using two other blogs as reference.",
+ "name": "Blog Writer",
+ "last_tested_version": "1.0.0a0",
+ "is_component": false
}
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json
index b7228b9e7..5fa8547a1 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json
@@ -1,1029 +1,933 @@
{
- "id": "fecbce42-6f11-454c-8ab2-db6eddbbbb0f",
- "data": {
- "nodes": [
- {
- "id": "Prompt-tHwPf",
- "type": "genericNode",
- "position": {
- "x": 585.7906101139403,
- "y": 117.52115876762832
- },
- "data": {
- "type": "Prompt",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "template": {
- "type": "prompt",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "Answer user's questions based on the document below:\n\n---\n\n{Document}\n\n---\n\nQuestion:\n{Question}\n\nAnswer:\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "template",
- "display_name": "Template",
- "advanced": false,
- "input_types": [
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "_type": "CustomComponent",
- "Document": {
- "field_type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "Document",
- "display_name": "Document",
- "advanced": false,
- "input_types": [
- "Document",
- "BaseOutputParser",
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "type": "str"
- },
- "Question": {
- "field_type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "Question",
- "display_name": "Question",
- "advanced": false,
- "input_types": [
- "Document",
- "BaseOutputParser",
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "type": "str"
- }
- },
- "description": "Create a prompt template with dynamic variables.",
- "icon": "prompts",
- "is_input": null,
- "is_output": null,
- "is_composition": null,
- "base_classes": [
- "object",
- "str",
- "Text"
- ],
- "name": "",
- "display_name": "Prompt",
- "documentation": "",
- "custom_fields": {
- "template": [
- "Document",
- "Question"
- ]
- },
- "output_types": [
- "Text"
- ],
- "full_path": null,
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false,
- "error": null
- },
- "id": "Prompt-tHwPf",
- "description": "A component for creating prompt templates using dynamic variables.",
- "display_name": "Prompt"
- },
- "selected": false,
- "width": 384,
- "height": 479,
- "positionAbsolute": {
- "x": 585.7906101139403,
- "y": 117.52115876762832
- },
- "dragging": false
+ "id": "fecbce42-6f11-454c-8ab2-db6eddbbbb0f",
+ "data": {
+ "nodes": [
+ {
+ "id": "Prompt-tHwPf",
+ "type": "genericNode",
+ "position": {
+ "x": 585.7906101139403,
+ "y": 117.52115876762832
+ },
+ "data": {
+ "type": "Prompt",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "template": {
+ "type": "prompt",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "Answer user's questions based on the document below:\n\n---\n\n{Document}\n\n---\n\nQuestion:\n{Question}\n\nAnswer:\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "template",
+ "display_name": "Template",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent",
+ "Document": {
+ "field_type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "Document",
+ "display_name": "Document",
+ "advanced": false,
+ "input_types": [
+ "Document",
+ "BaseOutputParser",
+ "Record",
+ "Text"
+ ],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "type": "str"
+ },
+ "Question": {
+ "field_type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "Question",
+ "display_name": "Question",
+ "advanced": false,
+ "input_types": [
+ "Document",
+ "BaseOutputParser",
+ "Record",
+ "Text"
+ ],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "type": "str"
+ }
},
- {
- "id": "File-6TEsD",
- "type": "genericNode",
- "position": {
- "x": -18.636536329280602,
- "y": 3.951948774836353
- },
- "data": {
- "type": "File",
- "node": {
- "template": {
- "path": {
- "type": "file",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [
- ".txt",
- ".md",
- ".mdx",
- ".csv",
- ".json",
- ".yaml",
- ".yml",
- ".xml",
- ".html",
- ".htm",
- ".pdf",
- ".docx"
- ],
- "password": false,
- "name": "path",
- "display_name": "Path",
- "advanced": false,
- "dynamic": false,
- "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx",
- "load_from_db": false,
- "title_case": false,
- "value": ""
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"Files\"\n description = \"A generic file loader.\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\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 record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "silent_errors": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "silent_errors",
- "display_name": "Silent Errors",
- "advanced": true,
- "dynamic": false,
- "info": "If true, errors will not raise an exception.",
- "load_from_db": false,
- "title_case": false
- },
- "_type": "CustomComponent"
- },
- "description": "A generic file loader.",
- "base_classes": [
- "Record"
- ],
- "display_name": "Files",
- "documentation": "",
- "custom_fields": {
- "path": null,
- "silent_errors": null
- },
- "output_types": [
- "Record"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "File-6TEsD"
- },
- "selected": false,
- "width": 384,
- "height": 282,
- "positionAbsolute": {
- "x": -18.636536329280602,
- "y": 3.951948774836353
- },
- "dragging": false
+ "description": "Create a prompt template with dynamic variables.",
+ "icon": "prompts",
+ "is_input": null,
+ "is_output": null,
+ "is_composition": null,
+ "base_classes": ["object", "str", "Text"],
+ "name": "",
+ "display_name": "Prompt",
+ "documentation": "",
+ "custom_fields": {
+ "template": ["Document", "Question"]
},
- {
- "id": "ChatInput-MsSJ9",
- "type": "genericNode",
- "position": {
- "x": -28.80036300619821,
- "y": 379.81180230285355
- },
- "data": {
- "type": "ChatInput",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "input_value": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Message",
- "advanced": false,
- "input_types": [],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "value": ""
- },
- "return_record": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "return_record",
- "display_name": "Return Record",
- "advanced": true,
- "dynamic": false,
- "info": "Return the message as a record containing the sender, sender_name, and session_id.",
- "load_from_db": false,
- "title_case": false
- },
- "sender": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "User",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "Machine",
- "User"
- ],
- "name": "sender",
- "display_name": "Sender Type",
- "advanced": true,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "sender_name": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "User",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "sender_name",
- "display_name": "Sender Name",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "session_id": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "session_id",
- "display_name": "Session ID",
- "advanced": true,
- "dynamic": false,
- "info": "If provided, the message will be stored in the memory.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "_type": "CustomComponent"
- },
- "description": "Get chat inputs from the Playground.",
- "icon": "ChatInput",
- "base_classes": [
- "str",
- "Record",
- "Text",
- "object"
- ],
- "display_name": "Chat Input",
- "documentation": "",
- "custom_fields": {
- "sender": null,
- "sender_name": null,
- "input_value": null,
- "session_id": null,
- "return_record": null
- },
- "output_types": [
- "Text",
- "Record"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "ChatInput-MsSJ9"
- },
- "selected": true,
- "width": 384,
- "height": 377,
- "positionAbsolute": {
- "x": -28.80036300619821,
- "y": 379.81180230285355
- },
- "dragging": false
+ "output_types": ["Text"],
+ "full_path": null,
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false,
+ "error": null
+ },
+ "id": "Prompt-tHwPf",
+ "description": "A component for creating prompt templates using dynamic variables.",
+ "display_name": "Prompt"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 479,
+ "positionAbsolute": {
+ "x": 585.7906101139403,
+ "y": 117.52115876762832
+ },
+ "dragging": false
+ },
+ {
+ "id": "File-6TEsD",
+ "type": "genericNode",
+ "position": {
+ "x": -18.636536329280602,
+ "y": 3.951948774836353
+ },
+ "data": {
+ "type": "File",
+ "node": {
+ "template": {
+ "path": {
+ "type": "file",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [
+ ".txt",
+ ".md",
+ ".mdx",
+ ".csv",
+ ".json",
+ ".yaml",
+ ".yml",
+ ".xml",
+ ".html",
+ ".htm",
+ ".pdf",
+ ".docx"
+ ],
+ "password": false,
+ "name": "path",
+ "display_name": "Path",
+ "advanced": false,
+ "dynamic": false,
+ "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx",
+ "load_from_db": false,
+ "title_case": false,
+ "value": ""
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"Files\"\n description = \"A generic file loader.\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\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 record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "silent_errors": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "silent_errors",
+ "display_name": "Silent Errors",
+ "advanced": true,
+ "dynamic": false,
+ "info": "If true, errors will not raise an exception.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent"
},
- {
- "id": "ChatOutput-F5Awj",
- "type": "genericNode",
- "position": {
- "x": 1733.3012915204283,
- "y": 168.76098809939327
- },
- "data": {
- "type": "ChatOutput",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
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- "value": "from typing import Optional\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 NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"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 \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
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+ "required": true,
+ "placeholder": "",
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+ "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
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- "id": "reactflow__edge-Prompt-tHwPf{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-tHwPf\u0153}-OpenAIModel-Bt067{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-Bt067\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}"
+ "output_types": ["Text", "Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "ChatInput-MsSJ9"
+ },
+ "selected": true,
+ "width": 384,
+ "height": 377,
+ "positionAbsolute": {
+ "x": -28.80036300619821,
+ "y": 379.81180230285355
+ },
+ "dragging": false
+ },
+ {
+ "id": "ChatOutput-F5Awj",
+ "type": "genericNode",
+ "position": {
+ "x": 1733.3012915204283,
+ "y": 168.76098809939327
+ },
+ "data": {
+ "type": "ChatOutput",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "input_value": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Message",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "return_record": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "return_record",
+ "display_name": "Return Record",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Return the message as a record containing the sender, sender_name, and session_id.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "sender": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "Machine",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": ["Machine", "User"],
+ "name": "sender",
+ "display_name": "Sender Type",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "sender_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "AI",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "sender_name",
+ "display_name": "Sender Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "session_id": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "session_id",
+ "display_name": "Session ID",
+ "advanced": true,
+ "dynamic": false,
+ "info": "If provided, the message will be stored in the memory.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
},
- {
- "source": "OpenAIModel-Bt067",
- "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-Bt067\u0153}",
- "target": "ChatOutput-F5Awj",
- "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-F5Awj\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
- "data": {
- "targetHandle": {
- "fieldName": "input_value",
- "id": "ChatOutput-F5Awj",
- "inputTypes": [
- "Text"
- ],
- "type": "str"
- },
- "sourceHandle": {
- "baseClasses": [
- "object",
- "str",
- "Text"
- ],
- "dataType": "OpenAIModel",
- "id": "OpenAIModel-Bt067"
- }
+ "description": "Display a chat message in the Playground.",
+ "icon": "ChatOutput",
+ "base_classes": ["str", "Record", "Text", "object"],
+ "display_name": "Chat Output",
+ "documentation": "",
+ "custom_fields": {
+ "sender": null,
+ "sender_name": null,
+ "input_value": null,
+ "session_id": null,
+ "return_record": null
+ },
+ "output_types": ["Text", "Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "ChatOutput-F5Awj"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 385,
+ "positionAbsolute": {
+ "x": 1733.3012915204283,
+ "y": 168.76098809939327
+ },
+ "dragging": false
+ },
+ {
+ "id": "OpenAIModel-Bt067",
+ "type": "genericNode",
+ "position": {
+ "x": 1137.6078582863759,
+ "y": -14.41920034020356
+ },
+ "data": {
+ "type": "OpenAIModel",
+ "node": {
+ "template": {
+ "input_value": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Input",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\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 NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"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 \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "max_tokens": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 256,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "max_tokens",
+ "display_name": "Max Tokens",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_kwargs": {
+ "type": "NestedDict",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": {},
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "model_kwargs",
+ "display_name": "Model Kwargs",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "gpt-4-turbo-preview",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": [
+ "gpt-4o",
+ "gpt-4-turbo",
+ "gpt-4-turbo-preview",
+ "gpt-3.5-turbo",
+ "gpt-3.5-turbo-0125"
+ ],
+ "name": "model_name",
+ "display_name": "Model Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_base": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "openai_api_base",
+ "display_name": "OpenAI API Base",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_key": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": true,
+ "name": "openai_api_key",
+ "display_name": "OpenAI API Key",
+ "advanced": false,
+ "dynamic": false,
+ "info": "The OpenAI API Key to use for the OpenAI model.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": "OPENAI_API_KEY"
+ },
+ "stream": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "stream",
+ "display_name": "Stream",
+ "advanced": false,
+ "dynamic": false,
+ "info": "Stream the response from the model. Streaming works only in Chat.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "system_message": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "system_message",
+ "display_name": "System Message",
+ "advanced": true,
+ "dynamic": false,
+ "info": "System message to pass to the model.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "temperature": {
+ "type": "float",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 0.1,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "temperature",
+ "display_name": "Temperature",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "rangeSpec": {
+ "step_type": "float",
+ "min": -1,
+ "max": 1,
+ "step": 0.1
},
- "style": {
- "stroke": "#555"
- },
- "className": "stroke-gray-900 stroke-connection",
- "id": "reactflow__edge-OpenAIModel-Bt067{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-Bt067\u0153}-ChatOutput-F5Awj{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-F5Awj\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}"
- }
- ],
- "viewport": {
- "x": 352.20899206064655,
- "y": 56.054900898593075,
- "zoom": 0.9023391400011
- }
- },
- "description": "This flow integrates PDF reading with a language model to answer document-specific questions. Ideal for small-scale texts, it facilitates direct queries with immediate insights.",
- "name": "Document QA",
- "last_tested_version": "1.0.0a0",
- "is_component": false
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent"
+ },
+ "description": "Generates text using OpenAI LLMs.",
+ "icon": "OpenAI",
+ "base_classes": ["object", "str", "Text"],
+ "display_name": "OpenAI",
+ "documentation": "",
+ "custom_fields": {
+ "input_value": null,
+ "openai_api_key": null,
+ "temperature": null,
+ "model_name": null,
+ "max_tokens": null,
+ "model_kwargs": null,
+ "openai_api_base": null,
+ "stream": null,
+ "system_message": null
+ },
+ "output_types": ["Text"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [
+ "max_tokens",
+ "model_kwargs",
+ "model_name",
+ "openai_api_base",
+ "openai_api_key",
+ "temperature",
+ "input_value",
+ "system_message",
+ "stream"
+ ],
+ "beta": false
+ },
+ "id": "OpenAIModel-Bt067"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 642,
+ "positionAbsolute": {
+ "x": 1137.6078582863759,
+ "y": -14.41920034020356
+ },
+ "dragging": false
+ }
+ ],
+ "edges": [
+ {
+ "source": "ChatInput-MsSJ9",
+ "sourceHandle": "{œbaseClassesœ:[œstrœ,œRecordœ,œTextœ,œobjectœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-MsSJ9œ}",
+ "target": "Prompt-tHwPf",
+ "targetHandle": "{œfieldNameœ:œQuestionœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "Question",
+ "id": "Prompt-tHwPf",
+ "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["str", "Record", "Text", "object"],
+ "dataType": "ChatInput",
+ "id": "ChatInput-MsSJ9"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-ChatInput-MsSJ9{œbaseClassesœ:[œstrœ,œRecordœ,œTextœ,œobjectœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-MsSJ9œ}-Prompt-tHwPf{œfieldNameœ:œQuestionœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "File-6TEsD",
+ "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-6TEsDœ}",
+ "target": "Prompt-tHwPf",
+ "targetHandle": "{œfieldNameœ:œDocumentœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "Document",
+ "id": "Prompt-tHwPf",
+ "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["Record"],
+ "dataType": "File",
+ "id": "File-6TEsD"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-File-6TEsD{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-6TEsDœ}-Prompt-tHwPf{œfieldNameœ:œDocumentœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "Prompt-tHwPf",
+ "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-tHwPfœ}",
+ "target": "OpenAIModel-Bt067",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-Bt067œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "OpenAIModel-Bt067",
+ "inputTypes": ["Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["object", "str", "Text"],
+ "dataType": "Prompt",
+ "id": "Prompt-tHwPf"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-Prompt-tHwPf{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-tHwPfœ}-OpenAIModel-Bt067{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-Bt067œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "OpenAIModel-Bt067",
+ "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Bt067œ}",
+ "target": "ChatOutput-F5Awj",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-F5Awjœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "ChatOutput-F5Awj",
+ "inputTypes": ["Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["object", "str", "Text"],
+ "dataType": "OpenAIModel",
+ "id": "OpenAIModel-Bt067"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-OpenAIModel-Bt067{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Bt067œ}-ChatOutput-F5Awj{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-F5Awjœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}"
+ }
+ ],
+ "viewport": {
+ "x": 352.20899206064655,
+ "y": 56.054900898593075,
+ "zoom": 0.9023391400011
+ }
+ },
+ "description": "This flow integrates PDF reading with a language model to answer document-specific questions. Ideal for small-scale texts, it facilitates direct queries with immediate insights.",
+ "name": "Document QA",
+ "last_tested_version": "1.0.0a0",
+ "is_component": false
}
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json
index 235af8f6f..1002b721a 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json
@@ -1,1272 +1,1137 @@
{
- "id": "08d5cccf-d098-4367-b14b-1078429c9ed9",
- "icon": "\ud83e\udd16",
- "icon_bg_color": "#FFD700",
- "data": {
- "nodes": [
- {
- "id": "ChatInput-t7F8v",
- "type": "genericNode",
- "position": {
- "x": 1283.2700598313072,
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- },
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- "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
- "fileTypes": [],
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- "name": "return_record",
- "display_name": "Return Record",
- "advanced": true,
- "dynamic": false,
- "info": "Return the message as a record containing the sender, sender_name, and session_id.",
- "load_from_db": false,
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- "sender": {
- "type": "str",
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- "value": "User",
- "fileTypes": [],
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- "password": false,
- "options": [
- "Machine",
- "User"
- ],
- "name": "sender",
- "display_name": "Sender Type",
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- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "sender_name": {
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- "multiline": false,
- "value": "User",
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- "file_path": "",
- "password": false,
- "name": "sender_name",
- "display_name": "Sender Name",
- "advanced": false,
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- "input_types": [
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- "advanced": false,
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- "info": "If provided, the message will be stored in the memory.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "MySessionID"
- },
- "_type": "CustomComponent"
- },
- "description": "Get chat inputs from the Playground.",
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- "id": "ChatInput-t7F8v"
- },
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- "height": 469,
- "positionAbsolute": {
- "x": 1283.2700598313072,
- "y": 982.5953650473145
- },
- "dragging": false
+ "id": "08d5cccf-d098-4367-b14b-1078429c9ed9",
+ "icon": "🤖",
+ "icon_bg_color": "#FFD700",
+ "data": {
+ "nodes": [
+ {
+ "id": "ChatInput-t7F8v",
+ "type": "genericNode",
+ "position": {
+ "x": 1283.2700598313072,
+ "y": 982.5953650473145
+ },
+ "data": {
+ "type": "ChatInput",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "input_value": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Message",
+ "advanced": false,
+ "input_types": [],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "value": ""
+ },
+ "return_record": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "return_record",
+ "display_name": "Return Record",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Return the message as a record containing the sender, sender_name, and session_id.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "sender": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "User",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": ["Machine", "User"],
+ "name": "sender",
+ "display_name": "Sender Type",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "sender_name": {
+ "type": "str",
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+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "User",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "sender_name",
+ "display_name": "Sender Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "session_id": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "session_id",
+ "display_name": "Session ID",
+ "advanced": false,
+ "dynamic": false,
+ "info": "If provided, the message will be stored in the memory.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": "MySessionID"
+ },
+ "_type": "CustomComponent"
},
- {
- "id": "ChatOutput-P1jEe",
- "type": "genericNode",
- "position": {
- "x": 3154.916355514023,
- "y": 851.051882666333
- },
- "data": {
- "type": "ChatOutput",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
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- },
- "input_value": {
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- "show": true,
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- "file_path": "",
- "password": false,
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- "display_name": "Message",
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- "input_types": [
- "Text"
- ],
- "dynamic": false,
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- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "return_record",
- "display_name": "Return Record",
- "advanced": true,
- "dynamic": false,
- "info": "Return the message as a record containing the sender, sender_name, and session_id.",
- "load_from_db": false,
- "title_case": false
- },
- "sender": {
- "type": "str",
- "required": false,
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- "value": "Machine",
- "fileTypes": [],
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- "options": [
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- "name": "sender",
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- "multiline": false,
- "value": "AI",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "sender_name",
- "display_name": "Sender Name",
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- "info": "",
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- "input_types": [
- "Text"
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- "info": "If provided, the message will be stored in the memory.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "MySessionID"
- },
- "_type": "CustomComponent"
- },
- "description": "Display a chat message in the Playground.",
- "icon": "ChatOutput",
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- "Text",
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- "display_name": "Chat Output",
- "documentation": "",
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- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "ChatOutput-P1jEe"
- },
- "selected": false,
- "width": 384,
- "height": 477,
- "dragging": false,
- "positionAbsolute": {
- "x": 3154.916355514023,
- "y": 851.051882666333
- }
+ "description": "Get chat inputs from the Playground.",
+ "icon": "ChatInput",
+ "base_classes": ["Text", "object", "Record", "str"],
+ "display_name": "Chat Input",
+ "documentation": "",
+ "custom_fields": {
+ "sender": null,
+ "sender_name": null,
+ "input_value": null,
+ "session_id": null,
+ "return_record": null
},
- {
- "id": "MemoryComponent-cdA1J",
- "type": "genericNode",
- "position": {
- "x": 1289.9606870058817,
- "y": 442.16804561053766
- },
- "data": {
- "type": "MemoryComponent",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional\n\nfrom langflow.base.memory.memory import BaseMemoryComponent\nfrom langflow.field_typing import Text\nfrom langflow.helpers.record import records_to_text\nfrom langflow.memory import get_messages\nfrom langflow.schema.schema import Record\n\n\nclass MemoryComponent(BaseMemoryComponent):\n display_name = \"Chat Memory\"\n description = \"Retrieves stored chat messages given a specific Session ID.\"\n beta: bool = True\n icon = \"history\"\n\n def build_config(self):\n return {\n \"sender\": {\n \"options\": [\"Machine\", \"User\", \"Machine and User\"],\n \"display_name\": \"Sender Type\",\n },\n \"sender_name\": {\"display_name\": \"Sender Name\", \"advanced\": True},\n \"n_messages\": {\n \"display_name\": \"Number of Messages\",\n \"info\": \"Number of messages to retrieve.\",\n },\n \"session_id\": {\n \"display_name\": \"Session ID\",\n \"info\": \"Session ID of the chat history.\",\n \"input_types\": [\"Text\"],\n },\n \"order\": {\n \"options\": [\"Ascending\", \"Descending\"],\n \"display_name\": \"Order\",\n \"info\": \"Order of the messages.\",\n \"advanced\": True,\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def get_messages(self, **kwargs) -> list[Record]:\n # Validate kwargs by checking if it contains the correct keys\n if \"sender\" not in kwargs:\n kwargs[\"sender\"] = None\n if \"sender_name\" not in kwargs:\n kwargs[\"sender_name\"] = None\n if \"session_id\" not in kwargs:\n kwargs[\"session_id\"] = None\n if \"limit\" not in kwargs:\n kwargs[\"limit\"] = 5\n if \"order\" not in kwargs:\n kwargs[\"order\"] = \"Descending\"\n\n kwargs[\"order\"] = \"DESC\" if kwargs[\"order\"] == \"Descending\" else \"ASC\"\n if kwargs[\"sender\"] == \"Machine and User\":\n kwargs[\"sender\"] = None\n return get_messages(**kwargs)\n\n def build(\n self,\n sender: Optional[str] = \"Machine and User\",\n sender_name: Optional[str] = None,\n session_id: Optional[str] = None,\n n_messages: int = 5,\n order: Optional[str] = \"Descending\",\n record_template: Optional[str] = \"{sender_name}: {text}\",\n ) -> Text:\n messages = self.get_messages(\n sender=sender,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n messages_str = records_to_text(template=record_template or \"\", records=messages)\n self.status = messages_str\n return messages_str\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
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- "title_case": false
- },
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- "list": false,
- "show": true,
- "multiline": false,
- "value": 5,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "n_messages",
- "display_name": "Number of Messages",
- "advanced": false,
- "dynamic": false,
- "info": "Number of messages to retrieve.",
- "load_from_db": false,
- "title_case": false
- },
- "order": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "Descending",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "Ascending",
- "Descending"
- ],
- "name": "order",
- "display_name": "Order",
- "advanced": true,
- "dynamic": false,
- "info": "Order of the messages.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "record_template": {
- "type": "str",
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- "list": false,
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- "value": "{sender_name}: {text}",
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- "file_path": "",
- "password": false,
- "name": "record_template",
- "display_name": "Record Template",
- "advanced": true,
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- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "sender": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "Machine and User",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "Machine",
- "User",
- "Machine and User"
- ],
- "name": "sender",
- "display_name": "Sender Type",
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- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
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- "sender_name": {
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- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "sender_name",
- "display_name": "Sender Name",
- "advanced": true,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "session_id": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "session_id",
- "display_name": "Session ID",
- "advanced": false,
- "input_types": [
- "Text"
- ],
- "dynamic": false,
- "info": "Session ID of the chat history.",
- "load_from_db": false,
- "title_case": false,
- "value": "MySessionID"
- },
- "_type": "CustomComponent"
- },
- "description": "Retrieves stored chat messages given a specific Session ID.",
- "icon": "history",
- "base_classes": [
- "str",
- "Text",
- "object"
- ],
- "display_name": "Chat Memory",
- "documentation": "",
- "custom_fields": {
- "sender": null,
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- "session_id": null,
- "n_messages": null,
- "order": null,
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- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": true
- },
- "id": "MemoryComponent-cdA1J",
- "description": "Retrieves stored chat messages given a specific Session ID.",
- "display_name": "Chat Memory"
- },
- "selected": false,
- "width": 384,
- "height": 489,
- "dragging": false,
- "positionAbsolute": {
- "x": 1289.9606870058817,
- "y": 442.16804561053766
- }
+ "output_types": ["Text", "Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "ChatInput-t7F8v"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 469,
+ "positionAbsolute": {
+ "x": 1283.2700598313072,
+ "y": 982.5953650473145
+ },
+ "dragging": false
+ },
+ {
+ "id": "ChatOutput-P1jEe",
+ "type": "genericNode",
+ "position": {
+ "x": 3154.916355514023,
+ "y": 851.051882666333
+ },
+ "data": {
+ "type": "ChatOutput",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "input_value": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Message",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "return_record": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "return_record",
+ "display_name": "Return Record",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Return the message as a record containing the sender, sender_name, and session_id.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "sender": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "Machine",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": ["Machine", "User"],
+ "name": "sender",
+ "display_name": "Sender Type",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "sender_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "AI",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "sender_name",
+ "display_name": "Sender Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "session_id": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "session_id",
+ "display_name": "Session ID",
+ "advanced": false,
+ "dynamic": false,
+ "info": "If provided, the message will be stored in the memory.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": "MySessionID"
+ },
+ "_type": "CustomComponent"
},
- {
- "id": "Prompt-ODkUx",
- "type": "genericNode",
- "position": {
- "x": 1894.594426342426,
- "y": 753.3797365481901
- },
- "data": {
- "type": "Prompt",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "template": {
- "type": "prompt",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "{context}\n\nUser: {user_message}\nAI: ",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "template",
- "display_name": "Template",
- "advanced": false,
- "input_types": [
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "_type": "CustomComponent",
- "context": {
- "field_type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "context",
- "display_name": "context",
- "advanced": false,
- "input_types": [
- "Document",
- "BaseOutputParser",
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "type": "str"
- },
- "user_message": {
- "field_type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "user_message",
- "display_name": "user_message",
- "advanced": false,
- "input_types": [
- "Document",
- "BaseOutputParser",
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "type": "str"
- }
- },
- "description": "Create a prompt template with dynamic variables.",
- "icon": "prompts",
- "is_input": null,
- "is_output": null,
- "is_composition": null,
- "base_classes": [
- "Text",
- "str",
- "object"
- ],
- "name": "",
- "display_name": "Prompt",
- "documentation": "",
- "custom_fields": {
- "template": [
- "context",
- "user_message"
- ]
- },
- "output_types": [
- "Text"
- ],
- "full_path": null,
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false,
- "error": null
- },
- "id": "Prompt-ODkUx",
- "description": "A component for creating prompt templates using dynamic variables.",
- "display_name": "Prompt"
- },
- "selected": false,
- "width": 384,
- "height": 477,
- "dragging": false,
- "positionAbsolute": {
- "x": 1894.594426342426,
- "y": 753.3797365481901
- }
+ "description": "Display a chat message in the Playground.",
+ "icon": "ChatOutput",
+ "base_classes": ["Text", "object", "Record", "str"],
+ "display_name": "Chat Output",
+ "documentation": "",
+ "custom_fields": {
+ "sender": null,
+ "sender_name": null,
+ "input_value": null,
+ "session_id": null,
+ "return_record": null
},
- {
- "id": "OpenAIModel-9RykF",
- "type": "genericNode",
- "position": {
- "x": 2561.5850334731617,
- "y": 553.2745131130916
- },
- "data": {
- "type": "OpenAIModel",
- "node": {
- "template": {
- "input_value": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Input",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional\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 NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"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 \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "max_tokens": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": 256,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "max_tokens",
- "display_name": "Max Tokens",
- "advanced": true,
- "dynamic": false,
- "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
- "load_from_db": false,
- "title_case": false
- },
- "model_kwargs": {
- "type": "NestedDict",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": {},
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "model_kwargs",
- "display_name": "Model Kwargs",
- "advanced": true,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "model_name": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "gpt-4-1106-preview",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "gpt-4o",
- "gpt-4-turbo",
- "gpt-4-turbo-preview",
- "gpt-3.5-turbo",
- "gpt-3.5-turbo-0125"
- ],
- "name": "model_name",
- "display_name": "Model Name",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "openai_api_base": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "openai_api_base",
- "display_name": "OpenAI API Base",
- "advanced": true,
- "dynamic": false,
- "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "openai_api_key": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": true,
- "name": "openai_api_key",
- "display_name": "OpenAI API Key",
- "advanced": false,
- "dynamic": false,
- "info": "The OpenAI API Key to use for the OpenAI model.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "OPENAI_API_KEY"
- },
- "stream": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "stream",
- "display_name": "Stream",
- "advanced": true,
- "dynamic": false,
- "info": "Stream the response from the model. Streaming works only in Chat.",
- "load_from_db": false,
- "title_case": false
- },
- "system_message": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "system_message",
- "display_name": "System Message",
- "advanced": true,
- "dynamic": false,
- "info": "System message to pass to the model.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "temperature": {
- "type": "float",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "0.2",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "temperature",
- "display_name": "Temperature",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "rangeSpec": {
- "step_type": "float",
- "min": -1,
- "max": 1,
- "step": 0.1
- },
- "load_from_db": false,
- "title_case": false
- },
- "_type": "CustomComponent"
- },
- "description": "Generates text using OpenAI LLMs.",
- "icon": "OpenAI",
- "base_classes": [
- "str",
- "object",
- "Text"
- ],
- "display_name": "OpenAI",
- "documentation": "",
- "custom_fields": {
- "input_value": null,
- "openai_api_key": null,
- "temperature": null,
- "model_name": null,
- "max_tokens": null,
- "model_kwargs": null,
- "openai_api_base": null,
- "stream": null,
- "system_message": null
- },
- "output_types": [
- "Text"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [
- "max_tokens",
- "model_kwargs",
- "model_name",
- "openai_api_base",
- "openai_api_key",
- "temperature",
- "input_value",
- "system_message",
- "stream"
- ],
- "beta": false
- },
- "id": "OpenAIModel-9RykF"
- },
- "selected": false,
- "width": 384,
- "height": 563,
- "positionAbsolute": {
- "x": 2561.5850334731617,
- "y": 553.2745131130916
- },
- "dragging": false
- },
- {
- "id": "TextOutput-vrs6T",
- "type": "genericNode",
- "position": {
- "x": 1911.4785906252087,
- "y": 247.39079954376987
- },
- "data": {
- "type": "TextOutput",
- "node": {
- "template": {
- "input_value": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Value",
- "advanced": false,
- "input_types": [
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "Text or Record to be passed as output.",
- "load_from_db": false,
- "title_case": false
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "record_template": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "record_template",
- "display_name": "Record Template",
- "advanced": true,
- "dynamic": false,
- "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "_type": "CustomComponent"
- },
- "description": "Display a text output in the Playground.",
- "icon": "type",
- "base_classes": [
- "str",
- "object",
- "Text"
- ],
- "display_name": "Inspect Memory",
- "documentation": "",
- "custom_fields": {
- "input_value": null,
- "record_template": null
- },
- "output_types": [
- "Text"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "TextOutput-vrs6T"
- },
- "selected": false,
- "width": 384,
- "height": 289,
- "positionAbsolute": {
- "x": 1911.4785906252087,
- "y": 247.39079954376987
- },
- "dragging": false
- }
- ],
- "edges": [
- {
- "source": "MemoryComponent-cdA1J",
- "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153MemoryComponent\u0153,\u0153id\u0153:\u0153MemoryComponent-cdA1J\u0153}",
- "target": "Prompt-ODkUx",
- "targetHandle": "{\u0153fieldName\u0153:\u0153context\u0153,\u0153id\u0153:\u0153Prompt-ODkUx\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
- "data": {
- "targetHandle": {
- "fieldName": "context",
- "type": "str",
- "id": "Prompt-ODkUx",
- "inputTypes": [
- "Document",
- "BaseOutputParser",
- "Record",
- "Text"
- ]
- },
- "sourceHandle": {
- "baseClasses": [
- "str",
- "Text",
- "object"
- ],
- "dataType": "MemoryComponent",
- "id": "MemoryComponent-cdA1J"
- }
- },
- "style": {
- "stroke": "#555"
- },
- "className": "stroke-gray-900 stroke-connection",
- "id": "reactflow__edge-MemoryComponent-cdA1J{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153MemoryComponent\u0153,\u0153id\u0153:\u0153MemoryComponent-cdA1J\u0153}-Prompt-ODkUx{\u0153fieldName\u0153:\u0153context\u0153,\u0153id\u0153:\u0153Prompt-ODkUx\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
- "selected": false
- },
- {
- "source": "ChatInput-t7F8v",
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- "target": "Prompt-ODkUx",
- "targetHandle": "{\u0153fieldName\u0153:\u0153user_message\u0153,\u0153id\u0153:\u0153Prompt-ODkUx\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
- "data": {
- "targetHandle": {
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- "dataType": "ChatInput",
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- "id": "reactflow__edge-ChatInput-t7F8v{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153object\u0153,\u0153Record\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-t7F8v\u0153}-Prompt-ODkUx{\u0153fieldName\u0153:\u0153user_message\u0153,\u0153id\u0153:\u0153Prompt-ODkUx\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
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- },
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- "source": "Prompt-ODkUx",
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- "target": "OpenAIModel-9RykF",
- "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-9RykF\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
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- "id": "OpenAIModel-9RykF",
- "inputTypes": [
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- "baseClasses": [
- "Text",
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- ],
- "dataType": "Prompt",
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- }
- },
- "style": {
- "stroke": "#555"
- },
- "className": "stroke-gray-900 stroke-connection",
- "id": "reactflow__edge-Prompt-ODkUx{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-ODkUx\u0153}-OpenAIModel-9RykF{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153OpenAIModel-9RykF\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}"
- },
- {
- "source": "OpenAIModel-9RykF",
- "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153object\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-9RykF\u0153}",
- "target": "ChatOutput-P1jEe",
- "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-P1jEe\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
- "data": {
- "targetHandle": {
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- "id": "ChatOutput-P1jEe",
- "inputTypes": [
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- "sourceHandle": {
- "baseClasses": [
- "str",
- "object",
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- ],
- "dataType": "OpenAIModel",
- "id": "OpenAIModel-9RykF"
- }
- },
- "style": {
- "stroke": "#555"
- },
- "className": "stroke-gray-900 stroke-connection",
- "id": "reactflow__edge-OpenAIModel-9RykF{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153object\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-9RykF\u0153}-ChatOutput-P1jEe{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-P1jEe\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}"
- },
- {
- "source": "MemoryComponent-cdA1J",
- "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153MemoryComponent\u0153,\u0153id\u0153:\u0153MemoryComponent-cdA1J\u0153}",
- "target": "TextOutput-vrs6T",
- "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-vrs6T\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
- "data": {
- "targetHandle": {
- "fieldName": "input_value",
- "id": "TextOutput-vrs6T",
- "inputTypes": [
- "Record",
- "Text"
- ],
- "type": "str"
- },
- "sourceHandle": {
- "baseClasses": [
- "str",
- "Text",
- "object"
- ],
- "dataType": "MemoryComponent",
- "id": "MemoryComponent-cdA1J"
- }
- },
- "style": {
- "stroke": "#555"
- },
- "className": "stroke-foreground stroke-connection",
- "id": "reactflow__edge-MemoryComponent-cdA1J{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153MemoryComponent\u0153,\u0153id\u0153:\u0153MemoryComponent-cdA1J\u0153}-TextOutput-vrs6T{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-vrs6T\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}"
- }
- ],
- "viewport": {
- "x": -569.862554459756,
- "y": -42.08339711050985,
- "zoom": 0.4868590524514978
+ "output_types": ["Text", "Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "ChatOutput-P1jEe"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 477,
+ "dragging": false,
+ "positionAbsolute": {
+ "x": 3154.916355514023,
+ "y": 851.051882666333
}
- },
- "description": "This project can be used as a starting point for building a Chat experience with user specific memory. You can set a different Session ID to start a new message history.",
- "name": "Memory Chatbot",
- "last_tested_version": "1.0.0a0",
- "is_component": false
+ },
+ {
+ "id": "MemoryComponent-cdA1J",
+ "type": "genericNode",
+ "position": {
+ "x": 1289.9606870058817,
+ "y": 442.16804561053766
+ },
+ "data": {
+ "type": "MemoryComponent",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\n\nfrom langflow.base.memory.memory import BaseMemoryComponent\nfrom langflow.field_typing import Text\nfrom langflow.helpers.record import records_to_text\nfrom langflow.memory import get_messages\nfrom langflow.schema.schema import Record\n\n\nclass MemoryComponent(BaseMemoryComponent):\n display_name = \"Chat Memory\"\n description = \"Retrieves stored chat messages given a specific Session ID.\"\n beta: bool = True\n icon = \"history\"\n\n def build_config(self):\n return {\n \"sender\": {\n \"options\": [\"Machine\", \"User\", \"Machine and User\"],\n \"display_name\": \"Sender Type\",\n },\n \"sender_name\": {\"display_name\": \"Sender Name\", \"advanced\": True},\n \"n_messages\": {\n \"display_name\": \"Number of Messages\",\n \"info\": \"Number of messages to retrieve.\",\n },\n \"session_id\": {\n \"display_name\": \"Session ID\",\n \"info\": \"Session ID of the chat history.\",\n \"input_types\": [\"Text\"],\n },\n \"order\": {\n \"options\": [\"Ascending\", \"Descending\"],\n \"display_name\": \"Order\",\n \"info\": \"Order of the messages.\",\n \"advanced\": True,\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def get_messages(self, **kwargs) -> list[Record]:\n # Validate kwargs by checking if it contains the correct keys\n if \"sender\" not in kwargs:\n kwargs[\"sender\"] = None\n if \"sender_name\" not in kwargs:\n kwargs[\"sender_name\"] = None\n if \"session_id\" not in kwargs:\n kwargs[\"session_id\"] = None\n if \"limit\" not in kwargs:\n kwargs[\"limit\"] = 5\n if \"order\" not in kwargs:\n kwargs[\"order\"] = \"Descending\"\n\n kwargs[\"order\"] = \"DESC\" if kwargs[\"order\"] == \"Descending\" else \"ASC\"\n if kwargs[\"sender\"] == \"Machine and User\":\n kwargs[\"sender\"] = None\n return get_messages(**kwargs)\n\n def build(\n self,\n sender: Optional[str] = \"Machine and User\",\n sender_name: Optional[str] = None,\n session_id: Optional[str] = None,\n n_messages: int = 5,\n order: Optional[str] = \"Descending\",\n record_template: Optional[str] = \"{sender_name}: {text}\",\n ) -> Text:\n messages = self.get_messages(\n sender=sender,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n messages_str = records_to_text(template=record_template or \"\", records=messages)\n self.status = messages_str\n return messages_str\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "n_messages": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 5,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "n_messages",
+ "display_name": "Number of Messages",
+ "advanced": false,
+ "dynamic": false,
+ "info": "Number of messages to retrieve.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "order": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "Descending",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": ["Ascending", "Descending"],
+ "name": "order",
+ "display_name": "Order",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Order of the messages.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "record_template": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "{sender_name}: {text}",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "record_template",
+ "display_name": "Record Template",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "sender": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "Machine and User",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": ["Machine", "User", "Machine and User"],
+ "name": "sender",
+ "display_name": "Sender Type",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "sender_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "sender_name",
+ "display_name": "Sender Name",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "session_id": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "session_id",
+ "display_name": "Session ID",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "Session ID of the chat history.",
+ "load_from_db": false,
+ "title_case": false,
+ "value": "MySessionID"
+ },
+ "_type": "CustomComponent"
+ },
+ "description": "Retrieves stored chat messages given a specific Session ID.",
+ "icon": "history",
+ "base_classes": ["str", "Text", "object"],
+ "display_name": "Chat Memory",
+ "documentation": "",
+ "custom_fields": {
+ "sender": null,
+ "sender_name": null,
+ "session_id": null,
+ "n_messages": null,
+ "order": null,
+ "record_template": null
+ },
+ "output_types": ["Text"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": true
+ },
+ "id": "MemoryComponent-cdA1J",
+ "description": "Retrieves stored chat messages given a specific Session ID.",
+ "display_name": "Chat Memory"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 489,
+ "dragging": false,
+ "positionAbsolute": {
+ "x": 1289.9606870058817,
+ "y": 442.16804561053766
+ }
+ },
+ {
+ "id": "Prompt-ODkUx",
+ "type": "genericNode",
+ "position": {
+ "x": 1894.594426342426,
+ "y": 753.3797365481901
+ },
+ "data": {
+ "type": "Prompt",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "template": {
+ "type": "prompt",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "{context}\n\nUser: {user_message}\nAI: ",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "template",
+ "display_name": "Template",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent",
+ "context": {
+ "field_type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "context",
+ "display_name": "context",
+ "advanced": false,
+ "input_types": [
+ "Document",
+ "BaseOutputParser",
+ "Record",
+ "Text"
+ ],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "type": "str"
+ },
+ "user_message": {
+ "field_type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "user_message",
+ "display_name": "user_message",
+ "advanced": false,
+ "input_types": [
+ "Document",
+ "BaseOutputParser",
+ "Record",
+ "Text"
+ ],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "type": "str"
+ }
+ },
+ "description": "Create a prompt template with dynamic variables.",
+ "icon": "prompts",
+ "is_input": null,
+ "is_output": null,
+ "is_composition": null,
+ "base_classes": ["Text", "str", "object"],
+ "name": "",
+ "display_name": "Prompt",
+ "documentation": "",
+ "custom_fields": {
+ "template": ["context", "user_message"]
+ },
+ "output_types": ["Text"],
+ "full_path": null,
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false,
+ "error": null
+ },
+ "id": "Prompt-ODkUx",
+ "description": "A component for creating prompt templates using dynamic variables.",
+ "display_name": "Prompt"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 477,
+ "dragging": false,
+ "positionAbsolute": {
+ "x": 1894.594426342426,
+ "y": 753.3797365481901
+ }
+ },
+ {
+ "id": "OpenAIModel-9RykF",
+ "type": "genericNode",
+ "position": {
+ "x": 2561.5850334731617,
+ "y": 553.2745131130916
+ },
+ "data": {
+ "type": "OpenAIModel",
+ "node": {
+ "template": {
+ "input_value": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Input",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\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 NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"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 \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "max_tokens": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 256,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "max_tokens",
+ "display_name": "Max Tokens",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_kwargs": {
+ "type": "NestedDict",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": {},
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "model_kwargs",
+ "display_name": "Model Kwargs",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "gpt-4-1106-preview",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": [
+ "gpt-4o",
+ "gpt-4-turbo",
+ "gpt-4-turbo-preview",
+ "gpt-3.5-turbo",
+ "gpt-3.5-turbo-0125"
+ ],
+ "name": "model_name",
+ "display_name": "Model Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_base": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "openai_api_base",
+ "display_name": "OpenAI API Base",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_key": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": true,
+ "name": "openai_api_key",
+ "display_name": "OpenAI API Key",
+ "advanced": false,
+ "dynamic": false,
+ "info": "The OpenAI API Key to use for the OpenAI model.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": "OPENAI_API_KEY"
+ },
+ "stream": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "stream",
+ "display_name": "Stream",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Stream the response from the model. Streaming works only in Chat.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "system_message": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "system_message",
+ "display_name": "System Message",
+ "advanced": true,
+ "dynamic": false,
+ "info": "System message to pass to the model.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "temperature": {
+ "type": "float",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "0.2",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "temperature",
+ "display_name": "Temperature",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "rangeSpec": {
+ "step_type": "float",
+ "min": -1,
+ "max": 1,
+ "step": 0.1
+ },
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent"
+ },
+ "description": "Generates text using OpenAI LLMs.",
+ "icon": "OpenAI",
+ "base_classes": ["str", "object", "Text"],
+ "display_name": "OpenAI",
+ "documentation": "",
+ "custom_fields": {
+ "input_value": null,
+ "openai_api_key": null,
+ "temperature": null,
+ "model_name": null,
+ "max_tokens": null,
+ "model_kwargs": null,
+ "openai_api_base": null,
+ "stream": null,
+ "system_message": null
+ },
+ "output_types": ["Text"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [
+ "max_tokens",
+ "model_kwargs",
+ "model_name",
+ "openai_api_base",
+ "openai_api_key",
+ "temperature",
+ "input_value",
+ "system_message",
+ "stream"
+ ],
+ "beta": false
+ },
+ "id": "OpenAIModel-9RykF"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 563,
+ "positionAbsolute": {
+ "x": 2561.5850334731617,
+ "y": 553.2745131130916
+ },
+ "dragging": false
+ },
+ {
+ "id": "TextOutput-vrs6T",
+ "type": "genericNode",
+ "position": {
+ "x": 1911.4785906252087,
+ "y": 247.39079954376987
+ },
+ "data": {
+ "type": "TextOutput",
+ "node": {
+ "template": {
+ "input_value": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Value",
+ "advanced": false,
+ "input_types": ["Record", "Text"],
+ "dynamic": false,
+ "info": "Text or Record to be passed as output.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "record_template": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "record_template",
+ "display_name": "Record Template",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
+ },
+ "description": "Display a text output in the Playground.",
+ "icon": "type",
+ "base_classes": ["str", "object", "Text"],
+ "display_name": "Inspect Memory",
+ "documentation": "",
+ "custom_fields": {
+ "input_value": null,
+ "record_template": null
+ },
+ "output_types": ["Text"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "TextOutput-vrs6T"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 289,
+ "positionAbsolute": {
+ "x": 1911.4785906252087,
+ "y": 247.39079954376987
+ },
+ "dragging": false
+ }
+ ],
+ "edges": [
+ {
+ "source": "MemoryComponent-cdA1J",
+ "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}",
+ "target": "Prompt-ODkUx",
+ "targetHandle": "{œfieldNameœ:œcontextœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "context",
+ "type": "str",
+ "id": "Prompt-ODkUx",
+ "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"]
+ },
+ "sourceHandle": {
+ "baseClasses": ["str", "Text", "object"],
+ "dataType": "MemoryComponent",
+ "id": "MemoryComponent-cdA1J"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-MemoryComponent-cdA1J{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}-Prompt-ODkUx{œfieldNameœ:œcontextœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
+ "selected": false
+ },
+ {
+ "source": "ChatInput-t7F8v",
+ "sourceHandle": "{œbaseClassesœ:[œTextœ,œobjectœ,œRecordœ,œstrœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-t7F8vœ}",
+ "target": "Prompt-ODkUx",
+ "targetHandle": "{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "user_message",
+ "type": "str",
+ "id": "Prompt-ODkUx",
+ "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"]
+ },
+ "sourceHandle": {
+ "baseClasses": ["Text", "object", "Record", "str"],
+ "dataType": "ChatInput",
+ "id": "ChatInput-t7F8v"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-ChatInput-t7F8v{œbaseClassesœ:[œTextœ,œobjectœ,œRecordœ,œstrœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-t7F8vœ}-Prompt-ODkUx{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
+ "selected": false
+ },
+ {
+ "source": "Prompt-ODkUx",
+ "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-ODkUxœ}",
+ "target": "OpenAIModel-9RykF",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-9RykFœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "OpenAIModel-9RykF",
+ "inputTypes": ["Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["Text", "str", "object"],
+ "dataType": "Prompt",
+ "id": "Prompt-ODkUx"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-Prompt-ODkUx{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-ODkUxœ}-OpenAIModel-9RykF{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-9RykFœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "OpenAIModel-9RykF",
+ "sourceHandle": "{œbaseClassesœ:[œstrœ,œobjectœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-9RykFœ}",
+ "target": "ChatOutput-P1jEe",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-P1jEeœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "ChatOutput-P1jEe",
+ "inputTypes": ["Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["str", "object", "Text"],
+ "dataType": "OpenAIModel",
+ "id": "OpenAIModel-9RykF"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-OpenAIModel-9RykF{œbaseClassesœ:[œstrœ,œobjectœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-9RykFœ}-ChatOutput-P1jEe{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-P1jEeœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "MemoryComponent-cdA1J",
+ "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}",
+ "target": "TextOutput-vrs6T",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-vrs6Tœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "TextOutput-vrs6T",
+ "inputTypes": ["Record", "Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["str", "Text", "object"],
+ "dataType": "MemoryComponent",
+ "id": "MemoryComponent-cdA1J"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-foreground stroke-connection",
+ "id": "reactflow__edge-MemoryComponent-cdA1J{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}-TextOutput-vrs6T{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-vrs6Tœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}"
+ }
+ ],
+ "viewport": {
+ "x": -569.862554459756,
+ "y": -42.08339711050985,
+ "zoom": 0.4868590524514978
+ }
+ },
+ "description": "This project can be used as a starting point for building a Chat experience with user specific memory. You can set a different Session ID to start a new message history.",
+ "name": "Memory Chatbot",
+ "last_tested_version": "1.0.0a0",
+ "is_component": false
}
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json
index dd1b1307f..d49eba054 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json
@@ -1,1769 +1,1586 @@
{
- "id": "85392e54-20f3-4ab5-a179-cb4bef16f639",
- "data": {
- "nodes": [
- {
- "id": "Prompt-amqBu",
- "type": "genericNode",
- "position": {
- "x": 2191.5837146441663,
- "y": 1047.9307944451873
- },
- "data": {
- "type": "Prompt",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "template": {
- "type": "prompt",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "You are a helpful assistant. Given a long document, your task is to create a concise summary that captures the main points and key details. The summary should be clear, accurate, and succinct. Please provide the summary in the format below:\n####\n{document}\n####\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "template",
- "display_name": "Template",
- "advanced": false,
- "input_types": [
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "_type": "CustomComponent",
- "document": {
- "field_type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "document",
- "display_name": "document",
- "advanced": false,
- "input_types": [
- "Document",
- "BaseOutputParser",
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "type": "str"
- }
- },
- "description": "Create a prompt template with dynamic variables.",
- "icon": "prompts",
- "is_input": null,
- "is_output": null,
- "is_composition": null,
- "base_classes": [
- "object",
- "str",
- "Text"
- ],
- "name": "",
- "display_name": "Prompt",
- "documentation": "",
- "custom_fields": {
- "template": [
- "document"
- ]
- },
- "output_types": [
- "Text"
- ],
- "full_path": null,
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false,
- "error": null
- },
- "id": "Prompt-amqBu",
- "description": "Create a prompt template with dynamic variables.",
- "display_name": "Prompt"
- },
- "selected": false,
- "width": 384,
- "height": 385,
- "positionAbsolute": {
- "x": 2191.5837146441663,
- "y": 1047.9307944451873
- },
- "dragging": false
+ "id": "85392e54-20f3-4ab5-a179-cb4bef16f639",
+ "data": {
+ "nodes": [
+ {
+ "id": "Prompt-amqBu",
+ "type": "genericNode",
+ "position": {
+ "x": 2191.5837146441663,
+ "y": 1047.9307944451873
+ },
+ "data": {
+ "type": "Prompt",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "template": {
+ "type": "prompt",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "You are a helpful assistant. Given a long document, your task is to create a concise summary that captures the main points and key details. The summary should be clear, accurate, and succinct. Please provide the summary in the format below:\n####\n{document}\n####\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "template",
+ "display_name": "Template",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent",
+ "document": {
+ "field_type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "document",
+ "display_name": "document",
+ "advanced": false,
+ "input_types": [
+ "Document",
+ "BaseOutputParser",
+ "Record",
+ "Text"
+ ],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "type": "str"
+ }
},
- {
- "id": "Prompt-gTNiz",
- "type": "genericNode",
- "position": {
- "x": 3731.0813766902447,
- "y": 799.631909121391
- },
- "data": {
- "type": "Prompt",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "template": {
- "type": "prompt",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "Given a summary of an article, please create two multiple-choice questions that cover the key points and details mentioned. Ensure the questions are clear and provide three options (A, B, C), with one correct answer.\n####\n{summary}\n####",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "template",
- "display_name": "Template",
- "advanced": false,
- "input_types": [
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "_type": "CustomComponent",
- "summary": {
- "field_type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "summary",
- "display_name": "summary",
- "advanced": false,
- "input_types": [
- "Document",
- "BaseOutputParser",
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "type": "str"
- }
- },
- "description": "Create a prompt template with dynamic variables.",
- "icon": "prompts",
- "is_input": null,
- "is_output": null,
- "is_composition": null,
- "base_classes": [
- "object",
- "str",
- "Text"
- ],
- "name": "",
- "display_name": "Prompt",
- "documentation": "",
- "custom_fields": {
- "template": [
- "summary"
- ]
- },
- "output_types": [
- "Text"
- ],
- "full_path": null,
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false,
- "error": null
- },
- "id": "Prompt-gTNiz",
- "description": "Create a prompt template with dynamic variables.",
- "display_name": "Prompt"
- },
- "selected": false,
- "width": 384,
- "height": 385,
- "dragging": false
+ "description": "Create a prompt template with dynamic variables.",
+ "icon": "prompts",
+ "is_input": null,
+ "is_output": null,
+ "is_composition": null,
+ "base_classes": ["object", "str", "Text"],
+ "name": "",
+ "display_name": "Prompt",
+ "documentation": "",
+ "custom_fields": {
+ "template": ["document"]
},
- {
- "id": "ChatOutput-EJkG3",
- "type": "genericNode",
- "position": {
- "x": 3722.1747844849388,
- "y": 1283.413553222214
- },
- "data": {
- "type": "ChatOutput",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "input_value": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Message",
- "advanced": false,
- "input_types": [
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "record_template": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "{text}",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "record_template",
- "display_name": "Record Template",
- "advanced": true,
- "dynamic": false,
- "info": "In case of Message being a Record, this template will be used to convert it to text.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "return_record": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "return_record",
- "display_name": "Return Record",
- "advanced": true,
- "dynamic": false,
- "info": "Return the message as a record containing the sender, sender_name, and session_id.",
- "load_from_db": false,
- "title_case": false
- },
- "sender": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "Machine",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "Machine",
- "User"
- ],
- "name": "sender",
- "display_name": "Sender Type",
- "advanced": true,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "sender_name": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "Summarizer",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "sender_name",
- "display_name": "Sender Name",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "session_id": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "session_id",
- "display_name": "Session ID",
- "advanced": true,
- "dynamic": false,
- "info": "If provided, the message will be stored in the memory.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "_type": "CustomComponent"
- },
- "description": "Display a chat message in the Playground.",
- "icon": "ChatOutput",
- "base_classes": [
- "object",
- "Record",
- "Text",
- "str"
- ],
- "display_name": "Chat Output",
- "documentation": "",
- "custom_fields": {
- "sender": null,
- "sender_name": null,
- "input_value": null,
- "session_id": null,
- "return_record": null,
- "record_template": null
- },
- "output_types": [
- "Text",
- "Record"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "ChatOutput-EJkG3"
- },
- "selected": false,
- "width": 384,
- "height": 385,
- "dragging": false
+ "output_types": ["Text"],
+ "full_path": null,
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false,
+ "error": null
+ },
+ "id": "Prompt-amqBu",
+ "description": "Create a prompt template with dynamic variables.",
+ "display_name": "Prompt"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 385,
+ "positionAbsolute": {
+ "x": 2191.5837146441663,
+ "y": 1047.9307944451873
+ },
+ "dragging": false
+ },
+ {
+ "id": "Prompt-gTNiz",
+ "type": "genericNode",
+ "position": {
+ "x": 3731.0813766902447,
+ "y": 799.631909121391
+ },
+ "data": {
+ "type": "Prompt",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "template": {
+ "type": "prompt",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "Given a summary of an article, please create two multiple-choice questions that cover the key points and details mentioned. Ensure the questions are clear and provide three options (A, B, C), with one correct answer.\n####\n{summary}\n####",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "template",
+ "display_name": "Template",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent",
+ "summary": {
+ "field_type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "summary",
+ "display_name": "summary",
+ "advanced": false,
+ "input_types": [
+ "Document",
+ "BaseOutputParser",
+ "Record",
+ "Text"
+ ],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "type": "str"
+ }
},
- {
- "id": "ChatOutput-DNmvg",
- "type": "genericNode",
- "position": {
- "x": 5077.71285886074,
- "y": 1232.9152769735522
- },
- "data": {
- "type": "ChatOutput",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "input_value": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Message",
- "advanced": false,
- "input_types": [
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "record_template": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "{text}",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "record_template",
- "display_name": "Record Template",
- "advanced": true,
- "dynamic": false,
- "info": "In case of Message being a Record, this template will be used to convert it to text.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "return_record": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "return_record",
- "display_name": "Return Record",
- "advanced": true,
- "dynamic": false,
- "info": "Return the message as a record containing the sender, sender_name, and session_id.",
- "load_from_db": false,
- "title_case": false
- },
- "sender": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "Machine",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "Machine",
- "User"
- ],
- "name": "sender",
- "display_name": "Sender Type",
- "advanced": true,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "sender_name": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "Question Generator",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "sender_name",
- "display_name": "Sender Name",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "session_id": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "session_id",
- "display_name": "Session ID",
- "advanced": true,
- "dynamic": false,
- "info": "If provided, the message will be stored in the memory.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "_type": "CustomComponent"
- },
- "description": "Display a chat message in the Playground.",
- "icon": "ChatOutput",
- "base_classes": [
- "object",
- "Record",
- "Text",
- "str"
- ],
- "display_name": "Chat Output",
- "documentation": "",
- "custom_fields": {
- "sender": null,
- "sender_name": null,
- "input_value": null,
- "session_id": null,
- "return_record": null,
- "record_template": null
- },
- "output_types": [
- "Text",
- "Record"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "ChatOutput-DNmvg"
- },
- "selected": false,
- "width": 384,
- "height": 385
+ "description": "Create a prompt template with dynamic variables.",
+ "icon": "prompts",
+ "is_input": null,
+ "is_output": null,
+ "is_composition": null,
+ "base_classes": ["object", "str", "Text"],
+ "name": "",
+ "display_name": "Prompt",
+ "documentation": "",
+ "custom_fields": {
+ "template": ["summary"]
},
- {
- "id": "TextInput-sptaH",
- "type": "genericNode",
- "position": {
- "x": 1700.5624822024752,
- "y": 1039.603088937466
- },
- "data": {
- "type": "TextInput",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[Text] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "input_value": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "Revolutionary Nano-Battery Technology Unveiled In a groundbreaking announcement yesterday, researchers from the fictional Tech Innovations Institute revealed the development of a new nano-battery technology that promises to revolutionize energy storage. The new battery, dubbed the \"EnerGCell\", uses advanced nanomaterials to achieve unprecedented efficiency and storage capacities. According to lead researcher Dr. Ada Byron, the EnerGCell can store up to ten times more energy than the best lithium-ion batteries available today, while charging in just a fraction of the time. \"We're talking about charging your electric vehicle in just five minutes for a range of over 1,000 miles,\" Dr. Byron stated during the press conference. The technology behind the EnerGCell involves a complex arrangement of nanostructured electrodes that allow for rapid ion transfer and extremely high energy density. This breakthrough was achieved after a decade of research into nanomaterials and their applications in energy storage. The implications of this technology are vast, promising to accelerate the adoption of renewable energy by making it more practical and affordable to store wind and solar power. It could also lead to significant advancements in electric vehicles, mobile devices, and any other technology that relies on batteries. Despite the excitement, some experts are calling for patience, noting that the EnerGCell is still in its early stages of development and may take several years before it's commercially available. However, the potential impact of such a technology on the environment and the global economy is undeniable. Tech Innovations Institute plans to continue refining the EnerGCell and begin pilot projects with select partners in the coming year. If successful, this nano-battery technology could indeed be the breakthrough needed to usher in a new era of clean energy and technology.",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Value",
- "advanced": false,
- "input_types": [
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- "Text"
- ],
- "dynamic": false,
- "info": "Text or Record to be passed as input.",
- "load_from_db": false,
- "title_case": false
- },
- "record_template": {
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- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "record_template",
- "display_name": "Record Template",
- "advanced": true,
- "dynamic": false,
- "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "_type": "CustomComponent"
- },
- "description": "Get text inputs from the Playground.",
- "icon": "type",
- "base_classes": [
- "str",
- "Text",
- "object"
- ],
- "display_name": "Text Input",
- "documentation": "",
- "custom_fields": {
- "input_value": null,
- "record_template": null
- },
- "output_types": [
- "Text"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "TextInput-sptaH"
- },
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- "width": 384,
- "height": 290,
- "positionAbsolute": {
- "x": 1700.5624822024752,
- "y": 1039.603088937466
- },
- "dragging": false
+ "output_types": ["Text"],
+ "full_path": null,
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false,
+ "error": null
+ },
+ "id": "Prompt-gTNiz",
+ "description": "Create a prompt template with dynamic variables.",
+ "display_name": "Prompt"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 385,
+ "dragging": false
+ },
+ {
+ "id": "ChatOutput-EJkG3",
+ "type": "genericNode",
+ "position": {
+ "x": 3722.1747844849388,
+ "y": 1283.413553222214
+ },
+ "data": {
+ "type": "ChatOutput",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "input_value": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Message",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "record_template": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "{text}",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "record_template",
+ "display_name": "Record Template",
+ "advanced": true,
+ "dynamic": false,
+ "info": "In case of Message being a Record, this template will be used to convert it to text.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "return_record": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "return_record",
+ "display_name": "Return Record",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Return the message as a record containing the sender, sender_name, and session_id.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "sender": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "Machine",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": ["Machine", "User"],
+ "name": "sender",
+ "display_name": "Sender Type",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "sender_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "Summarizer",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "sender_name",
+ "display_name": "Sender Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "session_id": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "session_id",
+ "display_name": "Session ID",
+ "advanced": true,
+ "dynamic": false,
+ "info": "If provided, the message will be stored in the memory.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
},
- {
- "id": "TextOutput-2MS4a",
- "type": "genericNode",
- "position": {
- "x": 2917.216113690115,
- "y": 513.0058511435552
- },
- "data": {
- "type": "TextOutput",
- "node": {
- "template": {
- "input_value": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Value",
- "advanced": false,
- "input_types": [
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "Text or Record to be passed as output.",
- "load_from_db": false,
- "title_case": false
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "record_template": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "record_template",
- "display_name": "Record Template",
- "advanced": true,
- "dynamic": false,
- "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "_type": "CustomComponent"
- },
- "description": "Display a text output in the Playground.",
- "icon": "type",
- "base_classes": [
- "str",
- "Text",
- "object"
- ],
- "display_name": "First Prompt",
- "documentation": "",
- "custom_fields": {
- "input_value": null,
- "record_template": null
- },
- "output_types": [
- "Text"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "TextOutput-2MS4a"
- },
- "selected": false,
- "width": 384,
- "height": 290,
- "positionAbsolute": {
- "x": 2917.216113690115,
- "y": 513.0058511435552
- },
- "dragging": false
+ "description": "Display a chat message in the Playground.",
+ "icon": "ChatOutput",
+ "base_classes": ["object", "Record", "Text", "str"],
+ "display_name": "Chat Output",
+ "documentation": "",
+ "custom_fields": {
+ "sender": null,
+ "sender_name": null,
+ "input_value": null,
+ "session_id": null,
+ "return_record": null,
+ "record_template": null
},
- {
- "id": "OpenAIModel-uYXZJ",
- "type": "genericNode",
- "position": {
- "x": 2925.784767523062,
- "y": 933.6465680967775
- },
- "data": {
- "type": "OpenAIModel",
- "node": {
- "template": {
- "input_value": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Input",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional\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 NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"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 \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "max_tokens": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": 256,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "max_tokens",
- "display_name": "Max Tokens",
- "advanced": true,
- "dynamic": false,
- "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
- "load_from_db": false,
- "title_case": false
- },
- "model_kwargs": {
- "type": "NestedDict",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": {},
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "model_kwargs",
- "display_name": "Model Kwargs",
- "advanced": true,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "model_name": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "gpt-4-turbo-preview",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "gpt-4o",
- "gpt-4-turbo",
- "gpt-4-turbo-preview",
- "gpt-3.5-turbo",
- "gpt-3.5-turbo-0125"
- ],
- "name": "model_name",
- "display_name": "Model Name",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "openai_api_base": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "openai_api_base",
- "display_name": "OpenAI API Base",
- "advanced": true,
- "dynamic": false,
- "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "openai_api_key": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": true,
- "name": "openai_api_key",
- "display_name": "OpenAI API Key",
- "advanced": false,
- "dynamic": false,
- "info": "The OpenAI API Key to use for the OpenAI model.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "OPENAI_API_KEY"
- },
- "stream": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "stream",
- "display_name": "Stream",
- "advanced": true,
- "dynamic": false,
- "info": "Stream the response from the model. Streaming works only in Chat.",
- "load_from_db": false,
- "title_case": false
- },
- "system_message": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "system_message",
- "display_name": "System Message",
- "advanced": true,
- "dynamic": false,
- "info": "System message to pass to the model.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "temperature": {
- "type": "float",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": 0.1,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "temperature",
- "display_name": "Temperature",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "rangeSpec": {
- "step_type": "float",
- "min": -1,
- "max": 1,
- "step": 0.1
- },
- "load_from_db": false,
- "title_case": false
- },
- "_type": "CustomComponent"
- },
- "description": "Generates text using OpenAI LLMs.",
- "icon": "OpenAI",
- "base_classes": [
- "str",
- "Text",
- "object"
- ],
- "display_name": "OpenAI",
- "documentation": "",
- "custom_fields": {
- "input_value": null,
- "openai_api_key": null,
- "temperature": null,
- "model_name": null,
- "max_tokens": null,
- "model_kwargs": null,
- "openai_api_base": null,
- "stream": null,
- "system_message": null
- },
- "output_types": [
- "Text"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [
- "max_tokens",
- "model_kwargs",
- "model_name",
- "openai_api_base",
- "openai_api_key",
- "temperature",
- "input_value",
- "system_message",
- "stream"
- ],
- "beta": false
- },
- "id": "OpenAIModel-uYXZJ"
- },
- "selected": false,
- "width": 384,
- "height": 565,
- "positionAbsolute": {
- "x": 2925.784767523062,
- "y": 933.6465680967775
- },
- "dragging": false
+ "output_types": ["Text", "Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "ChatOutput-EJkG3"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 385,
+ "dragging": false
+ },
+ {
+ "id": "ChatOutput-DNmvg",
+ "type": "genericNode",
+ "position": {
+ "x": 5077.71285886074,
+ "y": 1232.9152769735522
+ },
+ "data": {
+ "type": "ChatOutput",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "input_value": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Message",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "record_template": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "{text}",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "record_template",
+ "display_name": "Record Template",
+ "advanced": true,
+ "dynamic": false,
+ "info": "In case of Message being a Record, this template will be used to convert it to text.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "return_record": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "return_record",
+ "display_name": "Return Record",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Return the message as a record containing the sender, sender_name, and session_id.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "sender": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "Machine",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": ["Machine", "User"],
+ "name": "sender",
+ "display_name": "Sender Type",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "sender_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "Question Generator",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "sender_name",
+ "display_name": "Sender Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "session_id": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "session_id",
+ "display_name": "Session ID",
+ "advanced": true,
+ "dynamic": false,
+ "info": "If provided, the message will be stored in the memory.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
},
- {
- "id": "TextOutput-MUDOR",
- "type": "genericNode",
- "position": {
- "x": 4446.064323520379,
- "y": 633.833297518702
- },
- "data": {
- "type": "TextOutput",
- "node": {
- "template": {
- "input_value": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Value",
- "advanced": false,
- "input_types": [
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "Text or Record to be passed as output.",
- "load_from_db": false,
- "title_case": false
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
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- "advanced": true,
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- "list": false,
- "show": true,
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- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "record_template",
- "display_name": "Record Template",
- "advanced": true,
- "dynamic": false,
- "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "_type": "CustomComponent"
- },
- "description": "Display a text output in the Playground.",
- "icon": "type",
- "base_classes": [
- "str",
- "Text",
- "object"
- ],
- "display_name": "Second Prompt",
- "documentation": "",
- "custom_fields": {
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- },
- "output_types": [
- "Text"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "TextOutput-MUDOR"
- },
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- "width": 384,
- "height": 290,
- "dragging": false,
- "positionAbsolute": {
- "x": 4446.064323520379,
- "y": 633.833297518702
- }
+ "description": "Display a chat message in the Playground.",
+ "icon": "ChatOutput",
+ "base_classes": ["object", "Record", "Text", "str"],
+ "display_name": "Chat Output",
+ "documentation": "",
+ "custom_fields": {
+ "sender": null,
+ "sender_name": null,
+ "input_value": null,
+ "session_id": null,
+ "return_record": null,
+ "record_template": null
},
- {
- "id": "OpenAIModel-XawYB",
- "type": "genericNode",
- "position": {
- "x": 4500.152018344182,
- "y": 1027.7382026227656
- },
- "data": {
- "type": "OpenAIModel",
- "node": {
- "template": {
- "input_value": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Input",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional\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 NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"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 \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
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- "show": true,
- "multiline": false,
- "value": 256,
- "fileTypes": [],
- "file_path": "",
- "password": false,
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- "display_name": "Max Tokens",
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- "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
- "load_from_db": false,
- "title_case": false
- },
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- "type": "NestedDict",
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- "placeholder": "",
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- "multiline": false,
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- "fileTypes": [],
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- "advanced": true,
- "dynamic": false,
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- "load_from_db": false,
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- },
- "model_name": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "gpt-4-turbo-preview",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "gpt-4o",
- "gpt-4-turbo",
- "gpt-4-turbo-preview",
- "gpt-3.5-turbo",
- "gpt-3.5-turbo-0125"
- ],
- "name": "model_name",
- "display_name": "Model Name",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
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- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "openai_api_base",
- "display_name": "OpenAI API Base",
- "advanced": true,
- "dynamic": false,
- "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "openai_api_key": {
- "type": "str",
- "required": true,
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- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": true,
- "name": "openai_api_key",
- "display_name": "OpenAI API Key",
- "advanced": false,
- "dynamic": false,
- "info": "The OpenAI API Key to use for the OpenAI model.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": ""
- },
- "stream": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "stream",
- "display_name": "Stream",
- "advanced": true,
- "dynamic": false,
- "info": "Stream the response from the model. Streaming works only in Chat.",
- "load_from_db": false,
- "title_case": false
- },
- "system_message": {
- "type": "str",
- "required": false,
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- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "system_message",
- "display_name": "System Message",
- "advanced": true,
- "dynamic": false,
- "info": "System message to pass to the model.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "temperature": {
- "type": "float",
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- "show": true,
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- "value": 0.1,
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- "info": "",
- "rangeSpec": {
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- "min": -1,
- "max": 1,
- "step": 0.1
- },
- "load_from_db": false,
- "title_case": false
- },
- "_type": "CustomComponent"
- },
- "description": "Generates text using OpenAI LLMs.",
- "icon": "OpenAI",
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- "beta": false
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+ "output_types": ["Text", "Record"],
+ "field_formatters": {},
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+ "field_order": [],
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+ },
+ "id": "ChatOutput-DNmvg"
+ },
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+ {
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+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[Text] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
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+ "input_value": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "Revolutionary Nano-Battery Technology Unveiled In a groundbreaking announcement yesterday, researchers from the fictional Tech Innovations Institute revealed the development of a new nano-battery technology that promises to revolutionize energy storage. The new battery, dubbed the \"EnerGCell\", uses advanced nanomaterials to achieve unprecedented efficiency and storage capacities. According to lead researcher Dr. Ada Byron, the EnerGCell can store up to ten times more energy than the best lithium-ion batteries available today, while charging in just a fraction of the time. \"We're talking about charging your electric vehicle in just five minutes for a range of over 1,000 miles,\" Dr. Byron stated during the press conference. The technology behind the EnerGCell involves a complex arrangement of nanostructured electrodes that allow for rapid ion transfer and extremely high energy density. This breakthrough was achieved after a decade of research into nanomaterials and their applications in energy storage. The implications of this technology are vast, promising to accelerate the adoption of renewable energy by making it more practical and affordable to store wind and solar power. It could also lead to significant advancements in electric vehicles, mobile devices, and any other technology that relies on batteries. Despite the excitement, some experts are calling for patience, noting that the EnerGCell is still in its early stages of development and may take several years before it's commercially available. However, the potential impact of such a technology on the environment and the global economy is undeniable. Tech Innovations Institute plans to continue refining the EnerGCell and begin pilot projects with select partners in the coming year. If successful, this nano-battery technology could indeed be the breakthrough needed to usher in a new era of clean energy and technology.",
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+ "load_from_db": false,
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+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "record_template": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "record_template",
+ "display_name": "Record Template",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
},
- {
- "source": "OpenAIModel-uYXZJ",
- "sourceHandle": "{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153}",
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- "targetHandle": "{\u0153fieldName\u0153:\u0153summary\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
- "data": {
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- "str",
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- "dataType": "OpenAIModel",
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- }
- },
- "style": {
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- "id": "reactflow__edge-OpenAIModel-uYXZJ{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153}-Prompt-gTNiz{\u0153fieldName\u0153:\u0153summary\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}"
+ "description": "Display a text output in the Playground.",
+ "icon": "type",
+ "base_classes": ["str", "Text", "object"],
+ "display_name": "First Prompt",
+ "documentation": "",
+ "custom_fields": {
+ "input_value": null,
+ "record_template": null
},
- {
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- "data": {
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- "str",
- "Text",
- "object"
- ],
- "dataType": "OpenAIModel",
- "id": "OpenAIModel-uYXZJ"
- }
+ "output_types": ["Text"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "TextOutput-2MS4a"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 290,
+ "positionAbsolute": {
+ "x": 2917.216113690115,
+ "y": 513.0058511435552
+ },
+ "dragging": false
+ },
+ {
+ "id": "OpenAIModel-uYXZJ",
+ "type": "genericNode",
+ "position": {
+ "x": 2925.784767523062,
+ "y": 933.6465680967775
+ },
+ "data": {
+ "type": "OpenAIModel",
+ "node": {
+ "template": {
+ "input_value": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Input",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\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 NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"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 \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "max_tokens": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 256,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "max_tokens",
+ "display_name": "Max Tokens",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_kwargs": {
+ "type": "NestedDict",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": {},
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "model_kwargs",
+ "display_name": "Model Kwargs",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "gpt-4-turbo-preview",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": [
+ "gpt-4o",
+ "gpt-4-turbo",
+ "gpt-4-turbo-preview",
+ "gpt-3.5-turbo",
+ "gpt-3.5-turbo-0125"
+ ],
+ "name": "model_name",
+ "display_name": "Model Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_base": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "openai_api_base",
+ "display_name": "OpenAI API Base",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_key": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": true,
+ "name": "openai_api_key",
+ "display_name": "OpenAI API Key",
+ "advanced": false,
+ "dynamic": false,
+ "info": "The OpenAI API Key to use for the OpenAI model.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": "OPENAI_API_KEY"
+ },
+ "stream": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "stream",
+ "display_name": "Stream",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Stream the response from the model. Streaming works only in Chat.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "system_message": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "system_message",
+ "display_name": "System Message",
+ "advanced": true,
+ "dynamic": false,
+ "info": "System message to pass to the model.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "temperature": {
+ "type": "float",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 0.1,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "temperature",
+ "display_name": "Temperature",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "rangeSpec": {
+ "step_type": "float",
+ "min": -1,
+ "max": 1,
+ "step": 0.1
},
- "style": {
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- },
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- "id": "reactflow__edge-OpenAIModel-uYXZJ{\u0153baseClasses\u0153:[\u0153str\u0153,\u0153Text\u0153,\u0153object\u0153],\u0153dataType\u0153:\u0153OpenAIModel\u0153,\u0153id\u0153:\u0153OpenAIModel-uYXZJ\u0153}-ChatOutput-EJkG3{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-EJkG3\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}"
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent"
},
- {
- "source": "Prompt-gTNiz",
- "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153}",
- "target": "TextOutput-MUDOR",
- "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-MUDOR\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
- "data": {
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- "dataType": "Prompt",
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- },
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- },
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- "id": "reactflow__edge-Prompt-gTNiz{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153str\u0153,\u0153Text\u0153],\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-gTNiz\u0153}-TextOutput-MUDOR{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-MUDOR\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}"
+ "description": "Generates text using OpenAI LLMs.",
+ "icon": "OpenAI",
+ "base_classes": ["str", "Text", "object"],
+ "display_name": "OpenAI",
+ "documentation": "",
+ "custom_fields": {
+ "input_value": null,
+ "openai_api_key": null,
+ "temperature": null,
+ "model_name": null,
+ "max_tokens": null,
+ "model_kwargs": null,
+ "openai_api_base": null,
+ "stream": null,
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- "target": "OpenAIModel-XawYB",
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+ "output_types": ["Text"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [
+ "max_tokens",
+ "model_kwargs",
+ "model_name",
+ "openai_api_base",
+ "openai_api_key",
+ "temperature",
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+ "system_message",
+ "stream"
+ ],
+ "beta": false
+ },
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+ },
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+ "positionAbsolute": {
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+ "y": 933.6465680967775
+ },
+ "dragging": false
+ },
+ {
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+ "type": "genericNode",
+ "position": {
+ "x": 4446.064323520379,
+ "y": 633.833297518702
+ },
+ "data": {
+ "type": "TextOutput",
+ "node": {
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+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Value",
+ "advanced": false,
+ "input_types": ["Record", "Text"],
+ "dynamic": false,
+ "info": "Text or Record to be passed as output.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "record_template": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "record_template",
+ "display_name": "Record Template",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
},
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- }
- ],
- "viewport": {
- "x": -383.7251879618552,
- "y": 69.19813933800037,
- "zoom": 0.3105753483695743
+ "description": "Display a text output in the Playground.",
+ "icon": "type",
+ "base_classes": ["str", "Text", "object"],
+ "display_name": "Second Prompt",
+ "documentation": "",
+ "custom_fields": {
+ "input_value": null,
+ "record_template": null
+ },
+ "output_types": ["Text"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "TextOutput-MUDOR"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 290,
+ "dragging": false,
+ "positionAbsolute": {
+ "x": 4446.064323520379,
+ "y": 633.833297518702
}
- },
- "description": "The Prompt Chaining flow chains prompts with LLMs, refining outputs through iterative stages.",
- "name": "Prompt Chaining",
- "last_tested_version": "1.0.0a0",
- "is_component": false
+ },
+ {
+ "id": "OpenAIModel-XawYB",
+ "type": "genericNode",
+ "position": {
+ "x": 4500.152018344182,
+ "y": 1027.7382026227656
+ },
+ "data": {
+ "type": "OpenAIModel",
+ "node": {
+ "template": {
+ "input_value": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Input",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\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 NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"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 \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "max_tokens": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 256,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "max_tokens",
+ "display_name": "Max Tokens",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_kwargs": {
+ "type": "NestedDict",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": {},
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "model_kwargs",
+ "display_name": "Model Kwargs",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "gpt-4-turbo-preview",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": [
+ "gpt-4o",
+ "gpt-4-turbo",
+ "gpt-4-turbo-preview",
+ "gpt-3.5-turbo",
+ "gpt-3.5-turbo-0125"
+ ],
+ "name": "model_name",
+ "display_name": "Model Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_base": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "openai_api_base",
+ "display_name": "OpenAI API Base",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_key": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": true,
+ "name": "openai_api_key",
+ "display_name": "OpenAI API Key",
+ "advanced": false,
+ "dynamic": false,
+ "info": "The OpenAI API Key to use for the OpenAI model.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": ""
+ },
+ "stream": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "stream",
+ "display_name": "Stream",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Stream the response from the model. Streaming works only in Chat.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "system_message": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "system_message",
+ "display_name": "System Message",
+ "advanced": true,
+ "dynamic": false,
+ "info": "System message to pass to the model.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "temperature": {
+ "type": "float",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 0.1,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "temperature",
+ "display_name": "Temperature",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "rangeSpec": {
+ "step_type": "float",
+ "min": -1,
+ "max": 1,
+ "step": 0.1
+ },
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent"
+ },
+ "description": "Generates text using OpenAI LLMs.",
+ "icon": "OpenAI",
+ "base_classes": ["str", "Text", "object"],
+ "display_name": "OpenAI",
+ "documentation": "",
+ "custom_fields": {
+ "input_value": null,
+ "openai_api_key": null,
+ "temperature": null,
+ "model_name": null,
+ "max_tokens": null,
+ "model_kwargs": null,
+ "openai_api_base": null,
+ "stream": null,
+ "system_message": null
+ },
+ "output_types": ["Text"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [
+ "max_tokens",
+ "model_kwargs",
+ "model_name",
+ "openai_api_base",
+ "openai_api_key",
+ "temperature",
+ "input_value",
+ "system_message",
+ "stream"
+ ],
+ "beta": false
+ },
+ "id": "OpenAIModel-XawYB"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 565,
+ "positionAbsolute": {
+ "x": 4500.152018344182,
+ "y": 1027.7382026227656
+ },
+ "dragging": false
+ }
+ ],
+ "edges": [
+ {
+ "source": "TextInput-sptaH",
+ "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-sptaHœ}",
+ "target": "Prompt-amqBu",
+ "targetHandle": "{œfieldNameœ:œdocumentœ,œidœ:œPrompt-amqBuœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "document",
+ "id": "Prompt-amqBu",
+ "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["str", "Text", "object"],
+ "dataType": "TextInput",
+ "id": "TextInput-sptaH"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-TextInput-sptaH{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-sptaHœ}-Prompt-amqBu{œfieldNameœ:œdocumentœ,œidœ:œPrompt-amqBuœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "Prompt-amqBu",
+ "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}",
+ "target": "TextOutput-2MS4a",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-2MS4aœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "TextOutput-2MS4a",
+ "inputTypes": ["Record", "Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["object", "str", "Text"],
+ "dataType": "Prompt",
+ "id": "Prompt-amqBu"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-Prompt-amqBu{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}-TextOutput-2MS4a{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-2MS4aœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "Prompt-amqBu",
+ "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}",
+ "target": "OpenAIModel-uYXZJ",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-uYXZJœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "OpenAIModel-uYXZJ",
+ "inputTypes": ["Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["object", "str", "Text"],
+ "dataType": "Prompt",
+ "id": "Prompt-amqBu"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-Prompt-amqBu{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}-OpenAIModel-uYXZJ{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-uYXZJœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "OpenAIModel-uYXZJ",
+ "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}",
+ "target": "Prompt-gTNiz",
+ "targetHandle": "{œfieldNameœ:œsummaryœ,œidœ:œPrompt-gTNizœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "summary",
+ "id": "Prompt-gTNiz",
+ "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["str", "Text", "object"],
+ "dataType": "OpenAIModel",
+ "id": "OpenAIModel-uYXZJ"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-OpenAIModel-uYXZJ{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}-Prompt-gTNiz{œfieldNameœ:œsummaryœ,œidœ:œPrompt-gTNizœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "OpenAIModel-uYXZJ",
+ "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}",
+ "target": "ChatOutput-EJkG3",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-EJkG3œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "ChatOutput-EJkG3",
+ "inputTypes": ["Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["str", "Text", "object"],
+ "dataType": "OpenAIModel",
+ "id": "OpenAIModel-uYXZJ"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-OpenAIModel-uYXZJ{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}-ChatOutput-EJkG3{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-EJkG3œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "Prompt-gTNiz",
+ "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}",
+ "target": "TextOutput-MUDOR",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-MUDORœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "TextOutput-MUDOR",
+ "inputTypes": ["Record", "Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["object", "str", "Text"],
+ "dataType": "Prompt",
+ "id": "Prompt-gTNiz"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-Prompt-gTNiz{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}-TextOutput-MUDOR{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-MUDORœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "Prompt-gTNiz",
+ "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}",
+ "target": "OpenAIModel-XawYB",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-XawYBœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "OpenAIModel-XawYB",
+ "inputTypes": ["Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["object", "str", "Text"],
+ "dataType": "Prompt",
+ "id": "Prompt-gTNiz"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-Prompt-gTNiz{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}-OpenAIModel-XawYB{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-XawYBœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "OpenAIModel-XawYB",
+ "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-XawYBœ}",
+ "target": "ChatOutput-DNmvg",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-DNmvgœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "ChatOutput-DNmvg",
+ "inputTypes": ["Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["str", "Text", "object"],
+ "dataType": "OpenAIModel",
+ "id": "OpenAIModel-XawYB"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-OpenAIModel-XawYB{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-XawYBœ}-ChatOutput-DNmvg{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-DNmvgœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}"
+ }
+ ],
+ "viewport": {
+ "x": -383.7251879618552,
+ "y": 69.19813933800037,
+ "zoom": 0.3105753483695743
+ }
+ },
+ "description": "The Prompt Chaining flow chains prompts with LLMs, refining outputs through iterative stages.",
+ "name": "Prompt Chaining",
+ "last_tested_version": "1.0.0a0",
+ "is_component": false
}
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
index 489d1cc19..bbf59c8b1 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
+++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
@@ -1,3407 +1,3151 @@
{
- "id": "51e2b78a-199b-4054-9f32-e288eef6924c",
- "data": {
- "nodes": [
- {
- "id": "ChatInput-yxMKE",
- "type": "genericNode",
- "position": {
- "x": 1195.5276981160775,
- "y": 209.421875
- },
- "data": {
- "type": "ChatInput",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "input_value": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Message",
- "advanced": false,
- "input_types": [],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "value": "what is a line"
- },
- "return_record": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "return_record",
- "display_name": "Return Record",
- "advanced": true,
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- "name": "sender",
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- "input_types": [
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- "_type": "CustomComponent"
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- "field_order": [],
- "beta": false
- },
- "id": "ChatInput-yxMKE"
- },
- "selected": false,
- "width": 384,
- "height": 383
+ "id": "51e2b78a-199b-4054-9f32-e288eef6924c",
+ "data": {
+ "nodes": [
+ {
+ "id": "ChatInput-yxMKE",
+ "type": "genericNode",
+ "position": {
+ "x": 1195.5276981160775,
+ "y": 209.421875
+ },
+ "data": {
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+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "input_value": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Message",
+ "advanced": false,
+ "input_types": [],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "value": "what is a line"
+ },
+ "return_record": {
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+ "required": false,
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+ "list": false,
+ "show": true,
+ "multiline": false,
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+ "file_path": "",
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+ "name": "return_record",
+ "display_name": "Return Record",
+ "advanced": true,
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+ "info": "Return the message as a record containing the sender, sender_name, and session_id.",
+ "load_from_db": false,
+ "title_case": false
+ },
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+ "value": "User",
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+ "file_path": "",
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+ "name": "sender",
+ "display_name": "Sender Type",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "sender_name": {
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+ "show": true,
+ "multiline": false,
+ "value": "User",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "sender_name",
+ "display_name": "Sender Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "session_id": {
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+ "dynamic": false,
+ "info": "If provided, the message will be stored in the memory.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
},
- {
- "id": "TextOutput-BDknO",
- "type": "genericNode",
- "position": {
- "x": 2322.600672827879,
- "y": 604.9467307442569
- },
- "data": {
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- "advanced": false,
- "input_types": [
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- "Text"
- ],
- "dynamic": false,
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- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
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- "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
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- },
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- "positionAbsolute": {
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- },
- "dragging": false
+ "description": "Get chat inputs from the Playground.",
+ "icon": "ChatInput",
+ "base_classes": ["Text", "str", "object", "Record"],
+ "display_name": "Chat Input",
+ "documentation": "",
+ "custom_fields": {
+ "sender": null,
+ "sender_name": null,
+ "input_value": null,
+ "session_id": null,
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},
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- "x": 1183.667250865064,
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- "password": false,
- "name": "client",
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- "load_from_db": false,
- "title_case": false
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- "code": {
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- "value": "from typing import Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, NestedDict\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"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 },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n",
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+ "output_types": ["Text", "Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "ChatInput-yxMKE"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 383
+ },
+ {
+ "id": "TextOutput-BDknO",
+ "type": "genericNode",
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+ },
+ "data": {
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+ "template": {
+ "input_value": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Value",
+ "advanced": false,
+ "input_types": ["Record", "Text"],
+ "dynamic": false,
+ "info": "Text or Record to be passed as output.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Playground.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "record_template": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "{text}",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "record_template",
+ "display_name": "Record Template",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
},
- {
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- "type": "genericNode",
- "position": {
- "x": 3410.117202077183,
- "y": 431.2038048137648
- },
- "data": {
- "type": "OpenAIModel",
- "node": {
- "template": {
- "input_value": {
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- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
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- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
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- "code": {
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- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional\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 NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"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 \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
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- "gpt-4-turbo",
- "gpt-4-turbo-preview",
- "gpt-3.5-turbo",
- "gpt-3.5-turbo-0125"
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- "display_name": "Model Name",
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- "load_from_db": false,
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- "advanced": true,
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- "load_from_db": false,
- "title_case": false,
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- "password": true,
- "name": "openai_api_key",
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- "load_from_db": true,
- "title_case": false,
- "input_types": [
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- ],
- "value": "OPENAI_API_KEY"
- },
- "stream": {
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- "show": true,
- "multiline": false,
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- "password": false,
- "name": "stream",
- "display_name": "Stream",
- "advanced": true,
- "dynamic": false,
- "info": "Stream the response from the model. Streaming works only in Chat.",
- "load_from_db": false,
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+ "description": "Display a text output in the Playground.",
+ "icon": "type",
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+ "display_name": "Extracted Chunks",
+ "documentation": "",
+ "custom_fields": {
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+ "show": true,
+ "multiline": true,
+ "value": "from typing import Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, NestedDict\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"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 },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "default_headers": {
+ "type": "dict",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "default_headers",
+ "display_name": "Default Headers",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "default_query": {
+ "type": "NestedDict",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": {},
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "default_query",
+ "display_name": "Default Query",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "deployment": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "text-embedding-ada-002",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "deployment",
+ "display_name": "Deployment",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "disallowed_special": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": ["all"],
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "disallowed_special",
+ "display_name": "Disallowed Special",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "embedding_ctx_length": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 8191,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "embedding_ctx_length",
+ "display_name": "Embedding Context Length",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "max_retries": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 6,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "max_retries",
+ "display_name": "Max Retries",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "text-embedding-ada-002",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": [
+ "text-embedding-3-small",
+ "text-embedding-3-large",
+ "text-embedding-ada-002"
+ ],
+ "name": "model",
+ "display_name": "Model",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "model_kwargs": {
+ "type": "NestedDict",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": {},
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "model_kwargs",
+ "display_name": "Model Kwargs",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "openai_api_base": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": true,
+ "name": "openai_api_base",
+ "display_name": "OpenAI API Base",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_key": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": true,
+ "name": "openai_api_key",
+ "display_name": "OpenAI API Key",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": true,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": "OPENAI_API_KEY"
+ },
+ "openai_api_type": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": true,
+ "name": "openai_api_type",
+ "display_name": "OpenAI API Type",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_version": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "openai_api_version",
+ "display_name": "OpenAI API Version",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_organization": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "openai_organization",
+ "display_name": "OpenAI Organization",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_proxy": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "openai_proxy",
+ "display_name": "OpenAI Proxy",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "request_timeout": {
+ "type": "float",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "request_timeout",
+ "display_name": "Request Timeout",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "rangeSpec": {
+ "step_type": "float",
+ "min": -1,
+ "max": 1,
+ "step": 0.1
},
- "data": {
- "type": "Prompt",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "template": {
- "type": "prompt",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: ",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "template",
- "display_name": "Template",
- "advanced": false,
- "input_types": [
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "_type": "CustomComponent",
- "context": {
- "field_type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "context",
- "display_name": "context",
- "advanced": false,
- "input_types": [
- "Document",
- "BaseOutputParser",
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "type": "str"
- },
- "question": {
- "field_type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "question",
- "display_name": "question",
- "advanced": false,
- "input_types": [
- "Document",
- "BaseOutputParser",
- "Record",
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "type": "str"
- }
- },
- "description": "Create a prompt template with dynamic variables.",
- "icon": "prompts",
- "is_input": null,
- "is_output": null,
- "is_composition": null,
- "base_classes": [
- "object",
- "Text",
- "str"
- ],
- "name": "",
- "display_name": "Prompt",
- "documentation": "",
- "custom_fields": {
- "template": [
- "context",
- "question"
- ]
- },
- "output_types": [
- "Text"
- ],
- "full_path": null,
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false,
- "error": null
- },
- "id": "Prompt-xeI6K",
- "description": "Create a prompt template with dynamic variables.",
- "display_name": "Prompt"
- },
- "selected": false,
- "width": 384,
- "height": 477,
- "positionAbsolute": {
- "x": 2969.0261961391298,
- "y": 442.1613649809069
- },
- "dragging": false
+ "load_from_db": false,
+ "title_case": false
+ },
+ "show_progress_bar": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "show_progress_bar",
+ "display_name": "Show Progress Bar",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "skip_empty": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "skip_empty",
+ "display_name": "Skip Empty",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "tiktoken_enable": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": true,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "tiktoken_enable",
+ "display_name": "TikToken Enable",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "tiktoken_model_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "tiktoken_model_name",
+ "display_name": "TikToken Model Name",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "_type": "CustomComponent"
},
- {
- "id": "ChatOutput-Q39I8",
- "type": "genericNode",
- "position": {
- "x": 3887.2073667611485,
- "y": 588.4801225794856
- },
- "data": {
- "type": "ChatOutput",
- "node": {
- "template": {
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "input_value": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Message",
- "advanced": false,
- "input_types": [
- "Text"
- ],
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "record_template": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "{text}",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "record_template",
- "display_name": "Record Template",
- "advanced": true,
- "dynamic": false,
- "info": "In case of Message being a Record, this template will be used to convert it to text.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "return_record": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "return_record",
- "display_name": "Return Record",
- "advanced": true,
- "dynamic": false,
- "info": "Return the message as a record containing the sender, sender_name, and session_id.",
- "load_from_db": false,
- "title_case": false
- },
- "sender": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "Machine",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "Machine",
- "User"
- ],
- "name": "sender",
- "display_name": "Sender Type",
- "advanced": true,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "sender_name": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": "AI",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "sender_name",
- "display_name": "Sender Name",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "session_id": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "session_id",
- "display_name": "Session ID",
- "advanced": true,
- "dynamic": false,
- "info": "If provided, the message will be stored in the memory.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "_type": "CustomComponent"
- },
- "description": "Display a chat message in the Playground.",
- "icon": "ChatOutput",
- "base_classes": [
- "object",
- "Text",
- "Record",
- "str"
- ],
- "display_name": "Chat Output",
- "documentation": "",
- "custom_fields": {
- "sender": null,
- "sender_name": null,
- "input_value": null,
- "session_id": null,
- "return_record": null,
- "record_template": null
- },
- "output_types": [
- "Text",
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- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "ChatOutput-Q39I8"
- },
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- "height": 383,
- "positionAbsolute": {
- "x": 3887.2073667611485,
- "y": 588.4801225794856
- },
- "dragging": false
+ "description": "Generate embeddings using OpenAI models.",
+ "base_classes": ["Embeddings"],
+ "display_name": "OpenAI Embeddings",
+ "documentation": "",
+ "custom_fields": {
+ "openai_api_key": null,
+ "default_headers": null,
+ "default_query": null,
+ "allowed_special": null,
+ "disallowed_special": null,
+ "chunk_size": null,
+ "client": null,
+ "deployment": null,
+ "embedding_ctx_length": null,
+ "max_retries": null,
+ "model": null,
+ "model_kwargs": null,
+ "openai_api_base": 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
},
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- "type": "genericNode",
- "position": {
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- "y": 1747.5389618367233
+ "output_types": ["Embeddings"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "OpenAIEmbeddings-ZlOk1"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 383,
+ "dragging": false
+ },
+ {
+ "id": "OpenAIModel-EjXlN",
+ "type": "genericNode",
+ "position": {
+ "x": 3410.117202077183,
+ "y": 431.2038048137648
+ },
+ "data": {
+ "type": "OpenAIModel",
+ "node": {
+ "template": {
+ "input_value": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Input",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\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 NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"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 \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"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 \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\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,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "max_tokens": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 256,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "max_tokens",
+ "display_name": "Max Tokens",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_kwargs": {
+ "type": "NestedDict",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": {},
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "model_kwargs",
+ "display_name": "Model Kwargs",
+ "advanced": true,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "model_name": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "value": "gpt-3.5-turbo",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "options": [
+ "gpt-4o",
+ "gpt-4-turbo",
+ "gpt-4-turbo-preview",
+ "gpt-3.5-turbo",
+ "gpt-3.5-turbo-0125"
+ ],
+ "name": "model_name",
+ "display_name": "Model Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_base": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "openai_api_base",
+ "display_name": "OpenAI API Base",
+ "advanced": true,
+ "dynamic": false,
+ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "openai_api_key": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": true,
+ "name": "openai_api_key",
+ "display_name": "OpenAI API Key",
+ "advanced": false,
+ "dynamic": false,
+ "info": "The OpenAI API Key to use for the OpenAI model.",
+ "load_from_db": true,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": "OPENAI_API_KEY"
+ },
+ "stream": {
+ "type": "bool",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "stream",
+ "display_name": "Stream",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Stream the response from the model. Streaming works only in Chat.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "system_message": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "system_message",
+ "display_name": "System Message",
+ "advanced": true,
+ "dynamic": false,
+ "info": "System message to pass to the model.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "temperature": {
+ "type": "float",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 0.1,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "temperature",
+ "display_name": "Temperature",
+ "advanced": false,
+ "dynamic": false,
+ "info": "",
+ "rangeSpec": {
+ "step_type": "float",
+ "min": -1,
+ "max": 1,
+ "step": 0.1
},
- "data": {
- "type": "File",
- "node": {
- "template": {
- "path": {
- "type": "file",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [
- ".txt",
- ".md",
- ".mdx",
- ".csv",
- ".json",
- ".yaml",
- ".yml",
- ".xml",
- ".html",
- ".htm",
- ".pdf",
- ".docx",
- ".py",
- ".sh",
- ".sql",
- ".js",
- ".ts",
- ".tsx"
- ],
- "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf",
- "password": false,
- "name": "path",
- "display_name": "Path",
- "advanced": false,
- "dynamic": false,
- "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx",
- "load_from_db": false,
- "title_case": false,
- "value": ""
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\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 record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "silent_errors": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "silent_errors",
- "display_name": "Silent Errors",
- "advanced": true,
- "dynamic": false,
- "info": "If true, errors will not raise an exception.",
- "load_from_db": false,
- "title_case": false
- },
- "_type": "CustomComponent"
- },
- "description": "A generic file loader.",
- "icon": "file-text",
- "base_classes": [
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- ],
- "display_name": "File",
- "documentation": "",
- "custom_fields": {
- "path": null,
- "silent_errors": null
- },
- "output_types": [
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- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "File-t0a6a"
- },
- "selected": false,
- "width": 384,
- "height": 281,
- "positionAbsolute": {
- "x": 2257.233450682836,
- "y": 1747.5389618367233
- },
- "dragging": false
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent"
},
- {
- "id": "RecursiveCharacterTextSplitter-tR9QM",
- "type": "genericNode",
- "position": {
- "x": 2791.013514133929,
- "y": 1462.9588953494142
- },
- "data": {
- "type": "RecursiveCharacterTextSplitter",
- "node": {
- "template": {
- "inputs": {
- "type": "Document",
- "required": true,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "inputs",
- "display_name": "Input",
- "advanced": false,
- "input_types": [
- "Document",
- "Record"
- ],
- "dynamic": false,
- "info": "The texts to split.",
- "load_from_db": false,
- "title_case": false
- },
- "chunk_overlap": {
- "type": "int",
- "required": false,
- "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": "The amount of overlap between chunks.",
- "load_from_db": false,
- "title_case": false
- },
- "chunk_size": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": 1000,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "chunk_size",
- "display_name": "Chunk Size",
- "advanced": false,
- "dynamic": false,
- "info": "The maximum length of each chunk.",
- "load_from_db": false,
- "title_case": false
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import Optional\n\nfrom langchain_core.documents import Document\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
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- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "separators": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "separators",
- "display_name": "Separators",
- "advanced": false,
- "dynamic": false,
- "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": [
- ""
- ]
- },
- "_type": "CustomComponent"
- },
- "description": "Split text into chunks of a specified length.",
- "base_classes": [
- "Record"
- ],
- "display_name": "Recursive Character Text Splitter",
- "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter",
- "custom_fields": {
- "inputs": null,
- "separators": null,
- "chunk_size": null,
- "chunk_overlap": null
- },
- "output_types": [
- "Record"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [],
- "beta": false
- },
- "id": "RecursiveCharacterTextSplitter-tR9QM"
- },
- "selected": false,
- "width": 384,
- "height": 501,
- "positionAbsolute": {
- "x": 2791.013514133929,
- "y": 1462.9588953494142
- },
- "dragging": false
+ "description": "Generates text using OpenAI LLMs.",
+ "icon": "OpenAI",
+ "base_classes": ["object", "Text", "str"],
+ "display_name": "OpenAI",
+ "documentation": "",
+ "custom_fields": {
+ "input_value": null,
+ "openai_api_key": null,
+ "temperature": null,
+ "model_name": null,
+ "max_tokens": null,
+ "model_kwargs": null,
+ "openai_api_base": null,
+ "stream": null,
+ "system_message": null
},
- {
- "id": "AstraDBSearch-41nRz",
- "type": "genericNode",
- "position": {
- "x": 1723.976434815103,
- "y": 277.03317407245913
- },
- "data": {
- "type": "AstraDBSearch",
- "node": {
- "template": {
- "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": "Embedding to use",
- "load_from_db": false,
- "title_case": false
- },
- "input_value": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "input_value",
- "display_name": "Input Value",
- "advanced": false,
- "dynamic": false,
- "info": "Input value to search",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "api_endpoint": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "api_endpoint",
- "display_name": "API Endpoint",
- "advanced": false,
- "dynamic": false,
- "info": "API endpoint URL for the Astra DB service.",
- "load_from_db": true,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "ASTRA_DB_API_ENDPOINT"
- },
- "batch_size": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "batch_size",
- "display_name": "Batch Size",
- "advanced": true,
- "dynamic": false,
- "info": "Optional number of records to process in a single batch.",
- "load_from_db": false,
- "title_case": false
- },
- "bulk_delete_concurrency": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "bulk_delete_concurrency",
- "display_name": "Bulk Delete Concurrency",
- "advanced": true,
- "dynamic": false,
- "info": "Optional concurrency level for bulk delete operations.",
- "load_from_db": false,
- "title_case": false
- },
- "bulk_insert_batch_concurrency": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "bulk_insert_batch_concurrency",
- "display_name": "Bulk Insert Batch Concurrency",
- "advanced": true,
- "dynamic": false,
- "info": "Optional concurrency level for bulk insert operations.",
- "load_from_db": false,
- "title_case": false
- },
- "bulk_insert_overwrite_concurrency": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "bulk_insert_overwrite_concurrency",
- "display_name": "Bulk Insert Overwrite Concurrency",
- "advanced": true,
- "dynamic": false,
- "info": "Optional concurrency level for bulk insert operations that overwrite existing records.",
- "load_from_db": false,
- "title_case": false
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n display_name = \"Astra DB Search\"\n description = \"Searches an existing Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n def build_config(self):\n return {\n \"search_type\": {\n \"display_name\": \"Search Type\",\n \"options\": [\"Similarity\", \"MMR\"],\n },\n \"input_value\": {\n \"display_name\": \"Input Value\",\n \"info\": \"Input value to search\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"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 \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"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 \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"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 \"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 \"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 \"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 \"number_of_results\": {\n \"display_name\": \"Number of Results\",\n \"info\": \"Number of results to return.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n collection_name: str,\n input_value: Text,\n token: str,\n api_endpoint: str,\n search_type: str = \"Similarity\",\n number_of_results: int = 4,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> List[Record]:\n vector_store = AstraDBVectorStoreComponent().build(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n try:\n return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n except KeyError as e:\n if \"content\" in str(e):\n raise ValueError(\n \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'.\"\n )\n else:\n raise e\n",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "code",
- "advanced": true,
- "dynamic": true,
- "info": "",
- "load_from_db": false,
- "title_case": false
- },
- "collection_indexing_policy": {
- "type": "dict",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "collection_indexing_policy",
- "display_name": "Collection Indexing Policy",
- "advanced": true,
- "dynamic": false,
- "info": "Optional dictionary defining the indexing policy for the collection.",
- "load_from_db": false,
- "title_case": false
- },
- "collection_name": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "collection_name",
- "display_name": "Collection Name",
- "advanced": false,
- "dynamic": false,
- "info": "The name of the collection within Astra DB where the vectors will be stored.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "langflow"
- },
- "metadata_indexing_exclude": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "metadata_indexing_exclude",
- "display_name": "Metadata Indexing Exclude",
- "advanced": true,
- "dynamic": false,
- "info": "Optional list of metadata fields to exclude from the indexing.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "metadata_indexing_include": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "metadata_indexing_include",
- "display_name": "Metadata Indexing Include",
- "advanced": true,
- "dynamic": false,
- "info": "Optional list of metadata fields to include in the indexing.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "metric": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "metric",
- "display_name": "Metric",
- "advanced": true,
- "dynamic": false,
- "info": "Optional distance metric for vector comparisons in the vector store.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "namespace": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "namespace",
- "display_name": "Namespace",
- "advanced": true,
- "dynamic": false,
- "info": "Optional namespace within Astra DB to use for the collection.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "number_of_results": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": 4,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "number_of_results",
- "display_name": "Number of Results",
- "advanced": true,
- "dynamic": false,
- "info": "Number of results to return.",
- "load_from_db": false,
- "title_case": false
- },
- "pre_delete_collection": {
- "type": "bool",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "value": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "pre_delete_collection",
- "display_name": "Pre Delete Collection",
- "advanced": true,
- "dynamic": false,
- "info": "Boolean flag to determine whether to delete the collection before creating a new one.",
- "load_from_db": false,
- "title_case": false
- },
- "search_type": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "Similarity",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "Similarity",
- "MMR"
- ],
- "name": "search_type",
- "display_name": "Search Type",
- "advanced": false,
- "dynamic": false,
- "info": "",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "setup_mode": {
- "type": "str",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "value": "Sync",
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "options": [
- "Sync",
- "Async",
- "Off"
- ],
- "name": "setup_mode",
- "display_name": "Setup Mode",
- "advanced": true,
- "dynamic": false,
- "info": "Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.",
- "load_from_db": false,
- "title_case": false,
- "input_types": [
- "Text"
- ]
- },
- "token": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": true,
- "name": "token",
- "display_name": "Token",
- "advanced": false,
- "dynamic": false,
- "info": "Authentication token for accessing Astra DB.",
- "load_from_db": true,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "ASTRA_DB_APPLICATION_TOKEN"
- },
- "_type": "CustomComponent"
- },
- "description": "Searches an existing Astra DB Vector Store.",
- "icon": "AstraDB",
- "base_classes": [
- "Record"
- ],
- "display_name": "Astra DB Search",
- "documentation": "",
- "custom_fields": {
- "embedding": null,
- "collection_name": null,
- "input_value": null,
- "token": null,
- "api_endpoint": null,
- "search_type": null,
- "number_of_results": null,
- "namespace": null,
- "metric": null,
- "batch_size": null,
- "bulk_insert_batch_concurrency": null,
- "bulk_insert_overwrite_concurrency": null,
- "bulk_delete_concurrency": null,
- "setup_mode": null,
- "pre_delete_collection": null,
- "metadata_indexing_include": null,
- "metadata_indexing_exclude": null,
- "collection_indexing_policy": null
- },
- "output_types": [
- "Record"
- ],
- "field_formatters": {},
- "frozen": false,
- "field_order": [
- "token",
- "api_endpoint",
- "collection_name",
- "input_value",
- "embedding"
- ],
- "beta": false
- },
- "id": "AstraDBSearch-41nRz"
- },
- "selected": false,
- "width": 384,
- "height": 713,
- "dragging": false,
- "positionAbsolute": {
- "x": 1723.976434815103,
- "y": 277.03317407245913
- }
+ "output_types": ["Text"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [
+ "max_tokens",
+ "model_kwargs",
+ "model_name",
+ "openai_api_base",
+ "openai_api_key",
+ "temperature",
+ "input_value",
+ "system_message",
+ "stream"
+ ],
+ "beta": false
+ },
+ "id": "OpenAIModel-EjXlN"
+ },
+ "selected": true,
+ "width": 384,
+ "height": 563,
+ "positionAbsolute": {
+ "x": 3410.117202077183,
+ "y": 431.2038048137648
+ },
+ "dragging": false
+ },
+ {
+ "id": "Prompt-xeI6K",
+ "type": "genericNode",
+ "position": {
+ "x": 2969.0261961391298,
+ "y": 442.1613649809069
+ },
+ "data": {
+ "type": "Prompt",
+ "node": {
+ "template": {
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "template": {
+ "type": "prompt",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: ",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "template",
+ "display_name": "Template",
+ "advanced": false,
+ "input_types": ["Text"],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent",
+ "context": {
+ "field_type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "context",
+ "display_name": "context",
+ "advanced": false,
+ "input_types": [
+ "Document",
+ "BaseOutputParser",
+ "Record",
+ "Text"
+ ],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "type": "str"
+ },
+ "question": {
+ "field_type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "question",
+ "display_name": "question",
+ "advanced": false,
+ "input_types": [
+ "Document",
+ "BaseOutputParser",
+ "Record",
+ "Text"
+ ],
+ "dynamic": false,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false,
+ "type": "str"
+ }
},
- {
- "id": "AstraDB-eUCSS",
- "type": "genericNode",
- "position": {
- "x": 3372.04958055989,
- "y": 1611.0742035495277
- },
- "data": {
- "type": "AstraDB",
- "node": {
- "template": {
- "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": "Embedding to use",
- "load_from_db": false,
- "title_case": false
- },
- "inputs": {
- "type": "Record",
- "required": false,
- "placeholder": "",
- "list": true,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "inputs",
- "display_name": "Inputs",
- "advanced": false,
- "dynamic": false,
- "info": "Optional list of records to be processed and stored in the vector store.",
- "load_from_db": false,
- "title_case": false
- },
- "api_endpoint": {
- "type": "str",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "api_endpoint",
- "display_name": "API Endpoint",
- "advanced": false,
- "dynamic": false,
- "info": "API endpoint URL for the Astra DB service.",
- "load_from_db": true,
- "title_case": false,
- "input_types": [
- "Text"
- ],
- "value": "ASTRA_DB_API_ENDPOINT"
- },
- "batch_size": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "batch_size",
- "display_name": "Batch Size",
- "advanced": true,
- "dynamic": false,
- "info": "Optional number of records to process in a single batch.",
- "load_from_db": false,
- "title_case": false
- },
- "bulk_delete_concurrency": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "bulk_delete_concurrency",
- "display_name": "Bulk Delete Concurrency",
- "advanced": true,
- "dynamic": false,
- "info": "Optional concurrency level for bulk delete operations.",
- "load_from_db": false,
- "title_case": false
- },
- "bulk_insert_batch_concurrency": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "bulk_insert_batch_concurrency",
- "display_name": "Bulk Insert Batch Concurrency",
- "advanced": true,
- "dynamic": false,
- "info": "Optional concurrency level for bulk insert operations.",
- "load_from_db": false,
- "title_case": false
- },
- "bulk_insert_overwrite_concurrency": {
- "type": "int",
- "required": false,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": false,
- "fileTypes": [],
- "file_path": "",
- "password": false,
- "name": "bulk_insert_overwrite_concurrency",
- "display_name": "Bulk Insert Overwrite Concurrency",
- "advanced": true,
- "dynamic": false,
- "info": "Optional concurrency level for bulk insert operations that overwrite existing records.",
- "load_from_db": false,
- "title_case": false
- },
- "code": {
- "type": "code",
- "required": true,
- "placeholder": "",
- "list": false,
- "show": true,
- "multiline": true,
- "value": "from typing import List, Optional, Union\nfrom langchain_astradb import AstraDBVectorStore\nfrom langchain_astradb.utils.astradb import SetupMode\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, VectorStore\nfrom langflow.schema import Record\nfrom langchain_core.retrievers import BaseRetriever\n\n\nclass AstraDBVectorStoreComponent(CustomComponent):\n display_name = \"Astra DB\"\n description = \"Builds or loads an Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"inputs\", \"embedding\"]\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Inputs\",\n \"info\": \"Optional list of records to be processed and stored in the vector store.\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"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 \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"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 \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"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 \"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 \"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 \"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 }\n\n def build(\n self,\n embedding: Embeddings,\n token: str,\n api_endpoint: str,\n collection_name: str,\n inputs: Optional[List[Record]] = None,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> Union[VectorStore, BaseRetriever]:\n try:\n setup_mode_value = SetupMode[setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {setup_mode}\")\n if inputs:\n documents = [_input.to_lc_document() for _input in inputs]\n\n vector_store = AstraDBVectorStore.from_documents(\n documents=documents,\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n else:\n vector_store = AstraDBVectorStore(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n\n return vector_store\n",
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+ "value": "{text}",
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+ "advanced": true,
+ "dynamic": false,
+ "info": "In case of Message being a Record, this template will be used to convert it to text.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
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+ "name": "return_record",
+ "display_name": "Return Record",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Return the message as a record containing the sender, sender_name, and session_id.",
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+ "title_case": false
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+ "name": "sender",
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+ "info": "",
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+ "title_case": false,
+ "input_types": ["Text"]
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+ "name": "sender_name",
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+ "dynamic": false,
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+ "file_path": "",
+ "password": false,
+ "name": "session_id",
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+ "advanced": true,
+ "dynamic": false,
+ "info": "If provided, the message will be stored in the memory.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
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+ "output_types": ["Text", "Record"],
+ "field_formatters": {},
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+ "field_order": [],
+ "beta": false
+ },
+ "id": "ChatOutput-Q39I8"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 383,
+ "positionAbsolute": {
+ "x": 3887.2073667611485,
+ "y": 588.4801225794856
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+ "type": "file",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [
+ ".txt",
+ ".md",
+ ".mdx",
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+ ".yaml",
+ ".yml",
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+ ".html",
+ ".htm",
+ ".pdf",
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+ "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf",
+ "password": false,
+ "name": "path",
+ "display_name": "Path",
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+ "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx",
+ "load_from_db": false,
+ "title_case": false,
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+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
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+ "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\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 record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
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+ "load_from_db": false,
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+ "silent_errors": {
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+ "show": true,
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+ "value": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "silent_errors",
+ "display_name": "Silent Errors",
+ "advanced": true,
+ "dynamic": false,
+ "info": "If true, errors will not raise an exception.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "_type": "CustomComponent"
},
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- "id": "reactflow__edge-File-t0a6a{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153File\u0153,\u0153id\u0153:\u0153File-t0a6a\u0153}-RecursiveCharacterTextSplitter-tR9QM{\u0153fieldName\u0153:\u0153inputs\u0153,\u0153id\u0153:\u0153RecursiveCharacterTextSplitter-tR9QM\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153Record\u0153],\u0153type\u0153:\u0153Document\u0153}",
- "data": {
- "targetHandle": {
- "fieldName": "inputs",
- "id": "RecursiveCharacterTextSplitter-tR9QM",
- "inputTypes": [
- "Document",
- "Record"
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- "type": "Document"
- },
- "sourceHandle": {
- "baseClasses": [
- "Record"
- ],
- "dataType": "File",
- "id": "File-t0a6a"
- }
- },
- "style": {
- "stroke": "#555"
- },
- "className": "stroke-gray-900 stroke-connection",
- "selected": false
+ "output_types": ["Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "File-t0a6a"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 281,
+ "positionAbsolute": {
+ "x": 2257.233450682836,
+ "y": 1747.5389618367233
+ },
+ "dragging": false
+ },
+ {
+ "id": "RecursiveCharacterTextSplitter-tR9QM",
+ "type": "genericNode",
+ "position": {
+ "x": 2791.013514133929,
+ "y": 1462.9588953494142
+ },
+ "data": {
+ "type": "RecursiveCharacterTextSplitter",
+ "node": {
+ "template": {
+ "inputs": {
+ "type": "Document",
+ "required": true,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "inputs",
+ "display_name": "Input",
+ "advanced": false,
+ "input_types": ["Document", "Record"],
+ "dynamic": false,
+ "info": "The texts to split.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "chunk_overlap": {
+ "type": "int",
+ "required": false,
+ "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": "The amount of overlap between chunks.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "chunk_size": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 1000,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "chunk_size",
+ "display_name": "Chunk Size",
+ "advanced": false,
+ "dynamic": false,
+ "info": "The maximum length of each chunk.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import Optional\n\nfrom langchain_core.documents import Document\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "separators": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "separators",
+ "display_name": "Separators",
+ "advanced": false,
+ "dynamic": false,
+ "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": [""]
+ },
+ "_type": "CustomComponent"
},
- {
- "source": "OpenAIEmbeddings-ZlOk1",
- "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-ZlOk1\u0153}",
- "target": "AstraDBSearch-41nRz",
- "targetHandle": "{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}",
- "data": {
- "targetHandle": {
- "fieldName": "embedding",
- "id": "AstraDBSearch-41nRz",
- "inputTypes": null,
- "type": "Embeddings"
- },
- "sourceHandle": {
- "baseClasses": [
- "Embeddings"
- ],
- "dataType": "OpenAIEmbeddings",
- "id": "OpenAIEmbeddings-ZlOk1"
- }
- },
- "style": {
- "stroke": "#555"
- },
- "className": "stroke-gray-900 stroke-connection",
- "id": "reactflow__edge-OpenAIEmbeddings-ZlOk1{\u0153baseClasses\u0153:[\u0153Embeddings\u0153],\u0153dataType\u0153:\u0153OpenAIEmbeddings\u0153,\u0153id\u0153:\u0153OpenAIEmbeddings-ZlOk1\u0153}-AstraDBSearch-41nRz{\u0153fieldName\u0153:\u0153embedding\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:null,\u0153type\u0153:\u0153Embeddings\u0153}"
+ "description": "Split text into chunks of a specified length.",
+ "base_classes": ["Record"],
+ "display_name": "Recursive Character Text Splitter",
+ "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter",
+ "custom_fields": {
+ "inputs": null,
+ "separators": null,
+ "chunk_size": null,
+ "chunk_overlap": null
},
- {
- "source": "ChatInput-yxMKE",
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- "data": {
- "targetHandle": {
- "fieldName": "input_value",
- "id": "AstraDBSearch-41nRz",
- "inputTypes": [
- "Text"
- ],
- "type": "str"
- },
- "sourceHandle": {
- "baseClasses": [
- "Text",
- "str",
- "object",
- "Record"
- ],
- "dataType": "ChatInput",
- "id": "ChatInput-yxMKE"
- }
- },
- "style": {
- "stroke": "#555"
- },
- "className": "stroke-gray-900 stroke-connection",
- "id": "reactflow__edge-ChatInput-yxMKE{\u0153baseClasses\u0153:[\u0153Text\u0153,\u0153str\u0153,\u0153object\u0153,\u0153Record\u0153],\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-yxMKE\u0153}-AstraDBSearch-41nRz{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153,\u0153inputTypes\u0153:[\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}"
+ "output_types": ["Record"],
+ "field_formatters": {},
+ "frozen": false,
+ "field_order": [],
+ "beta": false
+ },
+ "id": "RecursiveCharacterTextSplitter-tR9QM"
+ },
+ "selected": false,
+ "width": 384,
+ "height": 501,
+ "positionAbsolute": {
+ "x": 2791.013514133929,
+ "y": 1462.9588953494142
+ },
+ "dragging": false
+ },
+ {
+ "id": "AstraDBSearch-41nRz",
+ "type": "genericNode",
+ "position": {
+ "x": 1723.976434815103,
+ "y": 277.03317407245913
+ },
+ "data": {
+ "type": "AstraDBSearch",
+ "node": {
+ "template": {
+ "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": "Embedding to use",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "input_value": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "input_value",
+ "display_name": "Input Value",
+ "advanced": false,
+ "dynamic": false,
+ "info": "Input value to search",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "api_endpoint": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "api_endpoint",
+ "display_name": "API Endpoint",
+ "advanced": false,
+ "dynamic": false,
+ "info": "API endpoint URL for the Astra DB service.",
+ "load_from_db": true,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": "ASTRA_DB_API_ENDPOINT"
+ },
+ "batch_size": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "batch_size",
+ "display_name": "Batch Size",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional number of records to process in a single batch.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "bulk_delete_concurrency": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "bulk_delete_concurrency",
+ "display_name": "Bulk Delete Concurrency",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional concurrency level for bulk delete operations.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "bulk_insert_batch_concurrency": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "bulk_insert_batch_concurrency",
+ "display_name": "Bulk Insert Batch Concurrency",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional concurrency level for bulk insert operations.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "bulk_insert_overwrite_concurrency": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "bulk_insert_overwrite_concurrency",
+ "display_name": "Bulk Insert Overwrite Concurrency",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional concurrency level for bulk insert operations that overwrite existing records.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "code": {
+ "type": "code",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": true,
+ "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n display_name = \"Astra DB Search\"\n description = \"Searches an existing Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n def build_config(self):\n return {\n \"search_type\": {\n \"display_name\": \"Search Type\",\n \"options\": [\"Similarity\", \"MMR\"],\n },\n \"input_value\": {\n \"display_name\": \"Input Value\",\n \"info\": \"Input value to search\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"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 \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"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 \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"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 },\n \"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 \"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 \"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 \"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 \"number_of_results\": {\n \"display_name\": \"Number of Results\",\n \"info\": \"Number of results to return.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n collection_name: str,\n input_value: Text,\n token: str,\n api_endpoint: str,\n search_type: str = \"Similarity\",\n number_of_results: int = 4,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> List[Record]:\n vector_store = AstraDBVectorStoreComponent().build(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n try:\n return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n except KeyError as e:\n if \"content\" in str(e):\n raise ValueError(\n \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'.\"\n )\n else:\n raise e\n",
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "code",
+ "advanced": true,
+ "dynamic": true,
+ "info": "",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "collection_indexing_policy": {
+ "type": "dict",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "collection_indexing_policy",
+ "display_name": "Collection Indexing Policy",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional dictionary defining the indexing policy for the collection.",
+ "load_from_db": false,
+ "title_case": false
+ },
+ "collection_name": {
+ "type": "str",
+ "required": true,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "collection_name",
+ "display_name": "Collection Name",
+ "advanced": false,
+ "dynamic": false,
+ "info": "The name of the collection within Astra DB where the vectors will be stored.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"],
+ "value": "langflow"
+ },
+ "metadata_indexing_exclude": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "metadata_indexing_exclude",
+ "display_name": "Metadata Indexing Exclude",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional list of metadata fields to exclude from the indexing.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "metadata_indexing_include": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": true,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "metadata_indexing_include",
+ "display_name": "Metadata Indexing Include",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional list of metadata fields to include in the indexing.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "metric": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "metric",
+ "display_name": "Metric",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional distance metric for vector comparisons in the vector store.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "namespace": {
+ "type": "str",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "namespace",
+ "display_name": "Namespace",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional namespace within Astra DB to use for the collection.",
+ "load_from_db": false,
+ "title_case": false,
+ "input_types": ["Text"]
+ },
+ "number_of_results": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "value": 4,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
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+ "dynamic": false,
+ "info": "Optional concurrency level for bulk insert operations.",
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+ "bulk_insert_overwrite_concurrency": {
+ "type": "int",
+ "required": false,
+ "placeholder": "",
+ "list": false,
+ "show": true,
+ "multiline": false,
+ "fileTypes": [],
+ "file_path": "",
+ "password": false,
+ "name": "bulk_insert_overwrite_concurrency",
+ "display_name": "Bulk Insert Overwrite Concurrency",
+ "advanced": true,
+ "dynamic": false,
+ "info": "Optional concurrency level for bulk insert operations that overwrite existing records.",
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+ "required": true,
+ "placeholder": "",
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+ "value": "from typing import List, Optional, Union\nfrom langchain_astradb import AstraDBVectorStore\nfrom langchain_astradb.utils.astradb import SetupMode\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, VectorStore\nfrom langflow.schema import Record\nfrom langchain_core.retrievers import BaseRetriever\n\n\nclass AstraDBVectorStoreComponent(CustomComponent):\n display_name = \"Astra DB\"\n description = \"Builds or loads an Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"inputs\", \"embedding\"]\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Inputs\",\n \"info\": \"Optional list of records to be processed and stored in the vector store.\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"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 \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"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 \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"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 },\n \"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 \"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 \"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 \"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 }\n\n def build(\n self,\n embedding: Embeddings,\n token: str,\n api_endpoint: str,\n collection_name: str,\n inputs: Optional[List[Record]] = None,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> Union[VectorStore, BaseRetriever]:\n try:\n setup_mode_value = SetupMode[setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {setup_mode}\")\n if inputs:\n documents = [_input.to_lc_document() for _input in inputs]\n\n vector_store = AstraDBVectorStore.from_documents(\n documents=documents,\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n else:\n vector_store = AstraDBVectorStore(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n\n return vector_store\n",
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+ "targetHandle": "{œfieldNameœ:œinputsœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ,œinputTypesœ:[œDocumentœ,œRecordœ],œtypeœ:œDocumentœ}",
+ "id": "reactflow__edge-File-t0a6a{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-t0a6aœ}-RecursiveCharacterTextSplitter-tR9QM{œfieldNameœ:œinputsœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ,œinputTypesœ:[œDocumentœ,œRecordœ],œtypeœ:œDocumentœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "inputs",
+ "id": "RecursiveCharacterTextSplitter-tR9QM",
+ "inputTypes": ["Document", "Record"],
+ "type": "Document"
+ },
+ "sourceHandle": {
+ "baseClasses": ["Record"],
+ "dataType": "File",
+ "id": "File-t0a6a"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "selected": false
+ },
+ {
+ "source": "OpenAIEmbeddings-ZlOk1",
+ "sourceHandle": "{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-ZlOk1œ}",
+ "target": "AstraDBSearch-41nRz",
+ "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "embedding",
+ "id": "AstraDBSearch-41nRz",
+ "inputTypes": null,
+ "type": "Embeddings"
+ },
+ "sourceHandle": {
+ "baseClasses": ["Embeddings"],
+ "dataType": "OpenAIEmbeddings",
+ "id": "OpenAIEmbeddings-ZlOk1"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-OpenAIEmbeddings-ZlOk1{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-ZlOk1œ}-AstraDBSearch-41nRz{œfieldNameœ:œembeddingœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}"
+ },
+ {
+ "source": "ChatInput-yxMKE",
+ "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}",
+ "target": "AstraDBSearch-41nRz",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "AstraDBSearch-41nRz",
+ "inputTypes": ["Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["Text", "str", "object", "Record"],
+ "dataType": "ChatInput",
+ "id": "ChatInput-yxMKE"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-ChatInput-yxMKE{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}-AstraDBSearch-41nRz{œfieldNameœ:œinput_valueœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}"
+ },
+ {
+ "source": "RecursiveCharacterTextSplitter-tR9QM",
+ "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ}",
+ "target": "AstraDB-eUCSS",
+ "targetHandle": "{œfieldNameœ:œinputsœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œRecordœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "inputs",
+ "id": "AstraDB-eUCSS",
+ "inputTypes": null,
+ "type": "Record"
+ },
+ "sourceHandle": {
+ "baseClasses": ["Record"],
+ "dataType": "RecursiveCharacterTextSplitter",
+ "id": "RecursiveCharacterTextSplitter-tR9QM"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-RecursiveCharacterTextSplitter-tR9QM{œbaseClassesœ:[œRecordœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ}-AstraDB-eUCSS{œfieldNameœ:œinputsœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œRecordœ}",
+ "selected": false
+ },
+ {
+ "source": "OpenAIEmbeddings-9TPjc",
+ "sourceHandle": "{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-9TPjcœ}",
+ "target": "AstraDB-eUCSS",
+ "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "embedding",
+ "id": "AstraDB-eUCSS",
+ "inputTypes": null,
+ "type": "Embeddings"
+ },
+ "sourceHandle": {
+ "baseClasses": ["Embeddings"],
+ "dataType": "OpenAIEmbeddings",
+ "id": "OpenAIEmbeddings-9TPjc"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-OpenAIEmbeddings-9TPjc{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-9TPjcœ}-AstraDB-eUCSS{œfieldNameœ:œembeddingœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}",
+ "selected": false
+ },
+ {
+ "source": "AstraDBSearch-41nRz",
+ "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œAstraDBSearchœ,œidœ:œAstraDBSearch-41nRzœ}",
+ "target": "TextOutput-BDknO",
+ "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-BDknOœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}",
+ "data": {
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "TextOutput-BDknO",
+ "inputTypes": ["Record", "Text"],
+ "type": "str"
+ },
+ "sourceHandle": {
+ "baseClasses": ["Record"],
+ "dataType": "AstraDBSearch",
+ "id": "AstraDBSearch-41nRz"
+ }
+ },
+ "style": {
+ "stroke": "#555"
+ },
+ "className": "stroke-gray-900 stroke-connection",
+ "id": "reactflow__edge-AstraDBSearch-41nRz{œbaseClassesœ:[œRecordœ],œdataTypeœ:œAstraDBSearchœ,œidœ:œAstraDBSearch-41nRzœ}-TextOutput-BDknO{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-BDknOœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}"
+ }
+ ],
+ "viewport": {
+ "x": -259.6782520315529,
+ "y": 90.3428735006047,
+ "zoom": 0.2687057134854984
+ }
+ },
+ "description": "Visit https://pre-release.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": "1.0.0a0",
+ "is_component": false
}
diff --git a/src/backend/base/langflow/load/__init__.py b/src/backend/base/langflow/load/__init__.py
index 2002e8bb1..59dbdf6e0 100644
--- a/src/backend/base/langflow/load/__init__.py
+++ b/src/backend/base/langflow/load/__init__.py
@@ -1,3 +1,4 @@
-from .load import load_flow_from_json, run_flow_from_json # noqa: F401
+from .load import load_flow_from_json, run_flow_from_json
+from .utils import upload_file, get_flow
-__all__ = ["load_flow_from_json", "run_flow_from_json"]
+__all__ = ["load_flow_from_json", "run_flow_from_json", "upload_file", "get_flow"]
diff --git a/src/backend/base/langflow/load/utils.py b/src/backend/base/langflow/load/utils.py
new file mode 100644
index 000000000..9c2918e91
--- /dev/null
+++ b/src/backend/base/langflow/load/utils.py
@@ -0,0 +1,89 @@
+import httpx
+
+from langflow.services.database.models.flow.model import FlowBase
+
+
+def upload(file_path, host, flow_id):
+ """
+ Upload a file to Langflow and return the file path.
+
+ Args:
+ file_path (str): The path to the file to be uploaded.
+ host (str): The host URL of Langflow.
+ flow_id (UUID): The ID of the flow to which the file belongs.
+
+ Returns:
+ dict: A dictionary containing the file path.
+
+ Raises:
+ Exception: If an error occurs during the upload process.
+ """
+ try:
+ url = f"{host}/api/v1/upload/{flow_id}"
+ response = httpx.post(url, files={"file": open(file_path, "rb")})
+ if response.status_code == 200:
+ return response.json()
+ else:
+ raise Exception(f"Error uploading file: {response.status_code}")
+ except Exception as e:
+ raise Exception(f"Error uploading file: {e}")
+
+
+def upload_file(file_path, host, flow_id, components, tweaks={}):
+ """
+ Upload a file to Langflow and return the file path.
+
+ Args:
+ file_path (str): The path to the file to be uploaded.
+ host (str): The host URL of Langflow.
+ port (int): The port number of Langflow.
+ flow_id (UUID): The ID of the flow to which the file belongs.
+ components (str): List of component IDs or names that need the file.
+ tweaks (dict): A dictionary of tweaks to be applied to the file.
+
+ Returns:
+ dict: A dictionary containing the file path and any tweaks that were applied.
+
+ Raises:
+ Exception: If an error occurs during the upload process.
+ """
+ try:
+ response = upload(file_path, host, flow_id)
+ if response["file_path"]:
+ for component in components:
+ if isinstance(component, str):
+ tweaks[component] = {"file_path": response["file_path"]}
+ else:
+ raise ValueError(f"Component ID or name must be a string. Got {type(component)}")
+ return tweaks
+ else:
+ raise ValueError("Error uploading file")
+ except Exception as e:
+ raise ValueError(f"Error uploading file: {e}")
+
+
+def get_flow(url: str, flow_id: str):
+ """Get the details of a flow from Langflow.
+
+ Args:
+ url (str): The host URL of Langflow.
+ port (int): The port number of Langflow.
+ flow_id (UUID): The ID of the flow to retrieve.
+
+ Returns:
+ dict: A dictionary containing the details of the flow.
+
+ Raises:
+ Exception: If an error occurs during the retrieval process.
+ """
+ try:
+ flow_url = f"{url}/api/v1/flows/{flow_id}"
+ response = httpx.get(flow_url)
+ if response.status_code == 200:
+ json_response = response.json()
+ flow = FlowBase(**json_response).model_dump()
+ return flow
+ else:
+ raise Exception(f"Error retrieving flow: {response.status_code}")
+ except Exception as e:
+ raise Exception(f"Error retrieving flow: {e}")
diff --git a/src/backend/base/langflow/services/database/models/flow/model.py b/src/backend/base/langflow/services/database/models/flow/model.py
index 4de1e0bc8..7727c7b86 100644
--- a/src/backend/base/langflow/services/database/models/flow/model.py
+++ b/src/backend/base/langflow/services/database/models/flow/model.py
@@ -29,7 +29,6 @@ class FlowBase(SQLModel):
is_component: Optional[bool] = Field(default=False, nullable=True)
updated_at: Optional[datetime] = Field(default_factory=lambda: datetime.now(timezone.utc), nullable=True)
webhook: Optional[bool] = Field(default=False, nullable=True, description="Can be used on the webhook endpoint")
- folder_id: Optional[UUID] = Field(default=None, nullable=True)
endpoint_name: Optional[str] = Field(default=None, nullable=True, index=True)
@field_validator("endpoint_name")
diff --git a/src/backend/base/langflow/services/monitor/schema.py b/src/backend/base/langflow/services/monitor/schema.py
index e7bc7a963..b3a9ce5c6 100644
--- a/src/backend/base/langflow/services/monitor/schema.py
+++ b/src/backend/base/langflow/services/monitor/schema.py
@@ -122,6 +122,13 @@ class MessageModelResponse(MessageModel):
return v
+class MessageModelRequest(MessageModel):
+ message: str = Field(default="")
+ sender: str = Field(default="")
+ sender_name: str = Field(default="")
+ session_id: str = Field(default="")
+
+
class VertexBuildModel(BaseModel):
index: Optional[int] = Field(default=None, alias="index", exclude=True)
id: Optional[str] = Field(default=None, alias="id")
diff --git a/src/backend/base/langflow/services/monitor/service.py b/src/backend/base/langflow/services/monitor/service.py
index 9fce7dd59..ab5a87f08 100644
--- a/src/backend/base/langflow/services/monitor/service.py
+++ b/src/backend/base/langflow/services/monitor/service.py
@@ -32,6 +32,10 @@ class MonitorService(Service):
except Exception as e:
logger.exception(f"Error initializing monitor service: {e}")
+ def exec_query(self, query: str):
+ with duckdb.connect(str(self.db_path)) as conn:
+ return conn.execute(query).df()
+
def to_df(self, table_name):
return self.load_table_as_dataframe(table_name)
@@ -69,7 +73,7 @@ class MonitorService(Service):
valid: Optional[bool] = None,
order_by: Optional[str] = "timestamp",
):
- query = "SELECT index,flow_id, valid, params, data, artifacts, timestamp FROM vertex_builds"
+ query = "SELECT id, index,flow_id, valid, params, data, artifacts, timestamp FROM vertex_builds"
conditions = []
if flow_id:
conditions.append(f"flow_id = '{flow_id}'")
@@ -88,6 +92,8 @@ class MonitorService(Service):
with duckdb.connect(str(self.db_path)) as conn:
df = conn.execute(query).df()
+ print(query)
+
return df.to_dict(orient="records")
def delete_vertex_builds(self, flow_id: Optional[str] = None):
@@ -98,11 +104,20 @@ class MonitorService(Service):
with duckdb.connect(str(self.db_path)) as conn:
conn.execute(query)
- def delete_messages(self, session_id: str):
+ def delete_messages_session(self, session_id: str):
query = f"DELETE FROM messages WHERE session_id = '{session_id}'"
- with duckdb.connect(str(self.db_path)) as conn:
- conn.execute(query)
+ return self.exec_query(query)
+
+ def delete_messages(self, message_ids: list[int]):
+ query = f"DELETE FROM messages WHERE index IN ({','.join(map(str, message_ids))})"
+
+ return self.exec_query(query)
+
+ def update_message(self, message_id: int, **kwargs):
+ query = f"""UPDATE messages SET {', '.join(f"{k} = '{v}'" for k, v in kwargs.items())} WHERE index = {message_id}"""
+
+ return self.exec_query(query)
def add_message(self, message: MessageModel):
self.add_row("messages", message)
diff --git a/src/backend/base/poetry.lock b/src/backend/base/poetry.lock
index 324dccc72..12fe93f21 100644
--- a/src/backend/base/poetry.lock
+++ b/src/backend/base/poetry.lock
@@ -463,43 +463,43 @@ files = [
[[package]]
name = "cryptography"
-version = "42.0.7"
+version = "42.0.8"
description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
optional = false
python-versions = ">=3.7"
files = [
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]
[package.dependencies]
@@ -2214,13 +2214,13 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
[[package]]
name = "pydantic-settings"
-version = "2.3.0"
+version = "2.3.1"
description = "Settings management using Pydantic"
optional = false
python-versions = ">=3.8"
files = [
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- {file = "pydantic_settings-2.3.0.tar.gz", hash = "sha256:78db28855a71503cfe47f39500a1dece523c640afd5280edb5c5c9c9cfa534c9"},
+ {file = "pydantic_settings-2.3.1-py3-none-any.whl", hash = "sha256:acb2c213140dfff9669f4fe9f8180d43914f51626db28ab2db7308a576cce51a"},
+ {file = "pydantic_settings-2.3.1.tar.gz", hash = "sha256:e34bbd649803a6bb3e2f0f58fb0edff1f0c7f556849fda106cc21bcce12c30ab"},
]
[package.dependencies]
diff --git a/src/backend/base/pyproject.toml b/src/backend/base/pyproject.toml
index 3d80fe194..14a7b87a8 100644
--- a/src/backend/base/pyproject.toml
+++ b/src/backend/base/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "langflow-base"
-version = "0.0.55"
+version = "0.0.57"
description = "A Python package with a built-in web application"
authors = ["Langflow "]
maintainers = [
diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json
index 363415ccf..ef33cb95d 100644
--- a/src/frontend/package-lock.json
+++ b/src/frontend/package-lock.json
@@ -26,6 +26,7 @@
"@radix-ui/react-slot": "^1.0.2",
"@radix-ui/react-switch": "^1.0.3",
"@radix-ui/react-tabs": "^1.0.4",
+ "@radix-ui/react-toggle": "^1.0.3",
"@radix-ui/react-tooltip": "^1.0.6",
"@tabler/icons-react": "^2.32.0",
"@tailwindcss/forms": "^0.5.6",
@@ -40,6 +41,7 @@
"class-variance-authority": "^0.6.1",
"clsx": "^1.2.1",
"cmdk": "^1.0.0",
+ "debounce-promise": "^3.1.2",
"dompurify": "^3.0.5",
"dotenv": "^16.4.5",
"esbuild": "^0.17.19",
@@ -2761,6 +2763,31 @@
}
}
},
+ "node_modules/@radix-ui/react-toggle": {
+ "version": "1.0.3",
+ "resolved": "https://registry.npmjs.org/@radix-ui/react-toggle/-/react-toggle-1.0.3.tgz",
+ "integrity": "sha512-Pkqg3+Bc98ftZGsl60CLANXQBBQ4W3mTFS9EJvNxKMZ7magklKV69/id1mlAlOFDDfHvlCms0fx8fA4CMKDJHg==",
+ "dependencies": {
+ "@babel/runtime": "^7.13.10",
+ "@radix-ui/primitive": "1.0.1",
+ "@radix-ui/react-primitive": "1.0.3",
+ "@radix-ui/react-use-controllable-state": "1.0.1"
+ },
+ "peerDependencies": {
+ "@types/react": "*",
+ "@types/react-dom": "*",
+ "react": "^16.8 || ^17.0 || ^18.0",
+ "react-dom": "^16.8 || ^17.0 || ^18.0"
+ },
+ "peerDependenciesMeta": {
+ "@types/react": {
+ "optional": true
+ },
+ "@types/react-dom": {
+ "optional": true
+ }
+ }
+ },
"node_modules/@radix-ui/react-tooltip": {
"version": "1.0.7",
"resolved": "https://registry.npmjs.org/@radix-ui/react-tooltip/-/react-tooltip-1.0.7.tgz",
@@ -5670,6 +5697,11 @@
"node": ">=12"
}
},
+ "node_modules/debounce-promise": {
+ "version": "3.1.2",
+ "resolved": "https://registry.npmjs.org/debounce-promise/-/debounce-promise-3.1.2.tgz",
+ "integrity": "sha512-rZHcgBkbYavBeD9ej6sP56XfG53d51CD4dnaw989YX/nZ/ZJfgRx/9ePKmTNiUiyQvh4mtrMoS3OAWW+yoYtpg=="
+ },
"node_modules/debug": {
"version": "4.3.4",
"resolved": "https://registry.npmjs.org/debug/-/debug-4.3.4.tgz",
diff --git a/src/frontend/package.json b/src/frontend/package.json
index 053a246ee..7a56d080e 100644
--- a/src/frontend/package.json
+++ b/src/frontend/package.json
@@ -21,6 +21,7 @@
"@radix-ui/react-slot": "^1.0.2",
"@radix-ui/react-switch": "^1.0.3",
"@radix-ui/react-tabs": "^1.0.4",
+ "@radix-ui/react-toggle": "^1.0.3",
"@radix-ui/react-tooltip": "^1.0.6",
"@tabler/icons-react": "^2.32.0",
"@tailwindcss/forms": "^0.5.6",
@@ -35,6 +36,7 @@
"class-variance-authority": "^0.6.1",
"clsx": "^1.2.1",
"cmdk": "^1.0.0",
+ "debounce-promise": "^3.1.2",
"dompurify": "^3.0.5",
"dotenv": "^16.4.5",
"esbuild": "^0.17.19",
diff --git a/src/frontend/playwright.config.ts b/src/frontend/playwright.config.ts
index 9535e0a15..eeb9497ae 100644
--- a/src/frontend/playwright.config.ts
+++ b/src/frontend/playwright.config.ts
@@ -15,7 +15,7 @@ dotenv.config({ path: path.resolve(__dirname, "../../.env") });
export default defineConfig({
testDir: "./tests",
/* Run tests in files in parallel */
- fullyParallel: true,
+ fullyParallel: false,
/* Fail the build on CI if you accidentally left test.only in the source code. */
forbidOnly: !!process.env.CI,
/* Retry on CI only */
@@ -52,18 +52,18 @@ export default defineConfig({
},
},
- {
- name: "firefox",
- use: {
- ...devices["Desktop Firefox"],
- launchOptions: {
- firefoxUserPrefs: {
- "dom.events.asyncClipboard.readText": true,
- "dom.events.testing.asyncClipboard": true,
- },
- },
- },
- },
+ // {
+ // name: "firefox",
+ // use: {
+ // ...devices["Desktop Firefox"],
+ // launchOptions: {
+ // firefoxUserPrefs: {
+ // "dom.events.asyncClipboard.readText": true,
+ // "dom.events.testing.asyncClipboard": true,
+ // },
+ // },
+ // },
+ // },
],
webServer: [
{
diff --git a/src/frontend/src/App.tsx b/src/frontend/src/App.tsx
index 3d144f194..b9e02a27d 100644
--- a/src/frontend/src/App.tsx
+++ b/src/frontend/src/App.tsx
@@ -30,10 +30,10 @@ export default function App() {
useTrackLastVisitedPath();
const removeFromTempNotificationList = useAlertStore(
- (state) => state.removeFromTempNotificationList
+ (state) => state.removeFromTempNotificationList,
);
const tempNotificationList = useAlertStore(
- (state) => state.tempNotificationList
+ (state) => state.tempNotificationList,
);
const [fetchError, setFetchError] = useState(false);
const isLoading = useFlowsManagerStore((state) => state.isLoading);
@@ -51,7 +51,7 @@ export default function App() {
const refreshVersion = useDarkStore((state) => state.refreshVersion);
const refreshStars = useDarkStore((state) => state.refreshStars);
const setGlobalVariables = useGlobalVariablesStore(
- (state) => state.setGlobalVariables
+ (state) => state.setGlobalVariables,
);
const checkHasStore = useStoreStore((state) => state.checkHasStore);
const navigate = useNavigate();
@@ -222,12 +222,19 @@ export default function App() {
id={alert.id}
removeAlert={removeAlert}
/>
+ ) : alert.type === "notice" ? (
+
) : (
- alert.type === "notice" && (
-
@@ -236,20 +243,6 @@ export default function App() {
+
+
+ 
+
+
+ 
+
+
+
+
+# 🖥️ Interface de Linha de Comando (CLI)
+
+O Langflow fornece uma interface de linha de comando (CLI) para fácil gerenciamento e configuração.
+
+## Uso
+
+Você pode executar o Langflow usando o seguinte comando:
+
+```shell
+langflow run [OPTIONS]
+```
+
+Cada opção é detalhada abaixo:
+
+- `--help`: Exibe todas as opções disponíveis.
+- `--host`: Define o host para vincular o servidor. Pode ser configurado usando a variável de ambiente `LANGFLOW_HOST`. O padrão é `127.0.0.1`.
+- `--workers`: Define o número de processos. Pode ser configurado usando a variável de ambiente `LANGFLOW_WORKERS`. O padrão é `1`.
+- `--timeout`: Define o tempo limite do worker em segundos. O padrão é `60`.
+- `--port`: Define a porta para escutar. Pode ser configurado usando a variável de ambiente `LANGFLOW_PORT`. O padrão é `7860`.
+- `--env-file`: Especifica o caminho para o arquivo .env contendo variáveis de ambiente. O padrão é `.env`.
+- `--log-level`: Define o nível de log. Pode ser configurado usando a variável de ambiente `LANGFLOW_LOG_LEVEL`. O padrão é `critical`.
+- `--components-path`: Especifica o caminho para o diretório contendo componentes personalizados. Pode ser configurado usando a variável de ambiente `LANGFLOW_COMPONENTS_PATH`. O padrão é `langflow/components`.
+- `--log-file`: Especifica o caminho para o arquivo de log. Pode ser configurado usando a variável de ambiente `LANGFLOW_LOG_FILE`. O padrão é `logs/langflow.log`.
+- `--cache`: Seleciona o tipo de cache a ser usado. As opções são `InMemoryCache` e `SQLiteCache`. Pode ser configurado usando a variável de ambiente `LANGFLOW_LANGCHAIN_CACHE`. O padrão é `SQLiteCache`.
+- `--dev/--no-dev`: Alterna o modo de desenvolvimento. O padrão é `no-dev`.
+- `--path`: Especifica o caminho para o diretório frontend contendo os arquivos de build. Esta opção é apenas para fins de desenvolvimento. Pode ser configurado usando a variável de ambiente `LANGFLOW_FRONTEND_PATH`.
+- `--open-browser/--no-open-browser`: Alterna a opção de abrir o navegador após iniciar o servidor. Pode ser configurado usando a variável de ambiente `LANGFLOW_OPEN_BROWSER`. O padrão é `open-browser`.
+- `--remove-api-keys/--no-remove-api-keys`: Alterna a opção de remover as chaves de API dos projetos salvos no banco de dados. Pode ser configurado usando a variável de ambiente `LANGFLOW_REMOVE_API_KEYS`. O padrão é `no-remove-api-keys`.
+- `--install-completion [bash|zsh|fish|powershell|pwsh]`: Instala a conclusão para o shell especificado.
+- `--show-completion [bash|zsh|fish|powershell|pwsh]`: Exibe a conclusão para o shell especificado, permitindo que você copie ou personalize a instalação.
+- `--backend-only`: Este parâmetro, com valor padrão `False`, permite executar apenas o servidor backend sem o frontend. Também pode ser configurado usando a variável de ambiente `LANGFLOW_BACKEND_ONLY`.
+- `--store`: Este parâmetro, com valor padrão `True`, ativa os recursos da loja, use `--no-store` para desativá-los. Pode ser configurado usando a variável de ambiente `LANGFLOW_STORE`.
+
+Esses parâmetros são importantes para usuários que precisam personalizar o comportamento do Langflow, especialmente em cenários de desenvolvimento ou deploy especializado.
+
+### Variáveis de Ambiente
+
+Você pode configurar muitas das opções de CLI usando variáveis de ambiente. Estas podem ser exportadas no seu sistema operacional ou adicionadas a um arquivo `.env` e carregadas usando a opção `--env-file`.
+
+Um arquivo de exemplo `.env` chamado `.env.example` está incluído no projeto. Copie este arquivo para um novo arquivo chamado `.env` e substitua os valores de exemplo pelas suas configurações reais. Se você estiver definindo valores tanto no seu sistema operacional quanto no arquivo `.env`, as configurações do `.env` terão precedência.
+
+# 👋 Contribuir
+
+Aceitamos contribuições de desenvolvedores de todos os níveis para nosso projeto open-source no GitHub. Se você deseja contribuir, por favor, confira nossas [diretrizes de contribuição](./CONTRIBUTING.md) e ajude a tornar o Langflow mais acessível.
+
+---
+
+[](https://star-history.com/#langflow-ai/langflow&Date)
+
+# 🌟 Contribuidores
+
+[](https://github.com/langflow-ai/langflow/graphs/contributors)
+
+# 📄 Licença
+
+O Langflow é lançado sob a licença MIT. Veja o arquivo [LICENSE](LICENSE) para detalhes.
diff --git a/README.md b/README.md
index 3c29e83e2..68c8fde29 100644
--- a/README.md
+++ b/README.md
@@ -25,13 +25,18 @@
+
+ 
+