docs: updates for release (#6762)
* chat-io-new-inputs * split-text-new-inputs-and-outputs * update-structured-data-io * bump-python-prereq-to-3.13 * reactflow * structured-output-component * chat-io-page-cleanup * typo * collapse-nav-by-default * move-up-api-in-sidebar * add-text-output-to-duck-duck-go * Apply suggestions from code review Co-authored-by: Edwin Jose <edwin.jose@datastax.com> * clarify-chat-and-text-inputs * changes-for-text-io * no-playground-output * Apply suggestions from code review Co-authored-by: KimberlyFields <46325568+KimberlyFields@users.noreply.github.com> * code-review --------- Co-authored-by: Edwin Jose <edwin.jose@datastax.com> Co-authored-by: KimberlyFields <46325568+KimberlyFields@users.noreply.github.com>
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9 changed files with 127 additions and 75 deletions
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@ -139,19 +139,39 @@ It provides flexibility in managing message storage and retrieval within a chat
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This component transforms LLM responses into structured data formats.
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### Input
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In this example from the **Financial Support Parser** template, the **Structured Output** component transforms unstructured financial reports into structured data.
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The connected LLM model is prompted by the **Structured Output** component's `Format Instructions` parameter to extract structured output from the unstructured text. `Format Instructions` is utilized as the system prompt for the **Structured Output** component.
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In the **Structured Output** component, click the **Open table** button to view the `Output Schema` table.
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The `Output Schema` parameter defines the structure and data types for the model's output using a table with the following fields:
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* **Name**: The name of the output field.
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* **Description**: The purpose of the output field.
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* **Type**: The data type of the output field. The available types are `str`, `int`, `float`, `bool`, `list`, or `dict`. The default is `text`.
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* **Multiple**: This feature is deprecated. Currently, it is set to `True` by default if you expect multiple values for a single field. For example, a `list` of `features` is set to `True` to contain multiple values, such as `["waterproof", "durable", "lightweight"]`. Default: `True`.
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The **Parse DataFrame** component parses the structured output into a template for orderly presentation in chat output. The template receives the values from the `output_schema` table with curly braces.
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For example, the template `EBITDA: {EBITDA} , Net Income: {NET_INCOME} , GROSS_PROFIT: {GROSS_PROFIT}` presents the extracted values in the **Playground** as `EBITDA: 900 million , Net Income: 500 million , GROSS_PROFIT: 1.2 billion`.
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### Inputs
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| Name | Display Name | Info |
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|------|--------------|------|
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| llm | Language Model | The language model to use to generate the structured output. |
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| input_value | Input message | The input message for the language model to process. |
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| schema_name | Schema Name | Provide a name for the output data schema. |
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| output_schema | Output Schema | Define the structure and data types for the model's output. |
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| multiple | Generate Multiple | Set to True if the model should generate a list of outputs instead of a single output. |
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| input_value | Input Message | The input message to the language model. |
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| system_prompt | Format Instructions | Instructions to the language model for formatting the output. |
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| schema_name | Schema Name | The name for the output data schema. |
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| output_schema | Output Schema | Defines the structure and data types for the model's output.|
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| multiple | Generate Multiple | [Deprecated] Always set to `True`. |
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### Output
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### Outputs
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| Name | Display Name | Info |
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|------|--------------|------|
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| stored_messages | Stored Messages | structured output based on the defined schema. |
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| structured_output | Structured Output | The structured output is a Data object based on the defined schema. |
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| structured_output_dataframe | DataFrame | The structured output converted to a [DataFrame](/concepts-objects#dataframe-object) format. |
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@ -5,30 +5,35 @@ slug: /components-io
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# Input and output components in Langflow
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This category of components defines where data enters and exits your flow. They dynamically alter the Playground and can be renamed to facilitate building and maintaining your flows.
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Input and output components define where data enters and exits your flow.
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The difference between Chat Input and Text Input components is the output format, the number of configurable fields, and the way they are displayed in the Playground.
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Both components accept user input and return a `Message` object, but serve different purposes.
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The **Text Input** component accepts a text string input and returns a `Message` object containing only the input text. The output does not appear in the **Playground**.
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The **Chat Input** component accepts multiple input types including text, files, and metadata, and returns a `Message` object containing the text along with sender information, session ID, and file attachments.
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The **Chat Input** component provides an interactive chat interface in the **Playground**.
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## Chat Input
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This component collects user input from the chat.
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This component collects user input as `Text` strings from the chat and wraps it in a [Message](/concepts-objects) object that includes the input text, sender information, session ID, file attachments, and styling properties.
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The Chat Input component creates a [Message](/concepts-objects) object that includes the input text, sender information, session ID, file attachments, and styling properties.
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It can optionally store the message in a chat history and supports customization of the message appearance.
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It can optionally store the message in a chat history.
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### Inputs
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| Name | Display Name | Info | Type |
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|------|--------------|------|------|
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|input_value|Text|Message to be passed as input.|MultilineInput|
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|should_store_message|Store Messages|Store the message in the history.|BoolInput|
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|sender|Sender Type|Type of sender.|DropdownInput|
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|sender_name|Sender Name|Name of the sender.|MessageTextInput|
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|session_id|Session ID|The session ID of the chat. If empty, the current session ID parameter will be used.|MessageTextInput|
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|files|Files|Files to be sent with the message.|FileInput|
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|background_color|Background Color|The background color of the icon.|MessageTextInput|
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|chat_icon|Icon|The icon of the message.|MessageTextInput|
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|text_color|Text Color|The text color of the name|MessageTextInput|
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| Name | Display Name | Info |
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|------|--------------|------|
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|input_value|Text|The Message to be passed as input.
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|should_store_message|Store Messages|Store the message in the history.|
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|sender|Sender Type|The type of sender.|
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|sender_name|Sender Name|The name of the sender.|
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|session_id|Session ID|The session ID of the chat. If empty, the current session ID parameter is used.|
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|files|Files|The files to be sent with the message.|
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|background_color|Background Color|The background color of the icon.|
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|chat_icon|Icon|The icon of the message.|
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|text_color|Text Color|The text color of the name.|
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### Outputs
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@ -36,17 +41,37 @@ It can optionally store the message in a chat history and supports customization
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|------|--------------|------|
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|message|Message|The resulting chat message object with all specified properties.|
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### Message method
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The `ChatInput` class provides an asynchronous method to create and store a `Message` object based on the input parameters.
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The `Message` object is created in the `message_response` method of the ChatInput class using the `Message.create()` factory method.
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```python
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message = await Message.create(
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text=self.input_value,
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sender=self.sender,
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sender_name=self.sender_name,
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session_id=self.session_id,
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files=self.files,
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properties={
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"background_color": background_color,
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"text_color": text_color,
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"icon": icon,
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},
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)
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```
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## Text Input
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The Text Input component adds an Input field on the Playground.
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The **Text Input** component accepts a text string input and returns a `Message` object containing only the input text.
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The Text Input component offers one input field for text, while the Chat Input has multiple fields for various chat-related features.
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The output does not appear in the **Playground**.
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### Inputs
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| Name | Display Name | Info | Type |
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|------|--------------|------|------|
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|input_value|Text|Text to be passed as input.|MultilineInput|
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| Name | Display Name | Info |
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|------|--------------|------|
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|input_value|Text|The text/content to be passed as output.|
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### Outputs
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## Chat Output
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The Chat Output component creates a [Message](/concepts-objects) object that includes the input text, sender information, session ID, and styling properties.
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It can optionally store the message in a chat history and supports customization of the message appearance, including background color, icon, and text color.
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The **Chat Output** component creates a [Message](/concepts-objects#message-object) object that includes the input text, sender information, session ID, and styling properties.
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The component accepts the following input types.
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* [Data](/concepts-objects#data-object)
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* [DataFrame](/concepts-objects#dataframe-object)
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* [Message](/concepts-objects#message-object)
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### Inputs
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| Name | Display Name | Info | Type |
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|------|--------------|------|------|
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|input_value|Text|Message to be passed as output.|MessageInput|
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|should_store_message|Store Messages|Store the message in the history.|BoolInput|
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|sender|Sender Type|Type of sender.|DropdownInput|
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|sender_name|Sender Name|Name of the sender.|MessageTextInput|
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|session_id|Session ID|The session ID of the chat. If empty, the current session ID parameter will be used.|MessageTextInput|
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|data_template|Data Template|Template to convert data to text. If left empty, it will be dynamically set to the data's text key.|MessageTextInput|
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|background_color|Background Color|The background color of the icon.|MessageTextInput|
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|chat_icon|Icon|The icon of the message.|MessageTextInput|
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|text_color|Text Color|The text color of the name|MessageTextInput|
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| Name | Display Name | Info |
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|------|--------------|------|
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|input_value|Text|The message to be passed as output.|
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|should_store_message|Store Messages|The flag to store the message in the history.|
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|sender|Sender Type|The type of sender.|
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|sender_name|Sender Name|The name of the sender.|
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|session_id|Session ID|The session ID of the chat. If empty, the current session ID parameter is used.|
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|data_template|Data Template|The template to convert Data to Text. If the option is left empty, it is dynamically set to the Data's text key.|
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|background_color|Background Color|The background color of the icon.|
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|chat_icon|Icon|The icon of the message.|
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|text_color|Text Color|The text color of the name.|
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|clean_data|Basic Clean Data|When enabled, `DataFrame` inputs are cleaned when converted to text. Cleaning removes empty rows, empty lines in cells, and multiple newlines.|
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### Outputs
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## Text Output
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The TextOutputComponent displays text output in the **Playground**.
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It takes a single input of text and returns a [Message](/concepts-objects) object containing that text.
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The component is simpler compared to the Chat Output but focuses solely on displaying text without additional chat-specific features or customizations.
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The **Text Output** takes a single input of text and returns a [Message](/concepts-objects) object containing that text.
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The output does not appear in the **Playground**.
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### Inputs
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| Name | Display Name | Info | Type |
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|------|--------------|------|------|
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|input_value|Text|Text to be passed as output.|MultilineInput|
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| Name | Display Name | Info |
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|------|--------------|------|
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|input_value|Text|The text to be passed as output.|
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### Outputs
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@ -326,17 +326,18 @@ This component splits text into chunks based on specified criteria.
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| Name | Display Name | Info |
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|------|--------------|------|
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| data_inputs | Data Inputs | The data to split |
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| chunk_overlap | Chunk Overlap | Number of characters to overlap between chunks |
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| chunk_size | Chunk Size | Maximum number of characters in each chunk |
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| separator | Separator | Character to split on (defaults to newline) |
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| data_inputs | Input Documents | The data to split.The component accepts [Data](/concepts-objects#data-object) or [DataFrame](/concepts-objects#dataframe-object) objects. |
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| chunk_overlap | Chunk Overlap | The number of characters to overlap between chunks. Default: `200`. |
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| chunk_size | Chunk Size | The maximum number of characters in each chunk. Default: `1000`. |
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| separator | Separator | The character to split on. Default: `newline`. |
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| text_key | Text Key | The key to use for the text column (advanced). Default: `text`. |
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### Outputs
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| Name | Display Name | Info |
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|------|--------------|------|
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| chunks | Chunks | List of split text chunks as [Data](/concepts-objects#data-object) objects. |
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| dataframe | DataFrame | The chunks as a DataFrame |
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| dataframe | DataFrame | List of split text chunks as [DataFrame](/concepts-objects#dataframe-object) objects. |
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## Update data
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@ -168,15 +168,16 @@ This component performs web searches using the [DuckDuckGo](https://www.duckduck
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| Name | Display Name | Info |
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|------|--------------|------|
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| input_value | Search Query | The search query to be used for the DuckDuckGo search |
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| max_results | Max Results | Maximum number of results to return |
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| max_snippet_length | Max Snippet Length | Maximum length of each result snippet |
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| input_value | Search Query | The search query to execute with DuckDuckGo. |
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| max_results | Max Results | The maximum number of search results to return. Default: `5`. |
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| max_snippet_length | Max Snippet Length | The maximum length of each result snippet. Default: `100`.|
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### Outputs
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| Name | Display Name | Info |
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|------|--------------|------|
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| data | Data | List of search results as Data objects |
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| data | [Data](/concepts-objects#data-object) | List of search results as Data objects containing snippets and full content. |
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| text | Text | Search results formatted as a single text string. |
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## Exa Search
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@ -21,7 +21,7 @@ A flow can be as simple as the [basic prompting flow](/get-started-quickstart),
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* Each component has a **Configuration** menu. Click the **Code** pane to see a component's underlying Python code.
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* Components are connected with **edges** to form flows.
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If you're familiar with [ReactFlow](https://reactflow.dev/learn), a **flow** is a node-based application, a **component** is a node, and the connections between components are **edges**.
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If you're familiar with [React Flow](https://reactflow.dev/learn), a **flow** is a node-based application, a **component** is a node, and the connections between components are **edges**.
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When a flow is run, Langflow builds a Directed Acyclic Graph (DAG) graph object from the nodes (components) and edges (connections between components), with the nodes sorted to determine the order of execution. The graph build calls the individual components' `def_build` functions to validate and prepare the nodes. This graph is then processed in dependency order. Each node is built and executed sequentially, with results from each built node being passed to nodes that are dependent on the previous node's results.
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@ -11,7 +11,7 @@ Install Langflow locally with [uv (recommended)](https://docs.astral.sh/uv/getti
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### Prerequisites
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- [Python 3.10 to 3.12](https://www.python.org/downloads/release/python-3100/) installed
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- [Python 3.10 to 3.13](https://www.python.org/downloads/release/python-3100/) installed
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- [uv](https://docs.astral.sh/uv/getting-started/installation/), [pip](https://pypi.org/project/pip/), or [pipx](https://pipx.pypa.io/stable/installation/) installed
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- Before installing Langflow, we recommend creating a virtual environment to isolate your Python dependencies with [uv](https://docs.astral.sh/uv/pip/environments), [venv](https://docs.python.org/3/library/venv.html), or [conda](https://anaconda.org/anaconda/conda)
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@ -37,7 +37,7 @@ const config = {
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docs: {
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routeBasePath: "/", // Serve the docs at the site's root
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sidebarPath: require.resolve("./sidebars.js"), // Use sidebars.js file
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sidebarCollapsed: false,
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sidebarCollapsed: true,
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beforeDefaultRemarkPlugins: [
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[
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remarkCodeHike,
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@ -96,6 +96,22 @@ module.exports = {
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"Deployment/deployment-render",
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],
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},
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{
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type: "category",
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label: "API reference",
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items: [
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{
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type: "link",
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label: "API documentation",
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href: "/api",
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},
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{
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type: "doc",
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id: "API-Reference/api-reference-api-examples",
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label: "API examples",
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},
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],
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},
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{
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type: "category",
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label: "Integrations",
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"Contributing/contributing-telemetry",
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],
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},
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{
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type: "category",
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label: "API reference",
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items: [
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{
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type: "link",
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label: "API documentation",
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href: "/api",
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},
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{
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type: "doc",
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id: "API-Reference/api-reference-api-examples",
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label: "API examples",
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},
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],
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},
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{
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type: "category",
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label: "Changelog",
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