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146 lines
7.3 KiB
Plaintext
146 lines
7.3 KiB
Plaintext
---
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title: Chat Input and Output
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slug: /chat-input-and-output
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---
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import Icon from "@site/src/components/icon";
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import PartialParams from '@site/docs/_partial-hidden-params.mdx';
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:::warning
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**Chat Input and Output** components are required to chat with your flow in the **Playground**.
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For more information, see [Test flows in the Playground](/concepts-playground).
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:::
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**Chat Input and Output** components are designed to handle conversational interactions in Langflow.
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## Chat Input
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The **Chat Input** component accepts text and file input, such as a chat message or a file.
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This data is passed to other components as [`Message` data](/data-types) containing the provided input as well as associated chat metadata, such as the sender, session ID, timestamp, and file attachments.
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Initial input should _not_ be provided as a complete `Message` object because the **Chat Input** component constructs the `Message` object that is then passed to other components in the flow.
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### Chat Input parameters
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<PartialParams />
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| Name | Display Name | Info |
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|------|--------------|------|
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|input_value|Input Text| Input parameter. The message text string to be passed as input. |
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|sender|Sender Type| Input parameter. Identifies the sender as either `User` or `Language Model`.|
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|sender_name|Sender Name| Input parameter. The name of the sender. If unspecified, defaults to `User` or `Language Model`. |
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|session_id|Session ID| Input parameter. The unique identifier for the chat session. If empty, the current session ID parameter is used.|
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|files|Files| Input parameter. The files to be sent with the message.|
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|background_color|Background Color| Input parameter. The background color of the icon.|
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|chat_icon|Icon| Input parameter. The icon of the message.|
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|should_store_message|Store Messages| Input parameter. Whether to store the message in chat history.|
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|text_color|Text Color| Input parameter. The text color of the name.|
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For information about the resulting `Message` object, including input parameters that are directly mapped to `Message` attributes, see [`Message` data](/data-types#message).
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<details>
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<summary>Message method for Chat Input</summary>
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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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</details>
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## Chat Output
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The **Chat Output** component ingests `Message`, `JSON`, or `Table` data from other components, transforms it into `Message` data if needed, and then emits the final output as a chat message.
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For information about these data types, see [Use Langflow data types](/data-types).
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In the **Playground**, chat output is limited to the parts of the `Message` object that are relevant to the chat interface, such as the text response, sender name, and file attachments.
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To see the metadata associated with a chat message, inspect the message logs in the **Playground**.
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When using the Langflow API, the API response includes the **Chat Output** `Message` object along with other response data from the flow run.
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Langflow API responses can be extremely verbose, so your applications must include code to extract relevant data from the response to return to the user.
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For an example, see the [Langflow quickstart](/get-started-quickstart).
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### Chat Output parameters
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<PartialParams />
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| Name | Display Name | Info |
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|------|--------------|------|
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|input_value|Inputs| Input parameter. The message text string to be passed as output. |
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|should_store_message|Store Messages| Input parameter. Whether to store the message in chat history.|
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|sender|Sender Type| Input parameter. Identifies the sender as either `User` or `Language Model`.|
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|sender_name|Sender Name| Input parameter. The name of the sender. If unspecified, defaults to `User` or `Language Model`. |
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|session_id|Session ID| Input parameter. The unique identifier for the chat session. If empty, the current session ID parameter is used.|
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|data_template|Data Template| Input parameter. The template to convert [`JSON` input](/data-types#json) to `text`. If empty, it is dynamically set to the `JSON` object's `text` key.|
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|background_color|Background Color| Input parameter. The background color of the icon.|
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|chat_icon|Icon| Input parameter. The icon of the message.|
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|text_color|Text Color| Input parameter. The text color of the name.|
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|clean_data|Basic Clean Data| Input parameter. When enabled, [`Table` input](/data-types#table) is cleaned when converted to text. Cleaning removes empty rows, empty lines in cells, and multiple newlines.|
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For information about the resulting `Message` object, including input parameters that are directly mapped to `Message` attributes, see [`Message` data](/data-types#message).
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## Use Chat Input and Output components in a flow
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To use the **Chat Input** and **Chat Output** components in a flow, connect them to components that accept or emit [`Message` data](/data-types#message).
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For example, the following flow connects **Chat Input** and **Chat Output** to a **Language Model** component, creating a simple LLM-based chat flow.
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:::tip
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For detailed examples of **Chat Input and Output** components in flows, see the following:
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* [Langflow quickstart](/get-started-quickstart): Create and run a basic agent flow.
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* **Basic Prompting** template: Create an LLM-based chat flow that accepts chat input as well as a prompt with additional instructions for the LLM. Many other Langflow templates also use **Chat Input and Output** components.
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* [Connect applications to agents](/agent-tutorial): Explore more advanced concepts around agent flows and prompting, including triggering agent flows from external applications.
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:::
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### Send chat input with the Langflow API
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You can use the Langflow API to run a flow by sending input to a **Chat Input** component:
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```bash
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curl --request POST \
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--url "http://$LANGFLOW_SERVER_ADDRESS/api/v1/run/$FLOW_ID" \
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--header "Content-Type: application/json" \
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--header "x-api-key: $LANGFLOW_API_KEY" \
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--data '{
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"input_value": "What's the recommended way to install Docker on Mac M1?",
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"output_type": "chat",
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"input_type": "chat"
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}'
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```
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When triggering flows with the Langflow API, the payload must contain the values for the **Chat Input** component's input parameters, such as `input_value`.
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Not all parameters need to be specified in the request.
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For example, `session_id` uses the flow's default session ID if omitted.
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If you want to use a custom session ID, include `session_id` in your request:
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```bash
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curl --request POST \
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--url "http://$LANGFLOW_SERVER_ADDRESS/api/v1/run/$FLOW_ID" \
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--header "Content-Type: application/json" \
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--header "x-api-key: $LANGFLOW_API_KEY" \
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--data '{
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"input_value": "Whats the recommended way to install Docker on Mac M1",
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"session_id": "$USER_ID",
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"output_type": "chat",
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"input_type": "chat"
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}'
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```
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For more information, see [Trigger flows with the Langflow API](/concepts-publish). |