Skip to content

Latest commit

 

History

History
125 lines (87 loc) · 4.18 KB

langflow.md

File metadata and controls

125 lines (87 loc) · 4.18 KB

LangFlow Pipeline Documentation

LangFlow Diagram

This document explains each component in the LangFlow pipeline to demonstrate how data flows through the system to generate a meaningful chat output based on user input. The components and their connections are outlined below:

1. File

  • Purpose: Loads a CSV file into the system to be used for data processing.

  • Path: mock_social_media_data.csv
    This file likely contains social media data for further analysis.

  • Output: Passes the loaded data into the Split Text node.

2. Split Text

  • Purpose: Splits the input text into smaller chunks for processing, based on specific criteria.
  • Inputs:
    • Chunk Overlap: 200
    • Chunk Size: 1000
    • Separator: Not specified but could be customized for specific text delimiters.
  • Output: Produces chunks of text that are easier for downstream components to handle.

3. Chat Input

  • Purpose: Takes user input from the playground for processing in the chat system.

  • Text: Example input: "What post type shows the most engagement?"

  • Output: Sends the user question as a message to downstream nodes.

4. Astra DB

  • Purpose: Implements a vector store using Astra DB to enable search capabilities based on embeddings.

  • Configuration:

    • Database: davedb
    • Collection: engagement
    • Embedding Model: Astra Vectorize
    • Embedding Provider: OpenAI
    • Model: text-embedding-3-small
    • API Keys: Required for authorization.

  • Output: Returns search results based on the query and embeddings stored in the database.

5. Parse Data

  • Purpose: Converts incoming data into plain text following a specified template.

  • Template: {text}
    This template formats the incoming data.

  • Output: Generates plain text to be used by downstream components.

6. OpenAI

  • Purpose: Generates a response using an OpenAI language model.
  • Inputs:
    • Model Name: gpt-4o-mini
    • Temperature: 0.1 (Controls the randomness of the output.)
    • Stream: Enabled to allow real-time streaming of results.
    • API Key: Required for accessing the OpenAI API.
  • Output: Returns a generated text response based on the input.

7. Text Input

  • Purpose: Accepts additional user-defined text input to refine or filter the results.

  • Text: Example input: "Provide only the final calculated result."

  • Output: Passes refined text for processing.

8. Combine Text

  • Purpose: Concatenates two text inputs into a single output, separated by a specified delimiter.

  • Inputs:

    • First Text: Receiving input from previous nodes.
    • Second Text: Receiving input from previous nodes.
    • Delimiter: Not explicitly defined in this flow.

  • Output: Produces combined text for further processing.

9. Chat Output

  • Purpose: Displays the final chat message in the playground as the system's response to the user's input.

  • Inputs:

    • Text: Receives the processed response from the OpenAI language model.

  • Output: Displays the response message for the user.

Flow Summary

  1. Input Phase:

    • User provides a query via Chat Input.
    • Relevant social media data is loaded via the File node.
    • The data is chunked for easier processing in the Split Text node.

  2. Processing Phase:

    • Data embeddings are retrieved and queried using Astra DB.
    • The data is parsed and converted into plain text using the Parse Data node.
    • The OpenAI language model generates a response based on the query.

  3. Finalization Phase:

    • Additional input or adjustments can be made through Text Input.
    • Outputs are combined using Combine Text for a cohesive response.
    • The final response is displayed using Chat Output.

Usage Example

  • User Query: "What post type shows the most engagement?"
  • System Response: The flow processes the input, retrieves the relevant insights from the database, generates a response using OpenAI, and outputs the response to the user.

If you need further clarification or additional details, feel free to ask!