Stock Q&A Workflow

Stock Q&A Workflow automates the retrieval and response process for stock-related inquiries. It integrates with LangChain and Google Drive to fetch and analyze data from PDFs, providing accurate answers through a webhook. This efficient system enhances user interaction by delivering timely responses, streamlining information access, and improving decision-making in stock analysis.

7/8/2025
17 nodes
Complex
webhookcomplexlangchainsticky noterespondtowebhookgoogle driveadvancedintegrationapi
Categories:
Complex WorkflowWebhook Triggered
Integrations:
LangChainSticky NoteRespondToWebhookGoogle Drive

Target Audience

  • Data Analysts: Those who need to analyze large datasets and extract insights quickly.
    - Developers: Individuals looking to integrate AI capabilities into their applications without extensive machine learning knowledge.
    - Business Professionals: Users who want to automate Q&A processes related to financial or operational data.
    - Researchers: Academics who need to retrieve and analyze information from documents efficiently.

    • Problem Solved

    This workflow addresses the challenge of quickly retrieving and processing information from large documents, specifically PDFs, by integrating AI models for question answering. It automates the process of fetching data from Google Drive, splitting documents into manageable chunks, and storing them in a vector store for efficient retrieval. This allows users to obtain accurate answers to queries based on the content of the documents without manual searching.

    Workflow Steps

  • Step 1: Fetch PDF from Google Drive - The workflow begins by downloading a specified PDF file from Google Drive.
    - Step 2: Document Processing - The PDF is converted into a document format suitable for further processing.
    - Step 3: Chunking the Document - The document is split into smaller chunks to facilitate easier retrieval and processing.
    - Step 4: Embedding Generation - Each chunk is transformed into embeddings using OpenAI's model, allowing for semantic understanding.
    - Step 5: Storage in Vector Store - The embeddings are stored in a Qdrant vector store, enabling efficient querying.
    - Step 6: Webhook Trigger - The workflow is triggered via a webhook, allowing users to send queries.
    - Step 7: Query Processing - The incoming query is processed to retrieve relevant chunks from the vector store.
    - Step 8: Response Generation - The relevant information is compiled, and a response is generated using OpenAI's chat model.
    - Step 9: Responding to Webhook - Finally, the response is sent back through the webhook, providing users with the answers they need.

    • Customization Guide

  • Change Document Source: Users can modify the Google Drive file ID to use different PDF documents.
    - Adjust Chunk Size: The chunk size and overlap parameters can be customized in the text splitter node to optimize retrieval based on document length.
    - Modify Query Logic: Users can adapt the query handling in the Retrieval QA Chain to tailor responses to specific use cases.
    - Integrate Additional AI Models: Users can replace the OpenAI models with different models available in LangChain to suit their needs better.
    - Add More Webhook Endpoints: Users can create additional webhook triggers to handle different types of queries or events.