AI-Powered Stock Q&A Workflow with Langchain and Qdrant
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This n8n workflow enables an AI-powered question-answering system for stock-related information. It uses Langchain nodes for embeddings and retrieval, a Qdrant Vector Store for data indexing, and OpenAI for generating responses. The workflow automates the process of fetching documents, embedding them, and querying them via a webhook.
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About This Workflow
Overview
This workflow is designed to create an intelligent question-answering system for stock-related data. It leverages the power of Langchain, integrated within n8n, to process documents, create vector embeddings, and perform semantic searches. The core logic involves fetching a document (e.g., a PDF from Google Drive), splitting it into manageable chunks, generating embeddings using OpenAI, and storing these in a Qdrant vector database. When a query is received via a webhook, the workflow retrieves relevant information from Qdrant, uses it to inform an OpenAI Chat Model, and then returns a concise answer. This automates the process of building a knowledge base and enabling natural language queries against it.
Key Features
- Document Ingestion: Automatically downloads files from Google Drive (e.g., PDFs).
- Text Splitting: Breaks down large documents into smaller, processable chunks using
Recursive Character Text Splitter. - AI Embeddings: Generates vector representations of text chunks using
Embeddings OpenAI. - Vector Database Storage: Stores embeddings in a
Qdrant Vector Storefor efficient semantic search. - AI-Powered Retrieval: Utilizes
Vector Store Retrieverto find relevant information based on user queries. - Conversational AI: Employs
OpenAI Chat Modelto understand context and generate human-like answers. - Webhook Integration: Accepts queries via a webhook (
Webhook1) and responds with the AI-generated answer (Respond to Webhook).
How To Use
- Configure Google Drive: Ensure your Google Drive account is connected and set up with the
Google Drivenode, specifying the file ID of the document you want to use. - Set up OpenAI: Connect your OpenAI API key via the
OpenAi accountcredential. - Configure Qdrant: Set up your Qdrant API connection and specify the collection name for your vector store in the
Qdrant Vector Storenodes. - Define Webhook Endpoint: Configure the
Webhook1node with a POST request path. This will be the endpoint where you send your queries. - Trigger the Workflow: Initially, manually trigger the workflow by clicking "Execute Workflow" to process and embed the document. After this, the webhook will be active for queries.
- Send Queries: Send POST requests to the configured webhook URL with a JSON body containing your question (e.g.,
{"input": "What is the Q3 revenue?")}.Thecompanyparameter inQdrant Vector Store1should also be set according to your data structure. - Receive Responses: The
Respond to Webhooknode will return the AI-generated answer.
Apps Used
Workflow JSON
{
"id": "c44afe53-94c2-4ef4-bbc5-988bff58249a",
"name": "AI-Powered Stock Q&A Workflow with Langchain and Qdrant",
"nodes": 0,
"category": "AI and LLMs",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: c44afe53-94c2...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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