Chat with GitHub OpenAPI Spec using RAG
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Chat with the GitHub API documentation using Retrieval Augmented Generation (RAG) with Pinecone and OpenAI.
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About This Workflow
Overview
This workflow demonstrates how to build an API documentation chatbot using Retrieval Augmented Generation (RAG) in n8n. It leverages Pinecone as a vector database and OpenAI for embeddings and response generation, allowing users to query the GitHub API specifications conversationally.
Key Features
- Fetches GitHub API OpenAPI specifications.
- Splits specifications into manageable chunks.
- Generates embeddings for text chunks using OpenAI.
- Stores embeddings in Pinecone vector database.
- Answers user queries by retrieving relevant information from the vector store.
- Utilizes OpenAI GPT 4o-mini for generating natural language responses.
How To Use
- Ensure you have accounts for OpenAI and Pinecone.
- Obtain API keys for both services.
- Configure n8n credentials for OpenAI and Pinecone.
- Create a Pinecone index named "n8n-demo" or adjust the workflow's Pinecone index name.
- Activate the workflow and interact with it via the chat interface.
Apps Used
Workflow JSON
{
"id": "e8e282db-5125-4c4f-9715-772c19604881",
"name": "Chat with GitHub OpenAPI Spec using RAG",
"nodes": 0,
"category": "AI / Machine Learning",
"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: e8e282db-5125...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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