Build a GitHub API RAG Chatbot with n8n and Pinecone
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This n8n workflow creates a chatbot that answers questions about the GitHub API using Retrieval Augmented Generation (RAG). It leverages OpenAI for embeddings and LLM, Pinecone for vector storage, and fetches API specs via HTTP Request.
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About This Workflow
Overview
This n8n workflow demonstrates a powerful application of Retrieval Augmented Generation (RAG) to create an intelligent chatbot capable of answering questions about the GitHub API. It automates the process of fetching GitHub's OpenAPI specifications, processing this data into a searchable format using embeddings, and then enabling natural language querying. The workflow solves the problem of efficiently accessing and understanding complex API documentation by providing a conversational interface.
Key Features
- Fetches GitHub OpenAPI specifications using an HTTP Request node.
- Processes and chunks API documentation using text splitter nodes.
- Generates embeddings for the documentation using OpenAI.
- Stores and indexes these embeddings in Pinecone for efficient retrieval.
- Utilizes an AI Agent with OpenAI Chat Model and Window Buffer Memory for conversational interactions.
- Implements a Vector Store Tool to query the Pinecone database for relevant information.
How To Use
- Ensure you have n8n installed and running.
- Create accounts for OpenAI and Pinecone, and obtain your API keys.
- Configure your OpenAI and Pinecone credentials within n8n.
- Make sure you have a Pinecone index named "n8n-demo" or update the Pinecone nodes with your index name.
- Trigger the workflow manually or via the chat trigger to index the GitHub API data.
- Interact with the chatbot through the chat interface, asking questions about the GitHub API.
Apps Used
Workflow JSON
{
"id": "e99a0696-9a44-466f-a5eb-e63b7053af52",
"name": "Build a GitHub API RAG Chatbot with n8n and Pinecone",
"nodes": 0,
"category": "AI_Research_RAG_and_Data_Analysis",
"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: e99a0696-9a44...
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