AI-Powered GitHub API Documentation Chatbot with RAG

This n8n workflow enables the creation of an intelligent chatbot that answers questions about the GitHub API using a Retrieval-Augmented Generation (RAG) approach. The process begins with a manual trigger, allowing users to test the workflow by fetching the GitHub OpenAPI specifications from GitHub’s repository via an HTTP request. The retrieved API schema is then processed by splitting it into manageable chunks, generating embeddings for each chunk using OpenAI’s language models, and storing these embeddings in a Pinecone vector database.

For handling user interactions, the workflow includes a webhook-triggered chat interface where incoming messages activate an AI agent. This agent leverages OpenAI’s language models to generate context-aware responses, querying the vector store to retrieve relevant API documentation as needed. Memory buffers ensure conversational context is maintained, leading to more accurate and helpful responses.

This workflow is highly practical for building API documentation assistants, developer support bots, or knowledge base tools that require fast, accurate answers pulled from complex API schemas. It showcases an effective use of AI, vector databases, and n8n automation for advanced information retrieval and conversational AI applications.