This n8n workflow demonstrates how to create an intelligent chatbot that provides information about the GitHub API using Retrieval-Augmented Generation (RAG) techniques. The workflow fetches GitHub API specifications, processes and embeds the content using OpenAI, and stores the data in Pinecone. When a user sends a chat message, the system retrieves relevant API information from the vector database, generates context-aware responses with OpenAI’s language model, and delivers accurate answers. This setup is ideal for developers and teams seeking an interactive API documentation assistant or chatbot integrated with n8n.
AI-Powered GitHub API Chatbot Using RAG in n8n
| Node Count | 11 – 20 Nodes |
|---|---|
| Nodes Used | @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.chatTrigger, @n8n/n8n-nodes-langchain.documentDefaultDataLoader, @n8n/n8n-nodes-langchain.embeddingsOpenAi, @n8n/n8n-nodes-langchain.lmChatOpenAi, @n8n/n8n-nodes-langchain.memoryBufferWindow, @n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter, @n8n/n8n-nodes-langchain.toolVectorStore, @n8n/n8n-nodes-langchain.vectorStorePinecone, httpRequest, manualTrigger, stickyNote |
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