Chat with GitHub Specifications Using RAG
Workflow Description
An automation that enables interactive questioning of GitHub project specifications through an intelligent chatbot. Documents are indexed in Pinecone and answers are generated via OpenAI using retrieval-augmented generation techniques for accurate, context-aware responses.
How it works
- 1.Load GitHub specifications and split content into searchable text chunks
- 2.Store chunks in Pinecone vector database for rapid semantic retrieval
- 3.Receive user questions through the chat interface and convert to embeddings
- 4.Search Pinecone for relevant documentation segments matching the query
- 5.Generate precise answers using OpenAI based on retrieved context
Use cases
- Answer technical questions about project architecture and requirements without manual file browsing
- Quickly locate specific information within large GitHub documentation repositories
Requirements
- Active Pinecone account with API key and configured vector index
- OpenAI API key for chat model and embedding generation access
Service Value
Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.
Apps Used
Details
How to Use
- 1.Click "Download Template"
- 2.Open your n8n dashboard
- 3.Go to Workflows > Import from File
- 4.Select downloaded file and configure credentials
Nodes Used (17)
When clicking ‘Test workflow’
Manual Trigger
HTTP Request
HTTP Request
Pinecone Vector Store
Vector Store Pinecone
Default Data Loader
Document Default Data Loader
Recursive Character Text Splitter
Text Splitter Recursive Character Text Splitter
When chat message received
Chat Trigger
AI Agent
Agent
OpenAI Chat Model
OpenAI
Window Buffer Memory
Memory Buffer Window
Vector Store Tool
Tool Vector Store
OpenAI Chat Model1
OpenAI
Sticky Note
Sticky Note
Sticky Note1
Sticky Note
Generate User Query Embedding
OpenAI
Pinecone Vector Store (Querying)
Vector Store Pinecone
Generate Embeddings
OpenAI
Sticky Note2
Sticky Note