AI Agents

Chat with GitHub specifications using RAG search

17 nodes 319 145 Automatic trigger
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Workflow Description

An intelligent conversation automation that connects OpenAI's language model to a Pinecone vector database to answer questions about GitHub project specifications. Users can query technical documentation and receive accurate responses grounded in the actual source materials.

How it works

  1. 1.Trigger chat interface and submit a question about project specifications
  2. 2.Parse and chunk specification documents, then generate embeddings and store in Pinecone
  3. 3.Search the vector database for content most relevant to the user's query
  4. 4.Pass retrieved context and question to the AI agent connected to OpenAI
  5. 5.Maintain conversation history to support multi-turn dialogue and context awareness
  6. 6.Return the answer with references to source documentation back to the user

Use cases

  • Answer technical questions about APIs, requirements, and design specifications in GitHub repos
  • Enable rapid full-text search across documentation without manual file review
  • Provide developers instant clarification on project specifications and architectural decisions

Requirements

  • Active OpenAI API key and configured Pinecone account
  • GitHub specification documents or URLs prepared for ingestion and processing
  • User-facing chat interface deployed and accessible

Service Value

Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.

Apps Used

Vector Store Document Default Data Loader Text Splitter Chat Trigger AI Agent OpenAI Memory

Details

Trigger Automatic trigger
Nodes 17
Apps 7
Views 319
Downloads 145

How to Use

  1. 1.Click "Download Template"
  2. 2.Open your n8n dashboard
  3. 3.Go to Workflows > Import from File
  4. 4.Select downloaded file and configure credentials

Nodes Used (17)

/

When clicking ‘Test workflow’

Manual Trigger

#1

HTTP Request

HTTP Request

#2

Pinecone Vector Store

Vector Store Pinecone

#3

Default Data Loader

Document Default Data Loader

#4

Recursive Character Text Splitter

Text Splitter Recursive Character Text Splitter

#5

When chat message received

Chat Trigger

#6

AI Agent

Agent

#7

OpenAI Chat Model

OpenAI

#8

Window Buffer Memory

Memory Buffer Window

#9

Vector Store Tool

Tool Vector Store

#10

OpenAI Chat Model1

OpenAI

#11

Sticky Note

Sticky Note

#12

Sticky Note1

Sticky Note

#13

Generate User Query Embedding

OpenAI

#14

Pinecone Vector Store (Querying)

Vector Store Pinecone

#15

Generate Embeddings

OpenAI

#16

Sticky Note2

Sticky Note

#17