skill-seekers-reference/examples/langchain-rag-pipeline/README.md

LangChain RAG Pipeline Example

Complete example showing how to build a RAG (Retrieval-Augmented Generation) pipeline using Skill Seekers documents with LangChain.

What This Example Does

1. Loads Skill Seekers-generated LangChain Documents
2. Creates a persistent Chroma vector store
3. Builds a RAG query engine with GPT-4
4. Queries the documentation with natural language

Prerequisites

# Install dependencies
pip install langchain langchain-community langchain-openai chromadb openai

# Set API key
export OPENAI_API_KEY=sk-...

Generate Documents

First, generate LangChain documents using Skill Seekers:

# Option 1: Use preset config (e.g., React)
skill-seekers scrape --config configs/react.json
skill-seekers package output/react --target langchain

# Option 2: From GitHub repo
skill-seekers github --repo facebook/react --name react
skill-seekers package output/react --target langchain

# Output: output/react-langchain.json

Run the Example

cd examples/langchain-rag-pipeline

# Run the quickstart script
python quickstart.py

What You'll See

1. Documents loaded from JSON file
2. Vector store created with embeddings
3. Example queries demonstrating RAG
4. Interactive mode to ask your own questions

Example Output

============================================================
LANGCHAIN RAG PIPELINE QUICKSTART
============================================================

Step 1: Loading documents...
✅ Loaded 150 documents
   Categories: {'overview', 'hooks', 'components', 'api'}

Step 2: Creating vector store...
✅ Vector store created at: ./chroma_db
   Documents indexed: 150

Step 3: Creating QA chain...
✅ QA chain created

Step 4: Running example queries...

============================================================
QUERY: How do I use React hooks?
============================================================

ANSWER:
React hooks are functions that let you use state and lifecycle features
in functional components. The most common hooks are useState and useEffect...

SOURCES:
  1. hooks (hooks.md)
     Preview: # React Hooks\n\nHooks are a way to reuse stateful logic...

  2. api (api_reference.md)
     Preview: ## useState\n\nReturns a stateful value and a function...

Files in This Example

• quickstart.py - Complete working example
• README.md - This file
• requirements.txt - Python dependencies

Next Steps

1. Customize - Modify the example for your use case
2. Experiment - Try different vector stores (FAISS, Pinecone)
3. Extend - Add conversational memory, filters, hybrid search
4. Deploy - Build a production RAG application

Troubleshooting

"Documents not found"

• Make sure you've generated documents first
• Check the path in quickstart.py matches your output location

"OpenAI API key not found"

• Set environment variable: export OPENAI_API_KEY=sk-...

"Module not found"

• Install dependencies: pip install -r requirements.txt

Related Examples

• LlamaIndex RAG Pipeline
• Pinecone Integration

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