localRAG/README.md

Local RAG Setup

Minimal RAG implementation with LangChain, Ollama, and FAISS.

Dependencies

Only 5 packages:

• langchain - Core framework
• langchain-ollama - Ollama integration
• faiss-cpu - Vector search
• sentence-transformers - Embeddings
• pypdf - PDF loading

Installation

# Create conda environment
conda create -n local_rag python=3.10 -y
conda activate local_rag

# Install dependencies
pip install -r requirements.txt

Setup Ollama

# Make sure Ollama is running
ollama serve

# Pull a model (in another terminal)
ollama pull llama2

Usage

Edit local_rag.py and uncomment the example code:

# Add documents
rag.add_documents([
    "path/to/document1.pdf",
    "path/to/document2.txt"
])

# Query
question = "What is this document about?"
answer = rag.query(question)
print(f"Answer: {answer}")

Run:

python local_rag.py

How it works

1. Load documents - PDFs or text files
2. Split into chunks - 1000 chars with 200 overlap
3. Create embeddings - Using sentence-transformers
4. Store in FAISS - Fast similarity search
5. Query - Retrieve relevant chunks and generate answer with Ollama

That's it! Simple and minimal.