# 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

```bash
# 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

```bash
# 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:

```python
# 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:
```bash
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.