test RAG with local Ollama models

initial commit

README.md

@@ -0,0 +1,65 @@
+# 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.