66 lines
1.2 KiB
Markdown
66 lines
1.2 KiB
Markdown
# Local RAG Setup
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Minimal RAG implementation with LangChain, Ollama, and FAISS.
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## Dependencies
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Only 5 packages:
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- `langchain` - Core framework
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- `langchain-ollama` - Ollama integration
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- `faiss-cpu` - Vector search
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- `sentence-transformers` - Embeddings
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- `pypdf` - PDF loading
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## Installation
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```bash
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# Create conda environment
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conda create -n local_rag python=3.10 -y
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conda activate local_rag
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# Install dependencies
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pip install -r requirements.txt
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```
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## Setup Ollama
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```bash
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# Make sure Ollama is running
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ollama serve
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# Pull a model (in another terminal)
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ollama pull llama2
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```
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## Usage
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Edit `local_rag.py` and uncomment the example code:
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```python
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# Add documents
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rag.add_documents([
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"path/to/document1.pdf",
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"path/to/document2.txt"
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])
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# Query
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question = "What is this document about?"
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answer = rag.query(question)
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print(f"Answer: {answer}")
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```
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Run:
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```bash
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python local_rag.py
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```
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## How it works
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1. **Load documents** - PDFs or text files
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2. **Split into chunks** - 1000 chars with 200 overlap
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3. **Create embeddings** - Using sentence-transformers
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4. **Store in FAISS** - Fast similarity search
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5. **Query** - Retrieve relevant chunks and generate answer with Ollama
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That's it! Simple and minimal.
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