Retrieval-Augmented Generation (RAG) Template 🚀

This repo provides a plug-and-play RAG pipeline in Python. Use it to index your documents and query them with an LLM (OpenAI, Gemini, HuggingFace, etc.).

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📂 Files Included

• rag_template.py → The main RAG script.
• sample_docs.txt → Example text file for testing.

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⚙️ Setup Instructions

1. Clone this repo

git clone https://github.com/yourusername/rag-template.git
cd rag-template

2. Create a virtual environment (recommended)

python -m venv venv
source venv/bin/activate   # On Mac/Linux
venv\Scripts\activate      # On Windows

3. Install dependencies

pip install -r requirements.txt

Minimal requirements (requirements.txt):

langchain
langchain-community
langchain-openai
faiss-cpu

4. Set your API Key

Export your API key for security:

export OPENAI_API_KEY="your_api_key_here"   # Mac/Linux
setx OPENAI_API_KEY "your_api_key_here"     # Windows

Or edit the script to place your key directly.

5. Run the RAG pipeline

python rag_template.py

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📝 How it Works

1. Load Documents → from .txt files (can swap for PDFs, CSVs, or DBs).
2. Split Text → into manageable chunks.
3. Embed Chunks → using OpenAI embeddings.
4. Store Vectors → inside FAISS (local vector database).
5. Ask Questions → queries are matched against your docs.
6. LLM Response → retrieved chunks are passed to the LLM for context.

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🔧 Customization

• Change TextLoader to PyPDFLoader, CSVLoader, etc.
• Swap embeddings: OpenAIEmbeddings → HuggingFaceEmbeddings.
• Replace FAISS with other vector DBs (Pinecone, Chroma, Weaviate).
• Use another LLM: ChatOpenAI → Gemini / HuggingFace / LLaMA.

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✅ Example Query

YOU: What is this document about?
RAG: This document explains how to set up a Retrieval-Augmented Generation pipeline.

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🤝 Contributing

Feel free to fork, modify, and use this template for your own projects.

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📜 License

MIT License. Free to use and modify.