🧠 RAG-Parse Chat

A Retrieval-Augmented Generation (RAG) system for answering questions based on custom documents.
Built with LangChain, Chroma, BeautifulSoup, FastAPI, Streamlit, and Docker Compose.

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✨ Features

• 💬 Interactive chat interface with Streamlit
• 📚 Document retrieval using LangChain + Chroma embeddings
• 🤖 Supports multiple LLM models: DeepSeek, OpenAI, K2-Think, Qwen, etc.. (https://ai.io.net/ai/api-keys (free api))
• ⚡ Backend powered by FastAPI
• 🔒 Safe API key storage using environment variables (.env)
• 🐳 Fully containerized with Docker Compose

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🖼 Screenshots

Writing Code (show.jpg)

Frontend (app.jpg)

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🛠 Tech Stack

• Python 3.10
• LangChain
• Chroma (vector database)
• BeautifulSoup (bs4)
• FastAPI
• Streamlit
• Docker Compose

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⚙️ Setup

1. Clone the repository:

git clone <your-repo-url>
cd RAG-start

2. Create a .env file:

LLM=your_api_token_here

Important: Do not commit this file; add it to .gitignore.

3. Build and run with Docker Compose:

docker compose up --build

• 🌐 Frontend (Streamlit): http://localhost:8501
• ⚡ Backend (FastAPI): http://localhost:8000/docs

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📝 Usage

1. Open Streamlit in your browser
2. Select a model from the sidebar
3. Ask a question in the chat input
4. The system retrieves relevant documents and generates a response using the selected LLM

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📂 Project Structure

RAG-start/
├─ api.py                 # API key helper
├─ streamlit_app.py       # Streamlit frontend
├─ query_rag.py           # FastAPI backend
├─ rag_data.py            # Embeddings & Chroma vector DB
├─ Dockerfile.api         # Backend Dockerfile
├─ Dockerfile.streamlit   # Frontend Dockerfile
├─ docker-compose.yml     # Docker Compose setup
├─ chroma_db/             # Persisted vector database
├─ show.jpg               # Screenshot of coding process
├─ app.jpg                # Screenshot of frontend
├─ requirements.txt       # Python dependencies
├─ .env                   # Environment variables (not in repo)

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🔐 Security

• API keys are never hardcoded
• Use .env files and Docker environment variables

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💡 Notes

• Works with multiple LLMs
• Fully containerized
• Designed for rapid prototyping and RAG experimentation
• Easy to share and deploy for collaborators

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🚀 Contribution

Contributions are welcome! Please fork the repo, create a feature branch, and submit a pull request.

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📫 Contact

Created by @nurikw3 (tg) – feel free to reach out for questions or collaborations.