Here is a polished, professional README for your agentic_rag project.
An advanced, intelligent Retrieval-Augmented Generation (RAG) framework designed to perform multi-step reasoning, dynamic context retrieval, and precise query answering.
- Autonomous Agent Routing: Dynamically decides whether to fetch external context, query vector databases, or compute directly based on user intent.
- Smart Context Retrieval: Filters and re-ranks retrieved chunks to eliminate noise and maximize response relevance.
- Seamless API Integration: Built to scale easily with modern LLM providers and custom vector databases.
- Modular Architecture: Cleanly separated core logic, search tools, and UI interfaces.
Below are the repository diagrams that explain the design and flow. They are included in the repository root — click to enlarge on GitHub.
git clone https://github.com/meddadaek/agentic_rag.git
cd agentic_rag
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
Create a .env file in the root directory and add your credentials:
OPENAI_API_KEY=your_openai_api_key
PINECONE_API_KEY=your_vector_db_key
python main.py
- Language: Python 3.10+
- Frameworks: LangChain / LlamaIndex
- Vector Store: Pinecone / FAISS / Qdrant
- Embeddings & LLMs: OpenAI / Hugging Face
Contributions are welcome! Feel free to open an issue or submit a pull request:
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AwesomeFeature) - Commit your Changes (
git commit -m 'Add some AwesomeFeature') - Push to the Branch (
git push origin feature/AwesomeFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
The five diagram files are included in the repository root. If you rename or move them, update the image paths above so they render correctly on GitHub.