anaboset/AskEFDA

AskEFDA - A Streamlit app leveraging RAG to provide accurate answers and summaries from Ethiopian Food and Drug Authority (EFDA) guidelines on medicine registration, import, approval and marketing. Built with LangChain, Groq LLM, and FAISS/BM25 indexing. Deployable on Streamlit Community Cloud. # AskEFDA AI-powered RAG assistant for querying Ethiopian Food and Drug Authority (EFDA) medical guidelines using natural language. Built with LangChain, FAISS/BM25 hybrid retrieval, Groq LLMs, and Streamlit. --- ## Features - Hybrid Retrieval (FAISS + BM25) - Cross-Encoder Reranking - Conversational Memory - PDF Upload Support - Summarization Mode - Streamlit Interface - Fast Responses with Groq API - Context-Aware Answers from EFDA Guidelines --- ## Architecture ```text PDF Documents ↓ Document Chunking ↓ Embeddings + BM25 Indexing ↓ Hybrid Retrieval ↓ Cross-Encoder Reranking ↓ Groq LLM ↓ Context-Aware Response ``` --- ## Tech Stack | Category | Tools | |---|---| | Framework | LangChain | | UI | Streamlit | | LLM | Groq (Llama 3.3 70B) | | Embeddings | Hugging Face all-MiniLM-L6-v2 | | Retrieval | FAISS + BM25 | | Reranker | cross-encoder/ms-marco-MiniLM-L-6-v2 | | PDF Processing | PyPDF2 | --- ## Project Structure ```bash . ├── helpers/ │ ├── chain.py │ ├── chunker.py │ ├── pdfloader.py │ ├── retriever.py │ └── vectorstore.py ├── app.py ├── process_pdfs.py ├── requirements.txt ├── README.md └── .env ``` --- ## Installation ### 1. Clone the Repository ```bash git clone github.com cd Medical-RAG-Assistant ``` ### 2. Create a Virtual Environment ```bash python -m venv venv ``` Activate the environment: #### Linux/macOS ```bash source venv/bin/activate ``` #### Windows ```bash venv\Scripts\activate ``` --- ### 3. Install Dependencies ```bash pip install -r requirements.txt ``` --- ### 4. Configure Environment Variables Create a `.env` file in the project root: ```env GROQ_API_KEY=your_groq_api_key ``` Get your API key from: console.groq.com --- ## Run the Application ```bash streamlit run app.py ``` --- ## Preprocessing PDFs To create FAISS and BM25 indexes from your PDF documents: ```bash python process_pdfs.py ``` This generates: - `chunks.pkl` - `chunks_faiss …