AskDocs AI: AI-Powered PDF Q&A Bot

AskDocs AI is an AI-powered chatbot that leverages Hybrid RAG (Retrieval-Augmented Generation) to answer your questions based on the content of uploaded PDFs. It combines semantic vector search with traditional keyword-based search for superior accuracy.

Key Features

• Hybrid Search: Combines ChromaDB (semantic) and BM25 (keyword) retrieval.
• LLM Powered: High-performance LLM via Groq Cloud.
• Async Processing: PDF ingestion and indexing are offloaded to background threads.
• Multimodal Support: Optimized for PDF extraction and processing.

Tech Stack

• Backend: FastAPI, LangChain (Classic), ChromaDB, Groq Cloud
• Frontend: Streamlit
• Search Engines: BM25 (Keyword), Vector (Cosine Similarity)
• Embeddings: HuggingFace (Sentence Transformers)
• Containerization: Docker & Docker Compose

Configuration

Control the behavior of the Hybrid Search by adjusting weights in your .env file or server/config.py:

Variable	Description	Default
HYBRID_SEARCH_BM25_WEIGHT	Weight for keyword search (0.0 to 1.0)	0.5
HYBRID_SEARCH_CHROMA_WEIGHT	Weight for semantic search (0.0 to 1.0)	0.5
GROQ_API_KEY	Your Groq Cloud API Key	Required

Optimization Features

• Split Dependencies: Client and Server have separate requirement files to minimize image sizes.
• CPU-Only Optimization: Server image is optimized for CPU-only environments, reducing size from ~12.8GB to ~2.3GB.
• Persistent Memory: Uses Docker volumes to persist the ChromaDB vector store and uploaded files.

Setup Instruction

1. Set up environment variables

Create a .env file in the root directory:

GROQ_API_KEY=your_api_key_here

2. Run with Docker (Recommended)

docker-compose up -d --build

• Streamlit UI: http://localhost:8501
• FastAPI Docs: http://localhost:8000/docs

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Alternative: Local Setup

1. Create and activate a virtual environment

   python -m venv venv
   .\venv\Scripts\activate  # Windows

2. Install dependencies

   pip install -r requirements.client.txt
   pip install -r requirements.server.txt

3. Run the services

   # Backend
   python server/main.py
   
   # Frontend
   streamlit run client/main.py

Testing & Verification

To verify that the Hybrid Search mechanism and LLM integration are working correctly:

python server/tests/test_hybrid_search.py

This script validates:

1. Vectorstore connectivity.
2. BM25 index reconstruction.
3. Ensemble Retriever initialization.