TattvaQuest

Overview

An Agentic RAG system for multi-document question answering — chunking, embedding-based retrieval, hybrid search, query routing, tool calling, contextual reasoning, and retrieval-quality optimization.

Problem

Answering questions across multiple long documents requires more than naive vector search: chunking quality, retrieval strategy, routing, and reasoning over context all determine answer quality.

Approach

1. 01Document chunking + embedding-based retrieval pipeline
2. 02Hybrid search with query routing to the best retrieval path
3. 03Tool calling + contextual reasoning for multi-document synthesis
4. 04Retrieval-quality optimization loop

Architecture

Engineering

• Document chunking
• Embedding-based retrieval
• Hybrid search
• Query routing
• Tool calling
• Contextual reasoning
• Retrieval-quality optimization
• Multi-document Q&A

Results

No standalone benchmark is claimed for this project — results are documented at the system level on the homepage. Only metrics stated in the source resume are published.