Enterprise RAG System with MCP Agents
A production retrieval-augmented generation platform that answers questions over a hundred-thousand-document corpus with citations, used for 10K+ daily predictions.
- Retrieval accuracy
- 94% Retrieval accuracy
- Documents
- 100K+ Documents
- Faster search
- 70% Faster search
Project Overview
A production retrieval-augmented generation platform that answers questions over a hundred-thousand-document corpus with citations, used for 10K+ daily predictions.
The Problem
An enterprise knowledge base of 100K+ documents was effectively unsearchable. Staff spent hours hunting for answers that already existed somewhere in the corpus.
The Solution
A production RAG platform with chunking, hybrid retrieval, and citation grounding, extended with MCP agents for multi-step tool use, and served via FastAPI with caching, validation, and quality monitoring.
Key Features
Citation-grounded answers
Responses cite their sources from the 100K+ document corpus.
Hybrid retrieval
Chunking plus hybrid search tuned to 94% retrieval accuracy.
MCP tool-using agents
Agents reason over multiple steps and call tools through MCP.
Reliability layers
Redis caching and validation keep answers fast and trustworthy.
Production serving
FastAPI handling 10K+ daily predictions with quality monitoring.
Scalable corpus
Indexed and searchable over 100K+ enterprise documents.
Architecture & Flow
What I Delivered
- RAG pipeline (LangChain + Qdrant + GPT-4) at 94% retrieval accuracy
- MCP agent layer for multi-step tool use
- Redis caching and validation for reliability
- FastAPI service serving 10K+ daily predictions
- Retrieval-quality and latency monitoring
- 70% faster search across the corpus
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