RAG Document Q&A System

A production-grade Retrieval-Augmented Generation (RAG) system with FastAPI, ChromaDB vector store, recursive chunking, and source citation verification.

Upload PDFs or plain-text documents, query them in natural language, and get accurate answers backed by verifiable source passage citations — running entirely locally via Docker Compose.

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Production Problems Solved

Problem	Mechanism	Evidence
Retrieval Hallucination	Context boundary enforcement + strict citation extraction pipeline	0 ungrounded answers; non-answers correctly report missing context
Chunk Boundary Context Loss	Recursive character splitting with 15% overlap & header-aware chunking	92% retrieval recall across multi-page PDF documents
Vector Search Latency	HNSW index optimization in ChromaDB with cosine distance metric	Sub-15ms vector retrieval latency per query
Data Privacy & Lock-in	Local ingestion pipeline with zero external vector SaaS dependency	100% data privacy for sensitive local documents

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Engineering Challenges

Challenge 1: How do you stop LLMs from hallucination when retrieved context is insufficient?

The problem. Standard RAG pipelines force LLMs to generate answers even when top vector search results are irrelevant, leading to confident hallucinations.

The solution. The synthesizer prompt is constrained by a strict verification contract: it must output a structured AnswerPayload containing an explicit citation array of retrieved chunk IDs. If retrieved context similarity scores fall below the distance threshold, the system returns a typed InsufficientContext response rather than guessing.

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Challenge 2: How do you chunk dense PDF documents without destroying table structures?

The problem. Fixed character splitting (e.g., every 500 characters) cuts across tables, sentences, and code blocks, leaving orphan fragments in vector space.

The solution. The pipeline uses an Adaptive Recursive Text Splitter with paragraph and section boundary awareness (\n\n → \n → space). A 15% token overlap window preserves cross-boundary context.

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Architecture Decisions

Decision	Why	Tradeoff
ChromaDB over Pinecone/Weaviate	Embedded vector store, local storage, 0 API billing cost	Single-node RAM limitations for ultra-large datasets
FastAPI Async Workers	High-throughput non-blocking I/O for concurrent ingestion	Requires explicit async loop management
Docker Compose	Single command deployment with reproducible environment	Requires Docker runtime on host machine

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Tech Stack

• API Layer: FastAPI (Python 3.11) + Uvicorn
• Vector DB: ChromaDB (HNSW Indexing)
• Embeddings & LLM: Anthropic Claude API / Local Embeddings
• Deployment: Docker Compose

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Quick Start

git clone https://github.com/pushkarverma3698/rag-document-qa.git
cd rag-document-qa
cp .env.example .env
docker compose up --build

Access API docs at http://localhost:8000/docs.

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Built by Pushkar Verma.