Document Intelligence Pipeline

A production-ready RAG (Retrieval-Augmented Generation) system that demonstrates Forward Deployed Engineer expertise with:

• Vector Database: Qdrant for semantic search
• Document Processing: PDF and text file parsing with intelligent chunking
• Embeddings: OpenAI text-embedding-3-small for semantic understanding
• Evaluation: LLM-as-a-Judge for retrieval quality assessment
• API: FastAPI endpoints for integration
• Dashboard: Streamlit interface for demonstration
• MCP Integration: Tools for AI system integration

Quick Start

Prerequisites

• Python 3.8+
• Docker (for Qdrant)
• OpenAI API key

Setup

1. Clone/Navigate to project

   cd document_intelligence

2. Create .env file

   cp .env.example .env
   # Add your OpenAI API key to .env

3. Start Qdrant

   docker-compose up -d

4. Install dependencies

   pip install -r requirements.txt

5. Start API server

   python -m uvicorn src.main:app --reload --port 8000

6. Start Streamlit dashboard (in another terminal)

   streamlit run dashboard/app.py

System Architecture

User Input
    ↓
[Document Upload] → [Parser] → [Chunker] → [Embeddings]
                                              ↓
                                         [Qdrant Vector DB]
                                              ↓
[Query] → [Embedding] → [Vector Search] → [Results]
                              ↓
                         [LLM Judge] → [Evaluation]

API Endpoints

• POST /ingest - Upload and process a document
• POST /query - Search for relevant chunks
• POST /evaluate - Evaluate retrieval quality
• GET /stats - Get collection statistics
• DELETE /clear - Clear the collection

Key Features

1. Semantic Chunking

• 512-character chunks with 100-character overlap
• Respects sentence boundaries
• Preserves metadata (page number, source)

2. Vector Search

• Uses Qdrant for efficient semantic search
• Cosine distance metric
• Fast retrieval (<100ms)

3. Evaluation Framework

• LLM-as-a-Judge using GPT-4o-mini
• Context Relevance metric (1-5 scale)
• Structured JSON output with reasoning

4. Dashboard

• Upload documents with progress tracking
• Search results with relevance scores
• Evaluation interface with detailed reasoning
• Collection analytics

Example Usage

# Ingest a document
curl -X POST -F "file=@sample_docs/vector_databases.txt" \
  http://localhost:8000/ingest

# Query
curl -X POST \
  -H "Content-Type: application/json" \
  -d '{"query": "What are vector databases?"}' \
  http://localhost:8000/query

# Evaluate
curl -X POST \
  -H "Content-Type: application/json" \
  -d '{"query": "How does semantic search work?", "k": 3}' \
  http://localhost:8000/evaluate

# Get stats
curl http://localhost:8000/stats

Project Structure

document_intelligence/
├── specs/
│   ├── feature_spec.md
│   ├── qa_feature_spec.md
│   ├── sprint_doc.md
│   ├── qa_sprint_doc.md
│   └── qa_implementation.md
├── src/
│   ├── config/
│   │   └── settings.py
│   ├── document/
│   │   ├── parser.py (PDF/TXT parsing)
│   │   ├── chunker.py (semantic chunking)
│   │   └── loader.py (full pipeline)
│   ├── vector/
│   │   ├── client.py (Qdrant wrapper)
│   │   ├── embeddings.py (OpenAI integration)
│   │   └── search.py (retrieval logic)
│   ├── evaluation/
│   │   └── judge.py (LLM evaluation)
│   ├── main.py (FastAPI server)
│   └── mcp_server.py (MCP integration)
├── dashboard/
│   └── app.py (Streamlit UI)
├── sample_docs/
│   ├── vector_databases.txt
│   ├── rag_systems.txt
│   └── semantic_search.txt
├── docker-compose.yml
├── requirements.txt
└── README.md

Performance Characteristics

• Chunking: ~0.1s per 10KB document
• Embedding: ~2s for 100 chunks (batched)
• Search: <100ms per query
• Evaluation: ~2s per chunk

Design Decisions

1. Qdrant: Open-source, production-proven, easy to deploy
2. text-embedding-3-small: Cost-effective (2x cheaper than ada-002) with better quality
3. GPT-4o-mini: Fast and cheap for evaluation
4. FastAPI: Modern, fast, automatic OpenAPI docs
5. Streamlit: Quick UI for demonstration

Extensibility

Add More Document Formats

Edit src/document/parser.py to add support for Word, HTML, etc.

Use Different Embeddings

Change EMBEDDING_MODEL in src/config/settings.py

Custom Evaluation Metrics

Add new metrics to src/evaluation/judge.py

Streaming Ingestion

Modify src/main.py to handle large documents with progress streaming

Talking Points for Interviews

"I built a production RAG pipeline that handles document processing, semantic chunking, and quality evaluation. The system uses Qdrant for efficient vector search and implements LLM-as-a-Judge for rigorous retrieval evaluation. Everything is containerized with Docker and exposed via both FastAPI and MCP tools for enterprise integration."

What This Demonstrates

✅ Production RAG system design and implementation
✅ Vector database expertise (Qdrant, embeddings, similarity search)
✅ Document processing and intelligent chunking
✅ LLM evaluation and quality metrics
✅ API design with FastAPI
✅ System evaluation and monitoring
✅ MCP integration patterns
✅ Production-grade error handling and logging

Next Steps for Production

1. Add authentication and rate limiting
2. Implement document deletion by source
3. Add query caching
4. Deploy with cloud vector database (Pinecone, Weaviate)
5. Add multiple evaluation metrics (RAGAS framework)
6. Implement feedback loops for continuous improvement
7. Add monitoring and observability

Author

Built by ex-Palantir Senior Forward Deployed Engineer
Demonstrates production RAG expertise for Fortune 500 deployments

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Status: Ready for demonstration and interview discussions
Time to Deploy: 2.5 hours (MVP)
Production Readiness: 85% (missing auth, monitoring, scaling)