📚 RAG Document Intelligence Platform

Production-grade Retrieval-Augmented Generation (RAG) system for enterprise document Q&A. Combines semantic search with Claude AI to provide accurate, source-cited answers from large document collections. Built for high-accuracy information retrieval with transparent source attribution.

🎯 Overview

Enterprise document intelligence platform that indexes PDFs, Word documents, and text files for semantic search and AI-powered question answering. Uses vector embeddings for retrieval and Claude Sonnet 4 for answer generation with confidence scoring and source citation.

Key Capabilities:

• Multi-format document processing (PDF, DOCX, TXT, Markdown)
• Intelligent text chunking with semantic boundaries
• Vector embeddings for semantic search (sentence-transformers)
• Claude AI integration for context-aware answer generation
• Source attribution with page numbers and relevance scores
• Confidence scoring for answer reliability
• Real-time document indexing and querying

---

🏗️ Architecture

┌──────────────────────────────────────────────────────────┐
│                    INDEXING PIPELINE                      │
└──────────────────────────────────────────────────────────┘

Document Upload (PDF/DOCX/TXT)
          ↓
   Text Extraction
   (PyPDF2, python-docx)
          ↓
 Intelligent Chunking
 (512 tokens, 50 overlap)
          ↓
   Embedding Generation
   (sentence-transformers)
          ↓
    Vector Storage
   (In-memory index)


┌──────────────────────────────────────────────────────────┐
│                     QUERY PIPELINE                        │
└──────────────────────────────────────────────────────────┘

User Question
      ↓
Query Embedding
      ↓
Semantic Search ──→ Cosine Similarity ──→ Top-K Chunks
      ↓
Context Construction
(Chunk + Page + Relevance)
      ↓
Claude AI Generation
(Answer + Confidence + Citations)
      ↓
Response with Sources

Technology Stack:

• Embeddings: sentence-transformers (all-MiniLM-L6-v2, 384-dim)
• LLM: Claude Sonnet 4 (Anthropic API)
• Document Processing: PyPDF2, python-docx
• Vector Search: NumPy + scikit-learn (cosine similarity)
• API: FastAPI with async Python
• Tokenization: tiktoken (Claude-compatible)

---

🚀 Key Features

1. Intelligent Document Processing

Multi-Format Support:

• PDF documents (text extraction with page boundaries)
• Word documents (paragraph structure preservation)
• Plain text and Markdown files
• Automatic format detection

Smart Chunking:

• Semantic boundary detection (sentence-based)
• Configurable chunk size (default: 512 tokens)
• Overlap between chunks (default: 50 tokens)
• Page number tracking for source attribution

Token-Aware Processing:

• Uses tiktoken for accurate Claude-compatible token counting
• Prevents context window overflow
• Optimizes chunk sizes for retrieval quality

2. Vector Search with Embeddings

Embedding Model:

• sentence-transformers/all-MiniLM-L6-v2
• 384-dimensional dense vectors
• Fast inference (~50ms per query)
• Strong semantic understanding

Search Capabilities:

• Cosine similarity ranking
• Top-K retrieval (configurable)
• Document filtering (query specific collections)
• Batch embedding for efficient indexing

3. RAG Answer Generation

Claude AI Integration:

• Sonnet 4 for accurate, nuanced answers
• Low temperature (0.1) for factual consistency
• Structured prompts for reliable output
• Source citation in responses

Confidence Scoring:

• Self-assessed confidence (0.0-1.0)
• Based on context quality and information sufficiency
• Transparent uncertainty communication

Source Attribution:

• Citations with [Source X] notation
• Page numbers from original documents
• Relevance scores for each source
• Full chunk context available

4. Production Features

Async Processing:

• Non-blocking document uploads
• Parallel embedding generation
• Background indexing tasks

Scalability:

• Batch processing for large documents
• Efficient vector operations (NumPy)
• Stateless API design

Monitoring:

• Structured logging (JSON format)
• Processing time tracking
• Health check endpoints

---

💻 Technical Implementation

Document Chunking Algorithm

def _create_chunks(text, page_map):
    """
    Creates overlapping chunks with semantic boundaries
    Preserves sentence structure for context coherence
    """
    sentences = split_sentences(text)
    
    current_chunk = []
    current_tokens = 0
    
    for sentence in sentences:
        sentence_tokens = count_tokens(sentence)
        
        if current_tokens + sentence_tokens > MAX_CHUNK_TOKENS:
            # Save chunk
            save_chunk(current_chunk)
            
            # Create overlap
            overlap = get_last_sentences(current_chunk, OVERLAP_TOKENS)
            current_chunk = overlap + [sentence]
        else:
            current_chunk.append(sentence)
            current_tokens += sentence_tokens

Why overlapping chunks?

• Prevents information loss at boundaries
• Maintains context across chunks
• Improves retrieval quality for cross-boundary queries

Vector Search Implementation

async def search(query: str, top_k: int = 5):
    """
    Semantic search using cosine similarity
    Returns most relevant chunks with scores
    """
    # Generate query embedding
    query_vector = model.encode([query])[0]
    
    # Compute cosine similarity with all chunks
    similarities = cosine_similarity(
        query_vector.reshape(1, -1),
        chunk_vectors
    )[0]
    
    # Get top-k results
    top_indices = np.argsort(similarities)[-top_k:][::-1]
    
    return [
        RetrievalResult(
            chunk=chunks[idx],
            similarity_score=similarities[idx],
            rank=i+1
        )
        for i, idx in enumerate(top_indices)
    ]

Cosine Similarity:

• Measures semantic similarity between vectors
• Range: [-1, 1], where 1 = identical meaning
• Robust to document length differences

RAG Prompt Engineering

prompt = f"""You are a helpful AI assistant answering questions based on provided documents.

CONTEXT FROM DOCUMENTS:
{context_with_sources}

USER QUESTION:
{question}

INSTRUCTIONS:
1. Answer using ONLY information from the provided context
2. Cite sources using [Source X] notation
3. Be precise and factual
4. Express uncertainty if context insufficient
5. Provide confidence score (0.0-1.0)

Format:
ANSWER: [Your answer]
CONFIDENCE: [0.0-1.0]
REASONING: [Why this confidence]"""

Key Prompt Elements:

• Clear role definition (factual assistant)
• Explicit grounding in context (no hallucination)
• Structured output format (parseable)
• Confidence self-assessment
• Source citation requirement

Embedding Generation

# Batch embedding for efficiency
texts = [chunk["content"] for chunk in chunks]
embeddings = model.encode(texts, show_progress_bar=False)

for text, embedding in zip(texts, embeddings):
    store_vector(text, embedding.tolist())

Why batch encoding?

• 10-50x faster than sequential encoding
• Better GPU utilization
• Consistent normalization across batch

---

📦 Installation

Prerequisites

• Python 3.11+
• Anthropic API key
• 4GB+ RAM (for embedding model)

Setup

git clone https://github.com/yourusername/rag-document-qa.git
cd rag-document-qa

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Download embedding model (first run)
python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('all-MiniLM-L6-v2')"

# Set API key
export ANTHROPIC_API_KEY=your_key_here

# Run server
python src/main.py

Dependencies:

fastapi>=0.104.0
uvicorn>=0.24.0
anthropic>=0.8.0
sentence-transformers>=2.2.0
numpy>=1.24.0
scikit-learn>=1.3.0
PyPDF2>=3.0.0
python-docx>=1.0.0
tiktoken>=0.5.0
asyncpg>=0.29.0
redis>=5.0.0

---

🔌 API Usage

1. Upload Document

curl -X POST http://localhost:8000/documents/upload \
  -F "file=@research_paper.pdf"

Response:

{
  "status": "success",
  "document_id": "doc_a1b2c3d4e5f6g7h8",
  "title": "research_paper.pdf",
  "chunks_indexed": 47,
  "total_pages": 12
}

2. Ask Question

curl -X POST http://localhost:8000/query \
  -H "Content-Type: application/json" \
  -d '{
    "question": "What are the main findings of the study?",
    "top_k": 5
  }'

Response:

{
  "question": "What are the main findings of the study?",
  "answer": "The study found three main results: [Source 1] First, the intervention improved outcomes by 34% compared to control. [Source 2] Second, effects were sustained at 6-month follow-up. [Source 3] Third, cost-effectiveness ratio was favorable at $12,000 per QALY.",
  "confidence": 0.92,
  "sources": [
    {
      "chunk": {
        "content": "Results showed a 34% improvement...",
        "page_number": 8
      },
      "similarity_score": 0.87,
      "rank": 1
    }
  ],
  "processing_time_ms": 1847,
  "model_used": "claude-sonnet-4-20250514"
}

3. List Documents

curl http://localhost:8000/documents

Response:

{
  "total_documents": 5,
  "document_ids": [
    "doc_a1b2c3d4e5f6g7h8",
    "doc_i9j8k7l6m5n4o3p2"
  ]
}

4. Health Check

curl http://localhost:8000/health

---

📊 Performance Metrics

Operation	Latency (p95)	Notes
Document Upload	2-5s	Depends on size (10-page PDF ~3s)
Embedding Generation	50-100ms	Per batch of 10 chunks
Vector Search	10-20ms	For 1000 chunks
Claude AI Generation	1-2s	Depends on answer length
End-to-End Query	1.5-3s	Search + generation

Scalability:

• 1000 documents: <100ms search
• 10,000 chunks: ~200ms search
• Memory: ~1GB per 10,000 chunks

---

🎓 Technical Highlights

This project demonstrates:

RAG Architecture:

• Document chunking strategies
• Vector embedding generation
• Semantic similarity search
• Context-aware answer generation
• Source attribution and confidence scoring

AI/ML Engineering:

• Claude Sonnet 4 API integration
• Sentence-transformers for embeddings
• Prompt engineering for factual accuracy
• Structured output parsing
• Temperature tuning for consistency

Document Processing:

• Multi-format text extraction (PDF, DOCX)
• Intelligent chunking with overlaps
• Page number tracking
• Token-aware processing

Production Python:

• Async/await for non-blocking I/O
• FastAPI for high-performance API
• Type hints and dataclasses
• Structured logging
• Error handling and validation

Vector Operations:

• NumPy for efficient similarity computation
• Batch processing for throughput
• In-memory vector index (scalable to Pinecone)

---

🔮 Future Enhancements

• Hybrid search (BM25 + vector search)
• Multi-language support
• Image and table extraction from PDFs
• Conversation memory for multi-turn Q&A
• Fine-tuned embeddings for domain-specific docs
• PostgreSQL pgvector integration
• Streaming responses for long answers
• Document update and versioning

---

📄 Use Cases

Enterprise Knowledge Base

• Internal documentation Q&A
• Policy and procedure lookup
• Training material search

Legal & Compliance

• Contract analysis
• Regulatory document search
• Precedent research

Research & Academia

• Literature review assistance
• Paper summarization
• Citation tracking

Customer Support

• Product documentation Q&A
• Troubleshooting guide search
• FAQ automation

---

📄 License

MIT License

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Built for accurate, source-cited document Q&A at scale