# Phase 4: Citation System & API (Week 4-6)
## Comprehensive Citation Tracking + Production API

### 🎯 Phase Objectives
- Implement comprehensive citation tracking and enforcement  
- Build production-ready FastAPI backend
- Create citation verification and validation system
- Add confidence scoring for citations
- Develop web interface for testing and demo

### 📋 Deliverables Checklist
- [ ] Citation extraction and tracking system
- [ ] Citation verification engine
- [ ] Confidence scoring for sources
- [ ] FastAPI production backend
- [ ] Web interface for queries
- [ ] API documentation and testing
- [ ] Citation audit and reporting

### 🛠 Technical Implementation

#### 1. Citation Tracking System (Day 1-3)
**File**: `src/core/citation_tracker.py`
**Features**:
- Automatic citation extraction from responses
- Source document mapping and verification
- Citation confidence scoring
- Granular attribution (sentence/paragraph level)
- Citation format standardization

```python
class CitationTracker:
    def __init__(self):
        self.citation_pattern = r'\[Doc (\d+)\]'
        self.confidence_threshold = 0.7
    
    def extract_citations(self, response: str, source_docs: List[Document]) -> List[Citation]:
        # Parse citation markers from response
        # Map to source documents
        # Calculate confidence scores
        # Validate citation accuracy
        pass
    
    def verify_citation(self, claim: str, source_doc: Document) -> CitationVerification:
        # Semantic similarity between claim and source
        # Factual consistency checking
        # Confidence scoring
        pass
    
    def track_citation_usage(self, citations: List[Citation]) -> CitationStats:
        # Track which sources are most cited
        # Identify unused high-quality sources
        # Citation distribution analysis
        pass
```

#### 2. Citation Verification Engine (Day 4-5)
**File**: `src/core/citation_verifier.py`
**Features**:
- Semantic entailment checking
- Factual consistency validation
- Source text alignment
- Confidence calibration
- Contradiction detection

```python
class CitationVerifier:
    def __init__(self):
        self.entailment_model = pipeline("text-classification", 
                                        model="microsoft/DialoGPT-medium")
        self.similarity_model = SentenceTransformer('all-MiniLM-L6-v2')
    
    def verify_entailment(self, claim: str, source: str) -> EntailmentResult:
        # Check if source entails the claim
        # Return confidence score and reasoning
        pass
    
    def detect_contradictions(self, response: str, sources: List[str]) -> List[Contradiction]:
        # Find claims that contradict sources
        # Highlight problematic sections
        pass
    
    def calculate_citation_confidence(self, citation: Citation) -> float:
        # Combine multiple verification signals
        # Return calibrated confidence score
        pass
```

#### 3. Enhanced Response Generation (Day 6-7)
**File**: `src/core/enhanced_generator.py`
**Features**:
- Citation-aware generation prompts
- Real-time citation insertion
- Source-specific formatting
- Citation quality feedback loop

```python
class CitationAwareGenerator(RAGGenerator):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.citation_tracker = CitationTracker()
        self.citation_verifier = CitationVerifier()
    
    def generate_with_citations(self, query: str, context: List[Document]) -> CitedResponse:
        # Generate response with embedded citations
        # Verify citation accuracy
        # Provide confidence scores
        # Format for display
        pass
    
    def improve_citations(self, response: str, context: List[Document]) -> CitedResponse:
        # Post-process to improve citation quality
        # Add missing citations
        # Remove or flag weak citations
        pass
```

#### 4. FastAPI Backend (Day 8-10)
**File**: `src/api/main.py`
**Endpoints**:
- `POST /query` - Main RAG query endpoint
- `POST /documents/upload` - Document upload
- `GET /documents` - List documents
- `DELETE /documents/{doc_id}` - Remove document
- `GET /citations/{query_id}` - Get citations for a response
- `POST /evaluate` - Evaluate query performance
- `GET /health` - Health check
- `GET /metrics` - System metrics

```python
from fastapi import FastAPI, UploadFile, HTTPException
from src.api.models import QueryRequest, QueryResponse, DocumentInfo
from src.core.rag_pipeline import RAGPipeline

app = FastAPI(title="Production RAG API", version="1.0.0")
rag_pipeline = RAGPipeline()

@app.post("/query", response_model=QueryResponse)
async def query_documents(request: QueryRequest):
    try:
        result = await rag_pipeline.process_query(
            query=request.query,
            max_results=request.max_results,
            include_citations=request.include_citations
        )
        return QueryResponse(
            answer=result.answer,
            citations=result.citations,
            confidence=result.confidence,
            processing_time=result.processing_time
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/documents/upload")
async def upload_document(file: UploadFile):
    # Process and index uploaded document
    # Return processing status and document ID
    pass
```

#### 5. API Models and Schemas (Day 11)
**File**: `src/api/models.py`
**Pydantic Models**:
- Request/Response schemas
- Validation rules
- Error models
- Configuration models

#### 6. Web Interface (Day 12-13)
**File**: `src/web/templates/index.html`
**Features**:
- Clean query interface
- Real-time streaming responses
- Citation highlighting and links
- Document management interface
- Performance metrics dashboard

#### 7. API Testing and Documentation (Day 14)
**Features**:
- Comprehensive test suite
- API documentation with examples
- Performance benchmarking
- Load testing setup

### 🔧 New Dependencies
```txt
# API and web interface
fastapi>=0.100.0
uvicorn>=0.22.0
jinja2>=3.1.0
python-multipart>=0.0.6
aiofiles>=23.0.0
python-jose>=3.3.0
passlib>=1.7.4
pytest-asyncio>=0.21.0
httpx>=0.24.0
```

### 📊 Success Criteria
- [ ] Citation accuracy > 95% on test dataset
- [ ] API response time < 3 seconds (95th percentile)
- [ ] Support 50+ concurrent users
- [ ] Citation confidence scores correlate with human judgment
- [ ] Web interface loads in < 2 seconds

### 🧪 Testing Strategy

#### API Testing
1. **Unit Tests**: Each endpoint individually
2. **Integration Tests**: Full RAG pipeline through API
3. **Load Tests**: Concurrent user simulation
4. **Security Tests**: Input validation, rate limiting

#### Citation Testing  
1. **Accuracy Tests**: Manual verification on sample queries
2. **Confidence Calibration**: Human evaluation correlation
3. **Edge Cases**: Missing sources, ambiguous claims
4. **Performance Tests**: Citation processing speed

### 📋 Citation Quality Framework

#### Citation Levels
1. **Direct Quote**: Exact text match with source
2. **Paraphrase**: Semantic equivalent in different words  
3. **Inference**: Logical conclusion from source facts
4. **Synthesis**: Combination of multiple sources

#### Confidence Scoring
- **High (0.9-1.0)**: Direct quotes, exact facts
- **Medium (0.7-0.9)**: Clear paraphrases, well-supported inferences
- **Low (0.5-0.7)**: Weak connections, speculative claims
- **Very Low (<0.5)**: Unsupported or contradictory claims

### 🌐 Web Interface Features

#### Query Interface
- Auto-complete for common queries
- Query suggestion based on available documents
- Advanced filters (date, document type, confidence threshold)
- Query history and favorites

#### Response Display
- Highlighted citations with hover tooltips
- Expandable source document previews
- Confidence indicators for each claim
- Alternative phrasings and related queries

#### Document Management
- Upload progress tracking
- Document processing status
- Metadata editing and tagging
- Search and filter documents
- Batch operations

### ⏭ Phase 4 → Phase 5 Handoff
**Phase 4 Output**:
- Production-ready API with comprehensive citation system
- Web interface for testing and demonstration
- Citation quality metrics and benchmarks
- API documentation and test coverage

**Phase 5 Input Requirements**:
- Fully functional RAG system with API
- Citation accuracy baselines
- Performance benchmarks
- User feedback collection mechanisms