# Phase 2: Hybrid Retrieval System (Week 2-3)
## Implement Sophisticated BM25 + Vector Search with Fusion

### 🎯 Phase Objectives
- Build hybrid retrieval combining BM25 and vector search
- Implement reciprocal rank fusion (RRF)
- Create query processing and expansion
- Optimize retrieval performance
- Add retrieval evaluation metrics

### 📋 Deliverables Checklist
- [ ] Hybrid retriever implementation
- [ ] Query preprocessing pipeline
- [ ] Reciprocal rank fusion algorithm
- [ ] Retrieval performance optimization
- [ ] Evaluation metrics for retrieval
- [ ] Query expansion mechanisms
- [ ] Retrieval result caching

### 🛠 Technical Implementation

#### 1. Query Processing (Day 1-2)
**File**: `src/core/query_processor.py`
**Features**:
- Query normalization and cleaning
- Stop word handling
- Query expansion with synonyms
- Intent detection (factual vs exploratory)
- Query complexity analysis

```python
class QueryProcessor:
    def process_query(self, query: str) -> ProcessedQuery
    def expand_query(self, query: str) -> List[str]
    def detect_intent(self, query: str) -> QueryIntent
    def normalize_text(self, text: str) -> str
```

#### 2. Vector Search Implementation (Day 3-4)
**File**: `src/core/vector_search.py`
**Features**:
- Semantic similarity search
- Embedding-based retrieval
- Similarity threshold filtering
- Batch embedding generation
- Metadata-based filtering

```python
class VectorSearch:
    def search(self, query: str, k: int = 50) -> List[VectorResult]
    def embed_query(self, query: str) -> List[float]
    def similarity_search(self, embedding: List[float]) -> List[VectorResult]
    def filter_by_metadata(self, results: List[VectorResult], filters: dict) -> List[VectorResult]
```

#### 3. BM25 Search Enhancement (Day 5-6)
**File**: `src/core/bm25_search.py`
**Features**:
- Optimized BM25 implementation
- Custom tokenization strategies
- Field-based scoring (title, content, metadata)
- Query term highlighting
- Score normalization

```python
class BM25Search:
    def search(self, query: str, k: int = 50) -> List[BM25Result]
    def score_documents(self, query_terms: List[str]) -> Dict[str, float]
    def highlight_terms(self, text: str, query_terms: List[str]) -> str
```

#### 4. Hybrid Retriever Core (Day 7-9)
**File**: `src/core/hybrid_retriever.py`
**Features**:
- Dual retrieval execution
- Reciprocal rank fusion (RRF)
- Weighted combination strategies
- Dynamic weight adjustment
- Result deduplication and merging

```python
class HybridRetriever:
    def __init__(self, vector_search: VectorSearch, bm25_search: BM25Search):
        self.vector_search = vector_search
        self.bm25_search = bm25_search
        self.fusion_weights = {"vector": 0.6, "bm25": 0.4}
    
    def retrieve(self, query: str, k: int = 20) -> List[RetrievalResult]:
        # Execute both searches in parallel
        # Apply reciprocal rank fusion
        # Deduplicate and merge results
        # Return top-k ranked results
        pass
    
    def reciprocal_rank_fusion(self, results_list: List[List[Result]]) -> List[Result]:
        # Implement RRF algorithm
        pass
```

#### 5. Retrieval Result Processing (Day 10-11)
**File**: `src/core/retrieval_result.py`
**Features**:
- Result standardization
- Score normalization
- Confidence calculation
- Source tracking
- Result explanation generation

#### 6. Performance Optimization (Day 12-13)
**Features**:
- Query result caching (Redis)
- Batch processing for multiple queries
- Asynchronous retrieval execution
- Memory-efficient result handling
- Connection pooling for databases

#### 7. Evaluation Metrics (Day 14)
**File**: `src/evaluation/retrieval_metrics.py`
**Metrics**:
- Precision@K, Recall@K, F1@K
- Mean Reciprocal Rank (MRR)
- Normalized Discounted Cumulative Gain (NDCG)
- Mean Average Precision (MAP)
- Hit Rate and Coverage

### 🔧 New Dependencies
```txt
# Retrieval enhancement
redis>=4.5.0
numpy>=1.24.0
scipy>=1.10.0
scikit-learn>=1.3.0
asyncio-throttle>=1.0.0
aioredis>=2.0.0
```

### 📊 Success Criteria
- [ ] Sub-2-second retrieval for 95% of queries
- [ ] Precision@5 > 70% on evaluation dataset
- [ ] MRR > 0.65 on standard test queries
- [ ] Handle 100+ concurrent queries
- [ ] Memory usage < 4GB for 100K documents

### 🧪 Testing Strategy
1. **Retrieval Quality Tests**: Precision, recall, relevance
2. **Performance Tests**: Query latency, throughput
3. **Fusion Algorithm Tests**: RRF correctness
4. **Cache Tests**: Hit rates, invalidation
5. **Concurrent Access Tests**: Race conditions, consistency

### 🔍 Evaluation Setup
**Test Dataset Requirements**:
- 500+ query-document pairs with relevance scores
- Diverse query types (factual, complex, ambiguous)
- Ground truth annotations
- Performance benchmarks

**Evaluation Process**:
1. Baseline BM25-only performance
2. Baseline vector-only performance  
3. Hybrid performance with different fusion weights
4. Ablation studies on query processing steps

### ⏭ Phase 2 → Phase 3 Handoff
**Phase 2 Output**:
- High-quality retrieval results (top-20 candidates)
- Performance metrics and benchmarks
- Optimized query processing pipeline
- Caching system operational

**Phase 3 Input Requirements**:
- Retrieved candidates for reranking
- Retrieval confidence scores
- Query context and intent
- Performance baseline for comparison