feat: Add semantic search module

Add semantic search capabilities with:
- embedder.py: Text embedding operations
- processor.py: Document processing
- vector_store.py: Vector storage and retrieval

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

semantic_search/embedder.py

@@ -0,0 +1,173 @@
+# semantic_search/embedder.py
+
+import logging
+from typing import List, Optional
+import numpy as np
+from sentence_transformers import SentenceTransformer
+import torch
+
+logger = logging.getLogger(__name__)
+
+class EmbeddingGemma:
+    """
+    Wrapper for Google's EmbeddingGemma model.
+    Handles query and document encoding with proper prompt templates.
+    """
+    
+    def __init__(self, model_name: str = "google/embeddinggemma-300m", device: Optional[str] = None):
+        """
+        Initialize EmbeddingGemma model.
+        
+        Args:
+            model_name: HuggingFace model name
+            device: Device to run model on ('cuda', 'cpu', or None for auto)
+        """
+        self.model_name = model_name
+        
+        # Auto-detect device if not specified
+        if device is None:
+            self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        else:
+            self.device = device
+            
+        logger.info(f"Initializing EmbeddingGemma on device: {self.device}")
+        
+        try:
+            # Load model with float32 precision (EmbeddingGemma doesn't support float16)
+            self.model = SentenceTransformer(model_name, device=self.device)
+            self.model.eval()  # Set to evaluation mode
+            
+            # Set precision to float32 or bfloat16
+            if self.device == 'cuda' and torch.cuda.is_bf16_supported():
+                logger.info("Using bfloat16 precision for CUDA")
+                self.dtype = torch.bfloat16
+            else:
+                logger.info("Using float32 precision")
+                self.dtype = torch.float32
+                
+            logger.info(f"Successfully loaded model: {model_name}")
+            
+        except Exception as e:
+            logger.error(f"Failed to load EmbeddingGemma model: {e}")
+            raise
+    
+    def encode_query(self, query: str, task: str = "search result") -> np.ndarray:
+        """
+        Encode a search query with appropriate prompt template.
+        
+        Args:
+            query: The search query text
+            task: Task type for prompt template (search result, question answering, etc.)
+            
+        Returns:
+            Numpy array of embeddings (768 dimensions)
+        """
+        # Apply query prompt template
+        prompted_query = f"task: {task} | query: {query}"
+        
+        try:
+            with torch.no_grad():
+                # Encode with model
+                embeddings = self.model.encode(
+                    prompted_query,
+                    convert_to_numpy=True,
+                    normalize_embeddings=True,  # L2 normalization for cosine similarity
+                    show_progress_bar=False
+                )
+            
+            logger.debug(f"Encoded query: {query[:50]}... -> shape: {embeddings.shape}")
+            return embeddings
+            
+        except Exception as e:
+            logger.error(f"Failed to encode query: {e}")
+            raise
+    
+    def encode_documents(self, documents: List[str], titles: Optional[List[str]] = None) -> np.ndarray:
+        """
+        Encode multiple documents with appropriate prompt template.
+        
+        Args:
+            documents: List of document texts
+            titles: Optional list of document titles
+            
+        Returns:
+            Numpy array of embeddings (N x 768 dimensions)
+        """
+        if not documents:
+            return np.array([])
+        
+        # Apply document prompt template
+        prompted_docs = []
+        for i, doc in enumerate(documents):
+            title = titles[i] if titles and i < len(titles) else "none"
+            prompted_doc = f"title: {title} | text: {doc}"
+            prompted_docs.append(prompted_doc)
+        
+        try:
+            with torch.no_grad():
+                # Batch encode documents
+                embeddings = self.model.encode(
+                    prompted_docs,
+                    convert_to_numpy=True,
+                    normalize_embeddings=True,
+                    show_progress_bar=len(documents) > 10,
+                    batch_size=8  # Adjust based on memory
+                )
+            
+            logger.info(f"Encoded {len(documents)} documents -> shape: {embeddings.shape}")
+            return embeddings
+            
+        except Exception as e:
+            logger.error(f"Failed to encode documents: {e}")
+            raise
+    
+    def reduce_dimensions(self, embeddings: np.ndarray, target_dim: int = 512) -> np.ndarray:
+        """
+        Reduce embedding dimensions using Matryoshka Representation Learning.
+        
+        Args:
+            embeddings: Original embeddings (N x 768)
+            target_dim: Target dimension (512, 256, or 128)
+            
+        Returns:
+            Reduced embeddings (N x target_dim)
+        """
+        if target_dim not in [512, 256, 128]:
+            raise ValueError(f"Target dimension must be 512, 256, or 128, got {target_dim}")
+        
+        if len(embeddings.shape) == 1:
+            # Single embedding
+            reduced = embeddings[:target_dim]
+            # Re-normalize after truncation
+            norm = np.linalg.norm(reduced)
+            if norm > 0:
+                reduced = reduced / norm
+        else:
+            # Multiple embeddings
+            reduced = embeddings[:, :target_dim]
+            # Re-normalize each embedding
+            norms = np.linalg.norm(reduced, axis=1, keepdims=True)
+            reduced = reduced / (norms + 1e-8)  # Avoid division by zero
+        
+        logger.debug(f"Reduced dimensions: {embeddings.shape} -> {reduced.shape}")
+        return reduced
+    
+    def compute_similarity(self, query_embedding: np.ndarray, document_embeddings: np.ndarray) -> np.ndarray:
+        """
+        Compute cosine similarity between query and documents.
+        
+        Args:
+            query_embedding: Query embedding (768,)
+            document_embeddings: Document embeddings (N x 768)
+            
+        Returns:
+            Similarity scores (N,)
+        """
+        # Ensure query is 2D for matrix multiplication
+        if len(query_embedding.shape) == 1:
+            query_embedding = query_embedding.reshape(1, -1)
+        
+        # Compute cosine similarity (embeddings are already normalized)
+        similarities = np.dot(document_embeddings, query_embedding.T).squeeze()
+        
+        return similarities