vector_store_client.py

"""
Client for LanceDB vector store operations with lazy loading.

This module provides an optimized client for LanceDB with automatic
connection management and lazy table initialization.
"""

import lancedb
import asyncio
from typing import List, Dict, Any, Optional
from datetime import datetime
from loguru import logger


class VectorStoreClient:
    """
    Client for LanceDB vector store with lazy loading.
    
    Features:
    - Lazy connection and table initialization
    - Automatic reconnection on errors
    - Document validation and enrichment
    - Search with metadata filtering
    
    Attributes:
        uri: Database URI path
        table_name: Name of the table to use
        
    Examples:
        >>> client = VectorStoreClient(uri="./lancedb")
        >>> # No connection yet - happens on first use
        >>> client.add_documents([{"text": "...", "vector": [...]}])
        >>> # Connection established automatically
    """

    def __init__(self, uri: str, table_name: str = "a11y_expert"):
        """
        Initialize client with database URI and table name.
        
        Args:
            uri: Path to LanceDB database
            table_name: Name of the table (default: "a11y_expert")
        """
        self.uri = uri
        self.table_name = table_name
        self._db = None
        self._table = None

    @property
    def db(self):
        """
        Lazy database connection property.
        
        Connects to database on first access and returns cached connection.
        
        Returns:
            LanceDB database connection
        """
        if self._db is None:
            logger.info(f"Connecting to LanceDB at: {self.uri}")
            self._db = lancedb.connect(self.uri)
            logger.info("✅ Connected to LanceDB")
        return self._db
    
    @property
    def table(self):
        """
        Lazy table initialization property.
        
        Opens or creates table on first access.
        
        Returns:
            LanceDB table or None if table doesn't exist yet
        """
        if self._table is None:
            if self.table_name in self.db.table_names():
                logger.debug(f"Opening existing table: '{self.table_name}'")
                self._table = self.db.open_table(self.table_name)
            else:
                logger.debug(f"Table '{self.table_name}' doesn't exist yet")
                return None
        return self._table
    
    def connect(self):
        """
        Explicitly connect to database (optional - happens automatically).
        
        Provided for backward compatibility. Connection happens automatically
        when first accessing db or table properties.
        """
        _ = self.db  # Trigger lazy connection
        if self.table is not None:
            logger.info(f"Table '{self.table_name}' ready ({len(self.table)} docs)")
        else:
            logger.info(f"Table '{self.table_name}' will be created on first insert")

    
    def add_documents(self, documents: List[Dict[str, Any]]):
        """
        Add documents to the table with automatic validation.
        
        Validates required fields, adds timestamps, and creates table if needed.
        
        Args:
            documents: List of dicts with required keys:
                - text (str): Document text
                - vector (List[float]): Embedding vector
                - source (str): Source identifier
                - language (str): Language code (en/pl)
                - doc_type (str): Document type
                
        Examples:
            >>> client.add_documents([{
            ...     "text": "Content",
            ...     "vector": [0.1, 0.2, ...],
            ...     "source": "wcag",
            ...     "language": "en",
            ...     "doc_type": "specification"
            ... }])
        """
        # Validate and enrich documents
        valid_docs = []
        now = datetime.now()
        skipped_count = 0
        
        for doc in documents:
            try:
                # Ensure required fields exist
                required_fields = {"text", "vector", "source", "language", "doc_type"}
                missing = required_fields - set(doc.keys())
                if missing:
                    logger.warning(f"Skipping document with missing fields: {missing}")
                    skipped_count += 1
                    continue
                
                # Add timestamps if not present
                if "created_at" not in doc or doc["created_at"] is None:
                    doc["created_at"] = now
                if "updated_at" not in doc or doc["updated_at"] is None:
                    doc["updated_at"] = now
                
                valid_docs.append(doc)
                
            except Exception as e:
                logger.error(f"Failed to process document: {e}")
                skipped_count += 1
                continue
        
        if not valid_docs:
            logger.warning(f"No valid documents to add (skipped: {skipped_count})")
            return
        
        try:
            logger.info(f"Adding {len(valid_docs)} documents to '{self.table_name}'")
            
            # Create table on first insert or open existing
            if self.table_name not in self.db.table_names():
                self._table = self.db.create_table(self.table_name, data=valid_docs)
                logger.info(f"✅ Created table '{self.table_name}' with {len(valid_docs)} docs")
            else:
                # Refresh table reference and add
                self._table = self.db.open_table(self.table_name)
                self._table.add(valid_docs)
                logger.info(f"✅ Added {len(valid_docs)} documents to '{self.table_name}'")
            
            if skipped_count > 0:
                logger.warning(f"Skipped {skipped_count} invalid documents")
                
        except Exception as e:
            logger.error(f"Failed to add documents to LanceDB: {e}")
            raise


    def search(
        self, 
        query_embedding: List[float], 
        where: str = "", 
        top_k: int = 5
    ) -> List[Dict[str, Any]]:
        """
        Search for documents using vector similarity.
        
        Args:
            query_embedding: Query vector embedding
            where: Optional SQL-like filter (e.g., "language = 'en'")
            top_k: Number of results to return
            
        Returns:
            List of matching documents with similarity scores
            
        Examples:
            >>> results = client.search(embedding, where="language = 'pl'", top_k=3)
            >>> len(results)
            3
        """
        if self.table is None:
            logger.error(f"Table '{self.table_name}' doesn't exist")
            return []
        
        try:
            logger.debug(f"Searching for {top_k} documents" + (f" where: {where}" if where else ""))
            
            query = self.table.search(query_embedding)
            if where:
                query = query.where(where)
            
            results = query.limit(top_k).to_df()
            logger.debug(f"Found {len(results)} documents")
            return results.to_dict("records")
        except Exception as e:
            logger.error(f"Search failed: {e}")
            return []

    def count_documents(self) -> int:
        """
        Return total number of documents in table.
        
        Returns:
            Document count or 0 if table doesn't exist
        """
        if self.table is None:
            return 0
        return len(self.table)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get database statistics."""
        if self._db is None:
            self.connect()
        
        if self.table_name not in self._db.table_names():
            logger.warning(f"Table '{self.table_name}' does not exist yet")
            return {
                "total_documents": 0,
                "languages": {},
                "doc_types": {},
                "sources": [],
                "earliest_document": None,
                "latest_document": None,
            }
        
        try:
            table = self._db.open_table(self.table_name)
            df = table.to_pandas()
            
            stats = {
                "total_documents": len(df),
                "languages": df["language"].value_counts().to_dict() if "language" in df.columns else {},
                "doc_types": df["doc_type"].value_counts().to_dict() if "doc_type" in df.columns else {},
                "sources": df["source"].unique().tolist() if "source" in df.columns else [],
                "earliest_document": str(df["created_at"].min()) if "created_at" in df.columns else None,
                "latest_document": str(df["created_at"].max()) if "created_at" in df.columns else None,
            }
            
            logger.info(f"Database stats: {stats['total_documents']} documents")
            return stats
        except Exception as e:
            logger.error(f"Failed to get statistics: {e}")
            return {"error": str(e)}
    
    
    def get_recent_documents(self, limit: int = 20) -> List[Dict[str, Any]]:
        """
        Get recently added documents sorted by creation time.
        
        Args:
            limit: Maximum number of documents to return
            
        Returns:
            List of recent documents
        """
        if self.table is None:
            logger.warning(f"Table '{self.table_name}' doesn't exist")
            return []
        
        try:
            df = self.table.to_pandas()
            if "created_at" in df.columns:
                df = df.sort_values("created_at", ascending=False).head(limit)
            else:
                df = df.head(limit)
            
            return df.to_dict("records")
        except Exception as e:
            logger.error(f"Failed to get recent documents: {e}")
            return []
    
    
    def search_with_filters(
        self,
        query_embedding: List[float],
        language: Optional[str] = None,
        doc_type: Optional[str] = None,
        source: Optional[str] = None,
        top_k: int = 5
    ) -> List[Dict[str, Any]]:
        """
        Search with optional metadata filters.
        
        Args:
            query_embedding: Query vector embedding
            language: Filter by language code (e.g., 'en', 'pl')
            doc_type: Filter by document type (e.g., 'specification')
            source: Filter by source (e.g., 'wcag')
            top_k: Number of results to return
            
        Returns:
            List of matching documents
            
        Examples:
            >>> results = client.search_with_filters(
            ...     embedding, 
            ...     language='pl',
            ...     doc_type='specification',
            ...     top_k=5
            ... )
        """
        if self.table is None:
            logger.warning(f"Table '{self.table_name}' doesn't exist")
            return []
        
        # Build where clause
        conditions = []
        if language:
            conditions.append(f"language = '{language}'")
        if doc_type:
            conditions.append(f"doc_type = '{doc_type}'")
        if source:
            conditions.append(f"source = '{source}'")
        
        where_clause = " AND ".join(conditions) if conditions else ""
        
        try:
            query = self.table.search(query_embedding)
            if where_clause:
                query = query.where(where_clause)
            
            results = query.limit(top_k).to_df()
            logger.debug(f"Found {len(results)} documents with filters")
            return results.to_dict("records")
        except Exception as e:
            logger.error(f"Search with filters failed: {e}")
            return []

    def close(self):
        """
        Close database connection and clean up resources.
        
        Call this method when shutting down the application to properly
        release all database resources and prevent asyncio warnings.
        """
        try:
            if self._db is not None:
                # LanceDB connections are file-based and don't need explicit closing
                # but we clear references to help garbage collection
                self._table = None
                self._db = None
                logger.info("VectorStoreClient resources cleared")
        except Exception as e:
            logger.warning(f"Error during VectorStoreClient cleanup: {e}")