Vector Database Implementation

class ProductionVectorDB:
    def __init__(self, config):
        self.config = config
        self.client = self.initialize_client()
        self.connection_pool = self.setup_connection_pool()
        self.metrics = MetricsCollector()
        self.circuit_breaker = CircuitBreaker()
        
        # Performance optimizations
        self.query_cache = QueryCache(max_size=10000, ttl=300)
        self.batch_processor = BatchProcessor(
            max_batch_size=config.max_batch_size,
            max_wait_time_ms=config.max_wait_time_ms
        )
        
    async def similarity_search(self, 
                              query_vector: List[float],
                              top_k: int = 10,
                              metadata_filter: Dict = None,
                              use_cache: bool = True) -> List[SearchResult]:
        """Optimized similarity search with caching and batching"""
        
        # Create cache key
        cache_key = self.create_cache_key(query_vector, top_k, metadata_filter)
        
        # Check cache first
        if use_cache:
            cached_result = await self.query_cache.get(cache_key)
            if cached_result:
                await self.metrics.record_cache_hit()
                return cached_result
        
        # Use circuit breaker for fault tolerance
        if not self.circuit_breaker.can_execute():
            raise VectorDBUnavailableError("Circuit breaker is open")
        
        try:
            # Execute search with timeout
            start_time = time.time()
            
            results = await asyncio.wait_for(
                self._execute_similarity_search(
                    query_vector, top_k, metadata_filter
                ),
                timeout=self.config.query_timeout_seconds
            )
            
            # Record metrics
            query_time = time.time() - start_time
            await self.metrics.record_query(
                query_time=query_time,
                result_count=len(results),
                top_k=top_k
            )
            
            # Cache successful result
            if use_cache and results:
                await self.query_cache.set(cache_key, results)
            
            # Update circuit breaker
            self.circuit_breaker.record_success()
            
            return results
            
        except Exception as e:
            # Record failure
            self.circuit_breaker.record_failure()
            await self.metrics.record_error(str(e))
            raise
    
    async def _execute_similarity_search(self, 
                                       query_vector: List[float],
                                       top_k: int,
                                       metadata_filter: Dict) -> List[SearchResult]:
        """Execute the actual similarity search"""
        
        # Use batch processing if beneficial
        if self.should_batch_query(query_vector):
            return await self.batch_processor.add_query(
                query_vector, top_k, metadata_filter
            )
        
        # Direct query for single requests
        async with self.connection_pool.get_connection() as conn:
            raw_results = await conn.query(
                vector=query_vector,
                top_k=top_k,
                filter=metadata_filter,
                include_metadata=True,
                include_values=False  # Don't return vectors to save bandwidth
            )
        
        # Process and rank results
        return self.process_search_results(raw_results)
    
    async def upsert_vectors(self, 
                           vectors: List[VectorRecord],
                           batch_size: int = 100,
                           parallel_batches: int = 4) -> UpsertResult:
        """Efficient batch upsert with parallelization"""
        
        total_vectors = len(vectors)
        batches = [
            vectors[i:i + batch_size] 
            for i in range(0, total_vectors, batch_size)
        ]
        
        # Process batches in parallel with controlled concurrency
        semaphore = asyncio.Semaphore(parallel_batches)
        
        async def process_batch(batch):
            async with semaphore:
                return await self._upsert_batch(batch)
        
        # Execute all batches
        start_time = time.time()
        batch_results = await asyncio.gather(
            *[process_batch(batch) for batch in batches],
            return_exceptions=True
        )
        
        # Aggregate results
        successful_upserts = 0
        failed_upserts = 0
        errors = []
        
        for result in batch_results:
            if isinstance(result, Exception):
                failed_upserts += batch_size
                errors.append(str(result))
            else:
                successful_upserts += result.upserted_count
                if result.errors:
                    failed_upserts += len(result.errors)
                    errors.extend(result.errors)
        
        total_time = time.time() - start_time
        
        # Record metrics
        await self.metrics.record_upsert(
            total_vectors=total_vectors,
            successful=successful_upserts,
            failed=failed_upserts,
            duration=total_time,
            throughput=total_vectors / total_time
        )
        
        return UpsertResult(
            total_vectors=total_vectors,
            successful_upserts=successful_upserts,
            failed_upserts=failed_upserts,
            errors=errors,
            duration=total_time
        )
    
    async def _upsert_batch(self, batch: List[VectorRecord]) -> BatchUpsertResult:
        """Upsert a single batch of vectors"""
        
        try:
            async with self.connection_pool.get_connection() as conn:
                # Prepare batch data
                ids = [record.id for record in batch]
                vectors = [record.vector for record in batch]
                metadata = [record.metadata for record in batch]
                
                # Execute upsert
                result = await conn.upsert(
                    ids=ids,
                    vectors=vectors,
                    metadata=metadata
                )
                
                return BatchUpsertResult(
                    upserted_count=len(batch),
                    errors=[]
                )
                
        except Exception as e:
            return BatchUpsertResult(
                upserted_count=0,
                errors=[str(e)]
            )

class ConnectionPool:
    """Connection pool for vector database clients"""
    
    def __init__(self, config):
        self.config = config
        self.pool = asyncio.Queue(maxsize=config.max_connections)
        self.active_connections = 0
        
        # Initialize pool
        asyncio.create_task(self.initialize_pool())
    
    async def initialize_pool(self):
        """Initialize connection pool with minimum connections"""
        
        for _ in range(self.config.min_connections):
            connection = await self.create_connection()
            await self.pool.put(connection)
            self.active_connections += 1
    
    async def get_connection(self):
        """Get connection from pool or create new one"""
        
        try:
            # Try to get existing connection
            connection = await asyncio.wait_for(
                self.pool.get(), 
                timeout=self.config.connection_timeout
            )
            
            # Validate connection is still healthy
            if await self.validate_connection(connection):
                return ConnectionContext(connection, self.pool)
            else:
                # Connection is stale, create new one
                await self.close_connection(connection)
                self.active_connections -= 1
                
        except asyncio.TimeoutError:
            # No available connections, create new if under limit
            pass
        
        # Create new connection if under limit
        if self.active_connections < self.config.max_connections:
            connection = await self.create_connection()
            self.active_connections += 1
            return ConnectionContext(connection, self.pool)
        
        # Pool exhausted, wait for available connection
        connection = await self.pool.get()
        return ConnectionContext(connection, self.pool)

class CircuitBreaker:
    """Circuit breaker pattern for vector database resilience"""
    
    def __init__(self, 
                 failure_threshold: int = 5,
                 timeout_seconds: int = 60,
                 expected_exception_types: tuple = (Exception,)):
        
        self.failure_threshold = failure_threshold
        self.timeout_seconds = timeout_seconds
        self.expected_exception_types = expected_exception_types
        
        self.failure_count = 0
        self.last_failure_time = None
        self.state = 'CLOSED'  # CLOSED, OPEN, HALF_OPEN
        
    def can_execute(self) -> bool:
        """Check if execution is allowed"""
        
        if self.state == 'CLOSED':
            return True
        elif self.state == 'OPEN':
            # Check if timeout has passed
            if (time.time() - self.last_failure_time) > self.timeout_seconds:
                self.state = 'HALF_OPEN'
                return True
            return False
        elif self.state == 'HALF_OPEN':
            return True
        
        return False
    
    def record_success(self):
        """Record successful execution"""
        
        if self.state == 'HALF_OPEN':
            self.state = 'CLOSED'
        
        self.failure_count = 0
        self.last_failure_time = None
    
    def record_failure(self):
        """Record failed execution"""
        
        self.failure_count += 1
        self.last_failure_time = time.time()
        
        if self.failure_count >= self.failure_threshold:
            self.state = 'OPEN'