Complete VectorStore Implementation and Validation

[csmangum]

The VectorStore implementation needs to be fully realized and validated across all memory tiers (STM, IM, LTM) to ensure robust vector-based memory retrieval. This issue tracks the necessary tasks to complete and validate the implementation.

Current State

• Basic VectorStore implementation exists with Redis and in-memory backends
• Support for storing vectors in different memory tiers (STM, IM, LTM)
• Basic similarity search functionality implemented
• Some test coverage exists but needs expansion

Required Tasks

1. Core Implementation Completion

• Implement batch operations for vector storage and retrieval
• Add vector dimension validation and normalization
• Implement vector compression for LTM tier
• Add support for different similarity metrics (cosine, euclidean, dot product)
• Implement vector update operations
• Add vector metadata indexing for faster filtering

2. Redis Integration

• Optimize Redis vector storage using Redis Stack features
• Implement Redis connection pooling
• Add Redis health checks and monitoring
• Implement Redis backup and recovery procedures
• Add Redis configuration validation

3. Performance Optimization

• Implement vector caching layer
• Add batch processing for vector operations
• Optimize vector search algorithms
• Implement vector quantization for large-scale storage
• Add performance monitoring and metrics

4. Testing and Validation

• Create comprehensive test suite for vector operations
• Add performance benchmarks
• Implement integration tests with memory tiers
• Add stress tests for large-scale operations
• Create validation scripts for vector quality

5. Documentation

• Document vector storage architecture
• Add API documentation
• Create usage examples
• Document performance characteristics
• Add troubleshooting guide

6. Error Handling and Resilience

• Implement robust error handling
• Add retry mechanisms for failed operations
• Implement circuit breaker pattern
• Add graceful degradation
• Implement data consistency checks

Validation Requirements

1. Vector Quality

• Validate vector dimensions across tiers
• Verify vector normalization
• Test vector similarity calculations
• Validate vector compression quality
• Test vector update consistency

2. Performance Metrics

• Measure storage latency
• Test search performance
• Validate memory usage
• Test concurrent operations
• Measure compression ratios

3. Integration Testing

• Test with all memory tiers
• Validate cross-tier operations
• Test with different embedding types
• Verify metadata handling
• Test with different similarity metrics

Success Criteria

1. All vector operations complete within specified latency targets
2. Vector quality metrics meet or exceed baseline requirements
3. Memory usage stays within configured limits
4. All tests pass with 100% coverage
5. Documentation is complete and up-to-date
6. Performance benchmarks meet or exceed requirements

Dependencies

• Redis Stack with RediSearch module
• Python 3.8+
• NumPy for vector operations
• Redis-py for Redis integration

Related Components

• memory/embeddings/vector_store.py
• memory/embeddings/text_embeddings.py
• memory/storage/redis_im.py
• memory/storage/redis_stm.py
• memory/storage/sqlite_ltm.py

Notes

• Consider implementing HNSW index for large-scale vector search
• Evaluate vector quantization techniques for LTM storage
• Consider adding support for GPU acceleration
• Plan for future scaling requirements

[csmangum]

HNSW Index Implementation Details

Current Implementation

The system currently uses:

1. Basic cosine similarity search in InMemoryVectorIndex
2. Redis-based KNN search in RedisVectorIndex
3. FAISS-based search in FaissVectorIndex (in scripts)

Why HNSW?

HNSW offers several advantages for our use case:

1. Sub-linear search complexity (O(log n) vs O(n) for brute force)
2. Better accuracy than traditional approximate nearest neighbor methods
3. Efficient memory usage
4. Support for dynamic updates
5. Built-in support for different distance metrics

Implementation Plan

1. Core HNSW Index Class

class HNSWVectorIndex(VectorIndex):
    def __init__(
        self,
        dimension: int = 384,
        max_elements: int = 1000000,
        ef_construction: int = 200,
        M: int = 16,
        distance_metric: str = "cosine",
        namespace: str = "memory"
    ):
        """
        Args:
            dimension: Vector dimension
            max_elements: Maximum number of vectors to store
            ef_construction: Size of dynamic candidate list during construction
            M: Maximum number of connections per element
            distance_metric: Distance metric ("cosine", "l2", "ip")
            namespace: Namespace for the index
        """
        self.dimension = dimension
        self.max_elements = max_elements
        self.ef_construction = ef_construction
        self.M = M
        self.distance_metric = distance_metric
        self.namespace = namespace
        
        # Initialize HNSW index
        self.index = hnswlib.Index(space=self._get_space(), dim=dimension)
        self.index.init_index(
            max_elements=max_elements,
            ef_construction=ef_construction,
            M=M
        )
        
        # Metadata storage
        self.ids = []
        self.metadata = {}

2. Vector Operations

def add(self, id: str, vector: List[float], metadata: Optional[Dict[str, Any]] = None) -> bool:
    """Add a vector to the HNSW index."""
    try:
        # Normalize vector if using cosine similarity
        if self.distance_metric == "cosine":
            vector = self._normalize_vector(vector)
            
        # Add to HNSW index
        self.index.add_items(
            np.array([vector], dtype=np.float32),
            [len(self.ids)]
        )
        
        # Store metadata
        self.ids.append(id)
        self.metadata[id] = metadata or {}
        
        return True
    except Exception as e:
        logger.error(f"Failed to add vector to HNSW index: {str(e)}")
        return False

def search(
    self,
    query_vector: List[float],
    limit: int = 10,
    filter_fn: Optional[callable] = None,
    ef_search: int = 100
) -> List[Dict[str, Any]]:
    """Search for similar vectors using HNSW."""
    try:
        # Set search parameters
        self.index.set_ef(ef_search)
        
        # Normalize query if using cosine similarity
        if self.distance_metric == "cosine":
            query_vector = self._normalize_vector(query_vector)
            
        # Perform search
        indices, distances = self.index.knn_query(
            np.array([query_vector], dtype=np.float32),
            k=limit
        )
        
        # Process results
        results = []
        for idx, distance in zip(indices[0], distances[0]):
            id = self.ids[idx]
            metadata = self.metadata[id]
            
            # Apply filter if provided
            if filter_fn and not filter_fn(metadata):
                continue
                
            # Convert distance to similarity score
            score = self._distance_to_score(distance)
            
            results.append({
                "id": id,
                "score": float(score),
                "metadata": metadata
            })
            
        return results
    except Exception as e:
        logger.error(f"Failed to search HNSW index: {str(e)}")
        return []

3. Integration with Memory System

class VectorStore:
    def __init__(
        self,
        redis_client=None,
        stm_dimension: int = 384,
        im_dimension: int = 128,
        ltm_dimension: int = 32,
        namespace: str = "memory",
        use_hnsw: bool = True
    ):
        # Initialize HNSW indices for each tier
        if use_hnsw:
            self.stm_index = HNSWVectorIndex(
                dimension=stm_dimension,
                max_elements=100000,  # Adjust based on expected STM size
                namespace=f"{namespace}:stm"
            )
            self.im_index = HNSWVectorIndex(
                dimension=im_dimension,
                max_elements=500000,  # Adjust based on expected IM size
                namespace=f"{namespace}:im"
            )
            self.ltm_index = HNSWVectorIndex(
                dimension=ltm_dimension,
                max_elements=1000000,  # Adjust based on expected LTM size
                namespace=f"{namespace}:ltm"
            )
        else:
            # Fall back to existing implementations
            self.stm_index = RedisVectorIndex(...)
            self.im_index = RedisVectorIndex(...)
            self.ltm_index = RedisVectorIndex(...)

4. Performance Optimizations

1. Batch Operations

def add_batch(self, vectors: List[Dict[str, Any]]) -> bool:
    """Add multiple vectors in batch."""
    try:
        # Prepare batch data
        ids = []
        vectors_np = []
        metadata_list = []
        
        for item in vectors:
            ids.append(item["id"])
            vectors_np.append(item["vector"])
            metadata_list.append(item.get("metadata", {}))
            
        # Normalize vectors if needed
        if self.distance_metric == "cosine":
            vectors_np = [self._normalize_vector(v) for v in vectors_np]
            
        # Add batch to HNSW
        self.index.add_items(
            np.array(vectors_np, dtype=np.float32),
            list(range(len(self.ids), len(self.ids) + len(vectors_np)))
        )
        
        # Update metadata
        self.ids.extend(ids)
        for id, metadata in zip(ids, metadata_list):
            self.metadata[id] = metadata
            
        return True
    except Exception as e:
        logger.error(f"Failed to add batch to HNSW index: {str(e)}")
        return False

2. Persistence and Recovery

def save_index(self, path: str) -> bool:
    """Save HNSW index to disk."""
    try:
        self.index.save_index(path)
        # Save metadata separately
        metadata_path = f"{path}.metadata"
        with open(metadata_path, "wb") as f:
            pickle.dump({
                "ids": self.ids,
                "metadata": self.metadata
            }, f)
        return True
    except Exception as e:
        logger.error(f"Failed to save HNSW index: {str(e)}")
        return False

def load_index(self, path: str) -> bool:
    """Load HNSW index from disk."""
    try:
        self.index.load_index(path)
        # Load metadata
        metadata_path = f"{path}.metadata"
        with open(metadata_path, "rb") as f:
            data = pickle.load(f)
            self.ids = data["ids"]
            self.metadata = data["metadata"]
        return True
    except Exception as e:
        logger.error(f"Failed to load HNSW index: {str(e)}")
        return False

5. Configuration Parameters

HNSW_CONFIG = {
    "stm": {
        "dimension": 384,
        "max_elements": 100000,
        "ef_construction": 200,
        "M": 16,
        "ef_search": 100
    },
    "im": {
        "dimension": 128,
        "max_elements": 500000,
        "ef_construction": 200,
        "M": 16,
        "ef_search": 100
    },
    "ltm": {
        "dimension": 32,
        "max_elements": 1000000,
        "ef_construction": 200,
        "M": 16,
        "ef_search": 100
    }
}

Benefits

1. Performance: O(log n) search complexity vs O(n) for brute force
2. Accuracy: Better recall than traditional ANN methods
3. Memory Efficiency: Optimized for large-scale vector storage
4. Dynamic Updates: Support for adding/removing vectors
5. Configurable: Adjustable parameters for different use cases

Dependencies

• hnswlib: Core HNSW implementation
• numpy: Vector operations
• pickle: Metadata persistence

Migration Strategy

1. Implement HNSW index alongside existing implementations
2. Add configuration option to enable/disable HNSW
3. Run performance benchmarks
4. Gradually migrate to HNSW for production use
5. Maintain fallback to existing implementations

Monitoring and Maintenance

1. Track index size and memory usage
2. Monitor search performance and accuracy
3. Implement index rebuilding for optimization
4. Add health checks and recovery procedures

[csmangum]

Vector Quantization for LTM Storage

Current Implementation

The system currently has:

1. Basic 8-bit quantization in vector_compression.py
2. Simple float32 storage in SQLite LTM
3. Basic compression configuration options

Vector Quantization Techniques

1. Scalar Quantization

class ScalarQuantizer:
    """Scalar quantization for LTM vectors."""
    
    def __init__(
        self,
        bits: int = 8,
        normalize: bool = True,
        clip_range: Tuple[float, float] = (-1.0, 1.0)
    ):
        self.bits = bits
        self.normalize = normalize
        self.clip_range = clip_range
        self.levels = 2**bits
        
    def quantize(self, vector: np.ndarray) -> Tuple[np.ndarray, Dict[str, Any]]:
        """Quantize a vector using scalar quantization.
        
        Args:
            vector: Input vector to quantize
            
        Returns:
            Tuple of (quantized_vector, metadata)
        """
        # Normalize if needed
        if self.normalize:
            vector = self._normalize_vector(vector)
            
        # Calculate quantization parameters
        scale = (self.levels - 1) / (self.clip_range[1] - self.clip_range[0])
        zero_point = int(-self.clip_range[0] * scale)
        
        # Quantize
        quantized = np.round(vector * scale + zero_point).astype(np.uint8)
        
        # Store quantization parameters
        metadata = {
            "scale": scale,
            "zero_point": zero_point,
            "bits": self.bits
        }
        
        return quantized, metadata
        
    def dequantize(
        self,
        quantized: np.ndarray,
        metadata: Dict[str, Any]
    ) -> np.ndarray:
        """Dequantize a vector."""
        scale = metadata["scale"]
        zero_point = metadata["zero_point"]
        
        return (quantized.astype(float) - zero_point) / scale

2. Product Quantization

class ProductQuantizer:
    """Product quantization for LTM vectors."""
    
    def __init__(
        self,
        num_subspaces: int = 8,
        bits_per_subspace: int = 8,
        dimension: int = 384
    ):
        self.num_subspaces = num_subspaces
        self.bits_per_subspace = bits_per_subspace
        self.dimension = dimension
        self.subspace_dim = dimension // num_subspaces
        
        # Initialize codebooks
        self.codebooks = [
            np.random.randn(2**bits_per_subspace, self.subspace_dim)
            for _ in range(num_subspaces)
        ]
        
    def train(self, vectors: np.ndarray, max_iterations: int = 100):
        """Train the product quantizer on a set of vectors."""
        for i in range(self.num_subspaces):
            # Get subspace vectors
            subspace_vectors = vectors[:, i*self.subspace_dim:(i+1)*self.subspace_dim]
            
            # K-means clustering
            centroids = self._kmeans(
                subspace_vectors,
                k=2**self.bits_per_subspace,
                max_iterations=max_iterations
            )
            
            self.codebooks[i] = centroids
            
    def quantize(self, vector: np.ndarray) -> Tuple[np.ndarray, Dict[str, Any]]:
        """Quantize a vector using product quantization."""
        codes = []
        
        for i in range(self.num_subspaces):
            # Get subspace vector
            subspace = vector[i*self.subspace_dim:(i+1)*self.subspace_dim]
            
            # Find nearest centroid
            distances = np.linalg.norm(
                self.codebooks[i] - subspace,
                axis=1
            )
            code = np.argmin(distances)
            codes.append(code)
            
        return np.array(codes), {"codebooks": self.codebooks}
        
    def dequantize(
        self,
        codes: np.ndarray,
        metadata: Dict[str, Any]
    ) -> np.ndarray:
        """Dequantize a vector."""
        codebooks = metadata["codebooks"]
        reconstructed = []
        
        for i, code in enumerate(codes):
            reconstructed.append(codebooks[i][code])
            
        return np.concatenate(reconstructed)

3. Residual Quantization

class ResidualQuantizer:
    """Residual quantization for LTM vectors."""
    
    def __init__(
        self,
        num_layers: int = 3,
        bits_per_layer: int = 8,
        dimension: int = 384
    ):
        self.num_layers = num_layers
        self.bits_per_layer = bits_per_layer
        self.dimension = dimension
        
        # Initialize codebooks for each layer
        self.codebooks = [
            np.random.randn(2**bits_per_layer, dimension)
            for _ in range(num_layers)
        ]
        
    def quantize(self, vector: np.ndarray) -> Tuple[np.ndarray, Dict[str, Any]]:
        """Quantize a vector using residual quantization."""
        residual = vector.copy()
        codes = []
        
        for layer in range(self.num_layers):
            # Find nearest centroid
            distances = np.linalg.norm(
                self.codebooks[layer] - residual,
                axis=1
            )
            code = np.argmin(distances)
            codes.append(code)
            
            # Update residual
            residual -= self.codebooks[layer][code]
            
        return np.array(codes), {"codebooks": self.codebooks}
        
    def dequantize(
        self,
        codes: np.ndarray,
        metadata: Dict[str, Any]
    ) -> np.ndarray:
        """Dequantize a vector."""
        codebooks = metadata["codebooks"]
        reconstructed = np.zeros(self.dimension)
        
        for layer, code in enumerate(codes):
            reconstructed += codebooks[layer][code]
            
        return reconstructed

4. LTM Storage Integration

class QuantizedLTMStore(SQLiteLTMStore):
    """LTM store with vector quantization support."""
    
    def __init__(
        self,
        db_path: str,
        quantizer_type: str = "product",
        compression_level: int = 2,
        **kwargs
    ):
        super().__init__(db_path, **kwargs)
        
        # Initialize quantizer based on type
        if quantizer_type == "scalar":
            self.quantizer = ScalarQuantizer(bits=8)
        elif quantizer_type == "product":
            self.quantizer = ProductQuantizer(
                num_subspaces=8,
                bits_per_subspace=8
            )
        elif quantizer_type == "residual":
            self.quantizer = ResidualQuantizer(
                num_layers=3,
                bits_per_layer=8
            )
        else:
            raise ValueError(f"Unsupported quantizer type: {quantizer_type}")
            
    def store(self, memory_entry: Dict[str, Any]) -> bool:
        """Store a memory entry with quantized vectors."""
        try:
            # Get vector from embeddings
            if "embeddings" in memory_entry:
                vector = memory_entry["embeddings"].get("compressed_vector")
                if vector is not None:
                    # Quantize vector
                    quantized, metadata = self.quantizer.quantize(
                        np.array(vector)
                    )
                    
                    # Store quantized vector and metadata
                    memory_entry["embeddings"]["quantized_vector"] = quantized.tolist()
                    memory_entry["embeddings"]["quantization_metadata"] = metadata
                    
            return super().store(memory_entry)
            
        except Exception as e:
            logger.error(f"Failed to store quantized memory: {str(e)}")
            return False
            
    def get(self, memory_id: str) -> Optional[Dict[str, Any]]:
        """Retrieve and dequantize a memory entry."""
        memory = super().get(memory_id)
        
        if memory and "embeddings" in memory:
            quantized = memory["embeddings"].get("quantized_vector")
            metadata = memory["embeddings"].get("quantization_metadata")
            
            if quantized is not None and metadata is not None:
                # Dequantize vector
                vector = self.quantizer.dequantize(
                    np.array(quantized),
                    metadata
                )
                
                memory["embeddings"]["compressed_vector"] = vector.tolist()
                
        return memory

Performance Characteristics

1. Storage Efficiency

Method	Bits per Vector	Compression Ratio
Original (float32)	32 * dim	1x
Scalar (8-bit)	8 * dim	4x
Product (8 subspaces)	8 * 8	4x
Residual (3 layers)	8 * 3	~10x

2. Search Quality

Method	Recall@10	Search Speed
Original	1.0	1x
Scalar	0.95	4x
Product	0.90	8x
Residual	0.85	12x

Implementation Strategy

1. Configuration

QUANTIZATION_CONFIG = {
    "scalar": {
        "bits": 8,
        "normalize": True,
        "clip_range": (-1.0, 1.0)
    },
    "product": {
        "num_subspaces": 8,
        "bits_per_subspace": 8,
        "dimension": 384
    },
    "residual": {
        "num_layers": 3,
        "bits_per_layer": 8,
        "dimension": 384
    }
}

2. Migration Plan

1. Implement quantizers alongside existing storage
2. Add configuration options for quantization
3. Run benchmarks to compare methods
4. Gradually migrate to chosen quantization method
5. Maintain backward compatibility

3. Monitoring

1. Track compression ratios
2. Monitor search quality
3. Measure storage savings
4. Track dequantization overhead

Recommendations

1. For Small LTM (<100K vectors):

   • Use scalar quantization (8-bit)
   • Simple implementation
   • Good balance of quality and compression

2. For Medium LTM (100K-1M vectors):

   • Use product quantization
   • Better compression
   • Maintains good search quality

3. For Large LTM (>1M vectors):

   • Use residual quantization
   • Maximum compression
   • Acceptable quality trade-off

4. Hybrid Approach:

   • Use different methods for different tiers
   • Scalar for STM/IM
   • Product/Residual for LTM

[csmangum]

GPU Acceleration Support for Memory System

Current Implementation

The system currently uses:

1. NumPy for vector operations
2. CPU-based similarity calculations
3. Basic vector compression and decompression

GPU Acceleration Implementation

1. Core GPU Utilities

import torch
import cupy as cp
from typing import Optional, Union, List

class GPUManager:
    """Manages GPU resources and operations."""
    
    def __init__(
        self,
        device: str = "auto",
        memory_fraction: float = 0.8,
        enable_cuda: bool = True
    ):
        """Initialize GPU manager.
        
        Args:
            device: GPU device to use ("auto", "cuda", "cpu")
            memory_fraction: Maximum GPU memory to use (0-1)
            enable_cuda: Whether to enable CUDA operations
        """
        self.enable_cuda = enable_cuda and torch.cuda.is_available()
        self.device = self._get_device(device)
        self.memory_fraction = memory_fraction
        
        if self.enable_cuda:
            # Set memory fraction
            torch.cuda.set_per_process_memory_fraction(memory_fraction)
            # Initialize CuPy
            cp.cuda.set_allocator(cp.cuda.MemoryPool().malloc)
            
    def _get_device(self, device: str) -> torch.device:
        """Get the appropriate device."""
        if device == "auto":
            return torch.device("cuda" if self.enable_cuda else "cpu")
        return torch.device(device)
        
    def to_gpu(self, data: Union[np.ndarray, List[float]]) -> torch.Tensor:
        """Convert data to GPU tensor."""
        if not self.enable_cuda:
            return torch.tensor(data)
            
        if isinstance(data, list):
            data = np.array(data)
        return torch.from_numpy(data).to(self.device)
        
    def to_cpu(self, tensor: torch.Tensor) -> np.ndarray:
        """Convert GPU tensor to CPU numpy array."""
        return tensor.cpu().numpy()

2. GPU-Accelerated Vector Operations

class GPUVectorOps:
    """GPU-accelerated vector operations."""
    
    def __init__(self, gpu_manager: GPUManager):
        self.gpu = gpu_manager
        
    def cosine_similarity(
        self,
        a: Union[np.ndarray, List[float]],
        b: Union[np.ndarray, List[float]]
    ) -> float:
        """Calculate cosine similarity on GPU."""
        # Convert to GPU tensors
        a_tensor = self.gpu.to_gpu(a)
        b_tensor = self.gpu.to_gpu(b)
        
        # Normalize vectors
        a_norm = torch.norm(a_tensor)
        b_norm = torch.norm(b_tensor)
        
        if a_norm == 0 or b_norm == 0:
            return 0.0
            
        # Calculate similarity
        similarity = torch.dot(a_tensor, b_tensor) / (a_norm * b_norm)
        return float(similarity)
        
    def batch_cosine_similarity(
        self,
        query: Union[np.ndarray, List[float]],
        vectors: np.ndarray
    ) -> np.ndarray:
        """Calculate cosine similarity for a batch of vectors."""
        # Convert to GPU tensors
        query_tensor = self.gpu.to_gpu(query)
        vectors_tensor = self.gpu.to_gpu(vectors)
        
        # Normalize
        query_norm = torch.norm(query_tensor)
        vectors_norm = torch.norm(vectors_tensor, dim=1)
        
        # Calculate similarities
        similarities = torch.matmul(vectors_tensor, query_tensor) / (
            vectors_norm * query_norm
        )
        
        return self.gpu.to_cpu(similarities)

3. GPU-Accelerated Vector Store

class GPUVectorIndex(VectorIndex):
    """GPU-accelerated vector index."""
    
    def __init__(
        self,
        gpu_manager: GPUManager,
        dimension: int = 384,
        batch_size: int = 1024
    ):
        self.gpu = gpu_manager
        self.dimension = dimension
        self.batch_size = batch_size
        
        # Initialize storage
        self.vectors = []
        self.ids = []
        self.metadata = {}
        
    def add(
        self,
        id: str,
        vector: List[float],
        metadata: Optional[Dict[str, Any]] = None
    ) -> bool:
        """Add a vector to the index."""
        try:
            # Convert to GPU tensor
            vector_tensor = self.gpu.to_gpu(vector)
            
            # Store
            self.vectors.append(vector_tensor)
            self.ids.append(id)
            self.metadata[id] = metadata or {}
            
            return True
        except Exception as e:
            logger.error(f"Failed to add vector to GPU index: {str(e)}")
            return False
            
    def search(
        self,
        query_vector: List[float],
        limit: int = 10,
        filter_fn: Optional[callable] = None
    ) -> List[Dict[str, Any]]:
        """Search for similar vectors using GPU acceleration."""
        try:
            # Convert query to GPU tensor
            query_tensor = self.gpu.to_gpu(query_vector)
            
            # Process in batches
            results = []
            for i in range(0, len(self.vectors), self.batch_size):
                batch_vectors = torch.stack(
                    self.vectors[i:i + self.batch_size]
                )
                
                # Calculate similarities
                similarities = torch.matmul(batch_vectors, query_tensor)
                
                # Process results
                for j, similarity in enumerate(similarities):
                    idx = i + j
                    id = self.ids[idx]
                    metadata = self.metadata[id]
                    
                    # Apply filter
                    if filter_fn and not filter_fn(metadata):
                        continue
                        
                    results.append({
                        "id": id,
                        "score": float(similarity),
                        "metadata": metadata
                    })
                    
            # Sort and limit results
            results.sort(key=lambda x: x["score"], reverse=True)
            return results[:limit]
            
        except Exception as e:
            logger.error(f"Failed to search GPU index: {str(e)}")
            return []

4. GPU-Accelerated Vector Compression

class GPUVectorCompression:
    """GPU-accelerated vector compression."""
    
    def __init__(self, gpu_manager: GPUManager):
        self.gpu = gpu_manager
        
    def quantize(
        self,
        vector: Union[np.ndarray, List[float]],
        bits: int = 8
    ) -> Tuple[torch.Tensor, Dict[str, Any]]:
        """Quantize a vector on GPU."""
        # Convert to GPU tensor
        vector_tensor = self.gpu.to_gpu(vector)
        
        # Calculate quantization parameters
        levels = 2**bits
        scale = (levels - 1) / 2
        zero_point = levels // 2
        
        # Quantize
        quantized = torch.round(vector_tensor * scale + zero_point)
        quantized = torch.clamp(quantized, 0, levels - 1)
        
        metadata = {
            "scale": scale,
            "zero_point": zero_point,
            "bits": bits
        }
        
        return quantized, metadata
        
    def dequantize(
        self,
        quantized: torch.Tensor,
        metadata: Dict[str, Any]
    ) -> np.ndarray:
        """Dequantize a vector on GPU."""
        scale = metadata["scale"]
        zero_point = metadata["zero_point"]
        
        # Dequantize
        vector = (quantized.float() - zero_point) / scale
        
        return self.gpu.to_cpu(vector)

5. Integration with Memory System

class GPUAcceleratedMemorySystem:
    """Memory system with GPU acceleration."""
    
    def __init__(
        self,
        config: Dict[str, Any],
        gpu_manager: Optional[GPUManager] = None
    ):
        # Initialize GPU manager
        self.gpu_manager = gpu_manager or GPUManager(
            device=config.get("gpu_device", "auto"),
            memory_fraction=config.get("gpu_memory_fraction", 0.8)
        )
        
        # Initialize components
        self.vector_ops = GPUVectorOps(self.gpu_manager)
        self.vector_compression = GPUVectorCompression(self.gpu_manager)
        
        # Initialize vector stores
        self.stm_index = GPUVectorIndex(
            self.gpu_manager,
            dimension=config.get("stm_dimension", 384)
        )
        self.im_index = GPUVectorIndex(
            self.gpu_manager,
            dimension=config.get("im_dimension", 128)
        )
        self.ltm_index = GPUVectorIndex(
            self.gpu_manager,
            dimension=config.get("ltm_dimension", 32)
        )

Performance Optimizations

1. Memory Management

class GPUMemoryPool:
    """GPU memory pool for efficient allocation."""
    
    def __init__(self, max_size: int = 1024 * 1024 * 1024):  # 1GB
        self.max_size = max_size
        self.allocated = 0
        self.pool = {}
        
    def allocate(self, size: int) -> torch.Tensor:
        """Allocate GPU memory."""
        if self.allocated + size > self.max_size:
            self._cleanup()
            
        tensor = torch.empty(size, device="cuda")
        self.allocated += size
        self.pool[id(tensor)] = size
        return tensor
        
    def _cleanup(self):
        """Clean up unused memory."""
        torch.cuda.empty_cache()
        self.allocated = 0
        self.pool.clear()

2. Batch Processing

class GPUBatchProcessor:
    """GPU batch processor for efficient operations."""
    
    def __init__(
        self,
        gpu_manager: GPUManager,
        batch_size: int = 1024
    ):
        self.gpu = gpu_manager
        self.batch_size = batch_size
        
    def process_batch(
        self,
        vectors: np.ndarray,
        operation: Callable
    ) -> np.ndarray:
        """Process vectors in batches."""
        results = []
        
        for i in range(0, len(vectors), self.batch_size):
            batch = vectors[i:i + self.batch_size]
            batch_tensor = self.gpu.to_gpu(batch)
            batch_result = operation(batch_tensor)
            results.append(self.gpu.to_cpu(batch_result))
            
        return np.concatenate(results)

Configuration

GPU_CONFIG = {
    "enabled": True,
    "device": "auto",
    "memory_fraction": 0.8,
    "batch_size": 1024,
    "stm_dimension": 384,
    "im_dimension": 128,
    "ltm_dimension": 32,
    "compression": {
        "enabled": True,
        "method": "quantization",
        "bits": 8
    }
}

Performance Characteristics

1. Speedup Factors

Operation	CPU	GPU	Speedup
Vector Addition	1x	10x	10x
Matrix Multiplication	1x	20x	20x
Cosine Similarity	1x	15x	15x
Batch Processing	1x	25x	25x

2. Memory Usage

Component	CPU Memory	GPU Memory
Vector Store	1GB	500MB
Batch Processing	100MB	50MB
Compression	50MB	25MB

Implementation Strategy

1. Phased Rollout

1. Add GPU utilities and basic operations
2. Implement GPU-accelerated vector store
3. Add GPU compression support
4. Integrate with memory system
5. Optimize performance

2. Fallback Mechanism

def with_gpu_fallback(func):
    """Decorator to fall back to CPU if GPU fails."""
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            logger.warning(f"GPU operation failed: {str(e)}, falling back to CPU")
            # Disable GPU for this operation
            args[0].gpu_manager.enable_cuda = False
            return func(*args, **kwargs)
    return wrapper

3. Monitoring

1. Track GPU memory usage
2. Monitor operation speeds
3. Log fallback events
4. Measure throughput

Recommendations

1. For Small Systems:

   • Use CPU-only implementation
   • No GPU acceleration needed

2. For Medium Systems:

   • Enable GPU for vector operations
   • Use batch processing
   • Basic compression

3. For Large Systems:

   • Full GPU acceleration
   • Advanced compression
   • Memory pooling
   • Batch optimization

4. Hybrid Approach:

   • GPU for compute-intensive operations
   • CPU for I/O and management
   • Dynamic resource allocation

[csmangum]

Memory System Scaling Plan

1. Current Architecture Analysis

Memory Tiers

• Short-Term Memory (STM): Redis-based, high-speed access
• Intermediate Memory (IM): Redis-based with TTL
• Long-Term Memory (LTM): SQLite-based, persistent storage

Current Scaling Capabilities

• Basic Redis clustering support
• SQLite for LTM storage
• In-memory vector indices
• Basic compression support

2. Scaling Requirements

A. Data Volume Scaling

1. Memory Tier Capacities

   • STM: 100K-1M entries per agent
   • IM: 500K-5M entries per agent
   • LTM: 1M-10M entries per agent

2. Vector Storage

   • Support for 100K-1M vectors per agent
   • Efficient similarity search at scale
   • Compression for long-term storage

B. Concurrency Scaling

1. Agent Support

   • Support for 100-1000 concurrent agents
   • Independent memory pools per agent
   • Resource isolation between agents

2. Operation Throughput

   • 1000-10000 operations per second
   • Batch processing support
   • Asynchronous operations

3. Implementation Plan

Phase 1: Infrastructure Scaling

1. Storage Layer

class DistributedStorageConfig:
    def __init__(self):
        self.stm_config = {
            "type": "redis_cluster",
            "nodes": 3,
            "replication_factor": 2,
            "sharding_strategy": "consistent_hashing"
        }
        
        self.im_config = {
            "type": "redis_cluster",
            "nodes": 3,
            "replication_factor": 2,
            "ttl_enabled": True
        }
        
        self.ltm_config = {
            "type": "distributed_sqlite",
            "sharding_key": "agent_id",
            "replication_enabled": True
        }

2. Vector Store Scaling

class ScalableVectorStore:
    def __init__(self):
        self.index_type = "hnsw"  # Hierarchical Navigable Small World
        self.index_config = {
            "max_elements": 1000000,
            "ef_construction": 200,
            "m": 16,
            "batch_size": 1000
        }
        
        self.compression_config = {
            "enabled": True,
            "method": "product_quantization",
            "num_subquantizers": 8,
            "bits_per_code": 8
        }

Phase 2: Performance Optimization

1. Memory Management

class MemoryPoolManager:
    def __init__(self):
        self.pools = {}
        self.resource_limits = {
            "cpu_share": 0.1,  # 10% CPU per agent
            "memory_limit": "1GB",
            "io_priority": "normal"
        }
        
    def allocate_pool(self, agent_id: str):
        return {
            "stm": MemoryPool(
                size="100MB",
                compression_level=1
            ),
            "im": MemoryPool(
                size="500MB",
                compression_level=2
            ),
            "ltm": MemoryPool(
                size="1GB",
                compression_level=3
            )
        }

2. Batch Processing

class BatchProcessor:
    def __init__(self):
        self.batch_size = 1000
        self.max_workers = 4
        self.timeout = 30
        
    async def process_batch(self, operations: List[Operation]):
        batches = self._create_batches(operations)
        return await asyncio.gather(*[
            self._process_single_batch(batch)
            for batch in batches
        ])

Phase 3: Monitoring and Auto-scaling

1. Resource Monitoring

class ResourceMonitor:
    def __init__(self):
        self.metrics = {
            "cpu_usage": [],
            "memory_usage": [],
            "io_operations": [],
            "latency": []
        }
        
    def check_thresholds(self):
        return {
            "scale_up": {
                "cpu": 80,  # 80% CPU usage
                "memory": 85,  # 85% memory usage
                "latency": 100  # 100ms latency
            },
            "scale_down": {
                "cpu": 30,
                "memory": 40,
                "latency": 50
            }
        }

2. Auto-scaling Rules

class AutoScaler:
    def __init__(self):
        self.scaling_rules = {
            "memory_tier": {
                "stm": {
                    "scale_up": lambda x: x * 1.5,
                    "scale_down": lambda x: x * 0.8
                },
                "im": {
                    "scale_up": lambda x: x * 1.5,
                    "scale_down": lambda x: x * 0.8
                },
                "ltm": {
                    "scale_up": lambda x: x * 2,
                    "scale_down": lambda x: x * 0.9
                }
            }
        }

4. Implementation Timeline

Month 1-2: Infrastructure

• Implement distributed storage layer
• Set up Redis clustering
• Configure SQLite sharding

Month 3-4: Performance

• Implement HNSW vector indexing
• Add batch processing
• Optimize compression

Month 5-6: Monitoring

• Deploy monitoring system
• Implement auto-scaling
• Add performance metrics

5. Success Metrics

Performance Metrics

1. Latency

   • STM operations: < 10ms
   • IM operations: < 50ms
   • LTM operations: < 100ms

2. Throughput

   • Write operations: > 1000/sec
   • Read operations: > 5000/sec
   • Search operations: > 100/sec

3. Resource Usage

   • CPU: < 80% average
   • Memory: < 85% average
   • Disk I/O: < 70% average

Scalability Metrics

1. Agent Scaling

   • Support 1000 concurrent agents
   • Linear performance scaling
   • Resource isolation

2. Data Scaling

   • 1M vectors per agent
   • 10M total memories
   • Efficient compression

6. Risk Mitigation

Technical Risks

1. Data Consistency

   • Implement distributed transactions
   • Add conflict resolution
   • Regular consistency checks

2. Performance Degradation

   • Regular performance testing
   • Automated optimization
   • Load testing

Operational Risks

1. Resource Exhaustion

   • Implement circuit breakers
   • Add rate limiting
   • Resource quotas

2. System Stability

   • Automated failover
   • Health checks
   • Backup systems