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"""
FAISS backend adapter for advanced retriever.
This module provides a backend adapter that wraps the existing FAISS
functionality to work with the advanced retriever's unified backend
interface. This enables hot-swapping between FAISS and other backends.
"""
import logging
import time
from typing import List, Dict, Any, Optional, Tuple
import numpy as np
from src.core.interfaces import Document
from ..indices.faiss_index import FAISSIndex
from .weaviate_config import WeaviateBackendConfig # For consistent interface
logger = logging.getLogger(__name__)
class FAISSBackend:
"""
FAISS backend adapter for advanced retriever.
This adapter wraps the existing FAISSIndex implementation to provide
a consistent backend interface that can be hot-swapped with other
vector database backends like Weaviate.
Features:
- Wraps existing FAISS functionality
- Consistent interface with other backends
- Performance monitoring and statistics
- Graceful error handling and fallbacks
- Memory usage optimization
The adapter follows the same patterns as external service adapters
but wraps internal components instead of making network calls.
"""
def __init__(self, config: Dict[str, Any]):
"""
Initialize FAISS backend adapter.
Args:
config: Configuration dictionary for FAISS settings
"""
self.config = config
self.faiss_config = config.get("faiss", {})
# Initialize wrapped FAISS index
self.faiss_index = FAISSIndex(self.faiss_config)
# Performance tracking
self.stats = {
"total_operations": 0,
"total_time": 0.0,
"avg_time": 0.0,
"last_operation_time": 0.0,
"search_count": 0,
"add_count": 0,
"error_count": 0
}
# Backend identification
self.backend_type = "faiss"
self.backend_version = "wrapped"
logger.info("FAISS backend adapter initialized")
def initialize_index(self, embedding_dim: int) -> None:
"""
Initialize the FAISS index with specified dimension.
Args:
embedding_dim: Dimension of the embedding vectors
"""
start_time = time.time()
try:
self.faiss_index.initialize_index(embedding_dim)
# Update stats
elapsed_time = time.time() - start_time
self._update_stats("initialize", elapsed_time)
logger.info(f"FAISS backend index initialized with dimension {embedding_dim}")
except Exception as e:
self.stats["error_count"] += 1
logger.error(f"Failed to initialize FAISS backend: {str(e)}")
raise RuntimeError(f"FAISS backend initialization failed: {str(e)}") from e
def add_documents(self, documents: List[Document]) -> None:
"""
Add documents to the FAISS index.
Args:
documents: List of documents with embeddings to add
"""
start_time = time.time()
try:
if not documents:
raise ValueError("Cannot add empty document list")
# Validate embeddings
for i, doc in enumerate(documents):
if doc.embedding is None:
raise ValueError(f"Document {i} missing embedding")
self.faiss_index.add_documents(documents)
# Update stats
elapsed_time = time.time() - start_time
self._update_stats("add", elapsed_time)
self.stats["add_count"] += len(documents)
logger.info(f"Added {len(documents)} documents to FAISS backend")
except Exception as e:
self.stats["error_count"] += 1
logger.error(f"Failed to add documents to FAISS backend: {str(e)}")
raise RuntimeError(f"FAISS backend add failed: {str(e)}") from e
def search(self, query_embedding: np.ndarray, k: int = 5) -> List[Tuple[int, float]]:
"""
Search for similar documents using FAISS.
Args:
query_embedding: Query vector
k: Number of results to return
Returns:
List of (document_index, score) tuples
"""
start_time = time.time()
try:
if k <= 0:
raise ValueError("k must be positive")
results = self.faiss_index.search(query_embedding, k=k)
# Update stats
elapsed_time = time.time() - start_time
self._update_stats("search", elapsed_time)
self.stats["search_count"] += 1
logger.debug(f"FAISS backend search returned {len(results)} results in {elapsed_time:.4f}s")
return results
except Exception as e:
self.stats["error_count"] += 1
logger.error(f"FAISS backend search failed: {str(e)}")
raise RuntimeError(f"FAISS backend search failed: {str(e)}") from e
def get_document_count(self) -> int:
"""
Get the number of documents in the index.
Returns:
Number of indexed documents
"""
try:
return self.faiss_index.get_document_count()
except Exception as e:
logger.error(f"Failed to get document count: {str(e)}")
return 0
def is_trained(self) -> bool:
"""
Check if the index is trained and ready for use.
Returns:
True if index is trained, False otherwise
"""
try:
return self.faiss_index.is_trained()
except Exception as e:
logger.error(f"Failed to check training status: {str(e)}")
return False
def clear(self) -> None:
"""Clear all documents from the index."""
start_time = time.time()
try:
self.faiss_index.clear()
# Reset relevant stats
elapsed_time = time.time() - start_time
self._update_stats("clear", elapsed_time)
logger.info("FAISS backend cleared")
except Exception as e:
self.stats["error_count"] += 1
logger.error(f"Failed to clear FAISS backend: {str(e)}")
raise RuntimeError(f"FAISS backend clear failed: {str(e)}") from e
def get_backend_info(self) -> Dict[str, Any]:
"""
Get information about the backend.
Returns:
Dictionary with backend information
"""
faiss_info = self.faiss_index.get_index_info()
return {
"backend_type": self.backend_type,
"backend_version": self.backend_version,
"document_count": self.get_document_count(),
"is_trained": self.is_trained(),
"faiss_info": faiss_info,
"stats": self.stats.copy(),
"config": self.config
}
def get_performance_stats(self) -> Dict[str, Any]:
"""
Get detailed performance statistics.
Returns:
Dictionary with performance metrics
"""
return {
"backend_type": "faiss",
"total_operations": self.stats["total_operations"],
"total_time": self.stats["total_time"],
"avg_time": self.stats["avg_time"],
"last_operation_time": self.stats["last_operation_time"],
"search_count": self.stats["search_count"],
"add_count": self.stats["add_count"],
"error_count": self.stats["error_count"],
"error_rate": self.stats["error_count"] / max(1, self.stats["total_operations"]),
"document_count": self.get_document_count(),
"is_ready": self.is_trained()
}
def health_check(self) -> Dict[str, Any]:
"""
Perform a health check on the backend.
Returns:
Dictionary with health status
"""
try:
is_healthy = True
issues = []
# Check if index is initialized
if not self.is_trained():
is_healthy = False
issues.append("Index not trained")
# Check error rate
error_rate = self.stats["error_count"] / max(1, self.stats["total_operations"])
if error_rate > 0.1: # More than 10% errors
is_healthy = False
issues.append(f"High error rate: {error_rate:.2%}")
# Check if we have documents
doc_count = self.get_document_count()
if doc_count == 0:
issues.append("No documents indexed")
return {
"backend_type": "faiss",
"is_healthy": is_healthy,
"issues": issues,
"document_count": doc_count,
"error_rate": error_rate,
"total_operations": self.stats["total_operations"]
}
except Exception as e:
return {
"backend_type": "faiss",
"is_healthy": False,
"issues": [f"Health check failed: {str(e)}"],
"error": str(e)
}
def _update_stats(self, operation: str, elapsed_time: float) -> None:
"""
Update performance statistics.
Args:
operation: Name of the operation
elapsed_time: Time taken for the operation
"""
self.stats["total_operations"] += 1
self.stats["total_time"] += elapsed_time
self.stats["avg_time"] = self.stats["total_time"] / self.stats["total_operations"]
self.stats["last_operation_time"] = elapsed_time
def supports_hybrid_search(self) -> bool:
"""
Check if backend supports hybrid search.
Returns:
False for FAISS (pure vector search only)
"""
return False
def supports_filtering(self) -> bool:
"""
Check if backend supports metadata filtering.
Returns:
False for FAISS (no built-in filtering)
"""
return False
def get_configuration(self) -> Dict[str, Any]:
"""
Get the current configuration.
Returns:
Configuration dictionary
"""
return {
"backend_type": "faiss",
"config": self.config,
"faiss_config": self.faiss_config
} |