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"""
FAISS to Weaviate migration tool.
This module provides tools for migrating data from FAISS to Weaviate
while preserving document content, embeddings, and metadata. It includes
validation and rollback capabilities for safe migration.
"""
import logging
import time
from typing import List, Dict, Any, Optional, Tuple
from pathlib import Path
import json
import numpy as np
from src.core.interfaces import Document
from ..faiss_backend import FAISSBackend
from ..weaviate_backend import WeaviateBackend
from .data_validator import DataValidator
logger = logging.getLogger(__name__)
class MigrationError(Exception):
"""Raised when migration operations fail."""
pass
class FAISSToWeaviateMigrator:
"""
Tool for migrating data from FAISS to Weaviate backend.
This migrator handles the complete process of moving documents,
embeddings, and metadata from a FAISS index to Weaviate while
preserving data integrity and enabling validation.
Features:
- Complete data migration with validation
- Backup creation before migration
- Progress tracking and reporting
- Rollback capabilities
- Batch processing for performance
- Data integrity verification
The migration process:
1. Extract all documents from FAISS
2. Validate document integrity
3. Create backup of current state
4. Initialize Weaviate schema
5. Batch transfer documents
6. Validate migration success
7. Generate migration report
"""
def __init__(self,
faiss_backend: FAISSBackend,
weaviate_backend: WeaviateBackend,
batch_size: int = 100,
validation_enabled: bool = True):
"""
Initialize the migrator.
Args:
faiss_backend: Source FAISS backend
weaviate_backend: Target Weaviate backend
batch_size: Number of documents to process per batch
validation_enabled: Whether to perform validation
"""
self.faiss_backend = faiss_backend
self.weaviate_backend = weaviate_backend
self.batch_size = batch_size
self.validation_enabled = validation_enabled
# Migration tracking
self.migration_stats = {
"start_time": None,
"end_time": None,
"total_documents": 0,
"migrated_documents": 0,
"failed_documents": 0,
"validation_passed": False,
"backup_created": False,
"migration_id": None
}
# Initialize validator
if validation_enabled:
self.validator = DataValidator()
else:
self.validator = None
logger.info("FAISS to Weaviate migrator initialized")
def migrate(self,
documents: List[Document],
backup_path: Optional[Path] = None,
preserve_faiss: bool = True) -> Dict[str, Any]:
"""
Perform complete migration from FAISS to Weaviate.
Args:
documents: List of documents to migrate (from FAISS)
backup_path: Optional path to create backup
preserve_faiss: Whether to keep FAISS data after migration
Returns:
Dictionary with migration results and statistics
"""
migration_id = f"migration_{int(time.time())}"
self.migration_stats["migration_id"] = migration_id
self.migration_stats["start_time"] = time.time()
logger.info(f"Starting migration {migration_id}: {len(documents)} documents")
try:
# Step 1: Validate source data
if self.validation_enabled:
logger.info("Validating source documents...")
validation_result = self.validator.validate_documents(documents)
if not validation_result["is_valid"]:
raise MigrationError(f"Source validation failed: {validation_result['issues']}")
logger.info("Source validation passed")
# Step 2: Create backup if requested
if backup_path:
logger.info(f"Creating backup at {backup_path}...")
self._create_backup(documents, backup_path)
self.migration_stats["backup_created"] = True
logger.info("Backup created successfully")
# Step 3: Initialize Weaviate
logger.info("Initializing Weaviate backend...")
self._initialize_weaviate(documents)
logger.info("Weaviate backend initialized")
# Step 4: Migrate documents in batches
logger.info("Starting document migration...")
self._migrate_documents_batch(documents)
logger.info("Document migration completed")
# Step 5: Validate migration
if self.validation_enabled:
logger.info("Validating migration results...")
validation_passed = self._validate_migration(documents)
self.migration_stats["validation_passed"] = validation_passed
if not validation_passed:
raise MigrationError("Migration validation failed")
logger.info("Migration validation passed")
# Step 6: Clean up FAISS if requested
if not preserve_faiss:
logger.info("Clearing FAISS backend...")
self.faiss_backend.clear()
logger.info("FAISS backend cleared")
# Complete migration
self.migration_stats["end_time"] = time.time()
self.migration_stats["total_documents"] = len(documents)
# Generate report
report = self._generate_migration_report()
logger.info(f"Migration {migration_id} completed successfully")
return {
"success": True,
"migration_id": migration_id,
"report": report,
"stats": self.migration_stats.copy()
}
except Exception as e:
self.migration_stats["end_time"] = time.time()
logger.error(f"Migration {migration_id} failed: {str(e)}")
return {
"success": False,
"migration_id": migration_id,
"error": str(e),
"stats": self.migration_stats.copy()
}
def _initialize_weaviate(self, documents: List[Document]) -> None:
"""
Initialize Weaviate backend for migration.
Args:
documents: Documents to be migrated (for dimension detection)
"""
# Detect embedding dimension
embedding_dim = None
for doc in documents:
if doc.embedding:
embedding_dim = len(doc.embedding)
break
if embedding_dim is None:
raise MigrationError("No embeddings found in documents")
# Initialize Weaviate index
self.weaviate_backend.initialize_index(embedding_dim)
# Verify connection and schema
if not self.weaviate_backend.is_trained():
raise MigrationError("Weaviate backend not ready after initialization")
def _migrate_documents_batch(self, documents: List[Document]) -> None:
"""
Migrate documents in batches.
Args:
documents: Documents to migrate
"""
total_batches = (len(documents) + self.batch_size - 1) // self.batch_size
for batch_idx in range(total_batches):
start_idx = batch_idx * self.batch_size
end_idx = min(start_idx + self.batch_size, len(documents))
batch_documents = documents[start_idx:end_idx]
try:
logger.info(f"Migrating batch {batch_idx + 1}/{total_batches} ({len(batch_documents)} documents)")
# Add batch to Weaviate
self.weaviate_backend.add_documents(batch_documents)
# Update stats
self.migration_stats["migrated_documents"] += len(batch_documents)
logger.debug(f"Batch {batch_idx + 1} completed successfully")
except Exception as e:
self.migration_stats["failed_documents"] += len(batch_documents)
logger.error(f"Batch {batch_idx + 1} failed: {str(e)}")
raise MigrationError(f"Batch migration failed: {str(e)}") from e
def _validate_migration(self, original_documents: List[Document]) -> bool:
"""
Validate that migration was successful.
Args:
original_documents: Original documents from FAISS
Returns:
True if validation passes, False otherwise
"""
try:
# Check document count
weaviate_count = self.weaviate_backend.get_document_count()
expected_count = len(original_documents)
if weaviate_count != expected_count:
logger.error(f"Document count mismatch: expected {expected_count}, got {weaviate_count}")
return False
# Sample-based content validation
sample_size = min(10, len(original_documents))
sample_indices = np.random.choice(len(original_documents), sample_size, replace=False)
for idx in sample_indices:
original_doc = original_documents[idx]
# Search for the document in Weaviate
if original_doc.embedding:
results = self.weaviate_backend.search(
np.array(original_doc.embedding),
k=1
)
if not results:
logger.error(f"Document {idx} not found in Weaviate")
return False
# Note: Full content validation would require additional metadata
# to match documents exactly. For now, we verify presence.
logger.info("Migration validation completed successfully")
return True
except Exception as e:
logger.error(f"Migration validation failed: {str(e)}")
return False
def _create_backup(self, documents: List[Document], backup_path: Path) -> None:
"""
Create backup of documents before migration.
Args:
documents: Documents to backup
backup_path: Path to save backup
"""
backup_path.parent.mkdir(parents=True, exist_ok=True)
# Create backup data structure
backup_data = {
"metadata": {
"timestamp": time.time(),
"document_count": len(documents),
"migration_id": self.migration_stats["migration_id"]
},
"documents": []
}
# Add documents to backup
for i, doc in enumerate(documents):
doc_data = {
"index": i,
"content": doc.content,
"metadata": doc.metadata,
"embedding": doc.embedding
}
backup_data["documents"].append(doc_data)
# Save backup
with open(backup_path, 'w', encoding='utf-8') as f:
json.dump(backup_data, f, indent=2, ensure_ascii=False)
logger.info(f"Backup saved to {backup_path}")
def _generate_migration_report(self) -> Dict[str, Any]:
"""
Generate comprehensive migration report.
Returns:
Dictionary with migration report
"""
duration = self.migration_stats["end_time"] - self.migration_stats["start_time"]
return {
"migration_summary": {
"migration_id": self.migration_stats["migration_id"],
"duration_seconds": duration,
"total_documents": self.migration_stats["total_documents"],
"migrated_documents": self.migration_stats["migrated_documents"],
"failed_documents": self.migration_stats["failed_documents"],
"success_rate": (
self.migration_stats["migrated_documents"] /
max(1, self.migration_stats["total_documents"])
)
},
"validation": {
"enabled": self.validation_enabled,
"passed": self.migration_stats["validation_passed"]
},
"backup": {
"created": self.migration_stats["backup_created"]
},
"performance": {
"documents_per_second": (
self.migration_stats["migrated_documents"] / max(0.1, duration)
),
"batch_size": self.batch_size
},
"backend_status": {
"faiss": self.faiss_backend.get_backend_info(),
"weaviate": self.weaviate_backend.get_backend_info()
}
}
def rollback_migration(self, backup_path: Path) -> Dict[str, Any]:
"""
Rollback migration using backup data.
Args:
backup_path: Path to backup file
Returns:
Dictionary with rollback results
"""
logger.info(f"Starting rollback from backup {backup_path}")
try:
# Load backup
with open(backup_path, 'r', encoding='utf-8') as f:
backup_data = json.load(f)
# Reconstruct documents
documents = []
for doc_data in backup_data["documents"]:
doc = Document(
content=doc_data["content"],
metadata=doc_data["metadata"],
embedding=doc_data["embedding"]
)
documents.append(doc)
# Clear Weaviate
self.weaviate_backend.clear()
# Restore to FAISS
if documents:
# Detect embedding dimension
embedding_dim = len(documents[0].embedding)
self.faiss_backend.initialize_index(embedding_dim)
self.faiss_backend.add_documents(documents)
logger.info(f"Rollback completed: restored {len(documents)} documents")
return {
"success": True,
"restored_documents": len(documents),
"backup_metadata": backup_data["metadata"]
}
except Exception as e:
logger.error(f"Rollback failed: {str(e)}")
return {
"success": False,
"error": str(e)
}
def get_migration_status(self) -> Dict[str, Any]:
"""
Get current migration status.
Returns:
Dictionary with current status
"""
return {
"migration_id": self.migration_stats["migration_id"],
"is_running": (
self.migration_stats["start_time"] is not None and
self.migration_stats["end_time"] is None
),
"progress": {
"total": self.migration_stats["total_documents"],
"migrated": self.migration_stats["migrated_documents"],
"failed": self.migration_stats["failed_documents"]
},
"stats": self.migration_stats.copy()
} |