Advanced Memory Vector Database Configuration¶

# my_backends.py
import os
import json
from agent_memory_server.memory_vector_db import MemoryVectorDatabase

def create_configured_backend(embeddings) -> MemoryVectorDatabase:
    """Factory that reads configuration from environment."""
    config = json.loads(os.getenv("MEMORY_VECTOR_DB_CONFIG", "{}"))
    backend_type = os.getenv("BACKEND_TYPE", "redis")

    if backend_type == "redis":
        from agent_memory_server.memory_vector_db_factory import create_redis_memory_vector_db
        return create_redis_memory_vector_db(embeddings)
    elif backend_type == "custom":
        return MyCustomBackend(embeddings, **config)
    else:
        raise ValueError(f"Unsupported backend: {backend_type}")

# resilient_factory.py
import logging
from agent_memory_server.memory_vector_db import MemoryVectorDatabase

logger = logging.getLogger(__name__)

def create_resilient_backend(embeddings) -> MemoryVectorDatabase:
    """Factory with fallback to default Redis backend."""
    try:
        # Try custom backend first
        return create_custom_backend(embeddings)
    except Exception as e:
        logger.warning(f"Custom backend failed: {e}, falling back to Redis")
        from agent_memory_server.memory_vector_db_factory import create_redis_memory_vector_db
        return create_redis_memory_vector_db(embeddings)

# custom_backend.py
from agent_memory_server.memory_vector_db import MemoryVectorDatabase
from agent_memory_server.models import MemoryRecord, MemoryRecordResult, MemoryRecordResults
import logging

class AdvancedCustomBackend(MemoryVectorDatabase):
    """Advanced custom backend with caching and batch operations."""

    def __init__(self, embeddings, batch_size: int = 50):
        self.embeddings = embeddings
        self.logger = logging.getLogger(__name__)
        self._embedding_cache = {}
        self._batch_size = batch_size

    async def add_memories(self, memories: list[MemoryRecord]) -> list[str]:
        """Add memories with optimized batching."""
        if not memories:
            return []

        self.logger.info(f"Adding {len(memories)} memories in batches of {self._batch_size}")

        all_ids = []
        for i in range(0, len(memories), self._batch_size):
            batch = memories[i:i + self._batch_size]
            batch_ids = await self._add_memory_batch(batch)
            all_ids.extend(batch_ids)

        return all_ids

    async def _add_memory_batch(self, memories: list[MemoryRecord]) -> list[str]:
        """Add a batch of memories."""
        texts = [m.text for m in memories]

        # Generate embeddings
        embeddings = await self.embeddings.aembed_documents(texts)

        # Store in your backend
        ids = []
        for memory, embedding in zip(memories, embeddings):
            memory_id = await self._store_memory(memory, embedding)
            ids.append(memory_id)

        return ids

    async def _store_memory(self, memory: MemoryRecord, embedding: list[float]) -> str:
        """Store a single memory in your backend."""
        # Implement your storage logic here
        raise NotImplementedError

    async def search_memories(self, query: str, limit: int = 10, **kwargs) -> MemoryRecordResults:
        """Search with advanced filtering and ranking."""
        # Generate query embedding
        query_embedding = await self.embeddings.aembed_query(query)

        # Perform search in your backend
        results = await self._vector_search(query_embedding, limit, **kwargs)

        return results

    async def _vector_search(self, embedding: list[float], limit: int, **kwargs) -> MemoryRecordResults:
        """Perform vector search in your backend."""
        raise NotImplementedError

    async def delete_memories(self, memory_ids: list[str]) -> int:
        """Delete memories by ID."""
        raise NotImplementedError

    async def update_memories(self, memories: list[MemoryRecord]) -> int:
        """Update existing memories."""
        updated_ids = await self.add_memories(memories)
        return len(updated_ids)

    async def count_memories(self, **filter_kwargs) -> int:
        """Count memories matching filters."""
        results = await self.list_memories(limit=100000, **filter_kwargs)
        return results.total

    async def list_memories(self, offset: int = 0, limit: int = 100, **filter_kwargs) -> MemoryRecordResults:
        """List memories with optional filters."""
        raise NotImplementedError

def create_advanced_custom_backend(embeddings) -> AdvancedCustomBackend:
    """Factory for advanced custom backend."""
    return AdvancedCustomBackend(embeddings)

# migration_tools.py
import json
import asyncio
from datetime import datetime
from typing import Any
from agent_memory_client import MemoryAPIClient

class MemoryMigrator:
    """Tool for migrating data between memory vector databases."""

    def __init__(self, source_client: MemoryAPIClient, target_client: MemoryAPIClient):
        self.source = source_client
        self.target = target_client

    async def migrate_all_memories(self, batch_size: int = 100) -> dict[str, int]:
        """Migrate all memories from source to target."""
        print("Starting migration...")

        # Export all memories
        memories = await self.export_memories()
        print(f"Exported {len(memories)} memories")

        # Import in batches
        imported_count = await self.import_memories(memories, batch_size)

        # Verification
        verification_results = await self.verify_migration()

        return {
            "exported": len(memories),
            "imported": imported_count,
            "verification_passed": verification_results["success"],
            "missing_memories": verification_results["missing_count"]
        }

    async def export_memories(self, user_id: str = None, namespace: str = None) -> list[dict[str, Any]]:
        """Export memories from source system."""
        memories = []
        offset = 0
        batch_size = 1000

        while True:
            results = await self.source.search_long_term_memory(
                text="",
                user_id=user_id,
                namespace=namespace,
                limit=batch_size,
                offset=offset
            )

            if not results.memories:
                break

            for memory in results.memories:
                memory_dict = {
                    "id": memory.id,
                    "text": memory.text,
                    "memory_type": memory.memory_type,
                    "user_id": memory.user_id,
                    "session_id": memory.session_id,
                    "namespace": memory.namespace,
                    "topics": memory.topics,
                    "entities": memory.entities,
                    "created_at": memory.created_at.isoformat() if memory.created_at else None,
                    "updated_at": memory.updated_at.isoformat() if memory.updated_at else None,
                    "access_count": memory.access_count,
                    "pinned": getattr(memory, "pinned", False)
                }
                memories.append(memory_dict)

            offset += batch_size
            print(f"Exported {len(memories)} memories so far...")

        return memories

    async def import_memories(self, memories: list[dict[str, Any]], batch_size: int = 100) -> int:
        """Import memories to target system."""
        imported_count = 0

        for i in range(0, len(memories), batch_size):
            batch = memories[i:i + batch_size]
            memory_records = [{k: v for k, v in mem.items() if v is not None} for mem in batch]

            try:
                result = await self.target.create_long_term_memories(memory_records)
                imported_count += len(result.memories)
                print(f"Imported batch {i//batch_size + 1}: {len(result.memories)} memories")
                await asyncio.sleep(0.1)
            except Exception as e:
                print(f"Error importing batch {i//batch_size + 1}: {e}")

        return imported_count

    async def verify_migration(self, sample_size: int = 100) -> dict[str, Any]:
        """Verify migration by sampling memories."""
        source_sample = await self.source.search_long_term_memory(text="", limit=sample_size)

        missing_count = 0
        verified_count = 0

        for memory in source_sample.memories:
            target_results = await self.target.search_long_term_memory(
                text=memory.text[:100],
                user_id=memory.user_id,
                limit=5
            )

            found = any(
                result.id == memory.id or result.text == memory.text
                for result in target_results.memories
            )

            if found:
                verified_count += 1
            else:
                missing_count += 1

        success_rate = verified_count / len(source_sample.memories) if source_sample.memories else 0

        return {
            "success": success_rate > 0.95,
            "success_rate": success_rate,
            "verified_count": verified_count,
            "missing_count": missing_count,
            "sample_size": len(source_sample.memories)
        }

    async def export_to_file(self, filename: str, user_id: str = None, namespace: str = None):
        """Export memories to JSON file."""
        memories = await self.export_memories(user_id, namespace)

        export_data = {
            "export_timestamp": datetime.now().isoformat(),
            "total_count": len(memories),
            "user_id": user_id,
            "namespace": namespace,
            "memories": memories
        }

        with open(filename, 'w', encoding='utf-8') as f:
            json.dump(export_data, f, indent=2, ensure_ascii=False)

        print(f"Exported {len(memories)} memories to {filename}")

    async def import_from_file(self, filename: str, batch_size: int = 100) -> int:
        """Import memories from JSON file."""
        with open(filename, 'r', encoding='utf-8') as f:
            export_data = json.load(f)

        memories = export_data.get("memories", [])
        print(f"Found {len(memories)} memories in {filename}")

        return await self.import_memories(memories, batch_size)