LightRAG/lightrag/tools/migrate_llm_cache.py

#!/usr/bin/env python3
"""
LLM Cache Migration Tool for LightRAG

This tool migrates LLM response cache (default:extract:* and default:summary:*)
between different KV storage implementations while preserving workspace isolation.

Usage:
    python -m lightrag.tools.migrate_llm_cache
    # or
    python lightrag/tools/migrate_llm_cache.py

Supported KV Storage Types:
    - JsonKVStorage
    - RedisKVStorage
    - PGKVStorage
    - MongoKVStorage
"""

import asyncio
import os
import sys
import time
from typing import Any, Dict, List
from dataclasses import dataclass, field
from dotenv import load_dotenv

# Add project root to path for imports
sys.path.insert(
    0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
)

from lightrag.kg import STORAGE_ENV_REQUIREMENTS
from lightrag.namespace import NameSpace
from lightrag.utils import setup_logger

# Load environment variables
# use the .env that is inside the current folder
# allows to use different .env file for each lightrag instance
# the OS environment variables take precedence over the .env file
load_dotenv(dotenv_path=".env", override=False)

# Setup logger
setup_logger("lightrag", level="INFO")

# Storage type configurations
STORAGE_TYPES = {
    "1": "JsonKVStorage",
    "2": "RedisKVStorage",
    "3": "PGKVStorage",
    "4": "MongoKVStorage",
}

# Workspace environment variable mapping
WORKSPACE_ENV_MAP = {
    "PGKVStorage": "POSTGRES_WORKSPACE",
    "MongoKVStorage": "MONGODB_WORKSPACE",
    "RedisKVStorage": "REDIS_WORKSPACE",
}

# Default batch size for migration
DEFAULT_BATCH_SIZE = 1000


# Default count batch size for efficient counting
DEFAULT_COUNT_BATCH_SIZE = 1000

# ANSI color codes for terminal output
BOLD_CYAN = "\033[1;36m"
RESET = "\033[0m"


@dataclass
class MigrationStats:
    """Migration statistics and error tracking"""

    total_source_records: int = 0
    total_batches: int = 0
    successful_batches: int = 0
    failed_batches: int = 0
    successful_records: int = 0
    failed_records: int = 0
    errors: List[Dict[str, Any]] = field(default_factory=list)

    def add_error(self, batch_idx: int, error: Exception, batch_size: int):
        """Record batch error"""
        self.errors.append(
            {
                "batch": batch_idx,
                "error_type": type(error).__name__,
                "error_msg": str(error),
                "records_lost": batch_size,
                "timestamp": time.time(),
            }
        )
        self.failed_batches += 1
        self.failed_records += batch_size


class MigrationTool:
    """LLM Cache Migration Tool"""

    def __init__(self):
        self.source_storage = None
        self.target_storage = None
        self.source_workspace = ""
        self.target_workspace = ""
        self.batch_size = DEFAULT_BATCH_SIZE

    def get_workspace_for_storage(self, storage_name: str) -> str:
        """Get workspace for a specific storage type

        Priority: Storage-specific env var > WORKSPACE env var > empty string

        Args:
            storage_name: Storage implementation name

        Returns:
            Workspace name
        """
        # Check storage-specific workspace
        if storage_name in WORKSPACE_ENV_MAP:
            specific_workspace = os.getenv(WORKSPACE_ENV_MAP[storage_name])
            if specific_workspace:
                return specific_workspace

        # Check generic WORKSPACE
        workspace = os.getenv("WORKSPACE", "")
        return workspace

    def check_env_vars(self, storage_name: str) -> bool:
        """Check if all required environment variables exist

        Args:
            storage_name: Storage implementation name

        Returns:
            True if all required env vars exist, False otherwise
        """
        required_vars = STORAGE_ENV_REQUIREMENTS.get(storage_name, [])
        missing_vars = [var for var in required_vars if var not in os.environ]

        if missing_vars:
            print(
                f"✗ Missing required environment variables: {', '.join(missing_vars)}"
            )
            return False

        print("✓ All required environment variables are set")
        return True

    def get_storage_class(self, storage_name: str):
        """Dynamically import and return storage class

        Args:
            storage_name: Storage implementation name

        Returns:
            Storage class
        """
        if storage_name == "JsonKVStorage":
            from lightrag.kg.json_kv_impl import JsonKVStorage

            return JsonKVStorage
        elif storage_name == "RedisKVStorage":
            from lightrag.kg.redis_impl import RedisKVStorage

            return RedisKVStorage
        elif storage_name == "PGKVStorage":
            from lightrag.kg.postgres_impl import PGKVStorage

            return PGKVStorage
        elif storage_name == "MongoKVStorage":
            from lightrag.kg.mongo_impl import MongoKVStorage

            return MongoKVStorage
        else:
            raise ValueError(f"Unsupported storage type: {storage_name}")

    async def initialize_storage(self, storage_name: str, workspace: str):
        """Initialize storage instance

        Args:
            storage_name: Storage implementation name
            workspace: Workspace name

        Returns:
            Initialized storage instance
        """
        storage_class = self.get_storage_class(storage_name)

        # Create global config
        global_config = {
            "working_dir": os.getenv("WORKING_DIR", "./rag_storage"),
            "embedding_batch_num": 10,
        }

        # Initialize storage
        storage = storage_class(
            namespace=NameSpace.KV_STORE_LLM_RESPONSE_CACHE,
            workspace=workspace,
            global_config=global_config,
            embedding_func=None,
        )

        # Initialize the storage
        await storage.initialize()

        return storage

    async def get_default_caches_json(self, storage) -> Dict[str, Any]:
        """Get default caches from JsonKVStorage

        Args:
            storage: JsonKVStorage instance

        Returns:
            Dictionary of cache entries with default:extract:* or default:summary:* keys
        """
        # Access _data directly - it's a dict from shared_storage
        async with storage._storage_lock:
            filtered = {}
            for key, value in storage._data.items():
                if key.startswith("default:extract:") or key.startswith(
                    "default:summary:"
                ):
                    filtered[key] = value
            return filtered

    async def get_default_caches_redis(
        self, storage, batch_size: int = 1000
    ) -> Dict[str, Any]:
        """Get default caches from RedisKVStorage with pagination

        Args:
            storage: RedisKVStorage instance
            batch_size: Number of keys to process per batch

        Returns:
            Dictionary of cache entries with default:extract:* or default:summary:* keys
        """
        import json

        cache_data = {}

        # Use _get_redis_connection() context manager
        async with storage._get_redis_connection() as redis:
            for pattern in ["default:extract:*", "default:summary:*"]:
                # Add namespace prefix to pattern
                prefixed_pattern = f"{storage.final_namespace}:{pattern}"
                cursor = 0

                while True:
                    # SCAN already implements cursor-based pagination
                    cursor, keys = await redis.scan(
                        cursor, match=prefixed_pattern, count=batch_size
                    )

                    if keys:
                        # Process this batch using pipeline with error handling
                        try:
                            pipe = redis.pipeline()
                            for key in keys:
                                pipe.get(key)
                            values = await pipe.execute()

                            for key, value in zip(keys, values):
                                if value:
                                    key_str = (
                                        key.decode() if isinstance(key, bytes) else key
                                    )
                                    # Remove namespace prefix to get original key
                                    original_key = key_str.replace(
                                        f"{storage.final_namespace}:", "", 1
                                    )
                                    cache_data[original_key] = json.loads(value)

                        except Exception as e:
                            # Pipeline execution failed, fall back to individual gets
                            print(
                                f"⚠️  Pipeline execution failed for batch, using individual gets: {e}"
                            )
                            for key in keys:
                                try:
                                    value = await redis.get(key)
                                    if value:
                                        key_str = (
                                            key.decode()
                                            if isinstance(key, bytes)
                                            else key
                                        )
                                        original_key = key_str.replace(
                                            f"{storage.final_namespace}:", "", 1
                                        )
                                        cache_data[original_key] = json.loads(value)
                                except Exception as individual_error:
                                    print(
                                        f"⚠️  Failed to get individual key {key}: {individual_error}"
                                    )
                                    continue

                    if cursor == 0:
                        break

                    # Yield control periodically to avoid blocking
                    await asyncio.sleep(0)

        return cache_data

    async def get_default_caches_pg(
        self, storage, batch_size: int = 1000
    ) -> Dict[str, Any]:
        """Get default caches from PGKVStorage with pagination

        Args:
            storage: PGKVStorage instance
            batch_size: Number of records to fetch per batch

        Returns:
            Dictionary of cache entries with default:extract:* or default:summary:* keys
        """
        from lightrag.kg.postgres_impl import namespace_to_table_name

        cache_data = {}
        table_name = namespace_to_table_name(storage.namespace)
        offset = 0

        while True:
            # Use LIMIT and OFFSET for pagination
            query = f"""
                SELECT id as key, original_prompt, return_value, chunk_id, cache_type, queryparam,
                       EXTRACT(EPOCH FROM create_time)::BIGINT as create_time,
                       EXTRACT(EPOCH FROM update_time)::BIGINT as update_time
                FROM {table_name}
                WHERE workspace = $1
                AND (id LIKE 'default:extract:%' OR id LIKE 'default:summary:%')
                ORDER BY id
                LIMIT $2 OFFSET $3
            """

            results = await storage.db.query(
                query, [storage.workspace, batch_size, offset], multirows=True
            )

            if not results:
                break

            for row in results:
                # Map PostgreSQL fields to cache format
                cache_entry = {
                    "return": row.get("return_value", ""),
                    "cache_type": row.get("cache_type"),
                    "original_prompt": row.get("original_prompt", ""),
                    "chunk_id": row.get("chunk_id"),
                    "queryparam": row.get("queryparam"),
                    "create_time": row.get("create_time", 0),
                    "update_time": row.get("update_time", 0),
                }
                cache_data[row["key"]] = cache_entry

            # If we got fewer results than batch_size, we're done
            if len(results) < batch_size:
                break

            offset += batch_size

            # Yield control periodically
            await asyncio.sleep(0)

        return cache_data

    async def get_default_caches_mongo(
        self, storage, batch_size: int = 1000
    ) -> Dict[str, Any]:
        """Get default caches from MongoKVStorage with cursor-based pagination

        Args:
            storage: MongoKVStorage instance
            batch_size: Number of documents to process per batch

        Returns:
            Dictionary of cache entries with default:extract:* or default:summary:* keys
        """
        cache_data = {}

        # MongoDB query with regex - use _data not collection
        query = {"_id": {"$regex": "^default:(extract|summary):"}}

        # Use cursor without to_list() - process in batches
        cursor = storage._data.find(query).batch_size(batch_size)

        async for doc in cursor:
            # Process each document as it comes
            doc_copy = doc.copy()
            key = doc_copy.pop("_id")

            # Filter ALL MongoDB/database-specific fields
            # Following .clinerules: "Always filter deprecated/incompatible fields during deserialization"
            for field_name in ["namespace", "workspace", "_id", "content"]:
                doc_copy.pop(field_name, None)

            cache_data[key] = doc_copy

            # Periodically yield control (every batch_size documents)
            if len(cache_data) % batch_size == 0:
                await asyncio.sleep(0)

        return cache_data

    async def get_default_caches(self, storage, storage_name: str) -> Dict[str, Any]:
        """Get default caches from any storage type

        Args:
            storage: Storage instance
            storage_name: Storage type name

        Returns:
            Dictionary of cache entries
        """
        if storage_name == "JsonKVStorage":
            return await self.get_default_caches_json(storage)
        elif storage_name == "RedisKVStorage":
            return await self.get_default_caches_redis(storage)
        elif storage_name == "PGKVStorage":
            return await self.get_default_caches_pg(storage)
        elif storage_name == "MongoKVStorage":
            return await self.get_default_caches_mongo(storage)
        else:
            raise ValueError(f"Unsupported storage type: {storage_name}")

    async def count_default_caches_json(self, storage) -> int:
        """Count default caches in JsonKVStorage - O(N) but very fast in-memory

        Args:
            storage: JsonKVStorage instance

        Returns:
            Total count of cache records
        """
        async with storage._storage_lock:
            return sum(
                1
                for key in storage._data.keys()
                if key.startswith("default:extract:")
                or key.startswith("default:summary:")
            )

    async def count_default_caches_redis(self, storage) -> int:
        """Count default caches in RedisKVStorage using SCAN with progress display

        Args:
            storage: RedisKVStorage instance

        Returns:
            Total count of cache records
        """
        count = 0
        print("Scanning Redis keys...", end="", flush=True)

        async with storage._get_redis_connection() as redis:
            for pattern in ["default:extract:*", "default:summary:*"]:
                prefixed_pattern = f"{storage.final_namespace}:{pattern}"
                cursor = 0
                while True:
                    cursor, keys = await redis.scan(
                        cursor, match=prefixed_pattern, count=DEFAULT_COUNT_BATCH_SIZE
                    )
                    count += len(keys)

                    # Show progress
                    print(
                        f"\rScanning Redis keys... found {count:,} records",
                        end="",
                        flush=True,
                    )

                    if cursor == 0:
                        break

        print()  # New line after progress
        return count

    async def count_default_caches_pg(self, storage) -> int:
        """Count default caches in PostgreSQL using COUNT(*) with progress indicator

        Args:
            storage: PGKVStorage instance

        Returns:
            Total count of cache records
        """
        from lightrag.kg.postgres_impl import namespace_to_table_name

        table_name = namespace_to_table_name(storage.namespace)

        query = f"""
            SELECT COUNT(*) as count
            FROM {table_name}
            WHERE workspace = $1
            AND (id LIKE 'default:extract:%' OR id LIKE 'default:summary:%')
        """

        print("Counting PostgreSQL records...", end="", flush=True)
        start_time = time.time()

        result = await storage.db.query(query, [storage.workspace])

        elapsed = time.time() - start_time
        if elapsed > 1:
            print(f" (took {elapsed:.1f}s)", end="")
        print()  # New line

        return result["count"] if result else 0

    async def count_default_caches_mongo(self, storage) -> int:
        """Count default caches in MongoDB using count_documents with progress indicator

        Args:
            storage: MongoKVStorage instance

        Returns:
            Total count of cache records
        """
        query = {"_id": {"$regex": "^default:(extract|summary):"}}

        print("Counting MongoDB documents...", end="", flush=True)
        start_time = time.time()

        count = await storage._data.count_documents(query)

        elapsed = time.time() - start_time
        if elapsed > 1:
            print(f" (took {elapsed:.1f}s)", end="")
        print()  # New line

        return count

    async def count_default_caches(self, storage, storage_name: str) -> int:
        """Count default caches from any storage type efficiently

        Args:
            storage: Storage instance
            storage_name: Storage type name

        Returns:
            Total count of cache records
        """
        if storage_name == "JsonKVStorage":
            return await self.count_default_caches_json(storage)
        elif storage_name == "RedisKVStorage":
            return await self.count_default_caches_redis(storage)
        elif storage_name == "PGKVStorage":
            return await self.count_default_caches_pg(storage)
        elif storage_name == "MongoKVStorage":
            return await self.count_default_caches_mongo(storage)
        else:
            raise ValueError(f"Unsupported storage type: {storage_name}")

    async def stream_default_caches_json(self, storage, batch_size: int):
        """Stream default caches from JsonKVStorage - yields batches

        Args:
            storage: JsonKVStorage instance
            batch_size: Size of each batch to yield

        Yields:
            Dictionary batches of cache entries

        Note:
            This method creates a snapshot of matching items while holding the lock,
            then releases the lock before yielding batches. This prevents deadlock
            when the target storage (also JsonKVStorage) tries to acquire the same
            lock during upsert operations.
        """
        # Create a snapshot of matching items while holding the lock
        async with storage._storage_lock:
            matching_items = [
                (key, value)
                for key, value in storage._data.items()
                if key.startswith("default:extract:")
                or key.startswith("default:summary:")
            ]

        # Now iterate over snapshot without holding lock
        batch = {}
        for key, value in matching_items:
            batch[key] = value
            if len(batch) >= batch_size:
                yield batch
                batch = {}

        # Yield remaining items
        if batch:
            yield batch

    async def stream_default_caches_redis(self, storage, batch_size: int):
        """Stream default caches from RedisKVStorage - yields batches

        Args:
            storage: RedisKVStorage instance
            batch_size: Size of each batch to yield

        Yields:
            Dictionary batches of cache entries
        """
        import json

        async with storage._get_redis_connection() as redis:
            for pattern in ["default:extract:*", "default:summary:*"]:
                prefixed_pattern = f"{storage.final_namespace}:{pattern}"
                cursor = 0

                while True:
                    cursor, keys = await redis.scan(
                        cursor, match=prefixed_pattern, count=batch_size
                    )

                    if keys:
                        try:
                            pipe = redis.pipeline()
                            for key in keys:
                                pipe.get(key)
                            values = await pipe.execute()

                            batch = {}
                            for key, value in zip(keys, values):
                                if value:
                                    key_str = (
                                        key.decode() if isinstance(key, bytes) else key
                                    )
                                    original_key = key_str.replace(
                                        f"{storage.final_namespace}:", "", 1
                                    )
                                    batch[original_key] = json.loads(value)

                            if batch:
                                yield batch

                        except Exception as e:
                            print(f"⚠️  Pipeline execution failed for batch: {e}")
                            # Fall back to individual gets
                            batch = {}
                            for key in keys:
                                try:
                                    value = await redis.get(key)
                                    if value:
                                        key_str = (
                                            key.decode()
                                            if isinstance(key, bytes)
                                            else key
                                        )
                                        original_key = key_str.replace(
                                            f"{storage.final_namespace}:", "", 1
                                        )
                                        batch[original_key] = json.loads(value)
                                except Exception as individual_error:
                                    print(
                                        f"⚠️  Failed to get individual key {key}: {individual_error}"
                                    )
                                    continue

                            if batch:
                                yield batch

                    if cursor == 0:
                        break

                    await asyncio.sleep(0)

    async def stream_default_caches_pg(self, storage, batch_size: int):
        """Stream default caches from PostgreSQL - yields batches

        Args:
            storage: PGKVStorage instance
            batch_size: Size of each batch to yield

        Yields:
            Dictionary batches of cache entries
        """
        from lightrag.kg.postgres_impl import namespace_to_table_name

        table_name = namespace_to_table_name(storage.namespace)
        offset = 0

        while True:
            query = f"""
                SELECT id as key, original_prompt, return_value, chunk_id, cache_type, queryparam,
                       EXTRACT(EPOCH FROM create_time)::BIGINT as create_time,
                       EXTRACT(EPOCH FROM update_time)::BIGINT as update_time
                FROM {table_name}
                WHERE workspace = $1
                AND (id LIKE 'default:extract:%' OR id LIKE 'default:summary:%')
                ORDER BY id
                LIMIT $2 OFFSET $3
            """

            results = await storage.db.query(
                query, [storage.workspace, batch_size, offset], multirows=True
            )

            if not results:
                break

            batch = {}
            for row in results:
                cache_entry = {
                    "return": row.get("return_value", ""),
                    "cache_type": row.get("cache_type"),
                    "original_prompt": row.get("original_prompt", ""),
                    "chunk_id": row.get("chunk_id"),
                    "queryparam": row.get("queryparam"),
                    "create_time": row.get("create_time", 0),
                    "update_time": row.get("update_time", 0),
                }
                batch[row["key"]] = cache_entry

            if batch:
                yield batch

            if len(results) < batch_size:
                break

            offset += batch_size
            await asyncio.sleep(0)

    async def stream_default_caches_mongo(self, storage, batch_size: int):
        """Stream default caches from MongoDB - yields batches

        Args:
            storage: MongoKVStorage instance
            batch_size: Size of each batch to yield

        Yields:
            Dictionary batches of cache entries
        """
        query = {"_id": {"$regex": "^default:(extract|summary):"}}
        cursor = storage._data.find(query).batch_size(batch_size)

        batch = {}
        async for doc in cursor:
            doc_copy = doc.copy()
            key = doc_copy.pop("_id")

            # Filter MongoDB/database-specific fields
            for field_name in ["namespace", "workspace", "_id", "content"]:
                doc_copy.pop(field_name, None)

            batch[key] = doc_copy

            if len(batch) >= batch_size:
                yield batch
                batch = {}

        # Yield remaining items
        if batch:
            yield batch

    async def stream_default_caches(
        self, storage, storage_name: str, batch_size: int = None
    ):
        """Stream default caches from any storage type - unified interface

        Args:
            storage: Storage instance
            storage_name: Storage type name
            batch_size: Size of each batch to yield (defaults to self.batch_size)

        Yields:
            Dictionary batches of cache entries
        """
        if batch_size is None:
            batch_size = self.batch_size

        if storage_name == "JsonKVStorage":
            async for batch in self.stream_default_caches_json(storage, batch_size):
                yield batch
        elif storage_name == "RedisKVStorage":
            async for batch in self.stream_default_caches_redis(storage, batch_size):
                yield batch
        elif storage_name == "PGKVStorage":
            async for batch in self.stream_default_caches_pg(storage, batch_size):
                yield batch
        elif storage_name == "MongoKVStorage":
            async for batch in self.stream_default_caches_mongo(storage, batch_size):
                yield batch
        else:
            raise ValueError(f"Unsupported storage type: {storage_name}")

    async def count_cache_types(self, cache_data: Dict[str, Any]) -> Dict[str, int]:
        """Count cache entries by type

        Args:
            cache_data: Dictionary of cache entries

        Returns:
            Dictionary with counts for each cache type
        """
        counts = {
            "extract": 0,
            "summary": 0,
        }

        for key in cache_data.keys():
            if key.startswith("default:extract:"):
                counts["extract"] += 1
            elif key.startswith("default:summary:"):
                counts["summary"] += 1

        return counts

    def print_header(self):
        """Print tool header"""
        print("\n" + "=" * 50)
        print("LLM Cache Migration Tool - LightRAG")
        print("=" * 50)

    def print_storage_types(self):
        """Print available storage types"""
        print("\nSupported KV Storage Types:")
        for key, value in STORAGE_TYPES.items():
            print(f"[{key}] {value}")

    def format_workspace(self, workspace: str) -> str:
        """Format workspace name with highlighting

        Args:
            workspace: Workspace name (may be empty)

        Returns:
            Formatted workspace string with ANSI color codes
        """
        if workspace:
            return f"{BOLD_CYAN}{workspace}{RESET}"
        else:
            return f"{BOLD_CYAN}(default){RESET}"

    def format_storage_name(self, storage_name: str) -> str:
        """Format storage type name with highlighting

        Args:
            storage_name: Storage type name

        Returns:
            Formatted storage name string with ANSI color codes
        """
        return f"{BOLD_CYAN}{storage_name}{RESET}"

    async def setup_storage(
        self,
        storage_type: str,
        use_streaming: bool = False,
        exclude_storage_name: str = None,
    ) -> tuple:
        """Setup and initialize storage

        Args:
            storage_type: Type label (source/target)
            use_streaming: If True, only count records without loading. If False, load all data (legacy mode)
            exclude_storage_name: Storage type to exclude from selection (e.g., to prevent selecting same as source)

        Returns:
            Tuple of (storage_instance, storage_name, workspace, total_count)
            Returns (None, None, None, 0) if user chooses to exit
        """
        print(f"\n=== {storage_type} Storage Setup ===")

        # Filter and remap available storage types if exclusion is specified
        if exclude_storage_name:
            # Get available storage types (excluding source)
            available_list = [
                (k, v) for k, v in STORAGE_TYPES.items() if v != exclude_storage_name
            ]

            # Remap to sequential numbering (1, 2, 3...)
            remapped_types = {
                str(i + 1): name for i, (_, name) in enumerate(available_list)
            }

            # Print available types with new sequential numbers
            print(
                f"\nAvailable Storage Types for Target (source: {exclude_storage_name} excluded):"
            )
            for key, value in remapped_types.items():
                print(f"[{key}] {value}")

            available_types = remapped_types
        else:
            # For source storage, use original numbering
            available_types = STORAGE_TYPES.copy()
            self.print_storage_types()

        # Generate dynamic prompt based on number of options
        num_options = len(available_types)
        if num_options == 1:
            prompt_range = "1"
        else:
            prompt_range = f"1-{num_options}"

        # Custom input handling with exit support
        while True:
            choice = input(
                f"\nSelect {storage_type} storage type ({prompt_range}) (Press Enter or 0 to exit): "
            ).strip()

            # Check for exit
            if choice == "" or choice == "0":
                print("\n✓ Migration cancelled by user")
                return None, None, None, 0

            # Check if choice is valid
            if choice in available_types:
                break

            print(
                f"✗ Invalid choice. Please enter one of: {', '.join(available_types.keys())}"
            )

        storage_name = available_types[choice]

        # Check environment variables
        print("\nChecking environment variables...")
        if not self.check_env_vars(storage_name):
            return None, None, None, 0

        # Get workspace
        workspace = self.get_workspace_for_storage(storage_name)

        # Initialize storage
        print(f"\nInitializing {storage_type} storage...")
        try:
            storage = await self.initialize_storage(storage_name, workspace)
            print(f"- Storage Type: {storage_name}")
            print(f"- Workspace: {workspace if workspace else '(default)'}")
            print("- Connection Status: ✓ Success")
        except Exception as e:
            print(f"✗ Initialization failed: {e}")
            return None, None, None, 0

        # Count cache records efficiently
        print(f"\n{'Counting' if use_streaming else 'Loading'} cache records...")
        try:
            if use_streaming:
                # Use efficient counting without loading data
                total_count = await self.count_default_caches(storage, storage_name)
                print(f"- Total: {total_count:,} records")
            else:
                # Legacy mode: load all data
                cache_data = await self.get_default_caches(storage, storage_name)
                counts = await self.count_cache_types(cache_data)
                total_count = len(cache_data)

                print(f"- default:extract: {counts['extract']:,} records")
                print(f"- default:summary: {counts['summary']:,} records")
                print(f"- Total: {total_count:,} records")
        except Exception as e:
            print(f"✗ {'Counting' if use_streaming else 'Loading'} failed: {e}")
            return None, None, None, 0

        return storage, storage_name, workspace, total_count

    async def migrate_caches(
        self, source_data: Dict[str, Any], target_storage, target_storage_name: str
    ) -> MigrationStats:
        """Migrate caches in batches with error tracking (Legacy mode - loads all data)

        Args:
            source_data: Source cache data
            target_storage: Target storage instance
            target_storage_name: Target storage type name

        Returns:
            MigrationStats object with migration results and errors
        """
        stats = MigrationStats()
        stats.total_source_records = len(source_data)

        if stats.total_source_records == 0:
            print("\nNo records to migrate")
            return stats

        # Convert to list for batching
        items = list(source_data.items())
        stats.total_batches = (
            stats.total_source_records + self.batch_size - 1
        ) // self.batch_size

        print("\n=== Starting Migration ===")

        for batch_idx in range(stats.total_batches):
            start_idx = batch_idx * self.batch_size
            end_idx = min((batch_idx + 1) * self.batch_size, stats.total_source_records)
            batch_items = items[start_idx:end_idx]
            batch_data = dict(batch_items)

            # Determine current cache type for display
            current_key = batch_items[0][0]
            cache_type = "extract" if "extract" in current_key else "summary"

            try:
                # Attempt to write batch
                await target_storage.upsert(batch_data)

                # Success - update stats
                stats.successful_batches += 1
                stats.successful_records += len(batch_data)

                # Calculate progress
                progress = (end_idx / stats.total_source_records) * 100
                bar_length = 20
                filled_length = int(bar_length * end_idx // stats.total_source_records)
                bar = "█" * filled_length + "░" * (bar_length - filled_length)

                print(
                    f"Batch {batch_idx + 1}/{stats.total_batches}: {bar} "
                    f"{end_idx:,}/{stats.total_source_records:,} ({progress:.0f}%) - "
                    f"default:{cache_type} ✓"
                )

            except Exception as e:
                # Error - record and continue
                stats.add_error(batch_idx + 1, e, len(batch_data))

                print(
                    f"Batch {batch_idx + 1}/{stats.total_batches}: ✗ FAILED - "
                    f"{type(e).__name__}: {str(e)}"
                )

        # Final persist
        print("\nPersisting data to disk...")
        try:
            await target_storage.index_done_callback()
            print("✓ Data persisted successfully")
        except Exception as e:
            print(f"✗ Persist failed: {e}")
            stats.add_error(0, e, 0)  # batch 0 = persist error

        return stats

    async def migrate_caches_streaming(
        self,
        source_storage,
        source_storage_name: str,
        target_storage,
        target_storage_name: str,
        total_records: int,
    ) -> MigrationStats:
        """Migrate caches using streaming approach - minimal memory footprint

        Args:
            source_storage: Source storage instance
            source_storage_name: Source storage type name
            target_storage: Target storage instance
            target_storage_name: Target storage type name
            total_records: Total number of records to migrate

        Returns:
            MigrationStats object with migration results and errors
        """
        stats = MigrationStats()
        stats.total_source_records = total_records

        if stats.total_source_records == 0:
            print("\nNo records to migrate")
            return stats

        # Calculate total batches
        stats.total_batches = (total_records + self.batch_size - 1) // self.batch_size

        print("\n=== Starting Streaming Migration ===")
        print(
            f"💡 Memory-optimized mode: Processing {self.batch_size:,} records at a time\n"
        )

        batch_idx = 0

        # Stream batches from source and write to target immediately
        async for batch in self.stream_default_caches(
            source_storage, source_storage_name
        ):
            batch_idx += 1

            # Determine current cache type for display
            if batch:
                first_key = next(iter(batch.keys()))
                cache_type = "extract" if "extract" in first_key else "summary"
            else:
                cache_type = "unknown"

            try:
                # Write batch to target storage
                await target_storage.upsert(batch)

                # Success - update stats
                stats.successful_batches += 1
                stats.successful_records += len(batch)

                # Calculate progress with known total
                progress = (stats.successful_records / total_records) * 100
                bar_length = 20
                filled_length = int(
                    bar_length * stats.successful_records // total_records
                )
                bar = "█" * filled_length + "░" * (bar_length - filled_length)

                print(
                    f"Batch {batch_idx}/{stats.total_batches}: {bar} "
                    f"{stats.successful_records:,}/{total_records:,} ({progress:.1f}%) - "
                    f"default:{cache_type} ✓"
                )

            except Exception as e:
                # Error - record and continue
                stats.add_error(batch_idx, e, len(batch))

                print(
                    f"Batch {batch_idx}/{stats.total_batches}: ✗ FAILED - "
                    f"{type(e).__name__}: {str(e)}"
                )

        # Final persist
        print("\nPersisting data to disk...")
        try:
            await target_storage.index_done_callback()
            print("✓ Data persisted successfully")
        except Exception as e:
            print(f"✗ Persist failed: {e}")
            stats.add_error(0, e, 0)  # batch 0 = persist error

        return stats

    def print_migration_report(self, stats: MigrationStats):
        """Print comprehensive migration report

        Args:
            stats: MigrationStats object with migration results
        """
        print("\n" + "=" * 60)
        print("Migration Complete - Final Report")
        print("=" * 60)

        # Overall statistics
        print("\n📊 Statistics:")
        print(f"  Total source records:    {stats.total_source_records:,}")
        print(f"  Total batches:           {stats.total_batches:,}")
        print(f"  Successful batches:      {stats.successful_batches:,}")
        print(f"  Failed batches:          {stats.failed_batches:,}")
        print(f"  Successfully migrated:   {stats.successful_records:,}")
        print(f"  Failed to migrate:       {stats.failed_records:,}")

        # Success rate
        success_rate = (
            (stats.successful_records / stats.total_source_records * 100)
            if stats.total_source_records > 0
            else 0
        )
        print(f"  Success rate:            {success_rate:.2f}%")

        # Error details
        if stats.errors:
            print(f"\n⚠️  Errors encountered: {len(stats.errors)}")
            print("\nError Details:")
            print("-" * 60)

            # Group errors by type
            error_types = {}
            for error in stats.errors:
                err_type = error["error_type"]
                error_types[err_type] = error_types.get(err_type, 0) + 1

            print("\nError Summary:")
            for err_type, count in sorted(error_types.items(), key=lambda x: -x[1]):
                print(f"  - {err_type}: {count} occurrence(s)")

            print("\nFirst 5 errors:")
            for i, error in enumerate(stats.errors[:5], 1):
                print(f"\n  {i}. Batch {error['batch']}")
                print(f"     Type: {error['error_type']}")
                print(f"     Message: {error['error_msg']}")
                print(f"     Records lost: {error['records_lost']:,}")

            if len(stats.errors) > 5:
                print(f"\n  ... and {len(stats.errors) - 5} more errors")

            print("\n" + "=" * 60)
            print("⚠️  WARNING: Migration completed with errors!")
            print("   Please review the error details above.")
            print("=" * 60)
        else:
            print("\n" + "=" * 60)
            print("✓ SUCCESS: All records migrated successfully!")
            print("=" * 60)

    async def run(self):
        """Run the migration tool with streaming approach"""
        try:
            # Initialize shared storage (REQUIRED for storage classes to work)
            from lightrag.kg.shared_storage import initialize_share_data

            initialize_share_data(workers=1)

            # Print header
            self.print_header()

            # Setup source storage with streaming (only count, don't load all data)
            (
                self.source_storage,
                source_storage_name,
                self.source_workspace,
                source_count,
            ) = await self.setup_storage("Source", use_streaming=True)

            # Check if user cancelled (setup_storage returns None for all fields)
            if self.source_storage is None:
                return

            if source_count == 0:
                print("\n⚠ Source storage has no cache records to migrate")
                # Cleanup
                await self.source_storage.finalize()
                return

            # Setup target storage with streaming (only count, don't load all data)
            # Exclude source storage type from target selection
            (
                self.target_storage,
                target_storage_name,
                self.target_workspace,
                target_count,
            ) = await self.setup_storage(
                "Target", use_streaming=True, exclude_storage_name=source_storage_name
            )

            if not self.target_storage:
                print("\n✗ Target storage setup failed")
                # Cleanup source
                await self.source_storage.finalize()
                return

            # Show migration summary
            print("\n" + "=" * 50)
            print("Migration Confirmation")
            print("=" * 50)
            print(
                f"Source: {self.format_storage_name(source_storage_name)} (workspace: {self.format_workspace(self.source_workspace)}) - {source_count:,} records"
            )
            print(
                f"Target: {self.format_storage_name(target_storage_name)} (workspace: {self.format_workspace(self.target_workspace)}) - {target_count:,} records"
            )
            print(f"Batch Size: {self.batch_size:,} records/batch")
            print("Memory Mode: Streaming (memory-optimized)")

            if target_count > 0:
                print(
                    f"\n⚠️ Warning: Target storage already has {target_count:,} records"
                )
                print("Migration will overwrite records with the same keys")

            # Confirm migration
            confirm = input("\nContinue? (y/n): ").strip().lower()
            if confirm != "y":
                print("\n✗ Migration cancelled")
                # Cleanup
                await self.source_storage.finalize()
                await self.target_storage.finalize()
                return

            # Perform streaming migration with error tracking
            stats = await self.migrate_caches_streaming(
                self.source_storage,
                source_storage_name,
                self.target_storage,
                target_storage_name,
                source_count,
            )

            # Print comprehensive migration report
            self.print_migration_report(stats)

            # Cleanup
            await self.source_storage.finalize()
            await self.target_storage.finalize()

        except KeyboardInterrupt:
            print("\n\n✗ Migration interrupted by user")
        except Exception as e:
            print(f"\n✗ Migration failed: {e}")
            import traceback

            traceback.print_exc()
        finally:
            # Ensure cleanup
            if self.source_storage:
                try:
                    await self.source_storage.finalize()
                except Exception:
                    pass
            if self.target_storage:
                try:
                    await self.target_storage.finalize()
                except Exception:
                    pass

            # Finalize shared storage
            try:
                from lightrag.kg.shared_storage import finalize_share_data

                finalize_share_data()
            except Exception:
                pass


async def main():
    """Main entry point"""
    tool = MigrationTool()
    await tool.run()


if __name__ == "__main__":
    asyncio.run(main())