import asyncio
import json
import os
import re
import datetime
from datetime import timezone
from dataclasses import dataclass, field
from typing import Any, Union, final
import numpy as np
import configparser
import ssl
import itertools

from lightrag.types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge

from tenacity import (
    retry,
    retry_if_exception_type,
    stop_after_attempt,
    wait_exponential,
)

from ..base import (
    BaseGraphStorage,
    BaseKVStorage,
    BaseVectorStorage,
    DocProcessingStatus,
    DocStatus,
    DocStatusStorage,
)
from ..namespace import NameSpace, is_namespace
from ..utils import logger
from ..constants import GRAPH_FIELD_SEP
from ..kg.shared_storage import get_data_init_lock, get_graph_db_lock, get_storage_lock

import pipmaster as pm

if not pm.is_installed("asyncpg"):
    pm.install("asyncpg")

import asyncpg  # type: ignore
from asyncpg import Pool  # type: ignore

from dotenv import load_dotenv

# 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)


class PostgreSQLDB:
    def __init__(self, config: dict[str, Any], **kwargs: Any):
        self.host = config["host"]
        self.port = config["port"]
        self.user = config["user"]
        self.password = config["password"]
        self.database = config["database"]
        self.workspace = config["workspace"]
        self.max = int(config["max_connections"])
        self.increment = 1
        self.pool: Pool | None = None

        # SSL configuration
        self.ssl_mode = config.get("ssl_mode")
        self.ssl_cert = config.get("ssl_cert")
        self.ssl_key = config.get("ssl_key")
        self.ssl_root_cert = config.get("ssl_root_cert")
        self.ssl_crl = config.get("ssl_crl")

        # Vector configuration
        self.vector_index_type = config.get("vector_index_type")
        self.hnsw_m = config.get("hnsw_m")
        self.hnsw_ef = config.get("hnsw_ef")
        self.ivfflat_lists = config.get("ivfflat_lists")

        if self.user is None or self.password is None or self.database is None:
            raise ValueError("Missing database user, password, or database")

    def _create_ssl_context(self) -> ssl.SSLContext | None:
        """Create SSL context based on configuration parameters."""
        if not self.ssl_mode:
            return None

        ssl_mode = self.ssl_mode.lower()

        # For simple modes that don't require custom context
        if ssl_mode in ["disable", "allow", "prefer", "require"]:
            if ssl_mode == "disable":
                return None
            elif ssl_mode in ["require", "prefer", "allow"]:
                # Return None for simple SSL requirement, handled in initdb
                return None

        # For modes that require certificate verification
        if ssl_mode in ["verify-ca", "verify-full"]:
            try:
                context = ssl.create_default_context(ssl.Purpose.SERVER_AUTH)

                # Configure certificate verification
                if ssl_mode == "verify-ca":
                    context.check_hostname = False
                elif ssl_mode == "verify-full":
                    context.check_hostname = True

                # Load root certificate if provided
                if self.ssl_root_cert:
                    if os.path.exists(self.ssl_root_cert):
                        context.load_verify_locations(cafile=self.ssl_root_cert)
                        logger.info(
                            f"PostgreSQL, Loaded SSL root certificate: {self.ssl_root_cert}"
                        )
                    else:
                        logger.warning(
                            f"PostgreSQL, SSL root certificate file not found: {self.ssl_root_cert}"
                        )

                # Load client certificate and key if provided
                if self.ssl_cert and self.ssl_key:
                    if os.path.exists(self.ssl_cert) and os.path.exists(self.ssl_key):
                        context.load_cert_chain(self.ssl_cert, self.ssl_key)
                        logger.info(
                            f"PostgreSQL, Loaded SSL client certificate: {self.ssl_cert}"
                        )
                    else:
                        logger.warning(
                            "PostgreSQL, SSL client certificate or key file not found"
                        )

                # Load certificate revocation list if provided
                if self.ssl_crl:
                    if os.path.exists(self.ssl_crl):
                        context.load_verify_locations(crlfile=self.ssl_crl)
                        logger.info(f"PostgreSQL, Loaded SSL CRL: {self.ssl_crl}")
                    else:
                        logger.warning(
                            f"PostgreSQL, SSL CRL file not found: {self.ssl_crl}"
                        )

                return context

            except Exception as e:
                logger.error(f"PostgreSQL, Failed to create SSL context: {e}")
                raise ValueError(f"SSL configuration error: {e}")

        # Unknown SSL mode
        logger.warning(f"PostgreSQL, Unknown SSL mode: {ssl_mode}, SSL disabled")
        return None

    async def initdb(self):
        try:
            # Prepare connection parameters
            connection_params = {
                "user": self.user,
                "password": self.password,
                "database": self.database,
                "host": self.host,
                "port": self.port,
                "min_size": 1,
                "max_size": self.max,
            }

            # Add SSL configuration if provided
            ssl_context = self._create_ssl_context()
            if ssl_context is not None:
                connection_params["ssl"] = ssl_context
                logger.info("PostgreSQL, SSL configuration applied")
            elif self.ssl_mode:
                # Handle simple SSL modes without custom context
                if self.ssl_mode.lower() in ["require", "prefer"]:
                    connection_params["ssl"] = True
                elif self.ssl_mode.lower() == "disable":
                    connection_params["ssl"] = False
                logger.info(f"PostgreSQL, SSL mode set to: {self.ssl_mode}")

            self.pool = await asyncpg.create_pool(**connection_params)  # type: ignore

            # Ensure VECTOR extension is available
            async with self.pool.acquire() as connection:
                await self.configure_vector_extension(connection)

            ssl_status = "with SSL" if connection_params.get("ssl") else "without SSL"
            logger.info(
                f"PostgreSQL, Connected to database at {self.host}:{self.port}/{self.database} {ssl_status}"
            )
        except Exception as e:
            logger.error(
                f"PostgreSQL, Failed to connect database at {self.host}:{self.port}/{self.database}, Got:{e}"
            )
            raise

    @staticmethod
    async def configure_vector_extension(connection: asyncpg.Connection) -> None:
        """Create VECTOR extension if it doesn't exist for vector similarity operations."""
        try:
            await connection.execute("CREATE EXTENSION IF NOT EXISTS vector")  # type: ignore
            logger.info("VECTOR extension ensured for PostgreSQL")
        except Exception as e:
            logger.warning(f"Could not create VECTOR extension: {e}")
            # Don't raise - let the system continue without vector extension

    @staticmethod
    async def configure_age_extension(connection: asyncpg.Connection) -> None:
        """Create AGE extension if it doesn't exist for graph operations."""
        try:
            await connection.execute("CREATE EXTENSION IF NOT EXISTS age")  # type: ignore
            logger.info("AGE extension ensured for PostgreSQL")
        except Exception as e:
            logger.warning(f"Could not create AGE extension: {e}")
            # Don't raise - let the system continue without AGE extension

    @staticmethod
    async def configure_age(connection: asyncpg.Connection, graph_name: str) -> None:
        """Set the Apache AGE environment and creates a graph if it does not exist.

        This method:
        - Sets the PostgreSQL `search_path` to include `ag_catalog`, ensuring that Apache AGE functions can be used without specifying the schema.
        - Attempts to create a new graph with the provided `graph_name` if it does not already exist.
        - Silently ignores errors related to the graph already existing.

        """
        try:
            await connection.execute(  # type: ignore
                'SET search_path = ag_catalog, "$user", public'
            )
            await connection.execute(  # type: ignore
                f"select create_graph('{graph_name}')"
            )
        except (
            asyncpg.exceptions.InvalidSchemaNameError,
            asyncpg.exceptions.UniqueViolationError,
        ):
            pass

    async def _migrate_llm_cache_schema(self):
        """Migrate LLM cache schema: add new columns and remove deprecated mode field"""
        try:
            # Check if all columns exist
            check_columns_sql = """
            SELECT column_name
            FROM information_schema.columns
            WHERE table_name = 'lightrag_llm_cache'
            AND column_name IN ('chunk_id', 'cache_type', 'queryparam', 'mode')
            """

            existing_columns = await self.query(check_columns_sql, multirows=True)
            existing_column_names = (
                {col["column_name"] for col in existing_columns}
                if existing_columns
                else set()
            )

            # Add missing chunk_id column
            if "chunk_id" not in existing_column_names:
                logger.info("Adding chunk_id column to LIGHTRAG_LLM_CACHE table")
                add_chunk_id_sql = """
                ALTER TABLE LIGHTRAG_LLM_CACHE
                ADD COLUMN chunk_id VARCHAR(255) NULL
                """
                await self.execute(add_chunk_id_sql)
                logger.info(
                    "Successfully added chunk_id column to LIGHTRAG_LLM_CACHE table"
                )
            else:
                logger.info(
                    "chunk_id column already exists in LIGHTRAG_LLM_CACHE table"
                )

            # Add missing cache_type column
            if "cache_type" not in existing_column_names:
                logger.info("Adding cache_type column to LIGHTRAG_LLM_CACHE table")
                add_cache_type_sql = """
                ALTER TABLE LIGHTRAG_LLM_CACHE
                ADD COLUMN cache_type VARCHAR(32) NULL
                """
                await self.execute(add_cache_type_sql)
                logger.info(
                    "Successfully added cache_type column to LIGHTRAG_LLM_CACHE table"
                )

                # Migrate existing data using optimized regex pattern
                logger.info(
                    "Migrating existing LLM cache data to populate cache_type field (optimized)"
                )
                optimized_update_sql = """
                UPDATE LIGHTRAG_LLM_CACHE
                SET cache_type = CASE
                    WHEN id ~ '^[^:]+:[^:]+:' THEN split_part(id, ':', 2)
                    ELSE 'extract'
                END
                WHERE cache_type IS NULL
                """
                await self.execute(optimized_update_sql)
                logger.info("Successfully migrated existing LLM cache data")
            else:
                logger.info(
                    "cache_type column already exists in LIGHTRAG_LLM_CACHE table"
                )

            # Add missing queryparam column
            if "queryparam" not in existing_column_names:
                logger.info("Adding queryparam column to LIGHTRAG_LLM_CACHE table")
                add_queryparam_sql = """
                ALTER TABLE LIGHTRAG_LLM_CACHE
                ADD COLUMN queryparam JSONB NULL
                """
                await self.execute(add_queryparam_sql)
                logger.info(
                    "Successfully added queryparam column to LIGHTRAG_LLM_CACHE table"
                )
            else:
                logger.info(
                    "queryparam column already exists in LIGHTRAG_LLM_CACHE table"
                )

            # Remove deprecated mode field if it exists
            if "mode" in existing_column_names:
                logger.info(
                    "Removing deprecated mode column from LIGHTRAG_LLM_CACHE table"
                )

                # First, drop the primary key constraint that includes mode
                drop_pk_sql = """
                ALTER TABLE LIGHTRAG_LLM_CACHE
                DROP CONSTRAINT IF EXISTS LIGHTRAG_LLM_CACHE_PK
                """
                await self.execute(drop_pk_sql)
                logger.info("Dropped old primary key constraint")

                # Drop the mode column
                drop_mode_sql = """
                ALTER TABLE LIGHTRAG_LLM_CACHE
                DROP COLUMN mode
                """
                await self.execute(drop_mode_sql)
                logger.info(
                    "Successfully removed mode column from LIGHTRAG_LLM_CACHE table"
                )

                # Create new primary key constraint without mode
                add_pk_sql = """
                ALTER TABLE LIGHTRAG_LLM_CACHE
                ADD CONSTRAINT LIGHTRAG_LLM_CACHE_PK PRIMARY KEY (workspace, id)
                """
                await self.execute(add_pk_sql)
                logger.info("Created new primary key constraint (workspace, id)")
            else:
                logger.info("mode column does not exist in LIGHTRAG_LLM_CACHE table")

        except Exception as e:
            logger.warning(f"Failed to migrate LLM cache schema: {e}")

    async def _migrate_timestamp_columns(self):
        """Migrate timestamp columns in tables to witimezone-free types, assuming original data is in UTC time"""
        # Tables and columns that need migration
        tables_to_migrate = {
            "LIGHTRAG_VDB_ENTITY": ["create_time", "update_time"],
            "LIGHTRAG_VDB_RELATION": ["create_time", "update_time"],
            "LIGHTRAG_DOC_CHUNKS": ["create_time", "update_time"],
            "LIGHTRAG_DOC_STATUS": ["created_at", "updated_at"],
        }

        for table_name, columns in tables_to_migrate.items():
            for column_name in columns:
                try:
                    # Check if column exists
                    check_column_sql = f"""
                    SELECT column_name, data_type
                    FROM information_schema.columns
                    WHERE table_name = '{table_name.lower()}'
                    AND column_name = '{column_name}'
                    """

                    column_info = await self.query(check_column_sql)
                    if not column_info:
                        logger.warning(
                            f"Column {table_name}.{column_name} does not exist, skipping migration"
                        )
                        continue

                    # Check column type
                    data_type = column_info.get("data_type")
                    if data_type == "timestamp without time zone":
                        logger.debug(
                            f"Column {table_name}.{column_name} is already witimezone-free, no migration needed"
                        )
                        continue

                    # Execute migration, explicitly specifying UTC timezone for interpreting original data
                    logger.info(
                        f"Migrating {table_name}.{column_name} from {data_type} to TIMESTAMP(0) type"
                    )
                    migration_sql = f"""
                    ALTER TABLE {table_name}
                    ALTER COLUMN {column_name} TYPE TIMESTAMP(0),
                    ALTER COLUMN {column_name} SET DEFAULT CURRENT_TIMESTAMP
                    """

                    await self.execute(migration_sql)
                    logger.info(
                        f"Successfully migrated {table_name}.{column_name} to timezone-free type"
                    )
                except Exception as e:
                    # Log error but don't interrupt the process
                    logger.warning(f"Failed to migrate {table_name}.{column_name}: {e}")

    async def _migrate_doc_chunks_to_vdb_chunks(self):
        """
        Migrate data from LIGHTRAG_DOC_CHUNKS to LIGHTRAG_VDB_CHUNKS if specific conditions are met.
        This migration is intended for users who are upgrading and have an older table structure
        where LIGHTRAG_DOC_CHUNKS contained a `content_vector` column.

        """
        try:
            # 1. Check if the new table LIGHTRAG_VDB_CHUNKS is empty
            vdb_chunks_count_sql = "SELECT COUNT(1) as count FROM LIGHTRAG_VDB_CHUNKS"
            vdb_chunks_count_result = await self.query(vdb_chunks_count_sql)
            if vdb_chunks_count_result and vdb_chunks_count_result["count"] > 0:
                logger.info(
                    "Skipping migration: LIGHTRAG_VDB_CHUNKS already contains data."
                )
                return

            # 2. Check if `content_vector` column exists in the old table
            check_column_sql = """
            SELECT 1 FROM information_schema.columns
            WHERE table_name = 'lightrag_doc_chunks' AND column_name = 'content_vector'
            """
            column_exists = await self.query(check_column_sql)
            if not column_exists:
                logger.info(
                    "Skipping migration: `content_vector` not found in LIGHTRAG_DOC_CHUNKS"
                )
                return

            # 3. Check if the old table LIGHTRAG_DOC_CHUNKS has data
            doc_chunks_count_sql = "SELECT COUNT(1) as count FROM LIGHTRAG_DOC_CHUNKS"
            doc_chunks_count_result = await self.query(doc_chunks_count_sql)
            if not doc_chunks_count_result or doc_chunks_count_result["count"] == 0:
                logger.info("Skipping migration: LIGHTRAG_DOC_CHUNKS is empty.")
                return

            # 4. Perform the migration
            logger.info(
                "Starting data migration from LIGHTRAG_DOC_CHUNKS to LIGHTRAG_VDB_CHUNKS..."
            )
            migration_sql = """
            INSERT INTO LIGHTRAG_VDB_CHUNKS (
                id, workspace, full_doc_id, chunk_order_index, tokens, content,
                content_vector, file_path, create_time, update_time
            )
            SELECT
                id, workspace, full_doc_id, chunk_order_index, tokens, content,
                content_vector, file_path, create_time, update_time
            FROM LIGHTRAG_DOC_CHUNKS
            ON CONFLICT (workspace, id) DO NOTHING;
            """
            await self.execute(migration_sql)
            logger.info("Data migration to LIGHTRAG_VDB_CHUNKS completed successfully.")

        except Exception as e:
            logger.error(f"Failed during data migration to LIGHTRAG_VDB_CHUNKS: {e}")
            # Do not re-raise, to allow the application to start

    async def _check_llm_cache_needs_migration(self):
        """Check if LLM cache data needs migration by examining any record with old format"""
        try:
            # Optimized query: directly check for old format records without sorting
            check_sql = """
            SELECT 1 FROM LIGHTRAG_LLM_CACHE
            WHERE id NOT LIKE '%:%'
            LIMIT 1
            """
            result = await self.query(check_sql)

            # If any old format record exists, migration is needed
            return result is not None

        except Exception as e:
            logger.warning(f"Failed to check LLM cache migration status: {e}")
            return False

    async def _migrate_llm_cache_to_flattened_keys(self):
        """Optimized version: directly execute single UPDATE migration to migrate old format cache keys to flattened format"""
        try:
            # Check if migration is needed
            check_sql = """
            SELECT COUNT(*) as count FROM LIGHTRAG_LLM_CACHE
            WHERE id NOT LIKE '%:%'
            """
            result = await self.query(check_sql)

            if not result or result["count"] == 0:
                logger.info("No old format LLM cache data found, skipping migration")
                return

            old_count = result["count"]
            logger.info(f"Found {old_count} old format cache records")

            # Check potential primary key conflicts (optional but recommended)
            conflict_check_sql = """
            WITH new_ids AS (
                SELECT
                    workspace,
                    mode,
                    id as old_id,
                    mode || ':' ||
                    CASE WHEN mode = 'default' THEN 'extract' ELSE 'unknown' END || ':' ||
                    md5(original_prompt) as new_id
                FROM LIGHTRAG_LLM_CACHE
                WHERE id NOT LIKE '%:%'
            )
            SELECT COUNT(*) as conflicts
            FROM new_ids n1
            JOIN LIGHTRAG_LLM_CACHE existing
            ON existing.workspace = n1.workspace
            AND existing.mode = n1.mode
            AND existing.id = n1.new_id
            WHERE existing.id LIKE '%:%'  -- Only check conflicts with existing new format records
            """

            conflict_result = await self.query(conflict_check_sql)
            if conflict_result and conflict_result["conflicts"] > 0:
                logger.warning(
                    f"Found {conflict_result['conflicts']} potential ID conflicts with existing records"
                )
                # Can choose to continue or abort, here we choose to continue and log warning

            # Execute single UPDATE migration
            logger.info("Starting optimized LLM cache migration...")
            migration_sql = """
            UPDATE LIGHTRAG_LLM_CACHE
            SET
                id = mode || ':' ||
                     CASE WHEN mode = 'default' THEN 'extract' ELSE 'unknown' END || ':' ||
                     md5(original_prompt),
                cache_type = CASE WHEN mode = 'default' THEN 'extract' ELSE 'unknown' END,
                update_time = CURRENT_TIMESTAMP
            WHERE id NOT LIKE '%:%'
            """

            # Execute migration
            await self.execute(migration_sql)

            # Verify migration results
            verify_sql = """
            SELECT COUNT(*) as remaining_old FROM LIGHTRAG_LLM_CACHE
            WHERE id NOT LIKE '%:%'
            """
            verify_result = await self.query(verify_sql)
            remaining = verify_result["remaining_old"] if verify_result else -1

            if remaining == 0:
                logger.info(
                    f"✅ Successfully migrated {old_count} LLM cache records to flattened format"
                )
            else:
                logger.warning(
                    f"⚠️ Migration completed but {remaining} old format records remain"
                )

        except Exception as e:
            logger.error(f"Optimized LLM cache migration failed: {e}")
            raise

    async def _migrate_doc_status_add_chunks_list(self):
        """Add chunks_list column to LIGHTRAG_DOC_STATUS table if it doesn't exist"""
        try:
            # Check if chunks_list column exists
            check_column_sql = """
            SELECT column_name
            FROM information_schema.columns
            WHERE table_name = 'lightrag_doc_status'
            AND column_name = 'chunks_list'
            """

            column_info = await self.query(check_column_sql)
            if not column_info:
                logger.info("Adding chunks_list column to LIGHTRAG_DOC_STATUS table")
                add_column_sql = """
                ALTER TABLE LIGHTRAG_DOC_STATUS
                ADD COLUMN chunks_list JSONB NULL DEFAULT '[]'::jsonb
                """
                await self.execute(add_column_sql)
                logger.info(
                    "Successfully added chunks_list column to LIGHTRAG_DOC_STATUS table"
                )
            else:
                logger.info(
                    "chunks_list column already exists in LIGHTRAG_DOC_STATUS table"
                )
        except Exception as e:
            logger.warning(
                f"Failed to add chunks_list column to LIGHTRAG_DOC_STATUS: {e}"
            )

    async def _migrate_text_chunks_add_llm_cache_list(self):
        """Add llm_cache_list column to LIGHTRAG_DOC_CHUNKS table if it doesn't exist"""
        try:
            # Check if llm_cache_list column exists
            check_column_sql = """
            SELECT column_name
            FROM information_schema.columns
            WHERE table_name = 'lightrag_doc_chunks'
            AND column_name = 'llm_cache_list'
            """

            column_info = await self.query(check_column_sql)
            if not column_info:
                logger.info("Adding llm_cache_list column to LIGHTRAG_DOC_CHUNKS table")
                add_column_sql = """
                ALTER TABLE LIGHTRAG_DOC_CHUNKS
                ADD COLUMN llm_cache_list JSONB NULL DEFAULT '[]'::jsonb
                """
                await self.execute(add_column_sql)
                logger.info(
                    "Successfully added llm_cache_list column to LIGHTRAG_DOC_CHUNKS table"
                )
            else:
                logger.info(
                    "llm_cache_list column already exists in LIGHTRAG_DOC_CHUNKS table"
                )
        except Exception as e:
            logger.warning(
                f"Failed to add llm_cache_list column to LIGHTRAG_DOC_CHUNKS: {e}"
            )

    async def _migrate_doc_status_add_track_id(self):
        """Add track_id column to LIGHTRAG_DOC_STATUS table if it doesn't exist and create index"""
        try:
            # Check if track_id column exists
            check_column_sql = """
            SELECT column_name
            FROM information_schema.columns
            WHERE table_name = 'lightrag_doc_status'
            AND column_name = 'track_id'
            """

            column_info = await self.query(check_column_sql)
            if not column_info:
                logger.info("Adding track_id column to LIGHTRAG_DOC_STATUS table")
                add_column_sql = """
                ALTER TABLE LIGHTRAG_DOC_STATUS
                ADD COLUMN track_id VARCHAR(255) NULL
                """
                await self.execute(add_column_sql)
                logger.info(
                    "Successfully added track_id column to LIGHTRAG_DOC_STATUS table"
                )
            else:
                logger.info(
                    "track_id column already exists in LIGHTRAG_DOC_STATUS table"
                )

            # Check if track_id index exists
            check_index_sql = """
            SELECT indexname
            FROM pg_indexes
            WHERE tablename = 'lightrag_doc_status'
            AND indexname = 'idx_lightrag_doc_status_track_id'
            """

            index_info = await self.query(check_index_sql)
            if not index_info:
                logger.info(
                    "Creating index on track_id column for LIGHTRAG_DOC_STATUS table"
                )
                create_index_sql = """
                CREATE INDEX idx_lightrag_doc_status_track_id ON LIGHTRAG_DOC_STATUS (track_id)
                """
                await self.execute(create_index_sql)
                logger.info(
                    "Successfully created index on track_id column for LIGHTRAG_DOC_STATUS table"
                )
            else:
                logger.info(
                    "Index on track_id column already exists for LIGHTRAG_DOC_STATUS table"
                )

        except Exception as e:
            logger.warning(
                f"Failed to add track_id column or index to LIGHTRAG_DOC_STATUS: {e}"
            )

    async def _migrate_doc_status_add_metadata_error_msg(self):
        """Add metadata and error_msg columns to LIGHTRAG_DOC_STATUS table if they don't exist"""
        try:
            # Check if metadata column exists
            check_metadata_sql = """
            SELECT column_name
            FROM information_schema.columns
            WHERE table_name = 'lightrag_doc_status'
            AND column_name = 'metadata'
            """

            metadata_info = await self.query(check_metadata_sql)
            if not metadata_info:
                logger.info("Adding metadata column to LIGHTRAG_DOC_STATUS table")
                add_metadata_sql = """
                ALTER TABLE LIGHTRAG_DOC_STATUS
                ADD COLUMN metadata JSONB NULL DEFAULT '{}'::jsonb
                """
                await self.execute(add_metadata_sql)
                logger.info(
                    "Successfully added metadata column to LIGHTRAG_DOC_STATUS table"
                )
            else:
                logger.info(
                    "metadata column already exists in LIGHTRAG_DOC_STATUS table"
                )

            # Check if error_msg column exists
            check_error_msg_sql = """
            SELECT column_name
            FROM information_schema.columns
            WHERE table_name = 'lightrag_doc_status'
            AND column_name = 'error_msg'
            """

            error_msg_info = await self.query(check_error_msg_sql)
            if not error_msg_info:
                logger.info("Adding error_msg column to LIGHTRAG_DOC_STATUS table")
                add_error_msg_sql = """
                ALTER TABLE LIGHTRAG_DOC_STATUS
                ADD COLUMN error_msg TEXT NULL
                """
                await self.execute(add_error_msg_sql)
                logger.info(
                    "Successfully added error_msg column to LIGHTRAG_DOC_STATUS table"
                )
            else:
                logger.info(
                    "error_msg column already exists in LIGHTRAG_DOC_STATUS table"
                )

        except Exception as e:
            logger.warning(
                f"Failed to add metadata/error_msg columns to LIGHTRAG_DOC_STATUS: {e}"
            )

    async def _migrate_field_lengths(self):
        """Migrate database field lengths: entity_name, source_id, target_id, and file_path"""
        # Define the field changes needed
        field_migrations = [
            {
                "table": "LIGHTRAG_VDB_ENTITY",
                "column": "entity_name",
                "old_type": "character varying(255)",
                "new_type": "VARCHAR(512)",
                "description": "entity_name from 255 to 512",
            },
            {
                "table": "LIGHTRAG_VDB_RELATION",
                "column": "source_id",
                "old_type": "character varying(256)",
                "new_type": "VARCHAR(512)",
                "description": "source_id from 256 to 512",
            },
            {
                "table": "LIGHTRAG_VDB_RELATION",
                "column": "target_id",
                "old_type": "character varying(256)",
                "new_type": "VARCHAR(512)",
                "description": "target_id from 256 to 512",
            },
            {
                "table": "LIGHTRAG_DOC_CHUNKS",
                "column": "file_path",
                "old_type": "character varying(256)",
                "new_type": "TEXT",
                "description": "file_path to TEXT NULL",
            },
            {
                "table": "LIGHTRAG_VDB_CHUNKS",
                "column": "file_path",
                "old_type": "character varying(256)",
                "new_type": "TEXT",
                "description": "file_path to TEXT NULL",
            },
        ]

        for migration in field_migrations:
            try:
                # Check current column definition
                check_column_sql = """
                SELECT column_name, data_type, character_maximum_length, is_nullable
                FROM information_schema.columns
                WHERE table_name = $1 AND column_name = $2
                """
                params = {
                    "table_name": migration["table"].lower(),
                    "column_name": migration["column"],
                }
                column_info = await self.query(
                    check_column_sql,
                    list(params.values()),
                )

                if not column_info:
                    logger.warning(
                        f"Column {migration['table']}.{migration['column']} does not exist, skipping migration"
                    )
                    continue

                current_type = column_info.get("data_type", "").lower()
                current_length = column_info.get("character_maximum_length")

                # Check if migration is needed
                needs_migration = False

                if migration["column"] == "entity_name" and current_length == 255:
                    needs_migration = True
                elif (
                    migration["column"] in ["source_id", "target_id"]
                    and current_length == 256
                ):
                    needs_migration = True
                elif (
                    migration["column"] == "file_path"
                    and current_type == "character varying"
                ):
                    needs_migration = True

                if needs_migration:
                    logger.info(
                        f"Migrating {migration['table']}.{migration['column']}: {migration['description']}"
                    )

                    # Execute the migration
                    alter_sql = f"""
                    ALTER TABLE {migration['table']}
                    ALTER COLUMN {migration['column']} TYPE {migration['new_type']}
                    """

                    await self.execute(alter_sql)
                    logger.info(
                        f"Successfully migrated {migration['table']}.{migration['column']}"
                    )
                else:
                    logger.debug(
                        f"Column {migration['table']}.{migration['column']} already has correct type, no migration needed"
                    )

            except Exception as e:
                # Log error but don't interrupt the process
                logger.warning(
                    f"Failed to migrate {migration['table']}.{migration['column']}: {e}"
                )

    async def check_tables(self):
        # First create all tables
        for k, v in TABLES.items():
            try:
                await self.query(f"SELECT 1 FROM {k} LIMIT 1")
            except Exception:
                try:
                    logger.info(f"PostgreSQL, Try Creating table {k} in database")
                    await self.execute(v["ddl"])
                    logger.info(
                        f"PostgreSQL, Creation success table {k} in PostgreSQL database"
                    )
                except Exception as e:
                    logger.error(
                        f"PostgreSQL, Failed to create table {k} in database, Please verify the connection with PostgreSQL database, Got: {e}"
                    )
                    raise e

            # Create index for id column in each table
            try:
                index_name = f"idx_{k.lower()}_id"
                check_index_sql = f"""
                SELECT 1 FROM pg_indexes
                WHERE indexname = '{index_name}'
                AND tablename = '{k.lower()}'
                """
                index_exists = await self.query(check_index_sql)

                if not index_exists:
                    create_index_sql = f"CREATE INDEX {index_name} ON {k}(id)"
                    logger.info(f"PostgreSQL, Creating index {index_name} on table {k}")
                    await self.execute(create_index_sql)
            except Exception as e:
                logger.error(
                    f"PostgreSQL, Failed to create index on table {k}, Got: {e}"
                )

            # Create composite index for (workspace, id) columns in each table
            try:
                composite_index_name = f"idx_{k.lower()}_workspace_id"
                check_composite_index_sql = f"""
                SELECT 1 FROM pg_indexes
                WHERE indexname = '{composite_index_name}'
                AND tablename = '{k.lower()}'
                """
                composite_index_exists = await self.query(check_composite_index_sql)

                if not composite_index_exists:
                    create_composite_index_sql = (
                        f"CREATE INDEX {composite_index_name} ON {k}(workspace, id)"
                    )
                    logger.info(
                        f"PostgreSQL, Creating composite index {composite_index_name} on table {k}"
                    )
                    await self.execute(create_composite_index_sql)
            except Exception as e:
                logger.error(
                    f"PostgreSQL, Failed to create composite index on table {k}, Got: {e}"
                )

        # Create vector indexs
        if self.vector_index_type:
            logger.info(
                f"PostgreSQL, Create vector indexs, type: {self.vector_index_type}"
            )
            try:
                if self.vector_index_type == "HNSW":
                    await self._create_hnsw_vector_indexes()
                elif self.vector_index_type == "IVFFLAT":
                    await self._create_ivfflat_vector_indexes()
                elif self.vector_index_type == "FLAT":
                    logger.warning(
                        "FLAT index type is not supported by pgvector. Skipping vector index creation. "
                        "Please use 'HNSW' or 'IVFFLAT' instead."
                    )
                else:
                    logger.warning(
                        "Doesn't support this vector index type: {self.vector_index_type}. "
                        "Supported types: HNSW, IVFFLAT"
                    )
            except Exception as e:
                logger.error(
                    f"PostgreSQL, Failed to create vector index, type: {self.vector_index_type}, Got: {e}"
                )
        # After all tables are created, attempt to migrate timestamp fields
        try:
            await self._migrate_timestamp_columns()
        except Exception as e:
            logger.error(f"PostgreSQL, Failed to migrate timestamp columns: {e}")
            # Don't throw an exception, allow the initialization process to continue

        # Migrate LLM cache schema: add new columns and remove deprecated mode field
        try:
            await self._migrate_llm_cache_schema()
        except Exception as e:
            logger.error(f"PostgreSQL, Failed to migrate LLM cache schema: {e}")
            # Don't throw an exception, allow the initialization process to continue

        # Finally, attempt to migrate old doc chunks data if needed
        try:
            await self._migrate_doc_chunks_to_vdb_chunks()
        except Exception as e:
            logger.error(f"PostgreSQL, Failed to migrate doc_chunks to vdb_chunks: {e}")

        # Check and migrate LLM cache to flattened keys if needed
        try:
            if await self._check_llm_cache_needs_migration():
                await self._migrate_llm_cache_to_flattened_keys()
        except Exception as e:
            logger.error(f"PostgreSQL, LLM cache migration failed: {e}")

        # Migrate doc status to add chunks_list field if needed
        try:
            await self._migrate_doc_status_add_chunks_list()
        except Exception as e:
            logger.error(
                f"PostgreSQL, Failed to migrate doc status chunks_list field: {e}"
            )

        # Migrate text chunks to add llm_cache_list field if needed
        try:
            await self._migrate_text_chunks_add_llm_cache_list()
        except Exception as e:
            logger.error(
                f"PostgreSQL, Failed to migrate text chunks llm_cache_list field: {e}"
            )

        # Migrate field lengths for entity_name, source_id, target_id, and file_path
        try:
            await self._migrate_field_lengths()
        except Exception as e:
            logger.error(f"PostgreSQL, Failed to migrate field lengths: {e}")

        # Migrate doc status to add track_id field if needed
        try:
            await self._migrate_doc_status_add_track_id()
        except Exception as e:
            logger.error(
                f"PostgreSQL, Failed to migrate doc status track_id field: {e}"
            )

        # Migrate doc status to add metadata and error_msg fields if needed
        try:
            await self._migrate_doc_status_add_metadata_error_msg()
        except Exception as e:
            logger.error(
                f"PostgreSQL, Failed to migrate doc status metadata/error_msg fields: {e}"
            )

        # Create pagination optimization indexes for LIGHTRAG_DOC_STATUS
        try:
            await self._create_pagination_indexes()
        except Exception as e:
            logger.error(f"PostgreSQL, Failed to create pagination indexes: {e}")

        # Migrate to ensure new tables LIGHTRAG_FULL_ENTITIES and LIGHTRAG_FULL_RELATIONS exist
        try:
            await self._migrate_create_full_entities_relations_tables()
        except Exception as e:
            logger.error(
                f"PostgreSQL, Failed to create full entities/relations tables: {e}"
            )

    async def _migrate_create_full_entities_relations_tables(self):
        """Create LIGHTRAG_FULL_ENTITIES and LIGHTRAG_FULL_RELATIONS tables if they don't exist"""
        tables_to_check = [
            {
                "name": "LIGHTRAG_FULL_ENTITIES",
                "ddl": TABLES["LIGHTRAG_FULL_ENTITIES"]["ddl"],
                "description": "Full entities storage table",
            },
            {
                "name": "LIGHTRAG_FULL_RELATIONS",
                "ddl": TABLES["LIGHTRAG_FULL_RELATIONS"]["ddl"],
                "description": "Full relations storage table",
            },
        ]

        for table_info in tables_to_check:
            table_name = table_info["name"]
            try:
                # Check if table exists
                check_table_sql = """
                SELECT table_name
                FROM information_schema.tables
                WHERE table_name = $1
                AND table_schema = 'public'
                """
                params = {"table_name": table_name.lower()}
                table_exists = await self.query(check_table_sql, list(params.values()))

                if not table_exists:
                    logger.info(f"Creating table {table_name}")
                    await self.execute(table_info["ddl"])
                    logger.info(
                        f"Successfully created {table_info['description']}: {table_name}"
                    )

                    # Create basic indexes for the new table
                    try:
                        # Create index for id column
                        index_name = f"idx_{table_name.lower()}_id"
                        create_index_sql = (
                            f"CREATE INDEX {index_name} ON {table_name}(id)"
                        )
                        await self.execute(create_index_sql)
                        logger.info(f"Created index {index_name} on table {table_name}")

                        # Create composite index for (workspace, id) columns
                        composite_index_name = f"idx_{table_name.lower()}_workspace_id"
                        create_composite_index_sql = f"CREATE INDEX {composite_index_name} ON {table_name}(workspace, id)"
                        await self.execute(create_composite_index_sql)
                        logger.info(
                            f"Created composite index {composite_index_name} on table {table_name}"
                        )

                    except Exception as e:
                        logger.warning(
                            f"Failed to create indexes for table {table_name}: {e}"
                        )

                else:
                    logger.debug(f"Table {table_name} already exists")

            except Exception as e:
                logger.error(f"Failed to create table {table_name}: {e}")

    async def _create_pagination_indexes(self):
        """Create indexes to optimize pagination queries for LIGHTRAG_DOC_STATUS"""
        indexes = [
            {
                "name": "idx_lightrag_doc_status_workspace_status_updated_at",
                "sql": "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_lightrag_doc_status_workspace_status_updated_at ON LIGHTRAG_DOC_STATUS (workspace, status, updated_at DESC)",
                "description": "Composite index for workspace + status + updated_at pagination",
            },
            {
                "name": "idx_lightrag_doc_status_workspace_status_created_at",
                "sql": "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_lightrag_doc_status_workspace_status_created_at ON LIGHTRAG_DOC_STATUS (workspace, status, created_at DESC)",
                "description": "Composite index for workspace + status + created_at pagination",
            },
            {
                "name": "idx_lightrag_doc_status_workspace_updated_at",
                "sql": "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_lightrag_doc_status_workspace_updated_at ON LIGHTRAG_DOC_STATUS (workspace, updated_at DESC)",
                "description": "Index for workspace + updated_at pagination (all statuses)",
            },
            {
                "name": "idx_lightrag_doc_status_workspace_created_at",
                "sql": "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_lightrag_doc_status_workspace_created_at ON LIGHTRAG_DOC_STATUS (workspace, created_at DESC)",
                "description": "Index for workspace + created_at pagination (all statuses)",
            },
            {
                "name": "idx_lightrag_doc_status_workspace_id",
                "sql": "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_lightrag_doc_status_workspace_id ON LIGHTRAG_DOC_STATUS (workspace, id)",
                "description": "Index for workspace + id sorting",
            },
            {
                "name": "idx_lightrag_doc_status_workspace_file_path",
                "sql": "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_lightrag_doc_status_workspace_file_path ON LIGHTRAG_DOC_STATUS (workspace, file_path)",
                "description": "Index for workspace + file_path sorting",
            },
        ]

        for index in indexes:
            try:
                # Check if index already exists
                check_sql = """
                SELECT indexname
                FROM pg_indexes
                WHERE tablename = 'lightrag_doc_status'
                AND indexname = $1
                """

                params = {"indexname": index["name"]}
                existing = await self.query(check_sql, list(params.values()))

                if not existing:
                    logger.info(f"Creating pagination index: {index['description']}")
                    await self.execute(index["sql"])
                    logger.info(f"Successfully created index: {index['name']}")
                else:
                    logger.debug(f"Index already exists: {index['name']}")

            except Exception as e:
                logger.warning(f"Failed to create index {index['name']}: {e}")

    async def _create_hnsw_vector_indexes(self):
        vdb_tables = [
            "LIGHTRAG_VDB_CHUNKS",
            "LIGHTRAG_VDB_ENTITY",
            "LIGHTRAG_VDB_RELATION",
        ]

        embedding_dim = int(os.environ.get("EMBEDDING_DIM", 1024))

        for k in vdb_tables:
            vector_index_name = f"idx_{k.lower()}_hnsw_cosine"
            check_vector_index_sql = f"""
                    SELECT 1 FROM pg_indexes
                    WHERE indexname = '{vector_index_name}'
                      AND tablename = '{k.lower()}'
                """
            try:
                vector_index_exists = await self.query(check_vector_index_sql)
                if not vector_index_exists:
                    # Only set vector dimension when index doesn't exist
                    alter_sql = f"ALTER TABLE {k} ALTER COLUMN content_vector TYPE VECTOR({embedding_dim})"
                    await self.execute(alter_sql)
                    logger.debug(f"Ensured vector dimension for {k}")

                    create_vector_index_sql = f"""
                            CREATE INDEX {vector_index_name}
                            ON {k} USING hnsw (content_vector vector_cosine_ops)
                            WITH (m = {self.hnsw_m}, ef_construction = {self.hnsw_ef})
                        """
                    logger.info(f"Creating hnsw index {vector_index_name} on table {k}")
                    await self.execute(create_vector_index_sql)
                    logger.info(
                        f"Successfully created vector index {vector_index_name} on table {k}"
                    )
                else:
                    logger.info(
                        f"HNSW vector index {vector_index_name} already exists on table {k}"
                    )
            except Exception as e:
                logger.error(f"Failed to create vector index on table {k}, Got: {e}")

    async def _create_ivfflat_vector_indexes(self):
        vdb_tables = [
            "LIGHTRAG_VDB_CHUNKS",
            "LIGHTRAG_VDB_ENTITY",
            "LIGHTRAG_VDB_RELATION",
        ]

        embedding_dim = int(os.environ.get("EMBEDDING_DIM", 1024))

        for k in vdb_tables:
            index_name = f"idx_{k.lower()}_ivfflat_cosine"
            check_index_sql = f"""
                    SELECT 1 FROM pg_indexes
                    WHERE indexname = '{index_name}' AND tablename = '{k.lower()}'
                """
            try:
                exists = await self.query(check_index_sql)
                if not exists:
                    # Only set vector dimension when index doesn't exist
                    alter_sql = f"ALTER TABLE {k} ALTER COLUMN content_vector TYPE VECTOR({embedding_dim})"
                    await self.execute(alter_sql)
                    logger.debug(f"Ensured vector dimension for {k}")

                    create_sql = f"""
                            CREATE INDEX {index_name}
                            ON {k} USING ivfflat (content_vector vector_cosine_ops)
                            WITH (lists = {self.ivfflat_lists})
                        """
                    logger.info(f"Creating ivfflat index {index_name} on table {k}")
                    await self.execute(create_sql)
                    logger.info(
                        f"Successfully created ivfflat index {index_name} on table {k}"
                    )
                else:
                    logger.info(
                        f"Ivfflat vector index {index_name} already exists on table {k}"
                    )
            except Exception as e:
                logger.error(f"Failed to create ivfflat index on {k}: {e}")

    async def query(
        self,
        sql: str,
        params: list[Any] | None = None,
        multirows: bool = False,
        with_age: bool = False,
        graph_name: str | None = None,
    ) -> dict[str, Any] | None | list[dict[str, Any]]:
        async with self.pool.acquire() as connection:  # type: ignore
            if with_age and graph_name:
                await self.configure_age(connection, graph_name)  # type: ignore
            elif with_age and not graph_name:
                raise ValueError("Graph name is required when with_age is True")

            try:
                if params:
                    rows = await connection.fetch(sql, *params)
                else:
                    rows = await connection.fetch(sql)

                if multirows:
                    if rows:
                        columns = [col for col in rows[0].keys()]
                        data = [dict(zip(columns, row)) for row in rows]
                    else:
                        data = []
                else:
                    if rows:
                        columns = rows[0].keys()
                        data = dict(zip(columns, rows[0]))
                    else:
                        data = None

                return data
            except Exception as e:
                logger.error(f"PostgreSQL database, error:{e}")
                raise

    async def execute(
        self,
        sql: str,
        data: dict[str, Any] | None = None,
        upsert: bool = False,
        ignore_if_exists: bool = False,
        with_age: bool = False,
        graph_name: str | None = None,
    ):
        try:
            async with self.pool.acquire() as connection:  # type: ignore
                if with_age and graph_name:
                    await self.configure_age(connection, graph_name)
                elif with_age and not graph_name:
                    raise ValueError("Graph name is required when with_age is True")

                if data is None:
                    await connection.execute(sql)
                else:
                    await connection.execute(sql, *data.values())
        except (
            asyncpg.exceptions.UniqueViolationError,
            asyncpg.exceptions.DuplicateTableError,
            asyncpg.exceptions.DuplicateObjectError,  # Catch "already exists" error
            asyncpg.exceptions.InvalidSchemaNameError,  # Also catch for AGE extension "already exists"
        ) as e:
            if ignore_if_exists:
                # If the flag is set, just ignore these specific errors
                pass
            elif upsert:
                print("Key value duplicate, but upsert succeeded.")
            else:
                logger.error(f"Upsert error: {e}")
        except Exception as e:
            logger.error(f"PostgreSQL database,\nsql:{sql},\ndata:{data},\nerror:{e}")
            raise


class ClientManager:
    _instances: dict[str, Any] = {"db": None, "ref_count": 0}
    _lock = asyncio.Lock()

    @staticmethod
    def get_config() -> dict[str, Any]:
        config = configparser.ConfigParser()
        config.read("config.ini", "utf-8")

        return {
            "host": os.environ.get(
                "POSTGRES_HOST",
                config.get("postgres", "host", fallback="localhost"),
            ),
            "port": os.environ.get(
                "POSTGRES_PORT", config.get("postgres", "port", fallback=5432)
            ),
            "user": os.environ.get(
                "POSTGRES_USER", config.get("postgres", "user", fallback="postgres")
            ),
            "password": os.environ.get(
                "POSTGRES_PASSWORD",
                config.get("postgres", "password", fallback=None),
            ),
            "database": os.environ.get(
                "POSTGRES_DATABASE",
                config.get("postgres", "database", fallback="postgres"),
            ),
            "workspace": os.environ.get(
                "POSTGRES_WORKSPACE",
                config.get("postgres", "workspace", fallback=None),
            ),
            "max_connections": os.environ.get(
                "POSTGRES_MAX_CONNECTIONS",
                config.get("postgres", "max_connections", fallback=20),
            ),
            # SSL configuration
            "ssl_mode": os.environ.get(
                "POSTGRES_SSL_MODE",
                config.get("postgres", "ssl_mode", fallback=None),
            ),
            "ssl_cert": os.environ.get(
                "POSTGRES_SSL_CERT",
                config.get("postgres", "ssl_cert", fallback=None),
            ),
            "ssl_key": os.environ.get(
                "POSTGRES_SSL_KEY",
                config.get("postgres", "ssl_key", fallback=None),
            ),
            "ssl_root_cert": os.environ.get(
                "POSTGRES_SSL_ROOT_CERT",
                config.get("postgres", "ssl_root_cert", fallback=None),
            ),
            "ssl_crl": os.environ.get(
                "POSTGRES_SSL_CRL",
                config.get("postgres", "ssl_crl", fallback=None),
            ),
            "vector_index_type": os.environ.get(
                "POSTGRES_VECTOR_INDEX_TYPE",
                config.get("postgres", "vector_index_type", fallback="HNSW"),
            ),
            "hnsw_m": int(
                os.environ.get(
                    "POSTGRES_HNSW_M",
                    config.get("postgres", "hnsw_m", fallback="16"),
                )
            ),
            "hnsw_ef": int(
                os.environ.get(
                    "POSTGRES_HNSW_EF",
                    config.get("postgres", "hnsw_ef", fallback="64"),
                )
            ),
            "ivfflat_lists": int(
                os.environ.get(
                    "POSTGRES_IVFFLAT_LISTS",
                    config.get("postgres", "ivfflat_lists", fallback="100"),
                )
            ),
        }

    @classmethod
    async def get_client(cls) -> PostgreSQLDB:
        async with cls._lock:
            if cls._instances["db"] is None:
                config = ClientManager.get_config()
                db = PostgreSQLDB(config)
                await db.initdb()
                await db.check_tables()
                cls._instances["db"] = db
                cls._instances["ref_count"] = 0
            cls._instances["ref_count"] += 1
            return cls._instances["db"]

    @classmethod
    async def release_client(cls, db: PostgreSQLDB):
        async with cls._lock:
            if db is not None:
                if db is cls._instances["db"]:
                    cls._instances["ref_count"] -= 1
                    if cls._instances["ref_count"] == 0:
                        await db.pool.close()
                        logger.info("Closed PostgreSQL database connection pool")
                        cls._instances["db"] = None
                else:
                    await db.pool.close()


@final
@dataclass
class PGKVStorage(BaseKVStorage):
    db: PostgreSQLDB = field(default=None)

    def __post_init__(self):
        self._max_batch_size = self.global_config["embedding_batch_num"]

    async def initialize(self):
        async with get_data_init_lock():
            if self.db is None:
                self.db = await ClientManager.get_client()

            # Implement workspace priority: PostgreSQLDB.workspace > self.workspace > "default"
            if self.db.workspace:
                # Use PostgreSQLDB's workspace (highest priority)
                self.workspace = self.db.workspace
            elif hasattr(self, "workspace") and self.workspace:
                # Use storage class's workspace (medium priority)
                pass
            else:
                # Use "default" for compatibility (lowest priority)
                self.workspace = "default"

    async def finalize(self):
        async with get_storage_lock():
            if self.db is not None:
                await ClientManager.release_client(self.db)
                self.db = None

    ################ QUERY METHODS ################
    async def get_all(self) -> dict[str, Any]:
        """Get all data from storage

        Returns:
            Dictionary containing all stored data
        """
        table_name = namespace_to_table_name(self.namespace)
        if not table_name:
            logger.error(
                f"[{self.workspace}] Unknown namespace for get_all: {self.namespace}"
            )
            return {}

        sql = f"SELECT * FROM {table_name} WHERE workspace=$1"
        params = {"workspace": self.workspace}

        try:
            results = await self.db.query(sql, list(params.values()), multirows=True)

            # Special handling for LLM cache to ensure compatibility with _get_cached_extraction_results
            if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE):
                processed_results = {}
                for row in results:
                    create_time = row.get("create_time", 0)
                    update_time = row.get("update_time", 0)
                    # Map field names and add cache_type for compatibility
                    processed_row = {
                        **row,
                        "return": row.get("return_value", ""),
                        "cache_type": row.get("original_prompt", "unknow"),
                        "original_prompt": row.get("original_prompt", ""),
                        "chunk_id": row.get("chunk_id"),
                        "mode": row.get("mode", "default"),
                        "create_time": create_time,
                        "update_time": create_time if update_time == 0 else update_time,
                    }
                    processed_results[row["id"]] = processed_row
                return processed_results

            # For text_chunks namespace, parse llm_cache_list JSON string back to list
            if is_namespace(self.namespace, NameSpace.KV_STORE_TEXT_CHUNKS):
                processed_results = {}
                for row in results:
                    llm_cache_list = row.get("llm_cache_list", [])
                    if isinstance(llm_cache_list, str):
                        try:
                            llm_cache_list = json.loads(llm_cache_list)
                        except json.JSONDecodeError:
                            llm_cache_list = []
                    row["llm_cache_list"] = llm_cache_list
                    create_time = row.get("create_time", 0)
                    update_time = row.get("update_time", 0)
                    row["create_time"] = create_time
                    row["update_time"] = (
                        create_time if update_time == 0 else update_time
                    )
                    processed_results[row["id"]] = row
                return processed_results

            # For FULL_ENTITIES namespace, parse entity_names JSON string back to list
            if is_namespace(self.namespace, NameSpace.KV_STORE_FULL_ENTITIES):
                processed_results = {}
                for row in results:
                    entity_names = row.get("entity_names", [])
                    if isinstance(entity_names, str):
                        try:
                            entity_names = json.loads(entity_names)
                        except json.JSONDecodeError:
                            entity_names = []
                    row["entity_names"] = entity_names
                    create_time = row.get("create_time", 0)
                    update_time = row.get("update_time", 0)
                    row["create_time"] = create_time
                    row["update_time"] = (
                        create_time if update_time == 0 else update_time
                    )
                    processed_results[row["id"]] = row
                return processed_results

            # For FULL_RELATIONS namespace, parse relation_pairs JSON string back to list
            if is_namespace(self.namespace, NameSpace.KV_STORE_FULL_RELATIONS):
                processed_results = {}
                for row in results:
                    relation_pairs = row.get("relation_pairs", [])
                    if isinstance(relation_pairs, str):
                        try:
                            relation_pairs = json.loads(relation_pairs)
                        except json.JSONDecodeError:
                            relation_pairs = []
                    row["relation_pairs"] = relation_pairs
                    create_time = row.get("create_time", 0)
                    update_time = row.get("update_time", 0)
                    row["create_time"] = create_time
                    row["update_time"] = (
                        create_time if update_time == 0 else update_time
                    )
                    processed_results[row["id"]] = row
                return processed_results

            # For other namespaces, return as-is
            return {row["id"]: row for row in results}
        except Exception as e:
            logger.error(
                f"[{self.workspace}] Error retrieving all data from {self.namespace}: {e}"
            )
            return {}

    async def get_by_id(self, id: str) -> dict[str, Any] | None:
        """Get data by id."""
        sql = SQL_TEMPLATES["get_by_id_" + self.namespace]
        params = {"workspace": self.workspace, "id": id}
        response = await self.db.query(sql, list(params.values()))

        if response and is_namespace(self.namespace, NameSpace.KV_STORE_TEXT_CHUNKS):
            # Parse llm_cache_list JSON string back to list
            llm_cache_list = response.get("llm_cache_list", [])
            if isinstance(llm_cache_list, str):
                try:
                    llm_cache_list = json.loads(llm_cache_list)
                except json.JSONDecodeError:
                    llm_cache_list = []
            response["llm_cache_list"] = llm_cache_list
            create_time = response.get("create_time", 0)
            update_time = response.get("update_time", 0)
            response["create_time"] = create_time
            response["update_time"] = create_time if update_time == 0 else update_time

        # Special handling for LLM cache to ensure compatibility with _get_cached_extraction_results
        if response and is_namespace(
            self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE
        ):
            create_time = response.get("create_time", 0)
            update_time = response.get("update_time", 0)
            # Parse queryparam JSON string back to dict
            queryparam = response.get("queryparam")
            if isinstance(queryparam, str):
                try:
                    queryparam = json.loads(queryparam)
                except json.JSONDecodeError:
                    queryparam = None
            # Map field names for compatibility (mode field removed)
            response = {
                **response,
                "return": response.get("return_value", ""),
                "cache_type": response.get("cache_type"),
                "original_prompt": response.get("original_prompt", ""),
                "chunk_id": response.get("chunk_id"),
                "queryparam": queryparam,
                "create_time": create_time,
                "update_time": create_time if update_time == 0 else update_time,
            }

        # Special handling for FULL_ENTITIES namespace
        if response and is_namespace(self.namespace, NameSpace.KV_STORE_FULL_ENTITIES):
            # Parse entity_names JSON string back to list
            entity_names = response.get("entity_names", [])
            if isinstance(entity_names, str):
                try:
                    entity_names = json.loads(entity_names)
                except json.JSONDecodeError:
                    entity_names = []
            response["entity_names"] = entity_names
            create_time = response.get("create_time", 0)
            update_time = response.get("update_time", 0)
            response["create_time"] = create_time
            response["update_time"] = create_time if update_time == 0 else update_time

        # Special handling for FULL_RELATIONS namespace
        if response and is_namespace(self.namespace, NameSpace.KV_STORE_FULL_RELATIONS):
            # Parse relation_pairs JSON string back to list
            relation_pairs = response.get("relation_pairs", [])
            if isinstance(relation_pairs, str):
                try:
                    relation_pairs = json.loads(relation_pairs)
                except json.JSONDecodeError:
                    relation_pairs = []
            response["relation_pairs"] = relation_pairs
            create_time = response.get("create_time", 0)
            update_time = response.get("update_time", 0)
            response["create_time"] = create_time
            response["update_time"] = create_time if update_time == 0 else update_time

        return response if response else None

    # Query by id
    async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
        """Get data by ids"""
        sql = SQL_TEMPLATES["get_by_ids_" + self.namespace].format(
            ids=",".join([f"'{id}'" for id in ids])
        )
        params = {"workspace": self.workspace}
        results = await self.db.query(sql, list(params.values()), multirows=True)

        if results and is_namespace(self.namespace, NameSpace.KV_STORE_TEXT_CHUNKS):
            # Parse llm_cache_list JSON string back to list for each result
            for result in results:
                llm_cache_list = result.get("llm_cache_list", [])
                if isinstance(llm_cache_list, str):
                    try:
                        llm_cache_list = json.loads(llm_cache_list)
                    except json.JSONDecodeError:
                        llm_cache_list = []
                result["llm_cache_list"] = llm_cache_list
                create_time = result.get("create_time", 0)
                update_time = result.get("update_time", 0)
                result["create_time"] = create_time
                result["update_time"] = create_time if update_time == 0 else update_time

        # Special handling for LLM cache to ensure compatibility with _get_cached_extraction_results
        if results and is_namespace(
            self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE
        ):
            processed_results = []
            for row in results:
                create_time = row.get("create_time", 0)
                update_time = row.get("update_time", 0)
                # Parse queryparam JSON string back to dict
                queryparam = row.get("queryparam")
                if isinstance(queryparam, str):
                    try:
                        queryparam = json.loads(queryparam)
                    except json.JSONDecodeError:
                        queryparam = None
                # Map field names for compatibility (mode field removed)
                processed_row = {
                    **row,
                    "return": row.get("return_value", ""),
                    "cache_type": row.get("cache_type"),
                    "original_prompt": row.get("original_prompt", ""),
                    "chunk_id": row.get("chunk_id"),
                    "queryparam": queryparam,
                    "create_time": create_time,
                    "update_time": create_time if update_time == 0 else update_time,
                }
                processed_results.append(processed_row)
            return processed_results

        # Special handling for FULL_ENTITIES namespace
        if results and is_namespace(self.namespace, NameSpace.KV_STORE_FULL_ENTITIES):
            for result in results:
                # Parse entity_names JSON string back to list
                entity_names = result.get("entity_names", [])
                if isinstance(entity_names, str):
                    try:
                        entity_names = json.loads(entity_names)
                    except json.JSONDecodeError:
                        entity_names = []
                result["entity_names"] = entity_names
                create_time = result.get("create_time", 0)
                update_time = result.get("update_time", 0)
                result["create_time"] = create_time
                result["update_time"] = create_time if update_time == 0 else update_time

        # Special handling for FULL_RELATIONS namespace
        if results and is_namespace(self.namespace, NameSpace.KV_STORE_FULL_RELATIONS):
            for result in results:
                # Parse relation_pairs JSON string back to list
                relation_pairs = result.get("relation_pairs", [])
                if isinstance(relation_pairs, str):
                    try:
                        relation_pairs = json.loads(relation_pairs)
                    except json.JSONDecodeError:
                        relation_pairs = []
                result["relation_pairs"] = relation_pairs
                create_time = result.get("create_time", 0)
                update_time = result.get("update_time", 0)
                result["create_time"] = create_time
                result["update_time"] = create_time if update_time == 0 else update_time

        return results if results else []

    async def filter_keys(self, keys: set[str]) -> set[str]:
        """Filter out duplicated content"""
        sql = SQL_TEMPLATES["filter_keys"].format(
            table_name=namespace_to_table_name(self.namespace),
            ids=",".join([f"'{id}'" for id in keys]),
        )
        params = {"workspace": self.workspace}
        try:
            res = await self.db.query(sql, list(params.values()), multirows=True)
            if res:
                exist_keys = [key["id"] for key in res]
            else:
                exist_keys = []
            new_keys = set([s for s in keys if s not in exist_keys])
            return new_keys
        except Exception as e:
            logger.error(
                f"[{self.workspace}] PostgreSQL database,\nsql:{sql},\nparams:{params},\nerror:{e}"
            )
            raise

    ################ INSERT METHODS ################
    async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
        logger.debug(f"[{self.workspace}] Inserting {len(data)} to {self.namespace}")
        if not data:
            return

        if is_namespace(self.namespace, NameSpace.KV_STORE_TEXT_CHUNKS):
            # Get current UTC time and convert to naive datetime for database storage
            current_time = datetime.datetime.now(timezone.utc).replace(tzinfo=None)
            for k, v in data.items():
                upsert_sql = SQL_TEMPLATES["upsert_text_chunk"]
                _data = {
                    "workspace": self.workspace,
                    "id": k,
                    "tokens": v["tokens"],
                    "chunk_order_index": v["chunk_order_index"],
                    "full_doc_id": v["full_doc_id"],
                    "content": v["content"],
                    "file_path": v["file_path"],
                    "llm_cache_list": json.dumps(v.get("llm_cache_list", [])),
                    "create_time": current_time,
                    "update_time": current_time,
                }
                await self.db.execute(upsert_sql, _data)
        elif is_namespace(self.namespace, NameSpace.KV_STORE_FULL_DOCS):
            for k, v in data.items():
                upsert_sql = SQL_TEMPLATES["upsert_doc_full"]
                _data = {
                    "id": k,
                    "content": v["content"],
                    "workspace": self.workspace,
                }
                await self.db.execute(upsert_sql, _data)
        elif is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE):
            for k, v in data.items():
                upsert_sql = SQL_TEMPLATES["upsert_llm_response_cache"]
                _data = {
                    "workspace": self.workspace,
                    "id": k,  # Use flattened key as id
                    "original_prompt": v["original_prompt"],
                    "return_value": v["return"],
                    "chunk_id": v.get("chunk_id"),
                    "cache_type": v.get(
                        "cache_type", "extract"
                    ),  # Get cache_type from data
                    "queryparam": json.dumps(v.get("queryparam"))
                    if v.get("queryparam")
                    else None,
                }

                await self.db.execute(upsert_sql, _data)
        elif is_namespace(self.namespace, NameSpace.KV_STORE_FULL_ENTITIES):
            # Get current UTC time and convert to naive datetime for database storage
            current_time = datetime.datetime.now(timezone.utc).replace(tzinfo=None)
            for k, v in data.items():
                upsert_sql = SQL_TEMPLATES["upsert_full_entities"]
                _data = {
                    "workspace": self.workspace,
                    "id": k,
                    "entity_names": json.dumps(v["entity_names"]),
                    "count": v["count"],
                    "create_time": current_time,
                    "update_time": current_time,
                }
                await self.db.execute(upsert_sql, _data)
        elif is_namespace(self.namespace, NameSpace.KV_STORE_FULL_RELATIONS):
            # Get current UTC time and convert to naive datetime for database storage
            current_time = datetime.datetime.now(timezone.utc).replace(tzinfo=None)
            for k, v in data.items():
                upsert_sql = SQL_TEMPLATES["upsert_full_relations"]
                _data = {
                    "workspace": self.workspace,
                    "id": k,
                    "relation_pairs": json.dumps(v["relation_pairs"]),
                    "count": v["count"],
                    "create_time": current_time,
                    "update_time": current_time,
                }
                await self.db.execute(upsert_sql, _data)

    async def index_done_callback(self) -> None:
        # PG handles persistence automatically
        pass

    async def delete(self, ids: list[str]) -> None:
        """Delete specific records from storage by their IDs

        Args:
            ids (list[str]): List of document IDs to be deleted from storage

        Returns:
            None
        """
        if not ids:
            return

        table_name = namespace_to_table_name(self.namespace)
        if not table_name:
            logger.error(
                f"[{self.workspace}] Unknown namespace for deletion: {self.namespace}"
            )
            return

        delete_sql = f"DELETE FROM {table_name} WHERE workspace=$1 AND id = ANY($2)"

        try:
            await self.db.execute(delete_sql, {"workspace": self.workspace, "ids": ids})
            logger.debug(
                f"[{self.workspace}] Successfully deleted {len(ids)} records from {self.namespace}"
            )
        except Exception as e:
            logger.error(
                f"[{self.workspace}] Error while deleting records from {self.namespace}: {e}"
            )

    async def drop(self) -> dict[str, str]:
        """Drop the storage"""
        async with get_storage_lock():
            try:
                table_name = namespace_to_table_name(self.namespace)
                if not table_name:
                    return {
                        "status": "error",
                        "message": f"Unknown namespace: {self.namespace}",
                    }

                drop_sql = SQL_TEMPLATES["drop_specifiy_table_workspace"].format(
                    table_name=table_name
                )
                await self.db.execute(drop_sql, {"workspace": self.workspace})
                return {"status": "success", "message": "data dropped"}
            except Exception as e:
                return {"status": "error", "message": str(e)}


@final
@dataclass
class PGVectorStorage(BaseVectorStorage):
    db: PostgreSQLDB | None = field(default=None)

    def __post_init__(self):
        self._max_batch_size = self.global_config["embedding_batch_num"]
        config = self.global_config.get("vector_db_storage_cls_kwargs", {})
        cosine_threshold = config.get("cosine_better_than_threshold")
        if cosine_threshold is None:
            raise ValueError(
                "cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs"
            )
        self.cosine_better_than_threshold = cosine_threshold

    async def initialize(self):
        async with get_data_init_lock():
            if self.db is None:
                self.db = await ClientManager.get_client()

            # Implement workspace priority: PostgreSQLDB.workspace > self.workspace > "default"
            if self.db.workspace:
                # Use PostgreSQLDB's workspace (highest priority)
                self.workspace = self.db.workspace
            elif hasattr(self, "workspace") and self.workspace:
                # Use storage class's workspace (medium priority)
                pass
            else:
                # Use "default" for compatibility (lowest priority)
                self.workspace = "default"

    async def finalize(self):
        async with get_storage_lock():
            if self.db is not None:
                await ClientManager.release_client(self.db)
                self.db = None

    def _upsert_chunks(
        self, item: dict[str, Any], current_time: datetime.datetime
    ) -> tuple[str, dict[str, Any]]:
        try:
            upsert_sql = SQL_TEMPLATES["upsert_chunk"]
            data: dict[str, Any] = {
                "workspace": self.workspace,
                "id": item["__id__"],
                "tokens": item["tokens"],
                "chunk_order_index": item["chunk_order_index"],
                "full_doc_id": item["full_doc_id"],
                "content": item["content"],
                "content_vector": json.dumps(item["__vector__"].tolist()),
                "file_path": item["file_path"],
                "create_time": current_time,
                "update_time": current_time,
            }
        except Exception as e:
            logger.error(
                f"[{self.workspace}] Error to prepare upsert,\nsql: {e}\nitem: {item}"
            )
            raise

        return upsert_sql, data

    def _upsert_entities(
        self, item: dict[str, Any], current_time: datetime.datetime
    ) -> tuple[str, dict[str, Any]]:
        upsert_sql = SQL_TEMPLATES["upsert_entity"]
        source_id = item["source_id"]
        if isinstance(source_id, str) and "<SEP>" in source_id:
            chunk_ids = source_id.split("<SEP>")
        else:
            chunk_ids = [source_id]

        data: dict[str, Any] = {
            "workspace": self.workspace,
            "id": item["__id__"],
            "entity_name": item["entity_name"],
            "content": item["content"],
            "content_vector": json.dumps(item["__vector__"].tolist()),
            "chunk_ids": chunk_ids,
            "file_path": item.get("file_path", None),
            "create_time": current_time,
            "update_time": current_time,
        }
        return upsert_sql, data

    def _upsert_relationships(
        self, item: dict[str, Any], current_time: datetime.datetime
    ) -> tuple[str, dict[str, Any]]:
        upsert_sql = SQL_TEMPLATES["upsert_relationship"]
        source_id = item["source_id"]
        if isinstance(source_id, str) and "<SEP>" in source_id:
            chunk_ids = source_id.split("<SEP>")
        else:
            chunk_ids = [source_id]

        data: dict[str, Any] = {
            "workspace": self.workspace,
            "id": item["__id__"],
            "source_id": item["src_id"],
            "target_id": item["tgt_id"],
            "content": item["content"],
            "content_vector": json.dumps(item["__vector__"].tolist()),
            "chunk_ids": chunk_ids,
            "file_path": item.get("file_path", None),
            "create_time": current_time,
            "update_time": current_time,
        }
        return upsert_sql, data

    async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
        logger.debug(f"[{self.workspace}] Inserting {len(data)} to {self.namespace}")
        if not data:
            return

        # Get current UTC time and convert to naive datetime for database storage
        current_time = datetime.datetime.now(timezone.utc).replace(tzinfo=None)
        list_data = [
            {
                "__id__": k,
                **{k1: v1 for k1, v1 in v.items()},
            }
            for k, v in data.items()
        ]
        contents = [v["content"] for v in data.values()]
        batches = [
            contents[i : i + self._max_batch_size]
            for i in range(0, len(contents), self._max_batch_size)
        ]

        embedding_tasks = [self.embedding_func(batch) for batch in batches]
        embeddings_list = await asyncio.gather(*embedding_tasks)

        embeddings = np.concatenate(embeddings_list)
        for i, d in enumerate(list_data):
            d["__vector__"] = embeddings[i]
        for item in list_data:
            if is_namespace(self.namespace, NameSpace.VECTOR_STORE_CHUNKS):
                upsert_sql, data = self._upsert_chunks(item, current_time)
            elif is_namespace(self.namespace, NameSpace.VECTOR_STORE_ENTITIES):
                upsert_sql, data = self._upsert_entities(item, current_time)
            elif is_namespace(self.namespace, NameSpace.VECTOR_STORE_RELATIONSHIPS):
                upsert_sql, data = self._upsert_relationships(item, current_time)
            else:
                raise ValueError(f"{self.namespace} is not supported")

            await self.db.execute(upsert_sql, data)

    #################### query method ###############
    async def query(
        self, query: str, top_k: int, query_embedding: list[float] = None
    ) -> list[dict[str, Any]]:
        if query_embedding is not None:
            embedding = query_embedding
        else:
            embeddings = await self.embedding_func(
                [query], _priority=5
            )  # higher priority for query
            embedding = embeddings[0]

        embedding_string = ",".join(map(str, embedding))

        sql = SQL_TEMPLATES[self.namespace].format(embedding_string=embedding_string)
        params = {
            "workspace": self.workspace,
            "closer_than_threshold": 1 - self.cosine_better_than_threshold,
            "top_k": top_k,
        }
        results = await self.db.query(sql, params=list(params.values()), multirows=True)
        return results

    async def index_done_callback(self) -> None:
        # PG handles persistence automatically
        pass

    async def delete(self, ids: list[str]) -> None:
        """Delete vectors with specified IDs from the storage.

        Args:
            ids: List of vector IDs to be deleted
        """
        if not ids:
            return

        table_name = namespace_to_table_name(self.namespace)
        if not table_name:
            logger.error(
                f"[{self.workspace}] Unknown namespace for vector deletion: {self.namespace}"
            )
            return

        delete_sql = f"DELETE FROM {table_name} WHERE workspace=$1 AND id = ANY($2)"

        try:
            await self.db.execute(delete_sql, {"workspace": self.workspace, "ids": ids})
            logger.debug(
                f"[{self.workspace}] Successfully deleted {len(ids)} vectors from {self.namespace}"
            )
        except Exception as e:
            logger.error(
                f"[{self.workspace}] Error while deleting vectors from {self.namespace}: {e}"
            )

    async def delete_entity(self, entity_name: str) -> None:
        """Delete an entity by its name from the vector storage.

        Args:
            entity_name: The name of the entity to delete
        """
        try:
            # Construct SQL to delete the entity
            delete_sql = """DELETE FROM LIGHTRAG_VDB_ENTITY
                            WHERE workspace=$1 AND entity_name=$2"""

            await self.db.execute(
                delete_sql, {"workspace": self.workspace, "entity_name": entity_name}
            )
            logger.debug(
                f"[{self.workspace}] Successfully deleted entity {entity_name}"
            )
        except Exception as e:
            logger.error(f"[{self.workspace}] Error deleting entity {entity_name}: {e}")

    async def delete_entity_relation(self, entity_name: str) -> None:
        """Delete all relations associated with an entity.

        Args:
            entity_name: The name of the entity whose relations should be deleted
        """
        try:
            # Delete relations where the entity is either the source or target
            delete_sql = """DELETE FROM LIGHTRAG_VDB_RELATION
                            WHERE workspace=$1 AND (source_id=$2 OR target_id=$2)"""

            await self.db.execute(
                delete_sql, {"workspace": self.workspace, "entity_name": entity_name}
            )
            logger.debug(
                f"[{self.workspace}] Successfully deleted relations for entity {entity_name}"
            )
        except Exception as e:
            logger.error(
                f"[{self.workspace}] Error deleting relations for entity {entity_name}: {e}"
            )

    async def get_by_id(self, id: str) -> dict[str, Any] | None:
        """Get vector data by its ID

        Args:
            id: The unique identifier of the vector

        Returns:
            The vector data if found, or None if not found
        """
        table_name = namespace_to_table_name(self.namespace)
        if not table_name:
            logger.error(
                f"[{self.workspace}] Unknown namespace for ID lookup: {self.namespace}"
            )
            return None

        query = f"SELECT *, EXTRACT(EPOCH FROM create_time)::BIGINT as created_at FROM {table_name} WHERE workspace=$1 AND id=$2"
        params = {"workspace": self.workspace, "id": id}

        try:
            result = await self.db.query(query, list(params.values()))
            if result:
                return dict(result)
            return None
        except Exception as e:
            logger.error(
                f"[{self.workspace}] Error retrieving vector data for ID {id}: {e}"
            )
            return None

    async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
        """Get multiple vector data by their IDs

        Args:
            ids: List of unique identifiers

        Returns:
            List of vector data objects that were found
        """
        if not ids:
            return []

        table_name = namespace_to_table_name(self.namespace)
        if not table_name:
            logger.error(
                f"[{self.workspace}] Unknown namespace for IDs lookup: {self.namespace}"
            )
            return []

        ids_str = ",".join([f"'{id}'" for id in ids])
        query = f"SELECT *, EXTRACT(EPOCH FROM create_time)::BIGINT as created_at FROM {table_name} WHERE workspace=$1 AND id IN ({ids_str})"
        params = {"workspace": self.workspace}

        try:
            results = await self.db.query(query, list(params.values()), multirows=True)
            return [dict(record) for record in results]
        except Exception as e:
            logger.error(
                f"[{self.workspace}] Error retrieving vector data for IDs {ids}: {e}"
            )
            return []

    async def get_vectors_by_ids(self, ids: list[str]) -> dict[str, list[float]]:
        """Get vectors by their IDs, returning only ID and vector data for efficiency

        Args:
            ids: List of unique identifiers

        Returns:
            Dictionary mapping IDs to their vector embeddings
            Format: {id: [vector_values], ...}
        """
        if not ids:
            return {}

        table_name = namespace_to_table_name(self.namespace)
        if not table_name:
            logger.error(
                f"[{self.workspace}] Unknown namespace for vector lookup: {self.namespace}"
            )
            return {}

        ids_str = ",".join([f"'{id}'" for id in ids])
        query = f"SELECT id, content_vector FROM {table_name} WHERE workspace=$1 AND id IN ({ids_str})"
        params = {"workspace": self.workspace}

        try:
            results = await self.db.query(query, list(params.values()), multirows=True)
            vectors_dict = {}

            for result in results:
                if result and "content_vector" in result and "id" in result:
                    try:
                        # Parse JSON string to get vector as list of floats
                        vector_data = json.loads(result["content_vector"])
                        if isinstance(vector_data, list):
                            vectors_dict[result["id"]] = vector_data
                    except (json.JSONDecodeError, TypeError) as e:
                        logger.warning(
                            f"[{self.workspace}] Failed to parse vector data for ID {result['id']}: {e}"
                        )

            return vectors_dict
        except Exception as e:
            logger.error(
                f"[{self.workspace}] Error retrieving vectors by IDs from {self.namespace}: {e}"
            )
            return {}

    async def drop(self) -> dict[str, str]:
        """Drop the storage"""
        async with get_storage_lock():
            try:
                table_name = namespace_to_table_name(self.namespace)
                if not table_name:
                    return {
                        "status": "error",
                        "message": f"Unknown namespace: {self.namespace}",
                    }

                drop_sql = SQL_TEMPLATES["drop_specifiy_table_workspace"].format(
                    table_name=table_name
                )
                await self.db.execute(drop_sql, {"workspace": self.workspace})
                return {"status": "success", "message": "data dropped"}
            except Exception as e:
                return {"status": "error", "message": str(e)}


@final
@dataclass
class PGDocStatusStorage(DocStatusStorage):
    db: PostgreSQLDB = field(default=None)

    def _format_datetime_with_timezone(self, dt):
        """Convert datetime to ISO format string with timezone info"""
        if dt is None:
            return None
        # If no timezone info, assume it's UTC time (as stored in database)
        if dt.tzinfo is None:
            dt = dt.replace(tzinfo=timezone.utc)
        # If datetime already has timezone info, keep it as is
        return dt.isoformat()

    async def initialize(self):
        async with get_data_init_lock():
            if self.db is None:
                self.db = await ClientManager.get_client()

            # Implement workspace priority: PostgreSQLDB.workspace > self.workspace > "default"
            if self.db.workspace:
                # Use PostgreSQLDB's workspace (highest priority)
                self.workspace = self.db.workspace
            elif hasattr(self, "workspace") and self.workspace:
                # Use storage class's workspace (medium priority)
                pass
            else:
                # Use "default" for compatibility (lowest priority)
                self.workspace = "default"

    async def finalize(self):
        async with get_storage_lock():
            if self.db is not None:
                await ClientManager.release_client(self.db)
                self.db = None

    async def filter_keys(self, keys: set[str]) -> set[str]:
        """Filter out duplicated content"""
        sql = SQL_TEMPLATES["filter_keys"].format(
            table_name=namespace_to_table_name(self.namespace),
            ids=",".join([f"'{id}'" for id in keys]),
        )
        params = {"workspace": self.workspace}
        try:
            res = await self.db.query(sql, list(params.values()), multirows=True)
            if res:
                exist_keys = [key["id"] for key in res]
            else:
                exist_keys = []
            new_keys = set([s for s in keys if s not in exist_keys])
            # print(f"keys: {keys}")
            # print(f"new_keys: {new_keys}")
            return new_keys
        except Exception as e:
            logger.error(
                f"[{self.workspace}] PostgreSQL database,\nsql:{sql},\nparams:{params},\nerror:{e}"
            )
            raise

    async def get_by_id(self, id: str) -> Union[dict[str, Any], None]:
        sql = "select * from LIGHTRAG_DOC_STATUS where workspace=$1 and id=$2"
        params = {"workspace": self.workspace, "id": id}
        result = await self.db.query(sql, list(params.values()), True)
        if result is None or result == []:
            return None
        else:
            # Parse chunks_list JSON string back to list
            chunks_list = result[0].get("chunks_list", [])
            if isinstance(chunks_list, str):
                try:
                    chunks_list = json.loads(chunks_list)
                except json.JSONDecodeError:
                    chunks_list = []

            # Parse metadata JSON string back to dict
            metadata = result[0].get("metadata", {})
            if isinstance(metadata, str):
                try:
                    metadata = json.loads(metadata)
                except json.JSONDecodeError:
                    metadata = {}

            # Convert datetime objects to ISO format strings with timezone info
            created_at = self._format_datetime_with_timezone(result[0]["created_at"])
            updated_at = self._format_datetime_with_timezone(result[0]["updated_at"])

            return dict(
                content_length=result[0]["content_length"],
                content_summary=result[0]["content_summary"],
                status=result[0]["status"],
                chunks_count=result[0]["chunks_count"],
                created_at=created_at,
                updated_at=updated_at,
                file_path=result[0]["file_path"],
                chunks_list=chunks_list,
                metadata=metadata,
                error_msg=result[0].get("error_msg"),
                track_id=result[0].get("track_id"),
            )

    async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
        """Get doc_chunks data by multiple IDs."""
        if not ids:
            return []

        sql = "SELECT * FROM LIGHTRAG_DOC_STATUS WHERE workspace=$1 AND id = ANY($2)"
        params = {"workspace": self.workspace, "ids": ids}

        results = await self.db.query(sql, list(params.values()), True)

        if not results:
            return []

        processed_results = []
        for row in results:
            # Parse chunks_list JSON string back to list
            chunks_list = row.get("chunks_list", [])
            if isinstance(chunks_list, str):
                try:
                    chunks_list = json.loads(chunks_list)
                except json.JSONDecodeError:
                    chunks_list = []

            # Parse metadata JSON string back to dict
            metadata = row.get("metadata", {})
            if isinstance(metadata, str):
                try:
                    metadata = json.loads(metadata)
                except json.JSONDecodeError:
                    metadata = {}

            # Convert datetime objects to ISO format strings with timezone info
            created_at = self._format_datetime_with_timezone(row["created_at"])
            updated_at = self._format_datetime_with_timezone(row["updated_at"])

            processed_results.append(
                {
                    "content_length": row["content_length"],
                    "content_summary": row["content_summary"],
                    "status": row["status"],
                    "chunks_count": row["chunks_count"],
                    "created_at": created_at,
                    "updated_at": updated_at,
                    "file_path": row["file_path"],
                    "chunks_list": chunks_list,
                    "metadata": metadata,
                    "error_msg": row.get("error_msg"),
                    "track_id": row.get("track_id"),
                }
            )

        return processed_results

    async def get_status_counts(self) -> dict[str, int]:
        """Get counts of documents in each status"""
        sql = """SELECT status as "status", COUNT(1) as "count"
                   FROM LIGHTRAG_DOC_STATUS
                  where workspace=$1 GROUP BY STATUS
                 """
        params = {"workspace": self.workspace}
        result = await self.db.query(sql, list(params.values()), True)
        counts = {}
        for doc in result:
            counts[doc["status"]] = doc["count"]
        return counts

    async def get_docs_by_status(
        self, status: DocStatus
    ) -> dict[str, DocProcessingStatus]:
        """all documents with a specific status"""
        sql = "select * from LIGHTRAG_DOC_STATUS where workspace=$1 and status=$2"
        params = {"workspace": self.workspace, "status": status.value}
        result = await self.db.query(sql, list(params.values()), True)

        docs_by_status = {}
        for element in result:
            # Parse chunks_list JSON string back to list
            chunks_list = element.get("chunks_list", [])
            if isinstance(chunks_list, str):
                try:
                    chunks_list = json.loads(chunks_list)
                except json.JSONDecodeError:
                    chunks_list = []

            # Parse metadata JSON string back to dict
            metadata = element.get("metadata", {})
            if isinstance(metadata, str):
                try:
                    metadata = json.loads(metadata)
                except json.JSONDecodeError:
                    metadata = {}
            # Ensure metadata is a dict
            if not isinstance(metadata, dict):
                metadata = {}

            # Safe handling for file_path
            file_path = element.get("file_path")
            if file_path is None:
                file_path = "no-file-path"

            # Convert datetime objects to ISO format strings with timezone info
            created_at = self._format_datetime_with_timezone(element["created_at"])
            updated_at = self._format_datetime_with_timezone(element["updated_at"])

            docs_by_status[element["id"]] = DocProcessingStatus(
                content_summary=element["content_summary"],
                content_length=element["content_length"],
                status=element["status"],
                created_at=created_at,
                updated_at=updated_at,
                chunks_count=element["chunks_count"],
                file_path=file_path,
                chunks_list=chunks_list,
                metadata=metadata,
                error_msg=element.get("error_msg"),
                track_id=element.get("track_id"),
            )

        return docs_by_status

    async def get_docs_by_track_id(
        self, track_id: str
    ) -> dict[str, DocProcessingStatus]:
        """Get all documents with a specific track_id"""
        sql = "select * from LIGHTRAG_DOC_STATUS where workspace=$1 and track_id=$2"
        params = {"workspace": self.workspace, "track_id": track_id}
        result = await self.db.query(sql, list(params.values()), True)

        docs_by_track_id = {}
        for element in result:
            # Parse chunks_list JSON string back to list
            chunks_list = element.get("chunks_list", [])
            if isinstance(chunks_list, str):
                try:
                    chunks_list = json.loads(chunks_list)
                except json.JSONDecodeError:
                    chunks_list = []

            # Parse metadata JSON string back to dict
            metadata = element.get("metadata", {})
            if isinstance(metadata, str):
                try:
                    metadata = json.loads(metadata)
                except json.JSONDecodeError:
                    metadata = {}
            # Ensure metadata is a dict
            if not isinstance(metadata, dict):
                metadata = {}

            # Safe handling for file_path
            file_path = element.get("file_path")
            if file_path is None:
                file_path = "no-file-path"

            # Convert datetime objects to ISO format strings with timezone info
            created_at = self._format_datetime_with_timezone(element["created_at"])
            updated_at = self._format_datetime_with_timezone(element["updated_at"])

            docs_by_track_id[element["id"]] = DocProcessingStatus(
                content_summary=element["content_summary"],
                content_length=element["content_length"],
                status=element["status"],
                created_at=created_at,
                updated_at=updated_at,
                chunks_count=element["chunks_count"],
                file_path=file_path,
                chunks_list=chunks_list,
                track_id=element.get("track_id"),
                metadata=metadata,
                error_msg=element.get("error_msg"),
            )

        return docs_by_track_id

    async def get_docs_paginated(
        self,
        status_filter: DocStatus | None = None,
        page: int = 1,
        page_size: int = 50,
        sort_field: str = "updated_at",
        sort_direction: str = "desc",
    ) -> tuple[list[tuple[str, DocProcessingStatus]], int]:
        """Get documents with pagination support

        Args:
            status_filter: Filter by document status, None for all statuses
            page: Page number (1-based)
            page_size: Number of documents per page (10-200)
            sort_field: Field to sort by ('created_at', 'updated_at', 'id')
            sort_direction: Sort direction ('asc' or 'desc')

        Returns:
            Tuple of (list of (doc_id, DocProcessingStatus) tuples, total_count)
        """
        # Validate parameters
        if page < 1:
            page = 1
        if page_size < 10:
            page_size = 10
        elif page_size > 200:
            page_size = 200

        if sort_field not in ["created_at", "updated_at", "id", "file_path"]:
            sort_field = "updated_at"

        if sort_direction.lower() not in ["asc", "desc"]:
            sort_direction = "desc"

        # Calculate offset
        offset = (page - 1) * page_size

        # Build WHERE clause
        where_clause = "WHERE workspace=$1"
        params = {"workspace": self.workspace}
        param_count = 1

        if status_filter is not None:
            param_count += 1
            where_clause += f" AND status=${param_count}"
            params["status"] = status_filter.value

        # Build ORDER BY clause
        order_clause = f"ORDER BY {sort_field} {sort_direction.upper()}"

        # Query for total count
        count_sql = f"SELECT COUNT(*) as total FROM LIGHTRAG_DOC_STATUS {where_clause}"
        count_result = await self.db.query(count_sql, list(params.values()))
        total_count = count_result["total"] if count_result else 0

        # Query for paginated data
        data_sql = f"""
            SELECT * FROM LIGHTRAG_DOC_STATUS
            {where_clause}
            {order_clause}
            LIMIT ${param_count + 1} OFFSET ${param_count + 2}
        """
        params["limit"] = page_size
        params["offset"] = offset

        result = await self.db.query(data_sql, list(params.values()), True)

        # Convert to (doc_id, DocProcessingStatus) tuples
        documents = []
        for element in result:
            doc_id = element["id"]

            # Parse chunks_list JSON string back to list
            chunks_list = element.get("chunks_list", [])
            if isinstance(chunks_list, str):
                try:
                    chunks_list = json.loads(chunks_list)
                except json.JSONDecodeError:
                    chunks_list = []

            # Parse metadata JSON string back to dict
            metadata = element.get("metadata", {})
            if isinstance(metadata, str):
                try:
                    metadata = json.loads(metadata)
                except json.JSONDecodeError:
                    metadata = {}

            # Convert datetime objects to ISO format strings with timezone info
            created_at = self._format_datetime_with_timezone(element["created_at"])
            updated_at = self._format_datetime_with_timezone(element["updated_at"])

            doc_status = DocProcessingStatus(
                content_summary=element["content_summary"],
                content_length=element["content_length"],
                status=element["status"],
                created_at=created_at,
                updated_at=updated_at,
                chunks_count=element["chunks_count"],
                file_path=element["file_path"],
                chunks_list=chunks_list,
                track_id=element.get("track_id"),
                metadata=metadata,
                error_msg=element.get("error_msg"),
            )
            documents.append((doc_id, doc_status))

        return documents, total_count

    async def get_all_status_counts(self) -> dict[str, int]:
        """Get counts of documents in each status for all documents

        Returns:
            Dictionary mapping status names to counts, including 'all' field
        """
        sql = """
            SELECT status, COUNT(*) as count
            FROM LIGHTRAG_DOC_STATUS
            WHERE workspace=$1
            GROUP BY status
        """
        params = {"workspace": self.workspace}
        result = await self.db.query(sql, list(params.values()), True)

        counts = {}
        total_count = 0
        for row in result:
            counts[row["status"]] = row["count"]
            total_count += row["count"]

        # Add 'all' field with total count
        counts["all"] = total_count

        return counts

    async def index_done_callback(self) -> None:
        # PG handles persistence automatically
        pass

    async def delete(self, ids: list[str]) -> None:
        """Delete specific records from storage by their IDs

        Args:
            ids (list[str]): List of document IDs to be deleted from storage

        Returns:
            None
        """
        if not ids:
            return

        table_name = namespace_to_table_name(self.namespace)
        if not table_name:
            logger.error(
                f"[{self.workspace}] Unknown namespace for deletion: {self.namespace}"
            )
            return

        delete_sql = f"DELETE FROM {table_name} WHERE workspace=$1 AND id = ANY($2)"

        try:
            await self.db.execute(delete_sql, {"workspace": self.workspace, "ids": ids})
            logger.debug(
                f"[{self.workspace}] Successfully deleted {len(ids)} records from {self.namespace}"
            )
        except Exception as e:
            logger.error(
                f"[{self.workspace}] Error while deleting records from {self.namespace}: {e}"
            )

    async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
        """Update or insert document status

        Args:
            data: dictionary of document IDs and their status data
        """
        logger.debug(f"[{self.workspace}] Inserting {len(data)} to {self.namespace}")
        if not data:
            return

        def parse_datetime(dt_str):
            """Parse datetime and ensure it's stored as UTC time in database"""
            if dt_str is None:
                return None
            if isinstance(dt_str, (datetime.date, datetime.datetime)):
                # If it's a datetime object
                if isinstance(dt_str, datetime.datetime):
                    # If no timezone info, assume it's UTC
                    if dt_str.tzinfo is None:
                        dt_str = dt_str.replace(tzinfo=timezone.utc)
                    # Convert to UTC and remove timezone info for storage
                    return dt_str.astimezone(timezone.utc).replace(tzinfo=None)
                return dt_str
            try:
                # Process ISO format string with timezone
                dt = datetime.datetime.fromisoformat(dt_str)
                # If no timezone info, assume it's UTC
                if dt.tzinfo is None:
                    dt = dt.replace(tzinfo=timezone.utc)
                # Convert to UTC and remove timezone info for storage
                return dt.astimezone(timezone.utc).replace(tzinfo=None)
            except (ValueError, TypeError):
                logger.warning(
                    f"[{self.workspace}] Unable to parse datetime string: {dt_str}"
                )
                return None

        # Modified SQL to include created_at, updated_at, chunks_list, track_id, metadata, and error_msg in both INSERT and UPDATE operations
        # All fields are updated from the input data in both INSERT and UPDATE cases
        sql = """insert into LIGHTRAG_DOC_STATUS(workspace,id,content_summary,content_length,chunks_count,status,file_path,chunks_list,track_id,metadata,error_msg,created_at,updated_at)
                 values($1,$2,$3,$4,$5,$6,$7,$8,$9,$10,$11,$12,$13)
                  on conflict(id,workspace) do update set
                  content_summary = EXCLUDED.content_summary,
                  content_length = EXCLUDED.content_length,
                  chunks_count = EXCLUDED.chunks_count,
                  status = EXCLUDED.status,
                  file_path = EXCLUDED.file_path,
                  chunks_list = EXCLUDED.chunks_list,
                  track_id = EXCLUDED.track_id,
                  metadata = EXCLUDED.metadata,
                  error_msg = EXCLUDED.error_msg,
                  created_at = EXCLUDED.created_at,
                  updated_at = EXCLUDED.updated_at"""
        for k, v in data.items():
            # Remove timezone information, store utc time in db
            created_at = parse_datetime(v.get("created_at"))
            updated_at = parse_datetime(v.get("updated_at"))

            # chunks_count, chunks_list, track_id, metadata, and error_msg are optional
            await self.db.execute(
                sql,
                {
                    "workspace": self.workspace,
                    "id": k,
                    "content_summary": v["content_summary"],
                    "content_length": v["content_length"],
                    "chunks_count": v["chunks_count"] if "chunks_count" in v else -1,
                    "status": v["status"],
                    "file_path": v["file_path"],
                    "chunks_list": json.dumps(v.get("chunks_list", [])),
                    "track_id": v.get("track_id"),  # Add track_id support
                    "metadata": json.dumps(
                        v.get("metadata", {})
                    ),  # Add metadata support
                    "error_msg": v.get("error_msg"),  # Add error_msg support
                    "created_at": created_at,  # Use the converted datetime object
                    "updated_at": updated_at,  # Use the converted datetime object
                },
            )

    async def drop(self) -> dict[str, str]:
        """Drop the storage"""
        async with get_storage_lock():
            try:
                table_name = namespace_to_table_name(self.namespace)
                if not table_name:
                    return {
                        "status": "error",
                        "message": f"Unknown namespace: {self.namespace}",
                    }

                drop_sql = SQL_TEMPLATES["drop_specifiy_table_workspace"].format(
                    table_name=table_name
                )
                await self.db.execute(drop_sql, {"workspace": self.workspace})
                return {"status": "success", "message": "data dropped"}
            except Exception as e:
                return {"status": "error", "message": str(e)}


class PGGraphQueryException(Exception):
    """Exception for the AGE queries."""

    def __init__(self, exception: Union[str, dict[str, Any]]) -> None:
        if isinstance(exception, dict):
            self.message = exception["message"] if "message" in exception else "unknown"
            self.details = exception["details"] if "details" in exception else "unknown"
        else:
            self.message = exception
            self.details = "unknown"

    def get_message(self) -> str:
        return self.message

    def get_details(self) -> Any:
        return self.details


@final
@dataclass
class PGGraphStorage(BaseGraphStorage):
    def __post_init__(self):
        # Graph name will be dynamically generated in initialize() based on workspace
        self.db: PostgreSQLDB | None = None

    def _get_workspace_graph_name(self) -> str:
        """
        Generate graph name based on workspace and namespace for data isolation.
        Rules:
        - If workspace is empty or "default": graph_name = namespace
        - If workspace has other value: graph_name = workspace_namespace

        Args:
            None

        Returns:
            str: The graph name for the current workspace
        """
        workspace = self.workspace
        namespace = self.namespace

        if workspace and workspace.strip() and workspace.strip().lower() != "default":
            # Ensure names comply with PostgreSQL identifier specifications
            safe_workspace = re.sub(r"[^a-zA-Z0-9_]", "_", workspace.strip())
            safe_namespace = re.sub(r"[^a-zA-Z0-9_]", "_", namespace)
            return f"{safe_workspace}_{safe_namespace}"
        else:
            # When the workspace is "default", use the namespace directly (for backward compatibility with legacy implementations)
            return re.sub(r"[^a-zA-Z0-9_]", "_", namespace)

    @staticmethod
    def _normalize_node_id(node_id: str) -> str:
        """
        Normalize node ID to ensure special characters are properly handled in Cypher queries.

        Args:
            node_id: The original node ID

        Returns:
            Normalized node ID suitable for Cypher queries
        """
        # Escape backslashes
        normalized_id = node_id
        normalized_id = normalized_id.replace("\\", "\\\\")
        normalized_id = normalized_id.replace('"', '\\"')
        return normalized_id

    async def initialize(self):
        async with get_data_init_lock():
            if self.db is None:
                self.db = await ClientManager.get_client()

            # Implement workspace priority: PostgreSQLDB.workspace > self.workspace > "default"
            if self.db.workspace:
                # Use PostgreSQLDB's workspace (highest priority)
                self.workspace = self.db.workspace
            elif hasattr(self, "workspace") and self.workspace:
                # Use storage class's workspace (medium priority)
                pass
            else:
                # Use "default" for compatibility (lowest priority)
                self.workspace = "default"

            # Dynamically generate graph name based on workspace
            self.graph_name = self._get_workspace_graph_name()

            # Log the graph initialization for debugging
            logger.info(
                f"[{self.workspace}] PostgreSQL Graph initialized: graph_name='{self.graph_name}'"
            )

            # Create AGE extension and configure graph environment once at initialization
            async with self.db.pool.acquire() as connection:
                # First ensure AGE extension is created
                await PostgreSQLDB.configure_age_extension(connection)

            # Execute each statement separately and ignore errors
            queries = [
                f"SELECT create_graph('{self.graph_name}')",
                f"SELECT create_vlabel('{self.graph_name}', 'base');",
                f"SELECT create_elabel('{self.graph_name}', 'DIRECTED');",
                # f'CREATE INDEX CONCURRENTLY vertex_p_idx ON {self.graph_name}."_ag_label_vertex" (id)',
                f'CREATE INDEX CONCURRENTLY vertex_idx_node_id ON {self.graph_name}."_ag_label_vertex" (ag_catalog.agtype_access_operator(properties, \'"entity_id"\'::agtype))',
                # f'CREATE INDEX CONCURRENTLY edge_p_idx ON {self.graph_name}."_ag_label_edge" (id)',
                f'CREATE INDEX CONCURRENTLY edge_sid_idx ON {self.graph_name}."_ag_label_edge" (start_id)',
                f'CREATE INDEX CONCURRENTLY edge_eid_idx ON {self.graph_name}."_ag_label_edge" (end_id)',
                f'CREATE INDEX CONCURRENTLY edge_seid_idx ON {self.graph_name}."_ag_label_edge" (start_id,end_id)',
                f'CREATE INDEX CONCURRENTLY directed_p_idx ON {self.graph_name}."DIRECTED" (id)',
                f'CREATE INDEX CONCURRENTLY directed_eid_idx ON {self.graph_name}."DIRECTED" (end_id)',
                f'CREATE INDEX CONCURRENTLY directed_sid_idx ON {self.graph_name}."DIRECTED" (start_id)',
                f'CREATE INDEX CONCURRENTLY directed_seid_idx ON {self.graph_name}."DIRECTED" (start_id,end_id)',
                f'CREATE INDEX CONCURRENTLY entity_p_idx ON {self.graph_name}."base" (id)',
                f'CREATE INDEX CONCURRENTLY entity_idx_node_id ON {self.graph_name}."base" (ag_catalog.agtype_access_operator(properties, \'"entity_id"\'::agtype))',
                f'CREATE INDEX CONCURRENTLY entity_node_id_gin_idx ON {self.graph_name}."base" using gin(properties)',
                f'ALTER TABLE {self.graph_name}."DIRECTED" CLUSTER ON directed_sid_idx',
            ]

            for query in queries:
                # Use the new flag to silently ignore "already exists" errors
                # at the source, preventing log spam.
                await self.db.execute(
                    query,
                    upsert=True,
                    ignore_if_exists=True,  # Pass the new flag
                    with_age=True,
                    graph_name=self.graph_name,
                )

    async def finalize(self):
        async with get_graph_db_lock():
            if self.db is not None:
                await ClientManager.release_client(self.db)
                self.db = None

    async def index_done_callback(self) -> None:
        # PG handles persistence automatically
        pass

    @staticmethod
    def _record_to_dict(record: asyncpg.Record) -> dict[str, Any]:
        """
        Convert a record returned from an age query to a dictionary

        Args:
            record (): a record from an age query result

        Returns:
            dict[str, Any]: a dictionary representation of the record where
                the dictionary key is the field name and the value is the
                value converted to a python type
        """

        @staticmethod
        def parse_agtype_string(agtype_str: str) -> tuple[str, str]:
            """
            Parse agtype string precisely, separating JSON content and type identifier

            Args:
                agtype_str: String like '{"json": "content"}::vertex'

            Returns:
                (json_content, type_identifier)
            """
            if not isinstance(agtype_str, str) or "::" not in agtype_str:
                return agtype_str, ""

            # Find the last :: from the right, which is the start of type identifier
            last_double_colon = agtype_str.rfind("::")

            if last_double_colon == -1:
                return agtype_str, ""

            # Separate JSON content and type identifier
            json_content = agtype_str[:last_double_colon]
            type_identifier = agtype_str[last_double_colon + 2 :]

            return json_content, type_identifier

        @staticmethod
        def safe_json_parse(json_str: str, context: str = "") -> dict:
            """
            Safe JSON parsing with simplified error logging
            """
            try:
                return json.loads(json_str)
            except json.JSONDecodeError as e:
                logger.error(f"JSON parsing failed ({context}): {e}")
                logger.error(f"Raw data (first 100 chars): {repr(json_str[:100])}")
                logger.error(f"Error position: line {e.lineno}, column {e.colno}")
                return None

        # result holder
        d = {}

        # prebuild a mapping of vertex_id to vertex mappings to be used
        # later to build edges
        vertices = {}

        # First pass: preprocess vertices
        for k in record.keys():
            v = record[k]
            if isinstance(v, str) and "::" in v:
                if v.startswith("[") and v.endswith("]"):
                    # Handle vertex arrays
                    json_content, type_id = parse_agtype_string(v)
                    if type_id == "vertex":
                        vertexes = safe_json_parse(
                            json_content, f"vertices array for {k}"
                        )
                        if vertexes:
                            for vertex in vertexes:
                                vertices[vertex["id"]] = vertex.get("properties")
                else:
                    # Handle single vertex
                    json_content, type_id = parse_agtype_string(v)
                    if type_id == "vertex":
                        vertex = safe_json_parse(json_content, f"single vertex for {k}")
                        if vertex:
                            vertices[vertex["id"]] = vertex.get("properties")

        # Second pass: process all fields
        for k in record.keys():
            v = record[k]
            if isinstance(v, str) and "::" in v:
                if v.startswith("[") and v.endswith("]"):
                    # Handle array types
                    json_content, type_id = parse_agtype_string(v)
                    if type_id in ["vertex", "edge"]:
                        parsed_data = safe_json_parse(
                            json_content, f"array {type_id} for field {k}"
                        )
                        d[k] = parsed_data if parsed_data is not None else None
                    else:
                        logger.warning(f"Unknown array type: {type_id}")
                        d[k] = None
                else:
                    # Handle single objects
                    json_content, type_id = parse_agtype_string(v)
                    if type_id in ["vertex", "edge"]:
                        parsed_data = safe_json_parse(
                            json_content, f"single {type_id} for field {k}"
                        )
                        d[k] = parsed_data if parsed_data is not None else None
                    else:
                        # May be other types of agtype data, keep as is
                        d[k] = v
            else:
                d[k] = v  # Keep as string

        return d

    @staticmethod
    def _format_properties(
        properties: dict[str, Any], _id: Union[str, None] = None
    ) -> str:
        """
        Convert a dictionary of properties to a string representation that
        can be used in a cypher query insert/merge statement.

        Args:
            properties (dict[str,str]): a dictionary containing node/edge properties
            _id (Union[str, None]): the id of the node or None if none exists

        Returns:
            str: the properties dictionary as a properly formatted string
        """
        props = []
        # wrap property key in backticks to escape
        for k, v in properties.items():
            prop = f"`{k}`: {json.dumps(v)}"
            props.append(prop)
        if _id is not None and "id" not in properties:
            props.append(
                f"id: {json.dumps(_id)}" if isinstance(_id, str) else f"id: {_id}"
            )
        return "{" + ", ".join(props) + "}"

    async def _query(
        self,
        query: str,
        readonly: bool = True,
        upsert: bool = False,
        params: dict[str, Any] | None = None,
    ) -> list[dict[str, Any]]:
        """
        Query the graph by taking a cypher query, converting it to an
        age compatible query, executing it and converting the result

        Args:
            query (str): a cypher query to be executed

        Returns:
            list[dict[str, Any]]: a list of dictionaries containing the result set
        """
        try:
            if readonly:
                data = await self.db.query(
                    query,
                    list(params.values()) if params else None,
                    multirows=True,
                    with_age=True,
                    graph_name=self.graph_name,
                )
            else:
                data = await self.db.execute(
                    query,
                    upsert=upsert,
                    with_age=True,
                    graph_name=self.graph_name,
                )

        except Exception as e:
            raise PGGraphQueryException(
                {
                    "message": f"Error executing graph query: {query}",
                    "wrapped": query,
                    "detail": str(e),
                }
            ) from e

        if data is None:
            result = []
        # decode records
        else:
            result = [self._record_to_dict(d) for d in data]

        return result

    async def has_node(self, node_id: str) -> bool:
        query = f"""
            SELECT EXISTS (
              SELECT 1
              FROM {self.graph_name}.base
              WHERE ag_catalog.agtype_access_operator(
                      VARIADIC ARRAY[properties, '"entity_id"'::agtype]
                    ) = (to_json($1::text)::text)::agtype
              LIMIT 1
            ) AS node_exists;
        """

        params = {"node_id": node_id}
        row = (await self._query(query, params=params))[0]
        return bool(row["node_exists"])

    async def has_edge(self, source_node_id: str, target_node_id: str) -> bool:
        query = f"""
            WITH a AS (
              SELECT id AS vid
              FROM {self.graph_name}.base
              WHERE ag_catalog.agtype_access_operator(
                      VARIADIC ARRAY[properties, '"entity_id"'::agtype]
                    ) = (to_json($1::text)::text)::agtype
            ),
            b AS (
              SELECT id AS vid
              FROM {self.graph_name}.base
              WHERE ag_catalog.agtype_access_operator(
                      VARIADIC ARRAY[properties, '"entity_id"'::agtype]
                    ) = (to_json($2::text)::text)::agtype
            )
            SELECT EXISTS (
              SELECT 1
              FROM {self.graph_name}."DIRECTED" d
              JOIN a ON d.start_id = a.vid
              JOIN b ON d.end_id   = b.vid
              LIMIT 1
            )
            OR EXISTS (
              SELECT 1
              FROM {self.graph_name}."DIRECTED" d
              JOIN a ON d.end_id   = a.vid
              JOIN b ON d.start_id = b.vid
              LIMIT 1
            ) AS edge_exists;
        """
        params = {
            "source_node_id": source_node_id,
            "target_node_id": target_node_id,
        }
        row = (await self._query(query, params=params))[0]
        return bool(row["edge_exists"])

    async def get_node(self, node_id: str) -> dict[str, str] | None:
        """Get node by its label identifier, return only node properties"""

        label = self._normalize_node_id(node_id)

        result = await self.get_nodes_batch(node_ids=[label])
        if result and node_id in result:
            return result[node_id]
        return None

    async def node_degree(self, node_id: str) -> int:
        label = self._normalize_node_id(node_id)

        result = await self.node_degrees_batch(node_ids=[label])
        if result and node_id in result:
            return result[node_id]

    async def edge_degree(self, src_id: str, tgt_id: str) -> int:
        result = await self.edge_degrees_batch(edges=[(src_id, tgt_id)])
        if result and (src_id, tgt_id) in result:
            return result[(src_id, tgt_id)]

    async def get_edge(
        self, source_node_id: str, target_node_id: str
    ) -> dict[str, str] | None:
        """Get edge properties between two nodes"""
        src_label = self._normalize_node_id(source_node_id)
        tgt_label = self._normalize_node_id(target_node_id)

        result = await self.get_edges_batch([{"src": src_label, "tgt": tgt_label}])
        if result and (src_label, tgt_label) in result:
            return result[(src_label, tgt_label)]
        return None

    async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None:
        """
        Retrieves all edges (relationships) for a particular node identified by its label.
        :return: list of dictionaries containing edge information
        """
        label = self._normalize_node_id(source_node_id)

        query = """SELECT * FROM cypher('%s', $$
                      MATCH (n:base {entity_id: "%s"})
                      OPTIONAL MATCH (n)-[]-(connected:base)
                      RETURN n.entity_id AS source_id, connected.entity_id AS connected_id
                    $$) AS (source_id text, connected_id text)""" % (
            self.graph_name,
            label,
        )

        results = await self._query(query)
        edges = []
        for record in results:
            source_id = record["source_id"]
            connected_id = record["connected_id"]

            if source_id and connected_id:
                edges.append((source_id, connected_id))

        return edges

    @retry(
        stop=stop_after_attempt(3),
        wait=wait_exponential(multiplier=1, min=4, max=10),
        retry=retry_if_exception_type((PGGraphQueryException,)),
    )
    async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None:
        """
        Upsert a node in the Neo4j database.

        Args:
            node_id: The unique identifier for the node (used as label)
            node_data: Dictionary of node properties
        """
        if "entity_id" not in node_data:
            raise ValueError(
                "PostgreSQL: node properties must contain an 'entity_id' field"
            )

        label = self._normalize_node_id(node_id)
        properties = self._format_properties(node_data)

        query = """SELECT * FROM cypher('%s', $$
                     MERGE (n:base {entity_id: "%s"})
                     SET n += %s
                     RETURN n
                   $$) AS (n agtype)""" % (
            self.graph_name,
            label,
            properties,
        )

        try:
            await self._query(query, readonly=False, upsert=True)

        except Exception:
            logger.error(
                f"[{self.workspace}] POSTGRES, upsert_node error on node_id: `{node_id}`"
            )
            raise

    @retry(
        stop=stop_after_attempt(3),
        wait=wait_exponential(multiplier=1, min=4, max=10),
        retry=retry_if_exception_type((PGGraphQueryException,)),
    )
    async def upsert_edge(
        self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
    ) -> None:
        """
        Upsert an edge and its properties between two nodes identified by their labels.

        Args:
            source_node_id (str): Label of the source node (used as identifier)
            target_node_id (str): Label of the target node (used as identifier)
            edge_data (dict): dictionary of properties to set on the edge
        """
        src_label = self._normalize_node_id(source_node_id)
        tgt_label = self._normalize_node_id(target_node_id)
        edge_properties = self._format_properties(edge_data)

        query = """SELECT * FROM cypher('%s', $$
                     MATCH (source:base {entity_id: "%s"})
                     WITH source
                     MATCH (target:base {entity_id: "%s"})
                     MERGE (source)-[r:DIRECTED]-(target)
                     SET r += %s
                     SET r += %s
                     RETURN r
                   $$) AS (r agtype)""" % (
            self.graph_name,
            src_label,
            tgt_label,
            edge_properties,
            edge_properties,  # https://github.com/HKUDS/LightRAG/issues/1438#issuecomment-2826000195
        )

        try:
            await self._query(query, readonly=False, upsert=True)

        except Exception:
            logger.error(
                f"[{self.workspace}] POSTGRES, upsert_edge error on edge: `{source_node_id}`-`{target_node_id}`"
            )
            raise

    async def delete_node(self, node_id: str) -> None:
        """
        Delete a node from the graph.

        Args:
            node_id (str): The ID of the node to delete.
        """
        label = self._normalize_node_id(node_id)

        query = """SELECT * FROM cypher('%s', $$
                     MATCH (n:base {entity_id: "%s"})
                     DETACH DELETE n
                   $$) AS (n agtype)""" % (self.graph_name, label)

        try:
            await self._query(query, readonly=False)
        except Exception as e:
            logger.error(f"[{self.workspace}] Error during node deletion: {e}")
            raise

    async def remove_nodes(self, node_ids: list[str]) -> None:
        """
        Remove multiple nodes from the graph.

        Args:
            node_ids (list[str]): A list of node IDs to remove.
        """
        node_ids = [self._normalize_node_id(node_id) for node_id in node_ids]
        node_id_list = ", ".join([f'"{node_id}"' for node_id in node_ids])

        query = """SELECT * FROM cypher('%s', $$
                     MATCH (n:base)
                     WHERE n.entity_id IN [%s]
                     DETACH DELETE n
                   $$) AS (n agtype)""" % (self.graph_name, node_id_list)

        try:
            await self._query(query, readonly=False)
        except Exception as e:
            logger.error(f"[{self.workspace}] Error during node removal: {e}")
            raise

    async def remove_edges(self, edges: list[tuple[str, str]]) -> None:
        """
        Remove multiple edges from the graph.

        Args:
            edges (list[tuple[str, str]]): A list of edges to remove, where each edge is a tuple of (source_node_id, target_node_id).
        """
        for source, target in edges:
            src_label = self._normalize_node_id(source)
            tgt_label = self._normalize_node_id(target)

            query = """SELECT * FROM cypher('%s', $$
                         MATCH (a:base {entity_id: "%s"})-[r]-(b:base {entity_id: "%s"})
                         DELETE r
                       $$) AS (r agtype)""" % (self.graph_name, src_label, tgt_label)

            try:
                await self._query(query, readonly=False)
                logger.debug(
                    f"[{self.workspace}] Deleted edge from '{source}' to '{target}'"
                )
            except Exception as e:
                logger.error(f"[{self.workspace}] Error during edge deletion: {str(e)}")
                raise

    async def get_nodes_batch(
        self, node_ids: list[str], batch_size: int = 1000
    ) -> dict[str, dict]:
        """
        Retrieve multiple nodes in one query using UNWIND.

        Args:
            node_ids: List of node entity IDs to fetch.
            batch_size: Batch size for the query

        Returns:
            A dictionary mapping each node_id to its node data (or None if not found).
        """
        if not node_ids:
            return {}

        seen = set()
        unique_ids = []
        for nid in node_ids:
            nid_norm = self._normalize_node_id(nid)
            if nid_norm not in seen:
                seen.add(nid_norm)
                unique_ids.append(nid_norm)

        # Build result dictionary
        nodes_dict = {}

        for i in range(0, len(unique_ids), batch_size):
            batch = unique_ids[i : i + batch_size]

            query = f"""
                WITH input(v, ord) AS (
                  SELECT v, ord
                  FROM unnest($1::text[]) WITH ORDINALITY AS t(v, ord)
                ),
                ids(node_id, ord) AS (
                  SELECT (to_json(v)::text)::agtype AS node_id, ord
                  FROM input
                )
                SELECT i.node_id::text AS node_id,
                       b.properties
                FROM {self.graph_name}.base AS b
                JOIN ids i
                  ON ag_catalog.agtype_access_operator(
                       VARIADIC ARRAY[b.properties, '"entity_id"'::agtype]
                     ) = i.node_id
                ORDER BY i.ord;
            """

            results = await self._query(query, params={"ids": batch})

            for result in results:
                if result["node_id"] and result["properties"]:
                    node_dict = result["properties"]

                    # Process string result, parse it to JSON dictionary
                    if isinstance(node_dict, str):
                        try:
                            node_dict = json.loads(node_dict)
                        except json.JSONDecodeError:
                            logger.warning(
                                f"Failed to parse node string in batch: {node_dict}"
                            )

                    nodes_dict[result["node_id"]] = node_dict

        return nodes_dict

    async def node_degrees_batch(
        self, node_ids: list[str], batch_size: int = 500
    ) -> dict[str, int]:
        """
        Retrieve the degree for multiple nodes in a single query using UNWIND.
        Calculates the total degree by counting distinct relationships.
        Uses separate queries for outgoing and incoming edges.

        Args:
            node_ids: List of node labels (entity_id values) to look up.
            batch_size: Batch size for the query

        Returns:
            A dictionary mapping each node_id to its degree (total number of relationships).
            If a node is not found, its degree will be set to 0.
        """
        if not node_ids:
            return {}

        seen = set()
        unique_ids: list[str] = []
        for nid in node_ids:
            n = self._normalize_node_id(nid)
            if n not in seen:
                seen.add(n)
                unique_ids.append(n)

        out_degrees = {}
        in_degrees = {}

        for i in range(0, len(unique_ids), batch_size):
            batch = unique_ids[i : i + batch_size]

            query = f"""
                    WITH input(v, ord) AS (
                      SELECT v, ord
                      FROM unnest($1::text[]) WITH ORDINALITY AS t(v, ord)
                    ),
                    ids(node_id, ord) AS (
                      SELECT (to_json(v)::text)::agtype AS node_id, ord
                      FROM input
                    ),
                    vids AS (
                      SELECT b.id AS vid, i.node_id, i.ord
                      FROM {self.graph_name}.base AS b
                      JOIN ids i
                        ON ag_catalog.agtype_access_operator(
                             VARIADIC ARRAY[b.properties, '"entity_id"'::agtype]
                           ) = i.node_id
                    ),
                    deg_out AS (
                      SELECT d.start_id AS vid, COUNT(*)::bigint AS out_degree
                      FROM {self.graph_name}."DIRECTED" AS d
                      JOIN vids v ON v.vid = d.start_id
                      GROUP BY d.start_id
                    ),
                    deg_in AS (
                      SELECT d.end_id AS vid, COUNT(*)::bigint AS in_degree
                      FROM {self.graph_name}."DIRECTED" AS d
                      JOIN vids v ON v.vid = d.end_id
                      GROUP BY d.end_id
                    )
                    SELECT v.node_id::text AS node_id,
                           COALESCE(o.out_degree, 0) AS out_degree,
                           COALESCE(n.in_degree, 0)  AS in_degree
                    FROM vids v
                    LEFT JOIN deg_out o ON o.vid = v.vid
                    LEFT JOIN deg_in  n ON n.vid = v.vid
                    ORDER BY v.ord;
                """

            combined_results = await self._query(query, params={"ids": batch})

            for row in combined_results:
                node_id = row["node_id"]
                if not node_id:
                    continue
                out_degrees[node_id] = int(row.get("out_degree", 0) or 0)
                in_degrees[node_id] = int(row.get("in_degree", 0) or 0)

        degrees_dict = {}
        for node_id in node_ids:
            out_degree = out_degrees.get(node_id, 0)
            in_degree = in_degrees.get(node_id, 0)
            degrees_dict[node_id] = out_degree + in_degree

        return degrees_dict

    async def edge_degrees_batch(
        self, edges: list[tuple[str, str]]
    ) -> dict[tuple[str, str], int]:
        """
        Calculate the combined degree for each edge (sum of the source and target node degrees)
        in batch using the already implemented node_degrees_batch.

        Args:
            edges: List of (source_node_id, target_node_id) tuples

        Returns:
            Dictionary mapping edge tuples to their combined degrees
        """
        if not edges:
            return {}

        # Use node_degrees_batch to get all node degrees efficiently
        all_nodes = set()
        for src, tgt in edges:
            all_nodes.add(src)
            all_nodes.add(tgt)

        node_degrees = await self.node_degrees_batch(list(all_nodes))

        # Calculate edge degrees
        edge_degrees_dict = {}
        for src, tgt in edges:
            src_degree = node_degrees.get(src, 0)
            tgt_degree = node_degrees.get(tgt, 0)
            edge_degrees_dict[(src, tgt)] = src_degree + tgt_degree

        return edge_degrees_dict

    async def get_edges_batch(
        self, pairs: list[dict[str, str]], batch_size: int = 500
    ) -> dict[tuple[str, str], dict]:
        """
        Retrieve edge properties for multiple (src, tgt) pairs in one query.
        Get forward and backward edges seperately and merge them before return

        Args:
            pairs: List of dictionaries, e.g. [{"src": "node1", "tgt": "node2"}, ...]
            batch_size: Batch size for the query

        Returns:
            A dictionary mapping (src, tgt) tuples to their edge properties.
        """
        if not pairs:
            return {}

        seen = set()
        uniq_pairs: list[dict[str, str]] = []
        for p in pairs:
            s = self._normalize_node_id(p["src"])
            t = self._normalize_node_id(p["tgt"])
            key = (s, t)
            if s and t and key not in seen:
                seen.add(key)
                uniq_pairs.append(p)

        edges_dict: dict[tuple[str, str], dict] = {}

        for i in range(0, len(uniq_pairs), batch_size):
            batch = uniq_pairs[i : i + batch_size]

            pairs = [{"src": p["src"], "tgt": p["tgt"]} for p in batch]

            forward_cypher = """
                         UNWIND $pairs AS p
                         WITH p.src AS src_eid, p.tgt AS tgt_eid
                         MATCH (a:base {entity_id: src_eid})
                         MATCH (b:base {entity_id: tgt_eid})
                         MATCH (a)-[r]->(b)
                         RETURN src_eid AS source, tgt_eid AS target, properties(r) AS edge_properties"""
            backward_cypher = """
                         UNWIND $pairs AS p
                         WITH p.src AS src_eid, p.tgt AS tgt_eid
                         MATCH (a:base {entity_id: src_eid})
                         MATCH (b:base {entity_id: tgt_eid})
                         MATCH (a)<-[r]-(b)
                         RETURN src_eid AS source, tgt_eid AS target, properties(r) AS edge_properties"""

            def dollar_quote(s: str, tag_prefix="AGE"):
                s = "" if s is None else str(s)
                for i in itertools.count(1):
                    tag = f"{tag_prefix}{i}"
                    wrapper = f"${tag}$"
                    if wrapper not in s:
                        return f"{wrapper}{s}{wrapper}"

            sql_fwd = f"""
            SELECT * FROM cypher({dollar_quote(self.graph_name)}::name,
                                 {dollar_quote(forward_cypher)}::cstring,
                                 $1::agtype)
              AS (source text, target text, edge_properties agtype)
            """

            sql_bwd = f"""
            SELECT * FROM cypher({dollar_quote(self.graph_name)}::name,
                                 {dollar_quote(backward_cypher)}::cstring,
                                 $1::agtype)
              AS (source text, target text, edge_properties agtype)
            """

            pg_params = {"params": json.dumps({"pairs": pairs}, ensure_ascii=False)}

            forward_results = await self._query(sql_fwd, params=pg_params)
            backward_results = await self._query(sql_bwd, params=pg_params)

            for result in forward_results:
                if result["source"] and result["target"] and result["edge_properties"]:
                    edge_props = result["edge_properties"]

                    # Process string result, parse it to JSON dictionary
                    if isinstance(edge_props, str):
                        try:
                            edge_props = json.loads(edge_props)
                        except json.JSONDecodeError:
                            logger.warning(
                                f"Failed to parse edge properties string: {edge_props}"
                            )
                            continue

                    edges_dict[(result["source"], result["target"])] = edge_props

            for result in backward_results:
                if result["source"] and result["target"] and result["edge_properties"]:
                    edge_props = result["edge_properties"]

                    # Process string result, parse it to JSON dictionary
                    if isinstance(edge_props, str):
                        try:
                            edge_props = json.loads(edge_props)
                        except json.JSONDecodeError:
                            logger.warning(
                                f"Failed to parse edge properties string: {edge_props}"
                            )
                            continue

                    edges_dict[(result["source"], result["target"])] = edge_props

        return edges_dict

    async def get_nodes_edges_batch(
        self, node_ids: list[str], batch_size: int = 500
    ) -> dict[str, list[tuple[str, str]]]:
        """
        Get all edges (both outgoing and incoming) for multiple nodes in a single batch operation.

        Args:
            node_ids: List of node IDs to get edges for
            batch_size: Batch size for the query

        Returns:
            Dictionary mapping node IDs to lists of (source, target) edge tuples
        """
        if not node_ids:
            return {}

        seen = set()
        unique_ids: list[str] = []
        for nid in node_ids:
            n = self._normalize_node_id(nid)
            if n and n not in seen:
                seen.add(n)
                unique_ids.append(n)

        edges_norm: dict[str, list[tuple[str, str]]] = {n: [] for n in unique_ids}

        for i in range(0, len(unique_ids), batch_size):
            batch = unique_ids[i : i + batch_size]
            # Format node IDs for the query
            formatted_ids = ", ".join([f'"{n}"' for n in batch])

            outgoing_query = """SELECT * FROM cypher('%s', $$
                         UNWIND [%s] AS node_id
                         MATCH (n:base {entity_id: node_id})
                         OPTIONAL MATCH (n:base)-[]->(connected:base)
                         RETURN node_id, connected.entity_id AS connected_id
                       $$) AS (node_id text, connected_id text)""" % (
                self.graph_name,
                formatted_ids,
            )

            incoming_query = """SELECT * FROM cypher('%s', $$
                         UNWIND [%s] AS node_id
                         MATCH (n:base {entity_id: node_id})
                         OPTIONAL MATCH (n:base)<-[]-(connected:base)
                         RETURN node_id, connected.entity_id AS connected_id
                       $$) AS (node_id text, connected_id text)""" % (
                self.graph_name,
                formatted_ids,
            )

            outgoing_results = await self._query(outgoing_query)
            incoming_results = await self._query(incoming_query)

            for result in outgoing_results:
                if result["node_id"] and result["connected_id"]:
                    edges_norm[result["node_id"]].append(
                        (result["node_id"], result["connected_id"])
                    )

            for result in incoming_results:
                if result["node_id"] and result["connected_id"]:
                    edges_norm[result["node_id"]].append(
                        (result["connected_id"], result["node_id"])
                    )

        out: dict[str, list[tuple[str, str]]] = {}
        for orig in node_ids:
            n = self._normalize_node_id(orig)
            out[orig] = edges_norm.get(n, [])

        return out

    async def get_all_labels(self) -> list[str]:
        """
        Get all labels (node IDs) in the graph.

        Returns:
            list[str]: A list of all labels in the graph.
        """
        query = (
            """SELECT * FROM cypher('%s', $$
                     MATCH (n:base)
                     WHERE n.entity_id IS NOT NULL
                     RETURN DISTINCT n.entity_id AS label
                     ORDER BY n.entity_id
                   $$) AS (label text)"""
            % self.graph_name
        )

        results = await self._query(query)
        labels = []
        for result in results:
            if result and isinstance(result, dict) and "label" in result:
                labels.append(result["label"])
        return labels

    async def get_nodes_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]:
        """
        Retrieves nodes from the graph that are associated with a given list of chunk IDs.
        This method uses a Cypher query with UNWIND to efficiently find all nodes
        where the `source_id` property contains any of the specified chunk IDs.
        """
        # The string representation of the list for the cypher query
        chunk_ids_str = json.dumps(chunk_ids)

        query = f"""
            SELECT * FROM cypher('{self.graph_name}', $$
                UNWIND {chunk_ids_str} AS chunk_id
                MATCH (n:base)
                WHERE n.source_id IS NOT NULL AND chunk_id IN split(n.source_id, '{GRAPH_FIELD_SEP}')
                RETURN n
            $$) AS (n agtype);
        """
        results = await self._query(query)

        # Build result list
        nodes = []
        for result in results:
            if result["n"]:
                node_dict = result["n"]["properties"]

                # Process string result, parse it to JSON dictionary
                if isinstance(node_dict, str):
                    try:
                        node_dict = json.loads(node_dict)
                    except json.JSONDecodeError:
                        logger.warning(
                            f"[{self.workspace}] Failed to parse node string in batch: {node_dict}"
                        )

                node_dict["id"] = node_dict["entity_id"]
                nodes.append(node_dict)

        return nodes

    async def get_edges_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]:
        """
        Retrieves edges from the graph that are associated with a given list of chunk IDs.
        This method uses a Cypher query with UNWIND to efficiently find all edges
        where the `source_id` property contains any of the specified chunk IDs.
        """
        chunk_ids_str = json.dumps(chunk_ids)

        query = f"""
            SELECT * FROM cypher('{self.graph_name}', $$
                UNWIND {chunk_ids_str} AS chunk_id
                MATCH ()-[r]-()
                WHERE r.source_id IS NOT NULL AND chunk_id IN split(r.source_id, '{GRAPH_FIELD_SEP}')
                RETURN DISTINCT r, startNode(r) AS source, endNode(r) AS target
            $$) AS (edge agtype, source agtype, target agtype);
        """
        results = await self._query(query)
        edges = []
        if results:
            for item in results:
                edge_agtype = item["edge"]["properties"]
                # Process string result, parse it to JSON dictionary
                if isinstance(edge_agtype, str):
                    try:
                        edge_agtype = json.loads(edge_agtype)
                    except json.JSONDecodeError:
                        logger.warning(
                            f"[{self.workspace}] Failed to parse edge string in batch: {edge_agtype}"
                        )

                source_agtype = item["source"]["properties"]
                # Process string result, parse it to JSON dictionary
                if isinstance(source_agtype, str):
                    try:
                        source_agtype = json.loads(source_agtype)
                    except json.JSONDecodeError:
                        logger.warning(
                            f"[{self.workspace}] Failed to parse node string in batch: {source_agtype}"
                        )

                target_agtype = item["target"]["properties"]
                # Process string result, parse it to JSON dictionary
                if isinstance(target_agtype, str):
                    try:
                        target_agtype = json.loads(target_agtype)
                    except json.JSONDecodeError:
                        logger.warning(
                            f"[{self.workspace}] Failed to parse node string in batch: {target_agtype}"
                        )

                if edge_agtype and source_agtype and target_agtype:
                    edge_properties = edge_agtype
                    edge_properties["source"] = source_agtype["entity_id"]
                    edge_properties["target"] = target_agtype["entity_id"]
                    edges.append(edge_properties)
        return edges

    async def _bfs_subgraph(
        self, node_label: str, max_depth: int, max_nodes: int
    ) -> KnowledgeGraph:
        """
        Implements a true breadth-first search algorithm for subgraph retrieval.
        This method is used as a fallback when the standard Cypher query is too slow
        or when we need to guarantee BFS ordering.

        Args:
            node_label: Label of the starting node
            max_depth: Maximum depth of the subgraph
            max_nodes: Maximum number of nodes to return

        Returns:
            KnowledgeGraph object containing nodes and edges
        """
        from collections import deque

        result = KnowledgeGraph()
        visited_nodes = set()
        visited_node_ids = set()
        visited_edges = set()
        visited_edge_pairs = set()

        # Get starting node data
        label = self._normalize_node_id(node_label)
        query = """SELECT * FROM cypher('%s', $$
                    MATCH (n:base {entity_id: "%s"})
                    RETURN id(n) as node_id, n
                  $$) AS (node_id bigint, n agtype)""" % (self.graph_name, label)

        node_result = await self._query(query)
        if not node_result or not node_result[0].get("n"):
            return result

        # Create initial KnowledgeGraphNode
        start_node_data = node_result[0]["n"]
        entity_id = start_node_data["properties"]["entity_id"]
        internal_id = str(start_node_data["id"])

        start_node = KnowledgeGraphNode(
            id=internal_id,
            labels=[entity_id],
            properties=start_node_data["properties"],
        )

        # Initialize BFS queue, each element is a tuple of (node, depth)
        queue = deque([(start_node, 0)])

        visited_nodes.add(entity_id)
        visited_node_ids.add(internal_id)
        result.nodes.append(start_node)

        result.is_truncated = False

        # BFS search main loop
        while queue:
            # Get all nodes at the current depth
            current_level_nodes = []
            current_depth = None

            # Determine current depth
            if queue:
                current_depth = queue[0][1]

            # Extract all nodes at current depth from the queue
            while queue and queue[0][1] == current_depth:
                node, depth = queue.popleft()
                if depth > max_depth:
                    continue
                current_level_nodes.append(node)

            if not current_level_nodes:
                continue

            # Check depth limit
            if current_depth > max_depth:
                continue

            # Prepare node IDs list
            node_ids = [node.labels[0] for node in current_level_nodes]
            formatted_ids = ", ".join(
                [f'"{self._normalize_node_id(node_id)}"' for node_id in node_ids]
            )

            # Construct batch query for outgoing edges
            outgoing_query = f"""SELECT * FROM cypher('{self.graph_name}', $$
                UNWIND [{formatted_ids}] AS node_id
                MATCH (n:base {{entity_id: node_id}})
                OPTIONAL MATCH (n)-[r]->(neighbor:base)
                RETURN node_id AS current_id,
                       id(n) AS current_internal_id,
                       id(neighbor) AS neighbor_internal_id,
                       neighbor.entity_id AS neighbor_id,
                       id(r) AS edge_id,
                       r,
                       neighbor,
                       true AS is_outgoing
              $$) AS (current_id text, current_internal_id bigint, neighbor_internal_id bigint,
                      neighbor_id text, edge_id bigint, r agtype, neighbor agtype, is_outgoing bool)"""

            # Construct batch query for incoming edges
            incoming_query = f"""SELECT * FROM cypher('{self.graph_name}', $$
                UNWIND [{formatted_ids}] AS node_id
                MATCH (n:base {{entity_id: node_id}})
                OPTIONAL MATCH (n)<-[r]-(neighbor:base)
                RETURN node_id AS current_id,
                       id(n) AS current_internal_id,
                       id(neighbor) AS neighbor_internal_id,
                       neighbor.entity_id AS neighbor_id,
                       id(r) AS edge_id,
                       r,
                       neighbor,
                       false AS is_outgoing
              $$) AS (current_id text, current_internal_id bigint, neighbor_internal_id bigint,
                      neighbor_id text, edge_id bigint, r agtype, neighbor agtype, is_outgoing bool)"""

            # Execute queries
            outgoing_results = await self._query(outgoing_query)
            incoming_results = await self._query(incoming_query)

            # Combine results
            neighbors = outgoing_results + incoming_results

            # Create mapping from node ID to node object
            node_map = {node.labels[0]: node for node in current_level_nodes}

            # Process all results in a single loop
            for record in neighbors:
                if not record.get("neighbor") or not record.get("r"):
                    continue

                # Get current node information
                current_entity_id = record["current_id"]
                current_node = node_map[current_entity_id]

                # Get neighbor node information
                neighbor_entity_id = record["neighbor_id"]
                neighbor_internal_id = str(record["neighbor_internal_id"])
                is_outgoing = record["is_outgoing"]

                # Determine edge direction
                if is_outgoing:
                    source_id = current_node.id
                    target_id = neighbor_internal_id
                else:
                    source_id = neighbor_internal_id
                    target_id = current_node.id

                if not neighbor_entity_id:
                    continue

                # Get edge and node information
                b_node = record["neighbor"]
                rel = record["r"]
                edge_id = str(record["edge_id"])

                # Create neighbor node object
                neighbor_node = KnowledgeGraphNode(
                    id=neighbor_internal_id,
                    labels=[neighbor_entity_id],
                    properties=b_node["properties"],
                )

                # Sort entity_ids to ensure (A,B) and (B,A) are treated as the same edge
                sorted_pair = tuple(sorted([current_entity_id, neighbor_entity_id]))

                # Create edge object
                edge = KnowledgeGraphEdge(
                    id=edge_id,
                    type=rel["label"],
                    source=source_id,
                    target=target_id,
                    properties=rel["properties"],
                )

                if neighbor_internal_id in visited_node_ids:
                    # Add backward edge if neighbor node is already visited
                    if (
                        edge_id not in visited_edges
                        and sorted_pair not in visited_edge_pairs
                    ):
                        result.edges.append(edge)
                        visited_edges.add(edge_id)
                        visited_edge_pairs.add(sorted_pair)
                else:
                    if len(visited_node_ids) < max_nodes and current_depth < max_depth:
                        # Add new node to result and queue
                        result.nodes.append(neighbor_node)
                        visited_nodes.add(neighbor_entity_id)
                        visited_node_ids.add(neighbor_internal_id)

                        # Add node to queue with incremented depth
                        queue.append((neighbor_node, current_depth + 1))

                        # Add forward edge
                        if (
                            edge_id not in visited_edges
                            and sorted_pair not in visited_edge_pairs
                        ):
                            result.edges.append(edge)
                            visited_edges.add(edge_id)
                            visited_edge_pairs.add(sorted_pair)
                    else:
                        if current_depth < max_depth:
                            result.is_truncated = True

        return result

    async def get_knowledge_graph(
        self,
        node_label: str,
        max_depth: int = 3,
        max_nodes: int = None,
    ) -> KnowledgeGraph:
        """
        Retrieve a connected subgraph of nodes where the label includes the specified `node_label`.

        Args:
            node_label: Label of the starting node, * means all nodes
            max_depth: Maximum depth of the subgraph, Defaults to 3
            max_nodes: Maximum nodes to return, Defaults to global_config max_graph_nodes

        Returns:
            KnowledgeGraph object containing nodes and edges, with an is_truncated flag
            indicating whether the graph was truncated due to max_nodes limit
        """
        # Use global_config max_graph_nodes as default if max_nodes is None
        if max_nodes is None:
            max_nodes = self.global_config.get("max_graph_nodes", 1000)
        else:
            # Limit max_nodes to not exceed global_config max_graph_nodes
            max_nodes = min(max_nodes, self.global_config.get("max_graph_nodes", 1000))
        kg = KnowledgeGraph()

        # Handle wildcard query - get all nodes
        if node_label == "*":
            # First check total node count to determine if graph should be truncated
            count_query = f"""SELECT * FROM cypher('{self.graph_name}', $$
                    MATCH (n:base)
                    RETURN count(distinct n) AS total_nodes
                    $$) AS (total_nodes bigint)"""

            count_result = await self._query(count_query)
            total_nodes = count_result[0]["total_nodes"] if count_result else 0
            is_truncated = total_nodes > max_nodes

            # Get max_nodes with highest degrees
            query_nodes = f"""SELECT * FROM cypher('{self.graph_name}', $$
                    MATCH (n:base)
                    OPTIONAL MATCH (n)-[r]->()
                    RETURN id(n) as node_id, count(r) as degree
                $$) AS (node_id BIGINT, degree BIGINT)
                ORDER BY degree DESC
                LIMIT {max_nodes}"""
            node_results = await self._query(query_nodes)

            node_ids = [str(result["node_id"]) for result in node_results]

            logger.info(
                f"[{self.workspace}] Total nodes: {total_nodes}, Selected nodes: {len(node_ids)}"
            )

            if node_ids:
                formatted_ids = ", ".join(node_ids)
                # Construct batch query for subgraph within max_nodes
                query = f"""SELECT * FROM cypher('{self.graph_name}', $$
                        WITH [{formatted_ids}] AS node_ids
                        MATCH (a)
                        WHERE id(a) IN node_ids
                        OPTIONAL MATCH (a)-[r]->(b)
                            WHERE id(b) IN node_ids
                        RETURN a, r, b
                    $$) AS (a AGTYPE, r AGTYPE, b AGTYPE)"""
                results = await self._query(query)

                # Process query results, deduplicate nodes and edges
                nodes_dict = {}
                edges_dict = {}
                for result in results:
                    # Process node a
                    if result.get("a") and isinstance(result["a"], dict):
                        node_a = result["a"]
                        node_id = str(node_a["id"])
                        if node_id not in nodes_dict and "properties" in node_a:
                            nodes_dict[node_id] = KnowledgeGraphNode(
                                id=node_id,
                                labels=[node_a["properties"]["entity_id"]],
                                properties=node_a["properties"],
                            )

                    # Process node b
                    if result.get("b") and isinstance(result["b"], dict):
                        node_b = result["b"]
                        node_id = str(node_b["id"])
                        if node_id not in nodes_dict and "properties" in node_b:
                            nodes_dict[node_id] = KnowledgeGraphNode(
                                id=node_id,
                                labels=[node_b["properties"]["entity_id"]],
                                properties=node_b["properties"],
                            )

                    # Process edge r
                    if result.get("r") and isinstance(result["r"], dict):
                        edge = result["r"]
                        edge_id = str(edge["id"])
                        if edge_id not in edges_dict:
                            edges_dict[edge_id] = KnowledgeGraphEdge(
                                id=edge_id,
                                type=edge["label"],
                                source=str(edge["start_id"]),
                                target=str(edge["end_id"]),
                                properties=edge["properties"],
                            )

                kg = KnowledgeGraph(
                    nodes=list(nodes_dict.values()),
                    edges=list(edges_dict.values()),
                    is_truncated=is_truncated,
                )
            else:
                # For single node query, use BFS algorithm
                kg = await self._bfs_subgraph(node_label, max_depth, max_nodes)

            logger.info(
                f"[{self.workspace}] Subgraph query successful | Node count: {len(kg.nodes)} | Edge count: {len(kg.edges)}"
            )
        else:
            # For non-wildcard queries, use the BFS algorithm
            kg = await self._bfs_subgraph(node_label, max_depth, max_nodes)
            logger.info(
                f"[{self.workspace}] Subgraph query for '{node_label}' successful | Node count: {len(kg.nodes)} | Edge count: {len(kg.edges)}"
            )

        return kg

    async def get_all_nodes(self) -> list[dict]:
        """Get all nodes in the graph.

        Returns:
            A list of all nodes, where each node is a dictionary of its properties
        """
        query = f"""SELECT * FROM cypher('{self.graph_name}', $$
                     MATCH (n:base)
                     RETURN n
                   $$) AS (n agtype)"""

        results = await self._query(query)
        nodes = []
        for result in results:
            if result["n"]:
                node_dict = result["n"]["properties"]

                # Process string result, parse it to JSON dictionary
                if isinstance(node_dict, str):
                    try:
                        node_dict = json.loads(node_dict)
                    except json.JSONDecodeError:
                        logger.warning(
                            f"[{self.workspace}] Failed to parse node string: {node_dict}"
                        )

                # Add node id (entity_id) to the dictionary for easier access
                node_dict["id"] = node_dict.get("entity_id")
                nodes.append(node_dict)
        return nodes

    async def get_all_edges(self) -> list[dict]:
        """Get all edges in the graph.

        Returns:
            A list of all edges, where each edge is a dictionary of its properties
            (The edge is bidirectional; deduplication must be handled by the caller)
        """
        query = f"""SELECT * FROM cypher('{self.graph_name}', $$
                     MATCH (a:base)-[r]-(b:base)
                     RETURN DISTINCT a.entity_id AS source, b.entity_id AS target, properties(r) AS properties
                   $$) AS (source text, target text, properties agtype)"""

        results = await self._query(query)
        edges = []
        for result in results:
            edge_properties = result["properties"]

            # Process string result, parse it to JSON dictionary
            if isinstance(edge_properties, str):
                try:
                    edge_properties = json.loads(edge_properties)
                except json.JSONDecodeError:
                    logger.warning(
                        f"[{self.workspace}] Failed to parse edge properties string: {edge_properties}"
                    )
                    edge_properties = {}

            edge_properties["source"] = result["source"]
            edge_properties["target"] = result["target"]
            edges.append(edge_properties)
        return edges

    async def get_popular_labels(self, limit: int = 300) -> list[str]:
        """Get popular labels by node degree (most connected entities) using native SQL for performance."""
        try:
            # Native SQL query to calculate node degrees directly from AGE's underlying tables
            # This is significantly faster than using the cypher() function wrapper
            query = f"""
            WITH node_degrees AS (
                SELECT
                    node_id,
                    COUNT(*) AS degree
                FROM (
                    SELECT start_id AS node_id FROM {self.graph_name}._ag_label_edge
                    UNION ALL
                    SELECT end_id AS node_id FROM {self.graph_name}._ag_label_edge
                ) AS all_edges
                GROUP BY node_id
            )
            SELECT
                (ag_catalog.agtype_access_operator(VARIADIC ARRAY[v.properties, '"entity_id"'::agtype]))::text AS label
            FROM
                node_degrees d
            JOIN
                {self.graph_name}._ag_label_vertex v ON d.node_id = v.id
            WHERE
                ag_catalog.agtype_access_operator(VARIADIC ARRAY[v.properties, '"entity_id"'::agtype]) IS NOT NULL
            ORDER BY
                d.degree DESC,
                label ASC
            LIMIT $1;
            """
            results = await self._query(query, params={"limit": limit})
            labels = [
                result["label"] for result in results if result and "label" in result
            ]

            logger.debug(
                f"[{self.workspace}] Retrieved {len(labels)} popular labels (limit: {limit})"
            )
            return labels
        except Exception as e:
            logger.error(f"[{self.workspace}] Error getting popular labels: {str(e)}")
            return []

    async def search_labels(self, query: str, limit: int = 50) -> list[str]:
        """Search labels with fuzzy matching using native, parameterized SQL for performance and security."""
        query_lower = query.lower().strip()
        if not query_lower:
            return []

        try:
            # Re-implementing with the correct agtype access operator and full scoring logic.
            sql_query = f"""
            WITH ranked_labels AS (
                SELECT
                    (ag_catalog.agtype_access_operator(VARIADIC ARRAY[properties, '"entity_id"'::agtype]))::text AS label,
                    LOWER((ag_catalog.agtype_access_operator(VARIADIC ARRAY[properties, '"entity_id"'::agtype]))::text) AS label_lower
                FROM
                    {self.graph_name}._ag_label_vertex
                WHERE
                    ag_catalog.agtype_access_operator(VARIADIC ARRAY[properties, '"entity_id"'::agtype]) IS NOT NULL
                    AND LOWER((ag_catalog.agtype_access_operator(VARIADIC ARRAY[properties, '"entity_id"'::agtype]))::text) ILIKE $1
            )
            SELECT
                label
            FROM (
                SELECT
                    label,
                    CASE
                        WHEN label_lower = $2 THEN 1000
                        WHEN label_lower LIKE $3 THEN 500
                        ELSE (100 - LENGTH(label))
                    END +
                    CASE
                        WHEN label_lower LIKE $4 OR label_lower LIKE $5 THEN 50
                        ELSE 0
                    END AS score
                FROM
                    ranked_labels
            ) AS scored_labels
            ORDER BY
                score DESC,
                label ASC
            LIMIT $6;
            """
            params = (
                f"%{query_lower}%",  # For the main ILIKE clause ($1)
                query_lower,  # For exact match ($2)
                f"{query_lower}%",  # For prefix match ($3)
                f"% {query_lower}%",  # For word boundary (space) ($4)
                f"%_{query_lower}%",  # For word boundary (underscore) ($5)
                limit,  # For LIMIT ($6)
            )
            results = await self._query(sql_query, params=dict(enumerate(params, 1)))
            labels = [
                result["label"] for result in results if result and "label" in result
            ]

            logger.debug(
                f"[{self.workspace}] Search query '{query}' returned {len(labels)} results (limit: {limit})"
            )
            return labels
        except Exception as e:
            logger.error(
                f"[{self.workspace}] Error searching labels with query '{query}': {str(e)}"
            )
            return []

    async def drop(self) -> dict[str, str]:
        """Drop the storage"""
        async with get_graph_db_lock():
            try:
                drop_query = f"""SELECT * FROM cypher('{self.graph_name}', $$
                                MATCH (n)
                                DETACH DELETE n
                                $$) AS (result agtype)"""

                await self._query(drop_query, readonly=False)
                return {
                    "status": "success",
                    "message": f"workspace '{self.workspace}' graph data dropped",
                }
            except Exception as e:
                logger.error(f"[{self.workspace}] Error dropping graph: {e}")
                return {"status": "error", "message": str(e)}


# Note: Order matters! More specific namespaces (e.g., "full_entities") must come before
# more general ones (e.g., "entities") because is_namespace() uses endswith() matching
NAMESPACE_TABLE_MAP = {
    NameSpace.KV_STORE_FULL_DOCS: "LIGHTRAG_DOC_FULL",
    NameSpace.KV_STORE_TEXT_CHUNKS: "LIGHTRAG_DOC_CHUNKS",
    NameSpace.KV_STORE_FULL_ENTITIES: "LIGHTRAG_FULL_ENTITIES",
    NameSpace.KV_STORE_FULL_RELATIONS: "LIGHTRAG_FULL_RELATIONS",
    NameSpace.KV_STORE_LLM_RESPONSE_CACHE: "LIGHTRAG_LLM_CACHE",
    NameSpace.VECTOR_STORE_CHUNKS: "LIGHTRAG_VDB_CHUNKS",
    NameSpace.VECTOR_STORE_ENTITIES: "LIGHTRAG_VDB_ENTITY",
    NameSpace.VECTOR_STORE_RELATIONSHIPS: "LIGHTRAG_VDB_RELATION",
    NameSpace.DOC_STATUS: "LIGHTRAG_DOC_STATUS",
}


def namespace_to_table_name(namespace: str) -> str:
    for k, v in NAMESPACE_TABLE_MAP.items():
        if is_namespace(namespace, k):
            return v


TABLES = {
    "LIGHTRAG_DOC_FULL": {
        "ddl": """CREATE TABLE LIGHTRAG_DOC_FULL (
                    id VARCHAR(255),
                    workspace VARCHAR(255),
                    doc_name VARCHAR(1024),
                    content TEXT,
                    meta JSONB,
                    create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
                    update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
	                CONSTRAINT LIGHTRAG_DOC_FULL_PK PRIMARY KEY (workspace, id)
                    )"""
    },
    "LIGHTRAG_DOC_CHUNKS": {
        "ddl": """CREATE TABLE LIGHTRAG_DOC_CHUNKS (
                    id VARCHAR(255),
                    workspace VARCHAR(255),
                    full_doc_id VARCHAR(256),
                    chunk_order_index INTEGER,
                    tokens INTEGER,
                    content TEXT,
                    file_path TEXT NULL,
                    llm_cache_list JSONB NULL DEFAULT '[]'::jsonb,
                    create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
                    update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
	                CONSTRAINT LIGHTRAG_DOC_CHUNKS_PK PRIMARY KEY (workspace, id)
                    )"""
    },
    "LIGHTRAG_VDB_CHUNKS": {
        "ddl": f"""CREATE TABLE LIGHTRAG_VDB_CHUNKS (
                    id VARCHAR(255),
                    workspace VARCHAR(255),
                    full_doc_id VARCHAR(256),
                    chunk_order_index INTEGER,
                    tokens INTEGER,
                    content TEXT,
                    content_vector VECTOR({os.environ.get("EMBEDDING_DIM", 1024)}),
                    file_path TEXT NULL,
                    create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
                    update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
	                CONSTRAINT LIGHTRAG_VDB_CHUNKS_PK PRIMARY KEY (workspace, id)
                    )"""
    },
    "LIGHTRAG_VDB_ENTITY": {
        "ddl": f"""CREATE TABLE LIGHTRAG_VDB_ENTITY (
                    id VARCHAR(255),
                    workspace VARCHAR(255),
                    entity_name VARCHAR(512),
                    content TEXT,
                    content_vector VECTOR({os.environ.get("EMBEDDING_DIM", 1024)}),
                    create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
                    update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
                    chunk_ids VARCHAR(255)[] NULL,
                    file_path TEXT NULL,
	                CONSTRAINT LIGHTRAG_VDB_ENTITY_PK PRIMARY KEY (workspace, id)
                    )"""
    },
    "LIGHTRAG_VDB_RELATION": {
        "ddl": f"""CREATE TABLE LIGHTRAG_VDB_RELATION (
                    id VARCHAR(255),
                    workspace VARCHAR(255),
                    source_id VARCHAR(512),
                    target_id VARCHAR(512),
                    content TEXT,
                    content_vector VECTOR({os.environ.get("EMBEDDING_DIM", 1024)}),
                    create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
                    update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
                    chunk_ids VARCHAR(255)[] NULL,
                    file_path TEXT NULL,
	                CONSTRAINT LIGHTRAG_VDB_RELATION_PK PRIMARY KEY (workspace, id)
                    )"""
    },
    "LIGHTRAG_LLM_CACHE": {
        "ddl": """CREATE TABLE LIGHTRAG_LLM_CACHE (
	                workspace varchar(255) NOT NULL,
	                id varchar(255) NOT NULL,
                    original_prompt TEXT,
                    return_value TEXT,
                    chunk_id VARCHAR(255) NULL,
                    cache_type VARCHAR(32),
                    queryparam JSONB NULL,
                    create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                    update_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
	                CONSTRAINT LIGHTRAG_LLM_CACHE_PK PRIMARY KEY (workspace, id)
                    )"""
    },
    "LIGHTRAG_DOC_STATUS": {
        "ddl": """CREATE TABLE LIGHTRAG_DOC_STATUS (
	               workspace varchar(255) NOT NULL,
	               id varchar(255) NOT NULL,
	               content_summary varchar(255) NULL,
	               content_length int4 NULL,
	               chunks_count int4 NULL,
	               status varchar(64) NULL,
	               file_path TEXT NULL,
	               chunks_list JSONB NULL DEFAULT '[]'::jsonb,
	               track_id varchar(255) NULL,
	               metadata JSONB NULL DEFAULT '{}'::jsonb,
	               error_msg TEXT NULL,
	               created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
	               updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
	               CONSTRAINT LIGHTRAG_DOC_STATUS_PK PRIMARY KEY (workspace, id)
	              )"""
    },
    "LIGHTRAG_FULL_ENTITIES": {
        "ddl": """CREATE TABLE LIGHTRAG_FULL_ENTITIES (
                    id VARCHAR(255),
                    workspace VARCHAR(255),
                    entity_names JSONB,
                    count INTEGER,
                    create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
                    update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
                    CONSTRAINT LIGHTRAG_FULL_ENTITIES_PK PRIMARY KEY (workspace, id)
                    )"""
    },
    "LIGHTRAG_FULL_RELATIONS": {
        "ddl": """CREATE TABLE LIGHTRAG_FULL_RELATIONS (
                    id VARCHAR(255),
                    workspace VARCHAR(255),
                    relation_pairs JSONB,
                    count INTEGER,
                    create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
                    update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
                    CONSTRAINT LIGHTRAG_FULL_RELATIONS_PK PRIMARY KEY (workspace, id)
                    )"""
    },
}


SQL_TEMPLATES = {
    # SQL for KVStorage
    "get_by_id_full_docs": """SELECT id, COALESCE(content, '') as content
                                FROM LIGHTRAG_DOC_FULL WHERE workspace=$1 AND id=$2
                            """,
    "get_by_id_text_chunks": """SELECT id, tokens, COALESCE(content, '') as content,
                                chunk_order_index, full_doc_id, file_path,
                                COALESCE(llm_cache_list, '[]'::jsonb) as llm_cache_list,
                                EXTRACT(EPOCH FROM create_time)::BIGINT as create_time,
                                EXTRACT(EPOCH FROM update_time)::BIGINT as update_time
                                FROM LIGHTRAG_DOC_CHUNKS WHERE workspace=$1 AND id=$2
                            """,
    "get_by_id_llm_response_cache": """SELECT id, 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 LIGHTRAG_LLM_CACHE WHERE workspace=$1 AND id=$2
                               """,
    "get_by_ids_full_docs": """SELECT id, COALESCE(content, '') as content
                                 FROM LIGHTRAG_DOC_FULL WHERE workspace=$1 AND id IN ({ids})
                            """,
    "get_by_ids_text_chunks": """SELECT id, tokens, COALESCE(content, '') as content,
                                  chunk_order_index, full_doc_id, file_path,
                                  COALESCE(llm_cache_list, '[]'::jsonb) as llm_cache_list,
                                  EXTRACT(EPOCH FROM create_time)::BIGINT as create_time,
                                  EXTRACT(EPOCH FROM update_time)::BIGINT as update_time
                                   FROM LIGHTRAG_DOC_CHUNKS WHERE workspace=$1 AND id IN ({ids})
                                """,
    "get_by_ids_llm_response_cache": """SELECT id, 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 LIGHTRAG_LLM_CACHE WHERE workspace=$1 AND id IN ({ids})
                                """,
    "get_by_id_full_entities": """SELECT id, entity_names, count,
                                EXTRACT(EPOCH FROM create_time)::BIGINT as create_time,
                                EXTRACT(EPOCH FROM update_time)::BIGINT as update_time
                                FROM LIGHTRAG_FULL_ENTITIES WHERE workspace=$1 AND id=$2
                               """,
    "get_by_id_full_relations": """SELECT id, relation_pairs, count,
                                EXTRACT(EPOCH FROM create_time)::BIGINT as create_time,
                                EXTRACT(EPOCH FROM update_time)::BIGINT as update_time
                                FROM LIGHTRAG_FULL_RELATIONS WHERE workspace=$1 AND id=$2
                               """,
    "get_by_ids_full_entities": """SELECT id, entity_names, count,
                                 EXTRACT(EPOCH FROM create_time)::BIGINT as create_time,
                                 EXTRACT(EPOCH FROM update_time)::BIGINT as update_time
                                 FROM LIGHTRAG_FULL_ENTITIES WHERE workspace=$1 AND id IN ({ids})
                                """,
    "get_by_ids_full_relations": """SELECT id, relation_pairs, count,
                                 EXTRACT(EPOCH FROM create_time)::BIGINT as create_time,
                                 EXTRACT(EPOCH FROM update_time)::BIGINT as update_time
                                 FROM LIGHTRAG_FULL_RELATIONS WHERE workspace=$1 AND id IN ({ids})
                                """,
    "filter_keys": "SELECT id FROM {table_name} WHERE workspace=$1 AND id IN ({ids})",
    "upsert_doc_full": """INSERT INTO LIGHTRAG_DOC_FULL (id, content, workspace)
                        VALUES ($1, $2, $3)
                        ON CONFLICT (workspace,id) DO UPDATE
                           SET content = $2, update_time = CURRENT_TIMESTAMP
                       """,
    "upsert_llm_response_cache": """INSERT INTO LIGHTRAG_LLM_CACHE(workspace,id,original_prompt,return_value,chunk_id,cache_type,queryparam)
                                      VALUES ($1, $2, $3, $4, $5, $6, $7)
                                      ON CONFLICT (workspace,id) DO UPDATE
                                      SET original_prompt = EXCLUDED.original_prompt,
                                      return_value=EXCLUDED.return_value,
                                      chunk_id=EXCLUDED.chunk_id,
                                      cache_type=EXCLUDED.cache_type,
                                      queryparam=EXCLUDED.queryparam,
                                      update_time = CURRENT_TIMESTAMP
                                     """,
    "upsert_text_chunk": """INSERT INTO LIGHTRAG_DOC_CHUNKS (workspace, id, tokens,
                      chunk_order_index, full_doc_id, content, file_path, llm_cache_list,
                      create_time, update_time)
                      VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10)
                      ON CONFLICT (workspace,id) DO UPDATE
                      SET tokens=EXCLUDED.tokens,
                      chunk_order_index=EXCLUDED.chunk_order_index,
                      full_doc_id=EXCLUDED.full_doc_id,
                      content = EXCLUDED.content,
                      file_path=EXCLUDED.file_path,
                      llm_cache_list=EXCLUDED.llm_cache_list,
                      update_time = EXCLUDED.update_time
                     """,
    "upsert_full_entities": """INSERT INTO LIGHTRAG_FULL_ENTITIES (workspace, id, entity_names, count,
                      create_time, update_time)
                      VALUES ($1, $2, $3, $4, $5, $6)
                      ON CONFLICT (workspace,id) DO UPDATE
                      SET entity_names=EXCLUDED.entity_names,
                      count=EXCLUDED.count,
                      update_time = EXCLUDED.update_time
                     """,
    "upsert_full_relations": """INSERT INTO LIGHTRAG_FULL_RELATIONS (workspace, id, relation_pairs, count,
                      create_time, update_time)
                      VALUES ($1, $2, $3, $4, $5, $6)
                      ON CONFLICT (workspace,id) DO UPDATE
                      SET relation_pairs=EXCLUDED.relation_pairs,
                      count=EXCLUDED.count,
                      update_time = EXCLUDED.update_time
                     """,
    # SQL for VectorStorage
    "upsert_chunk": """INSERT INTO LIGHTRAG_VDB_CHUNKS (workspace, id, tokens,
                      chunk_order_index, full_doc_id, content, content_vector, file_path,
                      create_time, update_time)
                      VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10)
                      ON CONFLICT (workspace,id) DO UPDATE
                      SET tokens=EXCLUDED.tokens,
                      chunk_order_index=EXCLUDED.chunk_order_index,
                      full_doc_id=EXCLUDED.full_doc_id,
                      content = EXCLUDED.content,
                      content_vector=EXCLUDED.content_vector,
                      file_path=EXCLUDED.file_path,
                      update_time = EXCLUDED.update_time
                     """,
    "upsert_entity": """INSERT INTO LIGHTRAG_VDB_ENTITY (workspace, id, entity_name, content,
                      content_vector, chunk_ids, file_path, create_time, update_time)
                      VALUES ($1, $2, $3, $4, $5, $6::varchar[], $7, $8, $9)
                      ON CONFLICT (workspace,id) DO UPDATE
                      SET entity_name=EXCLUDED.entity_name,
                      content=EXCLUDED.content,
                      content_vector=EXCLUDED.content_vector,
                      chunk_ids=EXCLUDED.chunk_ids,
                      file_path=EXCLUDED.file_path,
                      update_time=EXCLUDED.update_time
                     """,
    "upsert_relationship": """INSERT INTO LIGHTRAG_VDB_RELATION (workspace, id, source_id,
                      target_id, content, content_vector, chunk_ids, file_path, create_time, update_time)
                      VALUES ($1, $2, $3, $4, $5, $6, $7::varchar[], $8, $9, $10)
                      ON CONFLICT (workspace,id) DO UPDATE
                      SET source_id=EXCLUDED.source_id,
                      target_id=EXCLUDED.target_id,
                      content=EXCLUDED.content,
                      content_vector=EXCLUDED.content_vector,
                      chunk_ids=EXCLUDED.chunk_ids,
                      file_path=EXCLUDED.file_path,
                      update_time = EXCLUDED.update_time
                     """,
    "relationships": """
                     SELECT r.source_id AS src_id,
                            r.target_id AS tgt_id,
                            EXTRACT(EPOCH FROM r.create_time)::BIGINT AS created_at
                     FROM LIGHTRAG_VDB_RELATION r
                     WHERE r.workspace = $1
                       AND r.content_vector <=> '[{embedding_string}]'::vector < $2
                     ORDER BY r.content_vector <=> '[{embedding_string}]'::vector
                     LIMIT $3;
                     """,
    "entities": """
                SELECT e.entity_name,
                       EXTRACT(EPOCH FROM e.create_time)::BIGINT AS created_at
                FROM LIGHTRAG_VDB_ENTITY e
                WHERE e.workspace = $1
                  AND e.content_vector <=> '[{embedding_string}]'::vector < $2
                ORDER BY e.content_vector <=> '[{embedding_string}]'::vector
                LIMIT $3;
                """,
    "chunks": """
              SELECT c.id,
                     c.content,
                     c.file_path,
                     EXTRACT(EPOCH FROM c.create_time)::BIGINT AS created_at
              FROM LIGHTRAG_VDB_CHUNKS c
              WHERE c.workspace = $1
                AND c.content_vector <=> '[{embedding_string}]'::vector < $2
              ORDER BY c.content_vector <=> '[{embedding_string}]'::vector
              LIMIT $3;
              """,
    # DROP tables
    "drop_specifiy_table_workspace": """
        DELETE FROM {table_name} WHERE workspace=$1
       """,
}
