from __future__ import annotations

import traceback
import asyncio
import configparser
import os
import warnings
from dataclasses import asdict, dataclass, field
from datetime import datetime
from functools import partial
from typing import (
    Any,
    AsyncIterator,
    Callable,
    Iterator,
    cast,
    final,
    Literal,
    Optional,
    List,
    Dict,
)

from lightrag.kg import (
    STORAGES,
    verify_storage_implementation,
)

from lightrag.kg.shared_storage import (
    get_namespace_data,
    get_pipeline_status_lock,
)

from .base import (
    BaseGraphStorage,
    BaseKVStorage,
    BaseVectorStorage,
    DocProcessingStatus,
    DocStatus,
    DocStatusStorage,
    QueryParam,
    StorageNameSpace,
    StoragesStatus,
)
from .namespace import NameSpace, make_namespace
from .operate import (
    chunking_by_token_size,
    extract_entities,
    kg_query,
    mix_kg_vector_query,
    naive_query,
    query_with_keywords,
)
from .prompt import GRAPH_FIELD_SEP, PROMPTS
from .utils import (
    Tokenizer,
    TiktokenTokenizer,
    EmbeddingFunc,
    always_get_an_event_loop,
    compute_mdhash_id,
    convert_response_to_json,
    lazy_external_import,
    limit_async_func_call,
    get_content_summary,
    clean_text,
    check_storage_env_vars,
    logger,
)
from .types import KnowledgeGraph
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)

# TODO: TO REMOVE @Yannick
config = configparser.ConfigParser()
config.read("config.ini", "utf-8")


@final
@dataclass
class LightRAG:
    """LightRAG: Simple and Fast Retrieval-Augmented Generation."""

    # Directory
    # ---

    working_dir: str = field(
        default=f"./lightrag_cache_{datetime.now().strftime('%Y-%m-%d-%H:%M:%S')}"
    )
    """Directory where cache and temporary files are stored."""

    # Storage
    # ---

    kv_storage: str = field(default="JsonKVStorage")
    """Storage backend for key-value data."""

    vector_storage: str = field(default="NanoVectorDBStorage")
    """Storage backend for vector embeddings."""

    graph_storage: str = field(default="NetworkXStorage")
    """Storage backend for knowledge graphs."""

    doc_status_storage: str = field(default="JsonDocStatusStorage")
    """Storage type for tracking document processing statuses."""

    # Logging (Deprecated, use setup_logger in utils.py instead)
    # ---
    log_level: int | None = field(default=None)
    log_file_path: str | None = field(default=None)

    # Entity extraction
    # ---

    entity_extract_max_gleaning: int = field(default=1)
    """Maximum number of entity extraction attempts for ambiguous content."""

    summary_to_max_tokens: int = field(default=int(os.getenv("MAX_TOKEN_SUMMARY", 500)))

    force_llm_summary_on_merge: int = field(
        default=int(os.getenv("FORCE_LLM_SUMMARY_ON_MERGE", 6))
    )

    # Text chunking
    # ---

    chunk_token_size: int = field(default=int(os.getenv("CHUNK_SIZE", 1200)))
    """Maximum number of tokens per text chunk when splitting documents."""

    chunk_overlap_token_size: int = field(
        default=int(os.getenv("CHUNK_OVERLAP_SIZE", 100))
    )
    """Number of overlapping tokens between consecutive text chunks to preserve context."""

    tokenizer: Optional[Tokenizer] = field(default=None)
    """
    A function that returns a Tokenizer instance.
    If None, and a `tiktoken_model_name` is provided, a TiktokenTokenizer will be created.
    If both are None, the default TiktokenTokenizer is used.
    """

    tiktoken_model_name: str = field(default="gpt-4o-mini")
    """Model name used for tokenization when chunking text with tiktoken. Defaults to `gpt-4o-mini`."""

    chunking_func: Callable[
        [
            Tokenizer,
            str,
            Optional[str],
            bool,
            int,
            int,
        ],
        List[Dict[str, Any]],
    ] = field(default_factory=lambda: chunking_by_token_size)
    """
    Custom chunking function for splitting text into chunks before processing.

    The function should take the following parameters:

        - `tokenizer`: A Tokenizer instance to use for tokenization.
        - `content`: The text to be split into chunks.
        - `split_by_character`: The character to split the text on. If None, the text is split into chunks of `chunk_token_size` tokens.
        - `split_by_character_only`: If True, the text is split only on the specified character.
        - `chunk_token_size`: The maximum number of tokens per chunk.
        - `chunk_overlap_token_size`: The number of overlapping tokens between consecutive chunks.

    The function should return a list of dictionaries, where each dictionary contains the following keys:
        - `tokens`: The number of tokens in the chunk.
        - `content`: The text content of the chunk.

    Defaults to `chunking_by_token_size` if not specified.
    """

    # Embedding
    # ---

    embedding_func: EmbeddingFunc | None = field(default=None)
    """Function for computing text embeddings. Must be set before use."""

    embedding_batch_num: int = field(default=int(os.getenv("EMBEDDING_BATCH_NUM", 32)))
    """Batch size for embedding computations."""

    embedding_func_max_async: int = field(
        default=int(os.getenv("EMBEDDING_FUNC_MAX_ASYNC", 16))
    )
    """Maximum number of concurrent embedding function calls."""

    embedding_cache_config: dict[str, Any] = field(
        default_factory=lambda: {
            "enabled": False,
            "similarity_threshold": 0.95,
            "use_llm_check": False,
        }
    )
    """Configuration for embedding cache.
    - enabled: If True, enables caching to avoid redundant computations.
    - similarity_threshold: Minimum similarity score to use cached embeddings.
    - use_llm_check: If True, validates cached embeddings using an LLM.
    """

    # LLM Configuration
    # ---

    llm_model_func: Callable[..., object] | None = field(default=None)
    """Function for interacting with the large language model (LLM). Must be set before use."""

    llm_model_name: str = field(default="gpt-4o-mini")
    """Name of the LLM model used for generating responses."""

    llm_model_max_token_size: int = field(default=int(os.getenv("MAX_TOKENS", 32768)))
    """Maximum number of tokens allowed per LLM response."""

    llm_model_max_async: int = field(default=int(os.getenv("MAX_ASYNC", 4)))
    """Maximum number of concurrent LLM calls."""

    llm_model_kwargs: dict[str, Any] = field(default_factory=dict)
    """Additional keyword arguments passed to the LLM model function."""

    # Storage
    # ---

    vector_db_storage_cls_kwargs: dict[str, Any] = field(default_factory=dict)
    """Additional parameters for vector database storage."""

    # TODO：deprecated, remove in the future, use WORKSPACE instead
    namespace_prefix: str = field(default="")
    """Prefix for namespacing stored data across different environments."""

    enable_llm_cache: bool = field(default=True)
    """Enables caching for LLM responses to avoid redundant computations."""

    enable_llm_cache_for_entity_extract: bool = field(default=True)
    """If True, enables caching for entity extraction steps to reduce LLM costs."""

    # Extensions
    # ---

    max_parallel_insert: int = field(default=int(os.getenv("MAX_PARALLEL_INSERT", 2)))
    """Maximum number of parallel insert operations."""

    addon_params: dict[str, Any] = field(
        default_factory=lambda: {
            "language": os.getenv("SUMMARY_LANGUAGE", PROMPTS["DEFAULT_LANGUAGE"])
        }
    )

    # Storages Management
    # ---

    auto_manage_storages_states: bool = field(default=True)
    """If True, lightrag will automatically calls initialize_storages and finalize_storages at the appropriate times."""

    # Storages Management
    # ---

    convert_response_to_json_func: Callable[[str], dict[str, Any]] = field(
        default_factory=lambda: convert_response_to_json
    )
    """
    Custom function for converting LLM responses to JSON format.

    The default function is :func:`.utils.convert_response_to_json`.
    """

    cosine_better_than_threshold: float = field(
        default=float(os.getenv("COSINE_THRESHOLD", 0.2))
    )

    _storages_status: StoragesStatus = field(default=StoragesStatus.NOT_CREATED)

    def __post_init__(self):
        from lightrag.kg.shared_storage import (
            initialize_share_data,
        )

        # Handle deprecated parameters
        if self.log_level is not None:
            warnings.warn(
                "WARNING: log_level parameter is deprecated, use setup_logger in utils.py instead",
                UserWarning,
                stacklevel=2,
            )
        if self.log_file_path is not None:
            warnings.warn(
                "WARNING: log_file_path parameter is deprecated, use setup_logger in utils.py instead",
                UserWarning,
                stacklevel=2,
            )

        # Remove these attributes to prevent their use
        if hasattr(self, "log_level"):
            delattr(self, "log_level")
        if hasattr(self, "log_file_path"):
            delattr(self, "log_file_path")

        initialize_share_data()

        if not os.path.exists(self.working_dir):
            logger.info(f"Creating working directory {self.working_dir}")
            os.makedirs(self.working_dir)

        # Verify storage implementation compatibility and environment variables
        storage_configs = [
            ("KV_STORAGE", self.kv_storage),
            ("VECTOR_STORAGE", self.vector_storage),
            ("GRAPH_STORAGE", self.graph_storage),
            ("DOC_STATUS_STORAGE", self.doc_status_storage),
        ]

        for storage_type, storage_name in storage_configs:
            # Verify storage implementation compatibility
            verify_storage_implementation(storage_type, storage_name)
            # Check environment variables
            check_storage_env_vars(storage_name)

        # Ensure vector_db_storage_cls_kwargs has required fields
        self.vector_db_storage_cls_kwargs = {
            "cosine_better_than_threshold": self.cosine_better_than_threshold,
            **self.vector_db_storage_cls_kwargs,
        }

        # Init Tokenizer
        # Post-initialization hook to handle backward compatabile tokenizer initialization based on provided parameters
        if self.tokenizer is None:
            if self.tiktoken_model_name:
                self.tokenizer = TiktokenTokenizer(self.tiktoken_model_name)
            else:
                self.tokenizer = TiktokenTokenizer()

        # Fix global_config now
        global_config = asdict(self)
        _print_config = ",\n  ".join([f"{k} = {v}" for k, v in global_config.items()])
        logger.debug(f"LightRAG init with param:\n  {_print_config}\n")

        # Init Embedding
        self.embedding_func = limit_async_func_call(self.embedding_func_max_async)(  # type: ignore
            self.embedding_func
        )

        # Initialize all storages
        self.key_string_value_json_storage_cls: type[BaseKVStorage] = (
            self._get_storage_class(self.kv_storage)
        )  # type: ignore
        self.vector_db_storage_cls: type[BaseVectorStorage] = self._get_storage_class(
            self.vector_storage
        )  # type: ignore
        self.graph_storage_cls: type[BaseGraphStorage] = self._get_storage_class(
            self.graph_storage
        )  # type: ignore
        self.key_string_value_json_storage_cls = partial(  # type: ignore
            self.key_string_value_json_storage_cls, global_config=global_config
        )
        self.vector_db_storage_cls = partial(  # type: ignore
            self.vector_db_storage_cls, global_config=global_config
        )
        self.graph_storage_cls = partial(  # type: ignore
            self.graph_storage_cls, global_config=global_config
        )

        # Initialize document status storage
        self.doc_status_storage_cls = self._get_storage_class(self.doc_status_storage)

        self.llm_response_cache: BaseKVStorage = self.key_string_value_json_storage_cls(  # type: ignore
            namespace=make_namespace(
                self.namespace_prefix, NameSpace.KV_STORE_LLM_RESPONSE_CACHE
            ),
            global_config=asdict(
                self
            ),  # Add global_config to ensure cache works properly
            embedding_func=self.embedding_func,
        )

        self.full_docs: BaseKVStorage = self.key_string_value_json_storage_cls(  # type: ignore
            namespace=make_namespace(
                self.namespace_prefix, NameSpace.KV_STORE_FULL_DOCS
            ),
            embedding_func=self.embedding_func,
        )
        self.text_chunks: BaseKVStorage = self.key_string_value_json_storage_cls(  # type: ignore
            namespace=make_namespace(
                self.namespace_prefix, NameSpace.KV_STORE_TEXT_CHUNKS
            ),
            embedding_func=self.embedding_func,
        )
        self.chunk_entity_relation_graph: BaseGraphStorage = self.graph_storage_cls(  # type: ignore
            namespace=make_namespace(
                self.namespace_prefix, NameSpace.GRAPH_STORE_CHUNK_ENTITY_RELATION
            ),
            embedding_func=self.embedding_func,
        )

        self.entities_vdb: BaseVectorStorage = self.vector_db_storage_cls(  # type: ignore
            namespace=make_namespace(
                self.namespace_prefix, NameSpace.VECTOR_STORE_ENTITIES
            ),
            embedding_func=self.embedding_func,
            meta_fields={"entity_name", "source_id", "content", "file_path"},
        )
        self.relationships_vdb: BaseVectorStorage = self.vector_db_storage_cls(  # type: ignore
            namespace=make_namespace(
                self.namespace_prefix, NameSpace.VECTOR_STORE_RELATIONSHIPS
            ),
            embedding_func=self.embedding_func,
            meta_fields={"src_id", "tgt_id", "source_id", "content", "file_path"},
        )
        self.chunks_vdb: BaseVectorStorage = self.vector_db_storage_cls(  # type: ignore
            namespace=make_namespace(
                self.namespace_prefix, NameSpace.VECTOR_STORE_CHUNKS
            ),
            embedding_func=self.embedding_func,
            meta_fields={"full_doc_id", "content", "file_path"},
        )

        # Initialize document status storage
        self.doc_status: DocStatusStorage = self.doc_status_storage_cls(
            namespace=make_namespace(self.namespace_prefix, NameSpace.DOC_STATUS),
            global_config=global_config,
            embedding_func=None,
        )

        # Directly use llm_response_cache, don't create a new object
        hashing_kv = self.llm_response_cache

        self.llm_model_func = limit_async_func_call(self.llm_model_max_async)(
            partial(
                self.llm_model_func,  # type: ignore
                hashing_kv=hashing_kv,
                **self.llm_model_kwargs,
            )
        )

        self._storages_status = StoragesStatus.CREATED

        if self.auto_manage_storages_states:
            self._run_async_safely(self.initialize_storages, "Storage Initialization")

    def __del__(self):
        if self.auto_manage_storages_states:
            self._run_async_safely(self.finalize_storages, "Storage Finalization")

    def _run_async_safely(self, async_func, action_name=""):
        """Safely execute an async function, avoiding event loop conflicts."""
        try:
            loop = always_get_an_event_loop()
            if loop.is_running():
                task = loop.create_task(async_func())
                task.add_done_callback(
                    lambda t: logger.info(f"{action_name} completed!")
                )
            else:
                loop.run_until_complete(async_func())
        except RuntimeError:
            logger.warning(
                f"No running event loop, creating a new loop for {action_name}."
            )
            loop = asyncio.new_event_loop()
            loop.run_until_complete(async_func())
            loop.close()

    async def initialize_storages(self):
        """Asynchronously initialize the storages"""
        if self._storages_status == StoragesStatus.CREATED:
            tasks = []

            for storage in (
                self.full_docs,
                self.text_chunks,
                self.entities_vdb,
                self.relationships_vdb,
                self.chunks_vdb,
                self.chunk_entity_relation_graph,
                self.llm_response_cache,
                self.doc_status,
            ):
                if storage:
                    tasks.append(storage.initialize())

            await asyncio.gather(*tasks)

            self._storages_status = StoragesStatus.INITIALIZED
            logger.debug("Initialized Storages")

    async def finalize_storages(self):
        """Asynchronously finalize the storages"""
        if self._storages_status == StoragesStatus.INITIALIZED:
            tasks = []

            for storage in (
                self.full_docs,
                self.text_chunks,
                self.entities_vdb,
                self.relationships_vdb,
                self.chunks_vdb,
                self.chunk_entity_relation_graph,
                self.llm_response_cache,
                self.doc_status,
            ):
                if storage:
                    tasks.append(storage.finalize())

            await asyncio.gather(*tasks)

            self._storages_status = StoragesStatus.FINALIZED
            logger.debug("Finalized Storages")

    async def get_graph_labels(self):
        text = await self.chunk_entity_relation_graph.get_all_labels()
        return text

    async def get_knowledge_graph(
        self,
        node_label: str,
        max_depth: int = 3,
        max_nodes: int = 1000,
    ) -> KnowledgeGraph:
        """Get knowledge graph for a given label

        Args:
            node_label (str): Label to get knowledge graph for
            max_depth (int): Maximum depth of graph
            max_nodes (int, optional): Maximum number of nodes to return. Defaults to 1000.

        Returns:
            KnowledgeGraph: Knowledge graph containing nodes and edges
        """

        return await self.chunk_entity_relation_graph.get_knowledge_graph(
            node_label, max_depth, max_nodes
        )

    def _get_storage_class(self, storage_name: str) -> Callable[..., Any]:
        import_path = STORAGES[storage_name]
        storage_class = lazy_external_import(import_path, storage_name)
        return storage_class

    def insert(
        self,
        input: str | list[str],
        split_by_character: str | None = None,
        split_by_character_only: bool = False,
        ids: str | list[str] | None = None,
        file_paths: str | list[str] | None = None,
    ) -> None:
        """Sync Insert documents with checkpoint support

        Args:
            input: Single document string or list of document strings
            split_by_character: if split_by_character is not None, split the string by character, if chunk longer than
            chunk_token_size, it will be split again by token size.
            split_by_character_only: if split_by_character_only is True, split the string by character only, when
            split_by_character is None, this parameter is ignored.
            ids: single string of the document ID or list of unique document IDs, if not provided, MD5 hash IDs will be generated
            file_paths: single string of the file path or list of file paths, used for citation
        """
        loop = always_get_an_event_loop()
        loop.run_until_complete(
            self.ainsert(
                input, split_by_character, split_by_character_only, ids, file_paths
            )
        )

    async def ainsert(
        self,
        input: str | list[str],
        split_by_character: str | None = None,
        split_by_character_only: bool = False,
        ids: str | list[str] | None = None,
        file_paths: str | list[str] | None = None,
    ) -> None:
        """Async Insert documents with checkpoint support

        Args:
            input: Single document string or list of document strings
            split_by_character: if split_by_character is not None, split the string by character, if chunk longer than
            chunk_token_size, it will be split again by token size.
            split_by_character_only: if split_by_character_only is True, split the string by character only, when
            split_by_character is None, this parameter is ignored.
            ids: list of unique document IDs, if not provided, MD5 hash IDs will be generated
            file_paths: list of file paths corresponding to each document, used for citation
        """
        await self.apipeline_enqueue_documents(input, ids, file_paths)
        await self.apipeline_process_enqueue_documents(
            split_by_character, split_by_character_only
        )

    # TODO: deprecated, use insert instead
    def insert_custom_chunks(
        self,
        full_text: str,
        text_chunks: list[str],
        doc_id: str | list[str] | None = None,
    ) -> None:
        loop = always_get_an_event_loop()
        loop.run_until_complete(
            self.ainsert_custom_chunks(full_text, text_chunks, doc_id)
        )

    # TODO: deprecated, use ainsert instead
    async def ainsert_custom_chunks(
        self, full_text: str, text_chunks: list[str], doc_id: str | None = None
    ) -> None:
        update_storage = False
        try:
            # Clean input texts
            full_text = clean_text(full_text)
            text_chunks = [clean_text(chunk) for chunk in text_chunks]
            file_path = ""

            # Process cleaned texts
            if doc_id is None:
                doc_key = compute_mdhash_id(full_text, prefix="doc-")
            else:
                doc_key = doc_id
            new_docs = {doc_key: {"content": full_text}}

            _add_doc_keys = await self.full_docs.filter_keys({doc_key})
            new_docs = {k: v for k, v in new_docs.items() if k in _add_doc_keys}
            if not len(new_docs):
                logger.warning("This document is already in the storage.")
                return

            update_storage = True
            logger.info(f"Inserting {len(new_docs)} docs")

            inserting_chunks: dict[str, Any] = {}
            for index, chunk_text in enumerate(text_chunks):
                chunk_key = compute_mdhash_id(chunk_text, prefix="chunk-")
                tokens = len(self.tokenizer.encode(chunk_text))
                inserting_chunks[chunk_key] = {
                    "content": chunk_text,
                    "full_doc_id": doc_key,
                    "tokens": tokens,
                    "chunk_order_index": index,
                    "file_path": file_path,
                }

            doc_ids = set(inserting_chunks.keys())
            add_chunk_keys = await self.text_chunks.filter_keys(doc_ids)
            inserting_chunks = {
                k: v for k, v in inserting_chunks.items() if k in add_chunk_keys
            }
            if not len(inserting_chunks):
                logger.warning("All chunks are already in the storage.")
                return

            tasks = [
                self.chunks_vdb.upsert(inserting_chunks),
                self._process_entity_relation_graph(inserting_chunks),
                self.full_docs.upsert(new_docs),
                self.text_chunks.upsert(inserting_chunks),
            ]
            await asyncio.gather(*tasks)

        finally:
            if update_storage:
                await self._insert_done()

    async def apipeline_enqueue_documents(
        self,
        input: str | list[str],
        ids: list[str] | None = None,
        file_paths: str | list[str] | None = None,
    ) -> None:
        """
        Pipeline for Processing Documents

        1. Validate ids if provided or generate MD5 hash IDs
        2. Remove duplicate contents
        3. Generate document initial status
        4. Filter out already processed documents
        5. Enqueue document in status

        Args:
            input: Single document string or list of document strings
            ids: list of unique document IDs, if not provided, MD5 hash IDs will be generated
            file_paths: list of file paths corresponding to each document, used for citation
        """
        if isinstance(input, str):
            input = [input]
        if isinstance(ids, str):
            ids = [ids]
        if isinstance(file_paths, str):
            file_paths = [file_paths]

        # If file_paths is provided, ensure it matches the number of documents
        if file_paths is not None:
            if isinstance(file_paths, str):
                file_paths = [file_paths]
            if len(file_paths) != len(input):
                raise ValueError(
                    "Number of file paths must match the number of documents"
                )
        else:
            # If no file paths provided, use placeholder
            file_paths = ["unknown_source"] * len(input)

        # 1. Validate ids if provided or generate MD5 hash IDs
        if ids is not None:
            # Check if the number of IDs matches the number of documents
            if len(ids) != len(input):
                raise ValueError("Number of IDs must match the number of documents")

            # Check if IDs are unique
            if len(ids) != len(set(ids)):
                raise ValueError("IDs must be unique")

            # Generate contents dict of IDs provided by user and documents
            contents = {
                id_: {"content": doc, "file_path": path}
                for id_, doc, path in zip(ids, input, file_paths)
            }
        else:
            # Clean input text and remove duplicates
            cleaned_input = [
                (clean_text(doc), path) for doc, path in zip(input, file_paths)
            ]
            unique_content_with_paths = {}

            # Keep track of unique content and their paths
            for content, path in cleaned_input:
                if content not in unique_content_with_paths:
                    unique_content_with_paths[content] = path

            # Generate contents dict of MD5 hash IDs and documents with paths
            contents = {
                compute_mdhash_id(content, prefix="doc-"): {
                    "content": content,
                    "file_path": path,
                }
                for content, path in unique_content_with_paths.items()
            }

        # 2. Remove duplicate contents
        unique_contents = {}
        for id_, content_data in contents.items():
            content = content_data["content"]
            file_path = content_data["file_path"]
            if content not in unique_contents:
                unique_contents[content] = (id_, file_path)

        # Reconstruct contents with unique content
        contents = {
            id_: {"content": content, "file_path": file_path}
            for content, (id_, file_path) in unique_contents.items()
        }

        # 3. Generate document initial status
        new_docs: dict[str, Any] = {
            id_: {
                "status": DocStatus.PENDING,
                "content": content_data["content"],
                "content_summary": get_content_summary(content_data["content"]),
                "content_length": len(content_data["content"]),
                "created_at": datetime.now().isoformat(),
                "updated_at": datetime.now().isoformat(),
                "file_path": content_data[
                    "file_path"
                ],  # Store file path in document status
            }
            for id_, content_data in contents.items()
        }

        # 4. Filter out already processed documents
        # Get docs ids
        all_new_doc_ids = set(new_docs.keys())
        # Exclude IDs of documents that are already in progress
        unique_new_doc_ids = await self.doc_status.filter_keys(all_new_doc_ids)

        # Log ignored document IDs
        ignored_ids = [
            doc_id for doc_id in unique_new_doc_ids if doc_id not in new_docs
        ]
        if ignored_ids:
            logger.warning(
                f"Ignoring {len(ignored_ids)} document IDs not found in new_docs"
            )
            for doc_id in ignored_ids:
                logger.warning(f"Ignored document ID: {doc_id}")

        # Filter new_docs to only include documents with unique IDs
        new_docs = {
            doc_id: new_docs[doc_id]
            for doc_id in unique_new_doc_ids
            if doc_id in new_docs
        }

        if not new_docs:
            logger.info("No new unique documents were found.")
            return

        # 5. Store status document
        await self.doc_status.upsert(new_docs)
        logger.info(f"Stored {len(new_docs)} new unique documents")

    async def apipeline_process_enqueue_documents(
        self,
        split_by_character: str | None = None,
        split_by_character_only: bool = False,
    ) -> None:
        """
        Process pending documents by splitting them into chunks, processing
        each chunk for entity and relation extraction, and updating the
        document status.

        1. Get all pending, failed, and abnormally terminated processing documents.
        2. Split document content into chunks
        3. Process each chunk for entity and relation extraction
        4. Update the document status
        """

        # Get pipeline status shared data and lock
        pipeline_status = await get_namespace_data("pipeline_status")
        pipeline_status_lock = get_pipeline_status_lock()

        # Check if another process is already processing the queue
        async with pipeline_status_lock:
            # Ensure only one worker is processing documents
            if not pipeline_status.get("busy", False):
                processing_docs, failed_docs, pending_docs = await asyncio.gather(
                    self.doc_status.get_docs_by_status(DocStatus.PROCESSING),
                    self.doc_status.get_docs_by_status(DocStatus.FAILED),
                    self.doc_status.get_docs_by_status(DocStatus.PENDING),
                )

                to_process_docs: dict[str, DocProcessingStatus] = {}
                to_process_docs.update(processing_docs)
                to_process_docs.update(failed_docs)
                to_process_docs.update(pending_docs)

                if not to_process_docs:
                    logger.info("No documents to process")
                    return

                pipeline_status.update(
                    {
                        "busy": True,
                        "job_name": "Default Job",
                        "job_start": datetime.now().isoformat(),
                        "docs": 0,
                        "batchs": 0,  # Total number of files to be processed
                        "cur_batch": 0,  # Number of files already processed
                        "request_pending": False,  # Clear any previous request
                        "latest_message": "",
                    }
                )
                # Cleaning history_messages without breaking it as a shared list object
                del pipeline_status["history_messages"][:]
            else:
                # Another process is busy, just set request flag and return
                pipeline_status["request_pending"] = True
                logger.info(
                    "Another process is already processing the document queue. Request queued."
                )
                return

        try:
            # Process documents until no more documents or requests
            while True:
                if not to_process_docs:
                    log_message = "All documents have been processed or are duplicates"
                    logger.info(log_message)
                    pipeline_status["latest_message"] = log_message
                    pipeline_status["history_messages"].append(log_message)
                    break

                log_message = f"Processing {len(to_process_docs)} document(s)"
                logger.info(log_message)

                # Update pipeline_status, batchs now represents the total number of files to be processed
                pipeline_status["docs"] = len(to_process_docs)
                pipeline_status["batchs"] = len(to_process_docs)
                pipeline_status["cur_batch"] = 0
                pipeline_status["latest_message"] = log_message
                pipeline_status["history_messages"].append(log_message)

                # Get first document's file path and total count for job name
                first_doc_id, first_doc = next(iter(to_process_docs.items()))
                first_doc_path = first_doc.file_path
                path_prefix = first_doc_path[:20] + (
                    "..." if len(first_doc_path) > 20 else ""
                )
                total_files = len(to_process_docs)
                job_name = f"{path_prefix}[{total_files} files]"
                pipeline_status["job_name"] = job_name

                # Create a counter to track the number of processed files
                processed_count = 0
                # Create a semaphore to limit the number of concurrent file processing
                semaphore = asyncio.Semaphore(self.max_parallel_insert)

                async def process_document(
                    doc_id: str,
                    status_doc: DocProcessingStatus,
                    split_by_character: str | None,
                    split_by_character_only: bool,
                    pipeline_status: dict,
                    pipeline_status_lock: asyncio.Lock,
                    semaphore: asyncio.Semaphore,
                ) -> None:
                    """Process single document"""
                    async with semaphore:
                        nonlocal processed_count
                        current_file_number = 0
                        try:
                            # Get file path from status document
                            file_path = getattr(
                                status_doc, "file_path", "unknown_source"
                            )

                            async with pipeline_status_lock:
                                # Update processed file count and save current file number
                                processed_count += 1
                                current_file_number = (
                                    processed_count  # Save the current file number
                                )
                                pipeline_status["cur_batch"] = processed_count

                                log_message = f"Processing file ({current_file_number}/{total_files}): {file_path}"
                                logger.info(log_message)
                                pipeline_status["history_messages"].append(log_message)
                                log_message = f"Processing d-id: {doc_id}"
                                logger.info(log_message)
                                pipeline_status["latest_message"] = log_message
                                pipeline_status["history_messages"].append(log_message)

                            # Generate chunks from document
                            chunks: dict[str, Any] = {
                                compute_mdhash_id(dp["content"], prefix="chunk-"): {
                                    **dp,
                                    "full_doc_id": doc_id,
                                    "file_path": file_path,  # Add file path to each chunk
                                }
                                for dp in self.chunking_func(
                                    self.tokenizer,
                                    status_doc.content,
                                    split_by_character,
                                    split_by_character_only,
                                    self.chunk_overlap_token_size,
                                    self.chunk_token_size,
                                )
                            }

                            # Process document (text chunks and full docs) in parallel
                            # Create tasks with references for potential cancellation
                            doc_status_task = asyncio.create_task(
                                self.doc_status.upsert(
                                    {
                                        doc_id: {
                                            "status": DocStatus.PROCESSING,
                                            "chunks_count": len(chunks),
                                            "content": status_doc.content,
                                            "content_summary": status_doc.content_summary,
                                            "content_length": status_doc.content_length,
                                            "created_at": status_doc.created_at,
                                            "updated_at": datetime.now().isoformat(),
                                            "file_path": file_path,
                                        }
                                    }
                                )
                            )
                            chunks_vdb_task = asyncio.create_task(
                                self.chunks_vdb.upsert(chunks)
                            )
                            entity_relation_task = asyncio.create_task(
                                self._process_entity_relation_graph(
                                    chunks, pipeline_status, pipeline_status_lock
                                )
                            )
                            full_docs_task = asyncio.create_task(
                                self.full_docs.upsert(
                                    {doc_id: {"content": status_doc.content}}
                                )
                            )
                            text_chunks_task = asyncio.create_task(
                                self.text_chunks.upsert(chunks)
                            )
                            tasks = [
                                doc_status_task,
                                chunks_vdb_task,
                                entity_relation_task,
                                full_docs_task,
                                text_chunks_task,
                            ]
                            await asyncio.gather(*tasks)
                            await self.doc_status.upsert(
                                {
                                    doc_id: {
                                        "status": DocStatus.PROCESSED,
                                        "chunks_count": len(chunks),
                                        "content": status_doc.content,
                                        "content_summary": status_doc.content_summary,
                                        "content_length": status_doc.content_length,
                                        "created_at": status_doc.created_at,
                                        "updated_at": datetime.now().isoformat(),
                                        "file_path": file_path,
                                    }
                                }
                            )

                            # Call _insert_done after processing each file
                            await self._insert_done()

                            async with pipeline_status_lock:
                                log_message = f"Completed processing file {current_file_number}/{total_files}: {file_path}"
                                logger.info(log_message)
                                pipeline_status["latest_message"] = log_message
                                pipeline_status["history_messages"].append(log_message)

                        except Exception as e:
                            # Log error and update pipeline status
                            error_msg = f"Failed to process document {doc_id}: {traceback.format_exc()}"

                            logger.error(error_msg)
                            async with pipeline_status_lock:
                                pipeline_status["latest_message"] = error_msg
                                pipeline_status["history_messages"].append(error_msg)

                                # Cancel other tasks as they are no longer meaningful
                                for task in [
                                    chunks_vdb_task,
                                    entity_relation_task,
                                    full_docs_task,
                                    text_chunks_task,
                                ]:
                                    if not task.done():
                                        task.cancel()
                            # Update document status to failed
                            await self.doc_status.upsert(
                                {
                                    doc_id: {
                                        "status": DocStatus.FAILED,
                                        "error": str(e),
                                        "content": status_doc.content,
                                        "content_summary": status_doc.content_summary,
                                        "content_length": status_doc.content_length,
                                        "created_at": status_doc.created_at,
                                        "updated_at": datetime.now().isoformat(),
                                        "file_path": file_path,
                                    }
                                }
                            )

                # Create processing tasks for all documents
                doc_tasks = []
                for doc_id, status_doc in to_process_docs.items():
                    doc_tasks.append(
                        process_document(
                            doc_id,
                            status_doc,
                            split_by_character,
                            split_by_character_only,
                            pipeline_status,
                            pipeline_status_lock,
                            semaphore,
                        )
                    )

                # Wait for all document processing to complete
                await asyncio.gather(*doc_tasks)

                # Check if there's a pending request to process more documents (with lock)
                has_pending_request = False
                async with pipeline_status_lock:
                    has_pending_request = pipeline_status.get("request_pending", False)
                    if has_pending_request:
                        # Clear the request flag before checking for more documents
                        pipeline_status["request_pending"] = False

                if not has_pending_request:
                    break

                log_message = "Processing additional documents due to pending request"
                logger.info(log_message)
                pipeline_status["latest_message"] = log_message
                pipeline_status["history_messages"].append(log_message)

                # Check for pending documents again
                processing_docs, failed_docs, pending_docs = await asyncio.gather(
                    self.doc_status.get_docs_by_status(DocStatus.PROCESSING),
                    self.doc_status.get_docs_by_status(DocStatus.FAILED),
                    self.doc_status.get_docs_by_status(DocStatus.PENDING),
                )

                to_process_docs = {}
                to_process_docs.update(processing_docs)
                to_process_docs.update(failed_docs)
                to_process_docs.update(pending_docs)

        finally:
            log_message = "Document processing pipeline completed"
            logger.info(log_message)
            # Always reset busy status when done or if an exception occurs (with lock)
            async with pipeline_status_lock:
                pipeline_status["busy"] = False
                pipeline_status["latest_message"] = log_message
                pipeline_status["history_messages"].append(log_message)

    async def _process_entity_relation_graph(
        self, chunk: dict[str, Any], pipeline_status=None, pipeline_status_lock=None
    ) -> None:
        try:
            await extract_entities(
                chunk,
                knowledge_graph_inst=self.chunk_entity_relation_graph,
                entity_vdb=self.entities_vdb,
                relationships_vdb=self.relationships_vdb,
                global_config=asdict(self),
                pipeline_status=pipeline_status,
                pipeline_status_lock=pipeline_status_lock,
                llm_response_cache=self.llm_response_cache,
            )
        except Exception as e:
            error_msg = f"Failed to extract entities and relationships: {str(e)}"
            logger.error(error_msg)
            async with pipeline_status_lock:
                pipeline_status["latest_message"] = error_msg
                pipeline_status["history_messages"].append(error_msg)
            raise e

    async def _insert_done(
        self, pipeline_status=None, pipeline_status_lock=None
    ) -> None:
        tasks = [
            cast(StorageNameSpace, storage_inst).index_done_callback()
            for storage_inst in [  # type: ignore
                self.full_docs,
                self.text_chunks,
                self.llm_response_cache,
                self.entities_vdb,
                self.relationships_vdb,
                self.chunks_vdb,
                self.chunk_entity_relation_graph,
            ]
            if storage_inst is not None
        ]
        await asyncio.gather(*tasks)

        log_message = "In memory DB persist to disk"
        logger.info(log_message)

        if pipeline_status is not None and pipeline_status_lock is not None:
            async with pipeline_status_lock:
                pipeline_status["latest_message"] = log_message
                pipeline_status["history_messages"].append(log_message)

    def insert_custom_kg(
        self, custom_kg: dict[str, Any], full_doc_id: str = None
    ) -> None:
        loop = always_get_an_event_loop()
        loop.run_until_complete(self.ainsert_custom_kg(custom_kg, full_doc_id))

    async def ainsert_custom_kg(
        self,
        custom_kg: dict[str, Any],
        full_doc_id: str = None,
        file_path: str = "custom_kg",
    ) -> None:
        update_storage = False
        try:
            # Insert chunks into vector storage
            all_chunks_data: dict[str, dict[str, str]] = {}
            chunk_to_source_map: dict[str, str] = {}
            for chunk_data in custom_kg.get("chunks", []):
                chunk_content = clean_text(chunk_data["content"])
                source_id = chunk_data["source_id"]
                tokens = len(self.tokenizer.encode(chunk_content))
                chunk_order_index = (
                    0
                    if "chunk_order_index" not in chunk_data.keys()
                    else chunk_data["chunk_order_index"]
                )
                chunk_id = compute_mdhash_id(chunk_content, prefix="chunk-")

                chunk_entry = {
                    "content": chunk_content,
                    "source_id": source_id,
                    "tokens": tokens,
                    "chunk_order_index": chunk_order_index,
                    "full_doc_id": full_doc_id
                    if full_doc_id is not None
                    else source_id,
                    "file_path": file_path,  # Add file path
                    "status": DocStatus.PROCESSED,
                }
                all_chunks_data[chunk_id] = chunk_entry
                chunk_to_source_map[source_id] = chunk_id
                update_storage = True

            if all_chunks_data:
                await asyncio.gather(
                    self.chunks_vdb.upsert(all_chunks_data),
                    self.text_chunks.upsert(all_chunks_data),
                )

            # Insert entities into knowledge graph
            all_entities_data: list[dict[str, str]] = []
            for entity_data in custom_kg.get("entities", []):
                entity_name = entity_data["entity_name"]
                entity_type = entity_data.get("entity_type", "UNKNOWN")
                description = entity_data.get("description", "No description provided")
                source_chunk_id = entity_data.get("source_id", "UNKNOWN")
                source_id = chunk_to_source_map.get(source_chunk_id, "UNKNOWN")

                # Log if source_id is UNKNOWN
                if source_id == "UNKNOWN":
                    logger.warning(
                        f"Entity '{entity_name}' has an UNKNOWN source_id. Please check the source mapping."
                    )

                # Prepare node data
                node_data: dict[str, str] = {
                    "entity_id": entity_name,
                    "entity_type": entity_type,
                    "description": description,
                    "source_id": source_id,
                }
                # Insert node data into the knowledge graph
                await self.chunk_entity_relation_graph.upsert_node(
                    entity_name, node_data=node_data
                )
                node_data["entity_name"] = entity_name
                all_entities_data.append(node_data)
                update_storage = True

            # Insert relationships into knowledge graph
            all_relationships_data: list[dict[str, str]] = []
            for relationship_data in custom_kg.get("relationships", []):
                src_id = relationship_data["src_id"]
                tgt_id = relationship_data["tgt_id"]
                description = relationship_data["description"]
                keywords = relationship_data["keywords"]
                weight = relationship_data.get("weight", 1.0)
                source_chunk_id = relationship_data.get("source_id", "UNKNOWN")
                source_id = chunk_to_source_map.get(source_chunk_id, "UNKNOWN")

                # Log if source_id is UNKNOWN
                if source_id == "UNKNOWN":
                    logger.warning(
                        f"Relationship from '{src_id}' to '{tgt_id}' has an UNKNOWN source_id. Please check the source mapping."
                    )

                # Check if nodes exist in the knowledge graph
                for need_insert_id in [src_id, tgt_id]:
                    if not (
                        await self.chunk_entity_relation_graph.has_node(need_insert_id)
                    ):
                        await self.chunk_entity_relation_graph.upsert_node(
                            need_insert_id,
                            node_data={
                                "entity_id": need_insert_id,
                                "source_id": source_id,
                                "description": "UNKNOWN",
                                "entity_type": "UNKNOWN",
                            },
                        )

                # Insert edge into the knowledge graph
                await self.chunk_entity_relation_graph.upsert_edge(
                    src_id,
                    tgt_id,
                    edge_data={
                        "weight": weight,
                        "description": description,
                        "keywords": keywords,
                        "source_id": source_id,
                    },
                )
                edge_data: dict[str, str] = {
                    "src_id": src_id,
                    "tgt_id": tgt_id,
                    "description": description,
                    "keywords": keywords,
                    "source_id": source_id,
                    "weight": weight,
                }
                all_relationships_data.append(edge_data)
                update_storage = True

            # Insert entities into vector storage with consistent format
            data_for_vdb = {
                compute_mdhash_id(dp["entity_name"], prefix="ent-"): {
                    "content": dp["entity_name"] + "\n" + dp["description"],
                    "entity_name": dp["entity_name"],
                    "source_id": dp["source_id"],
                    "description": dp["description"],
                    "entity_type": dp["entity_type"],
                    "file_path": file_path,  # Add file path
                }
                for dp in all_entities_data
            }
            await self.entities_vdb.upsert(data_for_vdb)

            # Insert relationships into vector storage with consistent format
            data_for_vdb = {
                compute_mdhash_id(dp["src_id"] + dp["tgt_id"], prefix="rel-"): {
                    "src_id": dp["src_id"],
                    "tgt_id": dp["tgt_id"],
                    "source_id": dp["source_id"],
                    "content": f"{dp['keywords']}\t{dp['src_id']}\n{dp['tgt_id']}\n{dp['description']}",
                    "keywords": dp["keywords"],
                    "description": dp["description"],
                    "weight": dp["weight"],
                    "file_path": file_path,  # Add file path
                }
                for dp in all_relationships_data
            }
            await self.relationships_vdb.upsert(data_for_vdb)

        except Exception as e:
            logger.error(f"Error in ainsert_custom_kg: {e}")
            raise
        finally:
            if update_storage:
                await self._insert_done()

    def query(
        self,
        query: str,
        param: QueryParam = QueryParam(),
        system_prompt: str | None = None,
    ) -> str | Iterator[str]:
        """
        Perform a sync query.

        Args:
            query (str): The query to be executed.
            param (QueryParam): Configuration parameters for query execution.
            prompt (Optional[str]): Custom prompts for fine-tuned control over the system's behavior. Defaults to None, which uses PROMPTS["rag_response"].

        Returns:
            str: The result of the query execution.
        """
        loop = always_get_an_event_loop()

        return loop.run_until_complete(self.aquery(query, param, system_prompt))  # type: ignore

    async def aquery(
        self,
        query: str,
        param: QueryParam = QueryParam(),
        system_prompt: str | None = None,
    ) -> str | AsyncIterator[str]:
        """
        Perform a async query.

        Args:
            query (str): The query to be executed.
            param (QueryParam): Configuration parameters for query execution.
                If param.model_func is provided, it will be used instead of the global model.
            prompt (Optional[str]): Custom prompts for fine-tuned control over the system's behavior. Defaults to None, which uses PROMPTS["rag_response"].

        Returns:
            str: The result of the query execution.
        """
        # If a custom model is provided in param, temporarily update global config
        global_config = asdict(self)

        if param.mode in ["local", "global", "hybrid"]:
            response = await kg_query(
                query.strip(),
                self.chunk_entity_relation_graph,
                self.entities_vdb,
                self.relationships_vdb,
                self.text_chunks,
                param,
                global_config,
                hashing_kv=self.llm_response_cache,  # Directly use llm_response_cache
                system_prompt=system_prompt,
            )
        elif param.mode == "naive":
            response = await naive_query(
                query.strip(),
                self.chunks_vdb,
                self.text_chunks,
                param,
                global_config,
                hashing_kv=self.llm_response_cache,  # Directly use llm_response_cache
                system_prompt=system_prompt,
            )
        elif param.mode == "mix":
            response = await mix_kg_vector_query(
                query.strip(),
                self.chunk_entity_relation_graph,
                self.entities_vdb,
                self.relationships_vdb,
                self.chunks_vdb,
                self.text_chunks,
                param,
                global_config,
                hashing_kv=self.llm_response_cache,  # Directly use llm_response_cache
                system_prompt=system_prompt,
            )
        elif param.mode == "bypass":
            # Bypass mode: directly use LLM without knowledge retrieval
            use_llm_func = param.model_func or global_config["llm_model_func"]
            param.stream = True if param.stream is None else param.stream
            response = await use_llm_func(
                query.strip(),
                system_prompt=system_prompt,
                history_messages=param.conversation_history,
                stream=param.stream,
            )
        else:
            raise ValueError(f"Unknown mode {param.mode}")
        await self._query_done()
        return response

    def query_with_separate_keyword_extraction(
        self, query: str, prompt: str, param: QueryParam = QueryParam()
    ):
        """
        Query with separate keyword extraction step.

        This method extracts keywords from the query first, then uses them for the query.

        Args:
            query: User query
            prompt: Additional prompt for the query
            param: Query parameters

        Returns:
            Query response
        """
        loop = always_get_an_event_loop()
        return loop.run_until_complete(
            self.aquery_with_separate_keyword_extraction(query, prompt, param)
        )

    async def aquery_with_separate_keyword_extraction(
        self, query: str, prompt: str, param: QueryParam = QueryParam()
    ) -> str | AsyncIterator[str]:
        """
        Async version of query_with_separate_keyword_extraction.

        Args:
            query: User query
            prompt: Additional prompt for the query
            param: Query parameters

        Returns:
            Query response or async iterator
        """
        response = await query_with_keywords(
            query=query,
            prompt=prompt,
            param=param,
            knowledge_graph_inst=self.chunk_entity_relation_graph,
            entities_vdb=self.entities_vdb,
            relationships_vdb=self.relationships_vdb,
            chunks_vdb=self.chunks_vdb,
            text_chunks_db=self.text_chunks,
            global_config=asdict(self),
            hashing_kv=self.llm_response_cache,
        )

        await self._query_done()
        return response

    async def _query_done(self):
        await self.llm_response_cache.index_done_callback()

    async def aclear_cache(self, modes: list[str] | None = None) -> None:
        """Clear cache data from the LLM response cache storage.

        Args:
            modes (list[str] | None): Modes of cache to clear. Options: ["default", "naive", "local", "global", "hybrid", "mix"].
                             "default" represents extraction cache.
                             If None, clears all cache.

        Example:
            # Clear all cache
            await rag.aclear_cache()

            # Clear local mode cache
            await rag.aclear_cache(modes=["local"])

            # Clear extraction cache
            await rag.aclear_cache(modes=["default"])
        """
        if not self.llm_response_cache:
            logger.warning("No cache storage configured")
            return

        valid_modes = ["default", "naive", "local", "global", "hybrid", "mix"]

        # Validate input
        if modes and not all(mode in valid_modes for mode in modes):
            raise ValueError(f"Invalid mode. Valid modes are: {valid_modes}")

        try:
            # Reset the cache storage for specified mode
            if modes:
                success = await self.llm_response_cache.drop_cache_by_modes(modes)
                if success:
                    logger.info(f"Cleared cache for modes: {modes}")
                else:
                    logger.warning(f"Failed to clear cache for modes: {modes}")
            else:
                # Clear all modes
                success = await self.llm_response_cache.drop_cache_by_modes(valid_modes)
                if success:
                    logger.info("Cleared all cache")
                else:
                    logger.warning("Failed to clear all cache")

            await self.llm_response_cache.index_done_callback()

        except Exception as e:
            logger.error(f"Error while clearing cache: {e}")

    def clear_cache(self, modes: list[str] | None = None) -> None:
        """Synchronous version of aclear_cache."""
        return always_get_an_event_loop().run_until_complete(self.aclear_cache(modes))

    async def get_docs_by_status(
        self, status: DocStatus
    ) -> dict[str, DocProcessingStatus]:
        """Get documents by status

        Returns:
            Dict with document id is keys and document status is values
        """
        return await self.doc_status.get_docs_by_status(status)

    async def aget_docs_by_ids(
        self, ids: str | list[str]
    ) -> dict[str, DocProcessingStatus]:
        """Retrieves the processing status for one or more documents by their IDs.

        Args:
            ids: A single document ID (string) or a list of document IDs (list of strings).

        Returns:
            A dictionary where keys are the document IDs for which a status was found,
            and values are the corresponding DocProcessingStatus objects. IDs that
            are not found in the storage will be omitted from the result dictionary.
        """
        if isinstance(ids, str):
            # Ensure input is always a list of IDs for uniform processing
            id_list = [ids]
        elif (
            ids is None
        ):  # Handle potential None input gracefully, although type hint suggests str/list
            logger.warning(
                "aget_docs_by_ids called with None input, returning empty dict."
            )
            return {}
        else:
            # Assume input is already a list if not a string
            id_list = ids

        # Return early if the final list of IDs is empty
        if not id_list:
            logger.debug("aget_docs_by_ids called with an empty list of IDs.")
            return {}

        # Create tasks to fetch document statuses concurrently using the doc_status storage
        tasks = [self.doc_status.get_by_id(doc_id) for doc_id in id_list]
        # Execute tasks concurrently and gather the results. Results maintain order.
        # Type hint indicates results can be DocProcessingStatus or None if not found.
        results_list: list[Optional[DocProcessingStatus]] = await asyncio.gather(*tasks)

        # Build the result dictionary, mapping found IDs to their statuses
        found_statuses: dict[str, DocProcessingStatus] = {}
        # Keep track of IDs for which no status was found (for logging purposes)
        not_found_ids: list[str] = []

        # Iterate through the results, correlating them back to the original IDs
        for i, status_obj in enumerate(results_list):
            doc_id = id_list[
                i
            ]  # Get the original ID corresponding to this result index
            if status_obj:
                # If a status object was returned (not None), add it to the result dict
                found_statuses[doc_id] = status_obj
            else:
                # If status_obj is None, the document ID was not found in storage
                not_found_ids.append(doc_id)

        # Log a warning if any of the requested document IDs were not found
        if not_found_ids:
            logger.warning(
                f"Document statuses not found for the following IDs: {not_found_ids}"
            )

        # Return the dictionary containing statuses only for the found document IDs
        return found_statuses

    # TODO: Deprecated (Deleting documents can cause hallucinations in RAG.)
    # Document delete is not working properly for most of the storage implementations.
    async def adelete_by_doc_id(self, doc_id: str) -> None:
        """Delete a document and all its related data

        Args:
            doc_id: Document ID to delete
        """
        try:
            # 1. Get the document status and related data
            if not await self.doc_status.get_by_id(doc_id):
                logger.warning(f"Document {doc_id} not found")
                return

            logger.debug(f"Starting deletion for document {doc_id}")

            # 2. Get all chunks related to this document
            # Find all chunks where full_doc_id equals the current doc_id
            all_chunks = await self.text_chunks.get_all()
            related_chunks = {
                chunk_id: chunk_data
                for chunk_id, chunk_data in all_chunks.items()
                if isinstance(chunk_data, dict)
                and chunk_data.get("full_doc_id") == doc_id
            }

            if not related_chunks:
                logger.warning(f"No chunks found for document {doc_id}")
                return

            # Get all related chunk IDs
            chunk_ids = set(related_chunks.keys())
            logger.debug(f"Found {len(chunk_ids)} chunks to delete")

            # TODO: self.entities_vdb.client_storage only works for local storage, need to fix this

            # 3. Before deleting, check the related entities and relationships for these chunks
            for chunk_id in chunk_ids:
                # Check entities
                entities_storage = await self.entities_vdb.client_storage
                entities = [
                    dp
                    for dp in entities_storage["data"]
                    if chunk_id in dp.get("source_id")
                ]
                logger.debug(f"Chunk {chunk_id} has {len(entities)} related entities")

                # Check relationships
                relationships_storage = await self.relationships_vdb.client_storage
                relations = [
                    dp
                    for dp in relationships_storage["data"]
                    if chunk_id in dp.get("source_id")
                ]
                logger.debug(f"Chunk {chunk_id} has {len(relations)} related relations")

            # Continue with the original deletion process...

            # 4. Delete chunks from vector database
            if chunk_ids:
                await self.chunks_vdb.delete(chunk_ids)
                await self.text_chunks.delete(chunk_ids)

            # 5. Find and process entities and relationships that have these chunks as source
            # Get all nodes and edges from the graph storage using storage-agnostic methods
            entities_to_delete = set()
            entities_to_update = {}  # entity_name -> new_source_id
            relationships_to_delete = set()
            relationships_to_update = {}  # (src, tgt) -> new_source_id

            # Process entities - use storage-agnostic methods
            all_labels = await self.chunk_entity_relation_graph.get_all_labels()
            for node_label in all_labels:
                node_data = await self.chunk_entity_relation_graph.get_node(node_label)
                if node_data and "source_id" in node_data:
                    # Split source_id using GRAPH_FIELD_SEP
                    sources = set(node_data["source_id"].split(GRAPH_FIELD_SEP))
                    sources.difference_update(chunk_ids)
                    if not sources:
                        entities_to_delete.add(node_label)
                        logger.debug(
                            f"Entity {node_label} marked for deletion - no remaining sources"
                        )
                    else:
                        new_source_id = GRAPH_FIELD_SEP.join(sources)
                        entities_to_update[node_label] = new_source_id
                        logger.debug(
                            f"Entity {node_label} will be updated with new source_id: {new_source_id}"
                        )

            # Process relationships
            for node_label in all_labels:
                node_edges = await self.chunk_entity_relation_graph.get_node_edges(
                    node_label
                )
                if node_edges:
                    for src, tgt in node_edges:
                        edge_data = await self.chunk_entity_relation_graph.get_edge(
                            src, tgt
                        )
                        if edge_data and "source_id" in edge_data:
                            # Split source_id using GRAPH_FIELD_SEP
                            sources = set(edge_data["source_id"].split(GRAPH_FIELD_SEP))
                            sources.difference_update(chunk_ids)
                            if not sources:
                                relationships_to_delete.add((src, tgt))
                                logger.debug(
                                    f"Relationship {src}-{tgt} marked for deletion - no remaining sources"
                                )
                            else:
                                new_source_id = GRAPH_FIELD_SEP.join(sources)
                                relationships_to_update[(src, tgt)] = new_source_id
                                logger.debug(
                                    f"Relationship {src}-{tgt} will be updated with new source_id: {new_source_id}"
                                )

            # Delete entities
            if entities_to_delete:
                for entity in entities_to_delete:
                    await self.entities_vdb.delete_entity(entity)
                    logger.debug(f"Deleted entity {entity} from vector DB")
                await self.chunk_entity_relation_graph.remove_nodes(
                    list(entities_to_delete)
                )
                logger.debug(f"Deleted {len(entities_to_delete)} entities from graph")

            # Update entities
            for entity, new_source_id in entities_to_update.items():
                node_data = await self.chunk_entity_relation_graph.get_node(entity)
                if node_data:
                    node_data["source_id"] = new_source_id
                    await self.chunk_entity_relation_graph.upsert_node(
                        entity, node_data
                    )
                    logger.debug(
                        f"Updated entity {entity} with new source_id: {new_source_id}"
                    )

            # Delete relationships
            if relationships_to_delete:
                for src, tgt in relationships_to_delete:
                    rel_id_0 = compute_mdhash_id(src + tgt, prefix="rel-")
                    rel_id_1 = compute_mdhash_id(tgt + src, prefix="rel-")
                    await self.relationships_vdb.delete([rel_id_0, rel_id_1])
                    logger.debug(f"Deleted relationship {src}-{tgt} from vector DB")
                await self.chunk_entity_relation_graph.remove_edges(
                    list(relationships_to_delete)
                )
                logger.debug(
                    f"Deleted {len(relationships_to_delete)} relationships from graph"
                )

            # Update relationships
            for (src, tgt), new_source_id in relationships_to_update.items():
                edge_data = await self.chunk_entity_relation_graph.get_edge(src, tgt)
                if edge_data:
                    edge_data["source_id"] = new_source_id
                    await self.chunk_entity_relation_graph.upsert_edge(
                        src, tgt, edge_data
                    )
                    logger.debug(
                        f"Updated relationship {src}-{tgt} with new source_id: {new_source_id}"
                    )

            # 6. Delete original document and status
            await self.full_docs.delete([doc_id])
            await self.doc_status.delete([doc_id])

            # 7. Ensure all indexes are updated
            await self._insert_done()

            logger.info(
                f"Successfully deleted document {doc_id} and related data. "
                f"Deleted {len(entities_to_delete)} entities and {len(relationships_to_delete)} relationships. "
                f"Updated {len(entities_to_update)} entities and {len(relationships_to_update)} relationships."
            )

            async def process_data(data_type, vdb, chunk_id):
                # Check data (entities or relationships)
                storage = await vdb.client_storage
                data_with_chunk = [
                    dp
                    for dp in storage["data"]
                    if chunk_id in (dp.get("source_id") or "").split(GRAPH_FIELD_SEP)
                ]

                data_for_vdb = {}
                if data_with_chunk:
                    logger.warning(
                        f"found {len(data_with_chunk)} {data_type} still referencing chunk {chunk_id}"
                    )

                    for item in data_with_chunk:
                        old_sources = item["source_id"].split(GRAPH_FIELD_SEP)
                        new_sources = [src for src in old_sources if src != chunk_id]

                        if not new_sources:
                            logger.info(
                                f"{data_type} {item.get('entity_name', 'N/A')} is deleted because source_id is not exists"
                            )
                            await vdb.delete_entity(item)
                        else:
                            item["source_id"] = GRAPH_FIELD_SEP.join(new_sources)
                            item_id = item["__id__"]
                            data_for_vdb[item_id] = item.copy()
                            if data_type == "entities":
                                data_for_vdb[item_id]["content"] = data_for_vdb[
                                    item_id
                                ].get("content") or (
                                    item.get("entity_name", "")
                                    + (item.get("description") or "")
                                )
                            else:  # relationships
                                data_for_vdb[item_id]["content"] = data_for_vdb[
                                    item_id
                                ].get("content") or (
                                    (item.get("keywords") or "")
                                    + (item.get("src_id") or "")
                                    + (item.get("tgt_id") or "")
                                    + (item.get("description") or "")
                                )

                    if data_for_vdb:
                        await vdb.upsert(data_for_vdb)
                        logger.info(f"Successfully updated {data_type} in vector DB")

            # Add verification step
            async def verify_deletion():
                # Verify if the document has been deleted
                if await self.full_docs.get_by_id(doc_id):
                    logger.warning(f"Document {doc_id} still exists in full_docs")

                # Verify if chunks have been deleted
                all_remaining_chunks = await self.text_chunks.get_all()
                remaining_related_chunks = {
                    chunk_id: chunk_data
                    for chunk_id, chunk_data in all_remaining_chunks.items()
                    if isinstance(chunk_data, dict)
                    and chunk_data.get("full_doc_id") == doc_id
                }

                if remaining_related_chunks:
                    logger.warning(
                        f"Found {len(remaining_related_chunks)} remaining chunks"
                    )

                # Verify entities and relationships
                for chunk_id in chunk_ids:
                    await process_data("entities", self.entities_vdb, chunk_id)
                    await process_data(
                        "relationships", self.relationships_vdb, chunk_id
                    )

            await verify_deletion()

        except Exception as e:
            logger.error(f"Error while deleting document {doc_id}: {e}")

    async def adelete_by_entity(self, entity_name: str) -> None:
        """Asynchronously delete an entity and all its relationships.

        Args:
            entity_name: Name of the entity to delete
        """
        from .utils_graph import adelete_by_entity

        return await adelete_by_entity(
            self.chunk_entity_relation_graph,
            self.entities_vdb,
            self.relationships_vdb,
            entity_name,
        )

    def delete_by_entity(self, entity_name: str) -> None:
        loop = always_get_an_event_loop()
        return loop.run_until_complete(self.adelete_by_entity(entity_name))

    async def adelete_by_relation(self, source_entity: str, target_entity: str) -> None:
        """Asynchronously delete a relation between two entities.

        Args:
            source_entity: Name of the source entity
            target_entity: Name of the target entity
        """
        from .utils_graph import adelete_by_relation

        return await adelete_by_relation(
            self.chunk_entity_relation_graph,
            self.relationships_vdb,
            source_entity,
            target_entity,
        )

    def delete_by_relation(self, source_entity: str, target_entity: str) -> None:
        loop = always_get_an_event_loop()
        return loop.run_until_complete(
            self.adelete_by_relation(source_entity, target_entity)
        )

    async def get_processing_status(self) -> dict[str, int]:
        """Get current document processing status counts

        Returns:
            Dict with counts for each status
        """
        return await self.doc_status.get_status_counts()

    async def get_entity_info(
        self, entity_name: str, include_vector_data: bool = False
    ) -> dict[str, str | None | dict[str, str]]:
        """Get detailed information of an entity"""
        from .utils_graph import get_entity_info

        return await get_entity_info(
            self.chunk_entity_relation_graph,
            self.entities_vdb,
            entity_name,
            include_vector_data,
        )

    async def get_relation_info(
        self, src_entity: str, tgt_entity: str, include_vector_data: bool = False
    ) -> dict[str, str | None | dict[str, str]]:
        """Get detailed information of a relationship"""
        from .utils_graph import get_relation_info

        return await get_relation_info(
            self.chunk_entity_relation_graph,
            self.relationships_vdb,
            src_entity,
            tgt_entity,
            include_vector_data,
        )

    async def aedit_entity(
        self, entity_name: str, updated_data: dict[str, str], allow_rename: bool = True
    ) -> dict[str, Any]:
        """Asynchronously edit entity information.

        Updates entity information in the knowledge graph and re-embeds the entity in the vector database.

        Args:
            entity_name: Name of the entity to edit
            updated_data: Dictionary containing updated attributes, e.g. {"description": "new description", "entity_type": "new type"}
            allow_rename: Whether to allow entity renaming, defaults to True

        Returns:
            Dictionary containing updated entity information
        """
        from .utils_graph import aedit_entity

        return await aedit_entity(
            self.chunk_entity_relation_graph,
            self.entities_vdb,
            self.relationships_vdb,
            entity_name,
            updated_data,
            allow_rename,
        )

    def edit_entity(
        self, entity_name: str, updated_data: dict[str, str], allow_rename: bool = True
    ) -> dict[str, Any]:
        loop = always_get_an_event_loop()
        return loop.run_until_complete(
            self.aedit_entity(entity_name, updated_data, allow_rename)
        )

    async def aedit_relation(
        self, source_entity: str, target_entity: str, updated_data: dict[str, Any]
    ) -> dict[str, Any]:
        """Asynchronously edit relation information.

        Updates relation (edge) information in the knowledge graph and re-embeds the relation in the vector database.

        Args:
            source_entity: Name of the source entity
            target_entity: Name of the target entity
            updated_data: Dictionary containing updated attributes, e.g. {"description": "new description", "keywords": "new keywords"}

        Returns:
            Dictionary containing updated relation information
        """
        from .utils_graph import aedit_relation

        return await aedit_relation(
            self.chunk_entity_relation_graph,
            self.entities_vdb,
            self.relationships_vdb,
            source_entity,
            target_entity,
            updated_data,
        )

    def edit_relation(
        self, source_entity: str, target_entity: str, updated_data: dict[str, Any]
    ) -> dict[str, Any]:
        loop = always_get_an_event_loop()
        return loop.run_until_complete(
            self.aedit_relation(source_entity, target_entity, updated_data)
        )

    async def acreate_entity(
        self, entity_name: str, entity_data: dict[str, Any]
    ) -> dict[str, Any]:
        """Asynchronously create a new entity.

        Creates a new entity in the knowledge graph and adds it to the vector database.

        Args:
            entity_name: Name of the new entity
            entity_data: Dictionary containing entity attributes, e.g. {"description": "description", "entity_type": "type"}

        Returns:
            Dictionary containing created entity information
        """
        from .utils_graph import acreate_entity

        return await acreate_entity(
            self.chunk_entity_relation_graph,
            self.entities_vdb,
            self.relationships_vdb,
            entity_name,
            entity_data,
        )

    def create_entity(
        self, entity_name: str, entity_data: dict[str, Any]
    ) -> dict[str, Any]:
        loop = always_get_an_event_loop()
        return loop.run_until_complete(self.acreate_entity(entity_name, entity_data))

    async def acreate_relation(
        self, source_entity: str, target_entity: str, relation_data: dict[str, Any]
    ) -> dict[str, Any]:
        """Asynchronously create a new relation between entities.

        Creates a new relation (edge) in the knowledge graph and adds it to the vector database.

        Args:
            source_entity: Name of the source entity
            target_entity: Name of the target entity
            relation_data: Dictionary containing relation attributes, e.g. {"description": "description", "keywords": "keywords"}

        Returns:
            Dictionary containing created relation information
        """
        from .utils_graph import acreate_relation

        return await acreate_relation(
            self.chunk_entity_relation_graph,
            self.entities_vdb,
            self.relationships_vdb,
            source_entity,
            target_entity,
            relation_data,
        )

    def create_relation(
        self, source_entity: str, target_entity: str, relation_data: dict[str, Any]
    ) -> dict[str, Any]:
        loop = always_get_an_event_loop()
        return loop.run_until_complete(
            self.acreate_relation(source_entity, target_entity, relation_data)
        )

    async def amerge_entities(
        self,
        source_entities: list[str],
        target_entity: str,
        merge_strategy: dict[str, str] = None,
        target_entity_data: dict[str, Any] = None,
    ) -> dict[str, Any]:
        """Asynchronously merge multiple entities into one entity.

        Merges multiple source entities into a target entity, handling all relationships,
        and updating both the knowledge graph and vector database.

        Args:
            source_entities: List of source entity names to merge
            target_entity: Name of the target entity after merging
            merge_strategy: Merge strategy configuration, e.g. {"description": "concatenate", "entity_type": "keep_first"}
                Supported strategies:
                - "concatenate": Concatenate all values (for text fields)
                - "keep_first": Keep the first non-empty value
                - "keep_last": Keep the last non-empty value
                - "join_unique": Join all unique values (for fields separated by delimiter)
            target_entity_data: Dictionary of specific values to set for the target entity,
                overriding any merged values, e.g. {"description": "custom description", "entity_type": "PERSON"}

        Returns:
            Dictionary containing the merged entity information
        """
        from .utils_graph import amerge_entities

        return await amerge_entities(
            self.chunk_entity_relation_graph,
            self.entities_vdb,
            self.relationships_vdb,
            source_entities,
            target_entity,
            merge_strategy,
            target_entity_data,
        )

    def merge_entities(
        self,
        source_entities: list[str],
        target_entity: str,
        merge_strategy: dict[str, str] = None,
        target_entity_data: dict[str, Any] = None,
    ) -> dict[str, Any]:
        loop = always_get_an_event_loop()
        return loop.run_until_complete(
            self.amerge_entities(
                source_entities, target_entity, merge_strategy, target_entity_data
            )
        )

    async def aexport_data(
        self,
        output_path: str,
        file_format: Literal["csv", "excel", "md", "txt"] = "csv",
        include_vector_data: bool = False,
    ) -> None:
        """
        Asynchronously exports all entities, relations, and relationships to various formats.
        Args:
            output_path: The path to the output file (including extension).
            file_format: Output format - "csv", "excel", "md", "txt".
                - csv: Comma-separated values file
                - excel: Microsoft Excel file with multiple sheets
                - md: Markdown tables
                - txt: Plain text formatted output
                - table: Print formatted tables to console
            include_vector_data: Whether to include data from the vector database.
        """
        from .utils import aexport_data as utils_aexport_data

        await utils_aexport_data(
            self.chunk_entity_relation_graph,
            self.entities_vdb,
            self.relationships_vdb,
            output_path,
            file_format,
            include_vector_data,
        )

    def export_data(
        self,
        output_path: str,
        file_format: Literal["csv", "excel", "md", "txt"] = "csv",
        include_vector_data: bool = False,
    ) -> None:
        """
        Synchronously exports all entities, relations, and relationships to various formats.
        Args:
            output_path: The path to the output file (including extension).
            file_format: Output format - "csv", "excel", "md", "txt".
                - csv: Comma-separated values file
                - excel: Microsoft Excel file with multiple sheets
                - md: Markdown tables
                - txt: Plain text formatted output
                - table: Print formatted tables to console
            include_vector_data: Whether to include data from the vector database.
        """
        try:
            loop = asyncio.get_event_loop()
        except RuntimeError:
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)

        loop.run_until_complete(
            self.aexport_data(output_path, file_format, include_vector_data)
        )
