LightRAG/lightrag/lightrag.py

import asyncio
import os
from tqdm.asyncio import tqdm as tqdm_async
from dataclasses import asdict, dataclass, field
from datetime import datetime
from functools import partial
from typing import Type, cast, Dict

from .llm import (
    gpt_4o_mini_complete,
    openai_embedding,
)
from .operate import (
    chunking_by_token_size,
    extract_entities,
    # local_query,global_query,hybrid_query,
    kg_query,
    naive_query,
)

from .utils import (
    EmbeddingFunc,
    compute_mdhash_id,
    limit_async_func_call,
    convert_response_to_json,
    logger,
    set_logger,
)
from .base import (
    BaseGraphStorage,
    BaseKVStorage,
    BaseVectorStorage,
    StorageNameSpace,
    QueryParam,
    DocStatus,
)

from .storage import (
    JsonKVStorage,
    NanoVectorDBStorage,
    NetworkXStorage,
    JsonDocStatusStorage,
)

# future KG integrations

# from .kg.ArangoDB_impl import (
#     GraphStorage as ArangoDBStorage
# )


def lazy_external_import(module_name: str, class_name: str):
    """Lazily import a class from an external module based on the package of the caller."""

    # Get the caller's module and package
    import inspect

    caller_frame = inspect.currentframe().f_back
    module = inspect.getmodule(caller_frame)
    package = module.__package__ if module else None

    def import_class(*args, **kwargs):
        import importlib

        # Import the module using importlib
        module = importlib.import_module(module_name, package=package)

        # Get the class from the module and instantiate it
        cls = getattr(module, class_name)
        return cls(*args, **kwargs)

    return import_class


Neo4JStorage = lazy_external_import(".kg.neo4j_impl", "Neo4JStorage")
OracleKVStorage = lazy_external_import(".kg.oracle_impl", "OracleKVStorage")
OracleGraphStorage = lazy_external_import(".kg.oracle_impl", "OracleGraphStorage")
OracleVectorDBStorage = lazy_external_import(".kg.oracle_impl", "OracleVectorDBStorage")
MilvusVectorDBStorge = lazy_external_import(".kg.milvus_impl", "MilvusVectorDBStorge")
MongoKVStorage = lazy_external_import(".kg.mongo_impl", "MongoKVStorage")
ChromaVectorDBStorage = lazy_external_import(".kg.chroma_impl", "ChromaVectorDBStorage")
TiDBKVStorage = lazy_external_import(".kg.tidb_impl", "TiDBKVStorage")
TiDBVectorDBStorage = lazy_external_import(".kg.tidb_impl", "TiDBVectorDBStorage")
TiDBGraphStorage = lazy_external_import(".kg.tidb_impl", "TiDBGraphStorage")
AGEStorage = lazy_external_import(".kg.age_impl", "AGEStorage")
GremlinStorage = lazy_external_import(".kg.gremlin_impl", "GremlinStorage")


def always_get_an_event_loop() -> asyncio.AbstractEventLoop:
    """
    Ensure that there is always an event loop available.

    This function tries to get the current event loop. If the current event loop is closed or does not exist,
    it creates a new event loop and sets it as the current event loop.

    Returns:
        asyncio.AbstractEventLoop: The current or newly created event loop.
    """
    try:
        # Try to get the current event loop
        current_loop = asyncio.get_event_loop()
        if current_loop.is_closed():
            raise RuntimeError("Event loop is closed.")
        return current_loop

    except RuntimeError:
        # If no event loop exists or it is closed, create a new one
        logger.info("Creating a new event loop in main thread.")
        new_loop = asyncio.new_event_loop()
        asyncio.set_event_loop(new_loop)
        return new_loop


@dataclass
class LightRAG:
    working_dir: str = field(
        default_factory=lambda: f"./lightrag_cache_{datetime.now().strftime('%Y-%m-%d-%H:%M:%S')}"
    )
    # Default not to use embedding cache
    embedding_cache_config: dict = field(
        default_factory=lambda: {
            "enabled": False,
            "similarity_threshold": 0.95,
            "use_llm_check": False,
        }
    )
    kv_storage: str = field(default="JsonKVStorage")
    vector_storage: str = field(default="NanoVectorDBStorage")
    graph_storage: str = field(default="NetworkXStorage")

    current_log_level = logger.level
    log_level: str = field(default=current_log_level)

    # text chunking
    chunk_token_size: int = 1200
    chunk_overlap_token_size: int = 100
    tiktoken_model_name: str = "gpt-4o-mini"

    # entity extraction
    entity_extract_max_gleaning: int = 1
    entity_summary_to_max_tokens: int = 500

    # node embedding
    node_embedding_algorithm: str = "node2vec"
    node2vec_params: dict = field(
        default_factory=lambda: {
            "dimensions": 1536,
            "num_walks": 10,
            "walk_length": 40,
            "window_size": 2,
            "iterations": 3,
            "random_seed": 3,
        }
    )

    # embedding_func: EmbeddingFunc = field(default_factory=lambda:hf_embedding)
    embedding_func: EmbeddingFunc = field(default_factory=lambda: openai_embedding)
    embedding_batch_num: int = 32
    embedding_func_max_async: int = 16

    # LLM
    llm_model_func: callable = gpt_4o_mini_complete  # hf_model_complete#
    llm_model_name: str = "meta-llama/Llama-3.2-1B-Instruct"  #'meta-llama/Llama-3.2-1B'#'google/gemma-2-2b-it'
    llm_model_max_token_size: int = 32768
    llm_model_max_async: int = 16
    llm_model_kwargs: dict = field(default_factory=dict)

    # storage
    vector_db_storage_cls_kwargs: dict = field(default_factory=dict)

    enable_llm_cache: bool = True

    # extension
    addon_params: dict = field(default_factory=dict)
    convert_response_to_json_func: callable = convert_response_to_json

    # Add new field for document status storage type
    doc_status_storage: str = field(default="JsonDocStatusStorage")

    def __post_init__(self):
        log_file = os.path.join("lightrag.log")
        set_logger(log_file)
        logger.setLevel(self.log_level)

        logger.info(f"Logger initialized for working directory: {self.working_dir}")

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

        # @TODO: should move all storage setup here to leverage initial start params attached to self.

        self.key_string_value_json_storage_cls: Type[BaseKVStorage] = (
            self._get_storage_class()[self.kv_storage]
        )
        self.vector_db_storage_cls: Type[BaseVectorStorage] = self._get_storage_class()[
            self.vector_storage
        ]
        self.graph_storage_cls: Type[BaseGraphStorage] = self._get_storage_class()[
            self.graph_storage
        ]

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

        self.llm_response_cache = self.key_string_value_json_storage_cls(
            namespace="llm_response_cache",
            global_config=asdict(self),
            embedding_func=None,
        )

        self.embedding_func = limit_async_func_call(self.embedding_func_max_async)(
            self.embedding_func
        )

        ####
        # add embedding func by walter
        ####
        self.full_docs = self.key_string_value_json_storage_cls(
            namespace="full_docs",
            global_config=asdict(self),
            embedding_func=self.embedding_func,
        )
        self.text_chunks = self.key_string_value_json_storage_cls(
            namespace="text_chunks",
            global_config=asdict(self),
            embedding_func=self.embedding_func,
        )
        self.chunk_entity_relation_graph = self.graph_storage_cls(
            namespace="chunk_entity_relation",
            global_config=asdict(self),
            embedding_func=self.embedding_func,
        )
        ####
        # add embedding func by walter over
        ####

        self.entities_vdb = self.vector_db_storage_cls(
            namespace="entities",
            global_config=asdict(self),
            embedding_func=self.embedding_func,
            meta_fields={"entity_name"},
        )
        self.relationships_vdb = self.vector_db_storage_cls(
            namespace="relationships",
            global_config=asdict(self),
            embedding_func=self.embedding_func,
            meta_fields={"src_id", "tgt_id"},
        )
        self.chunks_vdb = self.vector_db_storage_cls(
            namespace="chunks",
            global_config=asdict(self),
            embedding_func=self.embedding_func,
        )

        self.llm_model_func = limit_async_func_call(self.llm_model_max_async)(
            partial(
                self.llm_model_func,
                hashing_kv=self.llm_response_cache
                if self.llm_response_cache
                and hasattr(self.llm_response_cache, "global_config")
                else self.key_string_value_json_storage_cls(
                    namespace="llm_response_cache",
                    global_config=asdict(self),
                    embedding_func=None,
                ),
                **self.llm_model_kwargs,
            )
        )

        # Initialize document status storage
        self.doc_status_storage_cls = self._get_storage_class()[self.doc_status_storage]
        self.doc_status = self.doc_status_storage_cls(
            namespace="doc_status",
            global_config=asdict(self),
            embedding_func=None,
        )

    def _get_storage_class(self) -> dict:
        return {
            # kv storage
            "JsonKVStorage": JsonKVStorage,
            "OracleKVStorage": OracleKVStorage,
            "MongoKVStorage": MongoKVStorage,
            "TiDBKVStorage": TiDBKVStorage,
            # vector storage
            "NanoVectorDBStorage": NanoVectorDBStorage,
            "OracleVectorDBStorage": OracleVectorDBStorage,
            "MilvusVectorDBStorge": MilvusVectorDBStorge,
            "ChromaVectorDBStorage": ChromaVectorDBStorage,
            "TiDBVectorDBStorage": TiDBVectorDBStorage,
            # graph storage
            "NetworkXStorage": NetworkXStorage,
            "Neo4JStorage": Neo4JStorage,
            "OracleGraphStorage": OracleGraphStorage,
            "AGEStorage": AGEStorage,
            "TiDBGraphStorage": TiDBGraphStorage,
            "GremlinStorage": GremlinStorage,
            # "ArangoDBStorage": ArangoDBStorage
            "JsonDocStatusStorage": JsonDocStatusStorage,
        }

    def insert(self, string_or_strings):
        loop = always_get_an_event_loop()
        return loop.run_until_complete(self.ainsert(string_or_strings))

    async def ainsert(self, string_or_strings):
        """Insert documents with checkpoint support

        Args:
            string_or_strings: Single document string or list of document strings
        """
        if isinstance(string_or_strings, str):
            string_or_strings = [string_or_strings]

        # 1. Remove duplicate contents from the list
        unique_contents = list(set(doc.strip() for doc in string_or_strings))

        # 2. Generate document IDs and initial status
        new_docs = {
            compute_mdhash_id(content, prefix="doc-"): {
                "content": content,
                "content_summary": self._get_content_summary(content),
                "content_length": len(content),
                "status": DocStatus.PENDING,
                "created_at": datetime.now().isoformat(),
                "updated_at": datetime.now().isoformat(),
            }
            for content in unique_contents
        }

        # 3. Filter out already processed documents
        _add_doc_keys = await self.doc_status.filter_keys(list(new_docs.keys()))
        new_docs = {k: v for k, v in new_docs.items() if k in _add_doc_keys}

        if not new_docs:
            logger.info("All documents have been processed or are duplicates")
            return

        logger.info(f"Processing {len(new_docs)} new unique documents")

        # Process documents in batches
        batch_size = self.addon_params.get("insert_batch_size", 10)
        for i in range(0, len(new_docs), batch_size):
            batch_docs = dict(list(new_docs.items())[i : i + batch_size])

            for doc_id, doc in tqdm_async(
                batch_docs.items(), desc=f"Processing batch {i//batch_size + 1}"
            ):
                try:
                    # Update status to processing
                    doc_status = {
                        "content_summary": doc["content_summary"],
                        "content_length": doc["content_length"],
                        "status": DocStatus.PROCESSING,
                        "created_at": doc["created_at"],
                        "updated_at": datetime.now().isoformat(),
                    }
                    await self.doc_status.upsert({doc_id: doc_status})

                    # Generate chunks from document
                    chunks = {
                        compute_mdhash_id(dp["content"], prefix="chunk-"): {
                            **dp,
                            "full_doc_id": doc_id,
                        }
                        for dp in chunking_by_token_size(
                            doc["content"],
                            overlap_token_size=self.chunk_overlap_token_size,
                            max_token_size=self.chunk_token_size,
                            tiktoken_model=self.tiktoken_model_name,
                        )
                    }

                    # Update status with chunks information
                    doc_status.update(
                        {
                            "chunks_count": len(chunks),
                            "updated_at": datetime.now().isoformat(),
                        }
                    )
                    await self.doc_status.upsert({doc_id: doc_status})

                    try:
                        # Store chunks in vector database
                        await self.chunks_vdb.upsert(chunks)

                        # Extract and store entities and relationships
                        maybe_new_kg = await extract_entities(
                            chunks,
                            knowledge_graph_inst=self.chunk_entity_relation_graph,
                            entity_vdb=self.entities_vdb,
                            relationships_vdb=self.relationships_vdb,
                            global_config=asdict(self),
                        )

                        if maybe_new_kg is None:
                            raise Exception(
                                "Failed to extract entities and relationships"
                            )

                        self.chunk_entity_relation_graph = maybe_new_kg

                        # Store original document and chunks
                        await self.full_docs.upsert(
                            {doc_id: {"content": doc["content"]}}
                        )
                        await self.text_chunks.upsert(chunks)

                        # Update status to processed
                        doc_status.update(
                            {
                                "status": DocStatus.PROCESSED,
                                "updated_at": datetime.now().isoformat(),
                            }
                        )
                        await self.doc_status.upsert({doc_id: doc_status})

                    except Exception as e:
                        # Mark as failed if any step fails
                        doc_status.update(
                            {
                                "status": DocStatus.FAILED,
                                "error": str(e),
                                "updated_at": datetime.now().isoformat(),
                            }
                        )
                        await self.doc_status.upsert({doc_id: doc_status})
                        raise e

                except Exception as e:
                    import traceback

                    error_msg = f"Failed to process document {doc_id}: {str(e)}\n{traceback.format_exc()}"
                    logger.error(error_msg)
                    continue

                finally:
                    # Ensure all indexes are updated after each document
                    await self._insert_done()

    async def _insert_done(self):
        tasks = []
        for storage_inst in [
            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 None:
                continue
            tasks.append(cast(StorageNameSpace, storage_inst).index_done_callback())
        await asyncio.gather(*tasks)

    def insert_custom_kg(self, custom_kg: dict):
        loop = always_get_an_event_loop()
        return loop.run_until_complete(self.ainsert_custom_kg(custom_kg))

    async def ainsert_custom_kg(self, custom_kg: dict):
        update_storage = False
        try:
            # Insert chunks into vector storage
            all_chunks_data = {}
            chunk_to_source_map = {}
            for chunk_data in custom_kg.get("chunks", []):
                chunk_content = chunk_data["content"]
                source_id = chunk_data["source_id"]
                chunk_id = compute_mdhash_id(chunk_content.strip(), prefix="chunk-")

                chunk_entry = {"content": chunk_content.strip(), "source_id": source_id}
                all_chunks_data[chunk_id] = chunk_entry
                chunk_to_source_map[source_id] = chunk_id
                update_storage = True

            if self.chunks_vdb is not None and all_chunks_data:
                await self.chunks_vdb.upsert(all_chunks_data)
            if self.text_chunks is not None and all_chunks_data:
                await self.text_chunks.upsert(all_chunks_data)

            # Insert entities into knowledge graph
            all_entities_data = []
            for entity_data in custom_kg.get("entities", []):
                entity_name = f'"{entity_data["entity_name"].upper()}"'
                entity_type = entity_data.get("entity_type", "UNKNOWN")
                description = entity_data.get("description", "No description provided")
                # source_id = entity_data["source_id"]
                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 = {
                    "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 = []
            for relationship_data in custom_kg.get("relationships", []):
                src_id = f'"{relationship_data["src_id"].upper()}"'
                tgt_id = f'"{relationship_data["tgt_id"].upper()}"'
                description = relationship_data["description"]
                keywords = relationship_data["keywords"]
                weight = relationship_data.get("weight", 1.0)
                # source_id = relationship_data["source_id"]
                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={
                                "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 = {
                    "src_id": src_id,
                    "tgt_id": tgt_id,
                    "description": description,
                    "keywords": keywords,
                }
                all_relationships_data.append(edge_data)
                update_storage = True

            # Insert entities into vector storage if needed
            if self.entities_vdb is not None:
                data_for_vdb = {
                    compute_mdhash_id(dp["entity_name"], prefix="ent-"): {
                        "content": dp["entity_name"] + dp["description"],
                        "entity_name": dp["entity_name"],
                    }
                    for dp in all_entities_data
                }
                await self.entities_vdb.upsert(data_for_vdb)

            # Insert relationships into vector storage if needed
            if self.relationships_vdb is not None:
                data_for_vdb = {
                    compute_mdhash_id(dp["src_id"] + dp["tgt_id"], prefix="rel-"): {
                        "src_id": dp["src_id"],
                        "tgt_id": dp["tgt_id"],
                        "content": dp["keywords"]
                        + dp["src_id"]
                        + dp["tgt_id"]
                        + dp["description"],
                    }
                    for dp in all_relationships_data
                }
                await self.relationships_vdb.upsert(data_for_vdb)
        finally:
            if update_storage:
                await self._insert_done()

    def query(self, query: str, param: QueryParam = QueryParam()):
        loop = always_get_an_event_loop()
        return loop.run_until_complete(self.aquery(query, param))

    async def aquery(self, query: str, param: QueryParam = QueryParam()):
        if param.mode in ["local", "global", "hybrid"]:
            response = await kg_query(
                query,
                self.chunk_entity_relation_graph,
                self.entities_vdb,
                self.relationships_vdb,
                self.text_chunks,
                param,
                asdict(self),
                hashing_kv=self.llm_response_cache
                if self.llm_response_cache
                and hasattr(self.llm_response_cache, "global_config")
                else self.key_string_value_json_storage_cls(
                    namespace="llm_response_cache",
                    global_config=asdict(self),
                    embedding_func=None,
                ),
            )
        elif param.mode == "naive":
            response = await naive_query(
                query,
                self.chunks_vdb,
                self.text_chunks,
                param,
                asdict(self),
                hashing_kv=self.llm_response_cache
                if self.llm_response_cache
                and hasattr(self.llm_response_cache, "global_config")
                else self.key_string_value_json_storage_cls(
                    namespace="llm_response_cache",
                    global_config=asdict(self),
                    embedding_func=None,
                ),
            )
        else:
            raise ValueError(f"Unknown mode {param.mode}")
        await self._query_done()
        return response

    async def _query_done(self):
        tasks = []
        for storage_inst in [self.llm_response_cache]:
            if storage_inst is None:
                continue
            tasks.append(cast(StorageNameSpace, storage_inst).index_done_callback())
        await asyncio.gather(*tasks)

    def delete_by_entity(self, entity_name: str):
        loop = always_get_an_event_loop()
        return loop.run_until_complete(self.adelete_by_entity(entity_name))

    async def adelete_by_entity(self, entity_name: str):
        entity_name = f'"{entity_name.upper()}"'

        try:
            await self.entities_vdb.delete_entity(entity_name)
            await self.relationships_vdb.delete_relation(entity_name)
            await self.chunk_entity_relation_graph.delete_node(entity_name)

            logger.info(
                f"Entity '{entity_name}' and its relationships have been deleted."
            )
            await self._delete_by_entity_done()
        except Exception as e:
            logger.error(f"Error while deleting entity '{entity_name}': {e}")

    async def _delete_by_entity_done(self):
        tasks = []
        for storage_inst in [
            self.entities_vdb,
            self.relationships_vdb,
            self.chunk_entity_relation_graph,
        ]:
            if storage_inst is None:
                continue
            tasks.append(cast(StorageNameSpace, storage_inst).index_done_callback())
        await asyncio.gather(*tasks)

    def _get_content_summary(self, content: str, max_length: int = 100) -> str:
        """Get summary of document content

        Args:
            content: Original document content
            max_length: Maximum length of summary

        Returns:
            Truncated content with ellipsis if needed
        """
        content = content.strip()
        if len(content) <= max_length:
            return content
        return content[:max_length] + "..."

    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()