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LightRAG/lightrag/lightrag.py
clssck 69358d830d test(lightrag,examples,api): comprehensive ruff formatting and type hints 
Format entire codebase with ruff and add type hints across all modules:
- Apply ruff formatting to all Python files (121 files, 17K insertions)
- Add type hints to function signatures throughout lightrag core and API
- Update test suite with improved type annotations and docstrings
- Add pyrightconfig.json for static type checking configuration
- Create prompt_optimized.py and test_extraction_prompt_ab.py test files
- Update ruff.toml and .gitignore for improved linting configuration
- Standardize code style across examples, reproduce scripts, and utilities
2025-12-05 15:17:06 +01:00

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from __future__ import annotations
import asyncio
import configparser
import inspect
import json
import os
import time
import traceback
import warnings
from collections.abc import AsyncIterator, Awaitable, Callable, Iterator
from dataclasses import asdict, dataclass, field
from datetime import datetime, timezone
from functools import partial
from typing import (
Any,
Literal,
cast,
final,
)
from dotenv import load_dotenv
from lightrag.base import (
BaseGraphStorage,
BaseKVStorage,
BaseVectorStorage,
DeletionResult,
DocProcessingStatus,
DocStatus,
DocStatusStorage,
OllamaServerInfos,
QueryParam,
QueryResult,
StorageNameSpace,
StoragesStatus,
)
from lightrag.constants import (
DEFAULT_CHUNK_TOP_K,
DEFAULT_COSINE_THRESHOLD,
DEFAULT_EMBEDDING_TIMEOUT,
DEFAULT_ENTITY_TYPES,
DEFAULT_FILE_PATH_MORE_PLACEHOLDER,
DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE,
DEFAULT_KG_CHUNK_PICK_METHOD,
DEFAULT_LLM_TIMEOUT,
DEFAULT_MAX_ASYNC,
DEFAULT_MAX_ENTITY_TOKENS,
DEFAULT_MAX_FILE_PATHS,
DEFAULT_MAX_GLEANING,
DEFAULT_MAX_GRAPH_NODES,
DEFAULT_MAX_PARALLEL_INSERT,
DEFAULT_MAX_RELATION_TOKENS,
DEFAULT_MAX_SOURCE_IDS_PER_ENTITY,
DEFAULT_MAX_SOURCE_IDS_PER_RELATION,
DEFAULT_MAX_TOTAL_TOKENS,
DEFAULT_MIN_RERANK_SCORE,
DEFAULT_RELATED_CHUNK_NUMBER,
DEFAULT_SOURCE_IDS_LIMIT_METHOD,
DEFAULT_SUMMARY_CONTEXT_SIZE,
DEFAULT_SUMMARY_LANGUAGE,
DEFAULT_SUMMARY_LENGTH_RECOMMENDED,
DEFAULT_SUMMARY_MAX_TOKENS,
DEFAULT_TOP_K,
GRAPH_FIELD_SEP,
)
from lightrag.entity_resolution import EntityResolutionConfig
from lightrag.exceptions import PipelineCancelledException
from lightrag.kg import (
STORAGES,
verify_storage_implementation,
)
from lightrag.kg.shared_storage import (
get_data_init_lock,
get_default_workspace,
get_namespace_data,
get_namespace_lock,
set_default_workspace,
)
from lightrag.namespace import NameSpace
from lightrag.operate import (
chunking_by_token_size,
extract_entities,
kg_query,
merge_nodes_and_edges,
naive_query,
rebuild_knowledge_from_chunks,
)
from lightrag.prompt import PROMPTS
from lightrag.types import KnowledgeGraph
from lightrag.utils import (
EmbeddingFunc,
TiktokenTokenizer,
Tokenizer,
always_get_an_event_loop,
check_storage_env_vars,
compute_mdhash_id,
convert_to_user_format,
generate_track_id,
get_content_summary,
get_env_value,
lazy_external_import,
logger,
make_relation_chunk_key,
normalize_source_ids_limit_method,
priority_limit_async_func_call,
sanitize_text_for_encoding,
subtract_source_ids,
)
# 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='./rag_storage')
"""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."""
# Workspace
# ---
workspace: str = field(default_factory=lambda: os.getenv('WORKSPACE', ''))
"""Workspace for data isolation. Defaults to empty string if WORKSPACE environment variable is not set."""
# Logging (Deprecated, use setup_logger in utils.py instead)
# ---
log_level: int | None = field(default=None)
log_file_path: str | None = field(default=None)
# Query parameters
# ---
top_k: int = field(default=get_env_value('TOP_K', DEFAULT_TOP_K, int))
"""Number of entities/relations to retrieve for each query."""
chunk_top_k: int = field(default=get_env_value('CHUNK_TOP_K', DEFAULT_CHUNK_TOP_K, int))
"""Maximum number of chunks in context."""
max_entity_tokens: int = field(default=get_env_value('MAX_ENTITY_TOKENS', DEFAULT_MAX_ENTITY_TOKENS, int))
"""Maximum number of tokens for entity in context."""
max_relation_tokens: int = field(default=get_env_value('MAX_RELATION_TOKENS', DEFAULT_MAX_RELATION_TOKENS, int))
"""Maximum number of tokens for relation in context."""
max_total_tokens: int = field(default=get_env_value('MAX_TOTAL_TOKENS', DEFAULT_MAX_TOTAL_TOKENS, int))
"""Maximum total tokens in context (including system prompt, entities, relations and chunks)."""
cosine_threshold: int = field(default=get_env_value('COSINE_THRESHOLD', DEFAULT_COSINE_THRESHOLD, int))
"""Cosine threshold of vector DB retrieval for entities, relations and chunks."""
related_chunk_number: int = field(default=get_env_value('RELATED_CHUNK_NUMBER', DEFAULT_RELATED_CHUNK_NUMBER, int))
"""Number of related chunks to grab from single entity or relation."""
kg_chunk_pick_method: str = field(default=get_env_value('KG_CHUNK_PICK_METHOD', DEFAULT_KG_CHUNK_PICK_METHOD, str))
"""Method for selecting text chunks: 'WEIGHT' for weight-based selection, 'VECTOR' for embedding similarity-based selection."""
# Entity extraction
# ---
entity_extract_max_gleaning: int = field(default=get_env_value('MAX_GLEANING', DEFAULT_MAX_GLEANING, int))
"""Maximum number of entity extraction attempts for ambiguous content."""
force_llm_summary_on_merge: int = field(
default=get_env_value('FORCE_LLM_SUMMARY_ON_MERGE', DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE, int)
)
entity_resolution_config: EntityResolutionConfig = field(default_factory=EntityResolutionConfig)
"""Configuration for entity resolution (deduplication).
Now enabled by default. Set to None to disable.
Resolves entities like 'FDA' → 'US Food and Drug Administration'."""
# Orphan connection
# ---
auto_connect_orphans: bool = field(default=get_env_value('AUTO_CONNECT_ORPHANS', False, bool))
"""Automatically run orphan connection after each document insert.
Orphans are entities with no relationships. This finds meaningful
connections using vector similarity + LLM validation."""
orphan_connection_threshold: float = field(default=get_env_value('ORPHAN_CONNECTION_THRESHOLD', 0.3, float))
"""Vector similarity threshold for orphan connection candidates.
Lower = more candidates (more LLM calls). Range: 0.0-1.0."""
orphan_confidence_threshold: float = field(default=get_env_value('ORPHAN_CONFIDENCE_THRESHOLD', 0.7, float))
"""LLM confidence threshold for creating orphan connections.
Higher = stricter validation. Range: 0.0-1.0."""
orphan_cross_connect: bool = field(default=get_env_value('ORPHAN_CROSS_CONNECT', True, bool))
"""Allow orphans to connect to other orphans, forming new clusters.
If False, orphans can only connect to already-connected entities."""
orphan_connection_max_degree: int = field(default=get_env_value('ORPHAN_CONNECTION_MAX_DEGREE', 0, int))
"""Maximum connection degree for entities to be targeted by auto-connect.
0 = True orphans only (no connections)
1 = Orphans + leaf nodes (0-1 connections)
2+ = Include sparsely connected nodes"""
# 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: Tokenizer | None = 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,
str | None,
bool,
int,
int,
],
list[dict[str, Any]] | Awaitable[list[dict[str, Any]]],
] = field(default_factory=lambda: chunking_by_token_size)
"""
Custom chunking function for splitting text into chunks before processing.
The function can be either synchronous or asynchronous.
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_overlap_token_size`: The number of overlapping tokens between consecutive chunks.
- `chunk_token_size`: The maximum number of tokens per chunk.
The function should return a list of dictionaries (or an awaitable that resolves to a list),
where each dictionary contains the following keys:
- `tokens` (int): The number of tokens in the chunk.
- `content` (str): The text content of the chunk.
- `chunk_order_index` (int): Zero-based index indicating the chunk's order in the document.
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_token_limit: int | None = field(default=None, init=False)
"""Token limit for embedding model. Set automatically from embedding_func.max_token_size in __post_init__."""
embedding_batch_num: int = field(default=int(os.getenv('EMBEDDING_BATCH_NUM', 10)))
"""Batch size for embedding computations."""
embedding_func_max_async: int = field(default=int(os.getenv('EMBEDDING_FUNC_MAX_ASYNC', 8)))
"""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.
"""
default_embedding_timeout: int = field(default=int(os.getenv('EMBEDDING_TIMEOUT', DEFAULT_EMBEDDING_TIMEOUT)))
# 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."""
summary_max_tokens: int = field(default=int(os.getenv('SUMMARY_MAX_TOKENS', DEFAULT_SUMMARY_MAX_TOKENS)))
"""Maximum tokens allowed for entity/relation description."""
summary_context_size: int = field(default=int(os.getenv('SUMMARY_CONTEXT_SIZE', DEFAULT_SUMMARY_CONTEXT_SIZE)))
"""Maximum number of tokens allowed per LLM response."""
summary_length_recommended: int = field(
default=int(os.getenv('SUMMARY_LENGTH_RECOMMENDED', DEFAULT_SUMMARY_LENGTH_RECOMMENDED))
)
"""Recommended length of LLM summary output."""
llm_model_max_async: int = field(default=int(os.getenv('MAX_ASYNC', DEFAULT_MAX_ASYNC)))
"""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."""
default_llm_timeout: int = field(default=int(os.getenv('LLM_TIMEOUT', DEFAULT_LLM_TIMEOUT)))
# Rerank Configuration
# ---
rerank_model_func: Callable[..., object] | None = field(default=None)
"""Function for reranking retrieved documents. All rerank configurations (model name, API keys, top_k, etc.) should be included in this function. Optional."""
min_rerank_score: float = field(default=get_env_value('MIN_RERANK_SCORE', DEFAULT_MIN_RERANK_SCORE, float))
"""Minimum rerank score threshold for filtering chunks after reranking."""
# Storage
# ---
vector_db_storage_cls_kwargs: dict[str, Any] = field(default_factory=dict)
"""Additional parameters for vector database storage."""
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', DEFAULT_MAX_PARALLEL_INSERT)))
"""Maximum number of parallel insert operations."""
max_graph_nodes: int = field(default=get_env_value('MAX_GRAPH_NODES', DEFAULT_MAX_GRAPH_NODES, int))
"""Maximum number of graph nodes to return in knowledge graph queries."""
max_source_ids_per_entity: int = field(
default=get_env_value('MAX_SOURCE_IDS_PER_ENTITY', DEFAULT_MAX_SOURCE_IDS_PER_ENTITY, int)
)
"""Maximum number of source (chunk) ids in entity Grpah + VDB."""
max_source_ids_per_relation: int = field(
default=get_env_value(
'MAX_SOURCE_IDS_PER_RELATION',
DEFAULT_MAX_SOURCE_IDS_PER_RELATION,
int,
)
)
"""Maximum number of source (chunk) ids in relation Graph + VDB."""
source_ids_limit_method: str = field(
default_factory=lambda: normalize_source_ids_limit_method(
get_env_value(
'SOURCE_IDS_LIMIT_METHOD',
DEFAULT_SOURCE_IDS_LIMIT_METHOD,
str,
)
)
)
"""Strategy for enforcing source_id limits: IGNORE_NEW or FIFO."""
max_file_paths: int = field(default=get_env_value('MAX_FILE_PATHS', DEFAULT_MAX_FILE_PATHS, int))
"""Maximum number of file paths to store in entity/relation file_path field."""
file_path_more_placeholder: str = field(default=DEFAULT_FILE_PATH_MORE_PLACEHOLDER)
"""Placeholder text when file paths exceed max_file_paths limit."""
addon_params: dict[str, Any] = field(
default_factory=lambda: {
'language': get_env_value('SUMMARY_LANGUAGE', DEFAULT_SUMMARY_LANGUAGE, str),
'entity_types': get_env_value('ENTITY_TYPES', DEFAULT_ENTITY_TYPES, list),
}
)
# Storages Management
# ---
# TODO: Deprecated (LightRAG will never initialize storage automatically on creation，and finalize should be call before destroying)
auto_manage_storages_states: bool = field(default=False)
"""If True, lightrag will automatically calls initialize_storages and finalize_storages at the appropriate times."""
cosine_better_than_threshold: float = field(default=float(os.getenv('COSINE_THRESHOLD', 0.2)))
ollama_server_infos: OllamaServerInfos | None = field(default=None)
"""Configuration for Ollama server information."""
_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()
# Initialize ollama_server_infos if not provided
if self.ollama_server_infos is None:
self.ollama_server_infos = OllamaServerInfos()
# Validate config
if self.force_llm_summary_on_merge < 3:
logger.warning(f'force_llm_summary_on_merge should be at least 3, got {self.force_llm_summary_on_merge}')
if self.summary_context_size > self.max_total_tokens:
logger.warning(
f'summary_context_size({self.summary_context_size}) should no greater than max_total_tokens({self.max_total_tokens})'
)
if self.summary_length_recommended > self.summary_max_tokens:
logger.warning(
f'max_total_tokens({self.summary_max_tokens}) should greater than summary_length_recommended({self.summary_length_recommended})'
)
# 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
# Step 1: Capture max_token_size before applying decorator (decorator strips dataclass attributes)
embedding_max_token_size = None
if self.embedding_func and hasattr(self.embedding_func, 'max_token_size'):
embedding_max_token_size = self.embedding_func.max_token_size
logger.debug(f'Captured embedding max_token_size: {embedding_max_token_size}')
self.embedding_token_limit = embedding_max_token_size
# Step 2: Apply priority wrapper decorator when an embedding function is provided
if self.embedding_func is not None:
wrapped = priority_limit_async_func_call(
self.embedding_func_max_async,
llm_timeout=self.default_embedding_timeout,
queue_name='Embedding func',
)(self.embedding_func)
# Preserve runtime type while satisfying static typing
self.embedding_func = cast(EmbeddingFunc, wrapped)
if self.embedding_func is None:
raise ValueError('embedding_func must be provided before initializing storages')
# 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=NameSpace.KV_STORE_LLM_RESPONSE_CACHE,
workspace=self.workspace,
global_config=global_config,
embedding_func=self.embedding_func,
)
self.text_chunks: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore
namespace=NameSpace.KV_STORE_TEXT_CHUNKS,
workspace=self.workspace,
embedding_func=self.embedding_func,
)
self.full_docs: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore
namespace=NameSpace.KV_STORE_FULL_DOCS,
workspace=self.workspace,
embedding_func=self.embedding_func,
)
self.full_entities: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore
namespace=NameSpace.KV_STORE_FULL_ENTITIES,
workspace=self.workspace,
embedding_func=self.embedding_func,
)
self.full_relations: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore
namespace=NameSpace.KV_STORE_FULL_RELATIONS,
workspace=self.workspace,
embedding_func=self.embedding_func,
)
self.entity_chunks: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore
namespace=NameSpace.KV_STORE_ENTITY_CHUNKS,
workspace=self.workspace,
embedding_func=self.embedding_func,
)
self.relation_chunks: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore
namespace=NameSpace.KV_STORE_RELATION_CHUNKS,
workspace=self.workspace,
embedding_func=self.embedding_func,
)
self.chunk_entity_relation_graph: BaseGraphStorage = self.graph_storage_cls( # type: ignore
namespace=NameSpace.GRAPH_STORE_CHUNK_ENTITY_RELATION,
workspace=self.workspace,
embedding_func=self.embedding_func,
)
self.entities_vdb: BaseVectorStorage = self.vector_db_storage_cls( # type: ignore
namespace=NameSpace.VECTOR_STORE_ENTITIES,
workspace=self.workspace,
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=NameSpace.VECTOR_STORE_RELATIONSHIPS,
workspace=self.workspace,
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=NameSpace.VECTOR_STORE_CHUNKS,
workspace=self.workspace,
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=NameSpace.DOC_STATUS,
workspace=self.workspace,
global_config=global_config,
embedding_func=None,
)
# Directly use llm_response_cache, don't create a new object
hashing_kv = self.llm_response_cache
# Get timeout from LLM model kwargs for dynamic timeout calculation
self.llm_model_func = priority_limit_async_func_call(
self.llm_model_max_async,
llm_timeout=self.default_llm_timeout,
queue_name='LLM func',
)(
partial(
self.llm_model_func, # type: ignore
hashing_kv=hashing_kv,
**self.llm_model_kwargs,
)
)
self._storages_status = StoragesStatus.CREATED
async def initialize_storages(self):
"""Storage initialization must be called one by one to prevent deadlock"""
if self._storages_status == StoragesStatus.CREATED:
# Set the first initialized workspace will set the default workspace
# Allows namespace operation without specifying workspace for backward compatibility
default_workspace = get_default_workspace()
if default_workspace is None:
set_default_workspace(self.workspace)
elif default_workspace != self.workspace:
logger.info(
f"Creating LightRAG instance with workspace='{self.workspace}' "
f"while default workspace is set to '{default_workspace}'"
)
# Auto-initialize pipeline_status for this workspace
from lightrag.kg.shared_storage import (
initialize_orphan_connection_status,
initialize_pipeline_status,
)
await initialize_pipeline_status(workspace=self.workspace)
await initialize_orphan_connection_status(workspace=self.workspace)
for storage in (
self.full_docs,
self.text_chunks,
self.full_entities,
self.full_relations,
self.entity_chunks,
self.relation_chunks,
self.entities_vdb,
self.relationships_vdb,
self.chunks_vdb,
self.chunk_entity_relation_graph,
self.llm_response_cache,
self.doc_status,
):
if storage:
# logger.debug(f"Initializing storage: {storage}")
await storage.initialize()
self._storages_status = StoragesStatus.INITIALIZED
logger.debug('All storage types initialized')
async def finalize_storages(self):
"""Asynchronously finalize the storages with improved error handling"""
if self._storages_status == StoragesStatus.INITIALIZED:
storages = [
('full_docs', self.full_docs),
('text_chunks', self.text_chunks),
('full_entities', self.full_entities),
('full_relations', self.full_relations),
('entity_chunks', self.entity_chunks),
('relation_chunks', self.relation_chunks),
('entities_vdb', self.entities_vdb),
('relationships_vdb', self.relationships_vdb),
('chunks_vdb', self.chunks_vdb),
('chunk_entity_relation_graph', self.chunk_entity_relation_graph),
('llm_response_cache', self.llm_response_cache),
('doc_status', self.doc_status),
]
# Finalize each storage individually to ensure one failure doesn't prevent others from closing
successful_finalizations = []
failed_finalizations = []
for storage_name, storage in storages:
if storage:
try:
await storage.finalize()
successful_finalizations.append(storage_name)
logger.debug(f'Successfully finalized {storage_name}')
except Exception as e:
error_msg = f'Failed to finalize {storage_name}: {e}'
logger.error(error_msg)
failed_finalizations.append(storage_name)
# Log summary of finalization results
if successful_finalizations:
logger.info(f'Successfully finalized {len(successful_finalizations)} storages')
if failed_finalizations:
logger.error(
f'Failed to finalize {len(failed_finalizations)} storages: {", ".join(failed_finalizations)}'
)
else:
logger.debug('All storages finalized successfully')
self._storages_status = StoragesStatus.FINALIZED
async def check_and_migrate_data(self):
"""Check if data migration is needed and perform migration if necessary"""
async with get_data_init_lock():
try:
# Check if migration is needed:
# 1. chunk_entity_relation_graph has entities and relations (count > 0)
# 2. full_entities and full_relations are empty
# Get all entity labels from graph
all_entity_labels = await self.chunk_entity_relation_graph.get_all_labels()
if not all_entity_labels:
logger.debug('No entities found in graph, skipping migration check')
return
try:
# Initialize chunk tracking storage after migration
await self._migrate_chunk_tracking_storage()
except Exception as e:
logger.error(f'Error during chunk_tracking migration: {e}')
raise e
# Check if full_entities and full_relations are empty
# Get all processed documents to check their entity/relation data
try:
processed_docs = await self.doc_status.get_docs_by_status(DocStatus.PROCESSED)
if not processed_docs:
logger.debug('No processed documents found, skipping migration')
return
# Check first few documents to see if they have full_entities/full_relations data
migration_needed = True
max_check = min(5, len(processed_docs)) # Check up to 5 documents
for _checked_count, doc_id in enumerate(list(processed_docs.keys())[:max_check], 1):
entity_data = await self.full_entities.get_by_id(doc_id)
relation_data = await self.full_relations.get_by_id(doc_id)
if entity_data or relation_data:
migration_needed = False
break
if not migration_needed:
logger.debug('Full entities/relations data already exists, no migration needed')
return
logger.info(
f'Data migration needed: found {len(all_entity_labels)} entities in graph but no full_entities/full_relations data'
)
# Perform migration
await self._migrate_entity_relation_data(processed_docs)
except Exception as e:
logger.error(f'Error during migration check: {e}')
raise e
except Exception as e:
logger.error(f'Error in data migration check: {e}')
raise e
async def _migrate_entity_relation_data(self, processed_docs: dict):
"""Migrate existing entity and relation data to full_entities and full_relations storage"""
logger.info(f'Starting data migration for {len(processed_docs)} documents')
# Create mapping from chunk_id to doc_id
chunk_to_doc = {}
for doc_id, doc_status in processed_docs.items():
chunk_ids = doc_status.chunks_list if hasattr(doc_status, 'chunks_list') and doc_status.chunks_list else []
for chunk_id in chunk_ids:
chunk_to_doc[chunk_id] = doc_id
# Initialize document entity and relation mappings
doc_entities = {} # doc_id -> set of entity_names
doc_relations = {} # doc_id -> set of relation_pairs (as tuples)
# Get all nodes and edges from graph
all_nodes = await self.chunk_entity_relation_graph.get_all_nodes()
all_edges = await self.chunk_entity_relation_graph.get_all_edges()
# Process all nodes once
for node in all_nodes:
if 'source_id' in node:
entity_id = node.get('entity_id') or node.get('id')
if not entity_id:
continue
# Get chunk IDs from source_id
source_ids = node['source_id'].split(GRAPH_FIELD_SEP)
# Find which documents this entity belongs to
for chunk_id in source_ids:
doc_id = chunk_to_doc.get(chunk_id)
if doc_id:
if doc_id not in doc_entities:
doc_entities[doc_id] = set()
doc_entities[doc_id].add(entity_id)
# Process all edges once
for edge in all_edges:
if 'source_id' in edge:
src = edge.get('source')
tgt = edge.get('target')
if not src or not tgt:
continue
# Get chunk IDs from source_id
source_ids = edge['source_id'].split(GRAPH_FIELD_SEP)
# Find which documents this relation belongs to
for chunk_id in source_ids:
doc_id = chunk_to_doc.get(chunk_id)
if doc_id:
if doc_id not in doc_relations:
doc_relations[doc_id] = set()
# Use tuple for set operations, convert to list later
doc_relations[doc_id].add(tuple(sorted((src, tgt))))
# Store the results in full_entities and full_relations
migration_count = 0
# Store entities
if doc_entities:
entities_data = {}
for doc_id, entity_set in doc_entities.items():
entities_data[doc_id] = {
'entity_names': list(entity_set),
'count': len(entity_set),
}
await self.full_entities.upsert(entities_data)
# Store relations
if doc_relations:
relations_data = {}
for doc_id, relation_set in doc_relations.items():
# Convert tuples back to lists
relations_data[doc_id] = {
'relation_pairs': [list(pair) for pair in relation_set],
'count': len(relation_set),
}
await self.full_relations.upsert(relations_data)
migration_count = len(set(list(doc_entities.keys()) + list(doc_relations.keys())))
# Persist the migrated data
await self.full_entities.index_done_callback()
await self.full_relations.index_done_callback()
logger.info(f'Data migration completed: migrated {migration_count} documents with entities/relations')
async def _migrate_chunk_tracking_storage(self) -> None:
"""Ensure entity/relation chunk tracking KV stores exist and are seeded."""
if not self.entity_chunks or not self.relation_chunks:
return
need_entity_migration = False
need_relation_migration = False
try:
need_entity_migration = await self.entity_chunks.is_empty()
except Exception as exc: # pragma: no cover - defensive logging
logger.error(f'Failed to check entity chunks storage: {exc}')
raise exc
try:
need_relation_migration = await self.relation_chunks.is_empty()
except Exception as exc: # pragma: no cover - defensive logging
logger.error(f'Failed to check relation chunks storage: {exc}')
raise exc
if not need_entity_migration and not need_relation_migration:
return
BATCH_SIZE = 500 # Process 500 records per batch
if need_entity_migration:
try:
nodes = await self.chunk_entity_relation_graph.get_all_nodes()
except Exception as exc:
logger.error(f'Failed to fetch nodes for chunk migration: {exc}')
nodes = []
logger.info(f'Starting chunk_tracking data migration: {len(nodes)} nodes')
# Process nodes in batches
total_nodes = len(nodes)
total_batches = (total_nodes + BATCH_SIZE - 1) // BATCH_SIZE
total_migrated = 0
for batch_idx in range(total_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = min((batch_idx + 1) * BATCH_SIZE, total_nodes)
batch_nodes = nodes[start_idx:end_idx]
upsert_payload: dict[str, dict[str, object]] = {}
for node in batch_nodes:
entity_id = node.get('entity_id') or node.get('id')
if not entity_id:
continue
raw_source = node.get('source_id') or ''
chunk_ids = [chunk_id for chunk_id in raw_source.split(GRAPH_FIELD_SEP) if chunk_id]
if not chunk_ids:
continue
upsert_payload[entity_id] = {
'chunk_ids': chunk_ids,
'count': len(chunk_ids),
}
if upsert_payload:
await self.entity_chunks.upsert(upsert_payload)
total_migrated += len(upsert_payload)
logger.info(
f'Processed entity batch {batch_idx + 1}/{total_batches}: {len(upsert_payload)} records (total: {total_migrated}/{total_nodes})'
)
if total_migrated > 0:
# Persist entity_chunks data to disk
await self.entity_chunks.index_done_callback()
logger.info(f'Entity chunk_tracking migration completed: {total_migrated} records persisted')
if need_relation_migration:
try:
edges = await self.chunk_entity_relation_graph.get_all_edges()
except Exception as exc:
logger.error(f'Failed to fetch edges for chunk migration: {exc}')
edges = []
logger.info(f'Starting chunk_tracking data migration: {len(edges)} edges')
# Process edges in batches
total_edges = len(edges)
total_batches = (total_edges + BATCH_SIZE - 1) // BATCH_SIZE
total_migrated = 0
for batch_idx in range(total_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = min((batch_idx + 1) * BATCH_SIZE, total_edges)
batch_edges = edges[start_idx:end_idx]
upsert_payload: dict[str, dict[str, object]] = {}
for edge in batch_edges:
src = edge.get('source') or edge.get('src_id') or edge.get('src')
tgt = edge.get('target') or edge.get('tgt_id') or edge.get('tgt')
if not src or not tgt:
continue
raw_source = edge.get('source_id') or ''
chunk_ids = [chunk_id for chunk_id in raw_source.split(GRAPH_FIELD_SEP) if chunk_id]
if not chunk_ids:
continue
storage_key = make_relation_chunk_key(src, tgt)
upsert_payload[storage_key] = {
'chunk_ids': chunk_ids,
'count': len(chunk_ids),
}
if upsert_payload:
await self.relation_chunks.upsert(upsert_payload)
total_migrated += len(upsert_payload)
logger.info(
f'Processed relation batch {batch_idx + 1}/{total_batches}: {len(upsert_payload)} records (total: {total_migrated}/{total_edges})'
)
if total_migrated > 0:
# Persist relation_chunks data to disk
await self.relation_chunks.index_done_callback()
logger.info(f'Relation chunk_tracking migration completed: {total_migrated} records persisted')
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 | None = None,
min_degree: int = 0,
include_orphans: bool = False,
) -> 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 self.max_graph_nodes.
min_degree (int): Minimum node degree to include. Defaults to 0 (no filtering).
include_orphans (bool): Include nodes with zero connections when min_degree > 0. Defaults to False.
Returns:
KnowledgeGraph: Knowledge graph containing nodes and edges
"""
# Use self.max_graph_nodes as default if max_nodes is None
# Limit max_nodes to not exceed self.max_graph_nodes
max_nodes = self.max_graph_nodes if max_nodes is None else min(max_nodes, self.max_graph_nodes)
return await self.chunk_entity_relation_graph.get_knowledge_graph(
node_label, max_depth, max_nodes, min_degree, include_orphans
)
def _get_storage_class(self, storage_name: str) -> Callable[..., Any]:
# Direct imports for default storage implementations
if storage_name == 'JsonKVStorage':
from lightrag.kg.json_kv_impl import JsonKVStorage
return JsonKVStorage
elif storage_name == 'NanoVectorDBStorage':
from lightrag.kg.nano_vector_db_impl import NanoVectorDBStorage
return NanoVectorDBStorage
elif storage_name == 'NetworkXStorage':
from lightrag.kg.networkx_impl import NetworkXStorage
return NetworkXStorage
elif storage_name == 'JsonDocStatusStorage':
from lightrag.kg.json_doc_status_impl import JsonDocStatusStorage
return JsonDocStatusStorage
else:
# Fallback to dynamic import for other storage implementations
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,
track_id: str | None = None,
) -> str:
"""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
track_id: tracking ID for monitoring processing status, if not provided, will be generated
Returns:
str: tracking ID for monitoring processing status
"""
loop = always_get_an_event_loop()
return loop.run_until_complete(
self.ainsert(
input,
split_by_character,
split_by_character_only,
ids,
file_paths,
track_id,
)
)
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,
track_id: str | None = None,
) -> str:
"""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
track_id: tracking ID for monitoring processing status, if not provided, will be generated
Returns:
str: tracking ID for monitoring processing status
"""
# Generate track_id if not provided
if track_id is None:
track_id = generate_track_id('insert')
await self.apipeline_enqueue_documents(input, ids, file_paths, track_id)
await self.apipeline_process_enqueue_documents(split_by_character, split_by_character_only)
return track_id
# TODO: deprecated, use insert instead
def insert_custom_chunks(
self,
full_text: str,
text_chunks: list[str],
doc_id: 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 = sanitize_text_for_encoding(full_text)
text_chunks = [sanitize_text_for_encoding(chunk) for chunk in text_chunks]
file_path = ''
# Process cleaned texts
doc_key = compute_mdhash_id(full_text, prefix='doc-') if doc_id is None else str(doc_id)
new_docs = {doc_key: {'content': full_text, 'file_path': file_path}}
_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')
if not self.tokenizer:
raise ValueError("Tokenizer is not initialized")
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_extract_entities(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: str | list[str] | None = None,
file_paths: str | list[str] | None = None,
track_id: str | None = None,
) -> str:
"""
Pipeline for Processing Documents
1. Validate ids if provided or generate MD5 hash IDs and remove duplicate contents
2. Generate document initial status
3. Filter out already processed documents
4. 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
track_id: tracking ID for monitoring processing status, if not provided, will be generated with "enqueue" prefix
Returns:
str: tracking ID for monitoring processing status
"""
# Generate track_id if not provided
if track_id is None or track_id.strip() == '':
track_id = generate_track_id('enqueue')
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 and remove duplicate contents
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 and remove duplicates in one pass
unique_contents = {}
for id_, doc, path in zip(ids, input, file_paths, strict=False):
cleaned_content = sanitize_text_for_encoding(doc)
if cleaned_content not in unique_contents:
unique_contents[cleaned_content] = (id_, path)
# Reconstruct contents with unique content
contents = {
id_: {'content': content, 'file_path': file_path}
for content, (id_, file_path) in unique_contents.items()
}
else:
# Clean input text and remove duplicates in one pass
unique_content_with_paths = {}
for doc, path in zip(input, file_paths, strict=False):
cleaned_content = sanitize_text_for_encoding(doc)
if cleaned_content not in unique_content_with_paths:
unique_content_with_paths[cleaned_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. Generate document initial status (without content)
new_docs: dict[str, Any] = {
id_: {
'status': DocStatus.PENDING,
'content_summary': get_content_summary(content_data['content']),
'content_length': len(content_data['content']),
'created_at': datetime.now(timezone.utc).isoformat(),
'updated_at': datetime.now(timezone.utc).isoformat(),
'file_path': content_data['file_path'], # Store file path in document status
'track_id': track_id, # Store track_id in document status
}
for id_, content_data in contents.items()
}
# 3. Filter out already processed documents
# Get docs ids
all_new_doc_ids = set(new_docs.keys())
# Exclude IDs of documents that are already enqueued
unique_new_doc_ids = await self.doc_status.filter_keys(all_new_doc_ids)
# Log ignored document IDs (documents that were filtered out because they already exist)
ignored_ids = list(all_new_doc_ids - unique_new_doc_ids)
if ignored_ids:
for doc_id in ignored_ids:
file_path = new_docs.get(doc_id, {}).get('file_path', 'unknown_source')
logger.warning(f'Ignoring document ID (already exists): {doc_id} ({file_path})')
if len(ignored_ids) > 3:
logger.warning(f'Total Ignoring {len(ignored_ids)} document IDs that already exist in storage')
# 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.warning("No new unique documents were found.")
return track_id
# 4. Store document content in full_docs and status in doc_status
# Store full document content separately
full_docs_data = {
doc_id: {
'content': contents[doc_id]['content'],
'file_path': contents[doc_id]['file_path'],
}
for doc_id in new_docs
}
await self.full_docs.upsert(full_docs_data)
# Persist data to disk immediately
await self.full_docs.index_done_callback()
# Store document status (without content)
await self.doc_status.upsert(new_docs)
logger.debug(f'Stored {len(new_docs)} new unique documents')
return track_id
async def apipeline_enqueue_error_documents(
self,
error_files: list[dict[str, Any]],
track_id: str | None = None,
) -> None:
"""
Record file extraction errors in doc_status storage.
This function creates error document entries in the doc_status storage for files
that failed during the extraction process. Each error entry contains information
about the failure to help with debugging and monitoring.
Args:
error_files: List of dictionaries containing error information for each failed file.
Each dictionary should contain:
- file_path: Original file name/path
- error_description: Brief error description (for content_summary)
- original_error: Full error message (for error_msg)
- file_size: File size in bytes (for content_length, 0 if unknown)
track_id: Optional tracking ID for grouping related operations
Returns:
None
"""
if not error_files:
logger.debug('No error files to record')
return
# Generate track_id if not provided
if track_id is None or track_id.strip() == '':
track_id = generate_track_id('error')
error_docs: dict[str, Any] = {}
current_time = datetime.now(timezone.utc).isoformat()
for error_file in error_files:
file_path = error_file.get('file_path', 'unknown_file')
error_description = error_file.get('error_description', 'File extraction failed')
original_error = error_file.get('original_error', 'Unknown error')
file_size = error_file.get('file_size', 0)
# Generate unique doc_id with "error-" prefix
doc_id_content = f'{file_path}-{error_description}'
doc_id = compute_mdhash_id(doc_id_content, prefix='error-')
error_docs[doc_id] = {
'status': DocStatus.FAILED,
'content_summary': error_description,
'content_length': file_size,
'error_msg': original_error,
'chunks_count': 0, # No chunks for failed files
'created_at': current_time,
'updated_at': current_time,
'file_path': file_path,
'track_id': track_id,
'metadata': {
'error_type': 'file_extraction_error',
},
}
# Store error documents in doc_status
if error_docs:
await self.doc_status.upsert(error_docs)
# Log each error for debugging
for doc_id, error_doc in error_docs.items():
logger.error(f'File processing error: - ID: {doc_id} {error_doc["file_path"]}')
async def _validate_and_fix_document_consistency(
self,
to_process_docs: dict[str, DocProcessingStatus],
pipeline_status: dict,
pipeline_status_lock: Any,
) -> dict[str, DocProcessingStatus]:
"""Validate and fix document data consistency by deleting inconsistent entries, but preserve failed documents"""
inconsistent_docs = []
failed_docs_to_preserve = []
successful_deletions = 0
# Check each document's data consistency
for doc_id, status_doc in to_process_docs.items():
# Check if corresponding content exists in full_docs
content_data = await self.full_docs.get_by_id(doc_id)
if not content_data:
# Check if this is a failed document that should be preserved
if hasattr(status_doc, 'status') and status_doc.status == DocStatus.FAILED:
failed_docs_to_preserve.append(doc_id)
else:
inconsistent_docs.append(doc_id)
# Log information about failed documents that will be preserved
if failed_docs_to_preserve:
async with pipeline_status_lock:
preserve_message = (
f'Preserving {len(failed_docs_to_preserve)} failed document entries for manual review'
)
logger.info(preserve_message)
pipeline_status['latest_message'] = preserve_message
pipeline_status['history_messages'].append(preserve_message)
# Remove failed documents from processing list but keep them in doc_status
for doc_id in failed_docs_to_preserve:
to_process_docs.pop(doc_id, None)
# Delete inconsistent document entries(excluding failed documents)
if inconsistent_docs:
async with pipeline_status_lock:
summary_message = f'Inconsistent document entries found: {len(inconsistent_docs)}'
logger.info(summary_message)
pipeline_status['latest_message'] = summary_message
pipeline_status['history_messages'].append(summary_message)
successful_deletions = 0
for doc_id in inconsistent_docs:
try:
status_doc = to_process_docs[doc_id]
file_path = getattr(status_doc, 'file_path', 'unknown_source')
# Delete doc_status entry
await self.doc_status.delete([doc_id])
successful_deletions += 1
# Log successful deletion
async with pipeline_status_lock:
log_message = f'Deleted inconsistent entry: {doc_id} ({file_path})'
logger.info(log_message)
pipeline_status['latest_message'] = log_message
pipeline_status['history_messages'].append(log_message)
# Remove from processing list
to_process_docs.pop(doc_id, None)
except Exception as e:
# Log deletion failure
async with pipeline_status_lock:
error_message = f'Failed to delete entry: {doc_id} - {e!s}'
logger.error(error_message)
pipeline_status['latest_message'] = error_message
pipeline_status['history_messages'].append(error_message)
# Final summary log
# async with pipeline_status_lock:
# final_message = f"Successfully deleted {successful_deletions} inconsistent entries, preserved {len(failed_docs_to_preserve)} failed documents"
# logger.info(final_message)
# pipeline_status["latest_message"] = final_message
# pipeline_status["history_messages"].append(final_message)
# Reset PROCESSING and FAILED documents that pass consistency checks to PENDING status
docs_to_reset = {}
reset_count = 0
for doc_id, status_doc in to_process_docs.items():
# Check if document has corresponding content in full_docs (consistency check)
content_data = await self.full_docs.get_by_id(doc_id)
if content_data and (
hasattr(status_doc, 'status') and status_doc.status in [DocStatus.PROCESSING, DocStatus.FAILED]
):
# Prepare document for status reset to PENDING
docs_to_reset[doc_id] = {
'status': DocStatus.PENDING,
'content_summary': status_doc.content_summary,
'content_length': status_doc.content_length,
'created_at': status_doc.created_at,
'updated_at': datetime.now(timezone.utc).isoformat(),
'file_path': getattr(status_doc, 'file_path', 'unknown_source'),
'track_id': getattr(status_doc, 'track_id', ''),
# Clear any error messages and processing metadata
'error_msg': '',
'metadata': {},
}
# Update the status in to_process_docs as well
status_doc.status = DocStatus.PENDING
reset_count += 1
# Update doc_status storage if there are documents to reset
if docs_to_reset:
await self.doc_status.upsert(docs_to_reset)
async with pipeline_status_lock:
reset_message = f'Reset {reset_count} documents from PROCESSING/FAILED to PENDING status'
logger.info(reset_message)
pipeline_status['latest_message'] = reset_message
pipeline_status['history_messages'].append(reset_message)
return to_process_docs
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. Validate document data consistency and fix any issues
3. Split document content into chunks
4. Process each chunk for entity and relation extraction
5. Update the document status
"""
# Get pipeline status shared data and lock
pipeline_status = await get_namespace_data('pipeline_status', workspace=self.workspace)
pipeline_status_lock = get_namespace_lock('pipeline_status', workspace=self.workspace)
# 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(timezone.utc).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
'cancellation_requested': False, # Initialize cancellation flag
'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:
# Check for cancellation request at the start of main loop
async with pipeline_status_lock:
if pipeline_status.get('cancellation_requested', False):
# Clear pending request
pipeline_status['request_pending'] = False
# Celar cancellation flag
pipeline_status['cancellation_requested'] = False
log_message = 'Pipeline cancelled by user'
logger.info(log_message)
pipeline_status['latest_message'] = log_message
pipeline_status['history_messages'].append(log_message)
# Exit directly, skipping request_pending check
return
if not to_process_docs:
log_message = 'All enqueued documents have been processed'
logger.info(log_message)
pipeline_status['latest_message'] = log_message
pipeline_status['history_messages'].append(log_message)
break
# Validate document data consistency and fix any issues as part of the pipeline
to_process_docs = await self._validate_and_fix_document_consistency(
to_process_docs, pipeline_status, pipeline_status_lock
)
if not to_process_docs:
log_message = 'No valid documents to process after consistency check'
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
# Handle cases where first_doc_path is None
if first_doc_path:
path_prefix = first_doc_path[:20] + ('...' if len(first_doc_path) > 20 else '')
else:
path_prefix = 'unknown_source'
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: Any,
semaphore: asyncio.Semaphore,
total_files: int = total_files,
) -> None:
"""Process single document"""
# Initialize variables at the start to prevent UnboundLocalError in error handling
file_path = 'unknown_source'
current_file_number = 0
file_extraction_stage_ok = False
processing_start_time = int(time.time())
first_stage_tasks: list[asyncio.Task[Any]] = []
entity_relation_task: asyncio.Task[Any] | None = None
chunk_results: list[Any] = []
chunks: dict[str, Any] = {}
async with semaphore:
nonlocal processed_count
# Initialize to prevent UnboundLocalError in error handling
first_stage_tasks = []
entity_relation_task = None
try:
# Check for cancellation before starting document processing
async with pipeline_status_lock:
if pipeline_status.get('cancellation_requested', False):
raise PipelineCancelledException('User cancelled')
# 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'Extracting stage {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)
# Prevent memory growth: keep only latest 5000 messages when exceeding 10000
if len(pipeline_status['history_messages']) > 10000:
logger.info(
f'Trimming pipeline history from {len(pipeline_status["history_messages"])} to 5000 messages'
)
pipeline_status['history_messages'] = pipeline_status['history_messages'][-5000:]
# Get document content from full_docs
content_data = await self.full_docs.get_by_id(doc_id)
if not content_data:
raise Exception(
f"Document content not found in full_docs for doc_id: {doc_id}"
)
content = content_data["content"]
if self.tokenizer is None:
raise ValueError("Tokenizer is not initialized")
# Call chunking function, supporting both sync and async implementations
chunking_result = self.chunking_func(
self.tokenizer,
content,
split_by_character,
split_by_character_only,
self.chunk_overlap_token_size,
self.chunk_token_size,
)
# If result is awaitable, await to get actual result
if inspect.isawaitable(chunking_result):
chunking_result = await chunking_result
# Validate return type
if not isinstance(chunking_result, (list, tuple)):
raise TypeError(
f'chunking_func must return a list or tuple of dicts, got {type(chunking_result)}'
)
# Build chunks dictionary
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
'llm_cache_list': [], # Initialize empty LLM cache list for each chunk
}
for dp in chunking_result
}
if not chunks:
logger.warning('No document chunks to process')
# Record processing start time
processing_start_time = int(time.time())
# Check for cancellation before entity extraction
async with pipeline_status_lock:
if pipeline_status.get('cancellation_requested', False):
raise PipelineCancelledException('User cancelled')
# Process document in two stages
# Stage 1: Process text chunks and docs (parallel execution)
doc_status_task = asyncio.create_task(
self.doc_status.upsert(
{
doc_id: {
'status': DocStatus.PROCESSING,
'chunks_count': len(chunks),
'chunks_list': list(chunks.keys()), # Save chunks list
'content_summary': status_doc.content_summary,
'content_length': status_doc.content_length,
'created_at': status_doc.created_at,
'updated_at': datetime.now(timezone.utc).isoformat(),
'file_path': file_path,
'track_id': status_doc.track_id, # Preserve existing track_id
'metadata': {'processing_start_time': processing_start_time},
}
}
)
)
chunks_vdb_task = asyncio.create_task(self.chunks_vdb.upsert(chunks))
text_chunks_task = asyncio.create_task(self.text_chunks.upsert(chunks))
# First stage tasks (parallel execution)
first_stage_tasks = [
doc_status_task,
chunks_vdb_task,
text_chunks_task,
]
entity_relation_task = None
# Execute first stage tasks
await asyncio.gather(*first_stage_tasks)
# Stage 2: Process entity relation graph (after text_chunks are saved)
entity_relation_task = asyncio.create_task(
self._process_extract_entities(chunks, pipeline_status, pipeline_status_lock)
)
chunk_results = await entity_relation_task
file_extraction_stage_ok = True
except Exception as e:
# Check if this is a user cancellation
if isinstance(e, PipelineCancelledException):
# User cancellation - log brief message only, no traceback
error_msg = f'User cancelled {current_file_number}/{total_files}: {file_path}'
logger.warning(error_msg)
async with pipeline_status_lock:
pipeline_status['latest_message'] = error_msg
pipeline_status['history_messages'].append(error_msg)
else:
# Other exceptions - log with traceback
logger.error(traceback.format_exc())
error_msg = (
f'Failed to extract document {current_file_number}/{total_files}: {file_path}'
)
logger.error(error_msg)
async with pipeline_status_lock:
pipeline_status['latest_message'] = error_msg
pipeline_status['history_messages'].append(traceback.format_exc())
pipeline_status['history_messages'].append(error_msg)
# Cancel tasks that are not yet completed
all_tasks = first_stage_tasks + ([entity_relation_task] if entity_relation_task else [])
for task in all_tasks:
if task and not task.done():
task.cancel()
# Persistent llm cache with error handling
if self.llm_response_cache:
try:
await self.llm_response_cache.index_done_callback()
except Exception as persist_error:
logger.error(f'Failed to persist LLM cache: {persist_error}')
# Record processing end time for failed case
processing_end_time = int(time.time())
# Update document status to failed
await self.doc_status.upsert(
{
doc_id: {
'status': DocStatus.FAILED,
'error_msg': str(e),
'content_summary': status_doc.content_summary,
'content_length': status_doc.content_length,
'created_at': status_doc.created_at,
'updated_at': datetime.now(timezone.utc).isoformat(),
'file_path': file_path,
'track_id': status_doc.track_id, # Preserve existing track_id
'metadata': {
'processing_start_time': processing_start_time,
'processing_end_time': processing_end_time,
},
}
}
)
# Concurrency is controlled by keyed lock for individual entities and relationships
if file_extraction_stage_ok:
try:
# Check for cancellation before merge
async with pipeline_status_lock:
if pipeline_status.get('cancellation_requested', False):
raise PipelineCancelledException('User cancelled')
# Use chunk_results from entity_relation_task
await merge_nodes_and_edges(
chunk_results=chunk_results, # result collected from entity_relation_task
knowledge_graph_inst=self.chunk_entity_relation_graph,
entity_vdb=self.entities_vdb,
relationships_vdb=self.relationships_vdb,
global_config=asdict(self),
full_entities_storage=self.full_entities,
full_relations_storage=self.full_relations,
doc_id=doc_id,
pipeline_status=pipeline_status,
pipeline_status_lock=pipeline_status_lock,
llm_response_cache=self.llm_response_cache,
entity_chunks_storage=self.entity_chunks,
relation_chunks_storage=self.relation_chunks,
current_file_number=current_file_number,
total_files=total_files,
file_path=file_path,
)
# Record processing end time
processing_end_time = int(time.time())
await self.doc_status.upsert(
{
doc_id: {
'status': DocStatus.PROCESSED,
'chunks_count': len(chunks),
'chunks_list': list(chunks.keys()),
'content_summary': status_doc.content_summary,
'content_length': status_doc.content_length,
'created_at': status_doc.created_at,
'updated_at': datetime.now(timezone.utc).isoformat(),
'file_path': file_path,
'track_id': status_doc.track_id, # Preserve existing track_id
'metadata': {
'processing_start_time': processing_start_time,
'processing_end_time': processing_end_time,
},
}
}
)
# 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:
# Check if this is a user cancellation
if isinstance(e, PipelineCancelledException):
# User cancellation - log brief message only, no traceback
error_msg = (
f'User cancelled during merge {current_file_number}/{total_files}: {file_path}'
)
logger.warning(error_msg)
async with pipeline_status_lock:
pipeline_status['latest_message'] = error_msg
pipeline_status['history_messages'].append(error_msg)
else:
# Other exceptions - log with traceback
logger.error(traceback.format_exc())
error_msg = f'Merging stage failed in document {current_file_number}/{total_files}: {file_path}'
logger.error(error_msg)
async with pipeline_status_lock:
pipeline_status['latest_message'] = error_msg
pipeline_status['history_messages'].append(traceback.format_exc())
pipeline_status['history_messages'].append(error_msg)
# Persistent llm cache with error handling
if self.llm_response_cache:
try:
await self.llm_response_cache.index_done_callback()
except Exception as persist_error:
logger.error(f'Failed to persist LLM cache: {persist_error}')
# Record processing end time for failed case
processing_end_time = int(time.time())
# Update document status to failed
await self.doc_status.upsert(
{
doc_id: {
'status': DocStatus.FAILED,
'error_msg': str(e),
'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,
'track_id': status_doc.track_id, # Preserve existing track_id
'metadata': {
'processing_start_time': processing_start_time,
'processing_end_time': processing_end_time,
},
}
}
)
# 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
try:
await asyncio.gather(*doc_tasks)
except PipelineCancelledException:
# Cancel all remaining tasks
for task in doc_tasks:
if not task.done():
task.cancel()
# Wait for all tasks to complete cancellation
await asyncio.wait(doc_tasks, return_when=asyncio.ALL_COMPLETED)
# Exit directly (document statuses already updated in process_document)
return
# 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 = 'Enqueued document processing pipeline stopped'
logger.info(log_message)
# Always reset busy status and cancellation flag when done or if an exception occurs (with lock)
async with pipeline_status_lock:
pipeline_status['busy'] = False
pipeline_status['cancellation_requested'] = False # Always reset cancellation flag
pipeline_status['latest_message'] = log_message
pipeline_status['history_messages'].append(log_message)
async def _process_extract_entities(
self, chunk: dict[str, Any], pipeline_status=None, pipeline_status_lock=None
) -> list:
try:
chunk_results = await extract_entities(
chunk,
global_config=asdict(self),
pipeline_status=pipeline_status,
pipeline_status_lock=pipeline_status_lock,
llm_response_cache=self.llm_response_cache,
text_chunks_storage=self.text_chunks,
)
return chunk_results
except Exception as e:
error_msg = f'Failed to extract entities and relationships: {e!s}'
logger.error(error_msg)
if pipeline_status is not None and pipeline_status_lock is not None:
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.doc_status,
self.text_chunks,
self.full_entities,
self.full_relations,
self.entity_chunks,
self.relation_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)
# Auto-connect orphan entities if enabled
if self.auto_connect_orphans:
try:
orphan_log = 'Running auto orphan connection...'
logger.info(orphan_log)
if pipeline_status is not None and pipeline_status_lock is not None:
async with pipeline_status_lock:
pipeline_status['latest_message'] = orphan_log
pipeline_status['history_messages'].append(orphan_log)
result = await self.aconnect_orphan_entities()
orphan_done_log = f'Orphan connection complete: {result["connections_made"]} connections made'
logger.info(orphan_done_log)
if pipeline_status is not None and pipeline_status_lock is not None:
async with pipeline_status_lock:
pipeline_status['latest_message'] = orphan_done_log
pipeline_status['history_messages'].append(orphan_done_log)
except Exception as e:
logger.warning(f'Auto orphan connection failed: {e}')
def insert_custom_kg(self, custom_kg: dict[str, Any], full_doc_id: str | None = 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 = None,
) -> None:
update_storage = False
try:
if self.tokenizer is None:
raise ValueError('Tokenizer is not initialized')
# Insert chunks into vector storage
all_chunks_data: dict[str, dict[str, str | int]] = {}
chunk_to_source_map: dict[str, str] = {}
for chunk_data in custom_kg.get('chunks', []):
chunk_content = sanitize_text_for_encoding(chunk_data['content'])
source_id = chunk_data['source_id']
file_path = chunk_data.get('file_path', 'custom_kg')
tokens = len(self.tokenizer.encode(chunk_content))
chunk_order_index = chunk_data.get('chunk_order_index', 0)
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,
'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')
file_path = entity_data.get('file_path', 'custom_kg')
# 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
created_at = str(int(time.time()))
node_data: dict[str, str] = {
'entity_id': entity_name,
'entity_type': entity_type,
'description': description,
'source_id': source_id,
'file_path': file_path,
'created_at': created_at,
}
# 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')
file_path = relationship_data.get('file_path', 'custom_kg')
# 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',
'file_path': file_path,
'created_at': str(int(time.time())),
},
)
# Insert edge into the knowledge graph
await self.chunk_entity_relation_graph.upsert_edge(
src_id,
tgt_id,
edge_data={
'weight': str(weight),
'description': description,
'keywords': keywords,
'source_id': source_id,
'file_path': file_path,
'created_at': str(int(time.time())),
},
)
edge_data: dict[str, str] = {
'src_id': src_id,
'tgt_id': tgt_id,
'description': description,
'keywords': keywords,
'source_id': source_id,
'weight': str(weight),
'file_path': file_path,
'created_at': str(int(time.time())),
}
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': dp.get('file_path', 'custom_kg'),
}
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': dp.get('file_path', 'custom_kg'),
}
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 | None = None,
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.
"""
if param is None:
param = QueryParam()
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 | None = None,
system_prompt: str | None = None,
) -> str | AsyncIterator[str]:
"""
Perform a async query (backward compatibility wrapper).
This function is now a wrapper around aquery_llm that maintains backward compatibility
by returning only the LLM response content in the original format.
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.
system_prompt (Optional[str]): Custom prompts for fine-tuned control over the system's behavior. Defaults to None, which uses PROMPTS["rag_response"].
Returns:
str | AsyncIterator[str]: The LLM response content.
- Non-streaming: Returns str
- Streaming: Returns AsyncIterator[str]
"""
if param is None:
param = QueryParam()
# Call the new aquery_llm function to get complete results
result = await self.aquery_llm(query, param, system_prompt)
# Extract and return only the LLM response for backward compatibility
llm_response = result.get('llm_response', {})
if llm_response.get('is_streaming'):
return llm_response.get('response_iterator')
else:
return llm_response.get('content', '')
def query_data(
self,
query: str,
param: QueryParam | None = None,
) -> dict[str, Any]:
"""
Synchronous data retrieval API: returns structured retrieval results without LLM generation.
This function is the synchronous version of aquery_data, providing the same functionality
for users who prefer synchronous interfaces.
Args:
query: Query text for retrieval.
param: Query parameters controlling retrieval behavior (same as aquery).
Returns:
dict[str, Any]: Same structured data result as aquery_data.
"""
if param is None:
param = QueryParam()
loop = always_get_an_event_loop()
return loop.run_until_complete(self.aquery_data(query, param))
async def aquery_data(
self,
query: str,
param: QueryParam | None = None,
) -> dict[str, Any]:
"""
Asynchronous data retrieval API: returns structured retrieval results without LLM generation.
This function reuses the same logic as aquery but stops before LLM generation,
returning the final processed entities, relationships, and chunks data that would be sent to LLM.
Args:
query: Query text for retrieval.
param: Query parameters controlling retrieval behavior (same as aquery).
Returns:
dict[str, Any]: Structured data result in the following format:
**Success Response:**
```python
{
"status": "success",
"message": "Query executed successfully",
"data": {
"entities": [
{
"entity_name": str, # Entity identifier
"entity_type": str, # Entity category/type
"description": str, # Entity description
"source_id": str, # Source chunk references
"file_path": str, # Origin file path
"created_at": str, # Creation timestamp
"reference_id": str # Reference identifier for citations
}
],
"relationships": [
{
"src_id": str, # Source entity name
"tgt_id": str, # Target entity name
"description": str, # Relationship description
"keywords": str, # Relationship keywords
"weight": float, # Relationship strength
"source_id": str, # Source chunk references
"file_path": str, # Origin file path
"created_at": str, # Creation timestamp
"reference_id": str # Reference identifier for citations
}
],
"chunks": [
{
"content": str, # Document chunk content
"file_path": str, # Origin file path
"chunk_id": str, # Unique chunk identifier
"reference_id": str # Reference identifier for citations
}
],
"references": [
{
"reference_id": str, # Reference identifier
"file_path": str # Corresponding file path
}
]
},
"metadata": {
"query_mode": str, # Query mode used ("local", "global", "hybrid", "mix", "naive", "bypass")
"keywords": {
"high_level": List[str], # High-level keywords extracted
"low_level": List[str] # Low-level keywords extracted
},
"processing_info": {
"total_entities_found": int, # Total entities before truncation
"total_relations_found": int, # Total relations before truncation
"entities_after_truncation": int, # Entities after token truncation
"relations_after_truncation": int, # Relations after token truncation
"merged_chunks_count": int, # Chunks before final processing
"final_chunks_count": int # Final chunks in result
}
}
}
```
**Query Mode Differences:**
- **local**: Focuses on entities and their related chunks based on low-level keywords
- **global**: Focuses on relationships and their connected entities based on high-level keywords
- **hybrid**: Combines local and global results using round-robin merging
- **mix**: Includes knowledge graph data plus vector-retrieved document chunks
- **naive**: Only vector-retrieved chunks, entities and relationships arrays are empty
- **bypass**: All data arrays are empty, used for direct LLM queries
** processing_info is optional and may not be present in all responses, especially when query result is empty**
**Failure Response:**
```python
{
"status": "failure",
"message": str, # Error description
"data": {} # Empty data object
}
```
**Common Failure Cases:**
- Empty query string
- Both high-level and low-level keywords are empty
- Query returns empty dataset
- Missing tokenizer or system configuration errors
Note:
The function adapts to the new data format from convert_to_user_format where
actual data is nested under the 'data' field, with 'status' and 'message'
fields at the top level.
"""
if param is None:
param = QueryParam()
global_config = asdict(self)
# Create a copy of param to avoid modifying the original
data_param = QueryParam(
mode=param.mode,
only_need_context=True, # Skip LLM generation, only get context and data
only_need_prompt=False,
response_type=param.response_type,
stream=False, # Data retrieval doesn't need streaming
top_k=param.top_k,
chunk_top_k=param.chunk_top_k,
max_entity_tokens=param.max_entity_tokens,
max_relation_tokens=param.max_relation_tokens,
max_total_tokens=param.max_total_tokens,
hl_keywords=param.hl_keywords,
ll_keywords=param.ll_keywords,
conversation_history=param.conversation_history,
history_turns=param.history_turns,
model_func=param.model_func,
user_prompt=param.user_prompt,
enable_rerank=param.enable_rerank,
)
query_result = None
if data_param.mode in ['local', 'global', 'hybrid', 'mix']:
logger.debug(f'[aquery_data] Using kg_query for mode: {data_param.mode}')
query_result = await kg_query(
query.strip(),
self.chunk_entity_relation_graph,
self.entities_vdb,
self.relationships_vdb,
self.text_chunks,
data_param, # Use data_param with only_need_context=True
global_config,
hashing_kv=self.llm_response_cache,
system_prompt=None,
chunks_vdb=self.chunks_vdb,
)
elif data_param.mode == 'naive':
logger.debug(f'[aquery_data] Using naive_query for mode: {data_param.mode}')
query_result = await naive_query(
query.strip(),
self.chunks_vdb,
data_param, # Use data_param with only_need_context=True
global_config,
hashing_kv=self.llm_response_cache,
system_prompt=None,
)
elif data_param.mode == 'bypass':
logger.debug('[aquery_data] Using bypass mode')
# bypass mode returns empty data using convert_to_user_format
empty_raw_data = convert_to_user_format(
[], # no entities
[], # no relationships
[], # no chunks
[], # no references
'bypass',
)
query_result = QueryResult(content='', raw_data=empty_raw_data)
else:
raise ValueError(f'Unknown mode {data_param.mode}')
if query_result is None:
no_result_message = 'Query returned no results'
if data_param.mode == 'naive':
no_result_message = 'No relevant document chunks found.'
final_data: dict[str, Any] = {
'status': 'failure',
'message': no_result_message,
'data': {},
'metadata': {
'failure_reason': 'no_results',
'mode': data_param.mode,
},
}
logger.info('[aquery_data] Query returned no results.')
else:
# Extract raw_data from QueryResult
final_data = query_result.raw_data or {}
# Log final result counts - adapt to new data format from convert_to_user_format
if final_data and 'data' in final_data:
data_section = final_data['data']
entities_count = len(data_section.get('entities', []))
relationships_count = len(data_section.get('relationships', []))
chunks_count = len(data_section.get('chunks', []))
logger.debug(
f'[aquery_data] Final result: {entities_count} entities, {relationships_count} relationships, {chunks_count} chunks'
)
else:
logger.warning('[aquery_data] No data section found in query result')
await self._query_done()
return final_data
async def aquery_llm(
self,
query: str,
param: QueryParam | None = None,
system_prompt: str | None = None,
) -> dict[str, Any]:
"""
Asynchronous complete query API: returns structured retrieval results with LLM generation.
This function performs a single query operation and returns both structured data and LLM response,
based on the original aquery logic to avoid duplicate calls.
Args:
query: Query text for retrieval and LLM generation.
param: Query parameters controlling retrieval and LLM behavior.
system_prompt: Optional custom system prompt for LLM generation.
Returns:
dict[str, Any]: Complete response with structured data and LLM response.
"""
if param is None:
param = QueryParam()
logger.debug(f'[aquery_llm] Query param: {param}')
global_config = asdict(self)
try:
query_result = None
if param.mode in ['local', 'global', 'hybrid', 'mix']:
query_result = 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,
system_prompt=system_prompt,
chunks_vdb=self.chunks_vdb,
)
elif param.mode == 'naive':
query_result = await naive_query(
query.strip(),
self.chunks_vdb,
param,
global_config,
hashing_kv=self.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']
# Apply higher priority (8) to entity/relation summary tasks
use_llm_func = partial(use_llm_func, _priority=8)
param.stream = True if param.stream is None else param.stream
llm_callable = cast(
Callable[..., Awaitable[str | AsyncIterator[str]]],
use_llm_func,
)
response = await llm_callable(
query.strip(),
system_prompt=system_prompt,
history_messages=param.conversation_history,
enable_cot=True,
stream=param.stream,
)
if type(response) is str:
return {
'status': 'success',
'message': 'Bypass mode LLM non streaming response',
'data': {},
'metadata': {},
'llm_response': {
'content': response,
'response_iterator': None,
'is_streaming': False,
},
}
else:
return {
'status': 'success',
'message': 'Bypass mode LLM streaming response',
'data': {},
'metadata': {},
'llm_response': {
'content': None,
'response_iterator': response,
'is_streaming': True,
},
}
else:
raise ValueError(f'Unknown mode {param.mode}')
await self._query_done()
# Check if query_result is None
if query_result is None:
return {
'status': 'failure',
'message': 'Query returned no results',
'data': {},
'metadata': {
'failure_reason': 'no_results',
'mode': param.mode,
},
'llm_response': {
'content': PROMPTS['fail_response'],
'response_iterator': None,
'is_streaming': False,
},
}
# Extract structured data from query result
raw_data = query_result.raw_data or {}
raw_data['llm_response'] = {
'content': query_result.content if not query_result.is_streaming else None,
'response_iterator': query_result.response_iterator if query_result.is_streaming else None,
'is_streaming': query_result.is_streaming,
}
return raw_data
except Exception as e:
logger.error(f'Query failed: {e}')
# Return error response
return {
'status': 'failure',
'message': f'Query failed: {e!s}',
'data': {},
'metadata': {},
'llm_response': {
'content': None,
'response_iterator': None,
'is_streaming': False,
},
}
def query_llm(
self,
query: str,
param: QueryParam | None = None,
system_prompt: str | None = None,
) -> dict[str, Any]:
"""
Synchronous complete query API: returns structured retrieval results with LLM generation.
This function is the synchronous version of aquery_llm, providing the same functionality
for users who prefer synchronous interfaces.
Args:
query: Query text for retrieval and LLM generation.
param: Query parameters controlling retrieval and LLM behavior.
system_prompt: Optional custom system prompt for LLM generation.
Returns:
dict[str, Any]: Same complete response format as aquery_llm.
"""
if param is None:
param = QueryParam()
loop = always_get_an_event_loop()
return loop.run_until_complete(self.aquery_llm(query, param, system_prompt))
async def _query_done(self):
await self.llm_response_cache.index_done_callback()
async def aclear_cache(self) -> None:
"""Clear all cache data from the LLM response cache storage.
This method clears all cached LLM responses regardless of mode.
Example:
# Clear all cache
await rag.aclear_cache()
"""
if not self.llm_response_cache:
logger.warning('No cache storage configured')
return
try:
# Clear all cache using drop method
success = await self.llm_response_cache.drop()
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) -> None:
"""Synchronous version of aclear_cache."""
return always_get_an_event_loop().run_until_complete(self.aclear_cache())
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, dict[str, Any]]:
"""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[dict[str, Any] | None] = await asyncio.gather(*tasks)
# Build the result dictionary, mapping found IDs to their statuses
found_statuses: dict[str, dict[str, Any]] = {}
# 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
async def adelete_by_doc_id(self, doc_id: str, delete_llm_cache: bool = False) -> DeletionResult:
"""Delete a document and all its related data, including chunks, graph elements.
This method orchestrates a comprehensive deletion process for a given document ID.
It ensures that not only the document itself but also all its derived and associated
data across different storage layers are removed or rebuiled. If entities or relationships
are partially affected, they will be rebuilded using LLM cached from remaining documents.
**Concurrency Control Design:**
This function implements a pipeline-based concurrency control to prevent data corruption:
1. **Single Document Deletion** (when WE acquire pipeline):
- Sets job_name to "Single document deletion" (NOT starting with "deleting")
- Prevents other adelete_by_doc_id calls from running concurrently
- Ensures exclusive access to graph operations for this deletion
2. **Batch Document Deletion** (when background_delete_documents acquires pipeline):
- Sets job_name to "Deleting {N} Documents" (starts with "deleting")
- Allows multiple adelete_by_doc_id calls to join the deletion queue
- Each call validates the job name to ensure it's part of a deletion operation
The validation logic `if not job_name.startswith("deleting") or "document" not in job_name`
ensures that:
- adelete_by_doc_id can only run when pipeline is idle OR during batch deletion
- Prevents concurrent single deletions that could cause race conditions
- Rejects operations when pipeline is busy with non-deletion tasks
Args:
doc_id (str): The unique identifier of the document to be deleted.
delete_llm_cache (bool): Whether to delete cached LLM extraction results
associated with the document. Defaults to False.
Returns:
DeletionResult: An object containing the outcome of the deletion process.
- `status` (str): "success", "not_found", "not_allowed", or "failure".
- `doc_id` (str): The ID of the document attempted to be deleted.
- `message` (str): A summary of the operation's result.
- `status_code` (int): HTTP status code (e.g., 200, 404, 403, 500).
- `file_path` (str | None): The file path of the deleted document, if available.
"""
# Get pipeline status shared data and lock for validation
pipeline_status = await get_namespace_data('pipeline_status', workspace=self.workspace)
pipeline_status_lock = get_namespace_lock('pipeline_status', workspace=self.workspace)
# Track whether WE acquired the pipeline
we_acquired_pipeline = False
# Check and acquire pipeline if needed
file_path = 'unknown_source'
async with pipeline_status_lock:
if not pipeline_status.get('busy', False):
# Pipeline is idle - WE acquire it for this deletion
we_acquired_pipeline = True
pipeline_status.update(
{
'busy': True,
'job_name': 'Single document deletion',
'job_start': datetime.now(timezone.utc).isoformat(),
'docs': 1,
'batchs': 1,
'cur_batch': 0,
'request_pending': False,
'cancellation_requested': False,
'latest_message': f'Starting deletion for document: {doc_id}',
}
)
# Initialize history messages
pipeline_status['history_messages'][:] = [f'Starting deletion for document: {doc_id}']
else:
# Pipeline already busy - verify it's a deletion job
job_name = pipeline_status.get('job_name', '').lower()
if not job_name.startswith('deleting') or 'document' not in job_name:
return DeletionResult(
status='fail',
doc_id=doc_id,
message=f"Deletion not allowed: current job '{pipeline_status.get('job_name')}' is not a document deletion job",
status_code=403,
file_path=None,
)
# Pipeline is busy with deletion - proceed without acquiring
deletion_operations_started = False
original_exception = None
doc_llm_cache_ids: list[str] = []
async with pipeline_status_lock:
log_message = f'Starting deletion process for document {doc_id}'
logger.info(log_message)
pipeline_status['latest_message'] = log_message
pipeline_status['history_messages'].append(log_message)
try:
# 1. Get the document status and related data
doc_status_data = await self.doc_status.get_by_id(doc_id)
file_path = doc_status_data.get('file_path') if doc_status_data else None
if not doc_status_data:
logger.warning(f'Document {doc_id} not found')
return DeletionResult(
status='not_found',
doc_id=doc_id,
message=f'Document {doc_id} not found.',
status_code=404,
file_path='',
)
# Check document status and log warning for non-completed documents
raw_status = doc_status_data.get('status')
try:
doc_status = DocStatus(raw_status)
except ValueError:
doc_status = raw_status
if doc_status != DocStatus.PROCESSED:
if doc_status == DocStatus.PENDING:
warning_msg = f'Deleting {doc_id} {file_path}(previous status: PENDING)'
elif doc_status == DocStatus.PROCESSING:
warning_msg = f'Deleting {doc_id} {file_path}(previous status: PROCESSING)'
elif doc_status == DocStatus.PREPROCESSED:
warning_msg = f'Deleting {doc_id} {file_path}(previous status: PREPROCESSED)'
elif doc_status == DocStatus.FAILED:
warning_msg = f'Deleting {doc_id} {file_path}(previous status: FAILED)'
else:
status_text = doc_status.value if isinstance(doc_status, DocStatus) else str(doc_status)
warning_msg = f'Deleting {doc_id} {file_path}(previous status: {status_text})'
logger.info(warning_msg)
# Update pipeline status for monitoring
async with pipeline_status_lock:
pipeline_status['latest_message'] = warning_msg
pipeline_status['history_messages'].append(warning_msg)
# 2. Get chunk IDs from document status
chunk_ids = list(set(doc_status_data.get('chunks_list', [])))
if not chunk_ids:
logger.warning(f'No chunks found for document {doc_id}')
# Mark that deletion operations have started
deletion_operations_started = True
try:
# Still need to delete the doc status and full doc
await self.full_docs.delete([doc_id])
await self.doc_status.delete([doc_id])
except Exception as e:
logger.error(f'Failed to delete document {doc_id} with no chunks: {e}')
raise Exception(f'Failed to delete document entry: {e}') from e
async with pipeline_status_lock:
log_message = f'Document deleted without associated chunks: {doc_id}'
logger.info(log_message)
pipeline_status['latest_message'] = log_message
pipeline_status['history_messages'].append(log_message)
return DeletionResult(
status='success',
doc_id=doc_id,
message=log_message,
status_code=200,
file_path=file_path,
)
# Mark that deletion operations have started
deletion_operations_started = True
if delete_llm_cache and chunk_ids:
if not self.llm_response_cache:
logger.info(
'Skipping LLM cache collection for document %s because cache storage is unavailable',
doc_id,
)
elif not self.text_chunks:
logger.info(
'Skipping LLM cache collection for document %s because text chunk storage is unavailable',
doc_id,
)
else:
try:
chunk_data_list = await self.text_chunks.get_by_ids(list(chunk_ids))
seen_cache_ids: set[str] = set()
for chunk_data in chunk_data_list:
if not chunk_data or not isinstance(chunk_data, dict):
continue
cache_ids = chunk_data.get('llm_cache_list', [])
if not isinstance(cache_ids, list):
continue
for cache_id in cache_ids:
if isinstance(cache_id, str) and cache_id and cache_id not in seen_cache_ids:
doc_llm_cache_ids.append(cache_id)
seen_cache_ids.add(cache_id)
if doc_llm_cache_ids:
logger.info(
'Collected %d LLM cache entries for document %s',
len(doc_llm_cache_ids),
doc_id,
)
else:
logger.info('No LLM cache entries found for document %s', doc_id)
except Exception as cache_collect_error:
logger.error(
'Failed to collect LLM cache ids for document %s: %s',
doc_id,
cache_collect_error,
)
raise Exception(
f'Failed to collect LLM cache ids for document {doc_id}: {cache_collect_error}'
) from cache_collect_error
# 4. Analyze entities and relationships that will be affected
entities_to_delete = set()
entities_to_rebuild = {} # entity_name -> remaining chunk id list
relationships_to_delete = set()
relationships_to_rebuild = {} # (src, tgt) -> remaining chunk id list
entity_chunk_updates: dict[str, list[str]] = {}
relation_chunk_updates: dict[tuple[str, str], list[str]] = {}
try:
# Get affected entities and relations from full_entities and full_relations storage
doc_entities_data = await self.full_entities.get_by_id(doc_id)
doc_relations_data = await self.full_relations.get_by_id(doc_id)
affected_nodes = []
affected_edges = []
# Get entity data from graph storage using entity names from full_entities
if doc_entities_data and 'entity_names' in doc_entities_data:
entity_names = doc_entities_data['entity_names']
# get_nodes_batch returns dict[str, dict], need to convert to list[dict]
nodes_dict = await self.chunk_entity_relation_graph.get_nodes_batch(entity_names)
for entity_name in entity_names:
node_data = nodes_dict.get(entity_name)
if node_data:
# Ensure compatibility with existing logic that expects "id" field
if 'id' not in node_data:
node_data['id'] = entity_name
affected_nodes.append(node_data)
# Get relation data from graph storage using relation pairs from full_relations
if doc_relations_data and 'relation_pairs' in doc_relations_data:
relation_pairs = doc_relations_data['relation_pairs']
edge_pairs_dicts = [{'src': pair[0], 'tgt': pair[1]} for pair in relation_pairs]
# get_edges_batch returns dict[tuple[str, str], dict], need to convert to list[dict]
edges_dict = await self.chunk_entity_relation_graph.get_edges_batch(edge_pairs_dicts)
for pair in relation_pairs:
src, tgt = pair[0], pair[1]
edge_key = (src, tgt)
edge_data = edges_dict.get(edge_key)
if edge_data:
# Ensure compatibility with existing logic that expects "source" and "target" fields
if 'source' not in edge_data:
edge_data['source'] = src
if 'target' not in edge_data:
edge_data['target'] = tgt
affected_edges.append(edge_data)
except Exception as e:
logger.error(f'Failed to analyze affected graph elements: {e}')
raise Exception(f'Failed to analyze graph dependencies: {e}') from e
try:
# Process entities
for node_data in affected_nodes:
node_label = node_data.get('entity_id')
if not node_label:
continue
existing_sources: list[str] = []
if self.entity_chunks:
stored_chunks = await self.entity_chunks.get_by_id(node_label)
if stored_chunks and isinstance(stored_chunks, dict):
existing_sources = [chunk_id for chunk_id in stored_chunks.get('chunk_ids', []) if chunk_id]
if not existing_sources and node_data.get('source_id'):
existing_sources = [
chunk_id for chunk_id in node_data['source_id'].split(GRAPH_FIELD_SEP) if chunk_id
]
if not existing_sources:
# No chunk references means this entity should be deleted
entities_to_delete.add(node_label)
entity_chunk_updates[node_label] = []
continue
remaining_sources = subtract_source_ids(existing_sources, chunk_ids)
if not remaining_sources:
entities_to_delete.add(node_label)
entity_chunk_updates[node_label] = []
elif remaining_sources != existing_sources:
entities_to_rebuild[node_label] = remaining_sources
entity_chunk_updates[node_label] = remaining_sources
else:
logger.info(f'Untouch entity: {node_label}')
async with pipeline_status_lock:
log_message = f'Found {len(entities_to_rebuild)} affected entities'
logger.info(log_message)
pipeline_status['latest_message'] = log_message
pipeline_status['history_messages'].append(log_message)
# Process relationships
for edge_data in affected_edges:
# source target is not in normalize order in graph db property
src = edge_data.get('source')
tgt = edge_data.get('target')
if not src or not tgt or 'source_id' not in edge_data:
continue
edge_tuple = tuple(sorted((src, tgt)))
if edge_tuple in relationships_to_delete or edge_tuple in relationships_to_rebuild:
continue
existing_sources: list[str] = []
if self.relation_chunks:
storage_key = make_relation_chunk_key(src, tgt)
stored_chunks = await self.relation_chunks.get_by_id(storage_key)
if stored_chunks and isinstance(stored_chunks, dict):
existing_sources = [chunk_id for chunk_id in stored_chunks.get('chunk_ids', []) if chunk_id]
if not existing_sources:
existing_sources = [
chunk_id for chunk_id in edge_data['source_id'].split(GRAPH_FIELD_SEP) if chunk_id
]
if not existing_sources:
# No chunk references means this relationship should be deleted
relationships_to_delete.add(edge_tuple)
relation_chunk_updates[edge_tuple] = []
continue
remaining_sources = subtract_source_ids(existing_sources, chunk_ids)
if not remaining_sources:
relationships_to_delete.add(edge_tuple)
relation_chunk_updates[edge_tuple] = []
elif remaining_sources != existing_sources:
relationships_to_rebuild[edge_tuple] = remaining_sources
relation_chunk_updates[edge_tuple] = remaining_sources
else:
logger.info(f'Untouch relation: {edge_tuple}')
async with pipeline_status_lock:
log_message = f'Found {len(relationships_to_rebuild)} affected relations'
logger.info(log_message)
pipeline_status['latest_message'] = log_message
pipeline_status['history_messages'].append(log_message)
current_time = int(time.time())
if entity_chunk_updates and self.entity_chunks:
entity_upsert_payload = {}
for entity_name, remaining in entity_chunk_updates.items():
if not remaining:
# Empty entities are deleted alongside graph nodes later
continue
entity_upsert_payload[entity_name] = {
'chunk_ids': remaining,
'count': len(remaining),
'updated_at': current_time,
}
if entity_upsert_payload:
await self.entity_chunks.upsert(entity_upsert_payload)
if relation_chunk_updates and self.relation_chunks:
relation_upsert_payload = {}
for edge_tuple, remaining in relation_chunk_updates.items():
if not remaining:
# Empty relations are deleted alongside graph edges later
continue
storage_key = make_relation_chunk_key(*edge_tuple)
relation_upsert_payload[storage_key] = {
'chunk_ids': remaining,
'count': len(remaining),
'updated_at': current_time,
}
if relation_upsert_payload:
await self.relation_chunks.upsert(relation_upsert_payload)
except Exception as e:
logger.error(f'Failed to process graph analysis results: {e}')
raise Exception(f'Failed to process graph dependencies: {e}') from e
# Data integrity is ensured by allowing only one process to hold pipeline at a time（no graph db lock is needed anymore)
# 5. Delete chunks from storage
if chunk_ids:
try:
await self.chunks_vdb.delete(chunk_ids)
await self.text_chunks.delete(chunk_ids)
async with pipeline_status_lock:
log_message = f'Successfully deleted {len(chunk_ids)} chunks from storage'
logger.info(log_message)
pipeline_status['latest_message'] = log_message
pipeline_status['history_messages'].append(log_message)
except Exception as e:
logger.error(f'Failed to delete chunks: {e}')
raise Exception(f'Failed to delete document chunks: {e}') from e
# 6. Delete relationships that have no remaining sources
if relationships_to_delete:
try:
# Delete from relation vdb
rel_ids_to_delete = []
for src, tgt in relationships_to_delete:
rel_ids_to_delete.extend(
[
compute_mdhash_id(src + tgt, prefix='rel-'),
compute_mdhash_id(tgt + src, prefix='rel-'),
]
)
await self.relationships_vdb.delete(rel_ids_to_delete)
# Delete from graph
await self.chunk_entity_relation_graph.remove_edges(list(relationships_to_delete))
# Delete from relation_chunks storage
if self.relation_chunks:
relation_storage_keys = [
make_relation_chunk_key(src, tgt) for src, tgt in relationships_to_delete
]
await self.relation_chunks.delete(relation_storage_keys)
async with pipeline_status_lock:
log_message = f'Successfully deleted {len(relationships_to_delete)} relations'
logger.info(log_message)
pipeline_status['latest_message'] = log_message
pipeline_status['history_messages'].append(log_message)
except Exception as e:
logger.error(f'Failed to delete relationships: {e}')
raise Exception(f'Failed to delete relationships: {e}') from e
# 7. Delete entities that have no remaining sources
if entities_to_delete:
try:
# Batch get all edges for entities to avoid N+1 query problem
nodes_edges_dict = await self.chunk_entity_relation_graph.get_nodes_edges_batch(
list(entities_to_delete)
)
# Debug: Check and log all edges before deleting nodes
edges_to_delete = set()
edges_still_exist = 0
for _entity, edges in nodes_edges_dict.items():
if edges:
for src, tgt in edges:
# Normalize edge representation (sorted for consistency)
edge_tuple = tuple(sorted((src, tgt)))
edges_to_delete.add(edge_tuple)
if src in entities_to_delete and tgt in entities_to_delete:
logger.warning(f'Edge still exists: {src} <-> {tgt}')
elif src in entities_to_delete:
logger.warning(f'Edge still exists: {src} --> {tgt}')
else:
logger.warning(f'Edge still exists: {src} <-- {tgt}')
edges_still_exist += 1
if edges_still_exist:
logger.warning(f'⚠️ {edges_still_exist} entities still has edges before deletion')
# Clean residual edges from VDB and storage before deleting nodes
if edges_to_delete:
# Delete from relationships_vdb
rel_ids_to_delete = []
for src, tgt in edges_to_delete:
rel_ids_to_delete.extend(
[
compute_mdhash_id(src + tgt, prefix='rel-'),
compute_mdhash_id(tgt + src, prefix='rel-'),
]
)
await self.relationships_vdb.delete(rel_ids_to_delete)
# Delete from relation_chunks storage
if self.relation_chunks:
relation_storage_keys = [make_relation_chunk_key(src, tgt) for src, tgt in edges_to_delete]
await self.relation_chunks.delete(relation_storage_keys)
logger.info(
f'Cleaned {len(edges_to_delete)} residual edges from VDB and chunk-tracking storage'
)
# Delete from graph (edges will be auto-deleted with nodes)
await self.chunk_entity_relation_graph.remove_nodes(list(entities_to_delete))
# Delete from vector vdb
entity_vdb_ids = [compute_mdhash_id(entity, prefix='ent-') for entity in entities_to_delete]
await self.entities_vdb.delete(entity_vdb_ids)
# Delete from entity_chunks storage
if self.entity_chunks:
await self.entity_chunks.delete(list(entities_to_delete))
async with pipeline_status_lock:
log_message = f'Successfully deleted {len(entities_to_delete)} entities'
logger.info(log_message)
pipeline_status['latest_message'] = log_message
pipeline_status['history_messages'].append(log_message)
except Exception as e:
logger.error(f'Failed to delete entities: {e}')
raise Exception(f'Failed to delete entities: {e}') from e
# Persist changes to graph database before entity and relationship rebuild
await self._insert_done()
# 8. Rebuild entities and relationships from remaining chunks
if entities_to_rebuild or relationships_to_rebuild:
try:
await rebuild_knowledge_from_chunks(
entities_to_rebuild=entities_to_rebuild,
relationships_to_rebuild=relationships_to_rebuild,
knowledge_graph_inst=self.chunk_entity_relation_graph,
entities_vdb=self.entities_vdb,
relationships_vdb=self.relationships_vdb,
text_chunks_storage=self.text_chunks,
llm_response_cache=self.llm_response_cache,
global_config=asdict(self),
pipeline_status=pipeline_status,
pipeline_status_lock=pipeline_status_lock,
entity_chunks_storage=self.entity_chunks,
relation_chunks_storage=self.relation_chunks,
)
except Exception as e:
logger.error(f'Failed to rebuild knowledge from chunks: {e}')
raise Exception(f'Failed to rebuild knowledge graph: {e}') from e
# 9. Delete from full_entities and full_relations storage
try:
await self.full_entities.delete([doc_id])
await self.full_relations.delete([doc_id])
except Exception as e:
logger.error(f'Failed to delete from full_entities/full_relations: {e}')
raise Exception(f'Failed to delete from full_entities/full_relations: {e}') from e
# 10. Delete original document and status
try:
await self.full_docs.delete([doc_id])
await self.doc_status.delete([doc_id])
except Exception as e:
logger.error(f'Failed to delete document and status: {e}')
raise Exception(f'Failed to delete document and status: {e}') from e
if delete_llm_cache and doc_llm_cache_ids and self.llm_response_cache:
try:
await self.llm_response_cache.delete(doc_llm_cache_ids)
cache_log_message = (
f'Successfully deleted {len(doc_llm_cache_ids)} LLM cache entries for document {doc_id}'
)
logger.info(cache_log_message)
async with pipeline_status_lock:
pipeline_status['latest_message'] = cache_log_message
pipeline_status['history_messages'].append(cache_log_message)
log_message = cache_log_message
except Exception as cache_delete_error:
log_message = f'Failed to delete LLM cache for document {doc_id}: {cache_delete_error}'
logger.error(log_message)
logger.error(traceback.format_exc())
async with pipeline_status_lock:
pipeline_status['latest_message'] = log_message
pipeline_status['history_messages'].append(log_message)
return DeletionResult(
status='success',
doc_id=doc_id,
message=log_message,
status_code=200,
file_path=file_path,
)
except Exception as e:
original_exception = e
error_message = f'Error while deleting document {doc_id}: {e}'
logger.error(error_message)
logger.error(traceback.format_exc())
return DeletionResult(
status='fail',
doc_id=doc_id,
message=error_message,
status_code=500,
file_path=file_path,
)
finally:
# ALWAYS ensure persistence if any deletion operations were started
if deletion_operations_started:
try:
await self._insert_done()
except Exception as persistence_error:
persistence_error_msg = (
f'Failed to persist data after deletion attempt for {doc_id}: {persistence_error}'
)
logger.error(persistence_error_msg)
logger.error(traceback.format_exc())
# If there was no original exception, this persistence error becomes the main error
if original_exception is None:
return DeletionResult(
status='fail',
doc_id=doc_id,
message=f'Deletion completed but failed to persist changes: {persistence_error}',
status_code=500,
file_path=file_path,
)
# If there was an original exception, log the persistence error but don't override the original error
# The original error result was already returned in the except block
else:
logger.debug(f'No deletion operations were started for document {doc_id}, skipping persistence')
# Release pipeline only if WE acquired it
if we_acquired_pipeline:
async with pipeline_status_lock:
pipeline_status['busy'] = False
pipeline_status['cancellation_requested'] = False
completion_msg = f'Deletion process completed for document: {doc_id}'
pipeline_status['latest_message'] = completion_msg
pipeline_status['history_messages'].append(completion_msg)
logger.info(completion_msg)
async def adelete_by_entity(self, entity_name: str) -> DeletionResult:
"""Asynchronously delete an entity and all its relationships.
Args:
entity_name: Name of the entity to delete.
Returns:
DeletionResult: An object containing the outcome of the deletion process.
"""
from lightrag.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) -> DeletionResult:
"""Synchronously delete an entity and all its relationships.
Args:
entity_name: Name of the entity to delete.
Returns:
DeletionResult: An object containing the outcome of the deletion process.
"""
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) -> DeletionResult:
"""Asynchronously delete a relation between two entities.
Args:
source_entity: Name of the source entity.
target_entity: Name of the target entity.
Returns:
DeletionResult: An object containing the outcome of the deletion process.
"""
from lightrag.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) -> DeletionResult:
"""Synchronously delete a relation between two entities.
Args:
source_entity: Name of the source entity.
target_entity: Name of the target entity.
Returns:
DeletionResult: An object containing the outcome of the deletion process.
"""
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 aget_docs_by_track_id(self, track_id: str) -> dict[str, DocProcessingStatus]:
"""Get documents by track_id
Args:
track_id: The tracking ID to search for
Returns:
Dict with document id as keys and document status as values
"""
return await self.doc_status.get_docs_by_track_id(track_id)
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 lightrag.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 lightrag.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,
allow_merge: bool = False,
) -> dict[str, Any]:
"""Asynchronously edit entity information.
Updates entity information in the knowledge graph and re-embeds the entity in the vector database.
Also synchronizes entity_chunks_storage and relation_chunks_storage to track chunk references.
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
allow_merge: Whether to merge into an existing entity when renaming to an existing name
Returns:
Dictionary containing updated entity information
"""
from lightrag.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,
allow_merge,
self.entity_chunks,
self.relation_chunks,
)
def edit_entity(
self,
entity_name: str,
updated_data: dict[str, str],
allow_rename: bool = True,
allow_merge: bool = False,
) -> dict[str, Any]:
loop = always_get_an_event_loop()
return loop.run_until_complete(self.aedit_entity(entity_name, updated_data, allow_rename, allow_merge))
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.
Also synchronizes the relation_chunks_storage to track which chunks reference this relation.
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 lightrag.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,
self.relation_chunks,
)
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 lightrag.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 lightrag.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 = None,
target_entity_data: dict[str, Any] | None = 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 lightrag.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,
self.entity_chunks,
self.relation_chunks,
)
def merge_entities(
self,
source_entities: list[str],
target_entity: str,
merge_strategy: dict[str, str] | None = None,
target_entity_data: dict[str, Any] | None = 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 lightrag.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))
async def aconnect_orphan_entities(
self,
max_candidates: int = 3,
similarity_threshold: float | None = None,
confidence_threshold: float | None = None,
cross_connect: bool | None = None,
max_degree: int | None = None,
) -> dict[str, Any]:
"""Asynchronously connect orphan/sparse entities to the knowledge graph.
Finds entities with few or no relationships, identifies potential
connections using vector similarity, validates them with LLM, and
creates meaningful relationships.
Args:
max_candidates: Maximum candidates to evaluate per entity (default: 3)
similarity_threshold: Vector similarity threshold (0.0-1.0). Uses config if None.
confidence_threshold: LLM confidence threshold (0.0-1.0). Uses config if None.
cross_connect: Allow orphan-to-orphan connections. Uses config if None.
max_degree: Maximum connection degree to target. Uses config if None.
- 0: True orphans only (no connections)
- 1: Orphans + leaf nodes (0-1 connections)
- 2+: Include sparsely connected nodes
Returns:
Dictionary containing:
- orphans_found: Number of target entities found
- connections_made: Number of new connections created
- connections: List of connection details
- errors: List of any errors encountered
"""
# Use config values if not explicitly provided
sim_threshold = similarity_threshold if similarity_threshold is not None else self.orphan_connection_threshold
conf_threshold = confidence_threshold if confidence_threshold is not None else self.orphan_confidence_threshold
allow_cross_connect = cross_connect if cross_connect is not None else self.orphan_cross_connect
target_max_degree = max_degree if max_degree is not None else self.orphan_connection_max_degree
result = {
'orphans_found': 0,
'connections_made': 0,
'connections': [],
'errors': [],
}
# Check if using PostgreSQL storage (required for this feature)
db = getattr(self.entities_vdb, 'db', None)
workspace = getattr(self.entities_vdb, 'workspace', self.workspace)
if db is None:
result['errors'].append('Orphan connection requires PostgreSQL vector storage')
return result
try:
from lightrag.kg.postgres_impl import SQL_TEMPLATES
# Step 1: Get target entities (orphans or sparse entities based on max_degree)
if target_max_degree == 0:
orphan_sql = SQL_TEMPLATES['get_orphan_entities']
orphans = await db.query(orphan_sql, [workspace], multirows=True)
entity_type = 'orphan'
else:
sparse_sql = SQL_TEMPLATES['get_sparse_entities']
orphans = await db.query(sparse_sql, [workspace, target_max_degree], multirows=True)
entity_type = f'degree ≤ {target_max_degree}'
if not orphans:
logger.info(f'[{workspace}] No {entity_type} entities found')
return result
result['orphans_found'] = len(orphans)
logger.info(f'[{workspace}] Found {len(orphans)} {entity_type} entities to process')
# Step 2: Process each orphan
for orphan in orphans:
orphan_name = orphan.get('entity_name', '')
orphan_content = orphan.get('content', '')
orphan_vector = orphan.get('content_vector', '')
if not orphan_vector:
result['errors'].append(f'No vector for orphan: {orphan_name}')
continue
# Step 3: Get candidate connections
# Choose query based on cross_connect setting
candidate_sql = (
SQL_TEMPLATES['get_orphan_candidates']
if allow_cross_connect
else SQL_TEMPLATES['get_connected_candidates']
)
# Format vector for PostgreSQL
vector_str = orphan_vector if isinstance(orphan_vector, str) else str(list(orphan_vector))
candidates = await db.query(
candidate_sql,
[workspace, vector_str, orphan_name, sim_threshold, max_candidates],
multirows=True,
)
if not candidates:
logger.debug(f'[{workspace}] No candidates found for orphan: {orphan_name}')
continue
# Step 4: Validate each candidate with LLM
for candidate in candidates:
candidate_name = candidate.get('entity_name', '')
candidate_content = candidate.get('content', '')
similarity = candidate.get('similarity', 0.0)
# Parse entity type from content (format: "entity_type: description")
orphan_type = orphan_content.split(':')[0].strip() if ':' in orphan_content else 'Unknown'
orphan_desc = orphan_content.split(':', 1)[1].strip() if ':' in orphan_content else orphan_content
candidate_type = candidate_content.split(':')[0].strip() if ':' in candidate_content else 'Unknown'
candidate_desc = (
candidate_content.split(':', 1)[1].strip() if ':' in candidate_content else candidate_content
)
# Build validation prompt
validation_prompt = PROMPTS['orphan_connection_validation'].format(
orphan_name=orphan_name,
orphan_type=orphan_type,
orphan_description=orphan_desc,
candidate_name=candidate_name,
candidate_type=candidate_type,
candidate_description=candidate_desc,
similarity_score=f'{similarity:.3f}',
)
try:
if not self.llm_model_func:
logger.warning("LLM model function not initialized")
continue
# Call LLM for validation
llm_callable = cast(Callable[[str], Awaitable[str]], self.llm_model_func)
llm_response = await llm_callable(validation_prompt)
# Parse JSON response
import re
# Extract JSON from response (handle markdown code blocks)
json_match = re.search(r'\{[^{}]*\}', llm_response, re.DOTALL)
if not json_match:
logger.warning(
f'[{workspace}] No JSON in LLM response for {orphan_name} -> {candidate_name}'
)
continue
validation = json.loads(json_match.group())
should_connect = validation.get('should_connect', False)
confidence = float(validation.get('confidence', 0.0))
# Step 5: Create connection if validated
if should_connect and confidence >= conf_threshold:
rel_type = validation.get('relationship_type', 'related_to')
rel_keywords = validation.get('relationship_keywords', 'connection')
rel_description = validation.get(
'relationship_description',
f'Connected via orphan resolution (confidence: {confidence:.2f})',
)
try:
await self.acreate_relation(
orphan_name,
candidate_name,
{
'description': rel_description,
'keywords': rel_keywords,
'source_id': 'orphan_connection',
},
)
result['connections_made'] += 1
result['connections'].append(
{
'orphan': orphan_name,
'connected_to': candidate_name,
'relationship_type': rel_type,
'keywords': rel_keywords,
'confidence': confidence,
'similarity': similarity,
}
)
logger.info(
f"[{workspace}] Connected orphan '{orphan_name}' -> '{candidate_name}' "
f'(confidence: {confidence:.2f}, similarity: {similarity:.3f})'
)
# Only connect to first valid candidate per orphan
break
except Exception as e:
result['errors'].append(
f'Failed to create relation {orphan_name} -> {candidate_name}: {e!s}'
)
else:
logger.debug(
f'[{workspace}] Rejected connection {orphan_name} -> {candidate_name} '
f'(should_connect={should_connect}, confidence={confidence:.2f})'
)
except json.JSONDecodeError as e:
result['errors'].append(f'JSON parse error for {orphan_name} -> {candidate_name}: {e!s}')
except Exception as e:
result['errors'].append(f'LLM validation error for {orphan_name} -> {candidate_name}: {e!s}')
except Exception as e:
result['errors'].append(f'Orphan connection failed: {e!s}')
logger.error(f'[{workspace}] Orphan connection error: {e}')
logger.info(
f'[{workspace}] Orphan connection complete: '
f'{result["connections_made"]}/{result["orphans_found"]} orphans connected'
)
return result
def connect_orphan_entities(
self,
max_candidates: int = 3,
similarity_threshold: float | None = None,
confidence_threshold: float | None = None,
cross_connect: bool | None = None,
) -> dict[str, Any]:
"""Synchronously connect orphan entities. See aconnect_orphan_entities for details."""
loop = always_get_an_event_loop()
return loop.run_until_complete(
self.aconnect_orphan_entities(max_candidates, similarity_threshold, confidence_threshold, cross_connect)
)
async def _append_orphan_message(self, message: str):
"""Add a timestamped message to the orphan connection history."""
from lightrag.kg.shared_storage import get_namespace_data, get_namespace_lock
orphan_status = await get_namespace_data('orphan_connection_status', workspace=self.workspace)
orphan_lock = get_namespace_lock('orphan_connection_status', workspace=self.workspace)
async with orphan_lock:
timestamp = datetime.now().strftime('%H:%M:%S')
full_message = f'[{timestamp}] {message}'
orphan_status['latest_message'] = full_message
orphan_status['history_messages'].append(full_message)
# Truncate if too long - use in-place modification to preserve Manager.list()
if len(orphan_status['history_messages']) > 10000:
# Get last 5000 messages, clear list, then extend with kept messages
messages_to_keep = list(orphan_status['history_messages'])[-5000:]
del orphan_status['history_messages'][:]
orphan_status['history_messages'].extend(messages_to_keep)
async def aprocess_orphan_connections_background(
self,
max_candidates: int = 3,
max_degree: int = 0,
) -> dict[str, Any]:
"""Background job for connecting orphan/sparse entities with progress tracking.
This method runs independently from the document pipeline and provides
real-time progress updates via the orphan_connection_status namespace.
Args:
max_candidates: Maximum candidates to evaluate per entity (default: 3)
max_degree: Maximum connection degree to target (default: 0)
- 0: True orphans only (no connections)
- 1: Orphans + leaf nodes (single connection)
- 2+: Include sparsely connected nodes
Returns:
Dictionary containing result summary or status if already running
"""
from lightrag.kg.shared_storage import get_namespace_data, get_namespace_lock
orphan_status = await get_namespace_data('orphan_connection_status', workspace=self.workspace)
orphan_lock = get_namespace_lock('orphan_connection_status', workspace=self.workspace)
# Check if already running
async with orphan_lock:
if orphan_status.get('busy'):
orphan_status['request_pending'] = True
logger.info(f'[{self.workspace}] Orphan connection already running, request pending')
return {'status': 'already_running'}
# Initialize job state
orphan_status['busy'] = True
orphan_status['job_start'] = datetime.now().isoformat()
degree_desc = 'orphan' if max_degree == 0 else f'degree ≤ {max_degree}'
orphan_status['job_name'] = f'Connecting {degree_desc} entities'
orphan_status['cancellation_requested'] = False
orphan_status['total_orphans'] = 0
orphan_status['processed_orphans'] = 0
orphan_status['connections_made'] = 0
# Clear history messages without breaking shared list reference
# Use del list[:] pattern (same as document pipeline) to preserve Manager.list()
del orphan_status['history_messages'][:]
result = {
'orphans_found': 0,
'connections_made': 0,
'connections': [],
'errors': [],
}
try:
await self._append_orphan_message('Starting orphan connection process...')
# Check if using PostgreSQL storage (required for this feature)
db = getattr(self.entities_vdb, 'db', None)
workspace = getattr(self.entities_vdb, 'workspace', self.workspace)
if db is None:
await self._append_orphan_message('Error: Orphan connection requires PostgreSQL vector storage')
result['errors'].append('Orphan connection requires PostgreSQL vector storage')
return result
from lightrag.kg.postgres_impl import SQL_TEMPLATES
# Step 1: Get target entities (orphans or sparse entities based on max_degree)
if max_degree == 0:
await self._append_orphan_message('Finding orphan entities (degree = 0)...')
orphan_sql = SQL_TEMPLATES['get_orphan_entities']
orphans = await db.query(orphan_sql, [workspace], multirows=True)
else:
await self._append_orphan_message(f'Finding sparse entities (degree ≤ {max_degree})...')
sparse_sql = SQL_TEMPLATES['get_sparse_entities']
orphans = await db.query(sparse_sql, [workspace, max_degree], multirows=True)
if not orphans:
entity_type = 'orphan' if max_degree == 0 else f'degree ≤ {max_degree}'
await self._append_orphan_message(f'No {entity_type} entities found')
return result
result['orphans_found'] = len(orphans)
entity_type = 'orphan' if max_degree == 0 else f'degree ≤ {max_degree}'
await self._append_orphan_message(f'Found {len(orphans)} {entity_type} entities')
# Update status with total count
async with orphan_lock:
orphan_status['total_orphans'] = len(orphans)
# Get thresholds from config
sim_threshold = self.orphan_connection_threshold
conf_threshold = self.orphan_confidence_threshold
allow_cross_connect = self.orphan_cross_connect
# Step 2: Process each orphan with progress tracking
batch_size = 10 # Process in smaller batches for more frequent updates
for i, orphan in enumerate(orphans):
# Check for cancellation
if orphan_status.get('cancellation_requested'):
await self._append_orphan_message('Cancellation requested, stopping...')
break
orphan_name = orphan.get('entity_name', '')
orphan_content = orphan.get('content', '')
orphan_vector = orphan.get('content_vector', '')
if not orphan_vector:
result['errors'].append(f'No vector for orphan: {orphan_name}')
async with orphan_lock:
orphan_status['processed_orphans'] += 1
continue
# Log progress every batch_size entities
if i % batch_size == 0:
await self._append_orphan_message(
f'Processing orphan {i + 1}/{len(orphans)}: {orphan_name[:50]}...'
)
# Get candidate connections
candidate_sql = (
SQL_TEMPLATES['get_orphan_candidates']
if allow_cross_connect
else SQL_TEMPLATES['get_connected_candidates']
)
vector_str = orphan_vector if isinstance(orphan_vector, str) else str(list(orphan_vector))
candidates = await db.query(
candidate_sql,
[workspace, vector_str, orphan_name, sim_threshold, max_candidates],
multirows=True,
)
if not candidates:
async with orphan_lock:
orphan_status['processed_orphans'] += 1
continue
# Validate each candidate with LLM
for candidate in candidates:
# Check for cancellation
if orphan_status.get('cancellation_requested'):
break
candidate_name = candidate.get('entity_name', '')
candidate_content = candidate.get('content', '')
similarity = candidate.get('similarity', 0.0)
# Parse entity type from content
orphan_type = orphan_content.split(':')[0].strip() if ':' in orphan_content else 'Unknown'
orphan_desc = orphan_content.split(':', 1)[1].strip() if ':' in orphan_content else orphan_content
candidate_type = candidate_content.split(':')[0].strip() if ':' in candidate_content else 'Unknown'
candidate_desc = (
candidate_content.split(':', 1)[1].strip() if ':' in candidate_content else candidate_content
)
# Build validation prompt
validation_prompt = PROMPTS['orphan_connection_validation'].format(
orphan_name=orphan_name,
orphan_type=orphan_type,
orphan_description=orphan_desc,
candidate_name=candidate_name,
candidate_type=candidate_type,
candidate_description=candidate_desc,
similarity_score=f'{similarity:.3f}',
)
try:
if not self.llm_model_func:
logger.warning('LLM model function not initialized for orphan validation')
continue
llm_callable = cast(Callable[[str], Awaitable[str]], self.llm_model_func)
llm_response = await llm_callable(validation_prompt)
import re
json_match = re.search(r'\{[^{}]*\}', llm_response, re.DOTALL)
if not json_match:
continue
validation = json.loads(json_match.group())
should_connect = validation.get('should_connect', False)
confidence = float(validation.get('confidence', 0.0))
if should_connect and confidence >= conf_threshold:
rel_type = validation.get('relationship_type', 'related_to')
rel_keywords = validation.get('relationship_keywords', 'connection')
rel_description = validation.get(
'relationship_description',
f'Connected via orphan resolution (confidence: {confidence:.2f})',
)
try:
await self.acreate_relation(
orphan_name,
candidate_name,
{
'description': rel_description,
'keywords': rel_keywords,
'source_id': 'orphan_connection',
},
)
result['connections_made'] += 1
result['connections'].append(
{
'orphan': orphan_name,
'connected_to': candidate_name,
'relationship_type': rel_type,
'keywords': rel_keywords,
'confidence': confidence,
'similarity': similarity,
}
)
# Update status
async with orphan_lock:
orphan_status['connections_made'] += 1
await self._append_orphan_message(
f'Connected: {orphan_name[:30]} -> {candidate_name[:30]} (conf: {confidence:.2f})'
)
# Only connect to first valid candidate per orphan
break
except Exception as e:
result['errors'].append(
f'Failed to create relation {orphan_name} -> {candidate_name}: {e!s}'
)
except Exception as e:
result['errors'].append(f'LLM validation error for {orphan_name}: {e!s}')
# Update processed count
async with orphan_lock:
orphan_status['processed_orphans'] += 1
# Final summary
await self._append_orphan_message(
f'Completed! Connected {result["connections_made"]} out of {result["orphans_found"]} orphan entities'
)
errors = cast(list, result['errors'])
if errors:
await self._append_orphan_message(f'Encountered {len(errors)} errors during processing')
except Exception as e:
error_msg = f'Orphan connection failed: {e!s}'
cast(list, result['errors']).append(error_msg)
await self._append_orphan_message(f'Error: {error_msg}')
logger.error(f'[{self.workspace}] Orphan connection error: {e}')
finally:
# Reset busy state
async with orphan_lock:
orphan_status['busy'] = False
if orphan_status.get('request_pending'):
orphan_status['request_pending'] = False
await self._append_orphan_message('Note: Another request was pending during processing')
return result
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