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Merge pull request #739 from YanSte/fixes

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Improve Parallelism & Fix Bugs After Testing
This commit is contained in:
zrguo
2025-02-10 13:52:19 +08:00
committed by GitHub
parent 832a9bb6fb bfd280450a
commit f1a0284294
9 changed files with 188 additions and 224 deletions
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30
lightrag/base.py
View File

@@ -1,24 +1,26 @@
from enum import Enum
import os import os
from dataclasses import dataclass, field from dataclasses import dataclass, field
from enum import Enum
from typing import ( from typing import (
Any,
Literal,
Optional, Optional,
TypedDict, TypedDict,
Union,
Literal,
TypeVar, TypeVar,
Any, Union,
) )
import numpy as np import numpy as np
from .utils import EmbeddingFunc from .utils import EmbeddingFunc
TextChunkSchema = TypedDict(
"TextChunkSchema", class TextChunkSchema(TypedDict):
{"tokens": int, "content": str, "full_doc_id": str, "chunk_order_index": int}, tokens: int
) content: str
full_doc_id: str
chunk_order_index: int
T = TypeVar("T") T = TypeVar("T")
@@ -57,11 +59,11 @@ class StorageNameSpace:
global_config: dict[str, Any] global_config: dict[str, Any]
async def index_done_callback(self): async def index_done_callback(self):
"""commit the storage operations after indexing""" """Commit the storage operations after indexing"""
pass pass
async def query_done_callback(self): async def query_done_callback(self):
"""commit the storage operations after querying""" """Commit the storage operations after querying"""
pass pass
@@ -84,14 +86,14 @@ class BaseVectorStorage(StorageNameSpace):
class BaseKVStorage(StorageNameSpace): class BaseKVStorage(StorageNameSpace):
embedding_func: EmbeddingFunc embedding_func: EmbeddingFunc
async def get_by_id(self, id: str) -> dict[str, Any]: async def get_by_id(self, id: str) -> Union[dict[str, Any], None]:
raise NotImplementedError raise NotImplementedError
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
raise NotImplementedError raise NotImplementedError
async def filter_keys(self, data: list[str]) -> set[str]: async def filter_keys(self, data: set[str]) -> set[str]:
"""return un-exist keys""" """Return un-exist keys"""
raise NotImplementedError raise NotImplementedError
async def upsert(self, data: dict[str, Any]) -> None: async def upsert(self, data: dict[str, Any]) -> None:

20
lightrag/kg/json_kv_impl.py
View File

@@ -1,16 +1,16 @@
import asyncio import asyncio
import os import os
from dataclasses import dataclass from dataclasses import dataclass
from typing import Any from typing import Any, Union
from lightrag.utils import (
logger,
load_json,
write_json,
)
from lightrag.base import ( from lightrag.base import (
BaseKVStorage, BaseKVStorage,
) )
from lightrag.utils import (
load_json,
logger,
write_json,
)
@dataclass @dataclass
@@ -25,8 +25,8 @@ class JsonKVStorage(BaseKVStorage):
async def index_done_callback(self): async def index_done_callback(self):
write_json(self._data, self._file_name) write_json(self._data, self._file_name)
async def get_by_id(self, id: str) -> dict[str, Any]: async def get_by_id(self, id: str) -> Union[dict[str, Any], None]:
return self._data.get(id, {}) return self._data.get(id)
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
return [ return [
@@ -38,8 +38,8 @@ class JsonKVStorage(BaseKVStorage):
for id in ids for id in ids
] ]
async def filter_keys(self, data: list[str]) -> set[str]: async def filter_keys(self, data: set[str]) -> set[str]:
return set([s for s in data if s not in self._data]) return set(data) - set(self._data.keys())
async def upsert(self, data: dict[str, dict[str, Any]]) -> None: async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
left_data = {k: v for k, v in data.items() if k not in self._data} left_data = {k: v for k, v in data.items() if k not in self._data}

53
lightrag/kg/jsondocstatus_impl.py
View File

@@ -48,21 +48,20 @@ Usage:
""" """
import os
from dataclasses import dataclass from dataclasses import dataclass
import os
from typing import Any, Union from typing import Any, Union
from lightrag.utils import (
logger,
load_json,
write_json,
)
from lightrag.base import ( from lightrag.base import (
DocStatus,
DocProcessingStatus, DocProcessingStatus,
DocStatus,
DocStatusStorage, DocStatusStorage,
) )
from lightrag.utils import (
load_json,
logger,
write_json,
)
@dataclass @dataclass
@@ -75,15 +74,17 @@ class JsonDocStatusStorage(DocStatusStorage):
self._data: dict[str, Any] = load_json(self._file_name) or {} self._data: dict[str, Any] = load_json(self._file_name) or {}
logger.info(f"Loaded document status storage with {len(self._data)} records") logger.info(f"Loaded document status storage with {len(self._data)} records")
async def filter_keys(self, data: list[str]) -> set[str]: async def filter_keys(self, data: set[str]) -> set[str]:
"""Return keys that should be processed (not in storage or not successfully processed)""" """Return keys that should be processed (not in storage or not successfully processed)"""
return set( return set(data) - set(self._data.keys())
[
k async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
for k in data result: list[dict[str, Any]] = []
if k not in self._data or self._data[k]["status"] != DocStatus.PROCESSED for id in ids:
] data = self._data.get(id, None)
) if data:
result.append(data)
return result
async def get_status_counts(self) -> dict[str, int]: async def get_status_counts(self) -> dict[str, int]:
"""Get counts of documents in each status""" """Get counts of documents in each status"""
@@ -94,11 +95,19 @@ class JsonDocStatusStorage(DocStatusStorage):
async def get_failed_docs(self) -> dict[str, DocProcessingStatus]: async def get_failed_docs(self) -> dict[str, DocProcessingStatus]:
"""Get all failed documents""" """Get all failed documents"""
return {k: v for k, v in self._data.items() if v["status"] == DocStatus.FAILED} return {
k: DocProcessingStatus(**v)
for k, v in self._data.items()
if v["status"] == DocStatus.FAILED
}
async def get_pending_docs(self) -> dict[str, DocProcessingStatus]: async def get_pending_docs(self) -> dict[str, DocProcessingStatus]:
"""Get all pending documents""" """Get all pending documents"""
return {k: v for k, v in self._data.items() if v["status"] == DocStatus.PENDING} return {
k: DocProcessingStatus(**v)
for k, v in self._data.items()
if v["status"] == DocStatus.PENDING
}
async def index_done_callback(self): async def index_done_callback(self):
"""Save data to file after indexing""" """Save data to file after indexing"""
@@ -113,12 +122,8 @@ class JsonDocStatusStorage(DocStatusStorage):
self._data.update(data) self._data.update(data)
await self.index_done_callback() await self.index_done_callback()
async def get_by_id(self, id: str) -> dict[str, Any]: async def get_by_id(self, id: str) -> Union[dict[str, Any], None]:
return self._data.get(id, {}) return self._data.get(id)
async def get(self, doc_id: str) -> Union[DocProcessingStatus, None]:
"""Get document status by ID"""
return self._data.get(doc_id)
async def delete(self, doc_ids: list[str]): async def delete(self, doc_ids: list[str]):
"""Delete document status by IDs""" """Delete document status by IDs"""

21
lightrag/kg/mongo_impl.py
View File

@@ -1,8 +1,9 @@
import os import os
from tqdm.asyncio import tqdm as tqdm_async
from dataclasses import dataclass from dataclasses import dataclass
import pipmaster as pm
import numpy as np import numpy as np
import pipmaster as pm
from tqdm.asyncio import tqdm as tqdm_async
if not pm.is_installed("pymongo"): if not pm.is_installed("pymongo"):
pm.install("pymongo") pm.install("pymongo")
@@ -10,13 +11,14 @@ if not pm.is_installed("pymongo"):
if not pm.is_installed("motor"): if not pm.is_installed("motor"):
pm.install("motor") pm.install("motor")
from pymongo import MongoClient from typing import Any, List, Tuple, Union
from motor.motor_asyncio import AsyncIOMotorClient
from typing import Any, Union, List, Tuple
from ..utils import logger from motor.motor_asyncio import AsyncIOMotorClient
from ..base import BaseKVStorage, BaseGraphStorage from pymongo import MongoClient
from ..base import BaseGraphStorage, BaseKVStorage
from ..namespace import NameSpace, is_namespace from ..namespace import NameSpace, is_namespace
from ..utils import logger
@dataclass @dataclass
@@ -29,13 +31,13 @@ class MongoKVStorage(BaseKVStorage):
self._data = database.get_collection(self.namespace) self._data = database.get_collection(self.namespace)
logger.info(f"Use MongoDB as KV {self.namespace}") logger.info(f"Use MongoDB as KV {self.namespace}")
async def get_by_id(self, id: str) -> dict[str, Any]: async def get_by_id(self, id: str) -> Union[dict[str, Any], None]:
return self._data.find_one({"_id": id}) return self._data.find_one({"_id": id})
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
return list(self._data.find({"_id": {"$in": ids}})) return list(self._data.find({"_id": {"$in": ids}}))
async def filter_keys(self, data: list[str]) -> set[str]: async def filter_keys(self, data: set[str]) -> set[str]:
existing_ids = [ existing_ids = [
str(x["_id"]) for x in self._data.find({"_id": {"$in": data}}, {"_id": 1}) str(x["_id"]) for x in self._data.find({"_id": {"$in": data}}, {"_id": 1})
] ]
@@ -170,7 +172,6 @@ class MongoGraphStorage(BaseGraphStorage):
But typically for a direct edge, we might just do a find_one. But typically for a direct edge, we might just do a find_one.
Below is a demonstration approach. Below is a demonstration approach.
""" """
# We can do a single-hop graphLookup (maxDepth=0 or 1). # We can do a single-hop graphLookup (maxDepth=0 or 1).
# Then check if the target_node appears among the edges array. # Then check if the target_node appears among the edges array.
pipeline = [ pipeline = [

24
lightrag/kg/oracle_impl.py
View File

@@ -1,27 +1,28 @@
import os import array
import asyncio import asyncio
import os
# import html # import html
# import os # import os
from dataclasses import dataclass from dataclasses import dataclass
from typing import Any, Union from typing import Any, Union
import numpy as np import numpy as np
import array
import pipmaster as pm import pipmaster as pm
if not pm.is_installed("oracledb"): if not pm.is_installed("oracledb"):
pm.install("oracledb") pm.install("oracledb")
from ..utils import logger import oracledb
from ..base import ( from ..base import (
BaseGraphStorage, BaseGraphStorage,
BaseKVStorage, BaseKVStorage,
BaseVectorStorage, BaseVectorStorage,
) )
from ..namespace import NameSpace, is_namespace from ..namespace import NameSpace, is_namespace
from ..utils import logger
import oracledb
class OracleDB: class OracleDB:
@@ -107,7 +108,7 @@ class OracleDB:
"SELECT id FROM GRAPH_TABLE (lightrag_graph MATCH (a) COLUMNS (a.id)) fetch first row only" "SELECT id FROM GRAPH_TABLE (lightrag_graph MATCH (a) COLUMNS (a.id)) fetch first row only"
) )
else: else:
await self.query("SELECT 1 FROM {k}".format(k=k)) await self.query(f"SELECT 1 FROM {k}")
except Exception as e: except Exception as e:
logger.error(f"Failed to check table {k} in Oracle database") logger.error(f"Failed to check table {k} in Oracle database")
logger.error(f"Oracle database error: {e}") logger.error(f"Oracle database error: {e}")
@@ -181,8 +182,8 @@ class OracleKVStorage(BaseKVStorage):
################ QUERY METHODS ################ ################ QUERY METHODS ################
async def get_by_id(self, id: str) -> dict[str, Any]: async def get_by_id(self, id: str) -> Union[dict[str, Any], None]:
"""get doc_full data based on id.""" """Get doc_full data based on id."""
SQL = SQL_TEMPLATES["get_by_id_" + self.namespace] SQL = SQL_TEMPLATES["get_by_id_" + self.namespace]
params = {"workspace": self.db.workspace, "id": id} params = {"workspace": self.db.workspace, "id": id}
# print("get_by_id:"+SQL) # print("get_by_id:"+SQL)
@@ -191,7 +192,10 @@ class OracleKVStorage(BaseKVStorage):
res = {} res = {}
for row in array_res: for row in array_res:
res[row["id"]] = row res[row["id"]] = row
return res if res:
return res
else:
return None
else: else:
return await self.db.query(SQL, params) return await self.db.query(SQL, params)
@@ -209,7 +213,7 @@ class OracleKVStorage(BaseKVStorage):
return None return None
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
"""get doc_chunks data based on id""" """Get doc_chunks data based on id"""
SQL = SQL_TEMPLATES["get_by_ids_" + self.namespace].format( SQL = SQL_TEMPLATES["get_by_ids_" + self.namespace].format(
ids=",".join([f"'{id}'" for id in ids]) ids=",".join([f"'{id}'" for id in ids])
) )

32
lightrag/kg/postgres_impl.py
View File

@@ -4,34 +4,35 @@ import json
import os import os
import time import time
from dataclasses import dataclass from dataclasses import dataclass
from typing import Union, List, Dict, Set, Any, Tuple from typing import Any, Dict, List, Set, Tuple, Union
import numpy as np
import numpy as np
import pipmaster as pm import pipmaster as pm
if not pm.is_installed("asyncpg"): if not pm.is_installed("asyncpg"):
pm.install("asyncpg") pm.install("asyncpg")
import asyncpg
import sys import sys
from tqdm.asyncio import tqdm as tqdm_async
import asyncpg
from tenacity import ( from tenacity import (
retry, retry,
retry_if_exception_type, retry_if_exception_type,
stop_after_attempt, stop_after_attempt,
wait_exponential, wait_exponential,
) )
from tqdm.asyncio import tqdm as tqdm_async
from ..utils import logger
from ..base import ( from ..base import (
BaseGraphStorage,
BaseKVStorage, BaseKVStorage,
BaseVectorStorage, BaseVectorStorage,
DocStatusStorage,
DocStatus,
DocProcessingStatus, DocProcessingStatus,
BaseGraphStorage, DocStatus,
DocStatusStorage,
) )
from ..namespace import NameSpace, is_namespace from ..namespace import NameSpace, is_namespace
from ..utils import logger
if sys.platform.startswith("win"): if sys.platform.startswith("win"):
import asyncio.windows_events import asyncio.windows_events
@@ -82,7 +83,7 @@ class PostgreSQLDB:
async def check_tables(self): async def check_tables(self):
for k, v in TABLES.items(): for k, v in TABLES.items():
try: try:
await self.query("SELECT 1 FROM {k} LIMIT 1".format(k=k)) await self.query(f"SELECT 1 FROM {k} LIMIT 1")
except Exception as e: except Exception as e:
logger.error(f"Failed to check table {k} in PostgreSQL database") logger.error(f"Failed to check table {k} in PostgreSQL database")
logger.error(f"PostgreSQL database error: {e}") logger.error(f"PostgreSQL database error: {e}")
@@ -183,7 +184,7 @@ class PGKVStorage(BaseKVStorage):
################ QUERY METHODS ################ ################ QUERY METHODS ################
async def get_by_id(self, id: str) -> dict[str, Any]: async def get_by_id(self, id: str) -> Union[dict[str, Any], None]:
"""Get doc_full data by id.""" """Get doc_full data by id."""
sql = SQL_TEMPLATES["get_by_id_" + self.namespace] sql = SQL_TEMPLATES["get_by_id_" + self.namespace]
params = {"workspace": self.db.workspace, "id": id} params = {"workspace": self.db.workspace, "id": id}
@@ -192,9 +193,10 @@ class PGKVStorage(BaseKVStorage):
res = {} res = {}
for row in array_res: for row in array_res:
res[row["id"]] = row res[row["id"]] = row
return res return res if res else None
else: else:
return await self.db.query(sql, params) response = await self.db.query(sql, params)
return response if response else None
async def get_by_mode_and_id(self, mode: str, id: str) -> Union[dict, None]: async def get_by_mode_and_id(self, mode: str, id: str) -> Union[dict, None]:
"""Specifically for llm_response_cache.""" """Specifically for llm_response_cache."""
@@ -421,7 +423,7 @@ class PGDocStatusStorage(DocStatusStorage):
def __post_init__(self): def __post_init__(self):
pass pass
async def filter_keys(self, data: list[str]) -> set[str]: async def filter_keys(self, data: set[str]) -> set[str]:
"""Return keys that don't exist in storage""" """Return keys that don't exist in storage"""
keys = ",".join([f"'{_id}'" for _id in data]) keys = ",".join([f"'{_id}'" for _id in data])
sql = ( sql = (
@@ -435,12 +437,12 @@ class PGDocStatusStorage(DocStatusStorage):
existed = set([element["id"] for element in result]) existed = set([element["id"] for element in result])
return set(data) - existed return set(data) - existed
async def get_by_id(self, id: str) -> dict[str, Any]: async def get_by_id(self, id: str) -> Union[dict[str, Any], None]:
sql = "select * from LIGHTRAG_DOC_STATUS where workspace=$1 and id=$2" sql = "select * from LIGHTRAG_DOC_STATUS where workspace=$1 and id=$2"
params = {"workspace": self.db.workspace, "id": id} params = {"workspace": self.db.workspace, "id": id}
result = await self.db.query(sql, params, True) result = await self.db.query(sql, params, True)
if result is None or result == []: if result is None or result == []:
return {} return None
else: else:
return DocProcessingStatus( return DocProcessingStatus(
content=result[0]["content"], content=result[0]["content"],

6
lightrag/kg/redis_impl.py
View File

@@ -1,5 +1,5 @@
import os import os
from typing import Any from typing import Any, Union
from tqdm.asyncio import tqdm as tqdm_async from tqdm.asyncio import tqdm as tqdm_async
from dataclasses import dataclass from dataclasses import dataclass
import pipmaster as pm import pipmaster as pm
@@ -21,7 +21,7 @@ class RedisKVStorage(BaseKVStorage):
self._redis = Redis.from_url(redis_url, decode_responses=True) self._redis = Redis.from_url(redis_url, decode_responses=True)
logger.info(f"Use Redis as KV {self.namespace}") logger.info(f"Use Redis as KV {self.namespace}")
async def get_by_id(self, id): async def get_by_id(self, id: str) -> Union[dict[str, Any], None]:
data = await self._redis.get(f"{self.namespace}:{id}") data = await self._redis.get(f"{self.namespace}:{id}")
return json.loads(data) if data else None return json.loads(data) if data else None
@@ -32,7 +32,7 @@ class RedisKVStorage(BaseKVStorage):
results = await pipe.execute() results = await pipe.execute()
return [json.loads(result) if result else None for result in results] return [json.loads(result) if result else None for result in results]
async def filter_keys(self, data: list[str]) -> set[str]: async def filter_keys(self, data: set[str]) -> set[str]:
pipe = self._redis.pipeline() pipe = self._redis.pipeline()
for key in data: for key in data:
pipe.exists(f"{self.namespace}:{key}") pipe.exists(f"{self.namespace}:{key}")

21
lightrag/kg/tidb_impl.py
View File

@@ -14,12 +14,12 @@ if not pm.is_installed("sqlalchemy"):
from sqlalchemy import create_engine, text from sqlalchemy import create_engine, text
from tqdm import tqdm from tqdm import tqdm
from ..base import BaseVectorStorage, BaseKVStorage, BaseGraphStorage from ..base import BaseGraphStorage, BaseKVStorage, BaseVectorStorage
from ..utils import logger
from ..namespace import NameSpace, is_namespace from ..namespace import NameSpace, is_namespace
from ..utils import logger
class TiDB(object): class TiDB:
def __init__(self, config, **kwargs): def __init__(self, config, **kwargs):
self.host = config.get("host", None) self.host = config.get("host", None)
self.port = config.get("port", None) self.port = config.get("port", None)
@@ -108,12 +108,12 @@ class TiDBKVStorage(BaseKVStorage):
################ QUERY METHODS ################ ################ QUERY METHODS ################
async def get_by_id(self, id: str) -> dict[str, Any]: async def get_by_id(self, id: str) -> Union[dict[str, Any], None]:
"""Fetch doc_full data by id.""" """Fetch doc_full data by id."""
SQL = SQL_TEMPLATES["get_by_id_" + self.namespace] SQL = SQL_TEMPLATES["get_by_id_" + self.namespace]
params = {"id": id} params = {"id": id}
# print("get_by_id:"+SQL) response = await self.db.query(SQL, params)
return await self.db.query(SQL, params) return response if response else None
# Query by id # Query by id
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
@@ -178,7 +178,7 @@ class TiDBKVStorage(BaseKVStorage):
"tokens": item["tokens"], "tokens": item["tokens"],
"chunk_order_index": item["chunk_order_index"], "chunk_order_index": item["chunk_order_index"],
"full_doc_id": item["full_doc_id"], "full_doc_id": item["full_doc_id"],
"content_vector": f"{item['__vector__'].tolist()}", "content_vector": f'{item["__vector__"].tolist()}',
"workspace": self.db.workspace, "workspace": self.db.workspace,
} }
) )
@@ -222,8 +222,7 @@ class TiDBVectorDBStorage(BaseVectorStorage):
) )
async def query(self, query: str, top_k: int) -> list[dict]: async def query(self, query: str, top_k: int) -> list[dict]:
"""search from tidb vector""" """Search from tidb vector"""
embeddings = await self.embedding_func([query]) embeddings = await self.embedding_func([query])
embedding = embeddings[0] embedding = embeddings[0]
@@ -286,7 +285,7 @@ class TiDBVectorDBStorage(BaseVectorStorage):
"id": item["id"], "id": item["id"],
"name": item["entity_name"], "name": item["entity_name"],
"content": item["content"], "content": item["content"],
"content_vector": f"{item['content_vector'].tolist()}", "content_vector": f'{item["content_vector"].tolist()}',
"workspace": self.db.workspace, "workspace": self.db.workspace,
} }
# update entity_id if node inserted by graph_storage_instance before # update entity_id if node inserted by graph_storage_instance before
@@ -308,7 +307,7 @@ class TiDBVectorDBStorage(BaseVectorStorage):
"source_name": item["src_id"], "source_name": item["src_id"],
"target_name": item["tgt_id"], "target_name": item["tgt_id"],
"content": item["content"], "content": item["content"],
"content_vector": f"{item['content_vector'].tolist()}", "content_vector": f'{item["content_vector"].tolist()}',
"workspace": self.db.workspace, "workspace": self.db.workspace,
} }
# update relation_id if node inserted by graph_storage_instance before # update relation_id if node inserted by graph_storage_instance before

205
lightrag/lightrag.py
View File

@@ -1,28 +1,10 @@
import asyncio import asyncio
import os import os
from tqdm.asyncio import tqdm as tqdm_async
from dataclasses import asdict, dataclass, field from dataclasses import asdict, dataclass, field
from datetime import datetime from datetime import datetime
from functools import partial from functools import partial
from typing import Any, Callable, Coroutine, Optional, Type, Union, cast from typing import Any, Callable, Optional, Type, Union, cast
from .operate import (
chunking_by_token_size,
extract_entities,
extract_keywords_only,
kg_query,
kg_query_with_keywords,
mix_kg_vector_query,
naive_query,
)
from .utils import (
EmbeddingFunc,
compute_mdhash_id,
limit_async_func_call,
convert_response_to_json,
logger,
set_logger,
)
from .base import ( from .base import (
BaseGraphStorage, BaseGraphStorage,
BaseKVStorage, BaseKVStorage,
@@ -33,10 +15,25 @@ from .base import (
QueryParam, QueryParam,
StorageNameSpace, StorageNameSpace,
) )
from .namespace import NameSpace, make_namespace from .namespace import NameSpace, make_namespace
from .operate import (
chunking_by_token_size,
extract_entities,
extract_keywords_only,
kg_query,
kg_query_with_keywords,
mix_kg_vector_query,
naive_query,
)
from .prompt import GRAPH_FIELD_SEP from .prompt import GRAPH_FIELD_SEP
from .utils import (
EmbeddingFunc,
compute_mdhash_id,
convert_response_to_json,
limit_async_func_call,
logger,
set_logger,
)
STORAGES = { STORAGES = {
"NetworkXStorage": ".kg.networkx_impl", "NetworkXStorage": ".kg.networkx_impl",
@@ -67,7 +64,6 @@ STORAGES = {
def lazy_external_import(module_name: str, class_name: str): def lazy_external_import(module_name: str, class_name: str):
"""Lazily import a class from an external module based on the package of the caller.""" """Lazily import a class from an external module based on the package of the caller."""
# Get the caller's module and package # Get the caller's module and package
import inspect import inspect
@@ -113,7 +109,7 @@ def always_get_an_event_loop() -> asyncio.AbstractEventLoop:
@dataclass @dataclass
class LightRAG: class LightRAG:
working_dir: str = field( working_dir: str = field(
default_factory=lambda: f"./lightrag_cache_{datetime.now().strftime('%Y-%m-%d-%H:%M:%S')}" default_factory=lambda: f'./lightrag_cache_{datetime.now().strftime("%Y-%m-%d-%H:%M:%S")}'
) )
# Default not to use embedding cache # Default not to use embedding cache
embedding_cache_config: dict = field( embedding_cache_config: dict = field(
@@ -412,7 +408,7 @@ class LightRAG:
doc_key = compute_mdhash_id(full_text.strip(), prefix="doc-") doc_key = compute_mdhash_id(full_text.strip(), prefix="doc-")
new_docs = {doc_key: {"content": full_text.strip()}} new_docs = {doc_key: {"content": full_text.strip()}}
_add_doc_keys = await self.full_docs.filter_keys([doc_key]) _add_doc_keys = await self.full_docs.filter_keys(set(doc_key))
new_docs = {k: v for k, v in new_docs.items() if k in _add_doc_keys} new_docs = {k: v for k, v in new_docs.items() if k in _add_doc_keys}
if not len(new_docs): if not len(new_docs):
logger.warning("This document is already in the storage.") logger.warning("This document is already in the storage.")
@@ -421,7 +417,7 @@ class LightRAG:
update_storage = True update_storage = True
logger.info(f"[New Docs] inserting {len(new_docs)} docs") logger.info(f"[New Docs] inserting {len(new_docs)} docs")
inserting_chunks = {} inserting_chunks: dict[str, Any] = {}
for chunk_text in text_chunks: for chunk_text in text_chunks:
chunk_text_stripped = chunk_text.strip() chunk_text_stripped = chunk_text.strip()
chunk_key = compute_mdhash_id(chunk_text_stripped, prefix="chunk-") chunk_key = compute_mdhash_id(chunk_text_stripped, prefix="chunk-")
@@ -431,37 +427,22 @@ class LightRAG:
"full_doc_id": doc_key, "full_doc_id": doc_key,
} }
_add_chunk_keys = await self.text_chunks.filter_keys( doc_ids = set(inserting_chunks.keys())
list(inserting_chunks.keys()) add_chunk_keys = await self.text_chunks.filter_keys(doc_ids)
)
inserting_chunks = { inserting_chunks = {
k: v for k, v in inserting_chunks.items() if k in _add_chunk_keys k: v for k, v in inserting_chunks.items() if k in add_chunk_keys
} }
if not len(inserting_chunks): if not len(inserting_chunks):
logger.warning("All chunks are already in the storage.") logger.warning("All chunks are already in the storage.")
return return
logger.info(f"[New Chunks] inserting {len(inserting_chunks)} chunks") tasks = [
self.chunks_vdb.upsert(inserting_chunks),
await self.chunks_vdb.upsert(inserting_chunks) self._process_entity_relation_graph(inserting_chunks),
self.full_docs.upsert(new_docs),
logger.info("[Entity Extraction]...") self.text_chunks.upsert(inserting_chunks),
maybe_new_kg = await extract_entities( ]
inserting_chunks, await asyncio.gather(*tasks)
knowledge_graph_inst=self.chunk_entity_relation_graph,
entity_vdb=self.entities_vdb,
relationships_vdb=self.relationships_vdb,
global_config=asdict(self),
)
if maybe_new_kg is None:
logger.warning("No new entities and relationships found")
return
else:
self.chunk_entity_relation_graph = maybe_new_kg
await self.full_docs.upsert(new_docs)
await self.text_chunks.upsert(inserting_chunks)
finally: finally:
if update_storage: if update_storage:
@@ -496,15 +477,12 @@ class LightRAG:
} }
# 3. Filter out already processed documents # 3. Filter out already processed documents
add_doc_keys: set[str] = set()
# Get docs ids # Get docs ids
in_process_keys = list(new_docs.keys()) all_new_doc_ids = set(new_docs.keys())
# Get in progress docs ids # Exclude IDs of documents that are already in progress
excluded_ids = await self.doc_status.get_by_ids(in_process_keys) unique_new_doc_ids = await self.doc_status.filter_keys(all_new_doc_ids)
# Exclude already in process # Filter new_docs to only include documents with unique IDs
add_doc_keys = new_docs.keys() - excluded_ids new_docs = {doc_id: new_docs[doc_id] for doc_id in unique_new_doc_ids}
# Filter
new_docs = {k: v for k, v in new_docs.items() if k in add_doc_keys}
if not new_docs: if not new_docs:
logger.info("All documents have been processed or are duplicates") logger.info("All documents have been processed or are duplicates")
@@ -535,47 +513,32 @@ class LightRAG:
# Fetch failed documents # Fetch failed documents
failed_docs = await self.doc_status.get_failed_docs() failed_docs = await self.doc_status.get_failed_docs()
to_process_docs.update(failed_docs) to_process_docs.update(failed_docs)
pendings_docs = await self.doc_status.get_pending_docs()
pending_docs = await self.doc_status.get_pending_docs() to_process_docs.update(pendings_docs)
to_process_docs.update(pending_docs)
if not to_process_docs: if not to_process_docs:
logger.info("All documents have been processed or are duplicates") logger.info("All documents have been processed or are duplicates")
return return
to_process_docs_ids = list(to_process_docs.keys())
# Get allready processed documents (text chunks and full docs)
text_chunks_processed_doc_ids = await self.text_chunks.filter_keys(
to_process_docs_ids
)
full_docs_processed_doc_ids = await self.full_docs.filter_keys(
to_process_docs_ids
)
# 2. split docs into chunks, insert chunks, update doc status # 2. split docs into chunks, insert chunks, update doc status
batch_size = self.addon_params.get("insert_batch_size", 10) batch_size = self.addon_params.get("insert_batch_size", 10)
batch_docs_list = [ docs_batches = [
list(to_process_docs.items())[i : i + batch_size] list(to_process_docs.items())[i : i + batch_size]
for i in range(0, len(to_process_docs), batch_size) for i in range(0, len(to_process_docs), batch_size)
] ]
logger.info(f"Number of batches to process: {len(docs_batches)}.")
# 3. iterate over batches # 3. iterate over batches
tasks: dict[str, list[Coroutine[Any, Any, None]]] = {} for batch_idx, docs_batch in enumerate(docs_batches):
for batch_idx, ids_doc_processing_status in tqdm_async(
enumerate(batch_docs_list),
desc="Process Batches",
):
# 4. iterate over batch # 4. iterate over batch
for id_doc_processing_status in tqdm_async( for doc_id_processing_status in docs_batch:
ids_doc_processing_status, doc_id, status_doc = doc_id_processing_status
desc=f"Process Batch {batch_idx}",
):
id_doc, status_doc = id_doc_processing_status
# Update status in processing # Update status in processing
doc_status_id = compute_mdhash_id(status_doc.content, prefix="doc-")
await self.doc_status.upsert( await self.doc_status.upsert(
{ {
id_doc: { doc_status_id: {
"status": DocStatus.PROCESSING, "status": DocStatus.PROCESSING,
"updated_at": datetime.now().isoformat(), "updated_at": datetime.now().isoformat(),
"content_summary": status_doc.content_summary, "content_summary": status_doc.content_summary,
@@ -588,7 +551,7 @@ class LightRAG:
chunks: dict[str, Any] = { chunks: dict[str, Any] = {
compute_mdhash_id(dp["content"], prefix="chunk-"): { compute_mdhash_id(dp["content"], prefix="chunk-"): {
**dp, **dp,
"full_doc_id": id_doc_processing_status, "full_doc_id": doc_id,
} }
for dp in self.chunking_func( for dp in self.chunking_func(
status_doc.content, status_doc.content,
@@ -600,50 +563,39 @@ class LightRAG:
) )
} }
# Ensure chunk insertion and graph processing happen sequentially, not in parallel
await self.chunks_vdb.upsert(chunks)
await self._process_entity_relation_graph(chunks)
tasks[id_doc] = []
# Check if document already processed the doc
if id_doc not in full_docs_processed_doc_ids:
tasks[id_doc].append(
self.full_docs.upsert({id_doc: {"content": status_doc.content}})
)
# Check if chunks already processed the doc
if id_doc not in text_chunks_processed_doc_ids:
tasks[id_doc].append(self.text_chunks.upsert(chunks))
# Process document (text chunks and full docs) in parallel # Process document (text chunks and full docs) in parallel
for id_doc_processing_status, task in tasks.items(): tasks = [
try: self.chunks_vdb.upsert(chunks),
await asyncio.gather(*task) self._process_entity_relation_graph(chunks),
await self.doc_status.upsert( self.full_docs.upsert({doc_id: {"content": status_doc.content}}),
{ self.text_chunks.upsert(chunks),
id_doc_processing_status: { ]
"status": DocStatus.PROCESSED, try:
"chunks_count": len(chunks), await asyncio.gather(*tasks)
"updated_at": datetime.now().isoformat(), await self.doc_status.upsert(
} {
doc_status_id: {
"status": DocStatus.PROCESSED,
"chunks_count": len(chunks),
"updated_at": datetime.now().isoformat(),
} }
) }
await self._insert_done() )
await self._insert_done()
except Exception as e: except Exception as e:
logger.error( logger.error(f"Failed to process document {doc_id}: {str(e)}")
f"Failed to process document {id_doc_processing_status}: {str(e)}" await self.doc_status.upsert(
) {
await self.doc_status.upsert( doc_status_id: {
{ "status": DocStatus.FAILED,
id_doc_processing_status: { "error": str(e),
"status": DocStatus.FAILED, "updated_at": datetime.now().isoformat(),
"error": str(e),
"updated_at": datetime.now().isoformat(),
}
} }
) }
continue )
continue
logger.info(f"Completed batch {batch_idx + 1} of {len(docs_batches)}.")
async def _process_entity_relation_graph(self, chunk: dict[str, Any]) -> None: async def _process_entity_relation_graph(self, chunk: dict[str, Any]) -> None:
try: try:
@@ -656,8 +608,9 @@ class LightRAG:
global_config=asdict(self), global_config=asdict(self),
) )
if new_kg is None: if new_kg is None:
logger.info("No entities or relationships extracted!") logger.info("No new entities or relationships extracted.")
else: else:
logger.info("New entities or relationships extracted.")
self.chunk_entity_relation_graph = new_kg self.chunk_entity_relation_graph = new_kg
except Exception as e: except Exception as e:
@@ -895,7 +848,6 @@ class LightRAG:
1. Extract keywords from the 'query' using new function in operate.py. 1. Extract keywords from the 'query' using new function in operate.py.
2. Then run the standard aquery() flow with the final prompt (formatted_question). 2. Then run the standard aquery() flow with the final prompt (formatted_question).
""" """
loop = always_get_an_event_loop() loop = always_get_an_event_loop()
return loop.run_until_complete( return loop.run_until_complete(
self.aquery_with_separate_keyword_extraction(query, prompt, param) self.aquery_with_separate_keyword_extraction(query, prompt, param)
@@ -908,7 +860,6 @@ class LightRAG:
1. Calls extract_keywords_only to get HL/LL keywords from 'query'. 1. Calls extract_keywords_only to get HL/LL keywords from 'query'.
2. Then calls kg_query(...) or naive_query(...), etc. as the main query, while also injecting the newly extracted keywords if needed. 2. Then calls kg_query(...) or naive_query(...), etc. as the main query, while also injecting the newly extracted keywords if needed.
""" """
# --------------------- # ---------------------
# STEP 1: Keyword Extraction # STEP 1: Keyword Extraction
# --------------------- # ---------------------