gardel/lightrag
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge branch 'main' into graph-viewer-webui

Browse Source
This commit is contained in:
ArnoChen
2025-02-10 00:54:05 +08:00
parent 10a0fa1530 832a9bb6fb
commit a62f2b382b
13 changed files with 352 additions and 610 deletions
Download Patch File Download Diff File Expand all files Collapse all files

67
lightrag/base.py
View File

@@ -1,20 +1,18 @@
from enum import Enum
import os
from dataclasses import dataclass, field
from typing import (
Optional,
TypedDict,
Union,
Literal,
Generic,
TypeVar,
Optional,
Dict,
Any,
List,
)
from enum import Enum
import numpy as np
from .utils import EmbeddingFunc
TextChunkSchema = TypedDict(
@@ -45,7 +43,7 @@ class QueryParam:
hl_keywords: list[str] = field(default_factory=list)
ll_keywords: list[str] = field(default_factory=list)
# Conversation history support
conversation_history: list[dict] = field(
conversation_history: list[dict[str, str]] = field(
default_factory=list
) # Format: [{"role": "user/assistant", "content": "message"}]
history_turns: int = (
@@ -56,7 +54,7 @@ class QueryParam:
@dataclass
class StorageNameSpace:
namespace: str
global_config: dict
global_config: dict[str, Any]
async def index_done_callback(self):
"""commit the storage operations after indexing"""
@@ -72,10 +70,10 @@ class BaseVectorStorage(StorageNameSpace):
embedding_func: EmbeddingFunc
meta_fields: set = field(default_factory=set)
async def query(self, query: str, top_k: int) -> list[dict]:
async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
raise NotImplementedError
async def upsert(self, data: dict[str, dict]):
async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
"""Use 'content' field from value for embedding, use key as id.
If embedding_func is None, use 'embedding' field from value
"""
@@ -83,28 +81,23 @@ class BaseVectorStorage(StorageNameSpace):
@dataclass
class BaseKVStorage(Generic[T], StorageNameSpace):
class BaseKVStorage(StorageNameSpace):
embedding_func: EmbeddingFunc
async def all_keys(self) -> list[str]:
async def get_by_id(self, id: str) -> dict[str, Any]:
raise NotImplementedError
async def get_by_id(self, id: str) -> Union[T, None]:
raise NotImplementedError
async def get_by_ids(
self, ids: list[str], fields: Union[set[str], None] = None
) -> list[Union[T, None]]:
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
raise NotImplementedError
async def filter_keys(self, data: list[str]) -> set[str]:
"""return un-exist keys"""
raise NotImplementedError
async def upsert(self, data: dict[str, T]):
async def upsert(self, data: dict[str, Any]) -> None:
raise NotImplementedError
async def drop(self):
async def drop(self) -> None:
raise NotImplementedError
@@ -151,12 +144,12 @@ class BaseGraphStorage(StorageNameSpace):
async def embed_nodes(self, algorithm: str) -> tuple[np.ndarray, list[str]]:
raise NotImplementedError("Node embedding is not used in lightrag.")
async def get_all_labels(self) -> List[str]:
async def get_all_labels(self) -> list[str]:
raise NotImplementedError
async def get_knowledge_graph(
self, node_label: str, max_depth: int = 5
) -> Dict[str, List[Dict]]:
) -> dict[str, list[dict]]:
raise NotImplementedError
@@ -173,27 +166,37 @@ class DocStatus(str, Enum):
class DocProcessingStatus:
"""Document processing status data structure"""
content_summary: str # First 100 chars of document content
content_length: int # Total length of document
status: DocStatus # Current processing status
created_at: str # ISO format timestamp
updated_at: str # ISO format timestamp
chunks_count: Optional[int] = None # Number of chunks after splitting
error: Optional[str] = None # Error message if failed
metadata: Dict[str, Any] = field(default_factory=dict) # Additional metadata
content: str
"""Original content of the document"""
content_summary: str
"""First 100 chars of document content, used for preview"""
content_length: int
"""Total length of document"""
status: DocStatus
"""Current processing status"""
created_at: str
"""ISO format timestamp when document was created"""
updated_at: str
"""ISO format timestamp when document was last updated"""
chunks_count: Optional[int] = None
"""Number of chunks after splitting, used for processing"""
error: Optional[str] = None
"""Error message if failed"""
metadata: dict[str, Any] = field(default_factory=dict)
"""Additional metadata"""
class DocStatusStorage(BaseKVStorage):
"""Base class for document status storage"""
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"""
raise NotImplementedError
async def get_failed_docs(self) -> Dict[str, DocProcessingStatus]:
async def get_failed_docs(self) -> dict[str, DocProcessingStatus]:
"""Get all failed documents"""
raise NotImplementedError
async def get_pending_docs(self) -> Dict[str, DocProcessingStatus]:
async def get_pending_docs(self) -> dict[str, DocProcessingStatus]:
"""Get all pending documents"""
raise NotImplementedError

101
lightrag/kg/json_kv_impl.py
View File

@@ -1,63 +1,13 @@
"""
JsonDocStatus Storage Module
=======================
This module provides a storage interface for graphs using NetworkX, a popular Python library for creating, manipulating, and studying the structure, dynamics, and functions of complex networks.
The `NetworkXStorage` class extends the `BaseGraphStorage` class from the LightRAG library, providing methods to load, save, manipulate, and query graphs using NetworkX.
Author: lightrag team
Created: 2024-01-25
License: MIT
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Version: 1.0.0
Dependencies:
- NetworkX
- NumPy
- LightRAG
- graspologic
Features:
- Load and save graphs in various formats (e.g., GEXF, GraphML, JSON)
- Query graph nodes and edges
- Calculate node and edge degrees
- Embed nodes using various algorithms (e.g., Node2Vec)
- Remove nodes and edges from the graph
Usage:
from lightrag.storage.networkx_storage import NetworkXStorage
"""
import asyncio
import os
from dataclasses import dataclass
from typing import Any
from lightrag.utils import (
logger,
load_json,
write_json,
)
from lightrag.base import (
BaseKVStorage,
)
@@ -68,25 +18,20 @@ class JsonKVStorage(BaseKVStorage):
def __post_init__(self):
working_dir = self.global_config["working_dir"]
self._file_name = os.path.join(working_dir, f"kv_store_{self.namespace}.json")
self._data = load_json(self._file_name) or {}
self._data: dict[str, Any] = load_json(self._file_name) or {}
self._lock = asyncio.Lock()
logger.info(f"Load KV {self.namespace} with {len(self._data)} data")
async def all_keys(self) -> list[str]:
return list(self._data.keys())
async def index_done_callback(self):
write_json(self._data, self._file_name)
async def get_by_id(self, id):
return self._data.get(id, None)
async def get_by_id(self, id: str) -> dict[str, Any]:
return self._data.get(id, {})
async def get_by_ids(self, ids, fields=None):
if fields is None:
return [self._data.get(id, None) for id in ids]
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
return [
(
{k: v for k, v in self._data[id].items() if k in fields}
{k: v for k, v in self._data[id].items()}
if self._data.get(id, None)
else None
)
@@ -96,39 +41,9 @@ class JsonKVStorage(BaseKVStorage):
async def filter_keys(self, data: list[str]) -> set[str]:
return set([s for s in data if s not in self._data])
async def upsert(self, data: dict[str, dict]):
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}
self._data.update(left_data)
return left_data
async def drop(self):
async def drop(self) -> None:
self._data = {}
async def filter(self, filter_func):
"""Filter key-value pairs based on a filter function
Args:
filter_func: The filter function, which takes a value as an argument and returns a boolean value
Returns:
Dict: Key-value pairs that meet the condition
"""
result = {}
async with self._lock:
for key, value in self._data.items():
if filter_func(value):
result[key] = value
return result
async def delete(self, ids: list[str]):
"""Delete data with specified IDs
Args:
ids: List of IDs to delete
"""
async with self._lock:
for id in ids:
if id in self._data:
del self._data[id]
await self.index_done_callback()
logger.info(f"Successfully deleted {len(ids)} items from {self.namespace}")

17
lightrag/kg/jsondocstatus_impl.py
View File

@@ -50,7 +50,7 @@ Usage:
import os
from dataclasses import dataclass
from typing import Union, Dict
from typing import Any, Union
from lightrag.utils import (
logger,
@@ -72,7 +72,7 @@ class JsonDocStatusStorage(DocStatusStorage):
def __post_init__(self):
working_dir = self.global_config["working_dir"]
self._file_name = os.path.join(working_dir, f"kv_store_{self.namespace}.json")
self._data = 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")
async def filter_keys(self, data: list[str]) -> set[str]:
@@ -85,18 +85,18 @@ class JsonDocStatusStorage(DocStatusStorage):
]
)
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"""
counts = {status: 0 for status in DocStatus}
for doc in self._data.values():
counts[doc["status"]] += 1
return counts
async def get_failed_docs(self) -> Dict[str, DocProcessingStatus]:
async def get_failed_docs(self) -> dict[str, DocProcessingStatus]:
"""Get all failed documents"""
return {k: 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"""
return {k: v for k, v in self._data.items() if v["status"] == DocStatus.PENDING}
@@ -104,7 +104,7 @@ class JsonDocStatusStorage(DocStatusStorage):
"""Save data to file after indexing"""
write_json(self._data, self._file_name)
async def upsert(self, data: dict[str, dict]):
async def upsert(self, data: dict[str, Any]) -> None:
"""Update or insert document status
Args:
@@ -112,10 +112,9 @@ class JsonDocStatusStorage(DocStatusStorage):
"""
self._data.update(data)
await self.index_done_callback()
return data
async def get_by_id(self, id: str):
return self._data.get(id)
async def get_by_id(self, id: str) -> dict[str, Any]:
return self._data.get(id, {})
async def get(self, doc_id: str) -> Union[DocProcessingStatus, None]:
"""Get document status by ID"""

27
lightrag/kg/mongo_impl.py
View File

@@ -12,7 +12,7 @@ if not pm.is_installed("motor"):
from pymongo import MongoClient
from motor.motor_asyncio import AsyncIOMotorClient
from typing import Union, List, Tuple
from typing import Any, Union, List, Tuple
from ..utils import logger
from ..base import BaseKVStorage, BaseGraphStorage
@@ -29,21 +29,11 @@ class MongoKVStorage(BaseKVStorage):
self._data = database.get_collection(self.namespace)
logger.info(f"Use MongoDB as KV {self.namespace}")
async def all_keys(self) -> list[str]:
return [x["_id"] for x in self._data.find({}, {"_id": 1})]
async def get_by_id(self, id):
async def get_by_id(self, id: str) -> dict[str, Any]:
return self._data.find_one({"_id": id})
async def get_by_ids(self, ids, fields=None):
if fields is None:
return list(self._data.find({"_id": {"$in": ids}}))
return list(
self._data.find(
{"_id": {"$in": ids}},
{field: 1 for field in fields},
)
)
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
return list(self._data.find({"_id": {"$in": ids}}))
async def filter_keys(self, data: list[str]) -> set[str]:
existing_ids = [
@@ -51,7 +41,7 @@ class MongoKVStorage(BaseKVStorage):
]
return set([s for s in data if s not in existing_ids])
async def upsert(self, data: dict[str, dict]):
async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE):
for mode, items in data.items():
for k, v in tqdm_async(items.items(), desc="Upserting"):
@@ -66,7 +56,6 @@ class MongoKVStorage(BaseKVStorage):
for k, v in tqdm_async(data.items(), desc="Upserting"):
self._data.update_one({"_id": k}, {"$set": v}, upsert=True)
data[k]["_id"] = k
return data
async def get_by_mode_and_id(self, mode: str, id: str) -> Union[dict, None]:
if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE):
@@ -81,9 +70,9 @@ class MongoKVStorage(BaseKVStorage):
else:
return None
async def drop(self):
""" """
pass
async def drop(self) -> None:
"""Drop the collection"""
await self._data.drop()
@dataclass

46
lightrag/kg/oracle_impl.py
View File

@@ -4,7 +4,7 @@ import asyncio
# import html
# import os
from dataclasses import dataclass
from typing import Union
from typing import Any, Union
import numpy as np
import array
import pipmaster as pm
@@ -181,7 +181,7 @@ class OracleKVStorage(BaseKVStorage):
################ QUERY METHODS ################
async def get_by_id(self, id: str) -> Union[dict, None]:
async def get_by_id(self, id: str) -> dict[str, Any]:
"""get doc_full data based on id."""
SQL = SQL_TEMPLATES["get_by_id_" + self.namespace]
params = {"workspace": self.db.workspace, "id": id}
@@ -191,12 +191,9 @@ class OracleKVStorage(BaseKVStorage):
res = {}
for row in array_res:
res[row["id"]] = row
else:
res = await self.db.query(SQL, params)
if res:
return res
else:
return None
return await self.db.query(SQL, params)
async def get_by_mode_and_id(self, mode: str, id: str) -> Union[dict, None]:
"""Specifically for llm_response_cache."""
@@ -211,7 +208,7 @@ class OracleKVStorage(BaseKVStorage):
else:
return None
async def get_by_ids(self, ids: list[str], fields=None) -> Union[list[dict], None]:
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
"""get doc_chunks data based on id"""
SQL = SQL_TEMPLATES["get_by_ids_" + self.namespace].format(
ids=",".join([f"'{id}'" for id in ids])
@@ -230,29 +227,7 @@ class OracleKVStorage(BaseKVStorage):
for row in res:
dict_res[row["mode"]][row["id"]] = row
res = [{k: v} for k, v in dict_res.items()]
if res:
data = res # [{"data":i} for i in res]
# print(data)
return data
else:
return None
async def get_by_status_and_ids(
self, status: str, ids: list[str]
) -> Union[list[dict], None]:
"""Specifically for llm_response_cache."""
if ids is not None:
SQL = SQL_TEMPLATES["get_by_status_ids_" + self.namespace].format(
ids=",".join([f"'{id}'" for id in ids])
)
else:
SQL = SQL_TEMPLATES["get_by_status_" + self.namespace]
params = {"workspace": self.db.workspace, "status": status}
res = await self.db.query(SQL, params, multirows=True)
if res:
return res
else:
return None
return res
async def filter_keys(self, keys: list[str]) -> set[str]:
"""Return keys that don't exist in storage"""
@@ -270,7 +245,7 @@ class OracleKVStorage(BaseKVStorage):
return set(keys)
################ INSERT METHODS ################
async def upsert(self, data: dict[str, dict]):
async def upsert(self, data: dict[str, Any]) -> None:
if is_namespace(self.namespace, NameSpace.KV_STORE_TEXT_CHUNKS):
list_data = [
{
@@ -328,14 +303,6 @@ class OracleKVStorage(BaseKVStorage):
}
await self.db.execute(upsert_sql, _data)
return None
async def change_status(self, id: str, status: str):
SQL = SQL_TEMPLATES["change_status"].format(
table_name=namespace_to_table_name(self.namespace)
)
params = {"workspace": self.db.workspace, "id": id, "status": status}
await self.db.execute(SQL, params)
async def index_done_callback(self):
if is_namespace(
@@ -745,7 +712,6 @@ SQL_TEMPLATES = {
"get_by_status_full_docs": "select id,status from LIGHTRAG_DOC_FULL t where workspace=:workspace AND status=:status",
"get_by_status_text_chunks": "select id,status from LIGHTRAG_DOC_CHUNKS where workspace=:workspace and status=:status",
"filter_keys": "select id from {table_name} where workspace=:workspace and id in ({ids})",
"change_status": "update {table_name} set status=:status,updatetime=SYSDATE where workspace=:workspace and id=:id",
"merge_doc_full": """MERGE INTO LIGHTRAG_DOC_FULL a
USING DUAL
ON (a.id = :id and a.workspace = :workspace)

44
lightrag/kg/postgres_impl.py
View File

@@ -30,7 +30,6 @@ from ..base import (
DocStatus,
DocProcessingStatus,
BaseGraphStorage,
T,
)
from ..namespace import NameSpace, is_namespace
@@ -184,7 +183,7 @@ class PGKVStorage(BaseKVStorage):
################ QUERY METHODS ################
async def get_by_id(self, id: str) -> Union[dict, None]:
async def get_by_id(self, id: str) -> dict[str, Any]:
"""Get doc_full data by id."""
sql = SQL_TEMPLATES["get_by_id_" + self.namespace]
params = {"workspace": self.db.workspace, "id": id}
@@ -193,12 +192,9 @@ class PGKVStorage(BaseKVStorage):
res = {}
for row in array_res:
res[row["id"]] = row
else:
res = await self.db.query(sql, params)
if res:
return res
else:
return None
return await self.db.query(sql, params)
async def get_by_mode_and_id(self, mode: str, id: str) -> Union[dict, None]:
"""Specifically for llm_response_cache."""
@@ -214,7 +210,7 @@ class PGKVStorage(BaseKVStorage):
return None
# Query by id
async def get_by_ids(self, ids: List[str], fields=None) -> Union[List[dict], None]:
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
"""Get doc_chunks data by id"""
sql = SQL_TEMPLATES["get_by_ids_" + self.namespace].format(
ids=",".join([f"'{id}'" for id in ids])
@@ -231,23 +227,15 @@ class PGKVStorage(BaseKVStorage):
dict_res[mode] = {}
for row in array_res:
dict_res[row["mode"]][row["id"]] = row
res = [{k: v} for k, v in dict_res.items()]
return [{k: v} for k, v in dict_res.items()]
else:
res = await self.db.query(sql, params, multirows=True)
if res:
return res
else:
return None
return await self.db.query(sql, params, multirows=True)
async def all_keys(self) -> list[dict]:
if is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE):
sql = "select workspace,mode,id from lightrag_llm_cache"
res = await self.db.query(sql, multirows=True)
return res
else:
logger.error(
f"all_keys is only implemented for llm_response_cache, not for {self.namespace}"
)
async def get_by_status(self, status: str) -> Union[list[dict[str, Any]], None]:
"""Specifically for llm_response_cache."""
SQL = SQL_TEMPLATES["get_by_status_" + self.namespace]
params = {"workspace": self.db.workspace, "status": status}
return await self.db.query(SQL, params, multirows=True)
async def filter_keys(self, keys: List[str]) -> Set[str]:
"""Filter out duplicated content"""
@@ -270,7 +258,7 @@ class PGKVStorage(BaseKVStorage):
print(params)
################ INSERT METHODS ################
async def upsert(self, data: Dict[str, dict]):
async def upsert(self, data: dict[str, Any]) -> None:
if is_namespace(self.namespace, NameSpace.KV_STORE_TEXT_CHUNKS):
pass
elif is_namespace(self.namespace, NameSpace.KV_STORE_FULL_DOCS):
@@ -447,14 +435,15 @@ class PGDocStatusStorage(DocStatusStorage):
existed = set([element["id"] for element in result])
return set(data) - existed
async def get_by_id(self, id: str) -> Union[T, None]:
async def get_by_id(self, id: str) -> dict[str, Any]:
sql = "select * from LIGHTRAG_DOC_STATUS where workspace=$1 and id=$2"
params = {"workspace": self.db.workspace, "id": id}
result = await self.db.query(sql, params, True)
if result is None or result == []:
return None
return {}
else:
return DocProcessingStatus(
content=result[0]["content"],
content_length=result[0]["content_length"],
content_summary=result[0]["content_summary"],
status=result[0]["status"],
@@ -483,10 +472,9 @@ class PGDocStatusStorage(DocStatusStorage):
sql = "select * from LIGHTRAG_DOC_STATUS where workspace=$1 and status=$1"
params = {"workspace": self.db.workspace, "status": status}
result = await self.db.query(sql, params, True)
# Result is like [{'id': 'id1', 'status': 'PENDING', 'updated_at': '2023-07-01 00:00:00'}, {'id': 'id2', 'status': 'PENDING', 'updated_at': '2023-07-01 00:00:00'}, ...]
# Converting to be a dict
return {
element["id"]: DocProcessingStatus(
content=result[0]["content"],
content_summary=element["content_summary"],
content_length=element["content_length"],
status=element["status"],
@@ -518,6 +506,7 @@ class PGDocStatusStorage(DocStatusStorage):
sql = """insert into LIGHTRAG_DOC_STATUS(workspace,id,content_summary,content_length,chunks_count,status)
values($1,$2,$3,$4,$5,$6)
on conflict(id,workspace) do update set
content = EXCLUDED.content,
content_summary = EXCLUDED.content_summary,
content_length = EXCLUDED.content_length,
chunks_count = EXCLUDED.chunks_count,
@@ -530,6 +519,7 @@ class PGDocStatusStorage(DocStatusStorage):
{
"workspace": self.db.workspace,
"id": k,
"content": v["content"],
"content_summary": v["content_summary"],
"content_length": v["content_length"],
"chunks_count": v["chunks_count"] if "chunks_count" in v else -1,

22
lightrag/kg/redis_impl.py
View File

@@ -1,4 +1,5 @@
import os
from typing import Any
from tqdm.asyncio import tqdm as tqdm_async
from dataclasses import dataclass
import pipmaster as pm
@@ -20,29 +21,15 @@ class RedisKVStorage(BaseKVStorage):
self._redis = Redis.from_url(redis_url, decode_responses=True)
logger.info(f"Use Redis as KV {self.namespace}")
async def all_keys(self) -> list[str]:
keys = await self._redis.keys(f"{self.namespace}:*")
return [key.split(":", 1)[-1] for key in keys]
async def get_by_id(self, id):
data = await self._redis.get(f"{self.namespace}:{id}")
return json.loads(data) if data else None
async def get_by_ids(self, ids, fields=None):
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
pipe = self._redis.pipeline()
for id in ids:
pipe.get(f"{self.namespace}:{id}")
results = await pipe.execute()
if fields:
# Filter fields if specified
return [
{field: value.get(field) for field in fields if field in value}
if (value := json.loads(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]:
@@ -54,7 +41,7 @@ class RedisKVStorage(BaseKVStorage):
existing_ids = {data[i] for i, exists in enumerate(results) if exists}
return set(data) - existing_ids
async def upsert(self, data: dict[str, dict]):
async def upsert(self, data: dict[str, Any]) -> None:
pipe = self._redis.pipeline()
for k, v in tqdm_async(data.items(), desc="Upserting"):
pipe.set(f"{self.namespace}:{k}", json.dumps(v))
@@ -62,9 +49,8 @@ class RedisKVStorage(BaseKVStorage):
for k in data:
data[k]["_id"] = k
return data
async def drop(self):
async def drop(self) -> None:
keys = await self._redis.keys(f"{self.namespace}:*")
if keys:
await self._redis.delete(*keys)

34
lightrag/kg/tidb_impl.py
View File

@@ -1,7 +1,7 @@
import asyncio
import os
from dataclasses import dataclass
from typing import Union
from typing import Any, Union
import numpy as np
import pipmaster as pm
@@ -108,33 +108,20 @@ class TiDBKVStorage(BaseKVStorage):
################ QUERY METHODS ################
async def get_by_id(self, id: str) -> Union[dict, None]:
"""根据 id 获取 doc_full 数据."""
async def get_by_id(self, id: str) -> dict[str, Any]:
"""Fetch doc_full data by id."""
SQL = SQL_TEMPLATES["get_by_id_" + self.namespace]
params = {"id": id}
# print("get_by_id:"+SQL)
res = await self.db.query(SQL, params)
if res:
data = res # {"data":res}
# print (data)
return data
else:
return None
return await self.db.query(SQL, params)
# Query by id
async def get_by_ids(self, ids: list[str], fields=None) -> Union[list[dict], None]:
"""根据 id 获取 doc_chunks 数据"""
async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
"""Fetch doc_chunks data by id"""
SQL = SQL_TEMPLATES["get_by_ids_" + self.namespace].format(
ids=",".join([f"'{id}'" for id in ids])
)
# print("get_by_ids:"+SQL)
res = await self.db.query(SQL, multirows=True)
if res:
data = res # [{"data":i} for i in res]
# print(data)
return data
else:
return None
return await self.db.query(SQL, multirows=True)
async def filter_keys(self, keys: list[str]) -> set[str]:
"""过滤掉重复内容"""
@@ -158,7 +145,7 @@ class TiDBKVStorage(BaseKVStorage):
return data
################ INSERT full_doc AND chunks ################
async def upsert(self, data: dict[str, dict]):
async def upsert(self, data: dict[str, Any]) -> None:
left_data = {k: v for k, v in data.items() if k not in self._data}
self._data.update(left_data)
if is_namespace(self.namespace, NameSpace.KV_STORE_TEXT_CHUNKS):
@@ -335,6 +322,11 @@ class TiDBVectorDBStorage(BaseVectorStorage):
merge_sql = SQL_TEMPLATES["insert_relationship"]
await self.db.execute(merge_sql, data)
async def get_by_status(self, status: str) -> Union[list[dict[str, Any]], None]:
SQL = SQL_TEMPLATES["get_by_status_" + self.namespace]
params = {"workspace": self.db.workspace, "status": status}
return await self.db.query(SQL, params, multirows=True)
@dataclass
class TiDBGraphStorage(BaseGraphStorage):

547
lightrag/lightrag.py
View File

@@ -4,17 +4,15 @@ from tqdm.asyncio import tqdm as tqdm_async
from dataclasses import asdict, dataclass, field
from datetime import datetime
from functools import partial
from typing import Type, cast, Dict
from typing import Any, Callable, Coroutine, Optional, Type, Union, cast
from .operate import (
chunking_by_token_size,
extract_entities,
# local_query,global_query,hybrid_query,
kg_query,
naive_query,
mix_kg_vector_query,
extract_keywords_only,
kg_query,
kg_query_with_keywords,
mix_kg_vector_query,
naive_query,
)
from .utils import (
@@ -24,15 +22,16 @@ from .utils import (
convert_response_to_json,
logger,
set_logger,
statistic_data,
)
from .base import (
BaseGraphStorage,
BaseKVStorage,
BaseVectorStorage,
StorageNameSpace,
QueryParam,
DocProcessingStatus,
DocStatus,
DocStatusStorage,
QueryParam,
StorageNameSpace,
)
from .namespace import NameSpace, make_namespace
@@ -176,15 +175,26 @@ class LightRAG:
enable_llm_cache_for_entity_extract: bool = True
# extension
addon_params: dict = field(default_factory=dict)
convert_response_to_json_func: callable = convert_response_to_json
addon_params: dict[str, Any] = field(default_factory=dict)
convert_response_to_json_func: Callable[[str], dict[str, Any]] = (
convert_response_to_json
)
# Add new field for document status storage type
doc_status_storage: str = field(default="JsonDocStatusStorage")
# Custom Chunking Function
chunking_func: callable = chunking_by_token_size
chunking_func_kwargs: dict = field(default_factory=dict)
chunking_func: Callable[
[
str,
Optional[str],
bool,
int,
int,
str,
],
list[dict[str, Any]],
] = chunking_by_token_size
def __post_init__(self):
os.makedirs(self.log_dir, exist_ok=True)
@@ -245,19 +255,19 @@ class LightRAG:
####
# add embedding func by walter
####
self.full_docs = self.key_string_value_json_storage_cls(
self.full_docs: BaseKVStorage = self.key_string_value_json_storage_cls(
namespace=make_namespace(
self.namespace_prefix, NameSpace.KV_STORE_FULL_DOCS
),
embedding_func=self.embedding_func,
)
self.text_chunks = self.key_string_value_json_storage_cls(
self.text_chunks: BaseKVStorage = self.key_string_value_json_storage_cls(
namespace=make_namespace(
self.namespace_prefix, NameSpace.KV_STORE_TEXT_CHUNKS
),
embedding_func=self.embedding_func,
)
self.chunk_entity_relation_graph = self.graph_storage_cls(
self.chunk_entity_relation_graph: BaseGraphStorage = self.graph_storage_cls(
namespace=make_namespace(
self.namespace_prefix, NameSpace.GRAPH_STORE_CHUNK_ENTITY_RELATION
),
@@ -281,7 +291,7 @@ class LightRAG:
embedding_func=self.embedding_func,
meta_fields={"src_id", "tgt_id"},
)
self.chunks_vdb = self.vector_db_storage_cls(
self.chunks_vdb: BaseVectorStorage = self.vector_db_storage_cls(
namespace=make_namespace(
self.namespace_prefix, NameSpace.VECTOR_STORE_CHUNKS
),
@@ -310,7 +320,7 @@ class LightRAG:
# Initialize document status storage
self.doc_status_storage_cls = self._get_storage_class(self.doc_status_storage)
self.doc_status = self.doc_status_storage_cls(
self.doc_status: DocStatusStorage = self.doc_status_storage_cls(
namespace=make_namespace(self.namespace_prefix, NameSpace.DOC_STATUS),
global_config=global_config,
embedding_func=None,
@@ -351,17 +361,12 @@ class LightRAG:
storage.db = db_client
def insert(
self, string_or_strings, split_by_character=None, split_by_character_only=False
self,
string_or_strings: Union[str, list[str]],
split_by_character: str | None = None,
split_by_character_only: bool = False,
):
loop = always_get_an_event_loop()
return loop.run_until_complete(
self.ainsert(string_or_strings, split_by_character, split_by_character_only)
)
async def ainsert(
self, string_or_strings, split_by_character=None, split_by_character_only=False
):
"""Insert documents with checkpoint support
"""Sync Insert documents with checkpoint support
Args:
string_or_strings: Single document string or list of document strings
@@ -370,154 +375,30 @@ class LightRAG:
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.
"""
if isinstance(string_or_strings, str):
string_or_strings = [string_or_strings]
loop = always_get_an_event_loop()
return loop.run_until_complete(
self.ainsert(string_or_strings, split_by_character, split_by_character_only)
)
# 1. Remove duplicate contents from the list
unique_contents = list(set(doc.strip() for doc in string_or_strings))
async def ainsert(
self,
string_or_strings: Union[str, list[str]],
split_by_character: str | None = None,
split_by_character_only: bool = False,
):
"""Async Insert documents with checkpoint support
# 2. Generate document IDs and initial status
new_docs = {
compute_mdhash_id(content, prefix="doc-"): {
"content": content,
"content_summary": self._get_content_summary(content),
"content_length": len(content),
"status": DocStatus.PENDING,
"created_at": datetime.now().isoformat(),
"updated_at": datetime.now().isoformat(),
}
for content in unique_contents
}
# 3. Filter out already processed documents
# _add_doc_keys = await self.doc_status.filter_keys(list(new_docs.keys()))
_add_doc_keys = set()
for doc_id in new_docs.keys():
current_doc = await self.doc_status.get_by_id(doc_id)
if current_doc is None:
_add_doc_keys.add(doc_id)
continue # skip to the next doc_id
status = None
if isinstance(current_doc, dict):
status = current_doc["status"]
else:
status = current_doc.status
if status == DocStatus.FAILED:
_add_doc_keys.add(doc_id)
new_docs = {k: v for k, v in new_docs.items() if k in _add_doc_keys}
if not new_docs:
logger.info("All documents have been processed or are duplicates")
return
logger.info(f"Processing {len(new_docs)} new unique documents")
# Process documents in batches
batch_size = self.addon_params.get("insert_batch_size", 10)
for i in range(0, len(new_docs), batch_size):
batch_docs = dict(list(new_docs.items())[i : i + batch_size])
for doc_id, doc in tqdm_async(
batch_docs.items(), desc=f"Processing batch {i // batch_size + 1}"
):
try:
# Update status to processing
doc_status = {
"content_summary": doc["content_summary"],
"content_length": doc["content_length"],
"status": DocStatus.PROCESSING,
"created_at": doc["created_at"],
"updated_at": datetime.now().isoformat(),
}
await self.doc_status.upsert({doc_id: doc_status})
# Generate chunks from document
chunks = {
compute_mdhash_id(dp["content"], prefix="chunk-"): {
**dp,
"full_doc_id": doc_id,
}
for dp in self.chunking_func(
doc["content"],
split_by_character=split_by_character,
split_by_character_only=split_by_character_only,
overlap_token_size=self.chunk_overlap_token_size,
max_token_size=self.chunk_token_size,
tiktoken_model=self.tiktoken_model_name,
**self.chunking_func_kwargs,
)
}
# Update status with chunks information
doc_status.update(
{
"chunks_count": len(chunks),
"updated_at": datetime.now().isoformat(),
}
)
await self.doc_status.upsert({doc_id: doc_status})
try:
# Store chunks in vector database
await self.chunks_vdb.upsert(chunks)
# Extract and store entities and relationships
maybe_new_kg = await extract_entities(
chunks,
knowledge_graph_inst=self.chunk_entity_relation_graph,
entity_vdb=self.entities_vdb,
relationships_vdb=self.relationships_vdb,
llm_response_cache=self.llm_response_cache,
global_config=asdict(self),
)
if maybe_new_kg is None:
raise Exception(
"Failed to extract entities and relationships"
)
self.chunk_entity_relation_graph = maybe_new_kg
# Store original document and chunks
await self.full_docs.upsert(
{doc_id: {"content": doc["content"]}}
)
await self.text_chunks.upsert(chunks)
# Update status to processed
doc_status.update(
{
"status": DocStatus.PROCESSED,
"updated_at": datetime.now().isoformat(),
}
)
await self.doc_status.upsert({doc_id: doc_status})
except Exception as e:
# Mark as failed if any step fails
doc_status.update(
{
"status": DocStatus.FAILED,
"error": str(e),
"updated_at": datetime.now().isoformat(),
}
)
await self.doc_status.upsert({doc_id: doc_status})
raise e
except Exception as e:
import traceback
error_msg = f"Failed to process document {doc_id}: {str(e)}\n{traceback.format_exc()}"
logger.error(error_msg)
continue
else:
# Only update index when processing succeeds
await self._insert_done()
Args:
string_or_strings: 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_size, split the sub chunk 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.
"""
await self.apipeline_enqueue_documents(string_or_strings)
await self.apipeline_process_enqueue_documents(
split_by_character, split_by_character_only
)
def insert_custom_chunks(self, full_text: str, text_chunks: list[str]):
loop = always_get_an_event_loop()
@@ -586,10 +467,14 @@ class LightRAG:
if update_storage:
await self._insert_done()
async def apipeline_process_documents(self, string_or_strings):
"""Input list remove duplicates, generate document IDs and initial pendding status, filter out already stored documents, store docs
Args:
string_or_strings: Single document string or list of document strings
async def apipeline_enqueue_documents(self, string_or_strings: str | list[str]):
"""
Pipeline for Processing Documents
1. Remove duplicate contents from the list
2. Generate document IDs and initial status
3. Filter out already processed documents
4. Enqueue document in status
"""
if isinstance(string_or_strings, str):
string_or_strings = [string_or_strings]
@@ -597,183 +482,187 @@ class LightRAG:
# 1. Remove duplicate contents from the list
unique_contents = list(set(doc.strip() for doc in string_or_strings))
logger.info(
f"Received {len(string_or_strings)} docs, contains {len(unique_contents)} new unique documents"
)
# 2. Generate document IDs and initial status
new_docs = {
new_docs: dict[str, Any] = {
compute_mdhash_id(content, prefix="doc-"): {
"content": content,
"content_summary": self._get_content_summary(content),
"content_length": len(content),
"status": DocStatus.PENDING,
"created_at": datetime.now().isoformat(),
"updated_at": None,
"updated_at": datetime.now().isoformat(),
}
for content in unique_contents
}
# 3. Filter out already processed documents
_not_stored_doc_keys = await self.full_docs.filter_keys(list(new_docs.keys()))
if len(_not_stored_doc_keys) < len(new_docs):
logger.info(
f"Skipping {len(new_docs) - len(_not_stored_doc_keys)} already existing documents"
)
new_docs = {k: v for k, v in new_docs.items() if k in _not_stored_doc_keys}
add_doc_keys: set[str] = set()
# Get docs ids
in_process_keys = list(new_docs.keys())
# Get in progress docs ids
excluded_ids = await self.doc_status.get_by_ids(in_process_keys)
# Exclude already in process
add_doc_keys = new_docs.keys() - excluded_ids
# Filter
new_docs = {k: v for k, v in new_docs.items() if k in add_doc_keys}
if not new_docs:
logger.info("All documents have been processed or are duplicates")
return None
return
# 4. Store original document
for doc_id, doc in new_docs.items():
await self.full_docs.upsert({doc_id: {"content": doc["content"]}})
await self.full_docs.change_status(doc_id, DocStatus.PENDING)
# 4. Store status document
await self.doc_status.upsert(new_docs)
logger.info(f"Stored {len(new_docs)} new unique documents")
async def apipeline_process_chunks(self):
"""Get pendding documents, split into chunks,insert chunks"""
# 1. get all pending and failed documents
_todo_doc_keys = []
_failed_doc = await self.full_docs.get_by_status_and_ids(
status=DocStatus.FAILED, ids=None
)
_pendding_doc = await self.full_docs.get_by_status_and_ids(
status=DocStatus.PENDING, ids=None
)
if _failed_doc:
_todo_doc_keys.extend([doc["id"] for doc in _failed_doc])
if _pendding_doc:
_todo_doc_keys.extend([doc["id"] for doc in _pendding_doc])
if not _todo_doc_keys:
logger.info("All documents have been processed or are duplicates")
return None
else:
logger.info(f"Filtered out {len(_todo_doc_keys)} not processed documents")
async def apipeline_process_enqueue_documents(
self,
split_by_character: str | None = None,
split_by_character_only: bool = False,
) -> None:
"""
Process pending documents by splitting them into chunks, processing
each chunk for entity and relation extraction, and updating the
document status.
new_docs = {
doc["id"]: doc for doc in await self.full_docs.get_by_ids(_todo_doc_keys)
}
1. Get all pending and failed documents
2. Split document content into chunks
3. Process each chunk for entity and relation extraction
4. Update the document status
"""
# 1. get all pending and failed documents
to_process_docs: dict[str, DocProcessingStatus] = {}
# Fetch failed documents
failed_docs = await self.doc_status.get_failed_docs()
to_process_docs.update(failed_docs)
pending_docs = await self.doc_status.get_pending_docs()
to_process_docs.update(pending_docs)
if not to_process_docs:
logger.info("All documents have been processed or are duplicates")
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
chunk_cnt = 0
batch_size = self.addon_params.get("insert_batch_size", 10)
for i in range(0, len(new_docs), batch_size):
batch_docs = dict(list(new_docs.items())[i : i + batch_size])
for doc_id, doc in tqdm_async(
batch_docs.items(),
desc=f"Level 1 - Spliting doc in batch {i // batch_size + 1}",
batch_docs_list = [
list(to_process_docs.items())[i : i + batch_size]
for i in range(0, len(to_process_docs), batch_size)
]
# 3. iterate over batches
tasks: dict[str, list[Coroutine[Any, Any, None]]] = {}
for batch_idx, ids_doc_processing_status in tqdm_async(
enumerate(batch_docs_list),
desc="Process Batches",
):
# 4. iterate over batch
for id_doc_processing_status in tqdm_async(
ids_doc_processing_status,
desc=f"Process Batch {batch_idx}",
):
try:
# Generate chunks from document
chunks = {
compute_mdhash_id(dp["content"], prefix="chunk-"): {
**dp,
"full_doc_id": doc_id,
"status": DocStatus.PENDING,
}
for dp in chunking_by_token_size(
doc["content"],
overlap_token_size=self.chunk_overlap_token_size,
max_token_size=self.chunk_token_size,
tiktoken_model=self.tiktoken_model_name,
)
}
chunk_cnt += len(chunks)
await self.text_chunks.upsert(chunks)
await self.text_chunks.change_status(doc_id, DocStatus.PROCESSING)
try:
# Store chunks in vector database
await self.chunks_vdb.upsert(chunks)
# Update doc status
await self.full_docs.change_status(doc_id, DocStatus.PROCESSED)
except Exception as e:
# Mark as failed if any step fails
await self.full_docs.change_status(doc_id, DocStatus.FAILED)
raise e
except Exception as e:
import traceback
error_msg = f"Failed to process document {doc_id}: {str(e)}\n{traceback.format_exc()}"
logger.error(error_msg)
continue
logger.info(f"Stored {chunk_cnt} chunks from {len(new_docs)} documents")
async def apipeline_process_extract_graph(self):
"""Get pendding or failed chunks, extract entities and relationships from each chunk"""
# 1. get all pending and failed chunks
_todo_chunk_keys = []
_failed_chunks = await self.text_chunks.get_by_status_and_ids(
status=DocStatus.FAILED, ids=None
)
_pendding_chunks = await self.text_chunks.get_by_status_and_ids(
status=DocStatus.PENDING, ids=None
)
if _failed_chunks:
_todo_chunk_keys.extend([doc["id"] for doc in _failed_chunks])
if _pendding_chunks:
_todo_chunk_keys.extend([doc["id"] for doc in _pendding_chunks])
if not _todo_chunk_keys:
logger.info("All chunks have been processed or are duplicates")
return None
# Process documents in batches
batch_size = self.addon_params.get("insert_batch_size", 10)
semaphore = asyncio.Semaphore(
batch_size
) # Control the number of tasks that are processed simultaneously
async def process_chunk(chunk_id):
async with semaphore:
chunks = {
i["id"]: i for i in await self.text_chunks.get_by_ids([chunk_id])
}
# Extract and store entities and relationships
try:
maybe_new_kg = await extract_entities(
chunks,
knowledge_graph_inst=self.chunk_entity_relation_graph,
entity_vdb=self.entities_vdb,
relationships_vdb=self.relationships_vdb,
llm_response_cache=self.llm_response_cache,
global_config=asdict(self),
)
if maybe_new_kg is None:
logger.info("No entities or relationships extracted!")
# Update status to processed
await self.text_chunks.change_status(chunk_id, DocStatus.PROCESSED)
except Exception as e:
logger.error("Failed to extract entities and relationships")
# Mark as failed if any step fails
await self.text_chunks.change_status(chunk_id, DocStatus.FAILED)
raise e
with tqdm_async(
total=len(_todo_chunk_keys),
desc="\nLevel 1 - Processing chunks",
unit="chunk",
position=0,
) as progress:
tasks = []
for chunk_id in _todo_chunk_keys:
task = asyncio.create_task(process_chunk(chunk_id))
tasks.append(task)
for future in asyncio.as_completed(tasks):
await future
progress.update(1)
progress.set_postfix(
id_doc, status_doc = id_doc_processing_status
# Update status in processing
await self.doc_status.upsert(
{
"LLM call": statistic_data["llm_call"],
"LLM cache": statistic_data["llm_cache"],
id_doc: {
"status": DocStatus.PROCESSING,
"updated_at": datetime.now().isoformat(),
"content_summary": status_doc.content_summary,
"content_length": status_doc.content_length,
"created_at": status_doc.created_at,
}
}
)
# Generate chunks from document
chunks: dict[str, Any] = {
compute_mdhash_id(dp["content"], prefix="chunk-"): {
**dp,
"full_doc_id": id_doc_processing_status,
}
for dp in self.chunking_func(
status_doc.content,
split_by_character,
split_by_character_only,
self.chunk_overlap_token_size,
self.chunk_token_size,
self.tiktoken_model_name,
)
}
# Ensure all indexes are updated after each document
await self._insert_done()
# 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
for id_doc_processing_status, task in tasks.items():
try:
await asyncio.gather(*task)
await self.doc_status.upsert(
{
id_doc_processing_status: {
"status": DocStatus.PROCESSED,
"chunks_count": len(chunks),
"updated_at": datetime.now().isoformat(),
}
}
)
await self._insert_done()
except Exception as e:
logger.error(
f"Failed to process document {id_doc_processing_status}: {str(e)}"
)
await self.doc_status.upsert(
{
id_doc_processing_status: {
"status": DocStatus.FAILED,
"error": str(e),
"updated_at": datetime.now().isoformat(),
}
}
)
continue
async def _process_entity_relation_graph(self, chunk: dict[str, Any]) -> None:
try:
new_kg = await extract_entities(
chunk,
knowledge_graph_inst=self.chunk_entity_relation_graph,
entity_vdb=self.entities_vdb,
relationships_vdb=self.relationships_vdb,
llm_response_cache=self.llm_response_cache,
global_config=asdict(self),
)
if new_kg is None:
logger.info("No entities or relationships extracted!")
else:
self.chunk_entity_relation_graph = new_kg
except Exception as e:
logger.error("Failed to extract entities and relationships")
raise e
async def _insert_done(self):
tasks = []
@@ -1169,7 +1058,7 @@ class LightRAG:
return content
return content[:max_length] + "..."
async def get_processing_status(self) -> Dict[str, int]:
async def get_processing_status(self) -> dict[str, int]:
"""Get current document processing status counts
Returns:

35
lightrag/operate.py
View File

@@ -2,7 +2,7 @@ import asyncio
import json
import re
from tqdm.asyncio import tqdm as tqdm_async
from typing import Union
from typing import Any, Union
from collections import Counter, defaultdict
from .utils import (
logger,
@@ -36,15 +36,14 @@ import time
def chunking_by_token_size(
content: str,
split_by_character=None,
split_by_character_only=False,
overlap_token_size=128,
max_token_size=1024,
tiktoken_model="gpt-4o",
**kwargs,
):
split_by_character: Union[str, None] = None,
split_by_character_only: bool = False,
overlap_token_size: int = 128,
max_token_size: int = 1024,
tiktoken_model: str = "gpt-4o",
) -> list[dict[str, Any]]:
tokens = encode_string_by_tiktoken(content, model_name=tiktoken_model)
results = []
results: list[dict[str, Any]] = []
if split_by_character:
raw_chunks = content.split(split_by_character)
new_chunks = []
@@ -568,7 +567,7 @@ async def kg_query(
knowledge_graph_inst: BaseGraphStorage,
entities_vdb: BaseVectorStorage,
relationships_vdb: BaseVectorStorage,
text_chunks_db: BaseKVStorage[TextChunkSchema],
text_chunks_db: BaseKVStorage,
query_param: QueryParam,
global_config: dict,
hashing_kv: BaseKVStorage = None,
@@ -777,7 +776,7 @@ async def mix_kg_vector_query(
entities_vdb: BaseVectorStorage,
relationships_vdb: BaseVectorStorage,
chunks_vdb: BaseVectorStorage,
text_chunks_db: BaseKVStorage[TextChunkSchema],
text_chunks_db: BaseKVStorage,
query_param: QueryParam,
global_config: dict,
hashing_kv: BaseKVStorage = None,
@@ -969,7 +968,7 @@ async def _build_query_context(
knowledge_graph_inst: BaseGraphStorage,
entities_vdb: BaseVectorStorage,
relationships_vdb: BaseVectorStorage,
text_chunks_db: BaseKVStorage[TextChunkSchema],
text_chunks_db: BaseKVStorage,
query_param: QueryParam,
):
# ll_entities_context, ll_relations_context, ll_text_units_context = "", "", ""
@@ -1052,7 +1051,7 @@ async def _get_node_data(
query,
knowledge_graph_inst: BaseGraphStorage,
entities_vdb: BaseVectorStorage,
text_chunks_db: BaseKVStorage[TextChunkSchema],
text_chunks_db: BaseKVStorage,
query_param: QueryParam,
):
# get similar entities
@@ -1145,7 +1144,7 @@ async def _get_node_data(
async def _find_most_related_text_unit_from_entities(
node_datas: list[dict],
query_param: QueryParam,
text_chunks_db: BaseKVStorage[TextChunkSchema],
text_chunks_db: BaseKVStorage,
knowledge_graph_inst: BaseGraphStorage,
):
text_units = [
@@ -1268,7 +1267,7 @@ async def _get_edge_data(
keywords,
knowledge_graph_inst: BaseGraphStorage,
relationships_vdb: BaseVectorStorage,
text_chunks_db: BaseKVStorage[TextChunkSchema],
text_chunks_db: BaseKVStorage,
query_param: QueryParam,
):
results = await relationships_vdb.query(keywords, top_k=query_param.top_k)
@@ -1421,7 +1420,7 @@ async def _find_most_related_entities_from_relationships(
async def _find_related_text_unit_from_relationships(
edge_datas: list[dict],
query_param: QueryParam,
text_chunks_db: BaseKVStorage[TextChunkSchema],
text_chunks_db: BaseKVStorage,
knowledge_graph_inst: BaseGraphStorage,
):
text_units = [
@@ -1496,7 +1495,7 @@ def combine_contexts(entities, relationships, sources):
async def naive_query(
query,
chunks_vdb: BaseVectorStorage,
text_chunks_db: BaseKVStorage[TextChunkSchema],
text_chunks_db: BaseKVStorage,
query_param: QueryParam,
global_config: dict,
hashing_kv: BaseKVStorage = None,
@@ -1599,7 +1598,7 @@ async def kg_query_with_keywords(
knowledge_graph_inst: BaseGraphStorage,
entities_vdb: BaseVectorStorage,
relationships_vdb: BaseVectorStorage,
text_chunks_db: BaseKVStorage[TextChunkSchema],
text_chunks_db: BaseKVStorage,
query_param: QueryParam,
global_config: dict,
hashing_kv: BaseKVStorage = None,

2
lightrag/utils.py
View File

@@ -98,7 +98,7 @@ def locate_json_string_body_from_string(content: str) -> Union[str, None]:
return None
def convert_response_to_json(response: str) -> dict:
def convert_response_to_json(response: str) -> dict[str, Any]:
json_str = locate_json_string_body_from_string(response)
assert json_str is not None, f"Unable to parse JSON from response: {response}"
try: