fix pre commit

examples/lightrag_api_oracle_demo..py

@@ -1,10 +1,10 @@
-
 from fastapi import FastAPI, HTTPException, File, UploadFile
 from contextlib import asynccontextmanager
 from pydantic import BaseModel
 from typing import Optional
 
-import sys, os
+import sys
+import os
 from pathlib import Path
 
 import asyncio
@@ -13,7 +13,6 @@ from lightrag import LightRAG, QueryParam
 from lightrag.llm import openai_complete_if_cache, openai_embedding
 from lightrag.utils import EmbeddingFunc
 import numpy as np
-from datetime import datetime
 
 from lightrag.kg.oracle_impl import OracleDB
 
@@ -24,8 +23,6 @@ script_directory = Path(__file__).resolve().parent.parent
 sys.path.append(os.path.abspath(script_directory))
 
 
-
-
 # Apply nest_asyncio to solve event loop issues
 nest_asyncio.apply()
 
@@ -51,6 +48,7 @@ print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}")
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 async def llm_model_func(
     prompt, system_prompt=None, history_messages=[], **kwargs
 ) -> str:
@@ -80,8 +78,8 @@ async def get_embedding_dim():
     embedding_dim = embedding.shape[1]
     return embedding_dim
 
+
 async def init():
-    
     # Detect embedding dimension
     embedding_dimension = await get_embedding_dim()
     print(f"Detected embedding dimension: {embedding_dimension}")
@@ -91,36 +89,36 @@ async def init():
     # We storage data in unified tables, so we need to set a `workspace` parameter to specify which docs we want to store and query
     # Below is an example of how to connect to Oracle Autonomous Database on Oracle Cloud
 
+    oracle_db = OracleDB(
+        config={
+            "user": "",
+            "password": "",
+            "dsn": "",
+            "config_dir": "",
+            "wallet_location": "",
+            "wallet_password": "",
+            "workspace": "",
+        }  # specify which docs you want to store and query
+    )
 
-    oracle_db = OracleDB(config={
-        "user":"",
-        "password":"",
-        "dsn":"",
-        "config_dir":"",
-        "wallet_location":"",
-        "wallet_password":"",
-        "workspace":""
-        }  # specify which docs you want to store and query               
-        )
-    
     # Check if Oracle DB tables exist, if not, tables will be created
     await oracle_db.check_tables()
     # Initialize LightRAG
-        # We use Oracle DB as the KV/vector/graph storage
+    # We use Oracle DB as the KV/vector/graph storage
     rag = LightRAG(
-            enable_llm_cache=False,
-            working_dir=WORKING_DIR,
-            chunk_token_size=512,
-            llm_model_func=llm_model_func,
-            embedding_func=EmbeddingFunc(
-                embedding_dim=embedding_dimension,
-                max_token_size=512,
-                func=embedding_func,
-                ),            
-            graph_storage = "OracleGraphStorage",
-            kv_storage="OracleKVStorage", 
-            vector_storage="OracleVectorDBStorage"
-        )
+        enable_llm_cache=False,
+        working_dir=WORKING_DIR,
+        chunk_token_size=512,
+        llm_model_func=llm_model_func,
+        embedding_func=EmbeddingFunc(
+            embedding_dim=embedding_dimension,
+            max_token_size=512,
+            func=embedding_func,
+        ),
+        graph_storage="OracleGraphStorage",
+        kv_storage="OracleKVStorage",
+        vector_storage="OracleVectorDBStorage",
+    )
 
     # Setthe KV/vector/graph storage's `db` property, so all operation will use same connection pool
     rag.graph_storage_cls.db = oracle_db
@@ -129,6 +127,7 @@ async def init():
 
     return rag
 
+
 # Data models
 
 
@@ -152,6 +151,7 @@ class Response(BaseModel):
 
 rag = None  # 定义为全局对象
 
+
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     global rag
@@ -160,18 +160,21 @@ async def lifespan(app: FastAPI):
     yield
 
 
-app = FastAPI(title="LightRAG API", description="API for RAG operations",lifespan=lifespan)
+app = FastAPI(
+    title="LightRAG API", description="API for RAG operations", lifespan=lifespan
+)
+
 
 @app.post("/query", response_model=Response)
 async def query_endpoint(request: QueryRequest):
     try:
         # loop = asyncio.get_event_loop()
         result = await rag.aquery(
-                request.query,
-                param=QueryParam(
-                    mode=request.mode, only_need_context=request.only_need_context
-                ),
-            )
+            request.query,
+            param=QueryParam(
+                mode=request.mode, only_need_context=request.only_need_context
+            ),
+        )
         return Response(status="success", data=result)
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
@@ -234,4 +237,4 @@ if __name__ == "__main__":
 # curl -X POST "http://127.0.0.1:8020/insert_file" -H "Content-Type: application/json" -d '{"file_path": "path/to/your/file.txt"}'
 
 # 4. Health check:
-# curl -X GET "http://127.0.0.1:8020/health"
+# curl -X GET "http://127.0.0.1:8020/health"

examples/lightrag_oracle_demo.py

@@ -1,11 +1,11 @@
-import sys, os
+import sys
+import os
 from pathlib import Path
 import asyncio
 from lightrag import LightRAG, QueryParam
 from lightrag.llm import openai_complete_if_cache, openai_embedding
 from lightrag.utils import EmbeddingFunc
 import numpy as np
-from datetime import datetime
 from lightrag.kg.oracle_impl import OracleDB
 
 print(os.getcwd())
@@ -25,6 +25,7 @@ EMBEDMODEL = "cohere.embed-multilingual-v3.0"
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 async def llm_model_func(
     prompt, system_prompt=None, history_messages=[], **kwargs
 ) -> str:
@@ -66,22 +67,21 @@ async def main():
         # More docs here https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html
         # We storage data in unified tables, so we need to set a `workspace` parameter to specify which docs we want to store and query
         # Below is an example of how to connect to Oracle Autonomous Database on Oracle Cloud
-        oracle_db = OracleDB(config={
-            "user":"username",
-            "password":"xxxxxxxxx",
-            "dsn":"xxxxxxx_medium",
-            "config_dir":"dir/path/to/oracle/config",
-            "wallet_location":"dir/path/to/oracle/wallet",
-            "wallet_password":"xxxxxxxxx",
-            "workspace":"company"  # specify which docs you want to store and query
+        oracle_db = OracleDB(
+            config={
+                "user": "username",
+                "password": "xxxxxxxxx",
+                "dsn": "xxxxxxx_medium",
+                "config_dir": "dir/path/to/oracle/config",
+                "wallet_location": "dir/path/to/oracle/wallet",
+                "wallet_password": "xxxxxxxxx",
+                "workspace": "company",  # specify which docs you want to store and query
             }
-            )
-        
+        )
 
         # Check if Oracle DB tables exist, if not, tables will be created
         await oracle_db.check_tables()
 
-
         # Initialize LightRAG
         # We use Oracle DB as the KV/vector/graph storage
         rag = LightRAG(
@@ -93,10 +93,10 @@ async def main():
                 embedding_dim=embedding_dimension,
                 max_token_size=512,
                 func=embedding_func,
-                ),            
-            graph_storage = "OracleGraphStorage",
-            kv_storage="OracleKVStorage", 
-            vector_storage="OracleVectorDBStorage"
+            ),
+            graph_storage="OracleGraphStorage",
+            kv_storage="OracleKVStorage",
+            vector_storage="OracleVectorDBStorage",
         )
 
         # Setthe KV/vector/graph storage's `db` property, so all operation will use same connection pool
@@ -106,18 +106,23 @@ async def main():
 
         # Extract and Insert into LightRAG storage
         with open("./dickens/demo.txt", "r", encoding="utf-8") as f:
-           await rag.ainsert(f.read())
+            await rag.ainsert(f.read())
 
         # Perform search in different modes
         modes = ["naive", "local", "global", "hybrid"]
         for mode in modes:
-            print("="*20, mode, "="*20)
-            print(await rag.aquery("What are the top themes in this story?", param=QueryParam(mode=mode)))
-            print("-"*100, "\n")
+            print("=" * 20, mode, "=" * 20)
+            print(
+                await rag.aquery(
+                    "What are the top themes in this story?",
+                    param=QueryParam(mode=mode),
+                )
+            )
+            print("-" * 100, "\n")
 
     except Exception as e:
         print(f"An error occurred: {e}")
 
 
 if __name__ == "__main__":
-    asyncio.run(main())
+    asyncio.run(main())

lightrag/base.py

@@ -60,6 +60,7 @@ class BaseVectorStorage(StorageNameSpace):
 @dataclass
 class BaseKVStorage(Generic[T], StorageNameSpace):
     embedding_func: EmbeddingFunc
+
     async def all_keys(self) -> list[str]:
         raise NotImplementedError
 
@@ -85,6 +86,7 @@ class BaseKVStorage(Generic[T], StorageNameSpace):
 @dataclass
 class BaseGraphStorage(StorageNameSpace):
     embedding_func: EmbeddingFunc = None
+
     async def has_node(self, node_id: str) -> bool:
         raise NotImplementedError
 

lightrag/kg/oracle_impl.py

@@ -1,9 +1,9 @@
 import asyncio
-#import html
-#import os
+
+# import html
+# import os
 from dataclasses import dataclass
-from typing import Any, Union, cast
-import networkx as nx
+from typing import Union
 import numpy as np
 import array
 
@@ -16,8 +16,9 @@ from ..base import (
 
 import oracledb
 
+
 class OracleDB:
-    def __init__(self,config,**kwargs):
+    def __init__(self, config, **kwargs):
         self.host = config.get("host", None)
         self.port = config.get("port", None)
         self.user = config.get("user", None)
@@ -32,21 +33,21 @@ class OracleDB:
         logger.info(f"Using the label {self.workspace} for Oracle Graph as identifier")
         if self.user is None or self.password is None:
             raise ValueError("Missing database user or password in addon_params")
-        
+
         try:
             oracledb.defaults.fetch_lobs = False
 
             self.pool = oracledb.create_pool_async(
-                user = self.user,
-                password = self.password,
-                dsn = self.dsn,
-                config_dir = self.config_dir,
-                wallet_location = self.wallet_location,
-                wallet_password = self.wallet_password,
-                min = 1,
-                max = self.max,
-                increment = self.increment
-                )
+                user=self.user,
+                password=self.password,
+                dsn=self.dsn,
+                config_dir=self.config_dir,
+                wallet_location=self.wallet_location,
+                wallet_password=self.wallet_password,
+                min=1,
+                max=self.max,
+                increment=self.increment,
+            )
             logger.info(f"Connected to Oracle database at {self.dsn}")
         except Exception as e:
             logger.error(f"Failed to connect to Oracle database at {self.dsn}")
@@ -90,12 +91,14 @@ class OracleDB:
                 arraysize=cursor.arraysize,
                 outconverter=self.numpy_converter_out,
             )
-        
+
     async def check_tables(self):
-        for k,v in TABLES.items():
+        for k, v in TABLES.items():
             try:
                 if k.lower() == "lightrag_graph":
-                    await self.query("SELECT id FROM GRAPH_TABLE (lightrag_graph MATCH (a) COLUMNS (a.id)) fetch first row only")
+                    await self.query(
+                        "SELECT id FROM GRAPH_TABLE (lightrag_graph MATCH (a) COLUMNS (a.id)) fetch first row only"
+                    )
                 else:
                     await self.query("SELECT 1 FROM {k}".format(k=k))
             except Exception as e:
@@ -108,12 +111,11 @@ class OracleDB:
                 except Exception as e:
                     logger.error(f"Failed to create table {k} in Oracle database")
                     logger.error(f"Oracle database error: {e}")
-                    
-        logger.info(f"Finished check all tables in Oracle database")
-        
-        
-    async def query(self,sql: str, multirows: bool = False) -> Union[dict, None]:
-       async with self.pool.acquire() as connection:
+
+        logger.info("Finished check all tables in Oracle database")
+
+    async def query(self, sql: str, multirows: bool = False) -> Union[dict, None]:
+        async with self.pool.acquire() as connection:
             connection.inputtypehandler = self.input_type_handler
             connection.outputtypehandler = self.output_type_handler
             with connection.cursor() as cursor:
@@ -136,9 +138,9 @@ class OracleDB:
                         data = dict(zip(columns, row))
                     else:
                         data = None
-                return data               
+                return data
 
-    async def execute(self,sql: str, data: list = None):
+    async def execute(self, sql: str, data: list = None):
         # logger.info("go into OracleDB execute method")
         try:
             async with self.pool.acquire() as connection:
@@ -148,58 +150,63 @@ class OracleDB:
                     if data is None:
                         await cursor.execute(sql)
                     else:
-                        #print(data)
-                        #print(sql)
-                        await cursor.execute(sql,data)
+                        # print(data)
+                        # print(sql)
+                        await cursor.execute(sql, data)
                     await connection.commit()
         except Exception as e:
-            logger.error(f"Oracle database error: {e}")            
+            logger.error(f"Oracle database error: {e}")
             print(sql)
             print(data)
             raise
 
+
 @dataclass
 class OracleKVStorage(BaseKVStorage):
-
     # should pass db object to self.db
     def __post_init__(self):
         self._data = {}
-        self._max_batch_size = self.global_config["embedding_batch_num"]     
-                                                           
+        self._max_batch_size = self.global_config["embedding_batch_num"]
+
     ################ QUERY METHODS ################
 
     async def get_by_id(self, id: str) -> Union[dict, None]:
         """根据 id 获取 doc_full 数据."""
-        SQL = SQL_TEMPLATES["get_by_id_"+self.namespace].format(workspace=self.db.workspace,id=id)
-        #print("get_by_id:"+SQL)
-        res = await self.db.query(SQL) 
+        SQL = SQL_TEMPLATES["get_by_id_" + self.namespace].format(
+            workspace=self.db.workspace, id=id
+        )
+        # print("get_by_id:"+SQL)
+        res = await self.db.query(SQL)
         if res:
-            data = res #{"data":res}
-            #print (data)
+            data = res  # {"data":res}
+            # print (data)
             return data
         else:
             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], fields=None) -> Union[list[dict], None]:
         """根据 id 获取 doc_chunks 数据"""
-        SQL = SQL_TEMPLATES["get_by_ids_"+self.namespace].format(workspace=self.db.workspace,
-                                                                 ids=",".join([f"'{id}'" for id in ids]))
-        #print("get_by_ids:"+SQL)
-        res = await self.db.query(SQL,multirows=True)
+        SQL = SQL_TEMPLATES["get_by_ids_" + self.namespace].format(
+            workspace=self.db.workspace, 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)
+            data = res  # [{"data":i} for i in res]
+            # print(data)
             return data
         else:
             return None
-   
+
     async def filter_keys(self, keys: list[str]) -> set[str]:
         """过滤掉重复内容"""
-        SQL = SQL_TEMPLATES["filter_keys"].format(table_name=N_T[self.namespace],
-                                                       workspace=self.db.workspace, 
-                                                       ids=",".join([f"'{k}'" for k in keys]))
-        res = await self.db.query(SQL,multirows=True)
+        SQL = SQL_TEMPLATES["filter_keys"].format(
+            table_name=N_T[self.namespace],
+            workspace=self.db.workspace,
+            ids=",".join([f"'{k}'" for k in keys]),
+        )
+        res = await self.db.query(SQL, multirows=True)
         data = None
         if res:
             exist_keys = [key["id"] for key in res]
@@ -208,14 +215,13 @@ class OracleKVStorage(BaseKVStorage):
             exist_keys = []
             data = set([s for s in keys if s not in exist_keys])
         return data
-    
-    
+
     ################ INSERT METHODS ################
     async def upsert(self, data: dict[str, dict]):
         left_data = {k: v for k, v in data.items() if k not in self._data}
         self._data.update(left_data)
-        #print(self._data)
-        #values = []
+        # print(self._data)
+        # values = []
         if self.namespace == "text_chunks":
             list_data = [
                 {
@@ -226,7 +232,7 @@ class OracleKVStorage(BaseKVStorage):
             ]
             contents = [v["content"] for v in data.values()]
             batches = [
-                contents[i: i + self._max_batch_size]
+                contents[i : i + self._max_batch_size]
                 for i in range(0, len(contents), self._max_batch_size)
             ]
             embeddings_list = await asyncio.gather(
@@ -235,42 +241,45 @@ class OracleKVStorage(BaseKVStorage):
             embeddings = np.concatenate(embeddings_list)
             for i, d in enumerate(list_data):
                 d["__vector__"] = embeddings[i]
-            #print(list_data)
+            # print(list_data)
             for item in list_data:
-                merge_sql = SQL_TEMPLATES["merge_chunk"].format(
-                    check_id=item["__id__"]
-                )
+                merge_sql = SQL_TEMPLATES["merge_chunk"].format(check_id=item["__id__"])
 
-                values = [item["__id__"], item["content"], self.db.workspace, item["tokens"],
-                               item["chunk_order_index"], item["full_doc_id"], item["__vector__"]]
-                #print(merge_sql)
+                values = [
+                    item["__id__"],
+                    item["content"],
+                    self.db.workspace,
+                    item["tokens"],
+                    item["chunk_order_index"],
+                    item["full_doc_id"],
+                    item["__vector__"],
+                ]
+                # print(merge_sql)
                 await self.db.execute(merge_sql, values)
 
         if self.namespace == "full_docs":
             for k, v in self._data.items():
-                #values.clear()
+                # values.clear()
                 merge_sql = SQL_TEMPLATES["merge_doc_full"].format(
                     check_id=k,
                 )
                 values = [k, self._data[k]["content"], self.db.workspace]
-                #print(merge_sql)
+                # print(merge_sql)
                 await self.db.execute(merge_sql, values)
         return left_data
 
-
     async def index_done_callback(self):
         if self.namespace in ["full_docs", "text_chunks"]:
             logger.info("full doc and chunk data had been saved into oracle db!")
 
 
-
 @dataclass
 class OracleVectorDBStorage(BaseVectorStorage):
     cosine_better_than_threshold: float = 0.2
 
     def __post_init__(self):
         pass
-    
+
     async def upsert(self, data: dict[str, dict]):
         """向向量数据库中插入数据"""
         pass
@@ -278,53 +287,51 @@ class OracleVectorDBStorage(BaseVectorStorage):
     async def index_done_callback(self):
         pass
 
-
     #################### query method ###############
     async def query(self, query: str, top_k=5) -> Union[dict, list[dict]]:
-        """从向量数据库中查询数据"""        
+        """从向量数据库中查询数据"""
         embeddings = await self.embedding_func([query])
         embedding = embeddings[0]
         # 转换精度
         dtype = str(embedding.dtype).upper()
         dimension = embedding.shape[0]
-        embedding_string = ', '.join(map(str, embedding.tolist()))
+        embedding_string = ", ".join(map(str, embedding.tolist()))
 
         SQL = SQL_TEMPLATES[self.namespace].format(
-                embedding_string=embedding_string,
-                dimension=dimension,
-                dtype=dtype,
-                workspace=self.db.workspace,
-                top_k=top_k,
-                better_than_threshold=self.cosine_better_than_threshold,
-            )
+            embedding_string=embedding_string,
+            dimension=dimension,
+            dtype=dtype,
+            workspace=self.db.workspace,
+            top_k=top_k,
+            better_than_threshold=self.cosine_better_than_threshold,
+        )
         # print(SQL)
         results = await self.db.query(SQL, multirows=True)
-        #print("vector search result:",results)
+        # print("vector search result:",results)
         return results
 
 
 @dataclass
-class OracleGraphStorage(BaseGraphStorage):    
+class OracleGraphStorage(BaseGraphStorage):
     """基于Oracle的图存储模块"""
-   
+
     def __post_init__(self):
         """从graphml文件加载图"""
         self._max_batch_size = self.global_config["embedding_batch_num"]
 
-
     #################### insert method ################
-    
+
     async def upsert_node(self, node_id: str, node_data: dict[str, str]):
         """插入或更新节点"""
-        #print("go into upsert node method")
+        # print("go into upsert node method")
         entity_name = node_id
         entity_type = node_data["entity_type"]
         description = node_data["description"]
-        source_id   = node_data["source_id"]
-        content = entity_name+description
+        source_id = node_data["source_id"]
+        content = entity_name + description
         contents = [content]
         batches = [
-            contents[i: i + self._max_batch_size]
+            contents[i : i + self._max_batch_size]
             for i in range(0, len(contents), self._max_batch_size)
         ]
         embeddings_list = await asyncio.gather(
@@ -333,27 +340,38 @@ class OracleGraphStorage(BaseGraphStorage):
         embeddings = np.concatenate(embeddings_list)
         content_vector = embeddings[0]
         merge_sql = SQL_TEMPLATES["merge_node"].format(
-            workspace=self.db.workspace,name=entity_name, source_chunk_id=source_id
+            workspace=self.db.workspace, name=entity_name, source_chunk_id=source_id
         )
-        #print(merge_sql)
-        await self.db.execute(merge_sql, [self.db.workspace,entity_name,entity_type,description,source_id,content,content_vector])
-        #self._graph.add_node(node_id, **node_data)
+        # print(merge_sql)
+        await self.db.execute(
+            merge_sql,
+            [
+                self.db.workspace,
+                entity_name,
+                entity_type,
+                description,
+                source_id,
+                content,
+                content_vector,
+            ],
+        )
+        # self._graph.add_node(node_id, **node_data)
 
     async def upsert_edge(
         self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
     ):
         """插入或更新边"""
-        #print("go into upsert edge method")
+        # print("go into upsert edge method")
         source_name = source_node_id
         target_name = target_node_id
         weight = edge_data["weight"]
         keywords = edge_data["keywords"]
         description = edge_data["description"]
         source_chunk_id = edge_data["source_id"]
-        content = keywords+source_name+target_name+description
+        content = keywords + source_name + target_name + description
         contents = [content]
         batches = [
-            contents[i: i + self._max_batch_size]
+            contents[i : i + self._max_batch_size]
             for i in range(0, len(contents), self._max_batch_size)
         ]
         embeddings_list = await asyncio.gather(
@@ -362,11 +380,27 @@ class OracleGraphStorage(BaseGraphStorage):
         embeddings = np.concatenate(embeddings_list)
         content_vector = embeddings[0]
         merge_sql = SQL_TEMPLATES["merge_edge"].format(
-            workspace=self.db.workspace,source_name=source_name, target_name=target_name, source_chunk_id=source_chunk_id
+            workspace=self.db.workspace,
+            source_name=source_name,
+            target_name=target_name,
+            source_chunk_id=source_chunk_id,
         )
-        #print(merge_sql)
-        await self.db.execute(merge_sql, [self.db.workspace,source_name,target_name,weight,keywords,description,source_chunk_id,content,content_vector])
-        #self._graph.add_edge(source_node_id, target_node_id, **edge_data)
+        # print(merge_sql)
+        await self.db.execute(
+            merge_sql,
+            [
+                self.db.workspace,
+                source_name,
+                target_name,
+                weight,
+                keywords,
+                description,
+                source_chunk_id,
+                content,
+                content_vector,
+            ],
+        )
+        # self._graph.add_edge(source_node_id, target_node_id, **edge_data)
 
     async def embed_nodes(self, algorithm: str) -> tuple[np.ndarray, list[str]]:
         """为节点生成向量"""
@@ -386,99 +420,109 @@ class OracleGraphStorage(BaseGraphStorage):
         nodes_ids = [self._graph.nodes[node_id]["id"] for node_id in nodes]
         return embeddings, nodes_ids
 
-
     async def index_done_callback(self):
         """写入graphhml图文件"""
-        logger.info("Node and edge data had been saved into oracle db already, so nothing to do here!")
-    
+        logger.info(
+            "Node and edge data had been saved into oracle db already, so nothing to do here!"
+        )
+
     #################### query method #################
     async def has_node(self, node_id: str) -> bool:
-        """根据节点id检查节点是否存在""" 
-        SQL = SQL_TEMPLATES["has_node"].format(workspace=self.db.workspace, node_id=node_id)
-        # print(SQL)   
-        #print(self.db.workspace, node_id)
+        """根据节点id检查节点是否存在"""
+        SQL = SQL_TEMPLATES["has_node"].format(
+            workspace=self.db.workspace, node_id=node_id
+        )
+        # print(SQL)
+        # print(self.db.workspace, node_id)
         res = await self.db.query(SQL)
         if res:
-            #print("Node exist!",res)
+            # print("Node exist!",res)
             return True
         else:
-            #print("Node not exist!")
+            # print("Node not exist!")
             return False
 
     async def has_edge(self, source_node_id: str, target_node_id: str) -> bool:
         """根据源和目标节点id检查边是否存在"""
-        SQL = SQL_TEMPLATES["has_edge"].format(workspace=self.db.workspace, 
-                                                source_node_id=source_node_id, 
-                                                target_node_id=target_node_id)
+        SQL = SQL_TEMPLATES["has_edge"].format(
+            workspace=self.db.workspace,
+            source_node_id=source_node_id,
+            target_node_id=target_node_id,
+        )
         # print(SQL)
         res = await self.db.query(SQL)
         if res:
-            #print("Edge exist!",res)
+            # print("Edge exist!",res)
             return True
         else:
-            #print("Edge not exist!")
+            # print("Edge not exist!")
             return False
 
     async def node_degree(self, node_id: str) -> int:
-        """根据节点id获取节点的度"""        
-        SQL = SQL_TEMPLATES["node_degree"].format(workspace=self.db.workspace, node_id=node_id)
+        """根据节点id获取节点的度"""
+        SQL = SQL_TEMPLATES["node_degree"].format(
+            workspace=self.db.workspace, node_id=node_id
+        )
         # print(SQL)
         res = await self.db.query(SQL)
         if res:
-            #print("Node degree",res["degree"])
+            # print("Node degree",res["degree"])
             return res["degree"]
         else:
-            #print("Edge not exist!")
+            # print("Edge not exist!")
             return 0
 
-    
     async def edge_degree(self, src_id: str, tgt_id: str) -> int:
         """根据源和目标节点id获取边的度"""
         degree = await self.node_degree(src_id) + await self.node_degree(tgt_id)
-        #print("Edge degree",degree)
+        # print("Edge degree",degree)
         return degree
 
-
     async def get_node(self, node_id: str) -> Union[dict, None]:
         """根据节点id获取节点数据"""
-        SQL = SQL_TEMPLATES["get_node"].format(workspace=self.db.workspace, node_id=node_id)
+        SQL = SQL_TEMPLATES["get_node"].format(
+            workspace=self.db.workspace, node_id=node_id
+        )
         # print(self.db.workspace, node_id)
         # print(SQL)
         res = await self.db.query(SQL)
         if res:
-            #print("Get node!",self.db.workspace, node_id,res)
+            # print("Get node!",self.db.workspace, node_id,res)
             return res
         else:
-            #print("Can't get node!",self.db.workspace, node_id)
+            # print("Can't get node!",self.db.workspace, node_id)
             return None
-    
+
     async def get_edge(
         self, source_node_id: str, target_node_id: str
     ) -> Union[dict, None]:
         """根据源和目标节点id获取边"""
-        SQL = SQL_TEMPLATES["get_edge"].format(workspace=self.db.workspace,
-                                                source_node_id=source_node_id,
-                                                target_node_id=target_node_id)
+        SQL = SQL_TEMPLATES["get_edge"].format(
+            workspace=self.db.workspace,
+            source_node_id=source_node_id,
+            target_node_id=target_node_id,
+        )
         res = await self.db.query(SQL)
         if res:
-            #print("Get edge!",self.db.workspace, source_node_id, target_node_id,res[0])
+            # print("Get edge!",self.db.workspace, source_node_id, target_node_id,res[0])
             return res
         else:
-            #print("Edge not exist!",self.db.workspace, source_node_id, target_node_id)
+            # print("Edge not exist!",self.db.workspace, source_node_id, target_node_id)
             return None
 
     async def get_node_edges(self, source_node_id: str):
         """根据节点id获取节点的所有边"""
         if await self.has_node(source_node_id):
-            SQL = SQL_TEMPLATES["get_node_edges"].format(workspace=self.db.workspace,
-                                                    source_node_id=source_node_id)
+            SQL = SQL_TEMPLATES["get_node_edges"].format(
+                workspace=self.db.workspace, source_node_id=source_node_id
+            )
             res = await self.db.query(sql=SQL, multirows=True)
             if res:
-                data = [(i["source_name"],i["target_name"]) for i in res]
-                #print("Get node edge!",self.db.workspace, source_node_id,data)
+                data = [(i["source_name"], i["target_name"]) for i in res]
+                # print("Get node edge!",self.db.workspace, source_node_id,data)
                 return data
             else:
-                #print("Node Edge not exist!",self.db.workspace, source_node_id)
+                # print("Node Edge not exist!",self.db.workspace, source_node_id)
                 return []
 
 
@@ -487,12 +531,12 @@ N_T = {
     "text_chunks": "LIGHTRAG_DOC_CHUNKS",
     "chunks": "LIGHTRAG_DOC_CHUNKS",
     "entities": "LIGHTRAG_GRAPH_NODES",
-    "relationships": "LIGHTRAG_GRAPH_EDGES"
+    "relationships": "LIGHTRAG_GRAPH_EDGES",
 }
 
 TABLES = {
-    "LIGHTRAG_DOC_FULL":
-          {"ddl":"""CREATE TABLE LIGHTRAG_DOC_FULL (
+    "LIGHTRAG_DOC_FULL": {
+        "ddl": """CREATE TABLE LIGHTRAG_DOC_FULL (
                     id varchar(256)PRIMARY KEY,
                     workspace varchar(1024),
                     doc_name varchar(1024),
@@ -500,61 +544,63 @@ TABLES = {
                     meta JSON,
                     createtime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                     updatetime TIMESTAMP DEFAULT NULL
-                    )"""},
-
-    "LIGHTRAG_DOC_CHUNKS":
-          {"ddl":"""CREATE TABLE LIGHTRAG_DOC_CHUNKS (
+                    )"""
+    },
+    "LIGHTRAG_DOC_CHUNKS": {
+        "ddl": """CREATE TABLE LIGHTRAG_DOC_CHUNKS (
                     id varchar(256) PRIMARY KEY,
                     workspace varchar(1024),
                     full_doc_id varchar(256),
                     chunk_order_index NUMBER,
-                    tokens NUMBER,    
+                    tokens NUMBER,
                     content CLOB,
                     content_vector VECTOR,
                     createtime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
-                    updatetime TIMESTAMP DEFAULT NULL    
-                    )"""},
-
-    "LIGHTRAG_GRAPH_NODES":
-          {"ddl":"""CREATE TABLE LIGHTRAG_GRAPH_NODES (
+                    updatetime TIMESTAMP DEFAULT NULL
+                    )"""
+    },
+    "LIGHTRAG_GRAPH_NODES": {
+        "ddl": """CREATE TABLE LIGHTRAG_GRAPH_NODES (
                     id NUMBER GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY,
                     workspace varchar(1024),
                     name varchar(2048),
-                    entity_type varchar(1024), 
+                    entity_type varchar(1024),
                     description CLOB,
                     source_chunk_id varchar(256),
                     content CLOB,
                     content_vector VECTOR,
                     createtime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                     updatetime TIMESTAMP DEFAULT NULL
-                    )"""},
-    "LIGHTRAG_GRAPH_EDGES":
-          {"ddl":"""CREATE TABLE LIGHTRAG_GRAPH_EDGES (
+                    )"""
+    },
+    "LIGHTRAG_GRAPH_EDGES": {
+        "ddl": """CREATE TABLE LIGHTRAG_GRAPH_EDGES (
                     id NUMBER GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY,
                     workspace varchar(1024),
                     source_name varchar(2048),
-                    target_name varchar(2048),    
+                    target_name varchar(2048),
                     weight NUMBER,
-                    keywords CLOB, 
+                    keywords CLOB,
                     description CLOB,
                     source_chunk_id varchar(256),
                     content CLOB,
                     content_vector VECTOR,
                     createtime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                     updatetime TIMESTAMP DEFAULT NULL
-                    )"""},
-    "LIGHTRAG_LLM_CACHE":
-          {"ddl":"""CREATE TABLE LIGHTRAG_LLM_CACHE (
+                    )"""
+    },
+    "LIGHTRAG_LLM_CACHE": {
+        "ddl": """CREATE TABLE LIGHTRAG_LLM_CACHE (
                     id varchar(256) PRIMARY KEY,
                     send clob,
                     return clob,
                     model varchar(1024),
                     createtime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                     updatetime TIMESTAMP DEFAULT NULL
-                    )"""},
-
-    "LIGHTRAG_GRAPH":
-          {"ddl":"""CREATE OR REPLACE PROPERTY GRAPH lightrag_graph
+                    )"""
+    },
+    "LIGHTRAG_GRAPH": {
+        "ddl": """CREATE OR REPLACE PROPERTY GRAPH lightrag_graph
                 VERTEX TABLES (
                     lightrag_graph_nodes KEY (id)
                         LABEL entity
@@ -565,93 +611,67 @@ TABLES = {
                         SOURCE KEY (source_name) REFERENCES lightrag_graph_nodes(name)
                         DESTINATION KEY (target_name) REFERENCES lightrag_graph_nodes(name)
                         LABEL  has_relation
-                        PROPERTIES (id,workspace,source_name,target_name) -- ,weight, keywords,description,source_chunk_id) 
-                ) OPTIONS(ALLOW MIXED PROPERTY TYPES)"""},
-    }
+                        PROPERTIES (id,workspace,source_name,target_name) -- ,weight, keywords,description,source_chunk_id)
+                ) OPTIONS(ALLOW MIXED PROPERTY TYPES)"""
+    },
+}
 
 
 SQL_TEMPLATES = {
     # SQL for KVStorage
-    "get_by_id_full_docs":
-        "select ID,NVL(content,'') as content from LIGHTRAG_DOC_FULL where workspace='{workspace}' and ID='{id}'",
-
-    "get_by_id_text_chunks":
-        "select ID,TOKENS,NVL(content,'') as content,CHUNK_ORDER_INDEX,FULL_DOC_ID from LIGHTRAG_DOC_CHUNKS where workspace='{workspace}' and ID='{id}'",
-
-    "get_by_ids_full_docs":
-        "select ID,NVL(content,'') as content from LIGHTRAG_DOC_FULL where workspace='{workspace}' and ID in ({ids})",
-    
-    "get_by_ids_text_chunks":
-        "select ID,TOKENS,NVL(content,'') as content,CHUNK_ORDER_INDEX,FULL_DOC_ID  from LIGHTRAG_DOC_CHUNKS where workspace='{workspace}' and ID in ({ids})",
-            
-    "filter_keys":
-        "select id from {table_name} where workspace='{workspace}' and id in ({ids})",
-
+    "get_by_id_full_docs": "select ID,NVL(content,'') as content from LIGHTRAG_DOC_FULL where workspace='{workspace}' and ID='{id}'",
+    "get_by_id_text_chunks": "select ID,TOKENS,NVL(content,'') as content,CHUNK_ORDER_INDEX,FULL_DOC_ID from LIGHTRAG_DOC_CHUNKS where workspace='{workspace}' and ID='{id}'",
+    "get_by_ids_full_docs": "select ID,NVL(content,'') as content from LIGHTRAG_DOC_FULL where workspace='{workspace}' and ID in ({ids})",
+    "get_by_ids_text_chunks": "select ID,TOKENS,NVL(content,'') as content,CHUNK_ORDER_INDEX,FULL_DOC_ID  from LIGHTRAG_DOC_CHUNKS where workspace='{workspace}' and ID in ({ids})",
+    "filter_keys": "select id from {table_name} where workspace='{workspace}' and id in ({ids})",
     "merge_doc_full": """ MERGE INTO LIGHTRAG_DOC_FULL a
                     USING DUAL
                     ON (a.id = '{check_id}')
                     WHEN NOT MATCHED THEN
                     INSERT(id,content,workspace) values(:1,:2,:3)
                     """,
-
     "merge_chunk": """MERGE INTO LIGHTRAG_DOC_CHUNKS a
                     USING DUAL
                     ON (a.id = '{check_id}')
                     WHEN NOT MATCHED THEN
                     INSERT(id,content,workspace,tokens,chunk_order_index,full_doc_id,content_vector)
                     values (:1,:2,:3,:4,:5,:6,:7) """,
-
     # SQL for VectorStorage
-    "entities":
-        """SELECT name as entity_name FROM 
-        (SELECT id,name,VECTOR_DISTANCE(content_vector,vector('[{embedding_string}]',{dimension},{dtype}),COSINE) as distance 
-        FROM LIGHTRAG_GRAPH_NODES WHERE workspace='{workspace}') 
+    "entities": """SELECT name as entity_name FROM
+        (SELECT id,name,VECTOR_DISTANCE(content_vector,vector('[{embedding_string}]',{dimension},{dtype}),COSINE) as distance
+        FROM LIGHTRAG_GRAPH_NODES WHERE workspace='{workspace}')
         WHERE distance>{better_than_threshold} ORDER BY distance ASC FETCH FIRST {top_k} ROWS ONLY""",
-
-    "relationships":
-        """SELECT source_name as src_id, target_name as tgt_id FROM 
-        (SELECT id,source_name,target_name,VECTOR_DISTANCE(content_vector,vector('[{embedding_string}]',{dimension},{dtype}),COSINE) as distance 
-        FROM LIGHTRAG_GRAPH_EDGES WHERE workspace='{workspace}') 
+    "relationships": """SELECT source_name as src_id, target_name as tgt_id FROM
+        (SELECT id,source_name,target_name,VECTOR_DISTANCE(content_vector,vector('[{embedding_string}]',{dimension},{dtype}),COSINE) as distance
+        FROM LIGHTRAG_GRAPH_EDGES WHERE workspace='{workspace}')
         WHERE distance>{better_than_threshold} ORDER BY distance ASC FETCH FIRST {top_k} ROWS ONLY""",
-
-    "chunks":
-        """SELECT id FROM 
-        (SELECT id,VECTOR_DISTANCE(content_vector,vector('[{embedding_string}]',{dimension},{dtype}),COSINE) as distance 
-        FROM LIGHTRAG_DOC_CHUNKS WHERE workspace='{workspace}') 
+    "chunks": """SELECT id FROM
+        (SELECT id,VECTOR_DISTANCE(content_vector,vector('[{embedding_string}]',{dimension},{dtype}),COSINE) as distance
+        FROM LIGHTRAG_DOC_CHUNKS WHERE workspace='{workspace}')
         WHERE distance>{better_than_threshold} ORDER BY distance ASC FETCH FIRST {top_k} ROWS ONLY""",
-
     # SQL for GraphStorage
-    "has_node":
-        """SELECT * FROM GRAPH_TABLE (lightrag_graph
+    "has_node": """SELECT * FROM GRAPH_TABLE (lightrag_graph
         MATCH (a)
         WHERE a.workspace='{workspace}' AND a.name='{node_id}'
         COLUMNS (a.name))""",
-
-    "has_edge":
-        """SELECT * FROM GRAPH_TABLE (lightrag_graph
+    "has_edge": """SELECT * FROM GRAPH_TABLE (lightrag_graph
         MATCH (a) -[e]-> (b)
         WHERE e.workspace='{workspace}' and a.workspace='{workspace}' and b.workspace='{workspace}'
         AND a.name='{source_node_id}' AND b.name='{target_node_id}'
         COLUMNS (e.source_name,e.target_name)  )""",
-
-    "node_degree":
-        """SELECT count(1) as degree FROM GRAPH_TABLE (lightrag_graph
+    "node_degree": """SELECT count(1) as degree FROM GRAPH_TABLE (lightrag_graph
         MATCH (a)-[e]->(b)
         WHERE a.workspace='{workspace}' and a.workspace='{workspace}' and b.workspace='{workspace}'
         AND a.name='{node_id}' or b.name = '{node_id}'
         COLUMNS (a.name))""",
-
-    "get_node":
-        """SELECT t1.name,t2.entity_type,t2.source_chunk_id as source_id,NVL(t2.description,'') AS description 
+    "get_node": """SELECT t1.name,t2.entity_type,t2.source_chunk_id as source_id,NVL(t2.description,'') AS description
         FROM GRAPH_TABLE (lightrag_graph
-        MATCH (a) 
+        MATCH (a)
         WHERE a.workspace='{workspace}' AND a.name='{node_id}'
         COLUMNS (a.name)
         ) t1 JOIN LIGHTRAG_GRAPH_NODES t2 on t1.name=t2.name
         WHERE t2.workspace='{workspace}'""",
-
-    "get_edge":
-        """SELECT t1.source_id,t2.weight,t2.source_chunk_id as source_id,t2.keywords,
+    "get_edge": """SELECT t1.source_id,t2.weight,t2.source_chunk_id as source_id,t2.keywords,
         NVL(t2.description,'') AS description,NVL(t2.KEYWORDS,'') AS keywords
         FROM GRAPH_TABLE (lightrag_graph
         MATCH (a)-[e]->(b)
@@ -659,15 +679,12 @@ SQL_TEMPLATES = {
         AND a.name='{source_node_id}' and b.name = '{target_node_id}'
         COLUMNS (e.id,a.name as source_id)
         ) t1 JOIN LIGHTRAG_GRAPH_EDGES t2 on t1.id=t2.id""",
-
-    "get_node_edges":
-        """SELECT source_name,target_name
+    "get_node_edges": """SELECT source_name,target_name
             FROM GRAPH_TABLE (lightrag_graph
             MATCH (a)-[e]->(b)
             WHERE e.workspace='{workspace}' and a.workspace='{workspace}' and b.workspace='{workspace}'
             AND a.name='{source_node_id}'
             COLUMNS (a.name as source_name,b.name as target_name))""",
-
     "merge_node": """MERGE INTO LIGHTRAG_GRAPH_NODES a
                     USING DUAL
                     ON (a.workspace = '{workspace}' and a.name='{name}' and a.source_chunk_id='{source_chunk_id}')
@@ -679,5 +696,5 @@ SQL_TEMPLATES = {
                     ON (a.workspace = '{workspace}' and a.source_name='{source_name}' and a.target_name='{target_name}' and a.source_chunk_id='{source_chunk_id}')
                 WHEN NOT MATCHED THEN
                     INSERT(workspace,source_name,target_name,weight,keywords,description,source_chunk_id,content,content_vector)
-                    values (:1,:2,:3,:4,:5,:6,:7,:8,:9) """
-    }
+                    values (:1,:2,:3,:4,:5,:6,:7,:8,:9) """,
+}

lightrag/lightrag.py

@@ -38,15 +38,11 @@ from .storage import (
     JsonKVStorage,
     NanoVectorDBStorage,
     NetworkXStorage,
-    )
+)
 
 from .kg.neo4j_impl import Neo4JStorage
 
-from .kg.oracle_impl import (
-    OracleKVStorage, 
-    OracleGraphStorage, 
-    OracleVectorDBStorage
-    )
+from .kg.oracle_impl import OracleKVStorage, OracleGraphStorage, OracleVectorDBStorage
 
 # future KG integrations
 
@@ -54,6 +50,7 @@ from .kg.oracle_impl import (
 #     GraphStorage as ArangoDBStorage
 # )
 
+
 def always_get_an_event_loop() -> asyncio.AbstractEventLoop:
     try:
         return asyncio.get_event_loop()
@@ -72,7 +69,7 @@ class LightRAG:
         default_factory=lambda: f"./lightrag_cache_{datetime.now().strftime('%Y-%m-%d-%H:%M:%S')}"
     )
 
-    kv_storage : str = field(default="JsonKVStorage")
+    kv_storage: str = field(default="JsonKVStorage")
     vector_storage: str = field(default="NanoVectorDBStorage")
     graph_storage: str = field(default="NetworkXStorage")
 
@@ -115,7 +112,7 @@ class LightRAG:
 
     # storage
     vector_db_storage_cls_kwargs: dict = field(default_factory=dict)
-        
+
     enable_llm_cache: bool = True
 
     # extension
@@ -134,18 +131,25 @@ class LightRAG:
 
         # @TODO: should move all storage setup here to leverage initial start params attached to self.
 
-        self.key_string_value_json_storage_cls: Type[BaseKVStorage] = self._get_storage_class()[self.kv_storage]
-        self.vector_db_storage_cls: Type[BaseVectorStorage] = self._get_storage_class()[self.vector_storage]        
-        self.graph_storage_cls: Type[BaseGraphStorage] = self._get_storage_class()[self.graph_storage]
+        self.key_string_value_json_storage_cls: Type[BaseKVStorage] = (
+            self._get_storage_class()[self.kv_storage]
+        )
+        self.vector_db_storage_cls: Type[BaseVectorStorage] = self._get_storage_class()[
+            self.vector_storage
+        ]
+        self.graph_storage_cls: Type[BaseGraphStorage] = self._get_storage_class()[
+            self.graph_storage
+        ]
 
         if not os.path.exists(self.working_dir):
             logger.info(f"Creating working directory {self.working_dir}")
             os.makedirs(self.working_dir)
 
-
         self.llm_response_cache = (
             self.key_string_value_json_storage_cls(
-                namespace="llm_response_cache", global_config=asdict(self),embedding_func=None
+                namespace="llm_response_cache",
+                global_config=asdict(self),
+                embedding_func=None,
             )
             if self.enable_llm_cache
             else None
@@ -159,13 +163,19 @@ class LightRAG:
         # add embedding func by walter
         ####
         self.full_docs = self.key_string_value_json_storage_cls(
-            namespace="full_docs", global_config=asdict(self), embedding_func=self.embedding_func
+            namespace="full_docs",
+            global_config=asdict(self),
+            embedding_func=self.embedding_func,
         )
         self.text_chunks = self.key_string_value_json_storage_cls(
-            namespace="text_chunks", global_config=asdict(self), embedding_func=self.embedding_func
+            namespace="text_chunks",
+            global_config=asdict(self),
+            embedding_func=self.embedding_func,
         )
         self.chunk_entity_relation_graph = self.graph_storage_cls(
-            namespace="chunk_entity_relation", global_config=asdict(self), embedding_func=self.embedding_func
+            namespace="chunk_entity_relation",
+            global_config=asdict(self),
+            embedding_func=self.embedding_func,
         )
         ####
         # add embedding func by walter over
@@ -200,13 +210,11 @@ class LightRAG:
     def _get_storage_class(self) -> Type[BaseGraphStorage]:
         return {
             # kv storage
-            "JsonKVStorage":JsonKVStorage,
-            "OracleKVStorage":OracleKVStorage,
-
+            "JsonKVStorage": JsonKVStorage,
+            "OracleKVStorage": OracleKVStorage,
             # vector storage
-            "NanoVectorDBStorage":NanoVectorDBStorage,
-            "OracleVectorDBStorage":OracleVectorDBStorage,
-
+            "NanoVectorDBStorage": NanoVectorDBStorage,
+            "OracleVectorDBStorage": OracleVectorDBStorage,
             # graph storage
             "NetworkXStorage": NetworkXStorage,
             "Neo4JStorage": Neo4JStorage,

lightrag/operate.py

@@ -16,7 +16,7 @@ from .utils import (
     split_string_by_multi_markers,
     truncate_list_by_token_size,
     process_combine_contexts,
-    locate_json_string_body_from_string
+    locate_json_string_body_from_string,
 )
 from .base import (
     BaseGraphStorage,

requirements.txt

@@ -1,22 +1,22 @@
 accelerate
+aioboto3
 aiohttp
+
+# database packages
+graspologic
+hnswlib
+nano-vectordb
+neo4j
+networkx
+ollama
+openai
+oracledb
 pyvis
 tenacity
-xxhash
 # lmdeploy[all]
 
 # LLM packages
 tiktoken
 torch
 transformers
-aioboto3
-ollama
-openai
-
-# database packages
-graspologic
-hnswlib
-networkx
-oracledb
-nano-vectordb
-neo4j
+xxhash