update Oracle support

add cache support, fix bug

examples/lightrag_oracle_demo.py

@@ -20,7 +20,8 @@ BASE_URL = "http://xxx.xxx.xxx.xxx:8088/v1/"
 APIKEY = "ocigenerativeai"
 CHATMODEL = "cohere.command-r-plus"
 EMBEDMODEL = "cohere.embed-multilingual-v3.0"
-
+CHUNK_TOKEN_SIZE = 1024
+MAX_TOKENS = 4000
 
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
@@ -86,27 +87,49 @@ async def main():
         # We use Oracle DB as the KV/vector/graph storage
         # You can add `addon_params={"example_number": 1, "language": "Simplfied Chinese"}` to control the prompt
         rag = LightRAG(
+            working_dir=WORKING_DIR,        
+            entity_extract_max_gleaning = 1,
+
             enable_llm_cache=False,
-            working_dir=WORKING_DIR,
+            embedding_cache_config= None,  # {"enabled": True,"similarity_threshold": 0.90},
-            chunk_token_size=512,
+            enable_llm_cache_for_entity_extract = True,
+
+            chunk_token_size=CHUNK_TOKEN_SIZE,
+            llm_model_max_token_size = MAX_TOKENS,
             llm_model_func=llm_model_func,
             embedding_func=EmbeddingFunc(
                 embedding_dim=embedding_dimension,
-                max_token_size=512,
+                max_token_size=500,
                 func=embedding_func,
-            ),
+                ),        
-            graph_storage="OracleGraphStorage",
+            
-            kv_storage="OracleKVStorage",
+            graph_storage = "OracleGraphStorage",
+            kv_storage = "OracleKVStorage", 
             vector_storage="OracleVectorDBStorage",
+            doc_status_storage="OracleDocStatusStorage",
+
+            addon_params = {"example_number":1, "language":"Simplfied Chinese"},
         )
 
-        # Setthe KV/vector/graph storage's `db` property, so all operation will use same connection pool
+        # Setthe KV/vector/graph storage's `db` property, so all operation will use same connection pool    
-        rag.graph_storage_cls.db = oracle_db
         rag.key_string_value_json_storage_cls.db = oracle_db
         rag.vector_db_storage_cls.db = oracle_db
-        # add embedding_func for graph database, it's deleted in commit 5661d76860436f7bf5aef2e50d9ee4a59660146c
+        rag.graph_storage_cls.db = oracle_db
-        rag.chunk_entity_relation_graph.embedding_func = rag.embedding_func
+        rag.doc_status_storage_cls.db = oracle_db
+        rag.doc_status.db = oracle_db
+        rag.full_docs.db = oracle_db
+        rag.text_chunks.db = oracle_db
+        rag.llm_response_cache.db = oracle_db
+        rag.key_string_value_json_storage_cls.db = oracle_db
+        rag.chunks_vdb.db = oracle_db
+        rag.relationships_vdb.db = oracle_db
+        rag.entities_vdb.db = oracle_db
+        rag.graph_storage_cls.db = oracle_db
+        rag.chunk_entity_relation_graph.db = oracle_db
+        rag.llm_response_cache.db = oracle_db
 
+        rag.chunk_entity_relation_graph.embedding_func = rag.embedding_func
+        
         # Extract and Insert into LightRAG storage
         with open("./dickens/demo.txt", "r", encoding="utf-8") as f:
             await rag.ainsert(f.read())

lightrag/kg/oracle_impl.py

@@ -3,7 +3,7 @@ import asyncio
 # import html
 # import os
 from dataclasses import dataclass
-from typing import Union
+from typing import Union, List, Dict, Set, Any, Tuple
 import numpy as np
 import array
 
@@ -12,6 +12,9 @@ from ..base import (
     BaseGraphStorage,
     BaseKVStorage,
     BaseVectorStorage,
+    DocStatusStorage,
+    DocStatus,
+    DocProcessingStatus,
 )
 
 import oracledb
@@ -167,6 +170,9 @@ class OracleDB:
 @dataclass
 class OracleKVStorage(BaseKVStorage):
     # should pass db object to self.db
+    db: OracleDB = None
+    meta_fields = None
+
     def __post_init__(self):
         self._data = {}
         self._max_batch_size = self.global_config["embedding_batch_num"]
@@ -174,28 +180,56 @@ class OracleKVStorage(BaseKVStorage):
     ################ QUERY METHODS ################
 
     async def get_by_id(self, id: str) -> Union[dict, None]:
-        """根据 id 获取 doc_full 数据."""
+        """get doc_full data based on id."""
         SQL = SQL_TEMPLATES["get_by_id_" + self.namespace]
         params = {"workspace": self.db.workspace, "id": id}
         # print("get_by_id:"+SQL)
-        res = await self.db.query(SQL, params)
+        if "llm_response_cache" == self.namespace:
+            array_res = await self.db.query(SQL, params, multirows=True)
+            res = {}
+            for row in array_res:
+                res[row["id"]] = row
+        else:
+            res = await self.db.query(SQL, params)
         if res:
-            data = res  # {"data":res}
+            return res
-            # print (data)
+        else:
-            return data
+            return None
+        
+    async def get_by_mode_and_id(self, mode: str, id: str) -> Union[dict, None]:
+        """Specifically for llm_response_cache."""
+        SQL = SQL_TEMPLATES["get_by_mode_id_" + self.namespace]
+        params = {"workspace": self.db.workspace, "cache_mode": mode, "id": id}
+        if "llm_response_cache" == self.namespace:
+            array_res = await self.db.query(SQL, params, multirows=True)
+            res = {}
+            for row in array_res:
+                res[row["id"]] = row
+            return res
         else:
             return None
 
     # 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"""
+        """get doc_chunks data based on id"""
         SQL = SQL_TEMPLATES["get_by_ids_" + self.namespace].format(
             ids=",".join([f"'{id}'" for id in ids])
         )
         params = {"workspace": self.db.workspace}
         # print("get_by_ids:"+SQL)
-        # print(params)
         res = await self.db.query(SQL, params, multirows=True)
+        if "llm_response_cache" == self.namespace:
+            modes = set()
+            dict_res: dict[str, dict] = {}
+            for row in res:
+                modes.add(row["mode"])
+            for mode in modes:
+                if mode not in dict_res:
+                    dict_res[mode] = {}
+            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)
@@ -204,7 +238,7 @@ class OracleKVStorage(BaseKVStorage):
             return None
 
     async def filter_keys(self, keys: list[str]) -> set[str]:
-        """过滤掉重复内容"""
+        """remove duplicated"""
         SQL = SQL_TEMPLATES["filter_keys"].format(
             table_name=N_T[self.namespace], ids=",".join([f"'{id}'" for id in keys])
         )
@@ -271,13 +305,26 @@ class OracleKVStorage(BaseKVStorage):
                 # values.clear()
                 merge_sql = SQL_TEMPLATES["merge_doc_full"]
                 data = {
-                    "check_id": k,
                     "id": k,
                     "content": v["content"],
                     "workspace": self.db.workspace,
                 }
                 # print(merge_sql)
                 await self.db.execute(merge_sql, data)
+
+        if self.namespace == "llm_response_cache":
+            for mode, items in data.items():
+                for k, v in items.items():
+                    upsert_sql = SQL_TEMPLATES["upsert_llm_response_cache"]
+                    _data = {
+                        "workspace": self.db.workspace,
+                        "id": k,
+                        "original_prompt": v["original_prompt"],
+                        "return_value": v["return"],
+                        "cache_mode": mode,
+                    }
+
+                    await self.db.execute(upsert_sql, _data)
         return left_data
 
     async def index_done_callback(self):
@@ -285,8 +332,99 @@ class OracleKVStorage(BaseKVStorage):
             logger.info("full doc and chunk data had been saved into oracle db!")
 
 
+@dataclass
+class OracleDocStatusStorage(DocStatusStorage):
+    """Oracle implementation of document status storage"""
+    # should pass db object to self.db
+    db: OracleDB = None
+    meta_fields = None
+
+    def __post_init__(self):
+        pass
+
+    async def filter_keys(self, ids: list[str]) -> set[str]:
+        """Return keys that don't exist in storage"""
+        SQL = SQL_TEMPLATES["get_by_id_" + self.namespace].format(
+            ids = ",".join([f"'{id}'" for id in ids])
+        )        
+        params = {"workspace": self.db.workspace}
+        res = await self.db.query(SQL, params, True)
+        # The result is like [{'id': 'id1'}, {'id': 'id2'}, ...].
+        if res:            
+            existed = set([element["id"] for element in res])
+            return set(ids) - existed
+        else:
+            return set(ids)
+
+    async def get_status_counts(self) -> Dict[str, int]:
+        """Get counts of documents in each status"""
+        SQL = SQL_TEMPLATES["get_status_counts"]
+        params = {"workspace": self.db.workspace}
+        res = await self.db.query(SQL, params, True)
+        # Result is like [{'status': 'PENDING', 'count': 1}, {'status': 'PROCESSING', 'count': 2}, ...]
+        counts = {}
+        for doc in res:
+            counts[doc["status"]] = doc["count"]
+        return counts
+
+    async def get_docs_by_status(self, status: DocStatus) -> Dict[str, DocProcessingStatus]:
+        """Get all documents by status"""
+        SQL = SQL_TEMPLATES["get_docs_by_status"]
+        params = {"workspace": self.db.workspace, "status": status}
+        res = 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_summary=element["content_summary"],
+                content_summary = "",
+                content_length=element["CONTENT_LENGTH"],
+                status=element["STATUS"],
+                created_at=element["CREATETIME"],
+                updated_at=element["UPDATETIME"],
+                chunks_count=-1,
+                #chunks_count=element["chunks_count"],
+            )
+            for element in res
+        }
+
+    async def get_failed_docs(self) -> Dict[str, DocProcessingStatus]:
+        """Get all failed documents"""
+        return await self.get_docs_by_status(DocStatus.FAILED)
+
+    async def get_pending_docs(self) -> Dict[str, DocProcessingStatus]:
+        """Get all pending documents"""
+        return await self.get_docs_by_status(DocStatus.PENDING)
+
+    async def index_done_callback(self):
+        """Save data after indexing, but for ORACLE, we already saved them during the upsert stage, so no action to take here"""
+        logger.info("Doc status had been saved into ORACLE db!")
+
+    async def upsert(self, data: dict[str, dict]):
+        """Update or insert document status
+
+        Args:
+            data: Dictionary of document IDs and their status data
+        """
+        SQL = SQL_TEMPLATES["merge_doc_status"]
+        for k, v in data.items():
+            # chunks_count is optional
+            params = {
+                    "workspace": self.db.workspace,
+                    "id": k,
+                    "content_summary": v["content_summary"],
+                    "content_length": v["content_length"],
+                    "chunks_count": v["chunks_count"] if "chunks_count" in v else -1,
+                    "status": v["status"],
+                }
+            await self.db.execute(SQL, params)
+        return data
+
+
 @dataclass
 class OracleVectorDBStorage(BaseVectorStorage):
+    # should pass db object to self.db
+    db: OracleDB = None
     cosine_better_than_threshold: float = 0.2
 
     def __post_init__(self):
@@ -564,13 +702,18 @@ N_T = {
 TABLES = {
     "LIGHTRAG_DOC_FULL": {
         "ddl": """CREATE TABLE LIGHTRAG_DOC_FULL (
-                    id varchar(256)PRIMARY KEY,
+                    id varchar(256),
                     workspace varchar(1024),
                     doc_name varchar(1024),
                     content CLOB,
                     meta JSON,
+                    content_summary varchar(1024),
+                    content_length NUMBER,
+                    status varchar(256),
+                    chunks_count NUMBER,
                     createtime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
-                    updatetime TIMESTAMP DEFAULT NULL
+                    updatetime TIMESTAMP DEFAULT NULL,
+                    error varchar(4096)
                     )"""
     },
     "LIGHTRAG_DOC_CHUNKS": {
@@ -618,10 +761,16 @@ TABLES = {
     },
     "LIGHTRAG_LLM_CACHE": {
         "ddl": """CREATE TABLE LIGHTRAG_LLM_CACHE (
-                    id varchar(256) PRIMARY KEY,
+                    id varchar(256) PRIMARY KEY, 
-                    send clob,
+                    workspace varchar(1024),                
-                    return clob,
+                    cache_mode varchar(256),
-                    model varchar(1024),
+                    model_name varchar(256),
+                    original_prompt clob,
+                    return_value clob,
+                    embedding CLOB,
+                    embedding_shape NUMBER,
+                    embedding_min NUMBER,
+                    embedding_max NUMBER,
                     createtime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                     updatetime TIMESTAMP DEFAULT NULL
                     )"""
@@ -647,22 +796,70 @@ TABLES = {
 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_id_llm_response_cache": """SELECT id, original_prompt, NVL(return_value, '') as "return", cache_mode as "mode"
+        FROM LIGHTRAG_LLM_CACHE WHERE workspace=:workspace AND id=:id""",
+    
+    "get_by_mode_id_llm_response_cache": """SELECT id, original_prompt, NVL(return_value, '') as "return", cache_mode as "mode"
+        FROM LIGHTRAG_LLM_CACHE WHERE workspace=:workspace AND cache_mode=:cache_mode AND id=:id""",
+
+    "get_by_ids_llm_response_cache": """SELECT id, original_prompt, NVL(return_value, '') as "return", cache_mode as "mode"
+        FROM LIGHTRAG_LLM_CACHE WHERE workspace=:workspace  AND id IN ({ids})""",
+    
     "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
+    "merge_doc_full": """MERGE INTO LIGHTRAG_DOC_FULL a
-                    ON (a.id = :check_id)
+        USING DUAL
-                    WHEN NOT MATCHED THEN
+        ON (a.id = :id and a.workspace = :workspace)
-                    INSERT(id,content,workspace) values(:id,:content,:workspace)
+        WHEN NOT MATCHED THEN
-                    """,
+        INSERT(id,content,workspace) values(:id,:content,:workspace)""",
+    
     "merge_chunk": """MERGE INTO LIGHTRAG_DOC_CHUNKS a
-                    USING DUAL
+        USING DUAL
-                    ON (a.id = :check_id)
+        ON (a.id = :check_id)
-                    WHEN NOT MATCHED THEN
+        WHEN NOT MATCHED THEN
-                    INSERT(id,content,workspace,tokens,chunk_order_index,full_doc_id,content_vector)
+        INSERT(id,content,workspace,tokens,chunk_order_index,full_doc_id,content_vector)
-                    values (:id,:content,:workspace,:tokens,:chunk_order_index,:full_doc_id,:content_vector) """,
+        values (:id,:content,:workspace,:tokens,:chunk_order_index,:full_doc_id,:content_vector) """,
+
+    "upsert_llm_response_cache": """MERGE INTO LIGHTRAG_LLM_CACHE a
+        USING DUAL
+        ON (a.id = :id)
+        WHEN NOT MATCHED THEN
+        INSERT (workspace,id,original_prompt,return_value,cache_mode)
+            VALUES (:workspace,:id,:original_prompt,:return_value,:cache_mode)
+        WHEN MATCHED THEN UPDATE
+            SET original_prompt = :original_prompt,
+            return_value = :return_value,
+            cache_mode = :cache_mode,
+            updatetime = SYSDATE""",
+
+    "get_by_id_doc_status": "SELECT id FROM LIGHTRAG_DOC_FULL WHERE workspace=:workspace AND id IN ({ids})",
+    
+    "get_status_counts":  """SELECT status as "status", COUNT(1) as "count"
+        FROM LIGHTRAG_DOC_FULL WHERE workspace=:workspace GROUP BY STATUS""",
+    
+    "get_docs_by_status": """select content_length,status,
+        TO_CHAR(created_at,'YYYY-MM-DD HH24:MI:SS') as created_at,TO_CHAR(updatetime,'YYYY-MM-DD HH24:MI:SS') as updatetime
+        from LIGHTRAG_DOC_STATUS where workspace=:workspace and status=:status""",
+    
+    "merge_doc_status":"""MERGE INTO LIGHTRAG_DOC_FULL a
+        USING DUAL
+        ON (a.id = :id and a.workspace = :workspace)
+        WHEN NOT MATCHED THEN
+            INSERT (id,content_summary,content_length,chunks_count,status) values(:id,:content_summary,:content_length,:chunks_count,:status)
+        WHEN MATCHED THEN UPDATE
+            SET content_summary = :content_summary,
+                content_length = :content_length,
+                chunks_count = :chunks_count,
+                status = :status,
+                updatetime = SYSDATE""",
+    
     # 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
@@ -714,16 +911,22 @@ SQL_TEMPLATES = {
             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)
+                    ON (a.workspace=:workspace and a.name=:name)
                 WHEN NOT MATCHED THEN
                     INSERT(workspace,name,entity_type,description,source_chunk_id,content,content_vector)
-                    values (:workspace,:name,:entity_type,:description,:source_chunk_id,:content,:content_vector) """,
+                    values (:workspace,:name,:entity_type,:description,:source_chunk_id,:content,:content_vector)
+                WHEN MATCHED THEN
+                    UPDATE SET 
+                    entity_type=:entity_type,description=:description,source_chunk_id=:source_chunk_id,content=:content,content_vector=:content_vector,updatetime=SYSDATE""",
     "merge_edge": """MERGE INTO LIGHTRAG_GRAPH_EDGES a
                     USING DUAL
-                    ON (a.workspace = :workspace and a.source_name=:source_name and a.target_name=:target_name and a.source_chunk_id=:source_chunk_id)
+                    ON (a.workspace=:workspace and a.source_name=:source_name and a.target_name=:target_name)
                 WHEN NOT MATCHED THEN
                     INSERT(workspace,source_name,target_name,weight,keywords,description,source_chunk_id,content,content_vector)
-                    values (:workspace,:source_name,:target_name,:weight,:keywords,:description,:source_chunk_id,:content,:content_vector) """,
+                    values (:workspace,:source_name,:target_name,:weight,:keywords,:description,:source_chunk_id,:content,:content_vector)
+                WHEN MATCHED THEN
+                    UPDATE SET 
+                    weight=:weight,keywords=:keywords,description=:description,source_chunk_id=:source_chunk_id,content=:content,content_vector=:content_vector,updatetime=SYSDATE""",
     "get_all_nodes": """WITH t0 AS (
                         SELECT name AS id, entity_type AS label, entity_type, description,
                             '["' || replace(source_chunk_id, '<SEP>', '","') || '"]'     source_chunk_ids

lightrag/lightrag.py

@@ -79,6 +79,7 @@ Neo4JStorage = lazy_external_import(".kg.neo4j_impl", "Neo4JStorage")
 OracleKVStorage = lazy_external_import(".kg.oracle_impl", "OracleKVStorage")
 OracleGraphStorage = lazy_external_import(".kg.oracle_impl", "OracleGraphStorage")
 OracleVectorDBStorage = lazy_external_import(".kg.oracle_impl", "OracleVectorDBStorage")
+OracleDocStatusStorage = lazy_external_import(".kg.oracle_impl", "OracleDocStatusStorage")
 MilvusVectorDBStorge = lazy_external_import(".kg.milvus_impl", "MilvusVectorDBStorge")
 MongoKVStorage = lazy_external_import(".kg.mongo_impl", "MongoKVStorage")
 ChromaVectorDBStorage = lazy_external_import(".kg.chroma_impl", "ChromaVectorDBStorage")
@@ -290,6 +291,7 @@ class LightRAG:
             # kv storage
             "JsonKVStorage": JsonKVStorage,
             "OracleKVStorage": OracleKVStorage,
+            "OracleDocStatusStorage":OracleDocStatusStorage,
             "MongoKVStorage": MongoKVStorage,
             "TiDBKVStorage": TiDBKVStorage,
             # vector storage

lightrag/operate.py

@@ -59,13 +59,15 @@ async def _handle_entity_relation_summary(
     description: str,
     global_config: dict,
 ) -> str:
+    """Handle entity relation summary
+    For each entity or relation, input is the combined description of already existing description and new description.
+    If too long, use LLM to summarize.
+    """
     use_llm_func: callable = global_config["llm_model_func"]
     llm_max_tokens = global_config["llm_model_max_token_size"]
     tiktoken_model_name = global_config["tiktoken_model_name"]
     summary_max_tokens = global_config["entity_summary_to_max_tokens"]
-    language = global_config["addon_params"].get(
+    language = global_config["addon_params"].get("language", PROMPTS["DEFAULT_LANGUAGE"])
-        "language", PROMPTS["DEFAULT_LANGUAGE"]
-    )
 
     tokens = encode_string_by_tiktoken(description, model_name=tiktoken_model_name)
     if len(tokens) < summary_max_tokens:  # No need for summary
@@ -139,6 +141,7 @@ async def _merge_nodes_then_upsert(
     knowledge_graph_inst: BaseGraphStorage,
     global_config: dict,
 ):
+    """Get existing nodes from knowledge graph use name,if exists, merge data, else create, then upsert."""
     already_entity_types = []
     already_source_ids = []
     already_description = []
@@ -319,7 +322,7 @@ async def extract_entities(
                 llm_response_cache.global_config = new_config
                 need_to_restore = True
             if history_messages:
-                history = json.dumps(history_messages)
+                history = json.dumps(history_messages,ensure_ascii=False)
                 _prompt = history + "\n" + input_text
             else:
                 _prompt = input_text
@@ -351,6 +354,11 @@ async def extract_entities(
             return await use_llm_func(input_text)
 
     async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]):
+        """"Prpocess a single chunk
+        Args:
+            chunk_key_dp (tuple[str, TextChunkSchema]):
+                ("chunck-xxxxxx", {"tokens": int, "content": str, "full_doc_id": str, "chunk_order_index": int})
+        """
         nonlocal already_processed, already_entities, already_relations
         chunk_key = chunk_key_dp[0]
         chunk_dp = chunk_key_dp[1]

lightrag/utils.py

@@ -36,7 +36,7 @@ logger = logging.getLogger("lightrag")
 def set_logger(log_file: str):
     logger.setLevel(logging.DEBUG)
 
-    file_handler = logging.FileHandler(log_file)
+    file_handler = logging.FileHandler(log_file, encoding='utf-8')
     file_handler.setLevel(logging.DEBUG)
 
     formatter = logging.Formatter(
@@ -473,7 +473,7 @@ async def handle_cache(hashing_kv, args_hash, prompt, mode="default"):
     quantized = min_val = max_val = None
     if is_embedding_cache_enabled:
         # Use embedding cache
-        embedding_model_func = hashing_kv.global_config["embedding_func"]["func"]
+        embedding_model_func = hashing_kv.global_config["embedding_func"].func  #["func"]
         llm_model_func = hashing_kv.global_config.get("llm_model_func")
 
         current_embedding = await embedding_model_func([prompt])