style: fix typo and format code (#2618)

### What problem does this PR solve?

- Fix typo
- Remove unused import
- Format code

### Type of change

- [x] Other (please describe): typo and format

api/db/services/llm_service.py

@@ -169,8 +169,8 @@ class TenantLLMService(CommonService):
 
         num = 0
         try:
-            for u in cls.query(tenant_id = tenant_id, llm_name=mdlnm):
-                num += cls.model.update(used_tokens = u.used_tokens + used_tokens)\
+            for u in cls.query(tenant_id=tenant_id, llm_name=mdlnm):
+                num += cls.model.update(used_tokens=u.used_tokens + used_tokens)\
                     .where(cls.model.tenant_id == tenant_id, cls.model.llm_name == mdlnm)\
                     .execute()
         except Exception as e:
@@ -252,7 +252,6 @@ class LLMBundle(object):
                 return
             yield chunk     
 
-    
     def chat(self, system, history, gen_conf):
         txt, used_tokens = self.mdl.chat(system, history, gen_conf)
         if not TenantLLMService.increase_usage(

graphrag/index.py

@@ -13,7 +13,6 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-import re
 from concurrent.futures import ThreadPoolExecutor
 import json
 from functools import reduce
@@ -24,7 +23,7 @@ from api.db.services.llm_service import LLMBundle
 from api.db.services.user_service import TenantService
 from graphrag.community_reports_extractor import CommunityReportsExtractor
 from graphrag.entity_resolution import EntityResolution
-from graphrag.graph_extractor import GraphExtractor
+from graphrag.graph_extractor import GraphExtractor, DEFAULT_ENTITY_TYPES
 from graphrag.mind_map_extractor import MindMapExtractor
 from rag.nlp import rag_tokenizer
 from rag.utils import num_tokens_from_string
@@ -52,7 +51,7 @@ def graph_merge(g1, g2):
     return g
 
 
-def build_knowlege_graph_chunks(tenant_id: str, chunks: List[str], callback, entity_types=["organization", "person", "location", "event", "time"]):
+def build_knowledge_graph_chunks(tenant_id: str, chunks: List[str], callback, entity_types=DEFAULT_ENTITY_TYPES):
     _, tenant = TenantService.get_by_id(tenant_id)
     llm_bdl = LLMBundle(tenant_id, LLMType.CHAT, tenant.llm_id)
     ext = GraphExtractor(llm_bdl)

rag/app/knowledge_graph.py

@@ -1,6 +1,6 @@
 import re
 
-from graphrag.index import build_knowlege_graph_chunks
+from graphrag.index import build_knowledge_graph_chunks
 from rag.app import naive
 from rag.nlp import rag_tokenizer, tokenize_chunks
 
@@ -15,9 +15,9 @@ def chunk(filename, binary, tenant_id, from_page=0, to_page=100000,
     parser_config["layout_recognize"] = False
     sections = naive.chunk(filename, binary, from_page=from_page, to_page=to_page, section_only=True,
                            parser_config=parser_config, callback=callback)
-    chunks = build_knowlege_graph_chunks(tenant_id, sections, callback,
-                                         parser_config.get("entity_types", ["organization", "person", "location", "event", "time"])
-                                         )
+    chunks = build_knowledge_graph_chunks(tenant_id, sections, callback,
+                                          parser_config.get("entity_types", ["organization", "person", "location", "event", "time"])
+                                          )
     for c in chunks: c["docnm_kwd"] = filename
 
     doc = {

rag/llm/chat_model.py

@@ -20,7 +20,6 @@ from abc import ABC
 from openai import OpenAI
 import openai
 from ollama import Client
-from volcengine.maas.v2 import MaasService
 from rag.nlp import is_english
 from rag.utils import num_tokens_from_string
 from groq import Groq
@@ -29,6 +28,7 @@ import json
 import requests
 import asyncio
 
+
 class Base(ABC):
     def __init__(self, key, model_name, base_url):
         self.client = OpenAI(api_key=key, base_url=base_url)

rag/utils/__init__.py

@@ -78,11 +78,9 @@ encoder = tiktoken.encoding_for_model("gpt-3.5-turbo")
 def num_tokens_from_string(string: str) -> int:
     """Returns the number of tokens in a text string."""
     try:
-        num_tokens = len(encoder.encode(string))
-        return num_tokens
-    except Exception as e:
-        pass
-    return 0
+        return len(encoder.encode(string))
+    except Exception:
+        return 0
 
 
 def truncate(string: str, max_len: int) -> str: