Linting

README.md

@@ -923,4 +923,3 @@ primaryClass={cs.IR}
 }
 ```
 **Thank you for your interest in our work!**
-

examples/lightrag_api_openai_compatible_demo.py

@@ -33,7 +33,7 @@ if not os.path.exists(WORKING_DIR):
 
 
 async def llm_model_func(
-        prompt, system_prompt=None, history_messages=[], **kwargs
+    prompt, system_prompt=None, history_messages=[], **kwargs
 ) -> str:
     return await openai_complete_if_cache(
         LLM_MODEL,
@@ -66,9 +66,11 @@ async def get_embedding_dim():
 rag = LightRAG(
     working_dir=WORKING_DIR,
     llm_model_func=llm_model_func,
-    embedding_func=EmbeddingFunc(embedding_dim=asyncio.run(get_embedding_dim()),
-                                 max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
-                                 func=embedding_func),
+    embedding_func=EmbeddingFunc(
+        embedding_dim=asyncio.run(get_embedding_dim()),
+        max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
+        func=embedding_func,
+    ),
 )
 
 
@@ -99,8 +101,13 @@ async def query_endpoint(request: QueryRequest):
     try:
         loop = asyncio.get_event_loop()
         result = await loop.run_in_executor(
-            None, lambda: rag.query(request.query,
-                                    param=QueryParam(mode=request.mode, only_need_context=request.only_need_context))
+            None,
+            lambda: rag.query(
+                request.query,
+                param=QueryParam(
+                    mode=request.mode, only_need_context=request.only_need_context
+                ),
+            ),
         )
         return Response(status="success", data=result)
     except Exception as e:

lightrag/__init__.py

@@ -1,5 +1,5 @@
 from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
 
-__version__ = "0.0.8"
+__version__ = "0.0.9"
 __author__ = "Zirui Guo"
 __url__ = "https://github.com/HKUDS/LightRAG"

lightrag/kg/__init__.py

@@ -1,3 +1 @@
 # print ("init package vars here. ......")
-
-

lightrag/lightrag.py

@@ -61,7 +61,6 @@ def always_get_an_event_loop() -> asyncio.AbstractEventLoop:
         return loop
 
 
-
 @dataclass
 class LightRAG:
     working_dir: str = field(

lightrag/operate.py

@@ -607,8 +607,7 @@ async def _find_most_related_text_unit_from_entities(
         return []
 
     all_text_units = sorted(
-        all_text_units, 
-        key=lambda x: (x["order"], -x["relation_counts"])
+        all_text_units, key=lambda x: (x["order"], -x["relation_counts"])
     )
 
     all_text_units = truncate_list_by_token_size(

test.py

@@ -1,6 +1,6 @@
 import os
 from lightrag import LightRAG, QueryParam
-from lightrag.llm import gpt_4o_mini_complete, gpt_4o_complete
+from lightrag.llm import gpt_4o_mini_complete
 #########
 # Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
 # import nest_asyncio

test_neo4j.py

@@ -1,6 +1,6 @@
 import os
 from lightrag import LightRAG, QueryParam
-from lightrag.llm import gpt_4o_mini_complete, gpt_4o_complete
+from lightrag.llm import gpt_4o_mini_complete
 
 
 #########