Linting

Author: LarFii

Dockerfile

@@ -53,4 +53,4 @@ VOLUME /data /logs
 EXPOSE 7474 7473 7687
 
 ENTRYPOINT ["tini", "-g", "--", "/startup/docker-entrypoint.sh"]
-CMD ["neo4j"]
+CMD ["neo4j"]

README.md

@@ -196,7 +196,7 @@ rag = LightRAG(
 ### Using Neo4J for Storage
 
 * For production level scenarios you will most likely want to leverage an enterprise solution
-* for KG storage. Running Neo4J in Docker is recommended for seamless local testing.  
+* for KG storage. Running Neo4J in Docker is recommended for seamless local testing.
 * See: https://hub.docker.com/_/neo4j
 
 
@@ -209,7 +209,7 @@ When you launch the project be sure to override the default KG: NetworkS
 by specifying kg="Neo4JStorage".
 
 # Note: Default settings use NetworkX
-#Initialize LightRAG with Neo4J implementation. 
+#Initialize LightRAG with Neo4J implementation.
 WORKING_DIR = "./local_neo4jWorkDir"
 
 rag = LightRAG(
@@ -503,8 +503,8 @@ pip install fastapi uvicorn pydantic
 export RAG_DIR="your_index_directory"  # Optional: Defaults to "index_default"
 export OPENAI_BASE_URL="Your OpenAI API base URL"  # Optional: Defaults to "https://api.openai.com/v1"
 export OPENAI_API_KEY="Your OpenAI API key"  # Required
-export LLM_MODEL="Your LLM model" # Optional: Defaults to "gpt-4o-mini" 
-export EMBEDDING_MODEL="Your embedding model" # Optional: Defaults to "text-embedding-3-large" 
+export LLM_MODEL="Your LLM model" # Optional: Defaults to "gpt-4o-mini"
+export EMBEDDING_MODEL="Your embedding model" # Optional: Defaults to "text-embedding-3-large"
 ```
 
 3. Run the API server:
@@ -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 @@
 
 
 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/kg/neo4j_impl.py

@@ -146,11 +146,11 @@ async def get_edge(
         entity_name_label_target = target_node_id.strip('"')
         """
         Find all edges between nodes of two given labels
-        
+
         Args:
             source_node_label (str): Label of the source nodes
             target_node_label (str): Label of the target nodes
-            
+
         Returns:
             list: List of all relationships/edges found
         """

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

@@ -560,19 +560,19 @@ async def _find_most_related_text_unit_from_entities(
         if not this_edges:
             continue
         all_one_hop_nodes.update([e[1] for e in this_edges])
-    
+
     all_one_hop_nodes = list(all_one_hop_nodes)
     all_one_hop_nodes_data = await asyncio.gather(
         *[knowledge_graph_inst.get_node(e) for e in all_one_hop_nodes]
     )
-    
+
     # Add null check for node data
     all_one_hop_text_units_lookup = {
         k: set(split_string_by_multi_markers(v["source_id"], [GRAPH_FIELD_SEP]))
         for k, v in zip(all_one_hop_nodes, all_one_hop_nodes_data)
         if v is not None and "source_id" in v  # Add source_id check
     }
-    
+
     all_text_units_lookup = {}
     for index, (this_text_units, this_edges) in enumerate(zip(text_units, edges)):
         for c_id in this_text_units:
@@ -586,37 +586,36 @@ async def _find_most_related_text_unit_from_entities(
                         and c_id in all_one_hop_text_units_lookup[e[1]]
                     ):
                         relation_counts += 1
-            
+
             chunk_data = await text_chunks_db.get_by_id(c_id)
             if chunk_data is not None and "content" in chunk_data:  # Add content check
                 all_text_units_lookup[c_id] = {
                     "data": chunk_data,
                     "order": index,
                     "relation_counts": relation_counts,
                 }
-    
+
     # Filter out None values and ensure data has content
     all_text_units = [
-        {"id": k, **v} 
-        for k, v in all_text_units_lookup.items() 
+        {"id": k, **v}
+        for k, v in all_text_units_lookup.items()
         if v is not None and v.get("data") is not None and "content" in v["data"]
     ]
-    
+
     if not all_text_units:
         logger.warning("No valid text units found")
         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(
         all_text_units,
         key=lambda x: x["data"]["content"],
         max_token_size=query_param.max_token_for_text_unit,
     )
-    
+
     all_text_units = [t["data"] for t in all_text_units]
     return all_text_units
 

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
 
 
 #########