fix bug

.gitignore

@@ -10,3 +10,4 @@ local_neo4jWorkDir/
 neo4jWorkDir/
 ignore_this.txt
 .venv/
+*.ignore.*

lightrag/base.py

@@ -84,7 +84,7 @@ class BaseKVStorage(Generic[T], StorageNameSpace):
 
 @dataclass
 class BaseGraphStorage(StorageNameSpace):
-    embedding_func: EmbeddingFunc
+    embedding_func: EmbeddingFunc = None
     async def has_node(self, node_id: str) -> bool:
         raise NotImplementedError
 

lightrag/operate.py

@@ -16,6 +16,7 @@ from .utils import (
     split_string_by_multi_markers,
     truncate_list_by_token_size,
     process_combine_contexts,
+    locate_json_string_body_from_string
 )
 from .base import (
     BaseGraphStorage,
@@ -403,9 +404,10 @@ async def local_query(
     kw_prompt_temp = PROMPTS["keywords_extraction"]
     kw_prompt = kw_prompt_temp.format(query=query)
     result = await use_model_func(kw_prompt)
+    json_text = locate_json_string_body_from_string(result)
 
     try:
-        keywords_data = json.loads(result)
+        keywords_data = json.loads(json_text)
         keywords = keywords_data.get("low_level_keywords", [])
         keywords = ", ".join(keywords)
     except json.JSONDecodeError:
@@ -670,9 +672,10 @@ async def global_query(
     kw_prompt_temp = PROMPTS["keywords_extraction"]
     kw_prompt = kw_prompt_temp.format(query=query)
     result = await use_model_func(kw_prompt)
+    json_text = locate_json_string_body_from_string(result)
 
     try:
-        keywords_data = json.loads(result)
+        keywords_data = json.loads(json_text)
         keywords = keywords_data.get("high_level_keywords", [])
         keywords = ", ".join(keywords)
     except json.JSONDecodeError:
@@ -911,8 +914,9 @@ async def hybrid_query(
     kw_prompt = kw_prompt_temp.format(query=query)
 
     result = await use_model_func(kw_prompt)
+    json_text = locate_json_string_body_from_string(result)
     try:
-        keywords_data = json.loads(result)
+        keywords_data = json.loads(json_text)
         hl_keywords = keywords_data.get("high_level_keywords", [])
         ll_keywords = keywords_data.get("low_level_keywords", [])
         hl_keywords = ", ".join(hl_keywords)

lightrag/prompt.py

@@ -14,7 +14,7 @@ Given a text document that is potentially relevant to this activity and a list o
 
 -Steps-
 1. Identify all entities. For each identified entity, extract the following information:
-- entity_name: Name of the entity, capitalized
+- entity_name: Name of the entity, use same language as input text. If English, capitalized the name.
 - entity_type: One of the following types: [{entity_types}]
 - entity_description: Comprehensive description of the entity's attributes and activities
 Format each entity as ("entity"{tuple_delimiter}<entity_name>{tuple_delimiter}<entity_type>{tuple_delimiter}<entity_description>

test.py

@@ -18,7 +18,7 @@ rag = LightRAG(
     # llm_model_func=gpt_4o_complete  # Optionally, use a stronger model
 )
 
-with open("./book.txt") as f:
+with open("./dickens/book.txt", "r", encoding="utf-8") as f:
     rag.insert(f.read())
 
 # Perform naive search