has method local been solved yet?

[Chenboshi114514]

(graphrag) D:\anaconda\env\graphrag>python -m graphrag.query --root ./ragtest --method local "本文的主旨是什么?"

INFO: Reading settings from ragtest\settings.yaml

INFO: Vector Store Args: {} creating llm client with {'api_key': 'REDACTED,len=9', 'type': "openai_chat", 'model': 'llama3.1:latest', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 1.0, 'n': 1, 'request_timeout': 180.0, 'api_base': 'http://localhost:11434/v1', 'api_version': None, 'organization': None, 'proxy': None, 'cognitive_services_endpoint': None, 'deployment_name': None, 'model_supports_json': True, 'tokens_per_minute': 0, 'requests_per_minute': 0, 'max_retries': 10, 'max_retry_wait': 10.0, 'sleep_on_rate_limit_recommendation': True, 'concurrent_requests': 25} creating embedding llm client with {'api_key': 'REDACTED,len=9', 'type': "openai_embedding", 'model': 'nomic-embed-text', 'max_tokens': 4000, 'temperature': 0, 'top_p': 1, 'n': 1, 'request_timeout': 180.0, 'api_base': 'http://localhost:11434/api', 'api_version': None, 'organization': None, 'proxy': None, 'cognitive_services_endpoint': None, 'deployment_name': None, 'model_supports_json': None, 'tokens_per_minute': 0, 'requests_per_minute': 0, 'max_retries': 10, 'max_retry_wait': 10.0, 'sleep_on_rate_limit_recommendation': True, 'concurrent_requests': 25} Error embedding chunk {'OpenAIEmbedding': "'NoneType' object is not iterable"} Traceback (most recent call last): File "", line 198, in _run_module_as_main File "", line 88, in _run_code File "D:\anaconda\env\graphrag\graphrag\query__main__.py", line 86, in run_local_search( File "D:\anaconda\env\graphrag\graphrag\query\cli.py", line 98, in run_local_search return asyncio.run( ^^^^^^^^^^^^ File "D:\anaconda\miniconda3\envs\graphrag\Lib\asyncio\runners.py", line 194, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "D:\anaconda\miniconda3\envs\graphrag\Lib\asyncio\runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\miniconda3\envs\graphrag\Lib\asyncio\base_events.py", line 687, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "D:\anaconda\env\graphrag\graphrag\query\api.py", line 190, in local_search result = await search_engine.asearch(query=query) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\env\graphrag\graphrag\query\structured_search\local_search\search.py", line 66, in asearch context_text, context_records = self.context_builder.build_context( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\env\graphrag\graphrag\query\structured_search\local_search\mixed_context.py", line 139, in build_context selected_entities = map_query_to_entities( ^^^^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\env\graphrag\graphrag\query\context_builder\entity_extraction.py", line 55, in map_query_to_entities search_results = text_embedding_vectorstore.similarity_search_by_text( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\env\graphrag\graphrag\vector_stores\lancedb.py", line 118, in similarity_search_by_text query_embedding = text_embedder(text) ^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\env\graphrag\graphrag\query\context_builder\entity_extraction.py", line 57, in text_embedder=lambda t: text_embedder.embed(t), ^^^^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\env\graphrag\graphrag\query\llm\oai\embedding.py", line 96, in embed chunk_embeddings = np.average(chunk_embeddings, axis=0, weights=chunk_lens) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\miniconda3\envs\graphrag\Lib\site-packages\numpy\lib\function_base.py", line 550, in average raise ZeroDivisionError( ZeroDivisionError: Weights sum to zero, can't be normalized

[kilimanj4r0]

Hi! Changing api_base: 'http://localhost:11434/api' to api_base: 'http://localhost:11434/v1' for embedding model config (in settings.yaml) solved the issue for me. Also, refer to this thread which explains what line to add to graphrag\graphrag\query\llm\oai\embedding.py.

[Chenboshi114514]

Hi! Changing api_base: 'http://localhost:11434/api' to api_base: 'http://localhost:11434/v1' for embedding model config (in settings.yaml) solved the issue for me. Also, refer to this thread which explains what line to add to graphrag\graphrag\query\llm\oai\embedding.py.

thx！