microsoft
[Bug]: Error executing verb "cluster_graph" in create_base_entity_graph: EmptyNetworkError details=None
Describe the bug
06:49:32,683 graphrag.index.reporting.file_workflow_callbacks INFO Error executing verb "cluster_graph" in create_base_entity_graph: EmptyNetworkError details=None
06:49:32,684 graphrag.index.run ERROR error running workflow create_base_entity_graph
Traceback (most recent call last):
File "/usr/local/lib/python3.10/site-packages/graphrag/index/run.py", line 323, in run_pipeline
result = await workflow.run(context, callbacks)
File "/usr/local/lib/python3.10/site-packages/datashaper/workflow/workflow.py", line 369, in run
timing = await self._execute_verb(node, context, callbacks)
File "/usr/local/lib/python3.10/site-packages/datashaper/workflow/workflow.py", line 410, in _execute_verb
result = node.verb.func(**verb_args)
File "/usr/local/lib/python3.10/site-packages/graphrag/index/verbs/graph/clustering/cluster_graph.py", line 61, in cluster_graph
results = output_df[column].apply(lambda graph: run_layout(strategy, graph))
File "/usr/local/lib/python3.10/site-packages/pandas/core/series.py", line 4924, in apply
).apply()
File "/usr/local/lib/python3.10/site-packages/pandas/core/apply.py", line 1427, in apply
return self.apply_standard()
File "/usr/local/lib/python3.10/site-packages/pandas/core/apply.py", line 1507, in apply_standard
mapped = obj._map_values(
File "/usr/local/lib/python3.10/site-packages/pandas/core/base.py", line 921, in _map_values
return algorithms.map_array(arr, mapper, na_action=na_action, convert=convert)
File "/usr/local/lib/python3.10/site-packages/pandas/core/algorithms.py", line 1743, in map_array
return lib.map_infer(values, mapper, convert=convert)
File "lib.pyx", line 2972, in pandas._libs.lib.map_infer
File "/usr/local/lib/python3.10/site-packages/graphrag/index/verbs/graph/clustering/cluster_graph.py", line 61, in
Steps to reproduce
I have configured the local model using ollama and lm studio gemma2:latest and nomic-embed-text-v1.5.Q5_K_M
settings.yaml llm: api_key: ollama type: openai_chat model: gemma2:latest model_supports_json: true api_base: http://192.168.1.107:11434/v1
parallelization: stagger: 0.3
async_mode: threaded
embeddings:
async_mode: threaded llm: api_key: lm-studio type: openai_embedding model: TheBloke/nomic-emb-GGUF/nomic-embed-text-v1.5.Q5_K_M.gguf api_base: http://192.168.1.24:1234/v1
Expected Behavior
No response
GraphRAG Config Used
No response
Logs and screenshots
No response
Additional Information
- GraphRAG Version: 0.1.1
- Operating System: linux
- Python Version: 3.10.14
- Related Issues:
same problem executing verb "cluster_graph" in create_base_entity_graph: Columns must be same length as key
maybe its llm models problem, qwen2:1.5b appear the same issue, but when using mistral:latest, it works
maybe its llm models problem, qwen2:1.5b appear the same issue, but when using mistral:latest, it works也许是它的llm型号问题，qwen2：1.5b出现了同样的问题，但当使用mistral：latest时，它工作正常
I replaced gemma2 and ran it a few more steps, but still have the same problem
mistral is useful
I got the same error after use the prompt tune，and found the new prompt have some issues（eg. lost of {record_delimiter}, wrong format... ）, modify manually according to the init prompt and the problem solved.
I got the same error after use the prompt tune，and found the new prompt have some issues（eg. lost of {record_delimiter}, wrong format... ）, modify manually according to the init prompt and the problem solved.
I don't quite understand, can you explain it in detail?
I got the same error after use the prompt tune，and found the new prompt have some issues（eg. lost of {record_delimiter}, wrong format... ）, modify manually according to the init prompt and the problem solved.
I don't quite understand, can you explain it in detail?
I mean if use the graphrag.promt_tune to generate new prompt or use manual prompt, the new entity extraction prompt may have some issue, such as lack of tuple delimeter or record delimeter，wrong format output in the example..., this may cause the entity extraction result in wrong format. So maybe you can check the entity extraction prompt, especially the output format in example. I think the entity output format should be same as the initial prompt.
I had the same problem but it was solved， My plan is ollama + graphrag，Simply changing the llm and embedding in the settings.yaml is not sufficient。like this： entity_extraction: prompt: "prompts/entity_extraction.txt" entity_types: [organization,person,geo,event] max_gleanings: 0 #here claim_extraction: prompt: "prompts/claim_extraction.txt" description: "Any claims or facts that could be relevant to information discovery." max_gleanings: 0 # here it works。
I had the same problem but it was solved， My plan is ollama + graphrag，Simply changing the llm and embedding in the settings.yaml is not sufficient。like this： entity_extraction: prompt: "prompts/entity_extraction.txt" entity_types: [organization,person,geo,event] max_gleanings: 0 #here claim_extraction: prompt: "prompts/claim_extraction.txt" description: "Any claims or facts that could be relevant to information discovery." max_gleanings: 0 # here it works。
how to say? 细说一下
I got the same error after use the prompt tune，and found the new prompt have some issues（eg. lost of {record_delimiter}, wrong format... ）, modify manually according to the init prompt and the problem solved.
I don't quite understand, can you explain it in detail?
I mean if use the graphrag.promt_tune to generate new prompt or use manual prompt, the new entity extraction prompt may have some issue, such as lack of tuple delimeter or record delimeter，wrong format output in the example..., this may cause the entity extraction result in wrong format. So maybe you can check the entity extraction prompt, especially the output format in example. I think the entity output format should be same as the initial prompt.
this is exactly my case. Thank you for your sharing.
细说就是配置设置的问题，虽然不知道是什么原理但是把我说的那几个地方改一下就能正常运行
在 2024-07-31 16:41:38，"Dr-jw" @.***> 写道：
I had the same problem but it was solved， My plan is ollama + graphrag，Simply changing the llm and embedding in the settings.yaml is not sufficient。like this： entity_extraction: prompt: "prompts/entity_extraction.txt" entity_types: [organization,person,geo,event] max_gleanings: 0 #here claim_extraction: prompt: "prompts/claim_extraction.txt" description: "Any claims or facts that could be relevant to information discovery." max_gleanings: 0 # here it works。
how to say? 细说一下
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