microsoft
[Bug]: <Error executing verb \"cluster_graph\" in create_base_entity_graph: EmptyNetworkError>
Describe the bug
Error executing verb "cluster_graph" in create_base_entity_graph: EmptyNetworkError With my own dataset, and openai API key. But do have the extracted entities in the entity_extraction folder and summarize_descriptions folders.
Steps to reproduce
No response
Expected Behavior
No response
GraphRAG Config Used
encoding_model: cl100k_base skip_workflows: [] llm: api_key: ${GRAPHRAG_API_KEY} type: openai_chat # or azure_openai_chat
model: gpt-4-turbo-preview
model: gpt-3.5-turbo-1106
model: gpt-4o-2024-05-13
model_supports_json: true # recommended if this is available for your model.
max_tokens: 4000
request_timeout: 180.0
api_base: https://.openai.azure.com
api_version: 2024-02-15-preview
organization: <organization_id>
deployment_name: <azure_model_deployment_name>
tokens_per_minute: 150_000 # set a leaky bucket throttle
requests_per_minute: 10_000 # set a leaky bucket throttle
max_retries: 10
max_retry_wait: 10.0
sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times
concurrent_requests: 25 # the number of parallel inflight requests that may be made
parallelization: stagger: 0.3
num_threads: 50 # the number of threads to use for parallel processing
async_mode: threaded # or asyncio
embeddings:
parallelization: override the global parallelization settings for embeddings
async_mode: threaded # or asyncio
llm:
api_key: ${GRAPHRAG_API_KEY}
type: openai_embedding # or azure_openai_embedding
model: text-embedding-3-small
# api_base: https://
chunks: size: 300 overlap: 100 group_by_columns: [id] # by default, we don't allow chunks to cross documents
input: type: file # or blob file_type: text # or csv base_dir: "input" file_encoding: utf-8 file_pattern: ".*\.txt$"
cache: type: file # or blob base_dir: "cache"
connection_string: <azure_blob_storage_connection_string>
container_name: <azure_blob_storage_container_name>
storage: type: file # or blob base_dir: "output/${timestamp}/artifacts"
connection_string: <azure_blob_storage_connection_string>
container_name: <azure_blob_storage_container_name>
reporting: type: file # or console, blob base_dir: "output/${timestamp}/reports"
connection_string: <azure_blob_storage_connection_string>
container_name: <azure_blob_storage_container_name>
entity_extraction:
llm: override the global llm settings for this task
parallelization: override the global parallelization settings for this task
async_mode: override the global async_mode settings for this task
prompt: "prompts/entity_extraction.txt" entity_types: [organization,person,geo,event] max_gleanings: 0
summarize_descriptions:
llm: override the global llm settings for this task
parallelization: override the global parallelization settings for this task
async_mode: override the global async_mode settings for this task
prompt: "prompts/summarize_descriptions.txt" max_length: 500
claim_extraction:
llm: override the global llm settings for this task
parallelization: override the global parallelization settings for this task
async_mode: override the global async_mode settings for this task
enabled: true
prompt: "prompts/claim_extraction.txt" description: "Any claims or facts that could be relevant to information discovery." max_gleanings: 0
community_report:
llm: override the global llm settings for this task
parallelization: override the global parallelization settings for this task
async_mode: override the global async_mode settings for this task
prompt: "prompts/community_report.txt" max_length: 2000 max_input_length: 8000
cluster_graph: max_cluster_size: 10
embed_graph: enabled: false # if true, will generate node2vec embeddings for nodes
num_walks: 10
walk_length: 40
window_size: 2
iterations: 3
random_seed: 597832
umap: enabled: false # if true, will generate UMAP embeddings for nodes
if we wanna graphml files as output -> turn graphml to true
snapshots: graphml: false raw_entities: false top_level_nodes: false
local_search:
text_unit_prop: 0.5
community_prop: 0.1
conversation_history_max_turns: 5
top_k_mapped_entities: 10
top_k_relationships: 10
max_tokens: 12000
global_search:
max_tokens: 12000
data_max_tokens: 12000
map_max_tokens: 1000
reduce_max_tokens: 2000
concurrency: 32
Logs and screenshots
{"type": "error", "data": "Error executing verb "cluster_graph" in create_base_entity_graph: EmptyNetworkError", "stack": "Traceback (most recent call last):\n File "/opt/anaconda3/envs/graphrag/lib/python3.11/site-packages/datashaper/workflow/workflow.py", line 410, in _execute_verb\n result = node.verb.func(**verb_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "/opt/anaconda3/envs/graphrag/lib/python3.11/site-packages/graphrag/index/verbs/graph/clustering/cluster_graph.py", line 61, in cluster_graph\n results = output_df[column].apply(lambda graph: run_layout(strategy, graph))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "/opt/anaconda3/envs/graphrag/lib/python3.11/site-packages/pandas/core/series.py", line 4924, in apply\n ).apply()\n ^^^^^^^\n File "/opt/anaconda3/envs/graphrag/lib/python3.11/site-packages/pandas/core/apply.py", line 1427, in apply\n return self.apply_standard()\n ^^^^^^^^^^^^^^^^^^^^^\n File "/opt/anaconda3/envs/graphrag/lib/python3.11/site-packages/pandas/core/apply.py", line 1507, in apply_standard\n mapped = obj._map_values(\n ^^^^^^^^^^^^^^^^\n File "/opt/anaconda3/envs/graphrag/lib/python3.11/site-packages/pandas/core/base.py", line 921, in _map_values\n return algorithms.map_array(arr, mapper, na_action=na_action, convert=convert)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "/opt/anaconda3/envs/graphrag/lib/python3.11/site-packages/pandas/core/algorithms.py", line 1743, in map_array\n return lib.map_infer(values, mapper, convert=convert)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "lib.pyx", line 2972, in pandas._libs.lib.map_infer\n File "/opt/anaconda3/envs/graphrag/lib/python3.11/site-packages/graphrag/index/verbs/graph/clustering/cluster_graph.py", line 61, in
Additional Information
- GraphRAG Version:
- Operating System:
- Python Version:
- Related Issues:
The issue might be due to rate limiting being enforced by endpoints. Please check that the configuration file specifically max_tokens is supported by the model context length you are using.
Alternatively, If you want to use open-source models, I've created a repository for deploying Hugging Face models to local endpoints, offering functionality similar to OpenAI APIs. You can find the repo here: https://github.com/rushizirpe/open-llm-server
Also, I've prepared a Colab notebook for Graphrag Demo here: https://colab.research.google.com/drive/1uhFDnih1WKrSRQHisU-L6xw6coapgR51?usp=sharing. If you don't have access to GPUs like the A100, you'll need a GROQ_API_KEY (which is free with certain limitations), you can obtain it from: https://console.groq.com/keys
This issue has been marked stale due to inactivity after repo maintainer or community member responses that request more information or suggest a solution. It will be closed after five additional days.
![github-actions[bot] avatar](https://avatars.githubusercontent.com/in/15368?v=4 "Published by a pub.dev verified publisher"){kind=small}
I have same error:
18:13:39,527 graphrag.index.reporting.file_workflow_callbacks INFO Error executing verb "cluster_graph" in create_base_entity_graph: EmptyNetworkError details=None
18:13:39,527 graphrag.index.run ERROR error running workflow create_base_entity_graph
Traceback (most recent call last):
File "D:\workspace\graphrag\index\run.py", line 323, in run_pipeline
result = await workflow.run(context, callbacks)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\workspace\venv\Lib\site-packages\datashaper\workflow\workflow.py", line 369, in run
timing = await self._execute_verb(node, context, callbacks)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\workspace\venv\Lib\site-packages\datashaper\workflow\workflow.py", line 410, in _execute_verb
result = node.verb.func(**verb_args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\workspace\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 "D:\workspace\venv\Lib\site-packages\pandas\core\series.py", line 4924, in apply
).apply()
^^^^^^^
File "D:\workspace\venv\Lib\site-packages\pandas\core\apply.py", line 1427, in apply
return self.apply_standard()
^^^^^^^^^^^^^^^^^^^^^
File "D:\workspace\venv\Lib\site-packages\pandas\core\apply.py", line 1507, in apply_standard
mapped = obj._map_values(
^^^^^^^^^^^^^^^^
File "D:\workspace\venv\Lib\site-packages\pandas\core\base.py", line 921, in _map_values
return algorithms.map_array(arr, mapper, na_action=na_action, convert=convert)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\workspace\venv\Lib\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 "D:\workspace\graphrag\index\verbs\graph\clustering\cluster_graph.py", line 61, in <lambda>
results = output_df[column].apply(lambda graph: run_layout(strategy, graph))
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\workspace\graphrag\index\verbs\graph\clustering\cluster_graph.py", line 167, in run_layout
clusters = run_leiden(graph, strategy)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\workspace\graphrag\index\verbs\graph\clustering\strategies\leiden.py", line 26, in run
node_id_to_community_map = _compute_leiden_communities(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\workspace\graphrag\index\verbs\graph\clustering\strategies\leiden.py", line 61, in _compute_leiden_communities
community_mapping = hierarchical_leiden(
^^^^^^^^^^^^^^^^^^^^
File "<@beartype(graspologic.partition.leiden.hierarchical_leiden) at 0x1a3bd57e0c0>", line 304, in hierarchical_leiden
File "D:\workspace\venv\Lib\site-packages\graspologic\partition\leiden.py", line 588, in hierarchical_leiden
hierarchical_clusters_native = gn.hierarchical_leiden(
^^^^^^^^^^^^^^^^^^^^^^^
leiden.EmptyNetworkError: EmptyNetworkError
18:13:39,531 graphrag.index.reporting.file_workflow_callbacks INFO Error running pipeline! details=None
Can folks getting this error upload a full indexing engine.log? The EmptyNetwork error usually happens late in the pipeline and masks a very earlier error with entity extraction, such as an invalid key, incorrect permissions, etc.
收到此错误的人们可以上传完整的索引engine.log吗？EmptyNetwork 错误通常发生在管道的后期，并通过实体提取掩盖了非常早期的错误，例如无效的键、不正确的权限等。
11:10:20,172 graphrag.config.read_dotenv INFO Loading pipeline .env file
11:10:20,174 graphrag.index.cli INFO using default configuration: {
"llm": {
"api_key": "REDACTED, length 6",
"type": "openai_chat",
"model": "qwen2:1.5b",
"max_tokens": 1024,
"temperature": 0.0,
"top_p": 1.0,
"request_timeout": 180.0,
"api_base": "http://localhost:11434/v1",
"api_version": null,
"proxy": null,
"cognitive_services_endpoint": null,
"deployment_name": null,
"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
},
"parallelization": {
"stagger": 0.3,
"num_threads": 50
},
"async_mode": "threaded",
"root_dir": "./ragtest",
"reporting": {
"type": "file",
"base_dir": "output/${timestamp}/reports",
"storage_account_blob_url": null
},
"storage": {
"type": "file",
"base_dir": "output/${timestamp}/artifacts",
"storage_account_blob_url": null
},
"cache": {
"type": "file",
"base_dir": "cache",
"storage_account_blob_url": null
},
"input": {
"type": "file",
"file_type": "text",
"base_dir": "input",
"storage_account_blob_url": null,
"encoding": "utf-8",
"file_pattern": ".\.txt$",
"file_filter": null,
"source_column": null,
"timestamp_column": null,
"timestamp_format": null,
"text_column": "text",
"title_column": null,
"document_attribute_columns": []
},
"embed_graph": {
"enabled": false,
"num_walks": 10,
"walk_length": 40,
"window_size": 2,
"iterations": 3,
"random_seed": 597832,
"strategy": null
},
"embeddings": {
"llm": {
"api_key": "REDACTED, length 9",
"type": "openai_embedding",
"model": "nomic-ai/nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.Q5_K_M.gguf",
"max_tokens": 4000,
"temperature": 0,
"top_p": 1,
"request_timeout": 180.0,
"api_base": "http://localhost:1234/v1",
"api_version": null,
"proxy": null,
"cognitive_services_endpoint": null,
"deployment_name": null,
"model_supports_json": null,
"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
},
"parallelization": {
"stagger": 0.3,
"num_threads": 50
},
"async_mode": "threaded",
"batch_size": 16,
"batch_max_tokens": 8191,
"target": "required",
"skip": [],
"vector_store": null,
"strategy": null
},
"chunks": {
"size": 200,
"overlap": 100,
"group_by_columns": [
"id"
],
"strategy": null
},
"snapshots": {
"graphml": true,
"raw_entities": true,
"top_level_nodes": true
},
"entity_extraction": {
"llm": {
"api_key": "REDACTED, length 6",
"type": "openai_chat",
"model": "qwen2:1.5b",
"max_tokens": 1024,
"temperature": 0.0,
"top_p": 1.0,
"request_timeout": 180.0,
"api_base": "http://localhost:11434/v1",
"api_version": null,
"proxy": null,
"cognitive_services_endpoint": null,
"deployment_name": null,
"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
},
"parallelization": {
"stagger": 0.3,
"num_threads": 50
},
"async_mode": "threaded",
"prompt": "prompts/entity_extraction.txt",
"entity_types": [
"organization",
"person",
"geo",
"event"
],
"max_gleanings": 0,
"strategy": null
},
"summarize_descriptions": {
"llm": {
"api_key": "REDACTED, length 6",
"type": "openai_chat",
"model": "qwen2:1.5b",
"max_tokens": 1024,
"temperature": 0.0,
"top_p": 1.0,
"request_timeout": 180.0,
"api_base": "http://localhost:11434/v1",
"api_version": null,
"proxy": null,
"cognitive_services_endpoint": null,
"deployment_name": null,
"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
},
"parallelization": {
"stagger": 0.3,
"num_threads": 50
},
"async_mode": "threaded",
"prompt": "prompts/summarize_descriptions.txt",
"max_length": 500,
"strategy": null
},
"community_reports": {
"llm": {
"api_key": "REDACTED, length 6",
"type": "openai_chat",
"model": "qwen2:1.5b",
"max_tokens": 1024,
"temperature": 0.0,
"top_p": 1.0,
"request_timeout": 180.0,
"api_base": "http://localhost:11434/v1",
"api_version": null,
"proxy": null,
"cognitive_services_endpoint": null,
"deployment_name": null,
"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
},
"parallelization": {
"stagger": 0.3,
"num_threads": 50
},
"async_mode": "threaded",
"prompt": null,
"max_length": 2000,
"max_input_length": 8000,
"strategy": null
},
"claim_extraction": {
"llm": {
"api_key": "REDACTED, length 6",
"type": "openai_chat",
"model": "qwen2:1.5b",
"max_tokens": 1024,
"temperature": 0.0,
"top_p": 1.0,
"request_timeout": 180.0,
"api_base": "http://localhost:11434/v1",
"api_version": null,
"proxy": null,
"cognitive_services_endpoint": null,
"deployment_name": null,
"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
},
"parallelization": {
"stagger": 0.3,
"num_threads": 50
},
"async_mode": "threaded",
"enabled": false,
"prompt": "prompts/claim_extraction.txt",
"description": "Any claims or facts that could be relevant to information discovery.",
"max_gleanings": 0,
"strategy": null
},
"cluster_graph": {
"max_cluster_size": 10,
"strategy": null
},
"umap": {
"enabled": false
},
"local_search": {
"text_unit_prop": 0.5,
"community_prop": 0.1,
"conversation_history_max_turns": 5,
"top_k_entities": 10,
"top_k_relationships": 10,
"max_tokens": 12000,
"llm_max_tokens": 2000
},
"global_search": {
"temperature": 0.0,
"top_p": 1.0,
"max_tokens": 12000,
"data_max_tokens": 12000,
"map_max_tokens": 1000,
"reduce_max_tokens": 2000,
"concurrency": 32
},
"encoding_model": "cl100k_base",
"skip_workflows": []
}
11:10:20,177 graphrag.index.create_pipeline_config INFO skipping workflows
11:10:20,177 graphrag.index.run INFO Running pipeline
11:10:20,177 graphrag.index.storage.file_pipeline_storage INFO Creating file storage at ragtest\output\20240815-111020\artifacts
11:10:20,178 graphrag.index.input.load_input INFO loading input from root_dir=input
11:10:20,178 graphrag.index.input.load_input INFO using file storage for input
11:10:20,180 graphrag.index.storage.file_pipeline_storage INFO search ragtest\input for files matching ..txt$
11:10:20,180 graphrag.index.input.text INFO found text files from input, found [('CNN_intro.txt', {}), ('GNN_intro.txt', {}), ('machinelearning_intro.txt', {}), ('Transformers_intro.txt', {})]
11:10:20,188 graphrag.index.workflows.load INFO Workflow Run Order: ['create_base_text_units', 'create_base_extracted_entities', 'create_summarized_entities', 'create_base_entity_graph', 'create_final_entities', 'create_final_nodes', 'create_final_communities', 'join_text_units_to_entity_ids', 'create_final_relationships', 'join_text_units_to_relationship_ids', 'create_final_community_reports', 'create_final_text_units', 'create_base_documents', 'create_final_documents']
11:10:20,188 graphrag.index.run INFO Final # of rows loaded: 4
11:10:20,268 graphrag.index.run INFO Running workflow: create_base_text_units...
11:10:20,268 graphrag.index.run INFO dependencies for create_base_text_units: []
11:10:20,271 datashaper.workflow.workflow INFO executing verb orderby
11:10:20,277 datashaper.workflow.workflow INFO executing verb zip
11:10:20,280 datashaper.workflow.workflow INFO executing verb aggregate_override
11:10:20,286 datashaper.workflow.workflow INFO executing verb chunk
11:10:20,423 datashaper.workflow.workflow INFO executing verb select
11:10:20,427 datashaper.workflow.workflow INFO executing verb unroll
11:10:20,432 datashaper.workflow.workflow INFO executing verb rename
11:10:20,436 datashaper.workflow.workflow INFO executing verb genid
11:10:20,440 datashaper.workflow.workflow INFO executing verb unzip
11:10:20,444 datashaper.workflow.workflow INFO executing verb copy
11:10:20,448 datashaper.workflow.workflow INFO executing verb filter
11:10:20,458 graphrag.index.emit.parquet_table_emitter INFO emitting parquet table create_base_text_units.parquet
11:10:20,580 graphrag.index.run INFO Running workflow: create_base_extracted_entities...
11:10:20,580 graphrag.index.run INFO dependencies for create_base_extracted_entities: ['create_base_text_units']
11:10:20,581 graphrag.index.run INFO read table from storage: create_base_text_units.parquet
11:10:20,609 datashaper.workflow.workflow INFO executing verb entity_extract
11:10:20,616 graphrag.llm.openai.create_openai_client INFO Creating OpenAI client base_url=http://localhost:11434/v1
11:10:20,724 graphrag.index.llm.load_llm INFO create TPM/RPM limiter for qwen2:1.5b: TPM=0, RPM=0
11:10:20,724 graphrag.index.llm.load_llm INFO create concurrency limiter for qwen2:1.5b: 25
11:10:26,827 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:26,829 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 6.046999999998661. input_tokens=2134, output_tokens=34
11:10:26,840 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:26,841 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 6.078000000001339. input_tokens=2134, output_tokens=33
11:10:28,800 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:28,801 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 8.04700000000048. input_tokens=2134, output_tokens=35
11:10:33,48 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:33,48 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 12.234000000000378. input_tokens=2134, output_tokens=145
11:10:35,750 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:35,751 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 15.015999999999622. input_tokens=2134, output_tokens=42
11:10:36,679 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:36,679 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 15.906000000000859. input_tokens=1934, output_tokens=135
11:10:43,840 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:43,841 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 23.04700000000048. input_tokens=2134, output_tokens=151
11:10:44,588 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:44,588 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 23.811999999999898. input_tokens=2134, output_tokens=146
11:10:46,574 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:46,575 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 25.82799999999952. input_tokens=2134, output_tokens=409
11:10:51,484 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:51,485 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 30.70299999999952. input_tokens=2134, output_tokens=140
11:10:52,605 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:52,607 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 31.843000000000757. input_tokens=2134, output_tokens=118
11:10:54,617 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:54,619 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 33.85900000000038. input_tokens=2134, output_tokens=36
11:10:57,226 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:57,228 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 36.42200000000048. input_tokens=2134, output_tokens=259
11:10:58,469 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:58,470 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 37.67200000000048. input_tokens=2134, output_tokens=121
11:10:59,955 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:10:59,956 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 39.15599999999904. input_tokens=2134, output_tokens=32
11:11:01,300 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:01,302 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 40.51599999999962. input_tokens=2082, output_tokens=34
11:11:02,814 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:02,815 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 42.03199999999924. input_tokens=2134, output_tokens=35
11:11:03,261 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:03,263 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 42.48499999999876. input_tokens=2134, output_tokens=126
11:11:04,302 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:04,303 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 43.5. input_tokens=2134, output_tokens=36
11:11:05,473 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:05,474 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 44.71900000000096. input_tokens=2134, output_tokens=36
11:11:08,637 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:08,639 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 47.82799999999952. input_tokens=2134, output_tokens=79
11:11:09,216 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:09,218 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 48.46900000000096. input_tokens=2134, output_tokens=752
11:11:10,935 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:10,936 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 44.11000000000058. input_tokens=2134, output_tokens=29
11:11:12,893 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:12,893 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 46.04699999999866. input_tokens=2134, output_tokens=34
11:11:13,747 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:13,749 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 52.9369999999999. input_tokens=2134, output_tokens=151
11:11:15,849 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:15,850 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 42.79700000000048. input_tokens=2134, output_tokens=36
11:11:17,982 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:17,983 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 42.23400000000038. input_tokens=2134, output_tokens=37
11:11:19,593 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:19,595 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 58.78100000000086. input_tokens=2052, output_tokens=292
11:11:21,778 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:21,780 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 52.98400000000038. input_tokens=2134, output_tokens=152
11:11:21,943 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:21,944 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 38.09399999999914. input_tokens=1959, output_tokens=29
11:11:26,257 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:26,260 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 65.42200000000048. input_tokens=2134, output_tokens=338
11:11:27,188 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:27,190 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "Process" with 0 retries took 50.51599999999962. input_tokens=2134, output_tokens=198
11:11:27,207 datashaper.workflow.workflow INFO executing verb snapshot
11:11:27,216 datashaper.workflow.workflow INFO executing verb merge_graphs
11:11:27,236 datashaper.workflow.workflow INFO executing verb snapshot_rows
11:11:27,240 graphrag.index.emit.parquet_table_emitter INFO emitting parquet table create_base_extracted_entities.parquet
11:11:27,425 graphrag.index.run INFO Running workflow: create_summarized_entities...
11:11:27,425 graphrag.index.run INFO dependencies for create_summarized_entities: ['create_base_extracted_entities']
11:11:27,426 graphrag.index.run INFO read table from storage: create_base_extracted_entities.parquet
11:11:27,454 datashaper.workflow.workflow INFO executing verb summarize_descriptions
11:11:30,49 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:30,50 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "summarize" with 0 retries took 2.5310000000008586. input_tokens=156, output_tokens=38
11:11:30,971 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:30,972 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "summarize" with 0 retries took 3.485000000000582. input_tokens=188, output_tokens=62
11:11:32,47 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:32,48 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "summarize" with 0 retries took 4.57799999999952. input_tokens=212, output_tokens=82
11:11:33,386 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:33,388 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "summarize" with 0 retries took 5.890999999999622. input_tokens=189, output_tokens=64
11:11:34,0 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:34,1 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "summarize" with 0 retries took 6.5. input_tokens=169, output_tokens=38
11:11:35,63 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:35,65 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "summarize" with 0 retries took 7.5789999999997235. input_tokens=167, output_tokens=86
11:11:35,142 httpx INFO HTTP Request: POST http://localhost:11434/v1/chat/completions "HTTP/1.1 200 OK"
11:11:35,144 graphrag.llm.base.rate_limiting_llm INFO perf - llm.chat "summarize" with 0 retries took 7.640999999999622. input_tokens=247, output_tokens=147
11:11:35,165 datashaper.workflow.workflow INFO executing verb snapshot_rows
11:11:35,172 graphrag.index.emit.parquet_table_emitter INFO emitting parquet table create_summarized_entities.parquet
11:11:35,367 graphrag.index.run INFO Running workflow: create_base_entity_graph...
11:11:35,367 graphrag.index.run INFO dependencies for create_base_entity_graph: ['create_summarized_entities']
11:11:35,368 graphrag.index.run INFO read table from storage: create_summarized_entities.parquet
11:11:35,399 datashaper.workflow.workflow INFO executing verb cluster_graph
11:11:35,410 datashaper.workflow.workflow ERROR Error executing verb "cluster_graph" in create_base_entity_graph: EmptyNetworkError
Traceback (most recent call last):
File "D:\software\Anaconda3\envs\graphrag\lib\site-packages\datashaper\workflow\workflow.py", line 410, in _execute_verb
result = node.verb.func(**verb_args)
File "E:\learn\graphrag\graphrag-local-ollama-main\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 "D:\software\Anaconda3\envs\graphrag\lib\site-packages\pandas\core\series.py", line 4924, in apply
).apply()
File "D:\software\Anaconda3\envs\graphrag\lib\site-packages\pandas\core\apply.py", line 1427, in apply
return self.apply_standard()
File "D:\software\Anaconda3\envs\graphrag\lib\site-packages\pandas\core\apply.py", line 1507, in apply_standard
mapped = obj._map_values(
File "D:\software\Anaconda3\envs\graphrag\lib\site-packages\pandas\core\base.py", line 921, in _map_values
return algorithms.map_array(arr, mapper, na_action=na_action, convert=convert)
File "D:\software\Anaconda3\envs\graphrag\lib\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 "E:\learn\graphrag\graphrag-local-ollama-main\graphrag\index\verbs\graph\clustering\cluster_graph.py", line 61, in
This issue has been marked stale due to inactivity after repo maintainer or community member responses that request more information or suggest a solution. It will be closed after five additional days.
This issue has been closed after being marked as stale for five days. Please reopen if needed.
still having this error
{ "type": "error", "data": "Error executing verb \"cluster_graph\" in create_base_entity_graph: EmptyNetworkError", "stack": "Traceback (most recent call last):\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/datashaper/workflow/workflow.py\", line 410, in _execute_verb\n result = node.verb.func(**verb_args)\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/graphrag/index/verbs/graph/clustering/cluster_graph.py\", line 61, in cluster_graph\n results = output_df[column].apply(lambda graph: run_layout(strategy, graph))\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/pandas/core/series.py\", line 4924, in apply\n ).apply()\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/pandas/core/apply.py\", line 1427, in apply\n return self.apply_standard()\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/pandas/core/apply.py\", line 1507, in apply_standard\n mapped = obj._map_values(\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/pandas/core/base.py\", line 921, in _map_values\n return algorithms.map_array(arr, mapper, na_action=na_action, convert=convert)\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/pandas/core/algorithms.py\", line 1743, in map_array\n return lib.map_infer(values, mapper, convert=convert)\n File \"lib.pyx\", line 2972, in pandas._libs.lib.map_infer\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/graphrag/index/verbs/graph/clustering/cluster_graph.py\", line 61, in <lambda>\n results = output_df[column].apply(lambda graph: run_layout(strategy, graph))\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/graphrag/index/verbs/graph/clustering/cluster_graph.py\", line 171, in run_layout\n clusters = run_leiden(graph, strategy)\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/graphrag/index/verbs/graph/clustering/strategies/leiden.py\", line 26, in run\n node_id_to_community_map = _compute_leiden_communities(\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/graphrag/index/verbs/graph/clustering/strategies/leiden.py\", line 61, in _compute_leiden_communities\n community_mapping = hierarchical_leiden(\n File \"<@beartype(graspologic.partition.leiden.hierarchical_leiden) at 0x7f5c9904ee60>\", line 304, in hierarchical_leiden\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/graspologic/partition/leiden.py\", line 588, in hierarchical_leiden\n hierarchical_clusters_native = gn.hierarchical_leiden(\nleiden.EmptyNetworkError: EmptyNetworkError\n", "source": "EmptyNetworkError", "details": null } { "type": "error", "data": "Error running pipeline!", "stack": "Traceback (most recent call last):\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/graphrag/index/run/run.py\", line 227, in run_pipeline\n result = await _process_workflow(\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/graphrag/index/run/workflow.py\", line 91, in _process_workflow\n result = await workflow.run(context, callbacks)\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/datashaper/workflow/workflow.py\", line 369, in run\n timing = await self._execute_verb(node, context, callbacks)\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/datashaper/workflow/workflow.py\", line 410, in _execute_verb\n result = node.verb.func(**verb_args)\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/graphrag/index/verbs/graph/clustering/cluster_graph.py\", line 61, in cluster_graph\n results = output_df[column].apply(lambda graph: run_layout(strategy, graph))\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/pandas/core/series.py\", line 4924, in apply\n ).apply()\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/pandas/core/apply.py\", line 1427, in apply\n return self.apply_standard()\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/pandas/core/apply.py\", line 1507, in apply_standard\n mapped = obj._map_values(\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/pandas/core/base.py\", line 921, in _map_values\n return algorithms.map_array(arr, mapper, na_action=na_action, convert=convert)\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/pandas/core/algorithms.py\", line 1743, in map_array\n return lib.map_infer(values, mapper, convert=convert)\n File \"lib.pyx\", line 2972, in pandas._libs.lib.map_infer\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/graphrag/index/verbs/graph/clustering/cluster_graph.py\", line 61, in <lambda>\n results = output_df[column].apply(lambda graph: run_layout(strategy, graph))\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/graphrag/index/verbs/graph/clustering/cluster_graph.py\", line 171, in run_layout\n clusters = run_leiden(graph, strategy)\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/graphrag/index/verbs/graph/clustering/strategies/leiden.py\", line 26, in run\n node_id_to_community_map = _compute_leiden_communities(\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/graphrag/index/verbs/graph/clustering/strategies/leiden.py\", line 61, in _compute_leiden_communities\n community_mapping = hierarchical_leiden(\n File \"<@beartype(graspologic.partition.leiden.hierarchical_leiden) at 0x7f5c9904ee60>\", line 304, in hierarchical_leiden\n File \"/home/zzm/anaconda3/envs/nodule_detection/lib/python3.10/site-packages/graspologic/partition/leiden.py\", line 588, in hierarchical_leiden\n hierarchical_clusters_native = gn.hierarchical_leiden(\nleiden.EmptyNetworkError: EmptyNetworkError\n", "source": "EmptyNetworkError", "details": null }
01:05:27,808 datashaper.workflow.workflow ERROR Error executing verb "cluster_graph" in create_base_entity_graph: EmptyNetworkError
Traceback (most recent call last):
File "/opt/conda/envs/graphrag/lib/python3.10/site-packages/datashaper/workflow/workflow.py", line 410, in _execute_verb
result = node.verb.func(**verb_args)
File "/inspire/hdd/ws-c6f77a66-a5f5-45dc-a4ce-1e856fe7a7b4/project/hejunjun-24017/jiyao/Project/llm-graph-rag-master/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 "/opt/conda/envs/graphrag/lib/python3.10/site-packages/pandas/core/series.py", line 4924, in apply
).apply()
File "/opt/conda/envs/graphrag/lib/python3.10/site-packages/pandas/core/apply.py", line 1427, in apply
return self.apply_standard()
File "/opt/conda/envs/graphrag/lib/python3.10/site-packages/pandas/core/apply.py", line 1507, in apply_standard
mapped = obj._map_values(
File "/opt/conda/envs/graphrag/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 "/opt/conda/envs/graphrag/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 "/inspire/hdd/ws-c6f77a66-a5f5-45dc-a4ce-1e856fe7a7b4/project/hejunjun-24017/jiyao/Project/llm-graph-rag-master/graphrag/index/verbs/graph/clustering/cluster_graph.py", line 61, in <lambda>
results = output_df[column].apply(lambda graph: run_layout(strategy, graph))
File "/inspire/hdd/ws-c6f77a66-a5f5-45dc-a4ce-1e856fe7a7b4/project/hejunjun-24017/jiyao/Project/llm-graph-rag-master/graphrag/index/verbs/graph/clustering/cluster_graph.py", line 167, in run_layout
clusters = run_leiden(graph, strategy)
File "/inspire/hdd/ws-c6f77a66-a5f5-45dc-a4ce-1e856fe7a7b4/project/hejunjun-24017/jiyao/Project/llm-graph-rag-master/graphrag/index/verbs/graph/clustering/strategies/leiden.py", line 26, in run
node_id_to_community_map = _compute_leiden_communities(
File "/inspire/hdd/ws-c6f77a66-a5f5-45dc-a4ce-1e856fe7a7b4/project/hejunjun-24017/jiyao/Project/llm-graph-rag-master/graphrag/index/verbs/graph/clustering/strategies/leiden.py", line 61, in _compute_leiden_communities
community_mapping = hierarchical_leiden(
File "<@beartype(graspologic.partition.leiden.hierarchical_leiden) at 0x7f7bd58beb90>", line 304, in hierarchical_leiden
File "/opt/conda/envs/graphrag/lib/python3.10/site-packages/graspologic/partition/leiden.py", line 588, in hierarchical_leiden
hierarchical_clusters_native = gn.hierarchical_leiden(
leiden.EmptyNetworkError: EmptyNetworkError
01:05:27,810 graphrag.index.reporting.file_workflow_callbacks INFO Error executing verb "cluster_graph" in create_base_entity_graph: EmptyNetworkError details=None
01:05:27,810 graphrag.index.run ERROR error running workflow create_base_entity_graph
Traceback (most recent call last):
File "/inspire/hdd/ws-c6f77a66-a5f5-45dc-a4ce-1e856fe7a7b4/project/hejunjun-24017/jiyao/Project/llm-graph-rag-master/graphrag/index/run.py", line 327, in run_pipeline
result = await workflow.run(context, callbacks)
File "/opt/conda/envs/graphrag/lib/python3.10/site-packages/datashaper/workflow/workflow.py", line 369, in run
timing = await self._execute_verb(node, context, callbacks)
File "/opt/conda/envs/graphrag/lib/python3.10/site-packages/datashaper/workflow/workflow.py", line 410, in _execute_verb
result = node.verb.func(**verb_args)
File "/inspire/hdd/ws-c6f77a66-a5f5-45dc-a4ce-1e856fe7a7b4/project/hejunjun-24017/jiyao/Project/llm-graph-rag-master/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 "/opt/conda/envs/graphrag/lib/python3.10/site-packages/pandas/core/series.py", line 4924, in apply
).apply()
File "/opt/conda/envs/graphrag/lib/python3.10/site-packages/pandas/core/apply.py", line 1427, in apply
return self.apply_standard()
File "/opt/conda/envs/graphrag/lib/python3.10/site-packages/pandas/core/apply.py", line 1507, in apply_standard
mapped = obj._map_values(
File "/opt/conda/envs/graphrag/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 "/opt/conda/envs/graphrag/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 "/inspire/hdd/ws-c6f77a66-a5f5-45dc-a4ce-1e856fe7a7b4/project/hejunjun-24017/jiyao/Project/llm-graph-rag-master/graphrag/index/verbs/graph/clustering/cluster_graph.py", line 61, in <lambda>
results = output_df[column].apply(lambda graph: run_layout(strategy, graph))
File "/inspire/hdd/ws-c6f77a66-a5f5-45dc-a4ce-1e856fe7a7b4/project/hejunjun-24017/jiyao/Project/llm-graph-rag-master/graphrag/index/verbs/graph/clustering/cluster_graph.py", line 167, in run_layout
clusters = run_leiden(graph, strategy)
File "/inspire/hdd/ws-c6f77a66-a5f5-45dc-a4ce-1e856fe7a7b4/project/hejunjun-24017/jiyao/Project/llm-graph-rag-master/graphrag/index/verbs/graph/clustering/strategies/leiden.py", line 26, in run
node_id_to_community_map = _compute_leiden_communities(
File "/inspire/hdd/ws-c6f77a66-a5f5-45dc-a4ce-1e856fe7a7b4/project/hejunjun-24017/jiyao/Project/llm-graph-rag-master/graphrag/index/verbs/graph/clustering/strategies/leiden.py", line 61, in _compute_leiden_communities
community_mapping = hierarchical_leiden(
File "<@beartype(graspologic.partition.leiden.hierarchical_leiden) at 0x7f7bd58beb90>", line 304, in hierarchical_leiden
File "/opt/conda/envs/graphrag/lib/python3.10/site-packages/graspologic/partition/leiden.py", line 588, in hierarchical_leiden
hierarchical_clusters_native = gn.hierarchical_leiden(
leiden.EmptyNetworkError: EmptyNetworkError
01:05:27,811 graphrag.index.reporting.file_workflow_callbacks INFO Error running pipeline! details=None
I encountered a similar issue as well. May I ask if there's a solution?
Here is my .env file configured with Ollama:
GRAPHRAG_API_BASE=http://localhost:11434/v1
GRAPHRAG_CHAT_API_KEY=ollama
GRAPHRAG_CHAT_MODEL=qwen2:latest
GRAPHRAG_EMBEDDING_API_KEY=ollama
GRAPHRAG_EMBEDDING_MODEL=nomic-embed-text:latest
