[Issue]: <NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.>

[chericher]

Do you need to file an issue?

• I have searched the existing issues and this bug is not already filed.
• My model is hosted on OpenAI or Azure. If not, please look at the "model providers" issue and don't file a new one here.
• I believe this is a legitimate bug, not just a question. If this is a question, please use the Discussions area.

Describe the issue

"I have locally deployed bge-large-zh-v1.5 + qwen2.5-3B-Instruct + GraphRAG 1.1.2, using Python 3.10.12 and torch 2.5. When I run the graphrag index --root ./ command, I encounter the following error:"

15:05:20,104 graphrag.utils.storage INFO reading table from storage: create_final_relationships.parquet
15:05:20,108 graphrag.utils.storage INFO reading table from storage: create_final_entities.parquet
15:05:20,113 graphrag.utils.storage INFO reading table from storage: create_final_communities.parquet
15:05:20,130 graphrag.index.operations.summarize_communities.prepare_community_reports INFO Number of nodes at level=0 => 3
15:05:24,750 httpx INFO HTTP Request: POST http://localhost:8000/v1/chat/completions "HTTP/1.1 200 OK"
15:05:24,912 graphrag.utils.storage INFO reading table from storage: create_final_documents.parquet
15:05:24,917 graphrag.utils.storage INFO reading table from storage: create_final_relationships.parquet
15:05:24,922 graphrag.utils.storage INFO reading table from storage: create_final_text_units.parquet
15:05:24,927 graphrag.utils.storage INFO reading table from storage: create_final_entities.parquet
15:05:24,932 graphrag.utils.storage INFO reading table from storage: create_final_community_reports.parquet
15:05:24,942 graphrag.index.flows.generate_text_embeddings INFO Creating embeddings
15:05:24,942 graphrag.index.operations.embed_text.embed_text INFO using vector store lancedb with container_name default for embedding entity.description: default-entity-description
15:05:25,143 graphrag.index.operations.embed_text.strategies.openai INFO embedding 3 inputs via 3 snippets using 1 batches. max_batch_size=16, max_tokens=8191
15:05:25,391 httpx INFO HTTP Request: POST http://localhost:8150/v1/embeddings "HTTP/1.1 200 OK"
15:05:25,432 graphrag.index.operations.embed_text.embed_text INFO using vector store lancedb with container_name default for embedding text_unit.text: default-text_unit-text
15:05:25,436 graphrag.index.operations.embed_text.strategies.openai INFO embedding 1 inputs via 1 snippets using 1 batches. max_batch_size=16, max_tokens=8191
15:05:25,445 graphrag.index.operations.embed_text.embed_text INFO using vector store lancedb with container_name default for embedding community.full_content: default-community-full_content
15:05:25,448 graphrag.index.operations.embed_text.strategies.openai INFO embedding 1 inputs via 1 snippets using 1 batches. max_batch_size=16, max_tokens=8191
15:05:25,471 httpx INFO HTTP Request: POST http://localhost:8150/v1/embeddings "HTTP/1.1 400 Bad Request"
15:05:25,475 graphrag.callbacks.file_workflow_callbacks INFO Error Invoking LLM details={'prompt': ["# Family A\n\nThe community revolves around the key entities A, F, and M, who are related by familial ties. A is the child of F and M, and both F and M are parents of A. This family structure is central to the community's dynamics.\n\n## F and M as parents\n\nF and M are the parents of A, and their roles as parents are central to the community's structure. Their relationship with A is crucial in understanding the dynamics of the family. [Data: Entities (1, 2), Relationships (0, 1, +more)]\n\n## A as the child\n\nA is the child of F and M, and their relationship with A is central to the community's structure. A's role as a child is significant in understanding the family dynamics and potential conflicts. [Data: Entities (0), Relationships (0, 1, +more)]\n\n## F and M's combined degree\n\nF and M have a combined degree of 3, indicating their significant role in the community. Their relationship with A is crucial in understanding the family dynamics and potential conflicts. [Data: Entities (1, 2), Relationships (0, 1, +more)]\n\n## A's relationship with F and M\n\nA's relationship with F and M is central to the community's structure. Their roles as parents and the relationship with A are significant in understanding the family dynamics and potential conflicts. [Data: Entities (0), Relationships (0, 1, +more)]\n\n## Family structure\n\nThe family structure is central to the community's dynamics, with F and M as parents and A as the child. This structure is significant in understanding the potential for family disputes or conflicts. [Data: Entities (1, 2), Relationships (0, 1, +more)]"], 'kwargs': {}}
15:05:25,476 graphrag.index.run.run_workflows ERROR error running workflow generate_text_embeddings
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/run/run_workflows.py", line 166, in _run_workflows
result = await run_workflow(
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/workflows/generate_text_embeddings.py", line 45, in run_workflow
await generate_text_embeddings(
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/flows/generate_text_embeddings.py", line 98, in generate_text_embeddings
await _run_and_snapshot_embeddings(
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/flows/generate_text_embeddings.py", line 121, in _run_and_snapshot_embeddings
data["embedding"] = await embed_text(
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/operations/embed_text/embed_text.py", line 89, in embed_text
return await _text_embed_with_vector_store(
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/operations/embed_text/embed_text.py", line 179, in _text_embed_with_vector_store
result = await strategy_exec(
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/operations/embed_text/strategies/openai.py", line 63, in run
embeddings = await _execute(llm, text_batches, ticker, semaphore)
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/operations/embed_text/strategies/openai.py", line 103, in _execute
results = await asyncio.gather(*futures)
File "/usr/local/lib/python3.10/dist-packages/graphrag/index/operations/embed_text/strategies/openai.py", line 97, in embed
chunk_embeddings = await llm(chunk)
File "/usr/local/lib/python3.10/dist-packages/fnllm/base/base.py", line 112, in call
return await self._invoke(prompt, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/fnllm/base/base.py", line 128, in _invoke
return await self._decorated_target(prompt, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/fnllm/services/retryer.py", line 109, in invoke
result = await execute_with_retry()
File "/usr/local/lib/python3.10/dist-packages/fnllm/services/retryer.py", line 93, in execute_with_retry
async for a in AsyncRetrying(
File "/usr/local/lib/python3.10/dist-packages/tenacity/asyncio/init.py", line 166, in anext
do = await self.iter(retry_state=self._retry_state)
File "/usr/local/lib/python3.10/dist-packages/tenacity/asyncio/init.py", line 153, in iter
result = await action(retry_state)
File "/usr/local/lib/python3.10/dist-packages/tenacity/_utils.py", line 99, in inner
return call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/tenacity/init.py", line 398, in
self._add_action_func(lambda rs: rs.outcome.result())
File "/usr/lib/python3.10/concurrent/futures/_base.py", line 451, in result
return self.__get_result()
File "/usr/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
raise self._exception
File "/usr/local/lib/python3.10/dist-packages/fnllm/services/retryer.py", line 101, in execute_with_retry
return await attempt()
File "/usr/local/lib/python3.10/dist-packages/fnllm/services/retryer.py", line 78, in attempt
return await delegate(prompt, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/fnllm/services/rate_limiter.py", line 70, in invoke
result = await delegate(prompt, **args)
File "/usr/local/lib/python3.10/dist-packages/fnllm/base/base.py", line 152, in _decorator_target
output = await self._execute_llm(prompt, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/fnllm/openai/llm/embeddings.py", line 133, in _execute_llm
response = await self._call_embeddings_or_cache(
File "/usr/local/lib/python3.10/dist-packages/fnllm/openai/llm/embeddings.py", line 110, in _call_embeddings_or_cache
return await self._cache.get_or_insert(
File "/usr/local/lib/python3.10/dist-packages/fnllm/services/cache_interactor.py", line 50, in get_or_insert
entry = await func()
File "/usr/local/lib/python3.10/dist-packages/openai/resources/embeddings.py", line 236, in create
return await self._post(
File "/usr/local/lib/python3.10/dist-packages/openai/_base_client.py", line 1849, in post
return await self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)
File "/usr/local/lib/python3.10/dist-packages/openai/_base_client.py", line 1543, in request
return await self._request(
File "/usr/local/lib/python3.10/dist-packages/openai/_base_client.py", line 1644, in _request
raise self._make_status_error_from_response(err.response) from None
openai.BadRequestError: Error code: 400 - {'object': 'error', 'message': 'NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.\n\n(The expanded size of the tensor (513) must match the existing size (512) at non-singleton dimension 1. Target sizes: [1, 513]. Tensor sizes: [1, 512])', 'code': 50001}
15:05:25,477 graphrag.callbacks.file_workflow_callbacks INFO Error running pipeline! details=None
15:05:25,554 graphrag.cli.index ERROR Errors occurred during the pipeline run, see logs for more details.

Steps to reproduce

No response

GraphRAG Config Used

### This config file contains required core defaults that must be set, along with a handful of common optional settings.
### For a full list of available settings, see https://microsoft.github.io/graphrag/config/yaml/

### LLM settings ###
## There are a number of settings to tune the threading and token limits for LLM calls - check the docs.

encoding_model: cl100k_base # this needs to be matched to your model!

llm:
  api_key: ${GRAPHRAG_API_KEY} # set this in the generated .env file
  type: openai_chat # or azure_openai_chat
  model: qwen3B
  model_supports_json: false # recommended if this is available for your model.
  # audience: "https://cognitiveservices.azure.com/.default"
  api_base: http://localhost:8000/v1
  # api_version: 2024-02-15-preview
  # organization: <organization_id>
  # deployment_name: <azure_model_deployment_name>

parallelization:
  stagger: 0.3
  # num_threads: 50

async_mode: threaded # or asyncio

embeddings:
  async_mode: threaded # or asyncio
  vector_store: 
    type: lancedb
    db_uri: 'output/lancedb'
    container_name: default
    overwrite: true
  llm:
    api_key: ${GRAPHRAG_API_KEY}
    type: openai_embedding # or azure_openai_embedding
    model: gpt-4
    api_base: http://localhost:8150/v1
    # api_version: 2024-02-15-preview
    # audience: "https://cognitiveservices.azure.com/.default"
    # organization: <organization_id>
    # deployment_name: <azure_model_deployment_name>

### Input settings ###

input:
  type: file # or blob
  file_type: text # or csv
  base_dir: "input"
  file_encoding: utf-8
  file_pattern: ".*\\.txt$"

chunks:
  size: 1200
  overlap: 100
  group_by_columns: [id]

### Storage settings ###
## If blob storage is specified in the following four sections,
## connection_string and container_name must be provided

cache:
  type: file # one of [blob, cosmosdb, file]
  base_dir: "cache"

reporting:
  type: file # or console, blob
  base_dir: "logs"

storage:
  type: file # one of [blob, cosmosdb, file]
  base_dir: "output"

## only turn this on if running `graphrag index` with custom settings
## we normally use `graphrag update` with the defaults
update_index_storage:
  # type: file # or blob
  # base_dir: "update_output"

### Workflow settings ###

skip_workflows: []

entity_extraction:
  prompt: "prompts/entity_extraction.txt"
  entity_types: [organization,person,geo,event]
  max_gleanings: 1

summarize_descriptions:
  prompt: "prompts/summarize_descriptions.txt"
  max_length: 1000

claim_extraction:
  enabled: false
  prompt: "prompts/claim_extraction.txt"
  description: "Any claims or facts that could be relevant to information discovery."
  max_gleanings: 1

community_reports:
  prompt: "prompts/community_report.txt"
  max_length: 1000
  max_input_length: 4000

cluster_graph:
  max_cluster_size: 10

embed_graph:
  enabled: false # if true, will generate node2vec embeddings for nodes

umap:
  enabled: false # if true, will generate UMAP embeddings for nodes (embed_graph must also be enabled)

snapshots:
  graphml: false
  embeddings: false
  transient: false

### Query settings ###
## The prompt locations are required here, but each search method has a number of optional knobs that can be tuned.
## See the config docs: https://microsoft.github.io/graphrag/config/yaml/#query

local_search:
  prompt: "prompts/local_search_system_prompt.txt"

global_search:
  map_prompt: "prompts/global_search_map_system_prompt.txt"
  reduce_prompt: "prompts/global_search_reduce_system_prompt.txt"
  knowledge_prompt: "prompts/global_search_knowledge_system_prompt.txt"

drift_search:
  prompt: "prompts/drift_search_system_prompt.txt"

basic_search:
  prompt: "prompts/basic_search_system_prompt.txt"

Logs and screenshots

(graphragtest) root@cdd2b6557714:/home/graphragtest# graphrag index --root ./

Logging enabled at /home/graphragtest/logs/indexing-engine.log
Running standard indexing.
🚀 create_base_text_units
id text document_ids n_tokens
0 b53ef702af00f35578b1cdbf74474a32866bd5bb89a30a... A的爸爸叫F。\n\nA的妈妈叫M。\n [10ae1eaa0dc9f3bd3cbbfc0ff5d391e0a4eb7ed2d604d... 22
🚀 create_final_documents
id human_readable_id title text text_unit_ids
0 10ae1eaa0dc9f3bd3cbbfc0ff5d391e0a4eb7ed2d604dd... 1 report.txt A的爸爸叫F。\n\nA的妈妈叫M。\n [b53ef702af00f35578b1cdbf74474a32866bd5bb89a30...
🚀 extract_graph
None
🚀 compute_communities
level community parent title
0 0 0 -1 A
0 0 0 -1 F
0 0 0 -1 M
🚀 create_final_entities
id human_readable_id title type description text_unit_ids
0 c137ae10-4252-48da-894b-ca30f7aef684 0 A PERSON A is a person [b53ef702af00f35578b1cdbf74474a32866bd5bb89a30...
1 5650c001-6bb1-4868-bbf9-08a8a3f95892 1 F PERSON F is the father of A [b53ef702af00f35578b1cdbf74474a32866bd5bb89a30...
2 b447f3a1-29d2-4130-b586-da16499a79a2 2 M PERSON M is the mother of A [b53ef702af00f35578b1cdbf74474a32866bd5bb89a30...
🚀 create_final_relationships
id human_readable_id source target description weight combined_degree text_unit_ids
0 69ebb419-9b02-4ca0-8d42-12335857355f 0 A F A's father is F 2.0 3 [b53ef702af00f35578b1cdbf74474a32866bd5bb89a30...
1 4860e8a2-b30f-4615-b127-64ddc3617535 1 A M A's mother is M 2.0 3 [b53ef702af00f35578b1cdbf74474a32866bd5bb89a30...
🚀 create_final_nodes
id human_readable_id title community level degree x y
0 c137ae10-4252-48da-894b-ca30f7aef684 0 A 0 0 2 0 0
1 5650c001-6bb1-4868-bbf9-08a8a3f95892 1 F 0 0 1 0 0
2 b447f3a1-29d2-4130-b586-da16499a79a2 2 M 0 0 1 0 0
🚀 create_final_communities
id human_readable_id community ... text_unit_ids period size
0 a48137e0-b5f5-4297-9919-50fb59ef270f 0 0 ... [b53ef702af00f35578b1cdbf74474a32866bd5bb89a30... 2025-01-10 3

[1 rows x 11 columns]
🚀 create_final_text_units
id ... relationship_ids
0 b53ef702af00f35578b1cdbf74474a32866bd5bb89a30a... ... [69ebb419-9b02-4ca0-8d42-12335857355f, 4860e8a...

[1 rows x 7 columns]
🚀 create_final_community_reports
id human_readable_id community ... full_content_json period size
0 54b5f0c3db3343f7a348d43a0ef6f086 0 0 ... {\n "title": "Family A",\n "summary": "T... 2025-01-10 3

[1 rows x 14 columns]
❌ generate_text_embeddings
None
⠼ GraphRAG Indexer
├── Loading Input (text) - 1 files loaded (0 filtered) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 0:00:00
├── create_base_text_units ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 0:00:00
├── create_final_documents ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 0:00:00
├── extract_graph ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 0:00:00
├── compute_communities ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 0:00:00
├── create_final_entities ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 0:00:00
├── create_final_relationships ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 0:00:00
├── create_final_nodes ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 0:00:00
├── create_final_communities ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 0:00:00
├── create_final_text_units ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 0:00:00
├── create_final_community_reports ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 0:00:00
❌ Errors occurred during the pipeline run, see logs for more details.

Additional Information

• GraphRAG Version:1.1.2
• Operating System:linux
• Python Version:3.10.12
• Related Issues:

[chericher]

Is not the true reason why run error ?
openai.BadRequestError: Error code: 400 - {'object': 'error', 'message': 'NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.\n\n(The expanded size of the tensor (513) must match the existing size (512) at non-singleton dimension 1. Target sizes: [1, 513]. Tensor sizes: [1, 512])', 'code': 50001}

it seems that tensor is too long. but my doc length is below 1000 tokens. i don`t know how to fix it