[Bug]: <title> ERROR Error executing verb "create_base_entity_graph" in create_base_entity_graph: 'name'

[Redhair957]

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 bug

graphrag 0.9

Traceback (most recent call last):
File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/datashaper/workflow/workflow.py", line 415, in _execute_verb
result = await result
File "/data/graphrag-dify/graphrag-0.9.0/graphrag/index/workflows/v1/subflows/create_base_entity_graph.py", line 47, in create_base_entity_graph
await create_base_entity_graph_flow(
File "/data/graphrag-dify/graphrag-0.9.0/graphrag/index/flows/create_base_entity_graph.py", line 58, in create_base_entity_graph
merged_entities = _merge_entities(entity_dfs)
File "/data/graphrag-dify/graphrag-0.9.0/graphrag/index/flows/create_base_entity_graph.py", line 119, in _merge_entities
all_entities.groupby(["name", "type"], sort=False)
File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/pandas/core/frame.py", line 9183, in groupby
return DataFrameGroupBy(
File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/pandas/core/groupby/groupby.py", line 1329, in init
grouper, exclusions, obj = get_grouper(
File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/pandas/core/groupby/grouper.py", line 1043, in get_grouper
raise KeyError(gpr)
KeyError: 'name'
17:32:39,625 graphrag.callbacks.file_workflow_callbacks INFO Error executing verb "create_base_entity_graph" in create_base_entity_graph: 'name' details=None
17:32:39,628 graphrag.index.run.run ERROR error running workflow create_base_entity_graph
Traceback (most recent call last):
File "/data/graphrag-dify/graphrag-0.9.0/graphrag/index/run/run.py", line 260, in run_pipeline
result = await _process_workflow(
File "/data/graphrag-dify/graphrag-0.9.0/graphrag/index/run/workflow.py", line 103, in _process_workflow
result = await workflow.run(context, callbacks)
File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/datashaper/workflow/workflow.py", line 369, in run
timing = await self._execute_verb(node, context, callbacks)
File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/datashaper/workflow/workflow.py", line 415, in _execute_verb
result = await result
File "/data/graphrag-dify/graphrag-0.9.0/graphrag/index/workflows/v1/subflows/create_base_entity_graph.py", line 47, in create_base_entity_graph
await create_base_entity_graph_flow(
File "/data/graphrag-dify/graphrag-0.9.0/graphrag/index/flows/create_base_entity_graph.py", line 58, in create_base_entity_graph
merged_entities = _merge_entities(entity_dfs)
File "/data/graphrag-dify/graphrag-0.9.0/graphrag/index/flows/create_base_entity_graph.py", line 119, in _merge_entities
all_entities.groupby(["name", "type"], sort=False)
File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/pandas/core/frame.py", line 9183, in groupby
return DataFrameGroupBy(
File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/pandas/core/groupby/groupby.py", line 1329, in init
grouper, exclusions, obj = get_grouper(
File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/pandas/core/groupby/grouper.py", line 1043, in get_grouper
raise KeyError(gpr)
KeyError: 'name'

{
"type": "error",
"data": "Error Invoking LLM",
"stack": "Traceback (most recent call last):\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/fnllm/base/base.py", line 112, in call\n return await self._invoke(prompt, **kwargs)\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/fnllm/base/base.py", line 128, in _invoke\n return await self._decorated_target(prompt, **kwargs)\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/fnllm/services/json.py", line 71, in invoke\n return await delegate(prompt, **kwargs)\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/fnllm/services/retryer.py", line 109, in invoke\n result = await execute_with_retry()\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/fnllm/services/retryer.py", line 93, in execute_with_retry\n async for a in AsyncRetrying(\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/tenacity/asyncio/init.py", line 166, in anext\n do = await self.iter(retry_state=self._retry_state)\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/tenacity/asyncio/init.py", line 153, in iter\n result = await action(retry_state)\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/tenacity/_utils.py", line 99, in inner\n return call(*args, **kwargs)\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/tenacity/init.py", line 398, in \n self._add_action_func(lambda rs: rs.outcome.result())\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/concurrent/futures/_base.py", line 438, in result\n return self.__get_result()\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/concurrent/futures/_base.py", line 390, in __get_result\n raise self._exception\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/fnllm/services/retryer.py", line 101, in execute_with_retry\n return await attempt()\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/fnllm/services/retryer.py", line 78, in attempt\n return await delegate(prompt, **kwargs)\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/fnllm/services/rate_limiter.py", line 70, in invoke\n result = await delegate(prompt, **args)\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/fnllm/base/base.py", line 152, in _decorator_target\n output = await self._execute_llm(prompt, **kwargs)\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/fnllm/openai/llm/chat_text.py", line 155, in _execute_llm\n completion = await self._call_completion_or_cache(\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/fnllm/openai/llm/chat_text.py", line 127, in _call_completion_or_cache\n return await self._cache.get_or_insert(\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/fnllm/services/cache_interactor.py", line 50, in get_or_insert\n entry = await func()\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/openai/resources/chat/completions.py", line 1661, in create\n return await self._post(\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/openai/_base_client.py", line 1843, in post\n return await self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/openai/_base_client.py", line 1537, in request\n return await self._request(\n File "/home/user/ENTER/envs/graphrag0.9/lib/python3.10/site-packages/openai/_base_client.py", line 1638, in _request\n raise self._make_status_error_from_response(err.response) from None\nopenai.APIStatusError: Error code: 402 - {'error': {'message': 'Insufficient Balance (request id: 2025010916490549354044674414679)', 'type': 'unknown_error', 'param': '', 'code': 'invalid_request_error'}}\n",
"source": "Error code: 402 - {'error': {'message': 'Insufficient Balance (request id: 2025010916490549354044674414679)', 'type': 'unknown_error', 'param': '', 'code': 'invalid_request_error'}}",
"details": {
"prompt": "\n-Goal-\nGiven a text document that is potentially relevant to this activity and a list of entity types, identify all entities of those types from the text and all relationships among the identified entities.\n\n-Steps-\n1. Identify all entities. For each identified entity, extract the following information:\n- entity_name: Name of the entity, capitalized\n- entity_type: One of the following types: [service_object, acceptance_conditions, processing_type, legal_processing_time, promised_processing_time, fee_status, consultation_method, implementing_authority, authority_nature, power_source, service_location, service_hours, administrative_level, processing_form, implementing_authority_code, service_object_type, application_materials, processing_flow, supervision_complaint_method, rights_and_obligations]\n- entity_description: Comprehensive description of the entity's attributes and activities\nFormat each entity as ("entity"<|><entity_name><|><entity_type><|><entity_description>)\n\n2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are clearly related to each other.\nFor each pair of related entities, extract the following information:\n- source_entity: name of the source entity, as identified in step 1\n- target_entity: name of the target entity, as identified in step 1\n- relationship_description: explanation as to why you think the source entity and the target entity are related to each other\n- relationship_strength: an integer score between 1 to 10, indicating strength of the relationship between the source entity and target entity\nFormat each relationship as ("relationship"<|><source_entity><|><target_entity><|><relationship_description><|><relationship_strength>)\n\n3. Return output in Chinese as a single list of all the entities and relationships identified in steps 1 and 2. Use ## as the list delimiter.\n4.Return Chinese format .\n\n5. When finished, output <|COMPLETE|>.\n\n-Examples-\n######################\n\nExample 1:\n\nentity_types: [service_object, acceptance_conditions, processing_type, legal_processing_time, promised_processing_time, fee_status, consultation_method, implementing_authority, authority_nature, power_source, service_location, service_hours, administrative_level, processing_form, implementing_authority_code, service_object_type, application_materials, processing_flow, supervision_complaint_method, rights_and_obligations]\ntext:\

Steps to reproduce

No response

Expected Behavior

No response

GraphRAG Config Used

encoding_model: cl100k_base
skip_workflows: []
llm:
  api_key: ${GRAPHRAG_CHAT_API_KEY}
  type: openai_chat # or azure_openai_chat
  model: ${GRAPHRAG_CHAT_MODEL}
  model_supports_json: false # recommended if this is available for your model.
  # audience: "https://cognitiveservices.azure.com/.default"
  # max_tokens: 4000
  # request_timeout: 180.0
  api_base: ${GRAPHRAG_API_BASE}
  # 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
#  temperature: 1 # temperature for sampling
#  top_p: 0.8 # top-p sampling
  # n: 1 # Number of completions to generate

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
  # target: required # or all
  # batch_size: 16 # the number of documents to send in a single request
  # batch_max_tokens: 8191 # the maximum number of tokens to send in a single request
  vector_store:
    type: lancedb
    db_uri: 'output/lancedb'
    container_name: default # A prefix for the vector store to create embedding containers. Default: 'default'.
    overwrite: true
  # vector_store: # configuration for AI Search
    # type: azure_ai_search
    # url: <ai_search_endpoint>
    # api_key: <api_key> # if not set, will attempt to use managed identity. Expects the `Search Index Data Contributor` RBAC role in this case.
    # audience: <optional> # if using managed identity, the audience to use for the token
    # overwrite: true # or false. Only applicable at index creation time
    # container_name: default # A prefix for the AzureAISearch to create indexes. Default: 'default'.
  llm:
    api_key: Xinference
    type: openai_embedding # or azure_openai_embedding
    model:  bge-m3-embeddings
    api_base: http://10.168.2.227:9997/v1
    # api_version: 2024-02-15-preview
    # audience: "https://cognitiveservices.azure.com/.default"
    # 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

chunks:
  size: 1200
  overlap: 200
  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"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

#update_index_storage: # Storage to save an updated index (for incremental indexing). Enabling this performs an incremental index run
#   type: file # or blob
#   base_dir: "update_output"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

reporting:
  type: file # or console, blob
  base_dir: "logs"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

entity_extraction:
  ## strategy: fully override the entity extraction strategy.
  ##   type: one of graph_intelligence, graph_intelligence_json and nltk
  ## 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: [业务办理项编码, 实施主体, 事项名称, 实施主体编码,权力来源]
  max_gleanings: 1

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: 1

community_reports:
  ## 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

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
  # llm_temperature: 0 # temperature for sampling
  # llm_top_p: 1 # top-p sampling
  # llm_n: 1 # Number of completions to generate
  # max_tokens: 12000

global_search:
  # llm_temperature: 0 # temperature for sampling
  # llm_top_p: 1 # top-p sampling
  # llm_n: 1 # Number of completions to generate
  # max_tokens: 12000
  # data_max_tokens: 12000
  # map_max_tokens: 1000
  # reduce_max_tokens: 2000
  # concurrency: 32

Logs and screenshots

No response

Additional Information

• GraphRAG Version: 0.9
• Operating System:
• Python Version:3.10
• Related Issues:

[Redhair957]

ubuntu