[Issue]: <title> Always stuck here create_base_entity_graph

[Redhair957]

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• I believe this is a legitimate bug, not just a question. If this is a question, please use the Discussions area.

Describe the issue

⠸ GraphRAG Indexer
├── Loading Input (text) - 1 files loaded (0 filtered) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 0:00:00
├── create_base_text_units
├── create_final_documents
└── create_base_entity_graph%

Steps to reproduce

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: ${GRAPHRAG_CHAT_API_KEY}
    type: openai_embedding # or azure_openai_embedding
    model:  text-embedding-3-small
    api_base:  ${GRAPHRAG_API_BASE}
    # 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: 600
  overlap: 50
  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: 200

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: 1000
  max_input_length: 2000

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:
• Operating System:
• Python Version:
• Related Issues:

[It-Is-Ishank]

same issue
these are the few errors that i get in the indexing log

1. INFO Error Invoking LLM
2. ERROR error extracting graph Traceback (most recent call last): File "c:\Users\ikapur\Desktop\testing\.venv\lib\site-packages\graphrag\index\operations\extract_entities\graph_extractor.py", line 127, in __call__ result = await self._process_document(text, prompt_variables) File "c:\Users\ikapur\Desktop\testing\.venv\lib\site-packages\graphrag\index\operations\extract_entities\graph_extractor.py", line 155, in _process_document response = await self._llm( File "c:\Users\ikapur\Desktop\testing\.venv\lib\site-packages\fnllm\openai\llm\chat.py", line 83, in __call__ return await self._text_chat_llm(prompt, **kwargs) File "c:\Users\ikapur\Desktop\testing\.venv\lib\site-packages\fnllm\openai\llm\features\tools_parsing.py", line 120, in __call__ return await self._delegate(prompt, **kwargs) File "c:\Users\ikapur\Desktop\testing\.venv\lib\site-packages\fnllm\base\base.py", line 112, in __call__ return await self._invoke(prompt, **kwargs) File "c:\Users\ikapur\Desktop\testing\.venv\lib\site-packages\fnllm\base\base.py", line 128, in _invoke return await self._decorated_target(prompt, **kwargs) File "c:\Users\ikapur\Desktop\testing\.venv\lib\site-packages\fnllm\services\json.py", line 71, in invoke return await delegate(prompt, **kwargs) File "c:\Users\ikapur\Desktop\testing\.venv\lib\site-packages\fnllm\services\retryer.py", line 109, in invoke result = await execute_with_retry() File "c:\Users\ikapur\Desktop\testing\.venv\lib\site-packages\fnllm\services\retryer.py", line 106, in execute_with_retry raise RetriesExhaustedError(name, self._max_retries) fnllm.services.errors.RetriesExhaustedError: Operation 'chat' failed - 10 retries exhausted.
3. INFO Entity Extraction Error
   ERROR error running workflow extract_graph
   Traceback (most recent call last):
   File "c:\Users\ikapur\Desktop\testing.venv\lib\site-packages\graphrag\index\run\run_workflows.py", line 166, in _run_workflows
   result = await run_workflow(
   File "c:\Users\ikapur\Desktop\testing.venv\lib\site-packages\graphrag\index\workflows\extract_graph.py", line 45, in run_workflow
   base_entity_nodes, base_relationship_edges = await extract_graph(
   File "c:\Users\ikapur\Desktop\testing.venv\lib\site-packages\graphrag\index\flows\extract_graph.py", line 33, in extract_graph
   entities, relationships = await extract_entities(
   File "c:\Users\ikapur\Desktop\testing.venv\lib\site-packages\graphrag\index\operations\extract_entities\extract_entities.py", line 136, in extract_entities
   entities = _merge_entities(entity_dfs)
   File "c:\Users\ikapur\Desktop\testing.venv\lib\site-packages\graphrag\index\operations\extract_entities\extract_entities.py", line 168, in _merge_entities
   all_entities.groupby(["title", "type"], sort=False)
   File "c:\Users\ikapur\Desktop\testing.venv\lib\site-packages\pandas\core\frame.py", line 9183, in groupby
   return DataFrameGroupBy(
   File "c:\Users\ikapur\Desktop\testing.venv\lib\site-packages\pandas\core\groupby\groupby.py", line 1329, in init
   grouper, exclusions, obj = get_grouper(
   File "c:\Users\ikapur\Desktop\testing.venv\lib\site-packages\pandas\core\groupby\grouper.py", line 1043, in get_grouper
   raise KeyError(gpr)
   KeyError: 'title'

[Lyzin]

same issue, Is there a solution？