[Bug]: 'sub_community' column does not exist

[adni03]

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• 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

When following the Global Search notebook (link), I am getting a KeyError raised from the read_indexer_communities method.

KeyError: "Column(s) ['sub_community'] do not exist"

I also noticed that read_indexer_communities method was not present when I installed graphrag via pip. When following the notebook, I copied the method manually into the file and re-ran indexing (deleted the cache and output dirs) before re-indexing.

At line 227

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: azure_openai_chat
  model_supports_json: true # recommended if this is available for your model.
  max_tokens: 4096
  # request_timeout: 180.0
  api_base: <REDACTED>
  api_version: 2024-02-15-preview
  deployment_name: <REDACTED>
  temperature: 0 # temperature for sampling
  top_p: 0.999 # 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
  llm:
    api_key: ${GRAPHRAG_API_KEY}
    type: azure_openai_embedding
    api_base: <REDACTED>
    api_version: 2024-02-15-preview
    deployment_name: <REDACTED>

chunks:
  size: 1200
  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"

storage:
  type: file # or blob
  base_dir: "output"

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

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: 500

claim_extraction:
  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: 2000
  max_input_length: 8000

cluster_graph:
  max_cluster_size: 10

umap:
  enabled: false # if true, will generate UMAP embeddings for nodes

snapshots:
  graphml: false
  raw_entities: false
  top_level_nodes: false

Logs and screenshots

No response

Additional Information

• GraphRAG Version: 0.4.1
• Operating System: MacOS
• Python Version: 3.12.5
• Related Issues:

[escano0]

same issue