[Issue]: <title> Error in Community Report Extraction – GraphRAG Indexing Pipeline

praman1870 commented 6 days ago

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[X] I have searched the existing issues and this bug is not already filed.
[X] 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 encountered an issue during the final stage of the GraphRAG indexing pipeline where the create_final_community_reports step failed, but the knowledge graph was successfully created. The error appears to be related to an unsupported response_format with the OpenAI model.

Steps to reproduce

Installed GraphRAG via pip install graphrag.
Ran the indexing pipeline using the following command: python -m graphrag.index --root .
The pipeline progressed successfully through stages like:
- create_base_text_units
- create_base_extracted_entities
- create_summarized_entities
- create_final_entities
- create_final_nodes
- create_final_relationships
- create_final_communities
It failed at the create_final_community_reports step.

GraphRAG Config Used

# Paste your config here

encoding_model: cl100k_base
skip_workflows: []
llm:
  api_key: ${GRAPHRAG_API_KEY}
  type: azure_openai_chat
  model: gpt-4
  model_supports_json: true # recommended if this is available for your model.
  # max_tokens: 4000
  # request_timeout: 180.0
  api_base: ${OPENAI_API_BASE}
  api_version: "2024-06-01"
  # organization: <organization_id>
  deployment_name: ${OPENAI_DEPLOYMENT_NAME}
  response_format: "json"
  tokens_per_minute: 10000 # set a leaky bucket throttle
  requests_per_minute: 60 # 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: 0 # temperature for sampling
  # top_p: 1 # 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
  llm:
    api_key: ${GRAPHRAG_API_KEY}
    type: azure_openai_embedding
    model: text-embedding-ada-002
    # api_base: https://<instance>.openai.azure.com
    # api_version: 2024-02-15-preview
    # organization: <organization_id>
    deployment_name: "text-embedding-ada-002-ea"
    response_format: "json"
    tokens_per_minute: 10000 # set a leaky bucket throttle
    requests_per_minute: 60 # 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
    # 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

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"
  # 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: 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

❌ create_final_community_reports None ❌ Errors occurred during the pipeline run, see logs for more details. { "type": "error", "data": "Community Report Extraction Error", "stack": "Traceback (most recent call last): ... openai.BadRequestError: Error code: 400 - {'error': {'message': \"Invalid parameter: 'response_format' of type 'json_object' is not supported with this model.\", 'type': 'invalid_request_error', 'param': 'response_format', 'code': None}}", }

Additional Information

GraphRAG Version: v0.3.6
Operating System: Microsoft Windows 10 Enterprise
Python Version: 3.12.0

natoverse commented 1 day ago

Are you still seeing this error? "json_object" is definitely supported so this seems like it was either a temporary glitch, or there is something else going on. Can you upload your indexing-engine.log?

praman1870 commented 20 hours ago

Are you still seeing this error? "json_object" is definitely supported so this seems like it was either a temporary glitch, or there is something else going on. Can you upload your indexing-engine.log?

Thank you for your response. But yes, I’m still facing the same issue with the "json_object" error. I've attached the indexing-engine.log file and the logs.json files. indexing-engine.log logs.json

microsoft / graphrag