[Issue]: <title> Configuration Error with o3 - 'max_tokens' expected an unsupported value, but got null instead

[buridan2]

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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

Attempting to run the GraphRAG PyPI pipeline, using the o3 model, and keep receiving this error:

"❌ LLM configuration error detected. Exiting... Error code: 400 - {'error': {'message': "Invalid type for 'max_tokens': expected an unsupported value, but got null instead.", 'type': 'invalid_request_error', 'param': 'max_tokens', 'code': 'invalid_type'}}"

Putting a max_tokens argument in the settings.yaml gives another error message ("unsupported parameter with this model").

My OpenAI is 1.86.0

Steps to reproduce

1. With settings.yaml as below, run graphrag index --root .
2. See error

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.

models:
  default_chat_model:
    type: openai_chat # or azure_openai_chat
    # api_base: https://<instance>.openai.azure.com
    # api_version: 2024-05-01-preview
    auth_type: api_key # or azure_managed_identity
    api_key: ${GRAPHRAG_API_KEY} # set this in the generated .env file
    # audience: "https://cognitiveservices.azure.com/.default"
    # organization: <organization_id>
    model: o3
    # deployment_name: <azure_model_deployment_name>
    # encoding_model: cl100k_base # automatically set by tiktoken if left undefined
    # max_tokens: 1 
    # max_completion_tokens: 4096 
    model_supports_json: true # recommended if this is available for your model.
    concurrent_requests: 25 # max number of simultaneous LLM requests allowed
    async_mode: threaded # or asyncio
    retry_strategy: native
    max_retries: 10
    tokens_per_minute: auto              # set to null to disable rate limiting
    requests_per_minute: auto            # set to null to disable rate limiting
  default_embedding_model:
    type: openai_embedding # or azure_openai_embedding
    # api_base: https://<instance>.openai.azure.com
    # api_version: 2024-05-01-preview
    auth_type: api_key # or azure_managed_identity
    api_key: ${GRAPHRAG_API_KEY}
    # audience: "https://cognitiveservices.azure.com/.default"
    # organization: <organization_id>
    model: text-embedding-3-small
    # deployment_name: <azure_model_deployment_name>
    # encoding_model: cl100k_base # automatically set by tiktoken if left undefined
    model_supports_json: true # recommended if this is available for your model.
    concurrent_requests: 5 # max number of simultaneous LLM requests allowed
    async_mode: threaded # or asyncio
    retry_strategy: native
    max_retries: 10
    tokens_per_minute: auto              # set to null to disable rate limiting
    requests_per_minute: auto            # set to null to disable rate limiting

### Input settings ###

input:
  type: file # or blob
  file_type: text # [csv, text, json]
  base_dir: "input"

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

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

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

reporting:
  type: file # [file, blob, cosmosdb]
  base_dir: "logs"

vector_store:
  default_vector_store:
    type: lancedb
    db_uri: output/lancedb
    container_name: default
    overwrite: True

### Workflow settings ###

embed_text:
  model_id: default_embedding_model
  vector_store_id: default_vector_store

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


summarize_descriptions:
  model_id: default_chat_model
  prompt: "prompts/summarize_descriptions.txt"
  max_length: 500
  batch_size: 4
  batch_max_tokens: 8191

extract_graph_nlp:
  text_analyzer:
    extractor_type: regex_english # [regex_english, syntactic_parser, cfg]

cluster_graph:
  max_cluster_size: 10

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

community_reports:
  model_id: default_chat_model
  graph_prompt: "prompts/community_report_graph.txt"
  text_prompt: "prompts/community_report_text.txt"
  max_length: 2000
  max_input_length: 8000

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

### 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:
  chat_model_id: default_chat_model
  embedding_model_id: default_embedding_model
  prompt: "prompts/local_search_system_prompt.txt"

global_search:
  chat_model_id: default_chat_model
  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:
  chat_model_id: default_chat_model
  embedding_model_id: default_embedding_model
  prompt: "prompts/drift_search_system_prompt.txt"
  reduce_prompt: "prompts/drift_search_reduce_prompt.txt"

basic_search:
  chat_model_id: default_chat_model
  embedding_model_id: default_embedding_model
  prompt: "prompts/basic_search_system_prompt.txt"

Logs and screenshots

No response

Additional Information

• GraphRAG Version: 2.3.0
• Operating System: Ubuntu Linux
• Python Version: 3.11
• Related Issues:

[Bc-Aqr]

I also encountered a similar error. I used the model fine-tuned with OpenAI 4.1, and the same error occurred. Could you tell me if you have found a better solution?

[Soroush77]

I also encountered this issue on GraphRAG. I am trying to use GPT-5-mini

[hydraxman]

A way to make it work is to go to the ragtest\venv\Lib\site-packages\openai\resources\chat\completions\completions.py, search 'max_tokens', and remove the line (for me it's line 2596):