❌ create_base_entity_graph ❌ Errors occurred during the pipeline run, see logs for more details.

[wy371900521]

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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 issue

Errors

❌ create_base_entity_graph None
⠙ GraphRAG Indexer ├── Loading Input (InputFileType.text) - 1 files loaded (0 filtered) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00 0:00:00 ├── create_base_text_units ├── create_base_extracted_entities ├── create_final_covariates ├── create_summarized_entities ├── join_text_units_to_covariate_ids └── create_base_entity_graph ❌ Errors occurred during the pipeline run, see logs for more details.

Steps to reproduce

logs

{"type": "error", "data": "Error Invoking LLM", "stack": "Traceback (most recent call last):\n File

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: true # recommended if this is available for your model.
  max_tokens: 2000
  # request_timeout: 180.0
  # api_base: https://<instance>.openai.azure.com
  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: 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
  llm:
    api_key: ${GRAPHRAG_EMBEDDING_API_KEY}
    type: openai_embedding # or azure_openai_embedding
    model: ${GRAPHRAG_EMBEDDING_MODEL}
    # api_base: https://<instance>.openai.azure.com
    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
    # 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
    # target: required # or optional



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"
  base_dir: ${GRAPHRAG_INPUT_DIR}
  file_encoding: utf-8
  file_pattern: ".*\\.txt$"

cache:
  type: file # or blob
  # base_dir: "cache"
  base_dir: ${GRAPHRAG_CACHE_DIR}
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

storage:
  type: file # or blob
  # base_dir: "output/${timestamp}/artifacts"
  # base_dir: "inputs/artifacts"
  base_dir: ${GRAPHRAG_STORAGE_DIR}
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

reporting:
  type: file # or console, blob
  # base_dir: "inputs/reports"
  base_dir: ${GRAPHRAG_REPORTING_DIR}
  # 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"
  prompt: ${GRAPHRAG_ENTITY_EXTRACTION_PROMPT_FILE}
  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"
  prompt: ${GRAPHRAG_SUMMARIZE_DESCRIPTIONS_PROMPT_FILE}
  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
  # 开启协变量
  enabled: true
  # prompt: "prompts/claim_extraction.txt"
  prompt: ${GRAPHRAG_CLAIM_EXTRACTION_PROMPT_FILE}
  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"
  prompt: ${GRAPHRAG_COMMUNITY_REPORT_PROMPT_FILE}
  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:
• Operating System:
• Python Version:
• Related Issues:

[wy371900521]

What's going on here? Did any of the big guys fix it? Get back to me. Thank you so much

[aleixlahozt]

I am having same issue as you. Models deployed to Azure OpenAI.

The error message indicates that the URL you are trying to request is missing the required 'http://' or 'https://' protocol prefix. This is causing an UnsupportedProtocol exception when making a request using the httpx library.

File "/opt/anaconda3/envs/graphragenv/lib/python3.10/site-packages/graphrag/llm/openai/openai_chat_llm.py", line 53, in _execute_llm\n completion = await self.client.chat.completions.create

[aleixlahozt]

check this:

https://github.com/microsoft/graphrag/blob/main/v1-breaking-changes.md

[yinzih]

Have you solved your problem?

[wy371900521]

没哦

[natoverse]

Can you upload your indexing-engine.log so we can see more error details?

[github-actions[bot]]

This issue has been marked stale due to inactivity after repo maintainer or community member responses that request more information or suggest a solution. It will be closed after five additional days.

[github-actions[bot]]

This issue has been marked stale due to inactivity after repo maintainer or community member responses that request more information or suggest a solution. It will be closed after five additional days.