[问题]: 运行到create_final_entities报错

[JKYtydt]

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• 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

运行到create_final_entities发生报错

16:32:28,146 datashaper.workflow.workflow ERROR Error executing verb "text_embed" in create_final_entities: list index out of range
Traceback (most recent call last):
  File "/data/anaconda3/envs/graphrag/lib/python3.12/site-packages/datashaper/workflow/workflow.py", line 415, in _execute_verb
    result = await result
             ^^^^^^^^^^^^
  File "/sdc/jky/GraphRAG-Local-UI/graphrag/graphrag/index/verbs/text/embed/text_embed.py", line 105, in text_embed
    return await _text_embed_in_memory(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/sdc/jky/GraphRAG-Local-UI/graphrag/graphrag/index/verbs/text/embed/text_embed.py", line 130, in _text_embed_in_memory
    result = await strategy_exec(texts, callbacks, cache, strategy_args)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/sdc/jky/GraphRAG-Local-UI/graphrag/graphrag/index/verbs/text/embed/strategies/openai.py", line 63, in run
    embeddings = _reconstitute_embeddings(embeddings, input_sizes)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/sdc/jky/GraphRAG-Local-UI/graphrag/graphrag/index/verbs/text/embed/strategies/openai.py", line 172, in _reconstitute_embeddings
    embedding = raw_embeddings[cursor]
                ~~~~~~~~~~~~~~^^^^^^^^
IndexError: list index out of range
16:32:28,164 graphrag.index.reporting.file_workflow_callbacks INFO Error executing verb "text_embed" in create_final_entities: list index out of range details=None
16:32:28,165 graphrag.index.run ERROR error running workflow create_final_entities

Steps to reproduce

本地运行

大模型服务
CUDA_VISIBLE_DEVICES=3 python -m vllm.entrypoints.openai.api_server --model /sdc/pre_trained_model/Qwen2-7B-Instruct --gpu-memory-utilization 0.6 --port 8003

向量服务
# !/bin/bash

# 启动控制器
python -m fastchat.serve.controller --host 0.0.0.0 --port 21003 &

# 为横型工作进程设置环境变量，并启动
export CUDA_VISIBLE_DEVICES=5
python -m fastchat.serve.model_worker --model-path /sdc/pre_trained_model/bge-large-zh/ --model-names gpt-4 --num-gpus 1 --host 0.0.0.0 --port 21005 --controller-address http://0.0.0.0:21003 &

python -m fastchat.serve.openai_api_server --host 0.0.0.0 --port 8200 --controller-address http://0.0.0.0:21003

python -m graphrag.index --root ./

GraphRAG Config Used

encoding_model: cl100k_base
skip_workflows: []
llm:
  api_key: ${GRAPHRAG_API_KEY}
  type: openai_chat # or azure_openai_chat
  model: /sdc/pre_trained_model/Qwen2-7B-Instruct
  model_supports_json: false # recommended if this is available for your model.
  # max_tokens: 4000
  # request_timeout: 180.0
  api_base: http://172.70.10.43:8003/v1
  # 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
embeddings:
  ## parallelization: override the global parallelization settings for embeddings
  async_mode: threaded # or asyncio
  llm:
    api_key: ${GRAPHRAG_API_KEY}
    type: openai_embedding # or azure_openai_embedding
    model: gpt-4
    api_base: http://172.70.10.43:8200/v1
    # 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: 8 # 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

Logs and screenshots

建索引报错

向量模型服务报错

Additional Information

• GraphRAG Version: 1.0.13.dev49
• Operating System:Ubuntu
• Python Version:3.12.0
• Related Issues:

[AlonsoGuevara]

Hi!
We are consolidating alternate model issues here: #657