[Issue]: <title> ❌ create_final_entities None AND ❌ Errors occurred during the pipeline run, see logs for more details.

[zw-change]

Describe the issue

and will be removed in a future version. Please use 'DataFrame.transpose' instead.
return bound(*args, **kwds)
获取嵌入向量时发生错误: [WinError 10061] 由于目标计算机积极拒绝，无法连接。
❌ create_final_entities

Steps to reproduce

ollama local instead the openai

GraphRAG Config Used

encoding_model: cl100k_base
skip_workflows: []
llm:
api_key: ${GRAPHRAG_API_KEY}
type: openai_chat # or azure_openai_chat
model: mistral
model_supports_json: true # recommended if this is available for your model.
api_base: http://192.168.0.17:11434/v1

max_tokens: 4000

request_timeout: 180.0

api_base: https://.openai.azure.com

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_API_KEY}
type: openai_embedding # or azure_openai_embedding
model: nomic-embed-text
api_base: http://192.168.0.17:11434/v1
# api_base: https://.openai.azure.com
# 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"
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

{"type": "error", "data": "Error executing verb "text_embed" in create_final_entities: iteration over a 0-d array", "stack": "Traceback (most recent call last):\n File "C:\Users\admin\AppData\Local\pypoetry\Cache\virtualenvs\graphrag-Me9XHZ9h-py3.11\Lib\site-packages\datashaper\workflow\workflow.py", line 415, in _execute_verb\n result = await result\n ^^^^^^^^^^^^\n File "E:\pythonworkspace\Graph_RAG\graphrag\graphrag\index\verbs\text\embed\text_embed.py", line 105, in text_embed\n return await _text_embed_in_memory(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "E:\pythonworkspace\Graph_RAG\graphrag\graphrag\index\verbs\text\embed\text_embed.py", line 130, in _text_embed_in_memory\n result = await strategy_exec(texts, callbacks, cache, strategy_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "E:\pythonworkspace\Graph_RAG\graphrag\graphrag\index\verbs\text\embed\strategies\openai.py", line 62, in run\n embeddings = await _execute(llm, text_batches, ticker, semaphore)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "E:\pythonworkspace\Graph_RAG\graphrag\graphrag\index\verbs\text\embed\strategies\openai.py", line 108, in _execute\n return [item for sublist in results for item in sublist]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "E:\pythonworkspace\Graph_RAG\graphrag\graphrag\index\verbs\text\embed\strategies\openai.py", line 108, in \n return [item for sublist in results for item in sublist]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: iteration over a 0-d array\n", "source": "iteration over a 0-d array", "details": null}
{"type": "error", "data": "Error running pipeline!", "stack": "Traceback (most recent call last):\n File "E:\pythonworkspace\Graph_RAG\graphrag\graphrag\index\run.py", line 323, in run_pipeline\n result = await workflow.run(context, callbacks)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "C:\Users\admin\AppData\Local\pypoetry\Cache\virtualenvs\graphrag-Me9XHZ9h-py3.11\Lib\site-packages\datashaper\workflow\workflow.py", line 369, in run\n timing = await self._execute_verb(node, context, callbacks)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "C:\Users\admin\AppData\Local\pypoetry\Cache\virtualenvs\graphrag-Me9XHZ9h-py3.11\Lib\site-packages\datashaper\workflow\workflow.py", line 415, in _execute_verb\n result = await result\n ^^^^^^^^^^^^\n File "E:\pythonworkspace\Graph_RAG\graphrag\graphrag\index\verbs\text\embed\text_embed.py", line 105, in text_embed\n return await _text_embed_in_memory(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "E:\pythonworkspace\Graph_RAG\graphrag\graphrag\index\verbs\text\embed\text_embed.py", line 130, in _text_embed_in_memory\n result = await strategy_exec(texts, callbacks, cache, strategy_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "E:\pythonworkspace\Graph_RAG\graphrag\graphrag\index\verbs\text\embed\strategies\openai.py", line 62, in run\n embeddings = await _execute(llm, text_batches, ticker, semaphore)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "E:\pythonworkspace\Graph_RAG\graphrag\graphrag\index\verbs\text\embed\strategies\openai.py", line 108, in _execute\n return [item for sublist in results for item in sublist]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "E:\pythonworkspace\Graph_RAG\graphrag\graphrag\index\verbs\text\embed\strategies\openai.py", line 108, in \n return [item for sublist in results for item in sublist]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: iteration over a 0-d array\n", "source": "iteration over a 0-d array", "details": null}
AND
14:56:36,162 graphrag.index.reporting.file_workflow_callbacks INFO Error executing verb "text_embed" in create_final_entities: iteration over a 0-d array details=None
14:56:36,162 graphrag.index.run ERROR error running workflow create_final_entities
Traceback (most recent call last):
TypeError: iteration over a 0-d array
14:56:36,164 graphrag.index.reporting.file_workflow_callbacks INFO Error running pipeline! details=None

Additional Information

• GraphRAG Version:
• Operating System:
• Python Version:
• Related Issues:

[natoverse]

Consolidating alternate model issues here: #657