[Bug]: Unable to use dirft mode and local mode, the error was traced to llm/openai/utils/try_parse_json_object, and json could not be loaded.

[shaoqing404]

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Describe the bug

I encountered a problem: when using local search, I received an error TypeError: Query column vector must be a vector. Got list<item: double>.

This also caused an error in the DRIFT search mode: RuntimeError: No intermediate answers found in primer response. Ensure that the primer response includes intermediate answers.

Steps to reproduce

Conduct a search on the database built in version 0.3.6 and version 0.4. The search code is as follows: When an aircraft cabin fire occurs, what emergency measures should ATC take?
graphrag query --method drift --query "When an aircraft cabin fire occurs, what emergency measures should ATC take?" --streaming

graphrag query --method drift --local"When an aircraft cabin fire occurs, what emergency measures should ATC take?" --streaming

Expected Behavior

normal operation

GraphRAG Config Used

encoding_model: cl100k_base
skip_workflows: []
llm:
  type: openai_chat # or azure_openai_chat
  model: gpt-4o
  model_supports_json: false # recommended if this is available for your model.
#  max_tokens: 8192
  # request_timeout: 180.0
  # api_base: https://<instance>.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
  temperature: 0.7
  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

parallelization:
  stagger: 0.3
  # num_threads: 50 # the number of threads to use for parallel processing

async_mode: threaded # or asyncio

embeddings:
  async_mode: threaded # or asyncio
  llm:
    type: openai_embedding # or azure_openai_embedding
    model: text-embedding-3-large
    # 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: 2000
  overlap: 200
  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"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

reporting:
  type: file # or console, blob
  base_dir: "output"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

entity_extraction:
  ## strategy: fully override the entity extraction strategy.
  ##   type: one of graph_intelligence, graph_intelligence_json and nltk
  ## 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, GOAL, EVENT, ACTIVITY, EQUIPMENT, FACILITIES, DEPARTMENT, ROLE, POSITION, OPERATION, TASK, PARAMETER, INDICATOR, DOCUMENT, SYSTEM, AIRCRAFT, VEHICLE, EVENT, COMPANY]
  max_gleanings: 10

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: 2000

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: 10

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: 3000
  max_input_length: 10000

cluster_graph:
  max_cluster_size: 10

embed_graph:
  enabled: true # if true, will generate node2vec embeddings for nodes
  num_walks: 10
  walk_length: 40
  window_size: 2
  iterations: 3
  random_seed: 597832


umap:
  enabled: true # if true, will generate UMAP embeddings for nodes

snapshots:
  graphml: true
  raw_entities: true
  top_level_nodes: true



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
  max_tokens: 12000

global_search:
  max_tokens: 12000
  data_max_tokens: 12000
  map_max_tokens: 1000
  reduce_max_tokens: 2000
  concurrency: 32

Logs and screenshots

creating llm client with {'api_key': 'REDACTED,len=51', 'type': "openai_chat", 'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.7, 'top_p': 1.0, 'n': 1, 'request_timeout': 180.0, 'api_base': 'https://api.gogoai.cloud', 'api_version': None, 'organization': None, 'proxy': None, 'audience': None, 'deployment_name': None, 'model_supports_json': False, 'tokens_per_minute': 0, 'requests_per_minute': 0, 'max_retries': 10, 'max_retry_wait': 10.0, 'sleep_on_rate_limit_recommendation': True, 'concurrent_requests': 25}
creating embedding llm client with {'api_key': 'REDACTED,len=49', 'type': "openai_embedding", 'model': 'embedding-2', 'max_tokens': 4000, 'temperature': 0, 'top_p': 1, 'n': 1, 'request_timeout': 180.0, 'api_base': 'https://open.bigmodel.cn/api/paas/v4', 'api_version': None, 'organization': None, 'proxy': None, 'audience': None, 'deployment_name': None, 'model_supports_json': None, 'tokens_per_minute': 0, 'requests_per_minute': 0, 'max_retries': 10, 'max_retry_wait': 10.0, 'sleep_on_rate_limit_recommendation': True, 'concurrent_requests': 25}
Failed to generate expansion for query: When an aircraft cabin fire occurs, what emergency measures should ATC take?
D:\element_path\miniconda\envs\graph\Lib\site-packages\numpy\core\fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.
return bound(*args, **kwds)
0%| | 0/5 [00:00<?, ?it/s]not expected dict type. type=<class 'str'>:
Traceback (most recent call last):
File "D:\element_path\miniconda\envs\graph\Lib\site-packages\graphrag\llm\openai\utils.py", line 130, in try_parse_json_object
result = json.loads(input)
^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json_init_.py", line 346, in loads
return _default_decoder.decode(s)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json\decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=w(s, 0).end())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json\decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
20%|████████████████████▍ | 1/5 [00:01<00:05, 1.39s/it]not expected dict type. type=<class 'str'>:
Traceback (most recent call last):
File "D:\element_path\miniconda\envs\graph\Lib\site-packages\graphrag\llm\openai\utils.py", line 130, in try_parse_json_object
result = json.loads(input)
^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json_init.py", line 346, in loads
return _default_decoder.decode(s)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json\decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=w(s, 0).end())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json\decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
40%|████████████████████████████████████████▊ | 2/5 [00:01<00:01, 1.57it/s]not expected dict type. type=<class 'str'>:
Traceback (most recent call last):
File "D:\element_path\miniconda\envs\graph\Lib\site-packages\graphrag\llm\openai\utils.py", line 130, in try_parse_json_object
result = json.loads(input)
^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json_init.py", line 346, in loads
return _default_decoder.decode(s)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json\decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=w(s, 0).end())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json\decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
60%|█████████████████████████████████████████████████████████████▏ | 3/5 [00:01<00:01, 1.97it/s]not expected dict type. type=<class 'str'>:
Traceback (most recent call last):
File "D:\element_path\miniconda\envs\graph\Lib\site-packages\graphrag\llm\openai\utils.py", line 130, in try_parse_json_object
result = json.loads(input)
^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json_init.py", line 346, in loads
return _default_decoder.decode(s)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json\decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=w(s, 0).end())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json\decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
80%|█████████████████████████████████████████████████████████████████████████████████▌ | 4/5 [00:02<00:00, 2.26it/s]not expected dict type. type=<class 'str'>:
Traceback (most recent call last):
File "D:\element_path\miniconda\envs\graph\Lib\site-packages\graphrag\llm\openai\utils.py", line 130, in try_parse_json_object
result = json.loads(input)
^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json_init.py", line 346, in loads
return _default_decoder.decode(s)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json\decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\element_path\miniconda\envs\graph\Lib\json\decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:02<00:00, 1.69it/s]
╭────────────────────────────────────────────────── Traceback (most recent call last) ───────────────────────────────────────────────────╮

Additional Information

• GraphRAG Version:0.4
• Operating System:windows
• Python Version:3.11
• Related Issues: