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microsoft / graphrag

A modular graph-based Retrieval-Augmented Generation (RAG) system
https://microsoft.github.io/graphrag/
MIT License
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[Issue]: AttributeError: 'str' object has no attribute 'embedding' #732

Closed wenmengzhou closed 4 months ago

wenmengzhou commented 4 months ago

Is there an existing issue for this?

Describe the issue

When use with self-host openAI embedding interface, run with following error:

372 Traceback (most recent call last):
373   File "/opt/anaconda3/envs/graphrag/lib/python3.10/site-packages/datashaper/workflow/workflow.py", line 415, in _execute_verb
374     result = await result
375   File "/Users/zhouwenmeng/work/code/llm/application/graphrag/graphrag/index/verbs/text/embed/text_embed.py", line 105, in text_embed
376     return await _text_embed_in_memory(
377   File "/Users/zhouwenmeng/work/code/llm/application/graphrag/graphrag/index/verbs/text/embed/text_embed.py", line 130, in _text_embed_in_memory
378     result = await strategy_exec(texts, callbacks, cache, strategy_args)
379   File "/Users/zhouwenmeng/work/code/llm/application/graphrag/graphrag/index/verbs/text/embed/strategies/openai.py", line 62, in run
380     embeddings = await _execute(llm, text_batches, ticker, semaphore)
381   File "/Users/zhouwenmeng/work/code/llm/application/graphrag/graphrag/index/verbs/text/embed/strategies/openai.py", line 106, in _execute
382     results = await asyncio.gather(*futures)
383   File "/Users/zhouwenmeng/work/code/llm/application/graphrag/graphrag/index/verbs/text/embed/strategies/openai.py", line 100, in embed
384     chunk_embeddings = await llm(chunk)
385   File "/Users/zhouwenmeng/work/code/llm/application/graphrag/graphrag/llm/base/caching_llm.py", line 104, in __call__
386     result = await self._delegate(input, **kwargs)
387   File "/Users/zhouwenmeng/work/code/llm/application/graphrag/graphrag/llm/base/rate_limiting_llm.py", line 177, in __call__
388     result, start = await execute_with_retry()
389   File "/Users/zhouwenmeng/work/code/llm/application/graphrag/graphrag/llm/base/rate_limiting_llm.py", line 159, in execute_with_retry
390     async for attempt in retryer:
391   File "/opt/anaconda3/envs/graphrag/lib/python3.10/site-packages/tenacity/asyncio/__init__.py", line 166, in __anext__
392     do = await self.iter(retry_state=self._retry_state)
393   File "/opt/anaconda3/envs/graphrag/lib/python3.10/site-packages/tenacity/asyncio/__init__.py", line 153, in iter
394     result = await action(retry_state)
395   File "/opt/anaconda3/envs/graphrag/lib/python3.10/site-packages/tenacity/_utils.py", line 99, in inner
396     return call(*args, **kwargs)
397   File "/opt/anaconda3/envs/graphrag/lib/python3.10/site-packages/tenacity/__init__.py", line 398, in <lambda>
398     self._add_action_func(lambda rs: rs.outcome.result())
399   File "/opt/anaconda3/envs/graphrag/lib/python3.10/concurrent/futures/_base.py", line 451, in result
400     return self.__get_result()
401   File "/opt/anaconda3/envs/graphrag/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
402     raise self._exception
403   File "/Users/zhouwenmeng/work/code/llm/application/graphrag/graphrag/llm/base/rate_limiting_llm.py", line 165, in execute_with_retry
404     return await do_attempt(), start
405   File "/Users/zhouwenmeng/work/code/llm/application/graphrag/graphrag/llm/base/rate_limiting_llm.py", line 147, in do_attempt
406     return await self._delegate(input, **kwargs)
407   File "/Users/zhouwenmeng/work/code/llm/application/graphrag/graphrag/llm/base/base_llm.py", line 49, in __call__
408     return await self._invoke(input, **kwargs)
409   File "/Users/zhouwenmeng/work/code/llm/application/graphrag/graphrag/llm/base/base_llm.py", line 53, in _invoke
410     output = await self._execute_llm(input, **kwargs)
411   File "/Users/zhouwenmeng/work/code/llm/application/graphrag/graphrag/llm/openai/openai_embeddings_llm.py", line 36, in _execute_llm
412     embedding = await self.client.embeddings.create(
413   File "/opt/anaconda3/envs/graphrag/lib/python3.10/site-packages/openai/resources/embeddings.py", line 215, in create
414     return await self._post(
415   File "/opt/anaconda3/envs/graphrag/lib/python3.10/site-packages/openai/_base_client.py", line 1826, in post
416     return await self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)
417   File "/opt/anaconda3/envs/graphrag/lib/python3.10/site-packages/openai/_base_client.py", line 1519, in request
418     return await self._request(
419   File "/opt/anaconda3/envs/graphrag/lib/python3.10/site-packages/openai/_base_client.py", line 1622, in _request
420     return await self._process_response(
421   File "/opt/anaconda3/envs/graphrag/lib/python3.10/site-packages/openai/_base_client.py", line 1714, in _process_response
422     return await api_response.parse()
423   File "/opt/anaconda3/envs/graphrag/lib/python3.10/site-packages/openai/_response.py", line 419, in parse
424     parsed = self._options.post_parser(parsed)
425   File "/opt/anaconda3/envs/graphrag/lib/python3.10/site-packages/openai/resources/embeddings.py", line 204, in parser
426     data = cast(object, embedding.embedding)
427 AttributeError: 'str' object has no attribute 'embedding'

Steps to reproduce

following the quick start tutorial: https://microsoft.github.io/graphrag/posts/get_started/

the config file is as follows:


encoding_model: cl100k_base
skip_workflows: []
llm:
  api_key:  my-secret-key
  type: openai_chat # or azure_openai_chat
  #￥type: azure_openai_chat # or azure_openai_chat
  # model: gpt-4-turbo-preview
  model: qwen-plus
  model_supports_json: false # recommended if this is available for your model.
  # max_tokens: 4000
  request_timeout: 180.0
  api_base: https://dashscope.aliyuncs.com/compatible-mode/v1
#  # 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: 0.99999999 # top-p sampling
  # n: 1 # Number of completions to generate

parallelization:
  stagger: 0.3
  num_threads: 1 # 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: eyJ0eXAiOiJqd3QiLCJhbGciOiJIUzI1NiJ9.eyJ1c2VybmFtZSI6IjMxNTEzMSIsInBhc3N3b3JkIjoiMzE1MTMxIiwiZXhwIjoyMDA1NjE0MzYxfQ.GdA72mKVMBS1Kj-HFV8o4gTxldjUAbooOX9aKszwIXs
    type: openai_embedding # or azure_openai_embedding
    model: text-embedding-3-small
    api_base: http://47.88.8.18:8088/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: 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:
encoding_model: cl100k_base
skip_workflows: []
llm:
  api_key: sk-72b9b57905b142e396722b0e01b982bf
  type: openai_chat # or azure_openai_chat
  #￥type: azure_openai_chat # or azure_openai_chat
  # model: gpt-4-turbo-preview
  model: qwen-plus
  model_supports_json: false # recommended if this is available for your model.
  # max_tokens: 4000
  request_timeout: 180.0
  api_base: https://dashscope.aliyuncs.com/compatible-mode/v1
#  # 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: 0.99999999 # top-p sampling
  # n: 1 # Number of completions to generate

parallelization:
  stagger: 0.3
  num_threads: 4 # 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:  my-secret-key
    type: openai_embedding # or azure_openai_embedding
    model: text-embedding-3-small
    api_base: http://my-embedding-host/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: 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: 0.999999 # 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: 0.999999 # 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

  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: 0.999999 # 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: 0.999999 # 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

GraphRAG Config Used

# Paste your config here

Logs and screenshots

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

No response

Additional Information

wenmengzhou commented 4 months ago

the error is caused by my own customized embedding interface which is not exact the same as openai embedding:

{"code": 200, "data": {"completion_tokens": 0, "messages": {"input": "Your text string goes here", "model": "text-embedding-3-small"}, "prompt_tokens": 5, "response": {"data": [{"embedding": [0.005172153, 0.017217182, -0.01868694, -0.018546963, -0.047256265, -0.030263046, 0.027659472, 0.0036639, 0.011233161, 0.0063969526, -0.001698097, 0.015859403, -0.001270292, -0.007873711, 0.059910193, 0.050307762, -0.027519494, 0.009910378, -0.040397387, 0.049999814, -0.00041380725, 0.03023505, -0.013717754, 0.032950606, 0.01728717, 0.016783252, -0.0017374655, 0.020422656, 0.04078932, -0.03773782, -0.026119724, -0.05002781, 0.024174042, -0.055122975, -0.032278717, 0.042357065, 0.06472541, 0.014697594, -0.015663436, -0.041321237, 0.022200365, 0.0073627946, 0.04496064, 0.0071073365, -0.024118051, 0.05240742, -0.020086711, -0.032222725, 0.016265336, 0.046444397, 0.0238241, -0.018882908, -0.0053716204, 0.043952804, -0.0061484934, 0.007306804, -0.014949553, 0.0726761, -0.0054871016, -0.037653834, 0.01606937, 0.03432238, 0.007138831, -0.0047417236, -0.026679631, 0.029003251, -0.038549688, 0.023194203, -0.0051651546, 0.011919049, 0.025517823, -0.014585612, -0.011107181, 0.0037863802, 0.00063470856, -0.023292186, -0.016321328, -0.004423276, 0.020730605, -0.004234307, 0.040285405, -0.0028292867, -0.025293859, -0.0201567, -0.048208106, 0.020660618, -0.075643614, 0.027953424, -0.017077204, 0.0083076395, -0.018336998, -0.012066024, -0.022634294, -0.0070513454, -0.015929392, 0.0055535906, -0.032782633, 0.004122325, -0.0058405437, -0.0384657, 0.047144283, -0.012073023, -0.020058716, 0.0026665635, -0.03152284, 0.011135177, 0.011954043, -0.0024408505, -0.04683633, -0.021332508, -0.08533003, 0.00720882, 0.0062779724, 0.02064662, 0.001258044, 0.0042133103, 0.06886873, -0.014515623, 0.021710446, -0.022690285, 0.025265863, -0.002834536, 0.031830788, -0.056858692, 0.0036429036, -0.041013286, 0.0056165806, -0.035526183, -0.03798978, 0.015467468, 0.021234524, -0.048516057, -0.0111491745, 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natoverse commented 4 months ago

Routing this to #657