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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]: Can't access Azure OpenAI embedding endpoint #590

Closed wodecki closed 1 month ago

wodecki commented 1 month ago

Describe the bug

When using AzureOpenAI llm and embeddings, local search generates the error: AttributeError: 'AzureOpenAIEmbeddings' object has no attribute 'embed'

Steps to reproduce

in https://github.com/microsoft/graphrag/blob/main/examples_notebooks/local_search.ipynb set-up:

api_key = "3dfc4da3414..da..8890dfcd1415b07"
api_base = "https://asystentai.openai.azure.com/"
api_version = "2024-02-15-preview"
llm_model = "gpt-4o"
#embedding_model = "text-embedding-3-large"
embedding_model = "text-embedding-ada-002"

llm = ChatOpenAI(
    api_key=api_key,
    model=llm_model,
    api_base=api_base,
    api_version=api_version,
    api_type=OpenaiApiType.OpenAI,  # OpenaiApiType.OpenAI or OpenaiApiType.AzureOpenAI
    max_retries=20,
)

token_encoder = tiktoken.get_encoding("cl100k_base")

text_embedder = OpenAIEmbedding(
    api_key=api_key,
    api_base=api_base,
    api_version=api_version,
    api_type=OpenaiApiType.OpenAI,
    model=embedding_model,
    deployment_name=embedding_model,
    organization=None,
    max_retries=20,
)

running: result = await search_engine.asearch("Tell me about Agent Mercer") results with: Error embedding chunk {'OpenAIEmbedding': "Error code: 404 - {'error': {'code': '404', 'message': 'Resource not found'}}"}

Expected Behavior

Answer to the question (no error)

GraphRAG Config Used

encoding_model: cl100k_base
skip_workflows: []
llm:
  api_key: ${GRAPHRAG_API_KEY}
  type: azure_openai_chat # or azure_openai_chat
  model: gpt-4o
  model_supports_json: true # recommended if this is available for your model.
  # max_tokens: 4000
  # request_timeout: 180.0
  api_base: https://asystentai.openai.azure.com/
  api_version: 2024-02-15-preview
  # organization: <organization_id>
  deployment_name: gpt-4o
  # 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

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: azure_openai_embedding # or azure_openai_embedding
    model: text-embedding-3-large
    api_base: https://asystentai.openai.azure.com/
    api_version: 2024-02-15-preview
    # organization: <organization_id>
    deployment_name: text-embedding-3-large
    # 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: 300
  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: 0

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: 0

community_report:
  ## 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
  # 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

No response

Additional Information

wodecki commented 1 month ago

Using CLI command: python -m graphrag.query --root ./ragtest --method global "Sample question" works correctly.

wodecki commented 1 month ago

Solved. Changed the model type to: api_type=OpenaiApiType.AzureOpenAI,

JamieVC commented 1 month ago

in my case, need to modify these parameter to work well. FYR

ahanine1 commented 1 day ago

Hello Sir, I am running in the same problem,

I tried the api_type=OpenaiApiType.AzureOpenAI,

But still got the same error, do youhave any idea why?

Thank you