[Bug]: Error when using Azure OpenAI for LLM and base OpenAI embedding

[KennyStryker]

Describe the bug

When you try to base OpenAI embeddings with Azure OpenAI LLM, it throws an error because it changes the base URL.

Steps to reproduce

1. For LLM configuration, use Azure deployment and credentials.
2. For embedding configuration, use OpenAI key, etc.
3. Try to index a document

Expected Behavior

I should be able to use OpenAI embeddings with Azure OpenAI LLM.

GraphRAG Config Used

encoding_model: cl100k_base
skip_workflows: []
llm:
 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://instance.openai.azure.com
 api_version: api-version
 # 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: openai_api_key
   type: openai_embedding # or azure_openai_embedding
   model: text-embedding-3-small
   # api_base: https://<instance>.openai.azure.com
   # api_version: 2024-02-15-preview
   # organization: <organization_id>
   # deployment_name: <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: 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

• GraphRAG Version: v0.1.1
• Operating System: Windows 10
• Python Version: 3.10
• Related Issues: N/A

[github-actions[bot]]

This issue has been marked stale due to inactivity after repo maintainer or community member responses that request more information or suggest a solution. It will be closed after five additional days.

[natoverse]

@AlonsoGuevara can you confirm #517 resolved this and it can be closed?