[pull] main from microsoft:main

.semversioner/next-release/minor-20251003221030515836.json

@@ -0,0 +1,4 @@
+{
+  "type": "minor",
+  "description": "Set LiteLLM as default in init_content."
+}

graphrag/config/defaults.py

@@ -6,7 +6,7 @@
 from collections.abc import Callable
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import ClassVar, Literal
+from typing import ClassVar
 
 from graphrag.config.embeddings import default_embeddings
 from graphrag.config.enums import (
@@ -46,13 +46,14 @@
 
 DEFAULT_OUTPUT_BASE_DIR = "output"
 DEFAULT_CHAT_MODEL_ID = "default_chat_model"
-DEFAULT_CHAT_MODEL_TYPE = ModelType.OpenAIChat
+DEFAULT_CHAT_MODEL_TYPE = ModelType.Chat
 DEFAULT_CHAT_MODEL = "gpt-4-turbo-preview"
 DEFAULT_CHAT_MODEL_AUTH_TYPE = AuthType.APIKey
 DEFAULT_EMBEDDING_MODEL_ID = "default_embedding_model"
-DEFAULT_EMBEDDING_MODEL_TYPE = ModelType.OpenAIEmbedding
+DEFAULT_EMBEDDING_MODEL_TYPE = ModelType.Embedding
 DEFAULT_EMBEDDING_MODEL = "text-embedding-3-small"
 DEFAULT_EMBEDDING_MODEL_AUTH_TYPE = AuthType.APIKey
+DEFAULT_MODEL_PROVIDER = "openai"
 DEFAULT_VECTOR_STORE_ID = "default_vector_store"
 
 ENCODING_MODEL = "cl100k_base"
@@ -325,10 +326,10 @@ class LanguageModelDefaults:
     proxy: None = None
     audience: None = None
     model_supports_json: None = None
-    tokens_per_minute: Literal["auto"] = "auto"
-    requests_per_minute: Literal["auto"] = "auto"
+    tokens_per_minute: None = None
+    requests_per_minute: None = None
     rate_limit_strategy: str | None = "static"
-    retry_strategy: str = "native"
+    retry_strategy: str = "exponential_backoff"
     max_retries: int = 10
     max_retry_wait: float = 10.0
     concurrent_requests: int = 25

graphrag/config/errors.py

@@ -33,15 +33,6 @@ def __init__(self, llm_type: str) -> None:
         super().__init__(msg)
 
 
-class AzureDeploymentNameMissingError(ValueError):
-    """Azure Deployment Name missing error."""
-
-    def __init__(self, llm_type: str) -> None:
-        """Init method definition."""
-        msg = f"Deployment name is required for {llm_type}. Please rerun `graphrag init` set the deployment_name."
-        super().__init__(msg)
-
-
 class LanguageModelConfigMissingError(ValueError):
     """Missing model configuration error."""
 

graphrag/config/init_content.py

@@ -19,41 +19,34 @@
 
 models:
   {defs.DEFAULT_CHAT_MODEL_ID}:
-    type: {defs.DEFAULT_CHAT_MODEL_TYPE.value} # or azure_openai_chat
-    # api_base: https://<instance>.openai.azure.com
-    # api_version: 2024-05-01-preview
+    type: {defs.DEFAULT_CHAT_MODEL_TYPE.value}
+    model_provider: {defs.DEFAULT_MODEL_PROVIDER}
     auth_type: {defs.DEFAULT_CHAT_MODEL_AUTH_TYPE.value} # or azure_managed_identity
-    api_key: ${{GRAPHRAG_API_KEY}} # set this in the generated .env file
-    # audience: "https://cognitiveservices.azure.com/.default"
-    # organization: <organization_id>
+    api_key: ${{GRAPHRAG_API_KEY}} # set this in the generated .env file, or remove if managed identity
     model: {defs.DEFAULT_CHAT_MODEL}
-    # deployment_name: <azure_model_deployment_name>
-    # encoding_model: {defs.ENCODING_MODEL} # automatically set by tiktoken if left undefined
+    # api_base: https://<instance>.openai.azure.com
+    # api_version: 2024-05-01-preview
     model_supports_json: true # recommended if this is available for your model.
-    concurrent_requests: {language_model_defaults.concurrent_requests} # max number of simultaneous LLM requests allowed
+    concurrent_requests: {language_model_defaults.concurrent_requests}
     async_mode: {language_model_defaults.async_mode.value} # or asyncio
-    retry_strategy: native
+    retry_strategy: {language_model_defaults.retry_strategy}
     max_retries: {language_model_defaults.max_retries}
-    tokens_per_minute: {language_model_defaults.tokens_per_minute}              # set to null to disable rate limiting
-    requests_per_minute: {language_model_defaults.requests_per_minute}            # set to null to disable rate limiting
+    tokens_per_minute: null
+    requests_per_minute: null
   {defs.DEFAULT_EMBEDDING_MODEL_ID}:
-    type: {defs.DEFAULT_EMBEDDING_MODEL_TYPE.value} # or azure_openai_embedding
-    # api_base: https://<instance>.openai.azure.com
-    # api_version: 2024-05-01-preview
-    auth_type: {defs.DEFAULT_EMBEDDING_MODEL_AUTH_TYPE.value} # or azure_managed_identity
+    type: {defs.DEFAULT_EMBEDDING_MODEL_TYPE.value}
+    model_provider: {defs.DEFAULT_MODEL_PROVIDER}
+    auth_type: {defs.DEFAULT_EMBEDDING_MODEL_AUTH_TYPE.value}
     api_key: ${{GRAPHRAG_API_KEY}}
-    # audience: "https://cognitiveservices.azure.com/.default"
-    # organization: <organization_id>
     model: {defs.DEFAULT_EMBEDDING_MODEL}
-    # deployment_name: <azure_model_deployment_name>
-    # encoding_model: {defs.ENCODING_MODEL} # automatically set by tiktoken if left undefined
-    model_supports_json: true # recommended if this is available for your model.
-    concurrent_requests: {language_model_defaults.concurrent_requests} # max number of simultaneous LLM requests allowed
+    # api_base: https://<instance>.openai.azure.com
+    # api_version: 2024-05-01-preview
+    concurrent_requests: {language_model_defaults.concurrent_requests}
     async_mode: {language_model_defaults.async_mode.value} # or asyncio
-    retry_strategy: native
+    retry_strategy: {language_model_defaults.retry_strategy}
     max_retries: {language_model_defaults.max_retries}
-    tokens_per_minute: null              # set to null to disable rate limiting or auto for dynamic
-    requests_per_minute: null            # set to null to disable rate limiting or auto for dynamic
+    tokens_per_minute: null
+    requests_per_minute: null
 
 ### Input settings ###
 
@@ -62,7 +55,6 @@
     type: {graphrag_config_defaults.input.storage.type.value} # or blob
     base_dir: "{graphrag_config_defaults.input.storage.base_dir}"
   file_type: {graphrag_config_defaults.input.file_type.value} # [csv, text, json]
-
 
 chunks:
   size: {graphrag_config_defaults.chunks.size}
@@ -90,7 +82,6 @@
     type: {vector_store_defaults.type}
     db_uri: {vector_store_defaults.db_uri}
     container_name: {vector_store_defaults.container_name}
-    overwrite: {vector_store_defaults.overwrite}
 
 ### Workflow settings ###
 

graphrag/config/models/language_model_config.py

@@ -3,6 +3,7 @@
 
 """Language model configuration."""
 
+import logging
 from typing import Literal
 
 import tiktoken
@@ -14,11 +15,12 @@
     ApiKeyMissingError,
     AzureApiBaseMissingError,
     AzureApiVersionMissingError,
-    AzureDeploymentNameMissingError,
     ConflictingSettingsError,
 )
 from graphrag.language_model.factory import ModelFactory
 
+logger = logging.getLogger(__name__)
+
 
 class LanguageModelConfig(BaseModel):
     """Language model configuration."""
@@ -214,7 +216,8 @@ def _validate_deployment_name(self) -> None:
             or self.type == ModelType.AzureOpenAIEmbedding
             or self.model_provider == "azure"  # indicates Litellm + AOI
         ) and (self.deployment_name is None or self.deployment_name.strip() == ""):
-            raise AzureDeploymentNameMissingError(self.type)
+            msg = f"deployment_name is not set for Azure-hosted model. This will default to your model name ({self.model}). If different, this should be set."
+            logger.debug(msg)
 
     organization: str | None = Field(
         description="The organization to use for the LLM service.",

tests/unit/config/fixtures/minimal_config/settings.yaml

@@ -1,9 +1,11 @@
 models:
   default_chat_model:
     api_key: ${CUSTOM_API_KEY}
-    type: openai_chat
+    type: chat
+    model_provider: openai
     model: gpt-4-turbo-preview
   default_embedding_model:
     api_key: ${CUSTOM_API_KEY}
-    type: openai_embedding
+    type: embedding
+    model_provider: openai
     model: text-embedding-3-small

tests/unit/config/fixtures/minimal_config_missing_env_var/settings.yaml

@@ -1,9 +1,11 @@
 models:
   default_chat_model:
     api_key: ${SOME_NON_EXISTENT_ENV_VAR}
-    type: openai_chat
+    type: chat
+    model_provider: openai
     model: gpt-4-turbo-preview
   default_embedding_model:
     api_key: ${SOME_NON_EXISTENT_ENV_VAR}
-    type: openai_embedding
+    type: embedding
+    model_provider: openai
     model: text-embedding-3-small

tests/unit/config/test_config.py

@@ -133,19 +133,6 @@ def test_missing_azure_api_version() -> None:
         })
 
 
-def test_missing_azure_deployment_name() -> None:
-    missing_deployment_name_config = base_azure_model_config.copy()
-    del missing_deployment_name_config["deployment_name"]
-
-    with pytest.raises(ValidationError):
-        create_graphrag_config({
-            "models": {
-                defs.DEFAULT_CHAT_MODEL_ID: missing_deployment_name_config,
-                defs.DEFAULT_EMBEDDING_MODEL_ID: DEFAULT_EMBEDDING_MODEL_CONFIG,
-            }
-        })
-
-
 def test_default_config() -> None:
     expected = get_default_graphrag_config()
     actual = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})

tests/unit/config/utils.py

@@ -41,12 +41,14 @@
     "api_key": FAKE_API_KEY,
     "type": defs.DEFAULT_CHAT_MODEL_TYPE.value,
     "model": defs.DEFAULT_CHAT_MODEL,
+    "model_provider": defs.DEFAULT_MODEL_PROVIDER,
 }
 
 DEFAULT_EMBEDDING_MODEL_CONFIG = {
     "api_key": FAKE_API_KEY,
     "type": defs.DEFAULT_EMBEDDING_MODEL_TYPE.value,
     "model": defs.DEFAULT_EMBEDDING_MODEL,
+    "model_provider": defs.DEFAULT_MODEL_PROVIDER,
 }
 
 DEFAULT_MODEL_CONFIG = {

tests/verbs/util.py

@@ -17,12 +17,14 @@
     "api_key": FAKE_API_KEY,
     "type": defs.DEFAULT_CHAT_MODEL_TYPE.value,
     "model": defs.DEFAULT_CHAT_MODEL,
+    "model_provider": defs.DEFAULT_MODEL_PROVIDER,
 }
 
 DEFAULT_EMBEDDING_MODEL_CONFIG = {
     "api_key": FAKE_API_KEY,
     "type": defs.DEFAULT_EMBEDDING_MODEL_TYPE.value,
     "model": defs.DEFAULT_EMBEDDING_MODEL,
+    "model_provider": defs.DEFAULT_MODEL_PROVIDER,
 }
 
 DEFAULT_MODEL_CONFIG = {