cherry-pick 45f4f823

lightrag/llm/openai.py

@@ -77,46 +77,86 @@ class InvalidResponseError(Exception):
 def create_openai_async_client(
     api_key: str | None = None,
     base_url: str | None = None,
+    use_azure: bool = False,
+    azure_deployment: str | None = None,
+    api_version: str | None = None,
+    timeout: int | None = None,
     client_configs: dict[str, Any] | None = None,
 ) -> AsyncOpenAI:
-    """Create an AsyncOpenAI client with the given configuration.
+    """Create an AsyncOpenAI or AsyncAzureOpenAI client with the given configuration.
 
     Args:
         api_key: OpenAI API key. If None, uses the OPENAI_API_KEY environment variable.
         base_url: Base URL for the OpenAI API. If None, uses the default OpenAI API URL.
+        use_azure: Whether to create an Azure OpenAI client. Default is False.
+        azure_deployment: Azure OpenAI deployment name (only used when use_azure=True).
+        api_version: Azure OpenAI API version (only used when use_azure=True).
+        timeout: Request timeout in seconds.
         client_configs: Additional configuration options for the AsyncOpenAI client.
             These will override any default configurations but will be overridden by
             explicit parameters (api_key, base_url).
 
     Returns:
-        An AsyncOpenAI client instance.
+        An AsyncOpenAI or AsyncAzureOpenAI client instance.
     """
-    if not api_key:
+    if use_azure:
-        api_key = os.environ["OPENAI_API_KEY"]
+        from openai import AsyncAzureOpenAI
 
-    default_headers = {
+        if not api_key:
-        "User-Agent": f"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) LightRAG/{__api_version__}",
+            api_key = os.environ.get("AZURE_OPENAI_API_KEY") or os.environ.get(
-        "Content-Type": "application/json",
+                "LLM_BINDING_API_KEY"
-    }
+            )
 
-    if client_configs is None:
+        if client_configs is None:
-        client_configs = {}
+            client_configs = {}
 
-    # Create a merged config dict with precedence: explicit params > client_configs > defaults
+        # Create a merged config dict with precedence: explicit params > client_configs
-    merged_configs = {
+        merged_configs = {
-        **client_configs,
+            **client_configs,
-        "default_headers": default_headers,
+            "api_key": api_key,
-        "api_key": api_key,
+        }
-    }
 
-    if base_url is not None:
+        # Add explicit parameters (override client_configs)
-        merged_configs["base_url"] = base_url
+        if base_url is not None:
+            merged_configs["azure_endpoint"] = base_url
+        if azure_deployment is not None:
+            merged_configs["azure_deployment"] = azure_deployment
+        if api_version is not None:
+            merged_configs["api_version"] = api_version
+        if timeout is not None:
+            merged_configs["timeout"] = timeout
+
+        return AsyncAzureOpenAI(**merged_configs)
     else:
-        merged_configs["base_url"] = os.environ.get(
+        if not api_key:
-            "OPENAI_API_BASE", "https://api.openai.com/v1"
+            api_key = os.environ["OPENAI_API_KEY"]
-        )
 
-    return AsyncOpenAI(**merged_configs)
+        default_headers = {
+            "User-Agent": f"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) LightRAG/{__api_version__}",
+            "Content-Type": "application/json",
+        }
+
+        if client_configs is None:
+            client_configs = {}
+
+        # Create a merged config dict with precedence: explicit params > client_configs > defaults
+        merged_configs = {
+            **client_configs,
+            "default_headers": default_headers,
+            "api_key": api_key,
+        }
+
+        if base_url is not None:
+            merged_configs["base_url"] = base_url
+        else:
+            merged_configs["base_url"] = os.environ.get(
+                "OPENAI_API_BASE", "https://api.openai.com/v1"
+            )
+
+        if timeout is not None:
+            merged_configs["timeout"] = timeout
+
+        return AsyncOpenAI(**merged_configs)
 
 
 @retry(
@@ -141,6 +181,9 @@ async def openai_complete_if_cache(
     stream: bool | None = None,
     timeout: int | None = None,
     keyword_extraction: bool = False,
+    use_azure: bool = False,
+    azure_deployment: str | None = None,
+    api_version: str | None = None,
     **kwargs: Any,
 ) -> str:
     """Complete a prompt using OpenAI's API with caching support and Chain of Thought (COT) integration.
@@ -207,10 +250,14 @@ async def openai_complete_if_cache(
     if keyword_extraction:
         kwargs["response_format"] = GPTKeywordExtractionFormat
 
-    # Create the OpenAI client
+    # Create the OpenAI client (supports both OpenAI and Azure)
     openai_async_client = create_openai_async_client(
         api_key=api_key,
         base_url=base_url,
+        use_azure=use_azure,
+        azure_deployment=azure_deployment,
+        api_version=api_version,
+        timeout=timeout,
         client_configs=client_configs,
     )
 
@@ -631,6 +678,9 @@ async def openai_embed(
     embedding_dim: int | None = None,
     client_configs: dict[str, Any] | None = None,
     token_tracker: Any | None = None,
+    use_azure: bool = False,
+    azure_deployment: str | None = None,
+    api_version: str | None = None,
 ) -> np.ndarray:
     """Generate embeddings for a list of texts using OpenAI's API.
 
@@ -658,9 +708,14 @@ async def openai_embed(
         RateLimitError: If the OpenAI API rate limit is exceeded.
         APITimeoutError: If the OpenAI API request times out.
     """
-    # Create the OpenAI client
+    # Create the OpenAI client (supports both OpenAI and Azure)
     openai_async_client = create_openai_async_client(
-        api_key=api_key, base_url=base_url, client_configs=client_configs
+        api_key=api_key,
+        base_url=base_url,
+        use_azure=use_azure,
+        azure_deployment=azure_deployment,
+        api_version=api_version,
+        client_configs=client_configs,
     )
 
     async with openai_async_client:
@@ -693,3 +748,157 @@ async def openai_embed(
                 for dp in response.data
             ]
         )
+
+
+# Azure OpenAI wrapper functions for backward compatibility
+async def azure_openai_complete_if_cache(
+    model,
+    prompt,
+    system_prompt: str | None = None,
+    history_messages: list[dict[str, Any]] | None = None,
+    enable_cot: bool = False,
+    base_url: str | None = None,
+    api_key: str | None = None,
+    api_version: str | None = None,
+    keyword_extraction: bool = False,
+    **kwargs,
+):
+    """Azure OpenAI completion wrapper function.
+
+    This function provides backward compatibility by wrapping the unified
+    openai_complete_if_cache implementation with Azure-specific parameter handling.
+    """
+    # Handle Azure-specific environment variables and parameters
+    deployment = os.getenv("AZURE_OPENAI_DEPLOYMENT") or model or os.getenv("LLM_MODEL")
+    base_url = (
+        base_url or os.getenv("AZURE_OPENAI_ENDPOINT") or os.getenv("LLM_BINDING_HOST")
+    )
+    api_key = (
+        api_key or os.getenv("AZURE_OPENAI_API_KEY") or os.getenv("LLM_BINDING_API_KEY")
+    )
+    api_version = (
+        api_version
+        or os.getenv("AZURE_OPENAI_API_VERSION")
+        or os.getenv("OPENAI_API_VERSION")
+    )
+
+    # Pop timeout from kwargs if present (will be handled by openai_complete_if_cache)
+    timeout = kwargs.pop("timeout", None)
+
+    # Call the unified implementation with Azure-specific parameters
+    return await openai_complete_if_cache(
+        model=model,
+        prompt=prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        enable_cot=enable_cot,
+        base_url=base_url,
+        api_key=api_key,
+        timeout=timeout,
+        use_azure=True,
+        azure_deployment=deployment,
+        api_version=api_version,
+        keyword_extraction=keyword_extraction,
+        **kwargs,
+    )
+
+
+async def azure_openai_complete(
+    prompt,
+    system_prompt=None,
+    history_messages=None,
+    keyword_extraction=False,
+    **kwargs,
+) -> str:
+    """Azure OpenAI complete wrapper function.
+
+    Provides backward compatibility for azure_openai_complete calls.
+    """
+    if history_messages is None:
+        history_messages = []
+    result = await azure_openai_complete_if_cache(
+        os.getenv("LLM_MODEL", "gpt-4o-mini"),
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        keyword_extraction=keyword_extraction,
+        **kwargs,
+    )
+    return result
+
+
+@wrap_embedding_func_with_attrs(embedding_dim=1536)
+async def azure_openai_embed(
+    texts: list[str],
+    model: str | None = None,
+    base_url: str | None = None,
+    api_key: str | None = None,
+    api_version: str | None = None,
+) -> np.ndarray:
+    """Azure OpenAI embedding wrapper function.
+
+    This function provides backward compatibility by wrapping the unified
+    openai_embed implementation with Azure-specific parameter handling.
+
+    IMPORTANT - Decorator Usage:
+
+    1. This function is decorated with @wrap_embedding_func_with_attrs to provide
+       the EmbeddingFunc interface for users who need to access embedding_dim
+       and other attributes.
+
+    2. This function does NOT use @retry decorator to avoid double-wrapping,
+       since the underlying openai_embed.func already has retry logic.
+
+    3. This function calls openai_embed.func (the unwrapped function) instead of
+       openai_embed (the EmbeddingFunc instance) to avoid double decoration issues:
+
+       ✅ Correct: await openai_embed.func(...)  # Calls unwrapped function with retry
+       ❌ Wrong:   await openai_embed(...)       # Would cause double EmbeddingFunc wrapping
+
+    Double decoration causes:
+    - Double injection of embedding_dim parameter
+    - Incorrect parameter passing to the underlying implementation
+    - Runtime errors due to parameter conflicts
+
+    The call chain with correct implementation:
+    azure_openai_embed(texts)
+    → EmbeddingFunc.__call__(texts)              # azure's decorator
+      → azure_openai_embed_impl(texts, embedding_dim=1536)
+        → openai_embed.func(texts, ...)
+          → @retry_wrapper(texts, ...)           # openai's retry (only one layer)
+            → openai_embed_impl(texts, ...)
+              → actual embedding computation
+    """
+    # Handle Azure-specific environment variables and parameters
+    deployment = (
+        os.getenv("AZURE_EMBEDDING_DEPLOYMENT")
+        or model
+        or os.getenv("EMBEDDING_MODEL", "text-embedding-3-small")
+    )
+    base_url = (
+        base_url
+        or os.getenv("AZURE_EMBEDDING_ENDPOINT")
+        or os.getenv("EMBEDDING_BINDING_HOST")
+    )
+    api_key = (
+        api_key
+        or os.getenv("AZURE_EMBEDDING_API_KEY")
+        or os.getenv("EMBEDDING_BINDING_API_KEY")
+    )
+    api_version = (
+        api_version
+        or os.getenv("AZURE_EMBEDDING_API_VERSION")
+        or os.getenv("OPENAI_API_VERSION")
+    )
+
+    # CRITICAL: Call openai_embed.func (unwrapped) to avoid double decoration
+    # openai_embed is an EmbeddingFunc instance, .func accesses the underlying function
+    return await openai_embed.func(
+        texts=texts,
+        model=model or deployment,
+        base_url=base_url,
+        api_key=api_key,
+        use_azure=True,
+        azure_deployment=deployment,
+        api_version=api_version,
+    )