fix: sync openai.py from upstream

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.
@@ -162,23 +205,33 @@ async def openai_complete_if_cache(
     6. For non-streaming: COT content is prepended to regular content with <think> tags.
 
     Args:
-        model: The OpenAI model to use.
+        model: The OpenAI model to use. For Azure, this can be the deployment name.
         prompt: The prompt to complete.
         system_prompt: Optional system prompt to include.
         history_messages: Optional list of previous messages in the conversation.
-        base_url: Optional base URL for the OpenAI API.
-        api_key: Optional OpenAI API key. If None, uses the OPENAI_API_KEY environment variable.
-        token_tracker: Optional token usage tracker for monitoring API usage.
         enable_cot: Whether to enable Chain of Thought (COT) processing. Default is False.
+        base_url: Optional base URL for the OpenAI API. For Azure, this should be the
+            Azure OpenAI endpoint (e.g., https://your-resource.openai.azure.com/).
+        api_key: Optional API key. For standard OpenAI, uses OPENAI_API_KEY environment
+            variable if None. For Azure, uses AZURE_OPENAI_API_KEY if None.
+        token_tracker: Optional token usage tracker for monitoring API usage.
         stream: Whether to stream the response. Default is False.
         timeout: Request timeout in seconds. Default is None.
         keyword_extraction: Whether to enable keyword extraction mode. When True, triggers
             special response formatting for keyword extraction. Default is False.
+        use_azure: Whether to use Azure OpenAI service instead of standard OpenAI.
+            When True, creates an AsyncAzureOpenAI client. Default is False.
+        azure_deployment: Azure OpenAI deployment name. Only used when use_azure=True.
+            If not specified, falls back to AZURE_OPENAI_DEPLOYMENT environment variable.
+        api_version: Azure OpenAI API version (e.g., "2024-02-15-preview"). Only used
+            when use_azure=True. If not specified, falls back to AZURE_OPENAI_API_VERSION
+            environment variable.
         **kwargs: Additional keyword arguments to pass to the OpenAI API.
             Special kwargs:
             - openai_client_configs: Dict of configuration options for the AsyncOpenAI client.
                 These will be passed to the client constructor but will be overridden by
-                explicit parameters (api_key, base_url).
+                explicit parameters (api_key, base_url). Supports proxy configuration,
+                custom headers, retry policies, etc.
 
     Returns:
         The completed text (with integrated COT content if available) or an async iterator
@@ -207,10 +260,14 @@ 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,
     )
 
@@ -238,15 +295,19 @@ async def openai_complete_if_cache(
     if timeout is not None:
         kwargs["timeout"] = timeout
 
+    # Determine the correct model identifier to use
+    # For Azure OpenAI, we must use the deployment name instead of the model name
+    api_model = azure_deployment if use_azure and azure_deployment else model
+
     try:
         # Don't use async with context manager, use client directly
         if "response_format" in kwargs:
-            response = await openai_async_client.beta.chat.completions.parse(
+            response = await openai_async_client.chat.completions.parse(
-                model=model, messages=messages, **kwargs
+                model=api_model, messages=messages, **kwargs
             )
         else:
             response = await openai_async_client.chat.completions.create(
-                model=model, messages=messages, **kwargs
+                model=api_model, messages=messages, **kwargs
             )
     except APITimeoutError as e:
         logger.error(f"OpenAI API Timeout Error: {e}")
@@ -291,7 +352,10 @@ async def openai_complete_if_cache(
 
                     # Check if choices exists and is not empty
                     if not hasattr(chunk, "choices") or not chunk.choices:
-                        logger.warning(f"Received chunk without choices: {chunk}")
+                        # Azure OpenAI sends content filter results in first chunk without choices
+                        logger.debug(
+                            f"Received chunk without choices (likely Azure content filter): {chunk}"
+                        )
                         continue
 
                     # Check if delta exists
@@ -453,46 +517,57 @@ async def openai_complete_if_cache(
                 raise InvalidResponseError("Invalid response from OpenAI API")
 
             message = response.choices[0].message
-            content = getattr(message, "content", None)
-            reasoning_content = getattr(message, "reasoning_content", "")
 
-            # Handle COT logic for non-streaming responses (only if enabled)
+            # Handle parsed responses (structured output via response_format)
-            final_content = ""
+            # When using beta.chat.completions.parse(), the response is in message.parsed
+            if hasattr(message, "parsed") and message.parsed is not None:
+                # Serialize the parsed structured response to JSON
+                final_content = message.parsed.model_dump_json()
+                logger.debug("Using parsed structured response from API")
+            else:
+                # Handle regular content responses
+                content = getattr(message, "content", None)
+                reasoning_content = getattr(message, "reasoning_content", "")
 
-            if enable_cot:
+                # Handle COT logic for non-streaming responses (only if enabled)
-                # Check if we should include reasoning content
+                final_content = ""
-                should_include_reasoning = False
+
-                if reasoning_content and reasoning_content.strip():
+                if enable_cot:
-                    if not content or content.strip() == "":
+                    # Check if we should include reasoning content
-                        # Case 1: Only reasoning content, should include COT
+                    should_include_reasoning = False
-                        should_include_reasoning = True
+                    if reasoning_content and reasoning_content.strip():
-                        final_content = (
+                        if not content or content.strip() == "":
-                            content or ""
+                            # Case 1: Only reasoning content, should include COT
-                        )  # Use empty string if content is None
+                            should_include_reasoning = True
+                            final_content = (
+                                content or ""
+                            )  # Use empty string if content is None
+                        else:
+                            # Case 3: Both content and reasoning_content present, ignore reasoning
+                            should_include_reasoning = False
+                            final_content = content
                     else:
-                        # Case 3: Both content and reasoning_content present, ignore reasoning
+                        # No reasoning content, use regular content
-                        should_include_reasoning = False
+                        final_content = content or ""
-                        final_content = content
+
+                    # Apply COT wrapping if needed
+                    if should_include_reasoning:
+                        if r"\u" in reasoning_content:
+                            reasoning_content = safe_unicode_decode(
+                                reasoning_content.encode("utf-8")
+                            )
+                        final_content = (
+                            f"<think>{reasoning_content}</think>{final_content}"
+                        )
                 else:
-                    # No reasoning content, use regular content
+                    # COT disabled, only use regular content
                     final_content = content or ""
 
-                # Apply COT wrapping if needed
+                # Validate final content
-                if should_include_reasoning:
+                if not final_content or final_content.strip() == "":
-                    if r"\u" in reasoning_content:
+                    logger.error("Received empty content from OpenAI API")
-                        reasoning_content = safe_unicode_decode(
+                    await openai_async_client.close()  # Ensure client is closed
-                            reasoning_content.encode("utf-8")
+                    raise InvalidResponseError("Received empty content from OpenAI API")
-                        )
-                    final_content = f"<think>{reasoning_content}</think>{final_content}"
-            else:
-                # COT disabled, only use regular content
-                final_content = content or ""
-
-            # Validate final content
-            if not final_content or final_content.strip() == "":
-                logger.error("Received empty content from OpenAI API")
-                await openai_async_client.close()  # Ensure client is closed
-                raise InvalidResponseError("Received empty content from OpenAI API")
 
             # Apply Unicode decoding to final content if needed
             if r"\u" in final_content:
@@ -620,24 +695,40 @@ 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.
 
+    This function supports both standard OpenAI and Azure OpenAI services.
+
     Args:
         texts: List of texts to embed.
-        model: The OpenAI embedding model to use.
+        model: The embedding model to use. For standard OpenAI (e.g., "text-embedding-3-small").
-        base_url: Optional base URL for the OpenAI API.
+            For Azure, this can be the deployment name.
-        api_key: Optional OpenAI API key. If None, uses the OPENAI_API_KEY environment variable.
+        base_url: Optional base URL for the API. For standard OpenAI, uses default OpenAI endpoint.
+            For Azure, this should be the Azure OpenAI endpoint (e.g., https://your-resource.openai.azure.com/).
+        api_key: Optional API key. For standard OpenAI, uses OPENAI_API_KEY environment variable if None.
+            For Azure, uses AZURE_EMBEDDING_API_KEY environment variable if None.
         embedding_dim: Optional embedding dimension for dynamic dimension reduction.
             **IMPORTANT**: This parameter is automatically injected by the EmbeddingFunc wrapper.
             Do NOT manually pass this parameter when calling the function directly.
             The dimension is controlled by the @wrap_embedding_func_with_attrs decorator.
             Manually passing a different value will trigger a warning and be ignored.
             When provided (by EmbeddingFunc), it will be passed to the OpenAI API for dimension reduction.
-        client_configs: Additional configuration options for the AsyncOpenAI client.
+        client_configs: Additional configuration options for the AsyncOpenAI/AsyncAzureOpenAI client.
             These will override any default configurations but will be overridden by
-            explicit parameters (api_key, base_url).
+            explicit parameters (api_key, base_url). Supports proxy configuration,
+            custom headers, retry policies, etc.
         token_tracker: Optional token usage tracker for monitoring API usage.
+        use_azure: Whether to use Azure OpenAI service instead of standard OpenAI.
+            When True, creates an AsyncAzureOpenAI client. Default is False.
+        azure_deployment: Azure OpenAI deployment name. Only used when use_azure=True.
+            If not specified, falls back to AZURE_EMBEDDING_DEPLOYMENT environment variable.
+        api_version: Azure OpenAI API version (e.g., "2024-02-15-preview"). Only used
+            when use_azure=True. If not specified, falls back to AZURE_EMBEDDING_API_VERSION
+            environment variable.
 
     Returns:
         A numpy array of embeddings, one per input text.
@@ -647,15 +738,24 @@ 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:
+        # Determine the correct model identifier to use
+        # For Azure OpenAI, we must use the deployment name instead of the model name
+        api_model = azure_deployment if use_azure and azure_deployment else model
+
         # Prepare API call parameters
         api_params = {
-            "model": model,
+            "model": api_model,
             "input": texts,
             "encoding_format": "base64",
         }
@@ -682,3 +782,172 @@ 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,
+    token_tracker: Any | None = None,
+    stream: bool | None = None,
+    timeout: int | 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.
+
+    All parameters from the underlying openai_complete_if_cache are exposed to ensure
+    full feature parity and API consistency.
+    """
+    # 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")
+        or "2024-08-01-preview"
+    )
+
+    # Call the unified implementation with Azure-specific parameters
+    return await openai_complete_if_cache(
+        model=deployment,
+        prompt=prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        enable_cot=enable_cot,
+        base_url=base_url,
+        api_key=api_key,
+        token_tracker=token_tracker,
+        stream=stream,
+        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, max_token_size=8192)
+async def azure_openai_embed(
+    texts: list[str],
+    model: str | None = None,
+    base_url: str | None = None,
+    api_key: str | None = None,
+    token_tracker: Any | None = None,
+    client_configs: dict[str, Any] | 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.
+
+    All parameters from the underlying openai_embed are exposed to ensure
+    full feature parity and API consistency.
+
+    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("AZURE_OPENAI_API_VERSION")
+        or os.getenv("OPENAI_API_VERSION")
+        or "2024-08-01-preview"
+    )
+
+    # 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=deployment,
+        base_url=base_url,
+        api_key=api_key,
+        token_tracker=token_tracker,
+        client_configs=client_configs,
+        use_azure=True,
+        azure_deployment=deployment,
+        api_version=api_version,
+    )