From d5f99db05001f513bfac23554bb762f184b048eb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20MANSUY?= Date: Thu, 4 Dec 2025 19:14:32 +0800 Subject: [PATCH] cherry-pick 45f4f823 --- lightrag/llm/openai.py | 259 +++++++++++++++++++++++++++++++++++++---- 1 file changed, 234 insertions(+), 25 deletions(-) diff --git a/lightrag/llm/openai.py b/lightrag/llm/openai.py index 6da79c2c..8e265d2c 100644 --- a/lightrag/llm/openai.py +++ b/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: - api_key = os.environ["OPENAI_API_KEY"] + if use_azure: + from openai import AsyncAzureOpenAI - default_headers = { - "User-Agent": f"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) LightRAG/{__api_version__}", - "Content-Type": "application/json", - } + if not api_key: + api_key = os.environ.get("AZURE_OPENAI_API_KEY") or os.environ.get( + "LLM_BINDING_API_KEY" + ) - if client_configs is None: - client_configs = {} + 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, - } + # Create a merged config dict with precedence: explicit params > client_configs + merged_configs = { + **client_configs, + "api_key": api_key, + } - if base_url is not None: - merged_configs["base_url"] = base_url + # Add explicit parameters (override client_configs) + 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( - "OPENAI_API_BASE", "https://api.openai.com/v1" - ) + if not api_key: + 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, + )