diff --git a/lightrag/llm/azure_openai.py b/lightrag/llm/azure_openai.py index cb8d68df..1fc6feef 100644 --- a/lightrag/llm/azure_openai.py +++ b/lightrag/llm/azure_openai.py @@ -1,193 +1,22 @@ -from collections.abc import Iterable -import os -import pipmaster as pm # Pipmaster for dynamic library install +""" +Azure OpenAI compatibility layer. -# install specific modules -if not pm.is_installed("openai"): - pm.install("openai") +This module provides backward compatibility by re-exporting Azure OpenAI functions +from the main openai module where the actual implementation resides. -from openai import ( - AsyncAzureOpenAI, - APIConnectionError, - RateLimitError, - APITimeoutError, -) -from openai.types.chat import ChatCompletionMessageParam +All core logic for both OpenAI and Azure OpenAI now lives in lightrag.llm.openai, +with this module serving as a thin compatibility wrapper for existing code that +imports from lightrag.llm.azure_openai. +""" -from tenacity import ( - retry, - stop_after_attempt, - wait_exponential, - retry_if_exception_type, +from lightrag.llm.openai import ( + azure_openai_complete_if_cache, + azure_openai_complete, + azure_openai_embed, ) -from lightrag.utils import ( - wrap_embedding_func_with_attrs, - safe_unicode_decode, - logger, -) -from lightrag.types import GPTKeywordExtractionFormat - -import numpy as np - - -@retry( - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=1, min=4, max=10), - retry=retry_if_exception_type( - (RateLimitError, APIConnectionError, APIConnectionError) - ), -) -async def azure_openai_complete_if_cache( - model, - prompt, - system_prompt: str | None = None, - history_messages: Iterable[ChatCompletionMessageParam] | 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, -): - if enable_cot: - logger.debug( - "enable_cot=True is not supported for the Azure OpenAI API and will be ignored." - ) - 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") - ) - - kwargs.pop("hashing_kv", None) - timeout = kwargs.pop("timeout", None) - - # Handle keyword extraction mode - if keyword_extraction: - kwargs["response_format"] = GPTKeywordExtractionFormat - - openai_async_client = AsyncAzureOpenAI( - azure_endpoint=base_url, - azure_deployment=deployment, - api_key=api_key, - api_version=api_version, - timeout=timeout, - ) - messages = [] - if system_prompt: - messages.append({"role": "system", "content": system_prompt}) - if history_messages: - messages.extend(history_messages) - if prompt is not None: - messages.append({"role": "user", "content": prompt}) - - if "response_format" in kwargs: - response = await openai_async_client.chat.completions.parse( - model=model, messages=messages, **kwargs - ) - else: - response = await openai_async_client.chat.completions.create( - model=model, messages=messages, **kwargs - ) - - if hasattr(response, "__aiter__"): - - async def inner(): - async for chunk in response: - if len(chunk.choices) == 0: - continue - content = chunk.choices[0].delta.content - if content is None: - continue - if r"\u" in content: - content = safe_unicode_decode(content.encode("utf-8")) - yield content - - return inner() - else: - message = response.choices[0].message - - # Handle parsed responses (structured output via response_format) - # 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 - content = message.parsed.model_dump_json() - logger.debug("Using parsed structured response from API") - else: - # Handle regular content responses - content = message.content - if content and r"\u" in content: - content = safe_unicode_decode(content.encode("utf-8")) - - return content - - -async def azure_openai_complete( - prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs -) -> str: - 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) -@retry( - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=1, min=4, max=10), - retry=retry_if_exception_type( - (RateLimitError, APIConnectionError, APITimeoutError) - ), -) -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: - 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") - ) - - openai_async_client = AsyncAzureOpenAI( - azure_endpoint=base_url, - azure_deployment=deployment, - api_key=api_key, - api_version=api_version, - ) - - response = await openai_async_client.embeddings.create( - model=model or deployment, input=texts, encoding_format="float" - ) - return np.array([dp.embedding for dp in response.data]) +__all__ = [ + "azure_openai_complete_if_cache", + "azure_openai_complete", + "azure_openai_embed", +] diff --git a/lightrag/llm/openai.py b/lightrag/llm/openai.py index 6da79c2c..a314d597 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. @@ -162,23 +205,33 @@ async def openai_complete_if_cache( 6. For non-streaming: COT content is prepended to regular content with 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, ) @@ -631,24 +688,40 @@ 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. - base_url: Optional base URL for the OpenAI API. - api_key: Optional OpenAI API key. If None, uses the OPENAI_API_KEY environment variable. + model: The embedding model to use. For standard OpenAI (e.g., "text-embedding-3-small"). + For Azure, this can be the deployment name. + 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. @@ -658,9 +731,14 @@ 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 +771,169 @@ 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") + ) + + # 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, + 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) +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("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, + token_tracker=token_tracker, + client_configs=client_configs, + use_azure=True, + azure_deployment=deployment, + api_version=api_version, + ) diff --git a/lightrag/utils.py b/lightrag/utils.py index 6a7237c0..65c1e4bc 100644 --- a/lightrag/utils.py +++ b/lightrag/utils.py @@ -1005,7 +1005,76 @@ def priority_limit_async_func_call( def wrap_embedding_func_with_attrs(**kwargs): - """Wrap a function with attributes""" + """Decorator to add embedding dimension and token limit attributes to embedding functions. + + This decorator wraps an async embedding function and returns an EmbeddingFunc instance + that automatically handles dimension parameter injection and attribute management. + + WARNING: DO NOT apply this decorator to wrapper functions that call other + decorated embedding functions. This will cause double decoration and parameter + injection conflicts. + + Correct usage patterns: + + 1. Direct implementation (decorated): + ```python + @wrap_embedding_func_with_attrs(embedding_dim=1536) + async def my_embed(texts, embedding_dim=None): + # Direct implementation + return embeddings + ``` + + 2. Wrapper calling decorated function (DO NOT decorate wrapper): + ```python + # my_embed is already decorated above + + async def my_wrapper(texts, **kwargs): # ❌ DO NOT decorate this! + # Must call .func to access unwrapped implementation + return await my_embed.func(texts, **kwargs) + ``` + + 3. Wrapper calling decorated function (properly decorated): + ```python + @wrap_embedding_func_with_attrs(embedding_dim=1536) + async def my_wrapper(texts, **kwargs): # ✅ Can decorate if calling .func + # Calling .func avoids double decoration + return await my_embed.func(texts, **kwargs) + ``` + + The decorated function becomes an EmbeddingFunc instance with: + - embedding_dim: The embedding dimension + - max_token_size: Maximum token limit (optional) + - func: The original unwrapped function (access via .func) + - __call__: Wrapper that injects embedding_dim parameter + + Double decoration causes: + - Double injection of embedding_dim parameter + - Incorrect parameter passing to the underlying implementation + - Runtime errors due to parameter conflicts + + Args: + embedding_dim: The dimension of embedding vectors + max_token_size: Maximum number of tokens (optional) + send_dimensions: Whether to inject embedding_dim as a keyword argument (optional) + + Returns: + A decorator that wraps the function as an EmbeddingFunc instance + + Example of correct wrapper implementation: + ```python + @wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192) + @retry(...) + async def openai_embed(texts, ...): + # Base implementation + pass + + @wrap_embedding_func_with_attrs(embedding_dim=1536) # Note: No @retry here! + async def azure_openai_embed(texts, ...): + # CRITICAL: Call .func to access unwrapped function + return await openai_embed.func(texts, ...) # ✅ Correct + # return await openai_embed(texts, ...) # ❌ Wrong - double decoration! + ``` + """ def final_decro(func) -> EmbeddingFunc: new_func = EmbeddingFunc(**kwargs, func=func)