Consolidate Azure OpenAI implementation into main OpenAI module
• Unified OpenAI/Azure client creation • Azure module now re-exports functions • Backward compatibility maintained • Reduced code duplication
This commit is contained in:
yangdx
parent
66d6c7dd6f
commit
b709f8f869
2 changed files with 213 additions and 215 deletions
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@ -1,193 +1,22 @@
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from collections.abc import Iterable
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import os
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import pipmaster as pm # Pipmaster for dynamic library install
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"""
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Azure OpenAI compatibility layer.
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# install specific modules
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if not pm.is_installed("openai"):
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pm.install("openai")
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This module provides backward compatibility by re-exporting Azure OpenAI functions
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from the main openai module where the actual implementation resides.
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from openai import (
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AsyncAzureOpenAI,
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APIConnectionError,
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RateLimitError,
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APITimeoutError,
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)
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from openai.types.chat import ChatCompletionMessageParam
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All core logic for both OpenAI and Azure OpenAI now lives in lightrag.llm.openai,
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with this module serving as a thin compatibility wrapper for existing code that
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imports from lightrag.llm.azure_openai.
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"""
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from tenacity import (
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retry,
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stop_after_attempt,
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wait_exponential,
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retry_if_exception_type,
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from lightrag.llm.openai import (
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azure_openai_complete_if_cache,
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azure_openai_complete,
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azure_openai_embed,
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)
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from lightrag.utils import (
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wrap_embedding_func_with_attrs,
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safe_unicode_decode,
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logger,
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)
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from lightrag.types import GPTKeywordExtractionFormat
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import numpy as np
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@retry(
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stop=stop_after_attempt(3),
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wait=wait_exponential(multiplier=1, min=4, max=10),
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retry=retry_if_exception_type(
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(RateLimitError, APIConnectionError, APIConnectionError)
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),
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)
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async def azure_openai_complete_if_cache(
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model,
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prompt,
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system_prompt: str | None = None,
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history_messages: Iterable[ChatCompletionMessageParam] | None = None,
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enable_cot: bool = False,
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base_url: str | None = None,
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api_key: str | None = None,
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api_version: str | None = None,
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keyword_extraction: bool = False,
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**kwargs,
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):
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if enable_cot:
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logger.debug(
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"enable_cot=True is not supported for the Azure OpenAI API and will be ignored."
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)
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deployment = os.getenv("AZURE_OPENAI_DEPLOYMENT") or model or os.getenv("LLM_MODEL")
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base_url = (
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base_url or os.getenv("AZURE_OPENAI_ENDPOINT") or os.getenv("LLM_BINDING_HOST")
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)
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api_key = (
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api_key or os.getenv("AZURE_OPENAI_API_KEY") or os.getenv("LLM_BINDING_API_KEY")
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)
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api_version = (
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api_version
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or os.getenv("AZURE_OPENAI_API_VERSION")
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or os.getenv("OPENAI_API_VERSION")
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)
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kwargs.pop("hashing_kv", None)
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timeout = kwargs.pop("timeout", None)
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# Handle keyword extraction mode
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if keyword_extraction:
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kwargs["response_format"] = GPTKeywordExtractionFormat
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openai_async_client = AsyncAzureOpenAI(
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azure_endpoint=base_url,
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azure_deployment=deployment,
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api_key=api_key,
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api_version=api_version,
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timeout=timeout,
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)
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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if history_messages:
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messages.extend(history_messages)
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if prompt is not None:
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messages.append({"role": "user", "content": prompt})
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if "response_format" in kwargs:
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response = await openai_async_client.chat.completions.parse(
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model=model, messages=messages, **kwargs
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)
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else:
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response = await openai_async_client.chat.completions.create(
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model=model, messages=messages, **kwargs
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)
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if hasattr(response, "__aiter__"):
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async def inner():
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async for chunk in response:
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if len(chunk.choices) == 0:
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continue
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content = chunk.choices[0].delta.content
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if content is None:
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continue
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if r"\u" in content:
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content = safe_unicode_decode(content.encode("utf-8"))
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yield content
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return inner()
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else:
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message = response.choices[0].message
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# Handle parsed responses (structured output via response_format)
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# When using beta.chat.completions.parse(), the response is in message.parsed
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if hasattr(message, "parsed") and message.parsed is not None:
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# Serialize the parsed structured response to JSON
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content = message.parsed.model_dump_json()
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logger.debug("Using parsed structured response from API")
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else:
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# Handle regular content responses
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content = message.content
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if content and r"\u" in content:
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content = safe_unicode_decode(content.encode("utf-8"))
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return content
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async def azure_openai_complete(
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prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
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) -> str:
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result = await azure_openai_complete_if_cache(
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os.getenv("LLM_MODEL", "gpt-4o-mini"),
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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keyword_extraction=keyword_extraction,
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**kwargs,
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)
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return result
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@wrap_embedding_func_with_attrs(embedding_dim=1536)
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@retry(
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stop=stop_after_attempt(3),
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wait=wait_exponential(multiplier=1, min=4, max=10),
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retry=retry_if_exception_type(
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(RateLimitError, APIConnectionError, APITimeoutError)
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),
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)
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async def azure_openai_embed(
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texts: list[str],
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model: str | None = None,
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base_url: str | None = None,
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api_key: str | None = None,
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api_version: str | None = None,
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) -> np.ndarray:
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deployment = (
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os.getenv("AZURE_EMBEDDING_DEPLOYMENT")
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or model
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or os.getenv("EMBEDDING_MODEL", "text-embedding-3-small")
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)
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base_url = (
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base_url
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or os.getenv("AZURE_EMBEDDING_ENDPOINT")
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or os.getenv("EMBEDDING_BINDING_HOST")
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)
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api_key = (
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api_key
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or os.getenv("AZURE_EMBEDDING_API_KEY")
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or os.getenv("EMBEDDING_BINDING_API_KEY")
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)
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api_version = (
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api_version
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or os.getenv("AZURE_EMBEDDING_API_VERSION")
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or os.getenv("OPENAI_API_VERSION")
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)
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openai_async_client = AsyncAzureOpenAI(
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azure_endpoint=base_url,
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azure_deployment=deployment,
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api_key=api_key,
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api_version=api_version,
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)
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response = await openai_async_client.embeddings.create(
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model=model or deployment, input=texts, encoding_format="float"
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)
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return np.array([dp.embedding for dp in response.data])
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__all__ = [
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"azure_openai_complete_if_cache",
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"azure_openai_complete",
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"azure_openai_embed",
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]
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@ -77,46 +77,73 @@ class InvalidResponseError(Exception):
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def create_openai_async_client(
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api_key: str | None = None,
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base_url: str | None = None,
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use_azure: bool = False,
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azure_deployment: str | None = None,
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api_version: str | None = None,
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timeout: int | None = None,
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client_configs: dict[str, Any] | None = None,
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) -> AsyncOpenAI:
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"""Create an AsyncOpenAI client with the given configuration.
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"""Create an AsyncOpenAI or AsyncAzureOpenAI client with the given configuration.
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Args:
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api_key: OpenAI API key. If None, uses the OPENAI_API_KEY environment variable.
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base_url: Base URL for the OpenAI API. If None, uses the default OpenAI API URL.
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use_azure: Whether to create an Azure OpenAI client. Default is False.
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azure_deployment: Azure OpenAI deployment name (only used when use_azure=True).
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api_version: Azure OpenAI API version (only used when use_azure=True).
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timeout: Request timeout in seconds.
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client_configs: Additional configuration options for the AsyncOpenAI client.
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These will override any default configurations but will be overridden by
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explicit parameters (api_key, base_url).
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Returns:
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An AsyncOpenAI client instance.
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An AsyncOpenAI or AsyncAzureOpenAI client instance.
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"""
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if not api_key:
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api_key = os.environ["OPENAI_API_KEY"]
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if use_azure:
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from openai import AsyncAzureOpenAI
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default_headers = {
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"User-Agent": f"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) LightRAG/{__api_version__}",
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"Content-Type": "application/json",
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}
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if not api_key:
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api_key = os.environ.get("AZURE_OPENAI_API_KEY") or os.environ.get(
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"LLM_BINDING_API_KEY"
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)
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if client_configs is None:
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client_configs = {}
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# Create a merged config dict with precedence: explicit params > client_configs > defaults
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merged_configs = {
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**client_configs,
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"default_headers": default_headers,
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"api_key": api_key,
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}
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if base_url is not None:
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merged_configs["base_url"] = base_url
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else:
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merged_configs["base_url"] = os.environ.get(
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"OPENAI_API_BASE", "https://api.openai.com/v1"
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return AsyncAzureOpenAI(
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azure_endpoint=base_url,
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azure_deployment=azure_deployment,
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api_key=api_key,
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api_version=api_version,
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timeout=timeout,
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)
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else:
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if not api_key:
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api_key = os.environ["OPENAI_API_KEY"]
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return AsyncOpenAI(**merged_configs)
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default_headers = {
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"User-Agent": f"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) LightRAG/{__api_version__}",
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"Content-Type": "application/json",
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}
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if client_configs is None:
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client_configs = {}
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# Create a merged config dict with precedence: explicit params > client_configs > defaults
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merged_configs = {
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**client_configs,
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"default_headers": default_headers,
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"api_key": api_key,
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}
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if base_url is not None:
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merged_configs["base_url"] = base_url
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else:
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merged_configs["base_url"] = os.environ.get(
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"OPENAI_API_BASE", "https://api.openai.com/v1"
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)
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if timeout is not None:
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merged_configs["timeout"] = timeout
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return AsyncOpenAI(**merged_configs)
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@retry(
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@ -141,6 +168,9 @@ async def openai_complete_if_cache(
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stream: bool | None = None,
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timeout: int | None = None,
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keyword_extraction: bool = False,
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use_azure: bool = False,
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azure_deployment: str | None = None,
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api_version: str | None = None,
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**kwargs: Any,
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) -> str:
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"""Complete a prompt using OpenAI's API with caching support and Chain of Thought (COT) integration.
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@ -207,10 +237,14 @@ async def openai_complete_if_cache(
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if keyword_extraction:
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kwargs["response_format"] = GPTKeywordExtractionFormat
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# Create the OpenAI client
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# Create the OpenAI client (supports both OpenAI and Azure)
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openai_async_client = create_openai_async_client(
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api_key=api_key,
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base_url=base_url,
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use_azure=use_azure,
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azure_deployment=azure_deployment,
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api_version=api_version,
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timeout=timeout,
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client_configs=client_configs,
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)
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@ -631,6 +665,9 @@ async def openai_embed(
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embedding_dim: int | None = None,
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client_configs: dict[str, Any] | None = None,
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token_tracker: Any | None = None,
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use_azure: bool = False,
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azure_deployment: str | None = None,
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api_version: str | None = None,
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) -> np.ndarray:
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"""Generate embeddings for a list of texts using OpenAI's API.
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@ -658,9 +695,14 @@ async def openai_embed(
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RateLimitError: If the OpenAI API rate limit is exceeded.
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APITimeoutError: If the OpenAI API request times out.
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"""
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# Create the OpenAI client
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# Create the OpenAI client (supports both OpenAI and Azure)
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openai_async_client = create_openai_async_client(
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api_key=api_key, base_url=base_url, client_configs=client_configs
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api_key=api_key,
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base_url=base_url,
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use_azure=use_azure,
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azure_deployment=azure_deployment,
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api_version=api_version,
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client_configs=client_configs,
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)
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async with openai_async_client:
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@ -693,3 +735,130 @@ async def openai_embed(
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for dp in response.data
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]
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)
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# Azure OpenAI wrapper functions for backward compatibility
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async def azure_openai_complete_if_cache(
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model,
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prompt,
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system_prompt: str | None = None,
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history_messages: list[dict[str, Any]] | None = None,
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enable_cot: bool = False,
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base_url: str | None = None,
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api_key: str | None = None,
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api_version: str | None = None,
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keyword_extraction: bool = False,
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**kwargs,
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):
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"""Azure OpenAI completion wrapper function.
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This function provides backward compatibility by wrapping the unified
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openai_complete_if_cache implementation with Azure-specific parameter handling.
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"""
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# Handle Azure-specific environment variables and parameters
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deployment = os.getenv("AZURE_OPENAI_DEPLOYMENT") or model or os.getenv("LLM_MODEL")
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base_url = (
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base_url or os.getenv("AZURE_OPENAI_ENDPOINT") or os.getenv("LLM_BINDING_HOST")
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)
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api_key = (
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api_key or os.getenv("AZURE_OPENAI_API_KEY") or os.getenv("LLM_BINDING_API_KEY")
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)
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api_version = (
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api_version
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or os.getenv("AZURE_OPENAI_API_VERSION")
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or os.getenv("OPENAI_API_VERSION")
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)
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# Pop timeout from kwargs if present (will be handled by openai_complete_if_cache)
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timeout = kwargs.pop("timeout", None)
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# Call the unified implementation with Azure-specific parameters
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return await openai_complete_if_cache(
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model=model,
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prompt=prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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enable_cot=enable_cot,
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base_url=base_url,
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api_key=api_key,
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timeout=timeout,
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use_azure=True,
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azure_deployment=deployment,
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api_version=api_version,
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keyword_extraction=keyword_extraction,
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**kwargs,
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)
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async def azure_openai_complete(
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prompt, system_prompt=None, history_messages=None, keyword_extraction=False, **kwargs
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) -> str:
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"""Azure OpenAI complete wrapper function.
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Provides backward compatibility for azure_openai_complete calls.
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"""
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if history_messages is None:
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history_messages = []
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result = await azure_openai_complete_if_cache(
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os.getenv("LLM_MODEL", "gpt-4o-mini"),
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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keyword_extraction=keyword_extraction,
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**kwargs,
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)
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return result
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@wrap_embedding_func_with_attrs(embedding_dim=1536)
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@retry(
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stop=stop_after_attempt(3),
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wait=wait_exponential(multiplier=1, min=4, max=10),
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retry=retry_if_exception_type(
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(RateLimitError, APIConnectionError, APITimeoutError)
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),
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)
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async def azure_openai_embed(
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texts: list[str],
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model: str | None = None,
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base_url: str | None = None,
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api_key: str | None = None,
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api_version: str | None = None,
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) -> np.ndarray:
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"""Azure OpenAI embedding wrapper function.
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|
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This function provides backward compatibility by wrapping the unified
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openai_embed implementation with Azure-specific parameter handling.
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"""
|
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# Handle Azure-specific environment variables and parameters
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deployment = (
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os.getenv("AZURE_EMBEDDING_DEPLOYMENT")
|
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or model
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or os.getenv("EMBEDDING_MODEL", "text-embedding-3-small")
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)
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base_url = (
|
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base_url
|
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or os.getenv("AZURE_EMBEDDING_ENDPOINT")
|
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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")
|
||||
)
|
||||
|
||||
# Call the unified implementation with Azure-specific parameters
|
||||
return await openai_embed(
|
||||
texts=texts,
|
||||
model=model or deployment,
|
||||
base_url=base_url,
|
||||
api_key=api_key,
|
||||
use_azure=True,
|
||||
azure_deployment=deployment,
|
||||
api_version=api_version,
|
||||
)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue