cherry-pick 9f69c5bf

lightrag/llm/azure_openai.py

@@ -90,7 +90,7 @@ async def azure_openai_complete_if_cache(
         messages.append({"role": "user", "content": prompt})
 
     if "response_format" in kwargs:
-        response = await openai_async_client.chat.completions.parse(
+        response = await openai_async_client.beta.chat.completions.parse(
             model=model, messages=messages, **kwargs
         )
     else:
@@ -114,7 +114,7 @@ async def azure_openai_complete_if_cache(
         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:
@@ -126,7 +126,7 @@ async def azure_openai_complete_if_cache(
             content = message.content
             if content and r"\u" in content:
                 content = safe_unicode_decode(content.encode("utf-8"))
-
+        
         return content
 
 

lightrag/llm/openai.py

@@ -77,86 +77,46 @@ 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 or AsyncAzureOpenAI client with the given configuration.
+    """Create an AsyncOpenAI 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 or AsyncAzureOpenAI client instance.
+        An AsyncOpenAI client instance.
     """
-    if use_azure:
+    if not api_key:
-        from openai import AsyncAzureOpenAI
+        api_key = os.environ["OPENAI_API_KEY"]
 
-        if not api_key:
+    default_headers = {
-            api_key = os.environ.get("AZURE_OPENAI_API_KEY") or os.environ.get(
+        "User-Agent": f"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) LightRAG/{__api_version__}",
-                "LLM_BINDING_API_KEY"
+        "Content-Type": "application/json",
-            )
+    }
 
-        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
+    # Create a merged config dict with precedence: explicit params > client_configs > defaults
-        merged_configs = {
+    merged_configs = {
-            **client_configs,
+        **client_configs,
-            "api_key": api_key,
+        "default_headers": default_headers,
-        }
+        "api_key": api_key,
+    }
 
-        # Add explicit parameters (override client_configs)
+    if base_url is not None:
-        if base_url is not None:
+        merged_configs["base_url"] = base_url
-            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:
-        if not api_key:
+        merged_configs["base_url"] = os.environ.get(
-            api_key = os.environ["OPENAI_API_KEY"]
+            "OPENAI_API_BASE", "https://api.openai.com/v1"
+        )
 
-        default_headers = {
+    return AsyncOpenAI(**merged_configs)
-            "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(
@@ -181,9 +141,6 @@ 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.
@@ -250,14 +207,10 @@ async def openai_complete_if_cache(
     if keyword_extraction:
         kwargs["response_format"] = GPTKeywordExtractionFormat
 
-    # Create the OpenAI client (supports both OpenAI and Azure)
+    # Create the OpenAI client
     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,
     )
 
@@ -288,7 +241,7 @@ async def openai_complete_if_cache(
     try:
         # Don't use async with context manager, use client directly
         if "response_format" in kwargs:
-            response = await openai_async_client.chat.completions.parse(
+            response = await openai_async_client.beta.chat.completions.parse(
                 model=model, messages=messages, **kwargs
             )
         else:
@@ -500,7 +453,7 @@ async def openai_complete_if_cache(
                 raise InvalidResponseError("Invalid response from OpenAI API")
 
             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:
@@ -539,9 +492,7 @@ async def openai_complete_if_cache(
                             reasoning_content = safe_unicode_decode(
                                 reasoning_content.encode("utf-8")
                             )
-                        final_content = (
+                        final_content = f"<think>{reasoning_content}</think>{final_content}"
-                            f"<think>{reasoning_content}</think>{final_content}"
-                        )
                 else:
                     # COT disabled, only use regular content
                     final_content = content or ""
@@ -678,9 +629,6 @@ 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.
 
@@ -708,14 +656,9 @@ 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 (supports both OpenAI and Azure)
+    # Create the OpenAI client
     openai_async_client = create_openai_async_client(
-        api_key=api_key,
+        api_key=api_key, base_url=base_url, client_configs=client_configs
-        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:
@@ -748,157 +691,3 @@ 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,
-    )