Merge pull request #2403 from danielaskdd/azure-cot-handling

Refact: Consolidate Azure OpenAI and OpenAI implementations

lightrag/llm/azure_openai.py

@@ -1,193 +1,22 @@
-from collections.abc import Iterable
+"""
-import os
+Azure OpenAI compatibility layer.
-import pipmaster as pm  # Pipmaster for dynamic library install
 
-# install specific modules
+This module provides backward compatibility by re-exporting Azure OpenAI functions
-if not pm.is_installed("openai"):
+from the main openai module where the actual implementation resides.
-    pm.install("openai")
 
-from openai import (
+All core logic for both OpenAI and Azure OpenAI now lives in lightrag.llm.openai,
-    AsyncAzureOpenAI,
+with this module serving as a thin compatibility wrapper for existing code that
-    APIConnectionError,
+imports from lightrag.llm.azure_openai.
-    RateLimitError,
+"""
-    APITimeoutError,
-)
-from openai.types.chat import ChatCompletionMessageParam
 
-from tenacity import (
+from lightrag.llm.openai import (
-    retry,
+    azure_openai_complete_if_cache,
-    stop_after_attempt,
+    azure_openai_complete,
-    wait_exponential,
+    azure_openai_embed,
-    retry_if_exception_type,
 )
 
-from lightrag.utils import (
+__all__ = [
-    wrap_embedding_func_with_attrs,
+    "azure_openai_complete_if_cache",
-    safe_unicode_decode,
+    "azure_openai_complete",
-    logger,
+    "azure_openai_embed",
-)
+]
-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])

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,
     )
 
@@ -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.
+        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.
@@ -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,
+    )

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)