gmakstutis/LightRAG
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions
LightRAG/lightrag/llm/openai.py
yangdx 0c4cba3860 Fix double decoration in azure_openai_embed and document decorator usage 
• Remove redundant @retry decorator
• Call openai_embed.func directly
• Add detailed decorator documentation
• Prevent double parameter injection
• Fix EmbeddingFunc wrapping issues
2025-11-21 18:03:53 +08:00

891 lines
34 KiB
Python
Raw Blame History

from ..utils import verbose_debug, VERBOSE_DEBUG
import os
import logging
from collections.abc import AsyncIterator
import pipmaster as pm
# install specific modules
if not pm.is_installed("openai"):
pm.install("openai")
from openai import (
APIConnectionError,
RateLimitError,
APITimeoutError,
)
from tenacity import (
retry,
stop_after_attempt,
wait_exponential,
retry_if_exception_type,
)
from lightrag.utils import (
wrap_embedding_func_with_attrs,
safe_unicode_decode,
logger,
)
from lightrag.types import GPTKeywordExtractionFormat
from lightrag.api import __api_version__
import numpy as np
import base64
from typing import Any, Union
from dotenv import load_dotenv
# Try to import Langfuse for LLM observability (optional)
# Falls back to standard OpenAI client if not available
# Langfuse requires proper configuration to work correctly
LANGFUSE_ENABLED = False
try:
# Check if required Langfuse environment variables are set
langfuse_public_key = os.environ.get("LANGFUSE_PUBLIC_KEY")
langfuse_secret_key = os.environ.get("LANGFUSE_SECRET_KEY")
# Only enable Langfuse if both keys are configured
if langfuse_public_key and langfuse_secret_key:
from langfuse.openai import AsyncOpenAI # type: ignore[import-untyped]
LANGFUSE_ENABLED = True
logger.info("Langfuse observability enabled for OpenAI client")
else:
from openai import AsyncOpenAI
logger.debug(
"Langfuse environment variables not configured, using standard OpenAI client"
)
except ImportError:
from openai import AsyncOpenAI
logger.debug("Langfuse not available, using standard OpenAI client")
# use the .env that is inside the current folder
# allows to use different .env file for each lightrag instance
# the OS environment variables take precedence over the .env file
load_dotenv(dotenv_path=".env", override=False)
class InvalidResponseError(Exception):
"""Custom exception class for triggering retry mechanism"""
pass
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.
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.
"""
if use_azure:
from openai import AsyncAzureOpenAI
if not api_key:
api_key = os.environ.get("AZURE_OPENAI_API_KEY") or os.environ.get(
"LLM_BINDING_API_KEY"
)
return AsyncAzureOpenAI(
azure_endpoint=base_url,
azure_deployment=azure_deployment,
api_key=api_key,
api_version=api_version,
timeout=timeout,
)
else:
if not api_key:
api_key = os.environ["OPENAI_API_KEY"]
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(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=4, max=10),
retry=(
retry_if_exception_type(RateLimitError)
| retry_if_exception_type(APIConnectionError)
| retry_if_exception_type(APITimeoutError)
| retry_if_exception_type(InvalidResponseError)
),
)
async def openai_complete_if_cache(
model: str,
prompt: str,
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,
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.
This function supports automatic integration of reasoning content from models that provide
Chain of Thought capabilities. The reasoning content is seamlessly integrated into the response
using <think>...</think> tags.
Note on `reasoning_content`: This feature relies on a Deepseek Style `reasoning_content`
in the API response, which may be provided by OpenAI-compatible endpoints that support
Chain of Thought.
COT Integration Rules:
1. COT content is accepted only when regular content is empty and `reasoning_content` has content.
2. COT processing stops when regular content becomes available.
3. If both `content` and `reasoning_content` are present simultaneously, reasoning is ignored.
4. If both fields have content from the start, COT is never activated.
5. For streaming: COT content is inserted into the content stream with <think> tags.
6. For non-streaming: COT content is prepended to regular content with <think> tags.
Args:
model: The OpenAI model to use.
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.
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.
**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).
Returns:
The completed text (with integrated COT content if available) or an async iterator
of text chunks if streaming. COT content is wrapped in <think>...</think> tags.
Raises:
InvalidResponseError: If the response from OpenAI is invalid or empty.
APIConnectionError: If there is a connection error with the OpenAI API.
RateLimitError: If the OpenAI API rate limit is exceeded.
APITimeoutError: If the OpenAI API request times out.
"""
if history_messages is None:
history_messages = []
# Set openai logger level to INFO when VERBOSE_DEBUG is off
if not VERBOSE_DEBUG and logger.level == logging.DEBUG:
logging.getLogger("openai").setLevel(logging.INFO)
# Remove special kwargs that shouldn't be passed to OpenAI
kwargs.pop("hashing_kv", None)
# Extract client configuration options
client_configs = kwargs.pop("openai_client_configs", {})
# Handle keyword extraction mode
if keyword_extraction:
kwargs["response_format"] = GPTKeywordExtractionFormat
# 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,
)
# Prepare messages
messages: list[dict[str, Any]] = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
messages.extend(history_messages)
messages.append({"role": "user", "content": prompt})
logger.debug("===== Entering func of LLM =====")
logger.debug(f"Model: {model} Base URL: {base_url}")
logger.debug(f"Client Configs: {client_configs}")
logger.debug(f"Additional kwargs: {kwargs}")
logger.debug(f"Num of history messages: {len(history_messages)}")
verbose_debug(f"System prompt: {system_prompt}")
verbose_debug(f"Query: {prompt}")
logger.debug("===== Sending Query to LLM =====")
messages = kwargs.pop("messages", messages)
# Add explicit parameters back to kwargs so they're passed to OpenAI API
if stream is not None:
kwargs["stream"] = stream
if timeout is not None:
kwargs["timeout"] = timeout
try:
# Don't use async with context manager, use client directly
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
)
except APIConnectionError as e:
logger.error(f"OpenAI API Connection Error: {e}")
await openai_async_client.close() # Ensure client is closed
raise
except RateLimitError as e:
logger.error(f"OpenAI API Rate Limit Error: {e}")
await openai_async_client.close() # Ensure client is closed
raise
except APITimeoutError as e:
logger.error(f"OpenAI API Timeout Error: {e}")
await openai_async_client.close() # Ensure client is closed
raise
except Exception as e:
logger.error(
f"OpenAI API Call Failed,\nModel: {model},\nParams: {kwargs}, Got: {e}"
)
await openai_async_client.close() # Ensure client is closed
raise
if hasattr(response, "__aiter__"):
async def inner():
# Track if we've started iterating
iteration_started = False
final_chunk_usage = None
# COT (Chain of Thought) state tracking
cot_active = False
cot_started = False
initial_content_seen = False
try:
iteration_started = True
async for chunk in response:
# Check if this chunk has usage information (final chunk)
if hasattr(chunk, "usage") and chunk.usage:
final_chunk_usage = chunk.usage
logger.debug(
f"Received usage info in streaming chunk: {chunk.usage}"
)
# Check if choices exists and is not empty
if not hasattr(chunk, "choices") or not chunk.choices:
logger.warning(f"Received chunk without choices: {chunk}")
continue
# Check if delta exists
if not hasattr(chunk.choices[0], "delta"):
# This might be the final chunk, continue to check for usage
continue
delta = chunk.choices[0].delta
content = getattr(delta, "content", None)
reasoning_content = getattr(delta, "reasoning_content", "")
# Handle COT logic for streaming (only if enabled)
if enable_cot:
if content:
# Regular content is present
if not initial_content_seen:
initial_content_seen = True
# If both content and reasoning_content are present initially, don't start COT
if reasoning_content:
cot_active = False
cot_started = False
# If COT was active, end it
if cot_active:
yield "</think>"
cot_active = False
# Process regular content
if r"\u" in content:
content = safe_unicode_decode(content.encode("utf-8"))
yield content
elif reasoning_content:
# Only reasoning content is present
if not initial_content_seen and not cot_started:
# Start COT if we haven't seen initial content yet
if not cot_active:
yield "<think>"
cot_active = True
cot_started = True
# Process reasoning content if COT is active
if cot_active:
if r"\u" in reasoning_content:
reasoning_content = safe_unicode_decode(
reasoning_content.encode("utf-8")
)
yield reasoning_content
else:
# COT disabled, only process regular content
if content:
if r"\u" in content:
content = safe_unicode_decode(content.encode("utf-8"))
yield content
# If neither content nor reasoning_content, continue to next chunk
if content is None and reasoning_content is None:
continue
# Ensure COT is properly closed if still active after stream ends
if enable_cot and cot_active:
yield "</think>"
cot_active = False
# After streaming is complete, track token usage
if token_tracker and final_chunk_usage:
# Use actual usage from the API
token_counts = {
"prompt_tokens": getattr(final_chunk_usage, "prompt_tokens", 0),
"completion_tokens": getattr(
final_chunk_usage, "completion_tokens", 0
),
"total_tokens": getattr(final_chunk_usage, "total_tokens", 0),
}
token_tracker.add_usage(token_counts)
logger.debug(f"Streaming token usage (from API): {token_counts}")
elif token_tracker:
logger.debug("No usage information available in streaming response")
except Exception as e:
# Ensure COT is properly closed before handling exception
if enable_cot and cot_active:
try:
yield "</think>"
cot_active = False
except Exception as close_error:
logger.warning(
f"Failed to close COT tag during exception handling: {close_error}"
)
logger.error(f"Error in stream response: {str(e)}")
# Try to clean up resources if possible
if (
iteration_started
and hasattr(response, "aclose")
and callable(getattr(response, "aclose", None))
):
try:
await response.aclose()
logger.debug("Successfully closed stream response after error")
except Exception as close_error:
logger.warning(
f"Failed to close stream response: {close_error}"
)
# Ensure client is closed in case of exception
await openai_async_client.close()
raise
finally:
# Final safety check for unclosed COT tags
if enable_cot and cot_active:
try:
yield "</think>"
cot_active = False
except Exception as final_close_error:
logger.warning(
f"Failed to close COT tag in finally block: {final_close_error}"
)
# Ensure resources are released even if no exception occurs
# Note: Some wrapped clients (e.g., Langfuse) may not implement aclose() properly
if iteration_started and hasattr(response, "aclose"):
aclose_method = getattr(response, "aclose", None)
if callable(aclose_method):
try:
await response.aclose()
logger.debug("Successfully closed stream response")
except (AttributeError, TypeError) as close_error:
# Some wrapper objects may report hasattr(aclose) but fail when called
# This is expected behavior for certain client wrappers
logger.debug(
f"Stream response cleanup not supported by client wrapper: {close_error}"
)
except Exception as close_error:
logger.warning(
f"Unexpected error during stream response cleanup: {close_error}"
)
# This prevents resource leaks since the caller doesn't handle closing
try:
await openai_async_client.close()
logger.debug(
"Successfully closed OpenAI client for streaming response"
)
except Exception as client_close_error:
logger.warning(
f"Failed to close OpenAI client in streaming finally block: {client_close_error}"
)
return inner()
else:
try:
if (
not response
or not response.choices
or not hasattr(response.choices[0], "message")
):
logger.error("Invalid response from OpenAI API")
await openai_async_client.close() # Ensure client is closed
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:
# Serialize the parsed structured response to JSON
final_content = message.parsed.model_dump_json()
logger.debug("Using parsed structured response from API")
else:
# Handle regular content responses
content = getattr(message, "content", None)
reasoning_content = getattr(message, "reasoning_content", "")
# Handle COT logic for non-streaming responses (only if enabled)
final_content = ""
if enable_cot:
# Check if we should include reasoning content
should_include_reasoning = False
if reasoning_content and reasoning_content.strip():
if not content or content.strip() == "":
# Case 1: Only reasoning content, should include COT
should_include_reasoning = True
final_content = (
content or ""
) # Use empty string if content is None
else:
# Case 3: Both content and reasoning_content present, ignore reasoning
should_include_reasoning = False
final_content = content
else:
# No reasoning content, use regular content
final_content = content or ""
# Apply COT wrapping if needed
if should_include_reasoning:
if r"\u" in reasoning_content:
reasoning_content = safe_unicode_decode(
reasoning_content.encode("utf-8")
)
final_content = (
f"<think>{reasoning_content}</think>{final_content}"
)
else:
# COT disabled, only use regular content
final_content = content or ""
# Validate final content
if not final_content or final_content.strip() == "":
logger.error("Received empty content from OpenAI API")
await openai_async_client.close() # Ensure client is closed
raise InvalidResponseError("Received empty content from OpenAI API")
# Apply Unicode decoding to final content if needed
if r"\u" in final_content:
final_content = safe_unicode_decode(final_content.encode("utf-8"))
if token_tracker and hasattr(response, "usage"):
token_counts = {
"prompt_tokens": getattr(response.usage, "prompt_tokens", 0),
"completion_tokens": getattr(
response.usage, "completion_tokens", 0
),
"total_tokens": getattr(response.usage, "total_tokens", 0),
}
token_tracker.add_usage(token_counts)
logger.debug(f"Response content len: {len(final_content)}")
verbose_debug(f"Response: {response}")
return final_content
finally:
# Ensure client is closed in all cases for non-streaming responses
await openai_async_client.close()
async def openai_complete(
prompt,
system_prompt=None,
history_messages=None,
keyword_extraction=False,
**kwargs,
) -> Union[str, AsyncIterator[str]]:
if history_messages is None:
history_messages = []
model_name = kwargs["hashing_kv"].global_config["llm_model_name"]
return await openai_complete_if_cache(
model_name,
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
keyword_extraction=keyword_extraction,
**kwargs,
)
async def gpt_4o_complete(
prompt,
system_prompt=None,
history_messages=None,
enable_cot: bool = False,
keyword_extraction=False,
**kwargs,
) -> str:
if history_messages is None:
history_messages = []
return await openai_complete_if_cache(
"gpt-4o",
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
enable_cot=enable_cot,
keyword_extraction=keyword_extraction,
**kwargs,
)
async def gpt_4o_mini_complete(
prompt,
system_prompt=None,
history_messages=None,
enable_cot: bool = False,
keyword_extraction=False,
**kwargs,
) -> str:
if history_messages is None:
history_messages = []
return await openai_complete_if_cache(
"gpt-4o-mini",
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
enable_cot=enable_cot,
keyword_extraction=keyword_extraction,
**kwargs,
)
async def nvidia_openai_complete(
prompt,
system_prompt=None,
history_messages=None,
enable_cot: bool = False,
keyword_extraction=False,
**kwargs,
) -> str:
if history_messages is None:
history_messages = []
result = await openai_complete_if_cache(
"nvidia/llama-3.1-nemotron-70b-instruct", # context length 128k
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
enable_cot=enable_cot,
keyword_extraction=keyword_extraction,
base_url="https://integrate.api.nvidia.com/v1",
**kwargs,
)
return result
@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192)
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=4, max=60),
retry=(
retry_if_exception_type(RateLimitError)
| retry_if_exception_type(APIConnectionError)
| retry_if_exception_type(APITimeoutError)
),
)
async def openai_embed(
texts: list[str],
model: str = "text-embedding-3-small",
base_url: str | None = None,
api_key: str | None = None,
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.
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.
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.
These will override any default configurations but will be overridden by
explicit parameters (api_key, base_url).
token_tracker: Optional token usage tracker for monitoring API usage.
Returns:
A numpy array of embeddings, one per input text.
Raises:
APIConnectionError: If there is a connection error with the OpenAI API.
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)
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,
client_configs=client_configs,
)
async with openai_async_client:
# Prepare API call parameters
api_params = {
"model": model,
"input": texts,
"encoding_format": "base64",
}
# Add dimensions parameter only if embedding_dim is provided
if embedding_dim is not None:
api_params["dimensions"] = embedding_dim
# Make API call
response = await openai_async_client.embeddings.create(**api_params)
if token_tracker and hasattr(response, "usage"):
token_counts = {
"prompt_tokens": getattr(response.usage, "prompt_tokens", 0),
"total_tokens": getattr(response.usage, "total_tokens", 0),
}
token_tracker.add_usage(token_counts)
return np.array(
[
np.array(dp.embedding, dtype=np.float32)
if isinstance(dp.embedding, list)
else np.frombuffer(base64.b64decode(dp.embedding), dtype=np.float32)
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,
)
Reference in a new issue View git blame Copy permalink