801 lines
32 KiB
Python
801 lines
32 KiB
Python
import math
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from typing import Dict, List, Optional, Union
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import tiktoken
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from openai import (APIError, AsyncAzureOpenAI, AsyncOpenAI,
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AuthenticationError, OpenAIError, RateLimitError)
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from openai.types.chat import ChatCompletion
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from openai.types.chat.chat_completion_message import ChatCompletionMessage
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from tenacity import (retry, retry_if_exception_type, stop_after_attempt,
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wait_random_exponential)
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from app.config import LLMSettings, config
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from app.exceptions import TokenLimitExceeded
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from app.logger import logger # Assuming a logger is set up in your app
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from app.schema import (ROLE_VALUES, TOOL_CHOICE_TYPE, TOOL_CHOICE_VALUES,
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Message, ToolChoice)
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REASONING_MODELS = ["o1", "o3-mini"]
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MULTIMODAL_MODELS = [
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"gpt-4-vision-preview",
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"gpt-4o",
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"gpt-4o-mini",
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"claude-3-opus-20240229",
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"claude-3-sonnet-20240229",
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"claude-3-haiku-20240307",
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]
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class TokenCounter:
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# Token constants
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BASE_MESSAGE_TOKENS = 4
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FORMAT_TOKENS = 2
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LOW_DETAIL_IMAGE_TOKENS = 85
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HIGH_DETAIL_TILE_TOKENS = 170
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# Image processing constants
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MAX_SIZE = 2048
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HIGH_DETAIL_TARGET_SHORT_SIDE = 768
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TILE_SIZE = 512
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def __init__(self, tokenizer):
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self.tokenizer = tokenizer
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def count_text(self, text: str) -> int:
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"""Calculate tokens for a text string"""
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return 0 if not text else len(self.tokenizer.encode(text))
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def count_image(self, image_item: dict) -> int:
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"""
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Calculate tokens for an image based on detail level and dimensions
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For "low" detail: fixed 85 tokens
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For "high" detail:
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1. Scale to fit in 2048x2048 square
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2. Scale shortest side to 768px
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3. Count 512px tiles (170 tokens each)
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4. Add 85 tokens
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"""
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detail = image_item.get("detail", "medium")
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# For low detail, always return fixed token count
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if detail == "low":
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return self.LOW_DETAIL_IMAGE_TOKENS
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# For medium detail (default in OpenAI), use high detail calculation
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# OpenAI doesn't specify a separate calculation for medium
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# For high detail, calculate based on dimensions if available
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if detail == "high" or detail == "medium":
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# If dimensions are provided in the image_item
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if "dimensions" in image_item:
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width, height = image_item["dimensions"]
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return self._calculate_high_detail_tokens(width, height)
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# Default values when dimensions aren't available or detail level is unknown
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if detail == "high":
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# Default to a 1024x1024 image calculation for high detail
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return self._calculate_high_detail_tokens(1024, 1024) # 765 tokens
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elif detail == "medium":
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# Default to a medium-sized image for medium detail
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return 1024 # This matches the original default
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else:
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# For unknown detail levels, use medium as default
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return 1024
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def _calculate_high_detail_tokens(self, width: int, height: int) -> int:
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"""Calculate tokens for high detail images based on dimensions"""
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# Step 1: Scale to fit in MAX_SIZE x MAX_SIZE square
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if width > self.MAX_SIZE or height > self.MAX_SIZE:
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scale = self.MAX_SIZE / max(width, height)
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width = int(width * scale)
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height = int(height * scale)
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# Step 2: Scale so shortest side is HIGH_DETAIL_TARGET_SHORT_SIDE
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scale = self.HIGH_DETAIL_TARGET_SHORT_SIDE / min(width, height)
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scaled_width = int(width * scale)
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scaled_height = int(height * scale)
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# Step 3: Count number of 512px tiles
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tiles_x = math.ceil(scaled_width / self.TILE_SIZE)
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tiles_y = math.ceil(scaled_height / self.TILE_SIZE)
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total_tiles = tiles_x * tiles_y
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# Step 4: Calculate final token count
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return (
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total_tiles * self.HIGH_DETAIL_TILE_TOKENS
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) + self.LOW_DETAIL_IMAGE_TOKENS
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def count_content(self, content: Union[str, List[Union[str, dict]]]) -> int:
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"""Calculate tokens for message content"""
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if not content:
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return 0
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if isinstance(content, str):
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return self.count_text(content)
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token_count = 0
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for item in content:
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if isinstance(item, str):
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token_count += self.count_text(item)
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elif isinstance(item, dict):
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if "text" in item:
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token_count += self.count_text(item["text"])
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elif "image_url" in item:
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token_count += self.count_image(item)
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return token_count
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def count_tool_calls(self, tool_calls: List[dict]) -> int:
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"""Calculate tokens for tool calls"""
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token_count = 0
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for tool_call in tool_calls:
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if "function" in tool_call:
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function = tool_call["function"]
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token_count += self.count_text(function.get("name", ""))
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token_count += self.count_text(function.get("arguments", ""))
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return token_count
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def count_message_tokens(self, messages: List[dict]) -> int:
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"""Calculate the total number of tokens in a message list"""
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total_tokens = self.FORMAT_TOKENS # Base format tokens
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for message in messages:
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tokens = self.BASE_MESSAGE_TOKENS # Base tokens per message
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# Add role tokens
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tokens += self.count_text(message.get("role", ""))
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# Add content tokens
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if "content" in message:
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tokens += self.count_content(message["content"])
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# Add tool calls tokens
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if "tool_calls" in message:
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tokens += self.count_tool_calls(message["tool_calls"])
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# Add name and tool_call_id tokens
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tokens += self.count_text(message.get("name", ""))
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tokens += self.count_text(message.get("tool_call_id", ""))
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total_tokens += tokens
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return total_tokens
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class LLM:
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_instances: Dict[str, "LLM"] = {}
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def __new__(
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cls, config_name: str = "default", llm_config: Optional[LLMSettings] = None
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):
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if config_name not in cls._instances:
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instance = super().__new__(cls)
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instance.__init__(config_name, llm_config)
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cls._instances[config_name] = instance
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return cls._instances[config_name]
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def __init__(
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self, config_name: str = "default", llm_config: Optional[LLMSettings] = None
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):
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if not hasattr(self, "client"): # Only initialize if not already initialized
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llm_config = llm_config or config.llm
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llm_config = llm_config.get(config_name, llm_config["default"])
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self.model = llm_config.model
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self.max_tokens = llm_config.max_tokens
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self.temperature = llm_config.temperature
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self.api_type = llm_config.api_type
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self.api_key = llm_config.api_key
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self.api_version = llm_config.api_version
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self.base_url = llm_config.base_url
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# Add token counting related attributes
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self.total_input_tokens = 0
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self.total_completion_tokens = 0
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self.max_input_tokens = (
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llm_config.max_input_tokens
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if hasattr(llm_config, "max_input_tokens")
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else None
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)
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# Initialize tokenizer
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try:
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self.tokenizer = tiktoken.encoding_for_model(self.model)
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except KeyError:
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# If the model is not in tiktoken's presets, use cl100k_base as default
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self.tokenizer = tiktoken.get_encoding("cl100k_base")
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if self.api_type == "azure":
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self.client = AsyncAzureOpenAI(
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base_url=self.base_url,
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api_key=self.api_key,
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api_version=self.api_version,
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)
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else:
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self.client = AsyncOpenAI(api_key=self.api_key, base_url=self.base_url)
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self.token_counter = TokenCounter(self.tokenizer)
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def count_tokens(self, text: str) -> int:
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"""Calculate the number of tokens in a text"""
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if not text:
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return 0
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return len(self.tokenizer.encode(text))
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def count_message_tokens(self, messages: List[dict]) -> int:
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return self.token_counter.count_message_tokens(messages)
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def update_token_count(self, input_tokens: int, completion_tokens: int = 0) -> None:
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"""Update token counts"""
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# Only track tokens if max_input_tokens is set
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self.total_input_tokens += input_tokens
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self.total_completion_tokens += completion_tokens
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logger.info(
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f"Token usage: Input={input_tokens}, Completion={completion_tokens}, "
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f"Cumulative Input={self.total_input_tokens}, Cumulative Completion={self.total_completion_tokens}, "
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f"Total={input_tokens + completion_tokens}, Cumulative Total={self.total_input_tokens + self.total_completion_tokens}"
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)
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def check_token_limit(self, input_tokens: int) -> bool:
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"""Check if token limits are exceeded"""
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if self.max_input_tokens is not None:
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return (self.total_input_tokens + input_tokens) <= self.max_input_tokens
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# If max_input_tokens is not set, always return True
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return True
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def get_limit_error_message(self, input_tokens: int) -> str:
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"""Generate error message for token limit exceeded"""
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if (
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self.max_input_tokens is not None
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and (self.total_input_tokens + input_tokens) > self.max_input_tokens
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):
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return f"Request may exceed input token limit (Current: {self.total_input_tokens}, Needed: {input_tokens}, Max: {self.max_input_tokens})"
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return "Token limit exceeded"
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@staticmethod
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def format_messages(
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messages: List[Union[dict, Message]], supports_images: bool = False
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) -> List[dict]:
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"""
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Format messages for LLM by converting them to OpenAI message format.
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Args:
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messages: List of messages that can be either dict or Message objects
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supports_images: Flag indicating if the target model supports image inputs
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Returns:
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List[dict]: List of formatted messages in OpenAI format
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Raises:
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ValueError: If messages are invalid or missing required fields
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TypeError: If unsupported message types are provided
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Examples:
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>>> msgs = [
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... Message.system_message("You are a helpful assistant"),
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... {"role": "user", "content": "Hello"},
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... Message.user_message("How are you?")
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... ]
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>>> formatted = LLM.format_messages(msgs)
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"""
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formatted_messages = []
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for message in messages:
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# Convert Message objects to dictionaries
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if isinstance(message, Message):
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message = message.to_dict()
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if isinstance(message, dict):
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# If message is a dict, ensure it has required fields
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if "role" not in message:
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raise ValueError("Message dict must contain 'role' field")
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# Process base64 images if present and model supports images
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if supports_images and message.get("base64_image"):
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# Initialize or convert content to appropriate format
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if not message.get("content"):
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message["content"] = []
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elif isinstance(message["content"], str):
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message["content"] = [
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{"type": "text", "text": message["content"]}
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]
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elif isinstance(message["content"], list):
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# Convert string items to proper text objects
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message["content"] = [
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(
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{"type": "text", "text": item}
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if isinstance(item, str)
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else item
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)
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for item in message["content"]
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]
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# Add the image to content
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message["content"].append(
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{message['base64_image']}"
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},
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}
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)
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# Remove the base64_image field
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del message["base64_image"]
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# If model doesn't support images but message has base64_image, handle gracefully
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elif not supports_images and message.get("base64_image"):
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# Just remove the base64_image field and keep the text content
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del message["base64_image"]
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if "content" in message or "tool_calls" in message:
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formatted_messages.append(message)
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# else: do not include the message
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else:
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raise TypeError(f"Unsupported message type: {type(message)}")
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# Validate all messages have required fields
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for msg in formatted_messages:
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if msg["role"] not in ROLE_VALUES:
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raise ValueError(f"Invalid role: {msg['role']}")
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return formatted_messages
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@retry(
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wait=wait_random_exponential(min=1, max=60),
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stop=stop_after_attempt(6),
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retry=retry_if_exception_type(
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(OpenAIError, Exception, ValueError)
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), # Don't retry TokenLimitExceeded
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)
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async def ask(
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self,
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messages: List[Union[dict, Message]],
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system_msgs: Optional[List[Union[dict, Message]]] = None,
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stream: bool = True,
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temperature: Optional[float] = None,
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) -> str:
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"""
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Send a prompt to the LLM and get the response.
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Args:
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messages: List of conversation messages
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system_msgs: Optional system messages to prepend
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stream (bool): Whether to stream the response
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temperature (float): Sampling temperature for the response
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Returns:
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str: The generated response
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Raises:
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TokenLimitExceeded: If token limits are exceeded
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ValueError: If messages are invalid or response is empty
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OpenAIError: If API call fails after retries
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Exception: For unexpected errors
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"""
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try:
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# Check if the model supports images
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supports_images = self.model in MULTIMODAL_MODELS
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# Format system and user messages with image support check
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if system_msgs:
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system_msgs = self.format_messages(system_msgs, supports_images)
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messages = system_msgs + self.format_messages(messages, supports_images)
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else:
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messages = self.format_messages(messages, supports_images)
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# Calculate input token count
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input_tokens = self.count_message_tokens(messages)
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# Check if token limits are exceeded
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if not self.check_token_limit(input_tokens):
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error_message = self.get_limit_error_message(input_tokens)
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# Raise a special exception that won't be retried
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raise TokenLimitExceeded(error_message)
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params = {
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"model": self.model,
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"messages": messages,
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}
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if self.model in REASONING_MODELS:
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params["max_completion_tokens"] = self.max_tokens
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else:
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params["max_tokens"] = self.max_tokens
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params["temperature"] = (
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temperature if temperature is not None else self.temperature
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)
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if not stream:
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# Non-streaming request
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response = await self.client.chat.completions.create(
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**params, stream=False
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)
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if not response.choices or not response.choices[0].message.content:
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raise ValueError("Empty or invalid response from LLM")
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# Update token counts
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self.update_token_count(
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response.usage.prompt_tokens, response.usage.completion_tokens
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)
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return response.choices[0].message.content
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# Streaming request, For streaming, update estimated token count before making the request
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self.update_token_count(input_tokens)
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response = await self.client.chat.completions.create(**params, stream=True)
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collected_messages = []
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completion_text = ""
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async for chunk in response:
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chunk_message = chunk.choices[0].delta.content or ""
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collected_messages.append(chunk_message)
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completion_text += chunk_message
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print(chunk_message, end="", flush=True)
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print() # Newline after streaming
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full_response = "".join(collected_messages).strip()
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if not full_response:
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raise ValueError("Empty response from streaming LLM")
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# estimate completion tokens for streaming response
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completion_tokens = self.count_tokens(completion_text)
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logger.info(
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f"Estimated completion tokens for streaming response: {completion_tokens}"
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)
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self.total_completion_tokens += completion_tokens
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return full_response
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except TokenLimitExceeded:
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# Re-raise token limit errors without logging
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raise
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except ValueError:
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logger.exception(f"Validation error")
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raise
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except OpenAIError as oe:
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logger.exception(f"OpenAI API error")
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if isinstance(oe, AuthenticationError):
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logger.error("Authentication failed. Check API key.")
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elif isinstance(oe, RateLimitError):
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logger.error("Rate limit exceeded. Consider increasing retry attempts.")
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elif isinstance(oe, APIError):
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logger.error(f"API error: {oe}")
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raise
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except Exception:
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logger.exception(f"Unexpected error in ask")
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raise
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@retry(
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wait=wait_random_exponential(min=1, max=60),
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stop=stop_after_attempt(6),
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retry=retry_if_exception_type(
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(OpenAIError, Exception, ValueError)
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), # Don't retry TokenLimitExceeded
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)
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async def ask_with_images(
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self,
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messages: List[Union[dict, Message]],
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images: List[Union[str, dict]],
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system_msgs: Optional[List[Union[dict, Message]]] = None,
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stream: bool = False,
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temperature: Optional[float] = None,
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) -> str:
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"""
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Send a prompt with images to the LLM and get the response.
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Args:
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messages: List of conversation messages
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images: List of image URLs or image data dictionaries
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system_msgs: Optional system messages to prepend
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stream (bool): Whether to stream the response
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temperature (float): Sampling temperature for the response
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Returns:
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str: The generated response
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Raises:
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TokenLimitExceeded: If token limits are exceeded
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ValueError: If messages are invalid or response is empty
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OpenAIError: If API call fails after retries
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Exception: For unexpected errors
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"""
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try:
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# For ask_with_images, we always set supports_images to True because
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# this method should only be called with models that support images
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if self.model not in MULTIMODAL_MODELS:
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raise ValueError(
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f"Model {self.model} does not support images. Use a model from {MULTIMODAL_MODELS}"
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)
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# Format messages with image support
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formatted_messages = self.format_messages(messages, supports_images=True)
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# Ensure the last message is from the user to attach images
|
||
if not formatted_messages or formatted_messages[-1]["role"] != "user":
|
||
raise ValueError(
|
||
"The last message must be from the user to attach images"
|
||
)
|
||
|
||
# Process the last user message to include images
|
||
last_message = formatted_messages[-1]
|
||
|
||
# Convert content to multimodal format if needed
|
||
content = last_message["content"]
|
||
multimodal_content = (
|
||
[{"type": "text", "text": content}]
|
||
if isinstance(content, str)
|
||
else content if isinstance(content, list) else []
|
||
)
|
||
|
||
# Add images to content
|
||
for image in images:
|
||
if isinstance(image, str):
|
||
multimodal_content.append(
|
||
{"type": "image_url", "image_url": {"url": image}}
|
||
)
|
||
elif isinstance(image, dict) and "url" in image:
|
||
multimodal_content.append({"type": "image_url", "image_url": image})
|
||
elif isinstance(image, dict) and "image_url" in image:
|
||
multimodal_content.append(image)
|
||
else:
|
||
raise ValueError(f"Unsupported image format: {image}")
|
||
|
||
# Update the message with multimodal content
|
||
last_message["content"] = multimodal_content
|
||
|
||
# Add system messages if provided
|
||
if system_msgs:
|
||
all_messages = (
|
||
self.format_messages(system_msgs, supports_images=True)
|
||
+ formatted_messages
|
||
)
|
||
else:
|
||
all_messages = formatted_messages
|
||
|
||
# Calculate tokens and check limits
|
||
input_tokens = self.count_message_tokens(all_messages)
|
||
if not self.check_token_limit(input_tokens):
|
||
raise TokenLimitExceeded(self.get_limit_error_message(input_tokens))
|
||
|
||
# Set up API parameters
|
||
params = {
|
||
"model": self.model,
|
||
"messages": all_messages,
|
||
"stream": stream,
|
||
}
|
||
|
||
# Add model-specific parameters
|
||
if self.model in REASONING_MODELS:
|
||
params["max_completion_tokens"] = self.max_tokens
|
||
else:
|
||
params["max_tokens"] = self.max_tokens
|
||
params["temperature"] = (
|
||
temperature if temperature is not None else self.temperature
|
||
)
|
||
|
||
# Handle non-streaming request
|
||
if not stream:
|
||
response = await self.client.chat.completions.create(**params)
|
||
|
||
if not response.choices or not response.choices[0].message.content:
|
||
raise ValueError("Empty or invalid response from LLM")
|
||
|
||
self.update_token_count(
|
||
response.usage.prompt_tokens, response.usage.completion_tokens
|
||
)
|
||
return response.choices[0].message.content
|
||
|
||
# Handle streaming request
|
||
response = await self.client.chat.completions.create(**params)
|
||
|
||
collected_messages = []
|
||
completion_text = ""
|
||
async for chunk in response:
|
||
chunk_message = chunk.choices[0].delta.content or ""
|
||
collected_messages.append(chunk_message)
|
||
completion_text += chunk_message
|
||
print(chunk_message, end="", flush=True)
|
||
|
||
print() # Newline after streaming
|
||
full_response = "".join(collected_messages).strip()
|
||
|
||
if not full_response:
|
||
raise ValueError("Empty response from streaming LLM")
|
||
|
||
completion_tokens = self.count_tokens(completion_text)
|
||
logger.info(
|
||
f"Estimated completion tokens for streaming response with images: {completion_tokens}"
|
||
)
|
||
self.update_token_count(input_tokens, completion_tokens)
|
||
|
||
return full_response
|
||
|
||
except TokenLimitExceeded:
|
||
raise
|
||
except ValueError as ve:
|
||
logger.error(f"Validation error in ask_with_images: {ve}")
|
||
raise
|
||
except OpenAIError as oe:
|
||
logger.error(f"OpenAI API error: {oe}")
|
||
if isinstance(oe, AuthenticationError):
|
||
logger.error("Authentication failed. Check API key.")
|
||
elif isinstance(oe, RateLimitError):
|
||
logger.error("Rate limit exceeded. Consider increasing retry attempts.")
|
||
elif isinstance(oe, APIError):
|
||
logger.error(f"API error: {oe}")
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"Unexpected error in ask_with_images: {e}")
|
||
raise
|
||
|
||
@retry(
|
||
wait=wait_random_exponential(min=1, max=60),
|
||
stop=stop_after_attempt(6),
|
||
retry=retry_if_exception_type(
|
||
(OpenAIError, Exception, ValueError)
|
||
), # Don't retry TokenLimitExceeded
|
||
)
|
||
async def ask_tool(
|
||
self,
|
||
messages: List[Union[dict, Message]],
|
||
system_msgs: Optional[List[Union[dict, Message]]] = None,
|
||
timeout: int = 300,
|
||
tools: Optional[List[dict]] = None,
|
||
tool_choice: TOOL_CHOICE_TYPE = ToolChoice.AUTO, # type: ignore
|
||
temperature: Optional[float] = None,
|
||
**kwargs,
|
||
) -> ChatCompletionMessage | None:
|
||
"""
|
||
Ask LLM using functions/tools and return the response.
|
||
|
||
Args:
|
||
messages: List of conversation messages
|
||
system_msgs: Optional system messages to prepend
|
||
timeout: Request timeout in seconds
|
||
tools: List of tools to use
|
||
tool_choice: Tool choice strategy
|
||
temperature: Sampling temperature for the response
|
||
**kwargs: Additional completion arguments
|
||
|
||
Returns:
|
||
ChatCompletionMessage: The model's response
|
||
|
||
Raises:
|
||
TokenLimitExceeded: If token limits are exceeded
|
||
ValueError: If tools, tool_choice, or messages are invalid
|
||
OpenAIError: If API call fails after retries
|
||
Exception: For unexpected errors
|
||
"""
|
||
try:
|
||
# Validate tool_choice
|
||
if tool_choice not in TOOL_CHOICE_VALUES:
|
||
raise ValueError(f"Invalid tool_choice: {tool_choice}")
|
||
|
||
# Check if the model supports images
|
||
supports_images = self.model in MULTIMODAL_MODELS
|
||
|
||
# Format messages
|
||
if system_msgs:
|
||
system_msgs = self.format_messages(system_msgs, supports_images)
|
||
formatted_messages = system_msgs + self.format_messages(messages, supports_images)
|
||
else:
|
||
formatted_messages = self.format_messages(messages, supports_images)
|
||
|
||
# 验证消息序列，确保tool消息前面有带tool_calls的assistant消息
|
||
valid_messages = []
|
||
tool_calls_ids = set() # 跟踪所有有效的tool_call IDs
|
||
|
||
for i, msg in enumerate(formatted_messages):
|
||
if isinstance(msg, dict):
|
||
role = msg.get("role")
|
||
tool_call_id = msg.get("tool_call_id")
|
||
else:
|
||
role = msg.role if hasattr(msg, "role") else None
|
||
tool_call_id = msg.tool_call_id if hasattr(msg, "tool_call_id") else None
|
||
|
||
# 如果是tool消息，验证它引用的tool_call_id是否存在
|
||
if role == "tool" and tool_call_id:
|
||
if tool_call_id not in tool_calls_ids:
|
||
logger.warning(f"发现无效的工具消息 - tool_call_id '{tool_call_id}' 未找到关联的tool_calls，将跳过此消息")
|
||
continue
|
||
|
||
# 如果是assistant消息且有tool_calls，记录所有tool_call IDs
|
||
elif role == "assistant":
|
||
tool_calls = []
|
||
if isinstance(msg, dict) and "tool_calls" in msg:
|
||
tool_calls = msg.get("tool_calls", [])
|
||
elif hasattr(msg, "tool_calls") and msg.tool_calls:
|
||
tool_calls = msg.tool_calls
|
||
|
||
# 收集所有工具调用ID
|
||
for call in tool_calls:
|
||
if isinstance(call, dict) and "id" in call:
|
||
tool_calls_ids.add(call["id"])
|
||
elif hasattr(call, "id"):
|
||
tool_calls_ids.add(call.id)
|
||
|
||
# 添加有效消息
|
||
valid_messages.append(msg)
|
||
|
||
# 使用验证过的消息序列替换原来的消息
|
||
formatted_messages = valid_messages
|
||
|
||
# Calculate input token count
|
||
input_tokens = self.count_message_tokens(formatted_messages)
|
||
|
||
# If there are tools, calculate token count for tool descriptions
|
||
tools_tokens = 0
|
||
if tools:
|
||
for tool in tools:
|
||
tools_tokens += self.count_tokens(str(tool))
|
||
|
||
input_tokens += tools_tokens
|
||
|
||
# Check if token limits are exceeded
|
||
if not self.check_token_limit(input_tokens):
|
||
error_message = self.get_limit_error_message(input_tokens)
|
||
# Raise a special exception that won't be retried
|
||
raise TokenLimitExceeded(error_message)
|
||
|
||
# Validate tools if provided
|
||
if tools:
|
||
for tool in tools:
|
||
if not isinstance(tool, dict) or "type" not in tool:
|
||
raise ValueError("Each tool must be a dict with 'type' field")
|
||
|
||
# Set up the completion request
|
||
params = {
|
||
"model": self.model,
|
||
"messages": formatted_messages,
|
||
"tools": tools,
|
||
"tool_choice": tool_choice,
|
||
"timeout": timeout,
|
||
**kwargs,
|
||
}
|
||
|
||
if self.model in REASONING_MODELS:
|
||
params["max_completion_tokens"] = self.max_tokens
|
||
else:
|
||
params["max_tokens"] = self.max_tokens
|
||
params["temperature"] = (
|
||
temperature if temperature is not None else self.temperature
|
||
)
|
||
|
||
response: ChatCompletion = await self.client.chat.completions.create(
|
||
**params, stream=False
|
||
)
|
||
|
||
# Check if response is valid
|
||
if not response.choices or not response.choices[0].message:
|
||
print(response)
|
||
# raise ValueError("Invalid or empty response from LLM")
|
||
return None
|
||
|
||
# Update token counts
|
||
self.update_token_count(
|
||
response.usage.prompt_tokens, response.usage.completion_tokens
|
||
)
|
||
|
||
return response.choices[0].message
|
||
|
||
except TokenLimitExceeded:
|
||
# Re-raise token limit errors without logging
|
||
raise
|
||
except ValueError as ve:
|
||
logger.error(f"Validation error in ask_tool: {ve}")
|
||
raise
|
||
except OpenAIError as oe:
|
||
logger.error(f"OpenAI API error: {oe}")
|
||
if isinstance(oe, AuthenticationError):
|
||
logger.error("Authentication failed. Check API key.")
|
||
elif isinstance(oe, RateLimitError):
|
||
logger.error("Rate limit exceeded. Consider increasing retry attempts.")
|
||
elif isinstance(oe, APIError):
|
||
logger.error(f"API error: {oe}")
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"Unexpected error in ask_tool: {e}")
|
||
raise
|