233 lines
8.4 KiB
Python
233 lines
8.4 KiB
Python
"""
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LLM wrapper for unified configuration across providers.
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"""
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import os
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import time
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import asyncio
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from typing import Optional, Any, Dict, List
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from openai import AsyncOpenAI, RateLimitError, APIError, APIStatusError, LengthFinishReasonError
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import logging
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logger = logging.getLogger(__name__)
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# Disable httpx logging
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logging.getLogger("httpx").setLevel(logging.WARNING)
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class OutputTooLongError(Exception):
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"""
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Bridge exception raised when LLM output exceeds token limits.
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This wraps provider-specific errors (e.g., OpenAI's LengthFinishReasonError)
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to allow callers to handle output length issues without depending on
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provider-specific implementations.
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"""
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pass
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class LLMConfig:
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"""Configuration for an LLM provider."""
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def __init__(
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self,
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provider: str,
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api_key: str,
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base_url: str,
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model: str,
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):
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"""
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Initialize LLM configuration.
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Args:
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provider: Provider name ("openai", "groq", "ollama"). Required.
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api_key: API key. Required.
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base_url: Base URL. Required.
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model: Model name. Required.
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"""
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self.provider = provider.lower()
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self.api_key = api_key
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self.base_url = base_url
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self.model = model
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# Validate provider
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if self.provider not in ["openai", "groq", "ollama"]:
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raise ValueError(
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f"Invalid LLM provider: {self.provider}. Must be 'openai', 'groq', or 'ollama'."
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)
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# Set default base URLs
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if not self.base_url:
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if self.provider == "groq":
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self.base_url = "https://api.groq.com/openai/v1"
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elif self.provider == "ollama":
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self.base_url = "http://localhost:11434/v1"
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# Validate API key (not needed for ollama)
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if self.provider != "ollama" and not self.api_key:
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raise ValueError(
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f"API key not found for {self.provider}"
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)
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# Create client
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if self.provider == "ollama":
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self.client = AsyncOpenAI(api_key="ollama", base_url=self.base_url)
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elif self.base_url:
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self.client = AsyncOpenAI(api_key=self.api_key, base_url=self.base_url)
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else:
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self.client = AsyncOpenAI(api_key=self.api_key)
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logger.info(
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f"Initialized LLM: provider={self.provider}, model={self.model}, base_url={self.base_url}"
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)
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async def call(
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self,
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messages: List[Dict[str, str]],
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response_format: Optional[Any] = None,
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scope: str = "memory",
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max_retries: int = 5,
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initial_backoff: float = 1.0,
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max_backoff: float = 60.0,
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**kwargs
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) -> Any:
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"""
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Make an LLM API call with consistent configuration and retry logic.
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Args:
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messages: List of message dicts with 'role' and 'content'
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response_format: Optional Pydantic model for structured output
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scope: Scope identifier (e.g., 'memory', 'judge') for future tracking
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max_retries: Maximum number of retry attempts (default: 5)
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initial_backoff: Initial backoff time in seconds (default: 1.0)
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max_backoff: Maximum backoff time in seconds (default: 60.0)
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**kwargs: Additional parameters to pass to the API (temperature, max_tokens, etc.)
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Returns:
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Parsed response if response_format is provided, otherwise the text content
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Raises:
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Exception: Re-raises any API errors after all retries are exhausted
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"""
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start_time = time.time()
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call_params = {
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"model": self.model,
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"messages": messages,
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**kwargs
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}
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if self.provider == "groq":
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call_params["extra_body"] = {"service_tier": "auto"}
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last_exception = None
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for attempt in range(max_retries + 1):
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try:
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if response_format is not None:
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# Use structured output parsing and return .parsed
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response = await self.client.beta.chat.completions.parse(
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response_format=response_format,
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**call_params
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)
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result = response.choices[0].message.parsed
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else:
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# Standard completion and return text content
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response = await self.client.chat.completions.create(**call_params)
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result = response.choices[0].message.content
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# Log call details on success
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duration = time.time() - start_time
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usage = response.usage
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logger.info(
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f"model={self.provider}/{self.model}, "
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f"input_tokens={usage.prompt_tokens}, output_tokens={usage.completion_tokens}, "
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f"total_tokens={usage.total_tokens}, time={duration:.3f}s"
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)
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return result
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except LengthFinishReasonError as e:
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# Output exceeded token limits - raise bridge exception for caller to handle
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logger.warning(f"LLM output exceeded token limits: {str(e)}")
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raise OutputTooLongError(
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f"LLM output exceeded token limits. Input may need to be split into smaller chunks."
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) from e
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except APIStatusError as e:
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last_exception = e
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if attempt < max_retries:
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# Calculate exponential backoff with jitter
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backoff = min(initial_backoff * (2 ** attempt), max_backoff)
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# Add jitter (±20%)
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jitter = backoff * 0.2 * (2 * (time.time() % 1) - 1)
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sleep_time = backoff + jitter
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logger.warning(
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f"LLM error on attempt {attempt + 1}/{max_retries + 1}. "
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f"Retrying in {sleep_time:.2f}s... Error: {str(e)}"
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)
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await asyncio.sleep(sleep_time)
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else:
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logger.error(f"Non-retryable API error after {max_retries + 1} attempts: {str(e)}")
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raise
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except Exception as e:
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logger.error(f"Unexpected error during LLM call: {type(e).__name__}: {str(e)}")
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raise
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# This should never be reached, but just in case
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if last_exception:
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raise last_exception
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raise RuntimeError("LLM call failed after all retries with no exception captured")
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@classmethod
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def for_memory(cls) -> "LLMConfig":
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"""Create configuration for memory operations from environment variables."""
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provider = os.getenv("MEMORY_LLM_PROVIDER", "groq")
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api_key = os.getenv("MEMORY_LLM_API_KEY")
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base_url = os.getenv("MEMORY_LLM_BASE_URL")
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model = os.getenv("MEMORY_LLM_MODEL", "openai/gpt-oss-120b")
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# Set default base URL if not provided
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if not base_url:
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if provider == "groq":
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base_url = "https://api.groq.com/openai/v1"
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elif provider == "ollama":
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base_url = "http://localhost:11434/v1"
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else:
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base_url = ""
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return cls(
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provider=provider,
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api_key=api_key,
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base_url=base_url,
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model=model,
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)
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@classmethod
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def for_judge(cls) -> "LLMConfig":
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"""
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Create configuration for judge/evaluator operations from environment variables.
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Falls back to memory LLM config if judge-specific config not set.
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"""
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# Check if judge-specific config exists, otherwise fall back to memory config
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provider = os.getenv("JUDGE_LLM_PROVIDER", os.getenv("MEMORY_LLM_PROVIDER", "groq"))
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api_key = os.getenv("JUDGE_LLM_API_KEY", os.getenv("MEMORY_LLM_API_KEY"))
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base_url = os.getenv("JUDGE_LLM_BASE_URL", os.getenv("MEMORY_LLM_BASE_URL"))
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model = os.getenv("JUDGE_LLM_MODEL", os.getenv("MEMORY_LLM_MODEL", "openai/gpt-oss-120b"))
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# Set default base URL if not provided
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if not base_url:
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if provider == "groq":
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base_url = "https://api.groq.com/openai/v1"
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elif provider == "ollama":
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base_url = "http://localhost:11434/v1"
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else:
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base_url = ""
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return cls(
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provider=provider,
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api_key=api_key,
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base_url=base_url,
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model=model,
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)
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