286 lines
12 KiB
Python
286 lines
12 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, APIConnectionError, LengthFinishReasonError
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import logging
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# Seed applied to every Groq request for deterministic behavior.
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DEFAULT_LLM_SEED = 4242
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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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# Global semaphore to limit concurrent LLM requests across all instances
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_global_llm_semaphore = asyncio.Semaphore(32)
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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 LLMProvider:
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"""
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Unified LLM provider using OpenAI-compatible API.
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Supports OpenAI, Groq, and Ollama (any OpenAI-compatible endpoint).
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"""
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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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reasoning_effort: str = "low",
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):
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"""
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Initialize LLM provider.
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Args:
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provider: Provider name ("openai", "groq", "ollama").
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api_key: API key.
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base_url: Base URL for the API.
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model: Model name.
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reasoning_effort: Reasoning effort level for supported providers.
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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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self.reasoning_effort = reasoning_effort
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# Validate provider
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valid_providers = ["openai", "groq", "ollama"]
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if self.provider not in valid_providers:
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raise ValueError(
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f"Invalid LLM provider: {self.provider}. Must be one of: {', '.join(valid_providers)}"
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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(f"API key not found for {self.provider}")
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# Create OpenAI-compatible client for all providers
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if self.provider == "ollama":
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self._client = AsyncOpenAI(api_key="ollama", base_url=self.base_url, max_retries=0)
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else:
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self._client = AsyncOpenAI(api_key=self.api_key, base_url=self.base_url, max_retries=0)
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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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max_completion_tokens: Optional[int] = None,
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temperature: Optional[float] = None,
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scope: str = "memory",
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max_retries: int = 10,
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initial_backoff: float = 1.0,
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max_backoff: float = 60.0,
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skip_validation: bool = False,
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) -> Any:
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"""
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Make an LLM API call with 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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max_completion_tokens: Maximum tokens in response.
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temperature: Sampling temperature (0.0-2.0).
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scope: Scope identifier for tracking.
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max_retries: Maximum retry attempts.
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initial_backoff: Initial backoff time in seconds.
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max_backoff: Maximum backoff time in seconds.
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skip_validation: Return raw JSON without Pydantic validation.
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Returns:
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Parsed response if response_format is provided, otherwise text content.
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Raises:
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OutputTooLongError: If output exceeds token limits.
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Exception: Re-raises API errors after retries exhausted.
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"""
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async with _global_llm_semaphore:
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start_time = time.time()
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import json
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call_params = {
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"model": self.model,
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"messages": messages,
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}
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if max_completion_tokens is not None:
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call_params["max_completion_tokens"] = max_completion_tokens
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if temperature is not None:
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call_params["temperature"] = temperature
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# Provider-specific parameters
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if self.provider == "groq":
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call_params["seed"] = DEFAULT_LLM_SEED
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call_params["extra_body"] = {
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"service_tier": "auto",
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"reasoning_effort": self.reasoning_effort,
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"include_reasoning": False,
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}
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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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# Add schema to system message for JSON mode
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if hasattr(response_format, 'model_json_schema'):
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schema = response_format.model_json_schema()
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schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"
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if call_params['messages'] and call_params['messages'][0].get('role') == 'system':
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call_params['messages'][0]['content'] += schema_msg
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elif call_params['messages']:
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call_params['messages'][0]['content'] = schema_msg + "\n\n" + call_params['messages'][0]['content']
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call_params['response_format'] = {"type": "json_object"}
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response = await self._client.chat.completions.create(**call_params)
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content = response.choices[0].message.content
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json_data = json.loads(content)
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if skip_validation:
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result = json_data
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else:
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result = response_format.model_validate(json_data)
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else:
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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 slow calls
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duration = time.time() - start_time
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usage = response.usage
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if duration > 10.0:
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ratio = max(1, usage.completion_tokens) / usage.prompt_tokens
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cached_tokens = 0
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if hasattr(usage, 'prompt_tokens_details') and usage.prompt_tokens_details:
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cached_tokens = getattr(usage.prompt_tokens_details, 'cached_tokens', 0) or 0
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cache_info = f", cached_tokens={cached_tokens}" if cached_tokens > 0 else ""
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logger.info(
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f"slow llm call: 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}{cache_info}, time={duration:.3f}s, ratio out/in={ratio:.2f}"
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)
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return result
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except LengthFinishReasonError as e:
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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 APIConnectionError as e:
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last_exception = e
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if attempt < max_retries:
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logger.warning(f"Connection error, retrying... (attempt {attempt + 1}/{max_retries + 1})")
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backoff = min(initial_backoff * (2 ** attempt), max_backoff)
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await asyncio.sleep(backoff)
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continue
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else:
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logger.error(f"Connection error after {max_retries + 1} attempts: {str(e)}")
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raise
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except APIStatusError as e:
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# Fast fail on 4xx client errors (except 429 rate limit and 498 which is treated as server error)
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if 400 <= e.status_code < 500 and e.status_code not in (429, 498):
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logger.error(f"Client error (HTTP {e.status_code}), not retrying: {str(e)}")
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raise
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last_exception = e
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if attempt < max_retries:
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backoff = min(initial_backoff * (2 ** attempt), max_backoff)
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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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await asyncio.sleep(sleep_time)
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else:
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logger.error(f"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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if last_exception:
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raise last_exception
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raise RuntimeError(f"LLM call failed after all retries with no exception captured")
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@classmethod
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def for_memory(cls) -> "LLMProvider":
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"""Create provider for memory operations from environment variables."""
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provider = os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq")
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api_key = os.getenv("HINDSIGHT_API_LLM_API_KEY")
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base_url = os.getenv("HINDSIGHT_API_LLM_BASE_URL", "")
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model = os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b")
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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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reasoning_effort="low"
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)
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@classmethod
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def for_answer_generation(cls) -> "LLMProvider":
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"""Create provider for answer generation. Falls back to memory config if not set."""
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provider = os.getenv("HINDSIGHT_API_ANSWER_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"))
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api_key = os.getenv("HINDSIGHT_API_ANSWER_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY"))
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base_url = os.getenv("HINDSIGHT_API_ANSWER_LLM_BASE_URL", os.getenv("HINDSIGHT_API_LLM_BASE_URL", ""))
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model = os.getenv("HINDSIGHT_API_ANSWER_LLM_MODEL", os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b"))
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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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reasoning_effort="high"
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)
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@classmethod
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def for_judge(cls) -> "LLMProvider":
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"""Create provider for judge/evaluator operations. Falls back to memory config if not set."""
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provider = os.getenv("HINDSIGHT_API_JUDGE_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"))
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api_key = os.getenv("HINDSIGHT_API_JUDGE_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY"))
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base_url = os.getenv("HINDSIGHT_API_JUDGE_LLM_BASE_URL", os.getenv("HINDSIGHT_API_LLM_BASE_URL", ""))
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model = os.getenv("HINDSIGHT_API_JUDGE_LLM_MODEL", os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b"))
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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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reasoning_effort="high"
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)
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# Backwards compatibility alias
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LLMConfig = LLMProvider
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