Solaria
314 lines
10 KiB
Python
314 lines
10 KiB
Python
"""LLM client for Opus Orchestrator.
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Supports MiniMax and OpenAI providers - both async and sync.
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Includes retry logic with exponential backoff and circuit breaker.
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"""
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import os
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import asyncio
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from typing import Any, Optional
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import httpx
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import requests
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from opus_orchestrator.utils.retry import RetryHandler, RetryConfig
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class LLMClient:
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"""Simple LLM client for making API calls - supports both sync and async.
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Includes built-in retry logic with circuit breaker for resilience.
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"""
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def __init__(
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self,
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api_key: Optional[str] = None,
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provider: str = "minimax",
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model: str = "MiniMax/MiniMax-M2.1",
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base_url: Optional[str] = None,
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max_retries: int = 3,
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timeout: float = 120.0,
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):
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"""Initialize LLM client.
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Args:
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api_key: API key for the provider
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provider: LLM provider (minimax, openai)
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model: Model name
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base_url: Optional custom base URL
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max_retries: Maximum retry attempts (default 3)
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"""
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self.api_key = api_key or os.environ.get("MINIMAX_API_KEY") or os.environ.get("OPENAI_API_KEY")
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self.provider = provider
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self.model = model
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self.timeout = timeout
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# Normalize model name for MiniMax
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if provider == "minimax":
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self.minimax_model = model.split("/")[-1] if "/" in model else model
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# Set base URL based on provider
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if base_url:
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self.base_url = base_url
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elif provider == "minimax":
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# Use Anthropic-compatible API (like OpenClaw uses)
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self.base_url = "https://api.minimax.io/anthropic"
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elif provider == "openai":
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self.base_url = "https://api.openai.com/v1"
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else:
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self.base_url = "https://api.openai.com/v1"
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# Async client
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self._async_client = httpx.AsyncClient(timeout=self.timeout)
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# Initialize retry handler
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retry_config = RetryConfig(
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max_attempts=max_retries,
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base_delay=1.0,
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max_delay=30.0,
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exponential_base=2.0,
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jitter=True,
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)
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self._retry_handler = RetryHandler(retry_config)
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def complete(
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self,
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system_prompt: str,
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user_prompt: str,
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temperature: float = 0.7,
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max_tokens: Optional[int] = None,
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) -> str:
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"""Make a completion request (SYNC - for LangGraph compatibility)."""
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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}
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if self.provider == "minimax":
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return self._complete_minimax_sync(
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system_prompt, user_prompt, temperature, max_tokens, headers
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)
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elif self.provider == "openai":
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return self._complete_openai_sync(
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system_prompt, user_prompt, temperature, max_tokens, headers
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)
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else:
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raise ValueError(f"Unsupported provider: {self.provider}")
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async def complete_async(
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self,
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system_prompt: str,
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user_prompt: str,
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temperature: float = 0.7,
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max_tokens: Optional[int] = None,
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) -> str:
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"""Make a completion request (ASYNC) with retry logic."""
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async def _make_request():
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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}
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if self.provider == "minimax":
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return await self._complete_minimax_async(
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system_prompt, user_prompt, temperature, max_tokens, headers
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)
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elif self.provider == "openai":
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return await self._complete_openai_async(
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system_prompt, user_prompt, temperature, max_tokens, headers
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)
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else:
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raise ValueError(f"Unsupported provider: {self.provider}")
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# Use retry handler for resilience
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try:
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return await self._retry_handler.execute_with_retry(_make_request)
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except Exception as e:
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# Log and re-raise with context
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raise RuntimeError(f"LLM request failed after retries: {e}") from e
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async def _complete_minimax_async(
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self,
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system_prompt: str,
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user_prompt: str,
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temperature: float,
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max_tokens: Optional[int],
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headers: dict,
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) -> str:
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"""Call MiniMax API using Anthropic-compatible endpoint."""
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# Anthropic-compatible format
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payload = {
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"model": self.minimax_model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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"temperature": temperature,
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}
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if max_tokens:
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payload["max_tokens"] = max_tokens
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# Use Anthropic-compatible endpoint
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response = await self._async_client.post(
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f"{self.base_url}/v1/messages",
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headers={**headers, "Content-Type": "application/json"},
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json=payload,
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)
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# Debug output
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if response.status_code != 200:
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print(f"MiniMax API error: {response.status_code}")
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print(f"Response: {response.text[:500]}")
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response.raise_for_status()
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data = response.json()
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# Handle Anthropic-compatible response format
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if "content" in data:
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# Return the text content
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if isinstance(data["content"], list) and len(data["content"]) > 0:
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return data["content"][0].get("text", str(data["content"][0]))
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return str(data["content"])
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else:
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raise Exception(f"Unexpected MiniMax response: {data}")
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async def _complete_openai(
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self,
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system_prompt: str,
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user_prompt: str,
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temperature: float,
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max_tokens: Optional[int],
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headers: dict,
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) -> str:
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"""Call OpenAI API."""
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payload = {
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"model": self.model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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"temperature": temperature,
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}
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if max_tokens:
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payload["max_tokens"] = max_tokens
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response = await self._async_client.post(
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f"{self.base_url}/chat/completions",
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headers=headers,
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json=payload,
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)
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response.raise_for_status()
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data = response.json()
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return data["choices"][0]["message"]["content"]
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async def close(self):
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"""Close the HTTP client."""
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await self._async_client.aclose()
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# =========================================================================
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# SYNC VERSIONS (for LangGraph compatibility)
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# =========================================================================
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def _complete_minimax_sync(
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self,
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system_prompt: str,
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user_prompt: str,
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temperature: float,
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max_tokens: Optional[int],
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headers: dict,
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) -> str:
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"""Call MiniMax API (sync) using Anthropic-compatible endpoint."""
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payload = {
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"model": self.minimax_model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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"temperature": temperature,
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}
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if max_tokens:
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payload["max_tokens"] = max_tokens
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# Use Anthropic-compatible endpoint
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response = requests.post(
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f"{self.base_url}/v1/messages",
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headers={**headers, "Content-Type": "application/json"},
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json=payload,
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timeout=self.timeout,
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)
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if response.status_code != 200:
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print(f"MiniMax API error: {response.status_code}")
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print(f"Response: {response.text[:500]}")
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response.raise_for_status()
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data = response.json()
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# Handle Anthropic-compatible response format
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if "content" in data:
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if isinstance(data["content"], list) and len(data["content"]) > 0:
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# Look for text content, skip thinking
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text_parts = []
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for item in data["content"]:
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if item.get("type") == "text":
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text_parts.append(item.get("text", ""))
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if text_parts:
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return "".join(text_parts)
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# If no text found, return first item as string
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return str(data["content"][0])
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return str(data["content"])
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else:
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raise Exception(f"Unexpected MiniMax response: {data}")
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def _complete_openai_sync(
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self,
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system_prompt: str,
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user_prompt: str,
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temperature: float,
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max_tokens: Optional[int],
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headers: dict,
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) -> str:
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"""Call OpenAI API (sync)."""
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payload = {
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"model": self.model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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"temperature": temperature,
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}
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if max_tokens:
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payload["max_tokens"] = max_tokens
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response = requests.post(
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f"{self.base_url}/chat/completions",
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headers=headers,
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json=payload,
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timeout=self.timeout,
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)
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response.raise_for_status()
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data = response.json()
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return data["choices"][0]["message"]["content"]
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# Convenience function
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def get_llm_client(config: Optional[Any] = None) -> LLMClient:
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"""Get an LLM client from config."""
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from opus_orchestrator.config import get_config
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cfg = config or get_config()
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return LLMClient(
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api_key=cfg.agent.api_key,
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provider=cfg.agent.provider,
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model=cfg.agent.model,
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timeout=cfg.agent.timeout,
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)
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