mrhavens
9692c89214
The main structure is in place. For production, the async event loop issues need resolution but core architecture is solid.
143 lines
4.1 KiB
Python
143 lines
4.1 KiB
Python
"""LLM client for Opus Orchestrator - Synchronous version.
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Uses synchronous httpx to avoid event loop issues with LangGraph.
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"""
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import os
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from typing import Any, Optional
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import requests
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class LLMClient:
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"""Synchronous LLM client for making API calls."""
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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 = "openai",
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model: str = "gpt-4o",
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base_url: Optional[str] = None,
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):
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"""Initialize LLM client."""
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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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if base_url:
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self.base_url = base_url
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elif provider == "minimax":
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self.base_url = "https://api.minimax.chat/v1"
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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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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 (synchronous)."""
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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(
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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(
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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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def _complete_minimax(
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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 (synchronous)."""
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minimax_model = self.model.split("/")[-1] if "/" in self.model else self.model
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payload = {
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"model": 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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response = requests.post(
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f"{self.base_url}/text/chatcompletion_v2",
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headers=headers,
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json=payload,
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timeout=120,
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)
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response.raise_for_status()
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data = response.json()
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if "choices" in data:
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return data["choices"][0]["message"]["content"]
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else:
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raise Exception(f"Unexpected MiniMax response: {data}")
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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 (synchronous)."""
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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=120,
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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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)
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