123 lines
4.1 KiB
Python
123 lines
4.1 KiB
Python
"""Unified LLM client supporting Ollama and OpenAI compatible APIs."""
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from __future__ import annotations
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import json
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from dataclasses import asdict
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from typing import Dict, Iterable, List, Optional
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import requests
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from app.utils.config import get_config
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from app.utils.logging import get_logger
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LOGGER = get_logger(__name__)
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LOG_EXTRA = {"stage": "llm"}
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class LLMError(RuntimeError):
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"""Raised when LLM provider returns an error response."""
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def _default_base_url(provider: str) -> str:
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if provider == "ollama":
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return "http://localhost:11434"
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return "https://api.openai.com"
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def _build_messages(prompt: str, system: Optional[str] = None) -> List[Dict[str, str]]:
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messages: List[Dict[str, str]] = []
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if system:
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messages.append({"role": "system", "content": system})
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messages.append({"role": "user", "content": prompt})
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return messages
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def _request_ollama(model: str, prompt: str, *, base_url: str, temperature: float, timeout: float, system: Optional[str]) -> str:
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url = f"{base_url.rstrip('/')}/api/chat"
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payload = {
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"model": model,
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"messages": _build_messages(prompt, system),
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"stream": False,
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"options": {"temperature": temperature},
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}
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LOGGER.debug("调用 Ollama: %s %s", model, url, extra=LOG_EXTRA)
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response = requests.post(url, json=payload, timeout=timeout)
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if response.status_code != 200:
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raise LLMError(f"Ollama 调用失败: {response.status_code} {response.text}")
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data = response.json()
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message = data.get("message", {})
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content = message.get("content", "")
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if isinstance(content, list):
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return "".join(chunk.get("text", "") or chunk.get("content", "") for chunk in content)
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return str(content)
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def _request_openai(model: str, prompt: str, *, base_url: str, api_key: str, temperature: float, timeout: float, system: Optional[str]) -> str:
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url = f"{base_url.rstrip('/')}/v1/chat/completions"
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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payload = {
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"model": model,
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"messages": _build_messages(prompt, system),
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"temperature": temperature,
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}
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LOGGER.debug("调用 OpenAI 兼容接口: %s %s", model, url, extra=LOG_EXTRA)
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response = requests.post(url, headers=headers, json=payload, timeout=timeout)
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if response.status_code != 200:
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raise LLMError(f"OpenAI API 调用失败: {response.status_code} {response.text}")
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data = response.json()
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try:
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return data["choices"][0]["message"]["content"].strip()
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except (KeyError, IndexError) as exc:
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raise LLMError(f"OpenAI 响应解析失败: {json.dumps(data, ensure_ascii=False)}") from exc
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def run_llm(prompt: str, *, system: Optional[str] = None) -> str:
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"""Execute the configured LLM provider with the given prompt."""
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cfg = get_config().llm
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provider = (cfg.provider or "ollama").lower()
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base_url = cfg.base_url or _default_base_url(provider)
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model = cfg.model
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temperature = max(0.0, min(cfg.temperature, 2.0))
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timeout = max(5.0, cfg.timeout or 30.0)
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LOGGER.info(
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"触发 LLM 请求：provider=%s model=%s base=%s", provider, model, base_url, extra=LOG_EXTRA
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)
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if provider == "openai":
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if not cfg.api_key:
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raise LLMError("缺少 OpenAI 兼容 API Key")
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return _request_openai(
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model,
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prompt,
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base_url=base_url,
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api_key=cfg.api_key,
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temperature=temperature,
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timeout=timeout,
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system=system,
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)
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if provider == "ollama":
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return _request_ollama(
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model,
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prompt,
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base_url=base_url,
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temperature=temperature,
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timeout=timeout,
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system=system,
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)
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raise LLMError(f"不支持的 LLM provider: {cfg.provider}")
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def llm_config_snapshot() -> Dict[str, object]:
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"""Return a sanitized snapshot of current LLM configuration for debugging."""
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cfg = get_config().llm
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data = asdict(cfg)
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if data.get("api_key"):
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data["api_key"] = "***"
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return data
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