f0f13faf00
- Add full 检修维护中心 (6.4): 3-type work orders (消缺/巡检/抄表), asset management, warehouse, work plans, billing settlement - Add AI智能分析 tab with LLM-powered diagnostics (StepFun + ZhipuAI) - Add AI模型配置 settings page (provider, temperature, prompts) - Fix station power accuracy: use API station total (station_power) instead of inverter-level computation — eliminates timing gaps - Add 7 new DB models, 4 new API routers, 5 new frontend pages - Migrations: 009 (maintenance expansion) + 010 (AI analysis) - Version bump: 1.6.1 → 2.0.0 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
108 lines
3.9 KiB
Python
108 lines
3.9 KiB
Python
"""LLM service for AI-powered analysis using OpenAI-compatible APIs (StepFun, ZhipuAI)."""
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import logging
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from openai import AsyncOpenAI
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logger = logging.getLogger(__name__)
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async def get_llm_client(settings: dict, use_fallback: bool = False) -> tuple[AsyncOpenAI, str]:
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"""Create an OpenAI-compatible client based on settings.
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Returns (client, model_name) tuple.
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"""
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if use_fallback:
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base_url = settings.get("ai_fallback_api_base_url", "")
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api_key = settings.get("ai_fallback_api_key", "")
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model = settings.get("ai_fallback_model_name", "codegeex-4")
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else:
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base_url = settings.get("ai_api_base_url", "")
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api_key = settings.get("ai_api_key", "")
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model = settings.get("ai_model_name", "step-2-16k")
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client = AsyncOpenAI(base_url=base_url, api_key=api_key, timeout=30.0)
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return client, model
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async def chat_completion(
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messages: list[dict],
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settings: dict,
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temperature: float | None = None,
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max_tokens: int | None = None,
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) -> str:
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"""Send a chat completion request with automatic fallback."""
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temp = temperature or float(settings.get("ai_temperature", "0.7"))
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tokens = max_tokens or int(settings.get("ai_max_tokens", "2000"))
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# Try primary
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try:
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client, model = await get_llm_client(settings, use_fallback=False)
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response = await client.chat.completions.create(
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model=model,
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messages=messages,
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temperature=temp,
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max_tokens=tokens,
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)
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return response.choices[0].message.content or ""
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except Exception as e:
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logger.warning(f"Primary LLM failed: {e}")
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# Try fallback if enabled
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if settings.get("ai_fallback_enabled") == "true":
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try:
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client, model = await get_llm_client(settings, use_fallback=True)
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response = await client.chat.completions.create(
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model=model,
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messages=messages,
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temperature=temp,
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max_tokens=tokens,
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)
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return response.choices[0].message.content or ""
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except Exception as e2:
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logger.error(f"Fallback LLM also failed: {e2}")
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raise Exception(f"All LLM providers failed. Primary: {e}, Fallback: {e2}")
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else:
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raise
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async def test_connection(settings: dict) -> dict:
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"""Test connection to the configured LLM provider."""
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results = {"primary": {"status": "unknown"}, "fallback": {"status": "unknown"}}
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# Test primary
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try:
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client, model = await get_llm_client(settings, use_fallback=False)
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response = await client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": "你好，请回复'连接成功'"}],
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max_tokens=20,
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temperature=0,
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)
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reply = response.choices[0].message.content or ""
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results["primary"] = {
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"status": "success",
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"model": model,
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"reply": reply[:100],
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}
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except Exception as e:
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results["primary"] = {"status": "error", "error": str(e)[:200]}
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# Test fallback
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if settings.get("ai_fallback_enabled") == "true":
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try:
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client, model = await get_llm_client(settings, use_fallback=True)
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response = await client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": "你好，请回复'连接成功'"}],
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max_tokens=20,
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temperature=0,
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)
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reply = response.choices[0].message.content or ""
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results["fallback"] = {
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"status": "success",
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"model": model,
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"reply": reply[:100],
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}
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except Exception as e:
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results["fallback"] = {"status": "error", "error": str(e)[:200]}
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return results
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