101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
from __future__ import annotations
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import json
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from typing import Any
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from backend.llm import transport as llm_transport
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from backend.features.screener.engine import FACTOR_FIELDS, REGIMES
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class LLMCompilerError(RuntimeError):
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pass
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def test_llm_connection(
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api_key: str,
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base_url: str,
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model: str,
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timeout: int = 30,
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) -> dict[str, Any]:
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if not api_key or not model:
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raise LLMCompilerError("API Key 或模型未配置。")
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try:
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result = llm_transport.chat_completion(
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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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messages=[{"role": "user", "content": "只回复 OK"}],
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timeout=timeout,
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user_agent="XiaobaiReviewWeb/0.5",
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)
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reply = str(result.content).strip()
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except llm_transport.OpenAIHTTPError as exc:
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raise LLMCompilerError(exc.describe("模型连接测试失败")) from exc
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except llm_transport.OpenAITransportError as exc:
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raise LLMCompilerError(f"模型连接测试失败：{exc}") from exc
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return {
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"ok": True,
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"model": model,
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"reply": reply[:100],
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"latency_ms": result.latency_ms,
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}
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def compile_strategy_with_llm(
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prompt: str,
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regime: 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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timeout: int = 45,
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) -> dict[str, Any]:
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if not api_key or not model:
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raise LLMCompilerError("尚未配置 LLM API Key 或模型。")
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schema = {
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"name": "策略名称",
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"description": "策略说明",
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"regimes": [regime],
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"formula": {
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"universe": {"exclude_st": True, "listed_days_min": 120},
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"filters": [{"field": "return_5d", "op": ">=", "value": 0}],
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"score": [{"field": "sector_strength", "weight": 0.3, "direction": "desc"}],
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"limit": 15,
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"min_score": 0.55,
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},
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}
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system_prompt = (
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"你是A股量化策略编译器。只输出JSON对象，不输出Markdown。"
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"不得生成Python、SQL、网络请求或未提供的因子。"
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f"当前市场阶段为{REGIMES.get(regime, regime)}。"
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f"可用因子为：{json.dumps(FACTOR_FIELDS, ensure_ascii=False)}。"
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"运算符只能使用 >, >=, <, <=, ==, !=, between, in。"
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"score权重均大于0且不超过1，direction只能是asc或desc。"
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"退潮和冰点策略必须提高门槛并允许结果为空。"
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f"严格遵循以下结构：{json.dumps(schema, ensure_ascii=False)}"
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)
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try:
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result = llm_transport.chat_completion(
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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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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt[:3000]},
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],
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timeout=timeout,
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user_agent="XiaobaiReviewWeb/0.4",
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)
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content = result.content.strip()
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if content.startswith("```"):
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content = content.strip("`")
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if content.startswith("json"):
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content = content[4:].strip()
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compiled = json.loads(content)
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except llm_transport.OpenAIHTTPError as exc:
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raise LLMCompilerError(exc.describe("LLM 策略编译失败")) from exc
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except (llm_transport.OpenAITransportError, json.JSONDecodeError) as exc:
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raise LLMCompilerError(f"LLM 策略编译失败：{exc}") from exc
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compiled["compiler"] = "llm"
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compiled["model"] = model
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return compiled
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