update
This commit is contained in:
sam
parent
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@ -58,9 +58,10 @@ export TUSHARE_TOKEN="<your-token>"
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### LLM 配置与测试
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- 默认使用本地 Ollama（`http://localhost:11434`），可在 Streamlit 的 “数据与设置” 页签切换到 OpenAI 兼容接口。
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- 支持本地 Ollama（`http://localhost:11434`）与多家 OpenAI 兼容云端供应商（如 DeepSeek、文心一言、OpenAI 等），可在 Streamlit 的 “数据与设置” 页签切换 Provider 并配置模型、Base URL、API Key。不同 Provider 默认映射的模型示例：Ollama → `llama3`，OpenAI → `gpt-4o-mini`，DeepSeek → `deepseek-chat`，文心一言 → `ERNIE-Speed`。
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- 修改 Provider/模型/Base URL/API Key 后点击 “保存 LLM 设置”，更新内容仅在当前会话生效。
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- 在 “自检测试” 页新增 “LLM 接口测试”，可输入 Prompt 快速验证调用结果，日志会记录限频与错误信息便于排查。
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- 未来可对同一功能的智能体并行调用多个 LLM，采用多数投票等策略增强鲁棒性，当前代码结构已为此预留扩展空间。
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## 快速开始
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@ -2,18 +2,18 @@
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from __future__ import annotations
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import json
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from collections import Counter
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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.config import DEFAULT_LLM_MODELS, LLMEndpoint, 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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@ -21,9 +21,18 @@ class LLMError(RuntimeError):
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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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if provider == "deepseek":
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return "https://api.deepseek.com"
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if provider == "wenxin":
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return "https://aip.baidubce.com"
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return "https://api.openai.com"
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def _default_model(provider: str) -> str:
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provider = (provider or "").lower()
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return DEFAULT_LLM_MODELS.get(provider, DEFAULT_LLM_MODELS["ollama"])
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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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@ -32,7 +41,15 @@ def _build_messages(prompt: str, system: Optional[str] = None) -> List[Dict[str,
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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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def _request_ollama(
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model: str,
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prompt: str,
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*,
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base_url: str,
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temperature: float,
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timeout: float,
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system: Optional[str],
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) -> 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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@ -52,7 +69,16 @@ def _request_ollama(model: str, prompt: str, *, base_url: str, temperature: floa
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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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def _request_openai(
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model: str,
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prompt: str,
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*,
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base_url: str,
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api_key: str,
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temperature: float,
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timeout: float,
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system: Optional[str],
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) -> 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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@ -74,28 +100,30 @@ def _request_openai(model: str, prompt: str, *, base_url: str, api_key: str, tem
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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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def _call_endpoint(endpoint: LLMEndpoint, prompt: str, system: Optional[str]) -> str:
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provider = (endpoint.provider or "ollama").lower()
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base_url = endpoint.base_url or _default_base_url(provider)
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model = endpoint.model or _default_model(provider)
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temperature = max(0.0, min(endpoint.temperature, 2.0))
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timeout = max(5.0, endpoint.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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"触发 LLM 请求：provider=%s model=%s base=%s",
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provider,
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model,
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base_url,
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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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if provider in {"openai", "deepseek", "wenxin"}:
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api_key = endpoint.api_key
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if not api_key:
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raise LLMError(f"缺少 {provider} API Key (model={model})")
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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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api_key=api_key,
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temperature=temperature,
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timeout=timeout,
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system=system,
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@ -109,14 +137,89 @@ def run_llm(prompt: str, *, system: Optional[str] = None) -> str:
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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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raise LLMError(f"不支持的 LLM provider: {endpoint.provider}")
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def _normalize_response(text: str) -> str:
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return " ".join(text.strip().split())
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def run_llm(prompt: str, *, system: Optional[str] = None) -> str:
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"""Execute the configured LLM strategy with the given prompt."""
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settings = get_config().llm
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if settings.strategy == "majority":
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return _run_majority_vote(settings, prompt, system)
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return _call_endpoint(settings.primary, prompt, system)
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def _run_majority_vote(config, prompt: str, system: Optional[str]) -> str:
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endpoints: List[LLMEndpoint] = [config.primary] + list(config.ensemble)
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responses: List[Dict[str, str]] = []
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failures: List[str] = []
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for idx, endpoint in enumerate(endpoints, start=1):
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try:
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result = _call_endpoint(endpoint, prompt, system)
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responses.append({
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"provider": endpoint.provider,
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"model": endpoint.model,
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"raw": result,
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"normalized": _normalize_response(result),
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})
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except Exception as exc: # noqa: BLE001
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summary = f"{endpoint.provider}:{endpoint.model} -> {exc}"
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failures.append(summary)
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LOGGER.warning("LLM 调用失败：%s", summary, extra=LOG_EXTRA)
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if not responses:
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raise LLMError("所有 LLM 调用均失败，无法返回结果。")
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threshold = max(1, config.majority_threshold)
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threshold = min(threshold, len(responses))
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counter = Counter(item["normalized"] for item in responses)
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top_value, top_count = counter.most_common(1)[0]
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if top_count >= threshold:
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chosen_raw = next(item["raw"] for item in responses if item["normalized"] == top_value)
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LOGGER.info(
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"LLM 多模型投票通过：value=%s votes=%s/%s threshold=%s",
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top_value[:80],
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top_count,
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len(responses),
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threshold,
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extra=LOG_EXTRA,
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)
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return chosen_raw
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LOGGER.info(
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"LLM 多模型投票未达门槛：votes=%s/%s threshold=%s，返回首个结果",
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top_count,
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len(responses),
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threshold,
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extra=LOG_EXTRA,
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)
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if failures:
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LOGGER.warning("LLM 调用失败列表：%s", failures, extra=LOG_EXTRA)
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return responses[0]["raw"]
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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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settings = get_config().llm
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primary = asdict(settings.primary)
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if primary.get("api_key"):
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primary["api_key"] = "***"
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ensemble = []
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for endpoint in settings.ensemble:
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record = asdict(endpoint)
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if record.get("api_key"):
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record["api_key"] = "***"
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ensemble.append(record)
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return {
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"strategy": settings.strategy,
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"majority_threshold": settings.majority_threshold,
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"primary": primary,
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"ensemble": ensemble,
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}
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@ -1,9 +1,12 @@
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"""Streamlit UI scaffold for the investment assistant."""
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from __future__ import annotations
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import json
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import sys
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from dataclasses import asdict
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from datetime import date, timedelta
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from pathlib import Path
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from typing import List
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ROOT = Path(__file__).resolve().parents[2]
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if str(ROOT) not in sys.path:
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@ -20,7 +23,7 @@ from app.ingest.checker import run_boot_check
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from app.ingest.tushare import FetchJob, run_ingestion
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from app.llm.client import llm_config_snapshot, run_llm
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from app.llm.explain import make_human_card
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from app.utils.config import get_config
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from app.utils.config import DEFAULT_LLM_MODELS, LLMEndpoint, get_config
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from app.utils.db import db_session
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from app.utils.logging import get_logger
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@ -194,25 +197,95 @@ def render_settings() -> None:
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st.divider()
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st.subheader("LLM 设置")
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llm_cfg = cfg.llm
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primary = llm_cfg.primary
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providers = ["ollama", "openai"]
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try:
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provider_index = providers.index((llm_cfg.provider or "ollama").lower())
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provider_index = providers.index((primary.provider or "ollama").lower())
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except ValueError:
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provider_index = 0
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selected_provider = st.selectbox("LLM Provider", providers, index=provider_index)
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llm_model = st.text_input("LLM 模型", value=llm_cfg.model)
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llm_base = st.text_input("LLM Base URL (可选)", value=llm_cfg.base_url or "")
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llm_api_key = st.text_input("LLM API Key (OpenAI 类需要)", value=llm_cfg.api_key or "", type="password")
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llm_temperature = st.slider("LLM 温度", min_value=0.0, max_value=2.0, value=float(llm_cfg.temperature), step=0.05)
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llm_timeout = st.number_input("请求超时时间 (秒)", min_value=5.0, max_value=120.0, value=float(llm_cfg.timeout), step=5.0)
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default_model_hint = DEFAULT_LLM_MODELS.get(selected_provider, DEFAULT_LLM_MODELS["ollama"])
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llm_model = st.text_input("LLM 模型", value=primary.model, help=f"默认推荐：{default_model_hint}")
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base_hints = {
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"ollama": "http://localhost:11434",
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"openai": "https://api.openai.com",
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"deepseek": "https://api.deepseek.com",
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"wenxin": "https://aip.baidubce.com",
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}
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default_base_hint = base_hints.get(selected_provider, "")
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llm_base = st.text_input("LLM Base URL (可选)", value=primary.base_url or "", help=f"默认推荐：{default_base_hint or '按供应商要求填写'}")
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llm_api_key = st.text_input("LLM API Key (OpenAI 类需要)", value=primary.api_key or "", type="password")
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llm_temperature = st.slider("LLM 温度", min_value=0.0, max_value=2.0, value=float(primary.temperature), step=0.05)
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llm_timeout = st.number_input("请求超时时间 (秒)", min_value=5.0, max_value=120.0, value=float(primary.timeout), step=5.0, format="%d")
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strategy_options = ["single", "majority"]
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try:
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strategy_index = strategy_options.index(llm_cfg.strategy)
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except ValueError:
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strategy_index = 0
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selected_strategy = st.selectbox("LLM 推理策略", strategy_options, index=strategy_index)
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majority_threshold = st.number_input(
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"多数投票门槛",
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min_value=1,
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max_value=10,
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value=int(llm_cfg.majority_threshold),
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step=1,
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format="%d",
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)
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ensemble_display = []
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for endpoint in llm_cfg.ensemble:
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data = asdict(endpoint)
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if data.get("api_key"):
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data["api_key"] = ""
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ensemble_display.append(data)
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ensemble_text = st.text_area(
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"LLM 集群配置 (JSON 数组)",
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value=json.dumps(ensemble_display or [], ensure_ascii=False, indent=2),
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height=220,
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)
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if st.button("保存 LLM 设置"):
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llm_cfg.provider = selected_provider
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llm_cfg.model = llm_model.strip() or llm_cfg.model
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llm_cfg.base_url = llm_base.strip() or None
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llm_cfg.api_key = llm_api_key.strip() or None
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llm_cfg.temperature = llm_temperature
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llm_cfg.timeout = llm_timeout
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original_provider = primary.provider
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original_model = primary.model
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primary.provider = selected_provider
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model_input = llm_model.strip()
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if not model_input:
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primary.model = DEFAULT_LLM_MODELS.get(selected_provider, DEFAULT_LLM_MODELS["ollama"])
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elif selected_provider != original_provider and model_input == original_model:
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primary.model = DEFAULT_LLM_MODELS.get(selected_provider, DEFAULT_LLM_MODELS["ollama"])
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else:
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primary.model = model_input
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primary.base_url = llm_base.strip() or None
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primary.temperature = llm_temperature
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primary.timeout = llm_timeout
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api_key_value = llm_api_key.strip()
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primary.api_key = api_key_value or None
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try:
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parsed = json.loads(ensemble_text or "[]")
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if not isinstance(parsed, list):
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raise ValueError("ensemble 配置必须是数组")
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except Exception as exc: # noqa: BLE001
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LOGGER.exception("解析 LLM 集群配置失败", extra=LOG_EXTRA)
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st.error(f"LLM 集群配置解析失败：{exc}")
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else:
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new_ensemble: List[LLMEndpoint] = []
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invalid = False
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for item in parsed:
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if not isinstance(item, dict):
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st.error("LLM 集群配置中的每个元素都必须是对象")
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invalid = True
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break
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fields = {key: item.get(key) for key in ("provider", "model", "base_url", "api_key", "temperature", "timeout")}
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endpoint = LLMEndpoint(**{k: v for k, v in fields.items() if v not in (None, "")})
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if not endpoint.provider:
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endpoint.provider = "ollama"
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new_ensemble.append(endpoint)
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if not invalid:
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llm_cfg.ensemble = new_ensemble
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llm_cfg.strategy = selected_strategy
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llm_cfg.majority_threshold = int(majority_threshold)
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LOGGER.info("LLM 配置已更新：%s", llm_config_snapshot(), extra=LOG_EXTRA)
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st.success("LLM 设置已保存，仅在当前会话生效。")
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st.json(llm_config_snapshot())
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@ -3,7 +3,7 @@ from __future__ import annotations
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Dict, Optional
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from typing import Dict, List, Optional
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def _default_root() -> Path:
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@ -44,17 +44,40 @@ class AgentWeights:
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"A_macro": self.macro,
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}
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@dataclass
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class LLMConfig:
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"""Configuration for LLM providers (Ollama / OpenAI-compatible)."""
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DEFAULT_LLM_MODELS: Dict[str, str] = {
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"ollama": "llama3",
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"openai": "gpt-4o-mini",
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"deepseek": "deepseek-chat",
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"wenxin": "ERNIE-Speed",
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}
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provider: str = "ollama" # Options: "ollama", "openai"
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model: str = "llama3"
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base_url: Optional[str] = None # Defaults resolved per provider
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@dataclass
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class LLMEndpoint:
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"""Single LLM endpoint configuration."""
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provider: str = "ollama"
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model: Optional[str] = None
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base_url: Optional[str] = None
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api_key: Optional[str] = None
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temperature: float = 0.2
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timeout: float = 30.0
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def __post_init__(self) -> None:
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self.provider = (self.provider or "ollama").lower()
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if not self.model:
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self.model = DEFAULT_LLM_MODELS.get(self.provider, DEFAULT_LLM_MODELS["ollama"])
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@dataclass
|
||||
class LLMConfig:
|
||||
"""LLM configuration allowing single or ensemble strategies."""
|
||||
|
||||
primary: LLMEndpoint = field(default_factory=LLMEndpoint)
|
||||
ensemble: List[LLMEndpoint] = field(default_factory=list)
|
||||
strategy: str = "single" # Options: single, majority
|
||||
majority_threshold: int = 3
|
||||
|
||||
|
||||
@dataclass
|
||||
class AppConfig:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user