update

.gitignore

@@ -19,6 +19,8 @@ env/
 app/data/*.db*
 app/data/backups/
 app/data/logs/
+app/data/*.json
+.json
 *.log
 
 # Streamlit temporary files

README.md

@@ -58,10 +58,13 @@ export TUSHARE_TOKEN="<your-token>"
 
 ### LLM 配置与测试
 
-- 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`。
+- 支持本地 Ollama 与多家 OpenAI 兼容云端供应商（如 DeepSeek、文心一言、OpenAI 等），可在 “数据与设置” 页签切换 Provider 并自动加载该 Provider 的候选模型、推荐 Base URL、默认温度与超时时间，亦可切换为自定义值。所有修改会持久化到 `app/data/config.json`，下次启动自动加载。
 - 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。
 - 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。
 - 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。
+- 若使用环境变量自动注入配置，可设置：
+  - `TUSHARE_TOKEN`
+  - `LLM_API_KEY`
 
 ## 快速开始
 

app/llm/client.py

@@ -8,7 +8,7 @@ from typing import Dict, Iterable, List, Optional
 
 import requests
 
-from app.utils.config import DEFAULT_LLM_MODELS, LLMEndpoint, get_config
+from app.utils.config import DEFAULT_LLM_BASE_URLS, DEFAULT_LLM_MODELS, LLMEndpoint, get_config
 from app.utils.logging import get_logger
 
 LOGGER = get_logger(__name__)
@@ -19,13 +19,8 @@ class LLMError(RuntimeError):
 
 
 def _default_base_url(provider: str) -> str:
-    if provider == "ollama":
+    provider = (provider or "openai").lower()
-        return "http://localhost:11434"
+    return DEFAULT_LLM_BASE_URLS.get(provider, DEFAULT_LLM_BASE_URLS["openai"])
-    if provider == "deepseek":
-        return "https://api.deepseek.com"
-    if provider == "wenxin":
-        return "https://aip.baidubce.com"
-    return "https://api.openai.com"
 
 
 def _default_model(provider: str) -> str:

app/ui/streamlit_app.py

@@ -23,7 +23,14 @@ from app.ingest.checker import run_boot_check
 from app.ingest.tushare import FetchJob, run_ingestion
 from app.llm.client import llm_config_snapshot, run_llm
 from app.llm.explain import make_human_card
-from app.utils.config import DEFAULT_LLM_MODELS, LLMEndpoint, get_config
+from app.utils.config import (
+    DEFAULT_LLM_BASE_URLS,
+    DEFAULT_LLM_MODEL_OPTIONS,
+    DEFAULT_LLM_MODELS,
+    LLMEndpoint,
+    get_config,
+    save_config,
+)
 from app.utils.db import db_session
 from app.utils.logging import get_logger
 
@@ -190,6 +197,7 @@ def render_settings() -> None:
         LOGGER.info("保存设置按钮被点击", extra=LOG_EXTRA)
         cfg.tushare_token = token.strip() or None
         LOGGER.info("TuShare Token 更新，是否为空=%s", cfg.tushare_token is None, extra=LOG_EXTRA)
+        save_config()
         st.success("设置已保存，仅在当前会话生效。")
 
     st.write("新闻源开关与数据库备份将在此配置。")
@@ -198,25 +206,76 @@ def render_settings() -> None:
     st.subheader("LLM 设置")
     llm_cfg = cfg.llm
     primary = llm_cfg.primary
-    providers = ["ollama", "openai"]
+    providers = sorted(DEFAULT_LLM_MODELS.keys())
     try:
         provider_index = providers.index((primary.provider or "ollama").lower())
     except ValueError:
         provider_index = 0
     selected_provider = st.selectbox("LLM Provider", providers, index=provider_index)
+    provider_info = DEFAULT_LLM_MODEL_OPTIONS.get(selected_provider, {})
+    model_options = provider_info.get("models", [])
+    custom_model_label = "自定义模型"
     default_model_hint = DEFAULT_LLM_MODELS.get(selected_provider, DEFAULT_LLM_MODELS["ollama"])
-    llm_model = st.text_input("LLM 模型", value=primary.model, help=f"默认推荐：{default_model_hint}")
+
-    base_hints = {
+    if model_options:
-        "ollama": "http://localhost:11434",
+        options_with_custom = model_options + [custom_model_label]
-        "openai": "https://api.openai.com",
+        if primary.model in model_options:
-        "deepseek": "https://api.deepseek.com",
+            model_index = options_with_custom.index(primary.model)
-        "wenxin": "https://aip.baidubce.com",
+        else:
-    }
+            model_index = len(options_with_custom) - 1
-    default_base_hint = base_hints.get(selected_provider, "")
+        selected_model_option = st.selectbox(
-    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'}")
+            "LLM 模型",
+            options_with_custom,
+            index=model_index,
+            help=f"可选模型：{', '.join(model_options)}",
+        )
+        if selected_model_option == custom_model_label:
+            custom_model_value = st.text_input(
+                "自定义模型名称",
+                value=primary.model if primary.model not in model_options else "",
+            )
+        else:
+            custom_model_value = selected_model_option
+    else:
+        custom_model_value = st.text_input(
+            "LLM 模型",
+            value=primary.model or default_model_hint,
+            help="未预设该 Provider 的模型列表，请手动填写",
+        )
+        selected_model_option = custom_model_label
+    default_base_hint = DEFAULT_LLM_BASE_URLS.get(selected_provider, "")
+    provider_default_temp = float(provider_info.get("temperature", 0.2))
+    provider_default_timeout = int(provider_info.get("timeout", 30.0))
+
+    if primary.provider == selected_provider:
+        base_value = primary.base_url or default_base_hint or ""
+        temp_value = float(primary.temperature)
+        timeout_value = int(primary.timeout)
+    else:
+        base_value = default_base_hint or ""
+        temp_value = provider_default_temp
+        timeout_value = provider_default_timeout
+
+    llm_base = st.text_input(
+        "LLM Base URL (可选)",
+        value=base_value,
+        help=f"默认推荐：{default_base_hint or '按供应商要求填写'}",
+    )
     llm_api_key = st.text_input("LLM API Key (OpenAI 类需要)", value=primary.api_key or "", type="password")
-    llm_temperature = st.slider("LLM 温度", min_value=0.0, max_value=2.0, value=float(primary.temperature), step=0.05)
+    llm_temperature = st.slider(
-    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")
+        "LLM 温度",
+        min_value=0.0,
+        max_value=2.0,
+        value=temp_value,
+        step=0.05,
+    )
+    llm_timeout = st.number_input(
+        "请求超时时间 (秒)",
+        min_value=5,
+        max_value=120,
+        value=timeout_value,
+        step=5,
+    )
 
     strategy_options = ["single", "majority"]
     try:
@@ -249,13 +308,18 @@ def render_settings() -> None:
         original_provider = primary.provider
         original_model = primary.model
         primary.provider = selected_provider
-        model_input = llm_model.strip()
+        if model_options:
-        if not model_input:
+            if selected_model_option == custom_model_label:
-            primary.model = DEFAULT_LLM_MODELS.get(selected_provider, DEFAULT_LLM_MODELS["ollama"])
+                model_input = custom_model_value.strip()
-        elif selected_provider != original_provider and model_input == original_model:
+                primary.model = model_input or DEFAULT_LLM_MODELS.get(
-            primary.model = DEFAULT_LLM_MODELS.get(selected_provider, DEFAULT_LLM_MODELS["ollama"])
+                    selected_provider, DEFAULT_LLM_MODELS["ollama"]
+                )
             else:
-            primary.model = model_input
+                primary.model = selected_model_option
+        else:
+            primary.model = custom_model_value.strip() or DEFAULT_LLM_MODELS.get(
+                selected_provider, DEFAULT_LLM_MODELS["ollama"]
+            )
         primary.base_url = llm_base.strip() or None
         primary.temperature = llm_temperature
         primary.timeout = llm_timeout
@@ -286,6 +350,7 @@ def render_settings() -> None:
                 llm_cfg.ensemble = new_ensemble
                 llm_cfg.strategy = selected_strategy
                 llm_cfg.majority_threshold = int(majority_threshold)
+                save_config()
                 LOGGER.info("LLM 配置已更新：%s", llm_config_snapshot(), extra=LOG_EXTRA)
                 st.success("LLM 设置已保存，仅在当前会话生效。")
                 st.json(llm_config_snapshot())
@@ -342,6 +407,7 @@ def render_tests() -> None:
     if force_refresh != cfg.force_refresh:
         cfg.force_refresh = force_refresh
         LOGGER.info("更新 force_refresh=%s", force_refresh, extra=LOG_EXTRA)
+        save_config()
 
     if st.button("执行开机检查"):
         LOGGER.info("点击执行开机检查按钮", extra=LOG_EXTRA)

app/utils/config.py

@@ -2,6 +2,8 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
+import json
+import os
 from pathlib import Path
 from typing import Dict, List, Optional
 
@@ -17,12 +19,14 @@ class DataPaths:
     root: Path = field(default_factory=_default_root)
     database: Path = field(init=False)
     backups: Path = field(init=False)
+    config_file: Path = field(init=False)
 
     def __post_init__(self) -> None:
         self.root.mkdir(parents=True, exist_ok=True)
         self.database = self.root / "llm_quant.db"
         self.backups = self.root / "backups"
         self.backups.mkdir(parents=True, exist_ok=True)
+        self.config_file = self.root / "config.json"
 
 
 @dataclass
@@ -44,11 +48,51 @@ class AgentWeights:
             "A_macro": self.macro,
         }
 
+DEFAULT_LLM_MODEL_OPTIONS: Dict[str, Dict[str, object]] = {
+    "ollama": {
+        "models": ["llama3", "phi3", "qwen2"],
+        "base_url": "http://localhost:11434",
+        "temperature": 0.2,
+        "timeout": 30.0,
+    },
+    "openai": {
+        "models": ["gpt-4o-mini", "gpt-4.1-mini", "gpt-3.5-turbo"],
+        "base_url": "https://api.openai.com",
+        "temperature": 0.2,
+        "timeout": 30.0,
+    },
+    "deepseek": {
+        "models": ["deepseek-chat", "deepseek-coder"],
+        "base_url": "https://api.deepseek.com",
+        "temperature": 0.2,
+        "timeout": 45.0,
+    },
+    "wenxin": {
+        "models": ["ERNIE-Speed", "ERNIE-Bot"],
+        "base_url": "https://aip.baidubce.com",
+        "temperature": 0.2,
+        "timeout": 60.0,
+    },
+}
+
 DEFAULT_LLM_MODELS: Dict[str, str] = {
-    "ollama": "llama3",
+    provider: info["models"][0]
-    "openai": "gpt-4o-mini",
+    for provider, info in DEFAULT_LLM_MODEL_OPTIONS.items()
-    "deepseek": "deepseek-chat",
+}
-    "wenxin": "ERNIE-Speed",
+
+DEFAULT_LLM_BASE_URLS: Dict[str, str] = {
+    provider: info["base_url"]
+    for provider, info in DEFAULT_LLM_MODEL_OPTIONS.items()
+}
+
+DEFAULT_LLM_TEMPERATURES: Dict[str, float] = {
+    provider: float(info.get("temperature", 0.2))
+    for provider, info in DEFAULT_LLM_MODEL_OPTIONS.items()
+}
+
+DEFAULT_LLM_TIMEOUTS: Dict[str, float] = {
+    provider: float(info.get("timeout", 30.0))
+    for provider, info in DEFAULT_LLM_MODEL_OPTIONS.items()
 }
 
 
@@ -67,6 +111,12 @@ class LLMEndpoint:
         self.provider = (self.provider or "ollama").lower()
         if not self.model:
             self.model = DEFAULT_LLM_MODELS.get(self.provider, DEFAULT_LLM_MODELS["ollama"])
+        if not self.base_url:
+            self.base_url = DEFAULT_LLM_BASE_URLS.get(self.provider)
+        if self.temperature == 0.2 or self.temperature is None:
+            self.temperature = DEFAULT_LLM_TEMPERATURES.get(self.provider, 0.2)
+        if self.timeout == 30.0 or self.timeout is None:
+            self.timeout = DEFAULT_LLM_TIMEOUTS.get(self.provider, 30.0)
 
 
 @dataclass
@@ -95,6 +145,105 @@ class AppConfig:
 CONFIG = AppConfig()
 
 
+def _endpoint_to_dict(endpoint: LLMEndpoint) -> Dict[str, object]:
+    return {
+        "provider": endpoint.provider,
+        "model": endpoint.model,
+        "base_url": endpoint.base_url,
+        "api_key": endpoint.api_key,
+        "temperature": endpoint.temperature,
+        "timeout": endpoint.timeout,
+    }
+
+
+def _dict_to_endpoint(data: Dict[str, object]) -> LLMEndpoint:
+    payload = {
+        key: data.get(key)
+        for key in ("provider", "model", "base_url", "api_key", "temperature", "timeout")
+        if data.get(key) is not None
+    }
+    return LLMEndpoint(**payload)
+
+
+def _load_from_file(cfg: AppConfig) -> None:
+    path = cfg.data_paths.config_file
+    if not path.exists():
+        return
+    try:
+        with path.open("r", encoding="utf-8") as fh:
+            payload = json.load(fh)
+    except (json.JSONDecodeError, OSError):
+        return
+
+    if isinstance(payload, dict):
+        if "tushare_token" in payload:
+            cfg.tushare_token = payload.get("tushare_token") or None
+        if "force_refresh" in payload:
+            cfg.force_refresh = bool(payload.get("force_refresh"))
+        if "decision_method" in payload:
+            cfg.decision_method = str(payload.get("decision_method") or cfg.decision_method)
+
+        llm_payload = payload.get("llm")
+        if isinstance(llm_payload, dict):
+            primary_data = llm_payload.get("primary")
+            if isinstance(primary_data, dict):
+                cfg.llm.primary = _dict_to_endpoint(primary_data)
+
+            ensemble_data = llm_payload.get("ensemble")
+            if isinstance(ensemble_data, list):
+                cfg.llm.ensemble = [
+                    _dict_to_endpoint(item)
+                    for item in ensemble_data
+                    if isinstance(item, dict)
+                ]
+
+            strategy = llm_payload.get("strategy")
+            if strategy in {"single", "majority"}:
+                cfg.llm.strategy = strategy
+
+            majority = llm_payload.get("majority_threshold")
+            if isinstance(majority, int) and majority > 0:
+                cfg.llm.majority_threshold = majority
+
+
+def save_config(cfg: AppConfig | None = None) -> None:
+    cfg = cfg or CONFIG
+    path = cfg.data_paths.config_file
+    payload = {
+        "tushare_token": cfg.tushare_token,
+        "force_refresh": cfg.force_refresh,
+        "decision_method": cfg.decision_method,
+        "llm": {
+            "strategy": cfg.llm.strategy,
+            "majority_threshold": cfg.llm.majority_threshold,
+            "primary": _endpoint_to_dict(cfg.llm.primary),
+            "ensemble": [_endpoint_to_dict(ep) for ep in cfg.llm.ensemble],
+        },
+    }
+    try:
+        path.parent.mkdir(parents=True, exist_ok=True)
+        with path.open("w", encoding="utf-8") as fh:
+            json.dump(payload, fh, ensure_ascii=False, indent=2)
+    except OSError:
+        pass
+
+
+def _load_env_defaults(cfg: AppConfig) -> None:
+    """Populate sensitive fields from environment variables if present."""
+
+    token = os.getenv("TUSHARE_TOKEN")
+    if token:
+        cfg.tushare_token = token.strip()
+
+    api_key = os.getenv("LLM_API_KEY")
+    if api_key:
+        cfg.llm.primary.api_key = api_key.strip()
+
+
+_load_from_file(CONFIG)
+_load_env_defaults(CONFIG)
+
+
 def get_config() -> AppConfig:
     """Return a mutable global configuration instance."""