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sam 2025-09-27 21:03:04 +08:00
parent 7c51831615
commit 5b4bd51199
5 changed files with 247 additions and 32 deletions
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2
.gitignore vendored
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@ -19,6 +19,8 @@ env/
app/data/*.db*
app/data/backups/
app/data/logs/
app/data/*.json
.json
*.log
# Streamlit temporary files

5
README.md
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@ -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

11
app/llm/client.py
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@ -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":
return "http://localhost:11434"
if provider == "deepseek":
return "https://api.deepseek.com"
if provider == "wenxin":
return "https://aip.baidubce.com"
return "https://api.openai.com"
provider = (provider or "openai").lower()
return DEFAULT_LLM_BASE_URLS.get(provider, DEFAULT_LLM_BASE_URLS["openai"])
def _default_model(provider: str) -> str:

104
app/ui/streamlit_app.py
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@ -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 = {
"ollama": "http://localhost:11434",
"openai": "https://api.openai.com",
"deepseek": "https://api.deepseek.com",
"wenxin": "https://aip.baidubce.com",
}
default_base_hint = base_hints.get(selected_provider, "")
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'}")
if model_options:
options_with_custom = model_options + [custom_model_label]
if primary.model in model_options:
model_index = options_with_custom.index(primary.model)
else:
model_index = len(options_with_custom) - 1
selected_model_option = st.selectbox(
"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_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_temperature = st.slider(
"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 not model_input:
primary.model = DEFAULT_LLM_MODELS.get(selected_provider, DEFAULT_LLM_MODELS["ollama"])
elif selected_provider != original_provider and model_input == original_model:
primary.model = DEFAULT_LLM_MODELS.get(selected_provider, DEFAULT_LLM_MODELS["ollama"])
if model_options:
if selected_model_option == custom_model_label:
model_input = custom_model_value.strip()
primary.model = model_input or DEFAULT_LLM_MODELS.get(
selected_provider, DEFAULT_LLM_MODELS["ollama"]
)
else:
primary.model = selected_model_option
else:
primary.model = model_input
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)

157
app/utils/config.py
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@ -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",
"openai": "gpt-4o-mini",
"deepseek": "deepseek-chat",
"wenxin": "ERNIE-Speed",
provider: info["models"][0]
for provider, info in DEFAULT_LLM_MODEL_OPTIONS.items()
}
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."""