update
This commit is contained in:
sam
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.gitignore
vendored
2
.gitignore
vendored
@ -19,6 +19,8 @@ env/
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app/data/*.db*
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app/data/backups/
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app/data/logs/
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app/data/*.json
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.json
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*.log
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# Streamlit temporary files
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@ -58,10 +58,13 @@ export TUSHARE_TOKEN="<your-token>"
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### LLM 配置与测试
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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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- 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。
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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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- 若使用环境变量自动注入配置，可设置：
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- `TUSHARE_TOKEN`
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- `LLM_API_KEY`
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## 快速开始
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@ -8,7 +8,7 @@ from typing import Dict, Iterable, List, Optional
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import requests
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from app.utils.config import DEFAULT_LLM_MODELS, LLMEndpoint, get_config
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from app.utils.config import DEFAULT_LLM_BASE_URLS, 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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@ -19,13 +19,8 @@ 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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provider = (provider or "openai").lower()
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return DEFAULT_LLM_BASE_URLS.get(provider, DEFAULT_LLM_BASE_URLS["openai"])
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def _default_model(provider: str) -> str:
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@ -23,7 +23,14 @@ 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 DEFAULT_LLM_MODELS, LLMEndpoint, get_config
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from app.utils.config import (
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DEFAULT_LLM_BASE_URLS,
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DEFAULT_LLM_MODEL_OPTIONS,
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DEFAULT_LLM_MODELS,
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LLMEndpoint,
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get_config,
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save_config,
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)
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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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@ -190,6 +197,7 @@ def render_settings() -> None:
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LOGGER.info("保存设置按钮被点击", extra=LOG_EXTRA)
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cfg.tushare_token = token.strip() or None
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LOGGER.info("TuShare Token 更新，是否为空=%s", cfg.tushare_token is None, extra=LOG_EXTRA)
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save_config()
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st.success("设置已保存，仅在当前会话生效。")
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st.write("新闻源开关与数据库备份将在此配置。")
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@ -198,25 +206,76 @@ def render_settings() -> None:
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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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providers = sorted(DEFAULT_LLM_MODELS.keys())
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try:
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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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provider_info = DEFAULT_LLM_MODEL_OPTIONS.get(selected_provider, {})
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model_options = provider_info.get("models", [])
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custom_model_label = "自定义模型"
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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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if model_options:
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options_with_custom = model_options + [custom_model_label]
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if primary.model in model_options:
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model_index = options_with_custom.index(primary.model)
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else:
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model_index = len(options_with_custom) - 1
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selected_model_option = st.selectbox(
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"LLM 模型",
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options_with_custom,
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index=model_index,
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help=f"可选模型：{', '.join(model_options)}",
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)
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if selected_model_option == custom_model_label:
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custom_model_value = st.text_input(
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"自定义模型名称",
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value=primary.model if primary.model not in model_options else "",
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)
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else:
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custom_model_value = selected_model_option
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else:
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custom_model_value = st.text_input(
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"LLM 模型",
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value=primary.model or default_model_hint,
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help="未预设该 Provider 的模型列表，请手动填写",
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)
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selected_model_option = custom_model_label
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default_base_hint = DEFAULT_LLM_BASE_URLS.get(selected_provider, "")
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provider_default_temp = float(provider_info.get("temperature", 0.2))
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provider_default_timeout = int(provider_info.get("timeout", 30.0))
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if primary.provider == selected_provider:
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base_value = primary.base_url or default_base_hint or ""
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temp_value = float(primary.temperature)
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timeout_value = int(primary.timeout)
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else:
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base_value = default_base_hint or ""
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temp_value = provider_default_temp
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timeout_value = provider_default_timeout
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llm_base = st.text_input(
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"LLM Base URL (可选)",
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value=base_value,
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help=f"默认推荐：{default_base_hint or '按供应商要求填写'}",
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)
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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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llm_temperature = st.slider(
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"LLM 温度",
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min_value=0.0,
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max_value=2.0,
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value=temp_value,
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step=0.05,
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)
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llm_timeout = st.number_input(
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"请求超时时间 (秒)",
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min_value=5,
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max_value=120,
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value=timeout_value,
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step=5,
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)
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strategy_options = ["single", "majority"]
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try:
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@ -249,13 +308,18 @@ def render_settings() -> None:
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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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if model_options:
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if selected_model_option == custom_model_label:
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model_input = custom_model_value.strip()
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primary.model = model_input or DEFAULT_LLM_MODELS.get(
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selected_provider, DEFAULT_LLM_MODELS["ollama"]
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)
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else:
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primary.model = selected_model_option
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else:
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primary.model = model_input
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primary.model = custom_model_value.strip() or DEFAULT_LLM_MODELS.get(
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selected_provider, DEFAULT_LLM_MODELS["ollama"]
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)
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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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@ -286,6 +350,7 @@ def render_settings() -> None:
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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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save_config()
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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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@ -342,6 +407,7 @@ def render_tests() -> None:
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if force_refresh != cfg.force_refresh:
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cfg.force_refresh = force_refresh
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LOGGER.info("更新 force_refresh=%s", force_refresh, extra=LOG_EXTRA)
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save_config()
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if st.button("执行开机检查"):
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LOGGER.info("点击执行开机检查按钮", extra=LOG_EXTRA)
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@ -2,6 +2,8 @@
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from __future__ import annotations
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from dataclasses import dataclass, field
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import json
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import os
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from pathlib import Path
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from typing import Dict, List, Optional
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@ -17,12 +19,14 @@ class DataPaths:
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root: Path = field(default_factory=_default_root)
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database: Path = field(init=False)
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backups: Path = field(init=False)
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config_file: Path = field(init=False)
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def __post_init__(self) -> None:
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self.root.mkdir(parents=True, exist_ok=True)
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self.database = self.root / "llm_quant.db"
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self.backups = self.root / "backups"
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self.backups.mkdir(parents=True, exist_ok=True)
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self.config_file = self.root / "config.json"
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@dataclass
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@ -44,11 +48,51 @@ class AgentWeights:
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"A_macro": self.macro,
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}
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DEFAULT_LLM_MODEL_OPTIONS: Dict[str, Dict[str, object]] = {
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"ollama": {
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"models": ["llama3", "phi3", "qwen2"],
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"base_url": "http://localhost:11434",
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"temperature": 0.2,
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"timeout": 30.0,
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},
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"openai": {
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"models": ["gpt-4o-mini", "gpt-4.1-mini", "gpt-3.5-turbo"],
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"base_url": "https://api.openai.com",
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"temperature": 0.2,
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"timeout": 30.0,
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},
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"deepseek": {
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"models": ["deepseek-chat", "deepseek-coder"],
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"base_url": "https://api.deepseek.com",
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"temperature": 0.2,
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"timeout": 45.0,
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},
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"wenxin": {
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"models": ["ERNIE-Speed", "ERNIE-Bot"],
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"base_url": "https://aip.baidubce.com",
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"temperature": 0.2,
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"timeout": 60.0,
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},
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}
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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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provider: info["models"][0]
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for provider, info in DEFAULT_LLM_MODEL_OPTIONS.items()
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}
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DEFAULT_LLM_BASE_URLS: Dict[str, str] = {
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provider: info["base_url"]
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for provider, info in DEFAULT_LLM_MODEL_OPTIONS.items()
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}
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DEFAULT_LLM_TEMPERATURES: Dict[str, float] = {
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provider: float(info.get("temperature", 0.2))
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for provider, info in DEFAULT_LLM_MODEL_OPTIONS.items()
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}
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DEFAULT_LLM_TIMEOUTS: Dict[str, float] = {
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provider: float(info.get("timeout", 30.0))
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for provider, info in DEFAULT_LLM_MODEL_OPTIONS.items()
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}
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@ -67,6 +111,12 @@ class LLMEndpoint:
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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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if not self.base_url:
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self.base_url = DEFAULT_LLM_BASE_URLS.get(self.provider)
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if self.temperature == 0.2 or self.temperature is None:
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self.temperature = DEFAULT_LLM_TEMPERATURES.get(self.provider, 0.2)
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if self.timeout == 30.0 or self.timeout is None:
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self.timeout = DEFAULT_LLM_TIMEOUTS.get(self.provider, 30.0)
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@dataclass
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@ -95,6 +145,105 @@ class AppConfig:
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CONFIG = AppConfig()
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def _endpoint_to_dict(endpoint: LLMEndpoint) -> Dict[str, object]:
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return {
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"provider": endpoint.provider,
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"model": endpoint.model,
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"base_url": endpoint.base_url,
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"api_key": endpoint.api_key,
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"temperature": endpoint.temperature,
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"timeout": endpoint.timeout,
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}
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def _dict_to_endpoint(data: Dict[str, object]) -> LLMEndpoint:
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payload = {
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key: data.get(key)
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for key in ("provider", "model", "base_url", "api_key", "temperature", "timeout")
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if data.get(key) is not None
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}
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return LLMEndpoint(**payload)
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def _load_from_file(cfg: AppConfig) -> None:
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path = cfg.data_paths.config_file
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if not path.exists():
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return
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try:
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with path.open("r", encoding="utf-8") as fh:
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payload = json.load(fh)
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except (json.JSONDecodeError, OSError):
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return
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if isinstance(payload, dict):
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if "tushare_token" in payload:
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cfg.tushare_token = payload.get("tushare_token") or None
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if "force_refresh" in payload:
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cfg.force_refresh = bool(payload.get("force_refresh"))
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if "decision_method" in payload:
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cfg.decision_method = str(payload.get("decision_method") or cfg.decision_method)
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llm_payload = payload.get("llm")
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if isinstance(llm_payload, dict):
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primary_data = llm_payload.get("primary")
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if isinstance(primary_data, dict):
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cfg.llm.primary = _dict_to_endpoint(primary_data)
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ensemble_data = llm_payload.get("ensemble")
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if isinstance(ensemble_data, list):
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cfg.llm.ensemble = [
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_dict_to_endpoint(item)
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for item in ensemble_data
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if isinstance(item, dict)
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]
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strategy = llm_payload.get("strategy")
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if strategy in {"single", "majority"}:
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cfg.llm.strategy = strategy
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majority = llm_payload.get("majority_threshold")
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if isinstance(majority, int) and majority > 0:
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cfg.llm.majority_threshold = majority
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def save_config(cfg: AppConfig | None = None) -> None:
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cfg = cfg or CONFIG
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path = cfg.data_paths.config_file
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payload = {
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"tushare_token": cfg.tushare_token,
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"force_refresh": cfg.force_refresh,
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"decision_method": cfg.decision_method,
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"llm": {
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"strategy": cfg.llm.strategy,
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"majority_threshold": cfg.llm.majority_threshold,
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"primary": _endpoint_to_dict(cfg.llm.primary),
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"ensemble": [_endpoint_to_dict(ep) for ep in cfg.llm.ensemble],
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},
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}
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try:
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path.parent.mkdir(parents=True, exist_ok=True)
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with path.open("w", encoding="utf-8") as fh:
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json.dump(payload, fh, ensure_ascii=False, indent=2)
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except OSError:
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pass
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def _load_env_defaults(cfg: AppConfig) -> None:
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"""Populate sensitive fields from environment variables if present."""
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token = os.getenv("TUSHARE_TOKEN")
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if token:
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cfg.tushare_token = token.strip()
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|
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api_key = os.getenv("LLM_API_KEY")
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if api_key:
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cfg.llm.primary.api_key = api_key.strip()
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_load_from_file(CONFIG)
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_load_env_defaults(CONFIG)
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def get_config() -> AppConfig:
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"""Return a mutable global configuration instance."""
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Reference in New Issue
Block a user