From 6384d7426c2527dbb919fc5f93784cdfbaba3f74 Mon Sep 17 00:00:00 2001 From: sam Date: Sat, 27 Sep 2025 20:03:04 +0800 Subject: [PATCH] update --- LICENSE | 27 +++++++++ README.md | 11 ++++ app/llm/client.py | 122 ++++++++++++++++++++++++++++++++++++++++ app/ui/streamlit_app.py | 48 ++++++++++++++++ app/utils/config.py | 12 ++++ requirements.txt | 1 + 6 files changed, 221 insertions(+) create mode 100644 LICENSE create mode 100644 app/llm/client.py diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..a89c06d --- /dev/null +++ b/LICENSE @@ -0,0 +1,27 @@ +LLM Quant Framework License v1.0 + +Copyright (c) 2025, Qiang + +Permission is hereby granted, free of charge, to any individual obtaining a copy +of this software and associated documentation files (the "Software"), to use, +copy, modify, and distribute the Software for personal, non-commercial purposes, +subject to the following conditions: + +1. Attribution. Any redistribution or derivative work must include a prominent + notice acknowledging the original source: + "Based on the LLM Quant Framework by Qiang" and provide a link to the + original repository if distributed electronically. + +2. Commercial Use. Any use of the Software, in whole or in part, for commercial + purposes requires prior written permission and a separate licensing agreement + with the copyright holder. Commercial purposes include, but are not limited + to, selling, licensing, offering as a hosted service, or integrating into a + commercial product. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md index e1a4244..0c1c0a6 100644 --- a/README.md +++ b/README.md @@ -54,6 +54,13 @@ export TUSHARE_TOKEN="" - Streamlit：交互式前端 - Plotly：行情与指标可视化 - TuShare：行情与基础面数据源 +- Requests：统一访问 Ollama / OpenAI 兼容 API + +### LLM 配置与测试 + +- 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。 +- 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。 ## 快速开始 @@ -77,3 +84,7 @@ Streamlit `自检测试` 页签提供： 2. 将代理效用写入 SQLite 的 `agent_utils` 和 `alloc_log` 表，驱动 UI 决策解释。 3. 使用轻量情感分析与热度计算填充 `news`、`heat_daily` 与热点指数。 4. 接入本地小模型或 API 完成人类可读的策略建议卡片，形成端到端体验。 + +## License + +本项目采用定制的 “LLM Quant Framework License v1.0”。个人使用、修改与分发需保留出处，任何商业用途须事先与版权方协商并签署付费协议。详情参见仓库根目录的 `LICENSE` 文件。 diff --git a/app/llm/client.py b/app/llm/client.py new file mode 100644 index 0000000..0114fdb --- /dev/null +++ b/app/llm/client.py @@ -0,0 +1,122 @@ +"""Unified LLM client supporting Ollama and OpenAI compatible APIs.""" +from __future__ import annotations + +import json +from dataclasses import asdict +from typing import Dict, Iterable, List, Optional + +import requests + +from app.utils.config import get_config +from app.utils.logging import get_logger + +LOGGER = get_logger(__name__) +LOG_EXTRA = {"stage": "llm"} + + +class LLMError(RuntimeError): + """Raised when LLM provider returns an error response.""" + + +def _default_base_url(provider: str) -> str: + if provider == "ollama": + return "http://localhost:11434" + return "https://api.openai.com" + + +def _build_messages(prompt: str, system: Optional[str] = None) -> List[Dict[str, str]]: + messages: List[Dict[str, str]] = [] + if system: + messages.append({"role": "system", "content": system}) + messages.append({"role": "user", "content": prompt}) + return messages + + +def _request_ollama(model: str, prompt: str, *, base_url: str, temperature: float, timeout: float, system: Optional[str]) -> str: + url = f"{base_url.rstrip('/')}/api/chat" + payload = { + "model": model, + "messages": _build_messages(prompt, system), + "stream": False, + "options": {"temperature": temperature}, + } + LOGGER.debug("调用 Ollama: %s %s", model, url, extra=LOG_EXTRA) + response = requests.post(url, json=payload, timeout=timeout) + if response.status_code != 200: + raise LLMError(f"Ollama 调用失败: {response.status_code} {response.text}") + data = response.json() + message = data.get("message", {}) + content = message.get("content", "") + if isinstance(content, list): + return "".join(chunk.get("text", "") or chunk.get("content", "") for chunk in content) + return str(content) + + +def _request_openai(model: str, prompt: str, *, base_url: str, api_key: str, temperature: float, timeout: float, system: Optional[str]) -> str: + url = f"{base_url.rstrip('/')}/v1/chat/completions" + headers = { + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json", + } + payload = { + "model": model, + "messages": _build_messages(prompt, system), + "temperature": temperature, + } + LOGGER.debug("调用 OpenAI 兼容接口: %s %s", model, url, extra=LOG_EXTRA) + response = requests.post(url, headers=headers, json=payload, timeout=timeout) + if response.status_code != 200: + raise LLMError(f"OpenAI API 调用失败: {response.status_code} {response.text}") + data = response.json() + try: + return data["choices"][0]["message"]["content"].strip() + except (KeyError, IndexError) as exc: + raise LLMError(f"OpenAI 响应解析失败: {json.dumps(data, ensure_ascii=False)}") from exc + + +def run_llm(prompt: str, *, system: Optional[str] = None) -> str: + """Execute the configured LLM provider with the given prompt.""" + + cfg = get_config().llm + provider = (cfg.provider or "ollama").lower() + base_url = cfg.base_url or _default_base_url(provider) + model = cfg.model + temperature = max(0.0, min(cfg.temperature, 2.0)) + timeout = max(5.0, cfg.timeout or 30.0) + + LOGGER.info( + "触发 LLM 请求：provider=%s model=%s base=%s", provider, model, base_url, extra=LOG_EXTRA + ) + + if provider == "openai": + if not cfg.api_key: + raise LLMError("缺少 OpenAI 兼容 API Key") + return _request_openai( + model, + prompt, + base_url=base_url, + api_key=cfg.api_key, + temperature=temperature, + timeout=timeout, + system=system, + ) + if provider == "ollama": + return _request_ollama( + model, + prompt, + base_url=base_url, + temperature=temperature, + timeout=timeout, + system=system, + ) + raise LLMError(f"不支持的 LLM provider: {cfg.provider}") + + +def llm_config_snapshot() -> Dict[str, object]: + """Return a sanitized snapshot of current LLM configuration for debugging.""" + + cfg = get_config().llm + data = asdict(cfg) + if data.get("api_key"): + data["api_key"] = "***" + return data diff --git a/app/ui/streamlit_app.py b/app/ui/streamlit_app.py index 50c24c9..ed2451e 100644 --- a/app/ui/streamlit_app.py +++ b/app/ui/streamlit_app.py @@ -18,6 +18,7 @@ from app.backtest.engine import BtConfig, run_backtest from app.data.schema import initialize_database 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 get_config from app.utils.db import db_session @@ -190,6 +191,32 @@ def render_settings() -> None: st.write("新闻源开关与数据库备份将在此配置。") + st.divider() + st.subheader("LLM 设置") + llm_cfg = cfg.llm + providers = ["ollama", "openai"] + try: + provider_index = providers.index((llm_cfg.provider or "ollama").lower()) + except ValueError: + provider_index = 0 + selected_provider = st.selectbox("LLM Provider", providers, index=provider_index) + llm_model = st.text_input("LLM 模型", value=llm_cfg.model) + llm_base = st.text_input("LLM Base URL (可选)", value=llm_cfg.base_url or "") + llm_api_key = st.text_input("LLM API Key (OpenAI 类需要)", value=llm_cfg.api_key or "", type="password") + llm_temperature = st.slider("LLM 温度", min_value=0.0, max_value=2.0, value=float(llm_cfg.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(llm_cfg.timeout), step=5.0) + + if st.button("保存 LLM 设置"): + llm_cfg.provider = selected_provider + llm_cfg.model = llm_model.strip() or llm_cfg.model + llm_cfg.base_url = llm_base.strip() or None + llm_cfg.api_key = llm_api_key.strip() or None + llm_cfg.temperature = llm_temperature + llm_cfg.timeout = llm_timeout + 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()) + def render_tests() -> None: LOGGER.info("渲染自检页面", extra=LOG_EXTRA) @@ -397,6 +424,27 @@ def render_tests() -> None: st.dataframe(df_reset.tail(20), width='stretch') LOGGER.info("行情可视化完成，展示行数=%s", len(df_reset), extra=LOG_EXTRA) + st.divider() + st.subheader("LLM 接口测试") + st.json(llm_config_snapshot()) + llm_prompt = st.text_area("测试 Prompt", value="请概述今天的市场重点。", height=160) + system_prompt = st.text_area( + "System Prompt (可选)", + value="你是一名量化策略研究助手，用简洁中文回答。", + height=100, + ) + if st.button("执行 LLM 测试"): + with st.spinner("正在调用 LLM..."): + try: + response = run_llm(llm_prompt, system=system_prompt or None) + except Exception as exc: # noqa: BLE001 + LOGGER.exception("LLM 测试失败", extra=LOG_EXTRA) + st.error(f"LLM 调用失败：{exc}") + else: + LOGGER.info("LLM 测试成功", extra=LOG_EXTRA) + st.success("LLM 调用成功，以下为返回内容：") + st.write(response) + def main() -> None: LOGGER.info("初始化 Streamlit UI", extra=LOG_EXTRA) diff --git a/app/utils/config.py b/app/utils/config.py index 007517a..c603733 100644 --- a/app/utils/config.py +++ b/app/utils/config.py @@ -44,6 +44,17 @@ class AgentWeights: "A_macro": self.macro, } +@dataclass +class LLMConfig: + """Configuration for LLM providers (Ollama / OpenAI-compatible).""" + + provider: str = "ollama" # Options: "ollama", "openai" + model: str = "llama3" + base_url: Optional[str] = None # Defaults resolved per provider + api_key: Optional[str] = None + temperature: float = 0.2 + timeout: float = 30.0 + @dataclass class AppConfig: @@ -55,6 +66,7 @@ class AppConfig: data_paths: DataPaths = field(default_factory=DataPaths) agent_weights: AgentWeights = field(default_factory=AgentWeights) force_refresh: bool = False + llm: LLMConfig = field(default_factory=LLMConfig) CONFIG = AppConfig() diff --git a/requirements.txt b/requirements.txt index ac1912c..9e94bbe 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,3 +2,4 @@ pandas>=2.0 plotly>=5.18 streamlit>=1.30 tushare>=1.2 +requests>=2.31