update

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.

README.md

@@ -54,6 +54,13 @@ export TUSHARE_TOKEN="<your-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。

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

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)

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()

requirements.txt

@@ -2,3 +2,4 @@ pandas>=2.0
 plotly>=5.18
 streamlit>=1.30
 tushare>=1.2
+requests>=2.31