update

README.md

@@ -58,9 +58,10 @@ export TUSHARE_TOKEN="<your-token>"
 
 ### 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。
+- 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`。
 - 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。
 
 ## 快速开始
 

app/llm/client.py

@@ -2,18 +2,18 @@
 from __future__ import annotations
 
 import json
+from collections import Counter
 from dataclasses import asdict
 from typing import Dict, Iterable, List, Optional
 
 import requests
 
-from app.utils.config import get_config
+from app.utils.config import DEFAULT_LLM_MODELS, LLMEndpoint, 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."""
 
@@ -21,9 +21,18 @@ 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"
 
 
+def _default_model(provider: str) -> str:
+    provider = (provider or "").lower()
+    return DEFAULT_LLM_MODELS.get(provider, DEFAULT_LLM_MODELS["ollama"])
+
+
 def _build_messages(prompt: str, system: Optional[str] = None) -> List[Dict[str, str]]:
     messages: List[Dict[str, str]] = []
     if system:
@@ -32,7 +41,15 @@ def _build_messages(prompt: str, system: Optional[str] = None) -> List[Dict[str,
     return messages
 
 
-def _request_ollama(model: str, prompt: str, *, base_url: str, temperature: float, timeout: float, system: Optional[str]) -> str:
+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,
@@ -52,7 +69,16 @@ def _request_ollama(model: str, prompt: str, *, base_url: str, temperature: floa
     return str(content)
 
 
-def _request_openai(model: str, prompt: str, *, base_url: str, api_key: str, temperature: float, timeout: float, system: Optional[str]) -> str:
+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}",
@@ -74,28 +100,30 @@ def _request_openai(model: str, prompt: str, *, base_url: str, api_key: str, tem
         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:
+def _call_endpoint(endpoint: LLMEndpoint, prompt: str, system: Optional[str]) -> str:
-    """Execute the configured LLM provider with the given prompt."""
+    provider = (endpoint.provider or "ollama").lower()
-
+    base_url = endpoint.base_url or _default_base_url(provider)
-    cfg = get_config().llm
+    model = endpoint.model or _default_model(provider)
-    provider = (cfg.provider or "ollama").lower()
+    temperature = max(0.0, min(endpoint.temperature, 2.0))
-    base_url = cfg.base_url or _default_base_url(provider)
+    timeout = max(5.0, endpoint.timeout or 30.0)
-    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
+        "触发 LLM 请求：provider=%s model=%s base=%s",
+        provider,
+        model,
+        base_url,
+        extra=LOG_EXTRA,
     )
 
-    if provider == "openai":
+    if provider in {"openai", "deepseek", "wenxin"}:
-        if not cfg.api_key:
+        api_key = endpoint.api_key
-            raise LLMError("缺少 OpenAI 兼容 API Key")
+        if not api_key:
+            raise LLMError(f"缺少 {provider} API Key (model={model})")
         return _request_openai(
             model,
             prompt,
             base_url=base_url,
-            api_key=cfg.api_key,
+            api_key=api_key,
             temperature=temperature,
             timeout=timeout,
             system=system,
@@ -109,14 +137,89 @@ def run_llm(prompt: str, *, system: Optional[str] = None) -> str:
             timeout=timeout,
             system=system,
         )
-    raise LLMError(f"不支持的 LLM provider: {cfg.provider}")
+    raise LLMError(f"不支持的 LLM provider: {endpoint.provider}")
+
+
+def _normalize_response(text: str) -> str:
+    return " ".join(text.strip().split())
+
+
+def run_llm(prompt: str, *, system: Optional[str] = None) -> str:
+    """Execute the configured LLM strategy with the given prompt."""
+
+    settings = get_config().llm
+    if settings.strategy == "majority":
+        return _run_majority_vote(settings, prompt, system)
+    return _call_endpoint(settings.primary, prompt, system)
+
+
+def _run_majority_vote(config, prompt: str, system: Optional[str]) -> str:
+    endpoints: List[LLMEndpoint] = [config.primary] + list(config.ensemble)
+    responses: List[Dict[str, str]] = []
+    failures: List[str] = []
+
+    for idx, endpoint in enumerate(endpoints, start=1):
+        try:
+            result = _call_endpoint(endpoint, prompt, system)
+            responses.append({
+                "provider": endpoint.provider,
+                "model": endpoint.model,
+                "raw": result,
+                "normalized": _normalize_response(result),
+            })
+        except Exception as exc:  # noqa: BLE001
+            summary = f"{endpoint.provider}:{endpoint.model} -> {exc}"
+            failures.append(summary)
+            LOGGER.warning("LLM 调用失败：%s", summary, extra=LOG_EXTRA)
+
+    if not responses:
+        raise LLMError("所有 LLM 调用均失败，无法返回结果。")
+
+    threshold = max(1, config.majority_threshold)
+    threshold = min(threshold, len(responses))
+
+    counter = Counter(item["normalized"] for item in responses)
+    top_value, top_count = counter.most_common(1)[0]
+    if top_count >= threshold:
+        chosen_raw = next(item["raw"] for item in responses if item["normalized"] == top_value)
+        LOGGER.info(
+            "LLM 多模型投票通过：value=%s votes=%s/%s threshold=%s",
+            top_value[:80],
+            top_count,
+            len(responses),
+            threshold,
+            extra=LOG_EXTRA,
+        )
+        return chosen_raw
+
+    LOGGER.info(
+        "LLM 多模型投票未达门槛：votes=%s/%s threshold=%s，返回首个结果",
+        top_count,
+        len(responses),
+        threshold,
+        extra=LOG_EXTRA,
+    )
+    if failures:
+        LOGGER.warning("LLM 调用失败列表：%s", failures, extra=LOG_EXTRA)
+    return responses[0]["raw"]
 
 
 def llm_config_snapshot() -> Dict[str, object]:
     """Return a sanitized snapshot of current LLM configuration for debugging."""
 
-    cfg = get_config().llm
+    settings = get_config().llm
-    data = asdict(cfg)
+    primary = asdict(settings.primary)
-    if data.get("api_key"):
+    if primary.get("api_key"):
-        data["api_key"] = "***"
+        primary["api_key"] = "***"
-    return data
+    ensemble = []
+    for endpoint in settings.ensemble:
+        record = asdict(endpoint)
+        if record.get("api_key"):
+            record["api_key"] = "***"
+        ensemble.append(record)
+    return {
+        "strategy": settings.strategy,
+        "majority_threshold": settings.majority_threshold,
+        "primary": primary,
+        "ensemble": ensemble,
+    }

app/ui/streamlit_app.py

@@ -1,9 +1,12 @@
 """Streamlit UI scaffold for the investment assistant."""
 from __future__ import annotations
 
+import json
 import sys
+from dataclasses import asdict
 from datetime import date, timedelta
 from pathlib import Path
+from typing import List
 
 ROOT = Path(__file__).resolve().parents[2]
 if str(ROOT) not in sys.path:
@@ -20,7 +23,7 @@ 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.config import DEFAULT_LLM_MODELS, LLMEndpoint, get_config
 from app.utils.db import db_session
 from app.utils.logging import get_logger
 
@@ -194,25 +197,95 @@ def render_settings() -> None:
     st.divider()
     st.subheader("LLM 设置")
     llm_cfg = cfg.llm
+    primary = llm_cfg.primary
     providers = ["ollama", "openai"]
     try:
-        provider_index = providers.index((llm_cfg.provider or "ollama").lower())
+        provider_index = providers.index((primary.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)
+    default_model_hint = DEFAULT_LLM_MODELS.get(selected_provider, DEFAULT_LLM_MODELS["ollama"])
-    llm_base = st.text_input("LLM Base URL (可选)", value=llm_cfg.base_url or "")
+    llm_model = st.text_input("LLM 模型", value=primary.model, help=f"默认推荐：{default_model_hint}")
-    llm_api_key = st.text_input("LLM API Key (OpenAI 类需要)", value=llm_cfg.api_key or "", type="password")
+    base_hints = {
-    llm_temperature = st.slider("LLM 温度", min_value=0.0, max_value=2.0, value=float(llm_cfg.temperature), step=0.05)
+        "ollama": "http://localhost:11434",
-    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)
+        "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'}")
+    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")
+
+    strategy_options = ["single", "majority"]
+    try:
+        strategy_index = strategy_options.index(llm_cfg.strategy)
+    except ValueError:
+        strategy_index = 0
+    selected_strategy = st.selectbox("LLM 推理策略", strategy_options, index=strategy_index)
+    majority_threshold = st.number_input(
+        "多数投票门槛",
+        min_value=1,
+        max_value=10,
+        value=int(llm_cfg.majority_threshold),
+        step=1,
+        format="%d",
+    )
+
+    ensemble_display = []
+    for endpoint in llm_cfg.ensemble:
+        data = asdict(endpoint)
+        if data.get("api_key"):
+            data["api_key"] = ""
+        ensemble_display.append(data)
+    ensemble_text = st.text_area(
+        "LLM 集群配置 (JSON 数组)",
+        value=json.dumps(ensemble_display or [], ensure_ascii=False, indent=2),
+        height=220,
+    )
 
     if st.button("保存 LLM 设置"):
-        llm_cfg.provider = selected_provider
+        original_provider = primary.provider
-        llm_cfg.model = llm_model.strip() or llm_cfg.model
+        original_model = primary.model
-        llm_cfg.base_url = llm_base.strip() or None
+        primary.provider = selected_provider
-        llm_cfg.api_key = llm_api_key.strip() or None
+        model_input = llm_model.strip()
-        llm_cfg.temperature = llm_temperature
+        if not model_input:
-        llm_cfg.timeout = llm_timeout
+            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"])
+        else:
+            primary.model = model_input
+        primary.base_url = llm_base.strip() or None
+        primary.temperature = llm_temperature
+        primary.timeout = llm_timeout
+        api_key_value = llm_api_key.strip()
+        primary.api_key = api_key_value or None
+
+        try:
+            parsed = json.loads(ensemble_text or "[]")
+            if not isinstance(parsed, list):
+                raise ValueError("ensemble 配置必须是数组")
+        except Exception as exc:  # noqa: BLE001
+            LOGGER.exception("解析 LLM 集群配置失败", extra=LOG_EXTRA)
+            st.error(f"LLM 集群配置解析失败：{exc}")
+        else:
+            new_ensemble: List[LLMEndpoint] = []
+            invalid = False
+            for item in parsed:
+                if not isinstance(item, dict):
+                    st.error("LLM 集群配置中的每个元素都必须是对象")
+                    invalid = True
+                    break
+                fields = {key: item.get(key) for key in ("provider", "model", "base_url", "api_key", "temperature", "timeout")}
+                endpoint = LLMEndpoint(**{k: v for k, v in fields.items() if v not in (None, "")})
+                if not endpoint.provider:
+                    endpoint.provider = "ollama"
+                new_ensemble.append(endpoint)
+            if not invalid:
+                llm_cfg.ensemble = new_ensemble
+                llm_cfg.strategy = selected_strategy
+                llm_cfg.majority_threshold = int(majority_threshold)
                 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())

app/utils/config.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Dict, Optional
+from typing import Dict, List, Optional
 
 
 def _default_root() -> Path:
@@ -44,17 +44,40 @@ class AgentWeights:
             "A_macro": self.macro,
         }
 
-@dataclass
+DEFAULT_LLM_MODELS: Dict[str, str] = {
-class LLMConfig:
+    "ollama": "llama3",
-    """Configuration for LLM providers (Ollama / OpenAI-compatible)."""
+    "openai": "gpt-4o-mini",
+    "deepseek": "deepseek-chat",
+    "wenxin": "ERNIE-Speed",
+}
 
-    provider: str = "ollama"  # Options: "ollama", "openai"
+
-    model: str = "llama3"
+@dataclass
-    base_url: Optional[str] = None  # Defaults resolved per provider
+class LLMEndpoint:
+    """Single LLM endpoint configuration."""
+
+    provider: str = "ollama"
+    model: Optional[str] = None
+    base_url: Optional[str] = None
     api_key: Optional[str] = None
     temperature: float = 0.2
     timeout: float = 30.0
 
+    def __post_init__(self) -> None:
+        self.provider = (self.provider or "ollama").lower()
+        if not self.model:
+            self.model = DEFAULT_LLM_MODELS.get(self.provider, DEFAULT_LLM_MODELS["ollama"])
+
+
+@dataclass
+class LLMConfig:
+    """LLM configuration allowing single or ensemble strategies."""
+
+    primary: LLMEndpoint = field(default_factory=LLMEndpoint)
+    ensemble: List[LLMEndpoint] = field(default_factory=list)
+    strategy: str = "single"  # Options: single, majority
+    majority_threshold: int = 3
+
 
 @dataclass
 class AppConfig: