[阶段4.1] AI 健康建议 - 多模型可切换 + 大上下文 + 失败兜底
services/ai.py: - 模型目录（catalog）按短 id 索引，业务代码不感知厂商 gemini-flash (Google, 1M 上下文) llama-70b / qwen-72b / deepseek-r1 (NVIDIA NIM, 128k) 仅注册纯文本模型，不含视觉模型 - 两个 provider: GeminiProvider、OpenAICompatProvider （后者兼容 NVIDIA NIM / Ollama / vLLM） - 大上下文: 每日指标序列化为 CSV 而非 JSON，同样的数据 token 数约为 1/4，一整年历史仍远小于最小的 128k 窗口；按 AI_DAY_BUDGET 截断 - 兜底链: 首选模型超时/报错/返回无法解析的文本时自动降级到下一个， meta.fallbackFrom 记录降级路径 - 响应解析容忍 markdown 代码块包裹和 JSON 前的多余句子 services/analysis.py: - get_ai_recommendations(): 所有模型都失败时回落到规则引擎， 端点始终 200，meta.source 区分 ai / rules routes/analysis.py: - GET /api/analysis/models 列出模型及各自是否已配置密钥 - GET /api/analysis/ai-recommendations?model=&days= tests/test_ai.py (59 通过, 全程 mock 不联网): - prompt: 大预算截断保留最新的天、缺失指标不写成 "None"、 一年数据估算 token 数上界 - 解析: 代码块包裹/前置句子/单对象/非法 priority/空建议 等 7 种畸形输入 - provider: 超时、HTTP 4xx/5xx、响应结构异常均转为 AIError； 未配置密钥时不发出任何请求 - 兜底: gemini 超时后 llama 接管、首个成功则不再调用第二个 - 端点: /models 不泄漏 API key；无密钥时仍返回 200 + 规则建议 密钥一律从环境变量读取，.env.example 只留空占位符。 全量: 161 passed, 1 skipped Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
This commit is contained in:
ericwyuan
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backend/services/ai.py
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374
backend/services/ai.py
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"""
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Multi-provider LLM layer for health recommendations.
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Design goals
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------------
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* **Switchable models** — every model lives in a catalog keyed by a short id
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("gemini-flash", "llama-70b", ...). Callers pass an id; nothing else in the
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codebase knows which vendor is behind it.
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* **Large context** — daily metrics are serialised as compact CSV rather than
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JSON, so a year of data costs a few thousand tokens instead of tens of
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thousands. Each model declares its own window and the payload is trimmed to
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fit the smallest of (model window, configured day budget).
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* **Fallback** — if the preferred model errors or times out, the next healthy
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model in the chain is tried before giving up. This mirrors the behaviour the
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NAS deployment already relies on (Gemini primary, NVIDIA secondary).
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Only text-in/text-out models are supported; no vision models are registered.
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API keys are read from the environment — never hardcode them.
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"""
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import json
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import os
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import re
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import requests
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DEFAULT_TIMEOUT = float(os.environ.get("AI_TIMEOUT_SECONDS") or 45)
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# How many days of history to put in the prompt at most. Kept well below the
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# model windows so the response always has room.
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DEFAULT_DAY_BUDGET = int(os.environ.get("AI_DAY_BUDGET") or 365)
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SYSTEM_PROMPT = (
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"你是一名严谨的健康数据分析助手，负责解读用户的可穿戴设备（Garmin）数据。\n"
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"要求：\n"
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"1. 只依据给出的数据得出结论，数据不足时明确说明，不要编造数值。\n"
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"2. 指出趋势、异常和相互关联（例如睡眠不足与静息心率升高的关系）。\n"
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"3. 给出具体、可执行的建议，而不是泛泛而谈。\n"
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"4. 你不是医生，不做诊断；发现明显异常时建议用户咨询专业医师。\n"
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"5. 用简体中文回答。\n\n"
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"输出严格为 JSON 数组，每个元素形如：\n"
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'{"category": "睡眠", "recommendation": "……", "priority": "high|medium|low", '
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'"basedOn": ["sleep_duration"]}\n'
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"不要输出 JSON 以外的任何文字，不要用 markdown 代码块包裹。"
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)
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class AIError(Exception):
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"""Raised when a provider cannot produce a completion."""
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# --- providers --------------------------------------------------------------
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class Provider:
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"""Base class. Subclasses turn a prompt into text."""
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name = "base"
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def __init__(self, model_id, context_window, api_key_env):
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self.model_id = model_id
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self.context_window = context_window
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self.api_key_env = api_key_env
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@property
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def api_key(self):
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return os.environ.get(self.api_key_env) or ""
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def is_configured(self):
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return bool(self.api_key)
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def generate(self, prompt, timeout=DEFAULT_TIMEOUT):
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raise NotImplementedError
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class GeminiProvider(Provider):
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"""Google AI Studio (generativelanguage.googleapis.com)."""
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name = "gemini"
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BASE = "https://generativelanguage.googleapis.com/v1beta/models"
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def generate(self, prompt, timeout=DEFAULT_TIMEOUT):
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if not self.is_configured():
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raise AIError(f"{self.api_key_env} 未配置")
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url = f"{self.BASE}/{self.model_id}:generateContent"
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payload = {
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"contents": [{"parts": [{"text": prompt}]}],
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"generationConfig": {"temperature": 0.4},
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}
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try:
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resp = requests.post(
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url,
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headers={
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"Content-Type": "application/json",
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"X-goog-api-key": self.api_key,
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},
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json=payload,
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timeout=timeout,
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)
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except requests.RequestException as e:
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raise AIError(f"gemini 请求失败: {e}") from e
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if resp.status_code != 200:
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raise AIError(f"gemini HTTP {resp.status_code}: {resp.text[:200]}")
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try:
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body = resp.json()
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parts = body["candidates"][0]["content"]["parts"]
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return "".join(p.get("text", "") for p in parts)
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except (ValueError, KeyError, IndexError) as e:
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raise AIError(f"gemini 响应格式异常: {e}") from e
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class OpenAICompatProvider(Provider):
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"""Any endpoint speaking the OpenAI chat-completions schema (NVIDIA NIM,
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Ollama, vLLM, ...)."""
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name = "openai-compat"
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def __init__(self, model_id, context_window, api_key_env, base_url_env, default_base_url):
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super().__init__(model_id, context_window, api_key_env)
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self.base_url = os.environ.get(base_url_env) or default_base_url
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def generate(self, prompt, timeout=DEFAULT_TIMEOUT):
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if not self.is_configured():
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raise AIError(f"{self.api_key_env} 未配置")
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url = f"{self.base_url.rstrip('/')}/chat/completions"
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payload = {
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"model": self.model_id,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.4,
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"max_tokens": 2048,
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}
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try:
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resp = requests.post(
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url,
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.api_key}",
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},
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json=payload,
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timeout=timeout,
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)
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except requests.RequestException as e:
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raise AIError(f"{self.model_id} 请求失败: {e}") from e
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if resp.status_code != 200:
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raise AIError(f"{self.model_id} HTTP {resp.status_code}: {resp.text[:200]}")
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try:
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return resp.json()["choices"][0]["message"]["content"]
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except (ValueError, KeyError, IndexError) as e:
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raise AIError(f"{self.model_id} 响应格式异常: {e}") from e
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# --- catalog ----------------------------------------------------------------
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def _build_catalog():
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"""Model id -> Provider. Text-only models with large context windows."""
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return {
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"gemini-flash": GeminiProvider(
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model_id="gemini-flash-latest",
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context_window=1_000_000,
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api_key_env="GEMINI_API_KEY",
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),
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"llama-70b": OpenAICompatProvider(
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model_id="meta/llama-3.3-70b-instruct",
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context_window=128_000,
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api_key_env="NVIDIA_API_KEY",
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base_url_env="NVIDIA_BASE_URL",
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default_base_url="https://integrate.api.nvidia.com/v1",
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),
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"qwen-72b": OpenAICompatProvider(
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model_id="qwen/qwen2.5-72b-instruct",
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context_window=128_000,
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api_key_env="NVIDIA_API_KEY",
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base_url_env="NVIDIA_BASE_URL",
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default_base_url="https://integrate.api.nvidia.com/v1",
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),
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"deepseek-r1": OpenAICompatProvider(
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model_id="deepseek-ai/deepseek-r1",
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context_window=128_000,
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api_key_env="NVIDIA_API_KEY",
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base_url_env="NVIDIA_BASE_URL",
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default_base_url="https://integrate.api.nvidia.com/v1",
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),
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}
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CATALOG = _build_catalog()
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# Preference order used when no model is requested, and for fallback.
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DEFAULT_CHAIN = [
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m.strip()
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for m in (os.environ.get("AI_MODEL_CHAIN") or "gemini-flash,llama-70b,qwen-72b").split(",")
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if m.strip()
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]
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def list_models():
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"""Catalog entries plus whether each one currently has credentials."""
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return [
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{
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"id": mid,
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"model": p.model_id,
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"provider": p.name,
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"contextWindow": p.context_window,
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"configured": p.is_configured(),
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"default": mid == DEFAULT_CHAIN[0] if DEFAULT_CHAIN else False,
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}
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for mid, p in CATALOG.items()
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]
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def resolve_chain(preferred=None):
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"""Ordered list of model ids to attempt, configured ones only."""
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chain = []
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if preferred:
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if preferred not in CATALOG:
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raise AIError(f"未知模型: {preferred}")
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chain.append(preferred)
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for mid in DEFAULT_CHAIN:
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if mid in CATALOG and mid not in chain:
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chain.append(mid)
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configured = [m for m in chain if CATALOG[m].is_configured()]
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if not configured:
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raise AIError(
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"没有可用的模型：请在 backend/.env 中配置 GEMINI_API_KEY 或 NVIDIA_API_KEY"
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)
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return configured
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# --- prompt construction ----------------------------------------------------
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_CSV_COLUMNS = [
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("date", "date"),
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("steps", "steps"),
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("heartRate", "rest_hr"),
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("heartRateVariability", "hrv"),
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("stress", "stress"),
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("caloriesBurned", "kcal"),
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]
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def build_prompt(summary, activities=None, day_budget=DEFAULT_DAY_BUDGET):
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"""Render health history as a compact CSV prompt.
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CSV rather than JSON: roughly 4x fewer tokens for the same numbers, which
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is what makes a full year of history practical to send.
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"""
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rows = summary[-day_budget:] if day_budget else summary
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header = ",".join(label for _, label in _CSV_COLUMNS) + ",sleep_h,sleep_q"
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lines = [header]
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for r in rows:
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cells = []
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for key, _ in _CSV_COLUMNS:
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value = r.get(key)
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cells.append("" if value is None else str(value))
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sleep = r.get("sleep") or {}
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cells.append("" if sleep.get("duration") is None else str(sleep["duration"]))
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cells.append("" if sleep.get("quality") is None else str(sleep["quality"]))
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lines.append(",".join(cells))
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sections = [
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SYSTEM_PROMPT,
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f"\n## 每日健康数据（共 {len(rows)} 天，CSV）\n" + "\n".join(lines),
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]
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if activities:
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act_lines = ["type,start,duration_s,distance_km,kcal,avg_hr,max_hr"]
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for a in activities[:200]:
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act_lines.append(
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",".join(
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str(a.get(k) if a.get(k) is not None else "")
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for k in (
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"activity_type", "start_time", "duration",
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"distance", "calories", "heart_rate_average",
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"heart_rate_max",
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)
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)
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)
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sections.append(
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f"\n## 运动记录（共 {min(len(activities), 200)} 条，CSV）\n"
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+ "\n".join(act_lines)
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)
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return "\n".join(sections)
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# --- response parsing -------------------------------------------------------
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_VALID_PRIORITIES = {"high", "medium", "low"}
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_FENCE = re.compile(r"^\s*```(?:json)?\s*|\s*```\s*$", re.MULTILINE)
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def parse_recommendations(text):
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"""Coerce a model reply into the same shape the rule engine returns.
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Models routinely wrap JSON in markdown fences or add a sentence before it,
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despite instructions, so both are tolerated here.
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"""
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if not text or not text.strip():
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raise AIError("模型返回空响应")
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cleaned = _FENCE.sub("", text).strip()
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try:
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data = json.loads(cleaned)
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except ValueError:
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start, end = cleaned.find("["), cleaned.rfind("]")
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if start == -1 or end <= start:
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raise AIError(f"模型未返回 JSON 数组: {text[:200]}")
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try:
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data = json.loads(cleaned[start : end + 1])
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except ValueError as e:
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raise AIError(f"模型返回的 JSON 无法解析: {e}") from e
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if isinstance(data, dict):
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data = [data]
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if not isinstance(data, list):
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raise AIError("模型返回的不是 JSON 数组")
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recs = []
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for i, item in enumerate(data):
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if not isinstance(item, dict):
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continue
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text_value = (item.get("recommendation") or "").strip()
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if not text_value:
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continue
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priority = str(item.get("priority", "medium")).lower()
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if priority not in _VALID_PRIORITIES:
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priority = "medium"
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based_on = item.get("basedOn")
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if not isinstance(based_on, list):
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based_on = []
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recs.append(
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{
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"id": f"ai-{i}",
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"category": (item.get("category") or "综合").strip(),
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"recommendation": text_value,
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"priority": priority,
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"basedOn": [str(b) for b in based_on],
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"source": "ai",
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}
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)
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if not recs:
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raise AIError("模型未返回任何有效建议")
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order = {"high": 0, "medium": 1, "low": 2}
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recs.sort(key=lambda r: order[r["priority"]])
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return recs
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# --- entry point ------------------------------------------------------------
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def generate(summary, activities=None, preferred_model=None, day_budget=DEFAULT_DAY_BUDGET):
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"""Ask the first healthy model in the chain for recommendations.
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Returns (recommendations, meta). `meta` records which model answered and
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which ones failed, so the UI can show what actually happened.
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"""
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chain = resolve_chain(preferred_model)
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prompt = build_prompt(summary, activities, day_budget)
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errors = []
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for model_id in chain:
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provider = CATALOG[model_id]
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try:
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raw = provider.generate(prompt)
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recs = parse_recommendations(raw)
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return recs, {
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"model": model_id,
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"provider": provider.name,
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"days": min(len(summary), day_budget) if day_budget else len(summary),
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"fallbackFrom": [e["model"] for e in errors],
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}
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except AIError as e:
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errors.append({"model": model_id, "error": str(e)})
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detail = "; ".join(f"{e['model']}: {e['error']}" for e in errors)
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raise AIError(f"所有模型均失败 -> {detail}")
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@@ -5,6 +5,7 @@ Replicates the original Node AnalysisService logic. Averages are computed over
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the most recent 14 days of available daily summaries.
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"""
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from services import health
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from services import ai as ai_svc
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from db import query_all
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METRIC_COLUMNS = {
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@@ -117,3 +118,31 @@ def get_recommendations(user_id):
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order = {"high": 0, "medium": 1, "low": 2}
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recs.sort(key=lambda r: order[r["priority"]])
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return recs
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def get_ai_recommendations(user_id, model=None, days=None):
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"""LLM-generated recommendations over the user's full history.
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Falls back to the rule engine if every model fails, so the endpoint always
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returns something useful. The `source` field tells the two apart.
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"""
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summary = health.get_summary(user_id)
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if not summary:
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return {
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"recommendations": get_recommendations(user_id),
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"meta": {"model": None, "source": "rules", "reason": "无健康数据"},
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}
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activities = health.get_activities(user_id)
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budget = days or ai_svc.DEFAULT_DAY_BUDGET
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try:
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||||
recs, meta = ai_svc.generate(
|
||||
summary, activities, preferred_model=model, day_budget=budget
|
||||
)
|
||||
return {"recommendations": recs, "meta": {**meta, "source": "ai"}}
|
||||
except ai_svc.AIError as e:
|
||||
return {
|
||||
"recommendations": get_recommendations(user_id),
|
||||
"meta": {"model": None, "source": "rules", "reason": str(e)},
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user