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AI 应用架构设计模式：从原型到生产级系统

news 2026/6/10 18:00:02

AI 应用架构设计模式：从原型到生产级系统

前言

构建一个 AI 应用原型很容易，但将其打造成生产级别的系统却需要深思熟虑的架构设计。我之前经历过多个 AI 项目，从简单的问答机器人到复杂的 Agent 系统，对 AI 应用架构有了一些经验总结。

今天分享一些常见的 AI 应用架构模式，以及如何根据场景选择合适的架构。

常见的 AI 应用架构

模式 1：直接调用（Direct Call）

最简单的架构，直接调用 LLM API：

class DirectLLMService: """直接调用模式""" def __init__(self, api_key: str): self.client = OpenAI(api_key=api_key) def chat(self, prompt: str, system_prompt: str = None) -> str: messages = [] if system_prompt: messages.append({"role": "system", "content": system_prompt}) messages.append({"role": "user", "content": prompt}) response = self.client.chat.completions.create( model="gpt-4", messages=messages, temperature=0.7 ) return response.choices[0].message.content

适用场景：

简单问答
一次性任务
快速原型

优点：简单、快速
缺点：无法使用外部工具、上下文有限

模式 2：RAG 架构

Retrieval-Augmented Generation，结合知识库：

class RAGApplication: """RAG 架构""" def __init__(self, llm, vector_store, embedding_model): self.llm = llm self.vector_store = vector_store self.embedding_model = embedding_model def query(self, question: str) -> str: # 1. 检索相关文档 query_embedding = self.embedding_model.encode(question) docs = self.vector_store.search(query_embedding, top_k=5) # 2. 构建上下文 context = "\n\n".join([doc.content for doc in docs]) prompt = f"基于以下内容回答问题：\n\n{context}\n\n问题：{question}" # 3. 生成回答 return self.llm.chat(prompt)

适用场景：

知识库问答
文档理解
需要最新信息

优点：利用外部知识、减少幻觉
缺点：依赖检索质量、延迟增加

模式 3：Agent 架构

带有工具调用能力的自主 Agent：

class ToolUsingAgent: """Agent 架构""" def __init__(self, llm, tools: list): self.llm = llm self.tools = {t.name: t for t in tools} self.max_iterations = 10 def run(self, task: str) -> str: messages = [{"role": "user", "content": task}] for _ in range(self.max_iterations): # 1. LLM 决定行动 response = self.llm.chat( messages=messages, tools=self._get_tools_schema() ) # 2. 检查是否需要工具调用 if not response.tool_calls: return response.content # 3. 执行工具 for call in response.tool_calls: result = self.tools[call.name].execute(**call.arguments) messages.append({ "role": "tool", "tool_call_id": call.id, "content": str(result) }) return "任务超时"

适用场景：

复杂多步骤任务
需要外部系统交互
自主决策

优点：能力强大、可处理复杂任务
缺点：不确定性强、调试困难

生产级架构组件

缓存层

from functools import wraps import hashlib import json import redis class SemanticCache: """语义缓存""" def __init__(self, redis_client, embedding_model, threshold=0.95): self.redis = redis_client self.embedding_model = embedding_model self.threshold = threshold def get(self, query: str) -> Optional[str]: """尝试从缓存获取""" query_hash = self._hash(query) cached = self.redis.get(f"cache:{query_hash}") if cached: return cached return None def set(self, query: str, response: str): """缓存响应""" query_hash = self._hash(query) self.redis.setex(f"cache:{query_hash}", 3600, response) def _hash(self, text: str) -> str: return hashlib.md5(text.encode()).hexdigest() class LLMCache: """LLM 响应缓存""" def __init__(self, cache: SemanticCache): self.cache = cache def cached_completion(self, prompt: str, **kwargs) -> str: """带缓存的 completion""" # 检查缓存 cached = self.cache.get(prompt) if cached: return cached # 调用 LLM response = openai.ChatCompletion.create(prompt=prompt, **kwargs) result = response.choices[0].message.content # 缓存结果 self.cache.set(prompt, result) return result

限流与回退

import time from collections import defaultdict class RateLimiter: """限流器""" def __init__(self, max_requests: int, window_seconds: int): self.max_requests = max_requests self.window_seconds = window_seconds self.requests = defaultdict(list) def is_allowed(self, user_id: str) -> bool: now = time.time() window_start = now - self.window_seconds # 清理过期请求 self.requests[user_id] = [ t for t in self.requests[user_id] if t > window_start ] # 检查限制 if len(self.requests[user_id]) >= self.max_requests: return False self.requests[user_id].append(now) return True class ModelFallback: """模型回退""" def __init__(self): self.models = [ "gpt-4", "gpt-4-turbo", "gpt-3.5-turbo" ] def chat_with_fallback(self, prompt: str) -> str: for model in self.models: try: response = openai.ChatCompletion.create( model=model, messages=[{"role": "user", "content": prompt}] ) return response.choices[0].message.content except Exception as e: print(f"Model {model} failed: {e}") continue raise Exception("All models failed")

异步处理

import asyncio from typing import List class AsyncLLMWrapper: """异步 LLM 包装器""" def __init__(self): self.semaphore = asyncio.Semaphore(10) # 最大并发 10 async def achat(self, prompt: str, system: str = None) -> str: """异步聊天""" async with self.semaphore: loop = asyncio.get_event_loop() return await loop.run_in_executor( None, lambda: self._sync_chat(prompt, system) ) async def achat_batch(self, prompts: List[str]) -> List[str]: """批量异步聊天""" tasks = [self.achat(p) for p in prompts] return await asyncio.gather(*tasks) def _sync_chat(self, prompt: str, system: str = None) -> str: messages = [] if system: messages.append({"role": "system", "content": system}) messages.append({"role": "user", "content": prompt}) response = openai.ChatCompletion.create( model="gpt-4", messages=messages ) return response.choices[0].message.content

完整生产架构

┌─────────────────────────────────────────────────────────────┐ │ API Gateway │ │ (认证、限流、日志) │ ├─────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ 缓存层 │ │ 限流层 │ │ 监控层 │ │ │ │ Redis │ │ Rate Limit │ │ Metrics │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ │ │ │ ┌──────────────────────────────────────────────────────┐ │ │ │ Application Layer │ │ │ │ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ │ │ │ │ │ QA │ │ RAG │ │ Agent │ │ 其他 │ │ │ │ │ └────────┘ └────────┘ └────────┘ └────────┘ │ │ │ └──────────────────────────────────────────────────────┘ │ │ │ │ ┌──────────────────────────────────────────────────────┐ │ │ │ LLM Layer │ │ │ │ ┌────────┐ ┌────────┐ ┌────────┐ │ │ │ │ │ OpenAI │ │ Claude │ │ 本地 │ │ │ │ │ └────────┘ └────────┘ └────────┘ │ │ │ └──────────────────────────────────────────────────────┘ │ │ │ │ ┌──────────────────────────────────────────────────────┐ │ │ │ External Services │ │ │ │ ┌────────┐ ┌────────┐ ┌────────┐ │ │ │ │ │Vector │ │ API │ │ Database│ │ │ │ │ │Store │ │服务 │ │ │ │ │ │ │ └────────┘ └────────┘ └────────┘ │ │ │ └──────────────────────────────────────────────────────┘ │ │ │ └─────────────────────────────────────────────────────────────┘

实际应用代码

class AIApplication: """完整的 AI 应用""" def __init__(self, config: dict): # 初始化各组件 self.llm = self._init_llm(config["llm"]) self.vector_store = self._init_vector_store(config["vector_store"]) self.cache = self._init_cache(config["cache"]) self.rate_limiter = RateLimiter(**config["rate_limit"]) # 初始化应用层 self.qa_service = QAService(self.llm) self.rag_service = RAGService(self.llm, self.vector_store) self.agent_service = AgentService(self.llm, self._init_tools()) async def handle_request(self, request: dict) -> dict: """处理请求""" user_id = request["user_id"] # 1. 限流检查 if not self.rate_limiter.is_allowed(user_id): return {"error": "Rate limit exceeded", "code": 429} # 2. 路由 request_type = request["type"] if request_type == "qa": result = await self.qa_service.answer(request["question"]) elif request_type == "rag": result = await self.rag_service.query(request["query"]) elif request_type == "agent": result = await self.agent_service.run(request["task"]) else: result = {"error": "Unknown request type"} # 3. 记录日志 await self._log_request(request, result) return result def _init_llm(self, config: dict) -> LLM: """初始化 LLM""" if config["provider"] == "openai": return OpenAILLM(api_key=config["api_key"]) elif config["provider"] == "anthropic": return AnthropicLLM(api_key=config["api_key"]) else: return LocalLLM(config["model_path"])

监控与可观测性

from prometheus_client import Counter, Histogram, Gauge import time # 指标定义 request_count = Counter('llm_requests_total', 'Total LLM requests', ['model', 'status']) request_duration = Histogram('llm_request_duration_seconds', 'Request duration') active_requests = Gauge('llm_active_requests', 'Active requests') cache_hit_rate = Gauge('llm_cache_hit_rate', 'Cache hit rate') class MonitoredLLM: """带监控的 LLM""" def __init__(self, llm): self.llm = llm def chat(self, prompt: str) -> str: model = self.llm.model_name active_requests.inc() start = time.time() try: result = self.llm.chat(prompt) request_count.labels(model=model, status="success").inc() return result except Exception as e: request_count.labels(model=model, status="error").inc() raise finally: request_duration.observe(time.time() - start) active_requests.dec()

部署架构

Docker 部署

FROM python:3.11-slim WORKDIR /app COPY requirements.txt . RUN pip install -r requirements.txt COPY . . CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Docker Compose

version: '3.8' services: api: build: . ports: - "8000:8000" environment: - OPENAI_API_KEY=${OPENAI_API_KEY} - REDIS_URL=redis://redis:6379 depends_on: - redis - qdrant redis: image: redis:7-alpine ports: - "6379:6379" qdrant: image: qdrant/qdrant ports: - "6333:6333" volumes: - qdrant_data:/qdrant/storage volumes: qdrant_data: