AI Agent的性能优化:从架构到代码级别的调优

在前面的文章中,我们讨论了 AI Agent 的部署和运维。今天,我想分享一下如何优化 AI Agent 的性能。说实话,这个话题我研究了很久,因为性能直接影响用户体验和运营成本。

从一个性能瓶颈说起

还记得去年我们的 AI 助手刚上线时的情况:

用户:这个响应也太慢了吧,要等好几秒
运营:API 费用每天都在涨
我:让我看看到底是哪里慢...

通过分析,我发现主要有三个问题:

  1. LLM 调用延迟高
  2. 向量检索效率低
  3. 内存占用过大

架构层面的优化

首先是架构层面的优化:

class OptimizedAgent:
    def __init__(
        self,
        config: Dict[str, Any]
    ):
        # 1. 模型池
        self.model_pool = ModelPool([
            {
                "model": "gpt-4",
                "max_tokens": 4000,
                "timeout": 30,
                "cost_per_token": 0.03
            },
            {
                "model": "gpt-3.5-turbo",
                "max_tokens": 2000,
                "timeout": 10,
                "cost_per_token": 0.002
            },
            {
                "model": "codellama-7b",
                "max_tokens": 2000,
                "timeout": 5,
                "cost_per_token": 0
            }
        ])
        
        # 2. 多级缓存
        self.cache = CacheManager([
            MemoryCache(max_size=1000),
            RedisCache(url=config["redis_url"]),
            DiskCache(path=config["cache_path"])
        ])
        
        # 3. 向量存储
        self.vector_store = VectorManager(
            primary=MilvusStore(
                host=config["milvus_host"],
                collection="embeddings",
                dimension=1536
            ),
            replica=FaissStore(
                index_path=config["faiss_path"],
                dimension=1536
            )
        )
        
    async def process(
        self,
        request: Dict[str, Any]
    ) -> Dict[str, Any]:
        # 1. 选择合适的模型
        model = await self._select_model(request)
        
        # 2. 检查缓存
        cache_key = self._generate_cache_key(request)
        if cached := await self.cache.get(cache_key):
            return cached
            
        # 3. 并行处理
        async with asyncio.TaskGroup() as group:
            # 3.1 向量检索
            search_task = group.create_task(
                self._search_relevant_docs(request)
            )
            
            # 3.2 上下文准备
            context_task = group.create_task(
                self._prepare_context(request)
            )
            
        docs = search_task.result()
        context = context_task.result()
        
        # 4. 生成响应
        response = await model.generate(
            prompt=self._build_prompt(
                request,
                docs,
                context
            )
        )
        
        # 5. 更新缓存
        await self.cache.set(
            cache_key,
            response,
            ttl=self._calculate_ttl(response)
        )
        
        return response
        
    async def _select_model(
        self,
        request: Dict[str, Any]
    ) -> BaseModel:
        # 根据请求特征选择合适的模型
        features = await self._extract_features(request)
        
        if features.complexity > 0.8:
            return self.model_pool.get("gpt-4")
        elif features.is_code_related:
            return self.model_pool.get("codellama-7b")
        else:
            return self.model_pool.get("gpt-3.5-turbo")

缓存系统优化

缓存是性能优化的关键:

class CacheManager:
    def __init__(
        self,
        backends: List[CacheBackend]
    ):
        self.backends = backends
        
    async def get(
        self,
        key: str
    ) -> Optional[Any]:
        # 从快到慢依次查找
        for backend in self.backends:
            if value := await backend.get(key):
                # 找到后,异步更新其他缓存
                asyncio.create_task(
                    self._update_other_caches(
                        key,
                        value,
                        backend
                    )
                )
                return value
        return None
        
    async def set(
        self,
        key: str,
        value: Any,
        ttl: int = None
    ):
        # 并行更新所有缓存
        await asyncio.gather(*[
            backend.set(key, value, ttl)
            for backend in self.backends
        ])
        
    async def _update_other_caches(
        self,
        key: str,
        value: Any,
        found_in: CacheBackend
    ):
        # 更新优先级更高的缓存
        tasks = []
        for backend in self.backends:
            if backend.priority < found_in.priority:
                tasks.append(
                    backend.set(key, value)
                )
        if tasks:
            await asyncio.gather(*tasks)

class MemoryCache:
    def __init__(self, max_size: int):
        self.cache = LRUCache(max_size)
        self.priority = 1
        
    async def get(self, key: str) -> Optional[Any]:
        return self.cache.get(key)
        
    async def set(
        self,
        key: str,
        value: Any,
        ttl: int = None
    ):
        self.cache.set(key, value)
        if ttl:
            asyncio.create_task(
                self._expire_key(key, ttl)
            )

class RedisCache:
    def __init__(self, url: str):
        self.redis = aioredis.from_url(url)
        self.priority = 2
        
    async def get(self, key: str) -> Optional[Any]:
        value = await self.redis.get(key)
        if value:
            return pickle.loads(value)
        return None
        
    async def set(
        self,
        key: str,
        value: Any,
        ttl: int = None
    ):
        await self.redis.set(
            key,
            pickle.dumps(value),
            ex=ttl
        )

向量检索优化

向量检索的优化也很重要:

class VectorManager:
    def __init__(
        self,
        primary: VectorStore,
        replica: VectorStore = None
    ):
        self.primary = primary
        self.replica = replica
        self.sync_interval = 3600  # 1小时
        
        # 启动同步任务
        if replica:
            asyncio.create_task(
                self._sync_loop()
            )
        
    async def search(
        self,
        query: str,
        limit: int = 5
    ) -> List[Document]:
        # 1. 计算查询向量
        vector = await self._compute_embedding(query)
        
        try:
            # 2. 优先从主存储搜索
            results = await self.primary.search(
                vector,
                limit=limit
            )
            
        except Exception as e:
            if not self.replica:
                raise
                
            # 3. 主存储失败,使用副本
            results = await self.replica.search(
                vector,
                limit=limit
            )
            
        return results
        
    async def _sync_loop(self):
        while True:
            try:
                # 同步数据
                await self._sync_data()
            except Exception as e:
                logger.error(f"同步失败: {e}")
            finally:
                await asyncio.sleep(
                    self.sync_interval
                )
                
    async def _sync_data(self):
        # 获取上次同步时间
        last_sync = await self._get_last_sync()
        
        # 增量同步数据
        vectors = await self.primary.get_updates(
            since=last_sync
        )
        
        if vectors:
            await self.replica.batch_add(vectors)
            
        # 更新同步时间
        await self._update_last_sync()

内存优化

内存管理也需要特别注意:

class MemoryOptimizer:
    def __init__(self):
        self.gc_threshold = 0.8  # 80%
        self.check_interval = 60  # 1分钟
        
    async def start(self):
        while True:
            try:
                await self._check_memory()
            except Exception as e:
                logger.error(f"内存检查失败: {e}")
            finally:
                await asyncio.sleep(
                    self.check_interval
                )
                
    async def _check_memory(self):
        usage = self._get_memory_usage()
        
        if usage > self.gc_threshold:
            # 触发垃圾回收
            await self._collect_garbage()
            
    def _get_memory_usage(self) -> float:
        import psutil
        process = psutil.Process()
        return process.memory_percent()
        
    async def _collect_garbage(self):
        # 1. 清理缓存
        await cache_manager.cleanup()
        
        # 2. 压缩向量索引
        await vector_store.optimize()
        
        # 3. 强制GC
        import gc
        gc.collect()

性能监控

要持续优化性能,需要好的监控:

class PerformanceMonitor:
    def __init__(self):
        self.metrics = {
            "latency": Histogram(
                name="request_latency",
                buckets=[0.1, 0.5, 1, 2, 5]
            ),
            "token_usage": Counter(
                name="token_usage",
                labels=["model"]
            ),
            "cache_hits": Counter(
                name="cache_hits",
                labels=["backend"]
            ),
            "vector_search_time": Histogram(
                name="vector_search_time",
                buckets=[0.01, 0.05, 0.1, 0.5]
            ),
            "memory_usage": Gauge(
                name="memory_usage_bytes"
            )
        }
        
    async def record_request(
        self,
        start_time: float,
        request: Dict[str, Any],
        response: Dict[str, Any]
    ):
        # 1. 记录延迟
        latency = time.time() - start_time
        self.metrics["latency"].observe(latency)
        
        # 2. 记录token使用
        tokens = self._count_tokens(
            request,
            response
        )
        self.metrics["token_usage"].inc(
            tokens,
            labels={"model": response["model"]}
        )
        
        # 3. 更新其他指标
        self._update_metrics(
            request,
            response
        )
        
    def _count_tokens(
        self,
        request: Dict[str, Any],
        response: Dict[str, Any]
    ) -> int:
        import tiktoken
        
        encoding = tiktoken.encoding_for_model(
            response["model"]
        )
        
        return len(encoding.encode(
            request["input"]
        )) + len(encoding.encode(
            response["output"]
        ))

成本优化

性能优化的同时也要考虑成本:

class CostOptimizer:
    def __init__(self):
        self.budget = {
            "daily_limit": 100,  # 美元
            "model_weights": {
                "gpt-4": 0.3,
                "gpt-3.5-turbo": 0.6,
                "codellama-7b": 0.1
            }
        }
        
    async def optimize_request(
        self,
        request: Dict[str, Any]
    ) -> Dict[str, Any]:
        # 1. 检查预算
        if await self._is_over_budget():
            # 切换到更便宜的模型
            request["model"] = "gpt-3.5-turbo"
            
        # 2. 优化prompt
        request["prompt"] = await self._optimize_prompt(
            request["prompt"]
        )
        
        # 3. 调整参数
        request["parameters"] = self._adjust_parameters(
            request["parameters"]
        )
        
        return request
        
    async def _is_over_budget(self) -> bool:
        usage = await self._get_daily_usage()
        return usage > self.budget["daily_limit"]
        
    async def _optimize_prompt(
        self,
        prompt: str
    ) -> str:
        # 1. 删除冗余内容
        prompt = self._remove_redundancy(prompt)
        
        # 2. 压缩上下文
        prompt = await self._compress_context(prompt)
        
        # 3. 优化指令
        prompt = self._optimize_instructions(prompt)
        
        return prompt
        
    def _adjust_parameters(
        self,
        params: Dict[str, Any]
    ) -> Dict[str, Any]:
        # 根据成本调整参数
        if self._is_peak_time():
            params["temperature"] = 0.3
            params["max_tokens"] = min(
                params.get("max_tokens", 2000),
                1000
            )
        return params

实践心得

在优化 AI Agent 性能的过程中,我总结了几点经验:

  1. 分层优化很重要

    • 架构层面要合理
    • 缓存策略要到位
    • 代码细节要注意
  2. 监控要做好

    • 实时性能监控
    • 成本使用跟踪
    • 及时发现问题
  3. 平衡很关键

    • 性能和成本
    • 实时性和准确性
    • 通用性和专用性

写在最后

性能优化是一个持续的过程,需要我们不断监控、分析和改进。就像调教一辆赛车,既要提升速度,又要控制油耗。

在下一篇文章中,我会讲解如何构建一个完整的 AI Agent 应用。如果你对性能优化有什么想法,欢迎在评论区交流。

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