AI Engineering Hub数据API：提供数据访问接口
项目地址: https://gitcode.com/GitHub_Trending/ai/ai-engineering-hub 🎯 痛点与承诺
还在为AI项目中的数据访问和集成而烦恼吗？面对分散的数据源、复杂的API调用和繁琐的集成工作，开发者往往需要花费大量时间在数据准备而非核心算法开发上。本文将通过AI Engineering Hub的数据API解决方案，为你提供一套完整、高效的数据访问接口，让你专注于AI模型的核心逻辑开发。
读完本文，你将获得：
- ✅ 理解AI Engineering Hub数据API的核心架构
- ✅ 掌握多种数据API的使用方法和最佳实践
- ✅ 学会如何快速集成外部数据源到AI项目中
- ✅ 了解性能优化和安全保障策略
- ✅ 获得完整的代码示例和部署指南
📊 AI Engineering Hub数据API架构概览
AI Engineering Hub采用模块化的数据API架构，支持多种数据源和访问模式：
核心组件功能表
| 组件 | 技术栈 | 主要功能 | 适用场景 |
|---|---|---|---|
| RESTful API | FastAPI + Pydantic | 结构化数据访问 | 文档查询、配置管理 |
| GraphQL API | Strawberry + GraphQL | 灵活数据查询 | 复杂数据关系查询 |
| WebSocket API | WebSockets + ASGI | 实时数据推送 | 实时监控、聊天应用 |
| 向量数据库 | Milvus + HuggingFace | 语义搜索 | RAG应用、相似性搜索 |
| 网页提取 | Firecrawl + BeautifulSoup | 网页内容提取 | 知识库构建、数据采集 |
🚀 快速开始：五分钟搭建数据API
环境准备
# 克隆项目仓库
git clone https://gitcode.com/GitHub_Trending/ai/ai-engineering-hub
cd ai-engineering-hub
# 安装依赖
pip install -r requirements.txt
# 设置环境变量
export GROQ_API_KEY=your_groq_api_key
export FIRECRAWL_API_KEY=your_firecrawl_key
基础数据API示例
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Optional
import os
app = FastAPI(title="AI Engineering Hub Data API")
class DocumentRequest(BaseModel):
url: str
extract_schema: Optional[dict] = None
timeout: int = 30
class VectorSearchRequest(BaseModel):
query: str
top_k: int = 5
collection_name: str
@app.post("/api/extract/document")
async def extract_document_data(request: DocumentRequest):
"""
从网页文档提取结构化数据
"""
try:
from firecrawl import FirecrawlApp
app = FirecrawlApp(api_key=os.getenv("FIRECRAWL_API_KEY"))
extract_params = {'prompt': '提取页面主要内容'}
if request.extract_schema:
extract_params['schema'] = request.extract_schema
result = app.extract([request.url], extract_params)
return {"success": True, "data": result}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/api/search/vector")
async def vector_semantic_search(request: VectorSearchRequest):
"""
向量语义搜索接口
"""
try:
from rag import EmbedData, MilvusVDB_BQ, Retriever
# 初始化嵌入模型
embedder = EmbedData()
query_embedding = embedder.embed_model.get_query_embedding(request.query)
# 搜索向量数据库
results = await search_milvus(
query_embedding,
request.collection_name,
request.top_k
)
return {"success": True, "results": results}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
🔧 核心API功能详解
1. 文档处理API
网页内容提取
@app.post("/api/documents/extract")
async def extract_web_content(
url: str,
schema: Optional[Dict[str, str]] = None
):
"""
高级网页内容提取API
支持自定义schema和智能内容识别
"""
extraction_params = {
'prompt': '提取所有重要信息',
'timeout': 60,
'waitForSelector': '.main-content'
}
if schema:
extraction_params['schema'] = schema
result = firecrawl_app.extract([url], extraction_params)
return process_extraction_result(result)
def process_extraction_result(data):
"""处理提取结果的标准流程"""
if isinstance(data['data'], list):
return {
"type": "tabular",
"count": len(data['data']),
"data": data['data']
}
else:
return {
"type": "structured",
"data": data['data']
}
批量文档处理
@app.post("/api/documents/batch")
async def batch_process_documents(
urls: List[str],
operation: str = "extract",
batch_size: int = 10
):
"""
批量文档处理API
支持并发处理多个文档源
"""
results = []
for i in range(0, len(urls), batch_size):
batch = urls[i:i + batch_size]
batch_results = await process_batch(batch, operation)
results.extend(batch_results)
return {
"processed": len(results),
"successful": sum(1 for r in results if r['success']),
"results": results
}
2. 向量搜索API
语义搜索接口
class SemanticSearchRequest(BaseModel):
query: str
collection: str = "default"
top_k: int = 10
score_threshold: float = 0.7
filters: Optional[Dict] = None
@app.post("/api/vector/search")
async def semantic_search(request: SemanticSearchRequest):
"""
高级语义搜索API
支持过滤、评分阈值和多种搜索模式
"""
# 生成查询嵌入
query_embedding = embedder.get_query_embedding(request.query)
# 构建搜索参数
search_params = {
"data": [query_embedding],
"anns_field": "embedding",
"limit": request.top_k,
"param": {"metric_type": "L2", "params": {"nprobe": 10}},
"expr": build_filter_expression(request.filters) if request.filters else None
}
# 执行搜索
results = milvus_client.search(
collection_name=request.collection,
**search_params
)
# 过滤和格式化结果
filtered_results = [
{
"id": hit.id,
"score": 1.0 / (1.0 + hit.distance),
"content": hit.entity.get('content', ''),
"metadata": hit.entity.get('metadata', {})
}
for hit in results[0]
if (1.0 / (1.0 + hit.distance)) >= request.score_threshold
]
return {"query": request.query, "results": filtered_results}
向量索引管理
@app.post("/api/vector/collections/{collection_name}")
async def create_vector_collection(
collection_name: str,
dimension: int = 1024,
index_type: str = "IVF_FLAT",
metric_type: str = "L2"
):
"""
创建和管理向量集合
"""
# 检查集合是否存在
if milvus_client.has_collection(collection_name):
raise HTTPException(400, f"Collection {collection_name} already exists")
# 定义schema
schema = milvus_client.create_schema(
auto_id=True,
enable_dynamic_field=True
)
schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="embedding", datatype=DataType.FLOAT_VECTOR, dim=dimension)
schema.add_field(field_name="content", datatype=DataType.VARCHAR, max_length=65535)
# 创建索引参数
index_params = milvus_client.prepare_index_params()
index_params.add_index(
field_name="embedding",
index_type=index_type,
metric_type=metric_type,
params={"nlist": 1024}
)
# 创建集合
milvus_client.create_collection(
collection_name=collection_name,
schema=schema,
index_params=index_params
)
return {"status": "created", "collection": collection_name}
3. 实时数据流API
WebSocket实时通信
from fastapi import WebSocket, WebSocketDisconnect
class ConnectionManager:
def __init__(self):
self.active_connections: List[WebSocket] = []
async def connect(self, websocket: WebSocket):
await websocket.accept()
self.active_connections.append(websocket)
def disconnect(self, websocket: WebSocket):
self.active_connections.remove(websocket)
async def broadcast(self, message: str):
for connection in self.active_connections:
await connection.send_text(message)
manager = ConnectionManager()
@app.websocket("/api/ws/updates")
async def websocket_endpoint(websocket: WebSocket):
await manager.connect(websocket)
try:
while True:
data = await websocket.receive_text()
# 处理实时数据更新
await process_real_time_data(data, websocket)
except WebSocketDisconnect:
manager.disconnect(websocket)
async def process_real_time_data(data, websocket):
"""处理实时数据流"""
try:
message = json.loads(data)
if message['type'] == 'subscribe':
# 订阅数据更新
await handle_subscription(message, websocket)
elif message['type'] == 'publish':
# 发布数据更新
await manager.broadcast(json.dumps(message))
except Exception as e:
await websocket.send_text(json.dumps({"error": str(e)}))
🏗️ API性能优化策略
缓存策略实现
from functools import lru_cache
from redis import Redis
import json
class APICache:
def __init__(self, redis_url: Optional[str] = None):
self.redis = Redis.from_url(redis_url) if redis_url else None
self.local_cache = {}
async def get(self, key: str):
"""获取缓存数据"""
if self.redis:
try:
cached = self.redis.get(key)
if cached:
return json.loads(cached)
except:
pass
return self.local_cache.get(key)
async def set(self, key: str, value: Any, expire: int = 300):
"""设置缓存数据"""
if self.redis:
try:
self.redis.setex(key, expire, json.dumps(value))
except:
pass
self.local_cache[key] = value
# 使用缓存装饰器
def cache_response(expire: int = 60):
def decorator(func):
@wraps(func)
async def wrapper(*args, **kwargs):
cache_key = f"{func.__name__}:{str(args)}:{str(kwargs)}"
cached = await cache_manager.get(cache_key)
if cached:
return cached
result = await func(*args, **kwargs)
await cache_manager.set(cache_key, result, expire)
return result
return wrapper
return decorator
# 在API中使用缓存
@app.get("/api/documents/{doc_id}")
@cache_response(expire=300)
async def get_document(doc_id: str):
"""带缓存的文档获取API"""
return await fetch_document_from_db(doc_id)
并发处理优化
import asyncio
from concurrent.futures import ThreadPoolExecutor
# 线程池执行器
thread_pool = ThreadPoolExecutor(max_workers=10)
@app.post("/api/process/batch")
async def batch_process_with_concurrency(
items: List[str],
max_concurrent: int = 5
):
"""
支持高并发的批量处理API
使用信号量控制并发度
"""
semaphore = asyncio.Semaphore(max_concurrent)
async def process_item(item):
async with semaphore:
return await process_single_item(item)
# 并发处理所有项目
tasks = [process_item(item) for item in items]
results = await asyncio.gather(*tasks, return_exceptions=True)
# 处理结果
successful = []
failed = []
for i, result in enumerate(results):
if isinstance(result, Exception):
failed.append({"item": items[i], "error": str(result)})
else:
successful.append(result)
return {
"total": len(items),
"successful": len(successful),
"failed": len(failed),
"results": successful,
"errors": failed
}
🔒 安全与认证机制
JWT认证集成
from fastapi import Depends, HTTPException, status
from fastapi.security import OAuth2PasswordBearer
from jose import JWTError, jwt
from passlib.context import CryptContext
# 安全配置
SECRET_KEY = "your-secret-key"
ALGORITHM = "HS256"
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="/api/auth/token")
pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
def verify_password(plain_password, hashed_password):
return pwd_context.verify(plain_password, hashed_password)
def get_password_hash(password):
return pwd_context.hash(password)
def create_access_token(data: dict, expires_delta: timedelta = None):
to_encode = data.copy()
if expires_delta:
expire = datetime.utcnow() + expires_delta
else:
expire = datetime.utcnow() + timedelta(minutes=15)
to_encode.update({"exp": expire})
encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
return encoded_jwt
async def get_current_user(token: str = Depends(oauth2_scheme)):
credentials_exception = HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Could not validate credentials",
headers={"WWW-Authenticate": "Bearer"},
)
try:
payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
username: str = payload.get("sub")
if username is None:
raise credentials_exception
except JWTError:
raise credentials_exception
user = await get_user(username)
if user is None:
raise credentials_exception
return user
# 受保护的API端点
@app.get("/api/protected/data")
async def protected_data(current_user: User = Depends(get_current_user)):
"""需要认证的数据访问API"""
return {"message": f"Hello {current_user.username}", "data": sensitive_data}
API速率限制
from slowapi import Limiter
from slowapi.util import get_remote_address
from slowapi.errors import RateLimitExceeded
limiter = Limiter(key_func=get_remote_address)
@app.exception_handler(RateLimitExceeded)
async def rate_limit_handler(request, exc):
return JSONResponse(
status_code=429,
content={"detail": "Rate limit exceeded", "retry_after": exc.retry_after}
)
@app.get("/api/data/limited")
@limiter.limit("5/minute")
async def limited_data(request: Request):
"""速率限制的API端点"""
return {"data": "This is rate limited data"}
# 基于用户的速率限制
def user_based_limiter(user_id: str):
return f"10/minute/{user_id}"
@app.get("/api/user/data")
@limiter.limit(user_based_limiter)
async def user_specific_data(
request: Request,
current_user: User = Depends(get_current_user)
):
"""基于用户的速率限制"""
return {"user_data": get_user_data(current_user.id)}
📈 监控与日志记录
综合监控系统
import logging
from prometheus_client import Counter, Histogram
from starlette.middleware.base import BaseHTTPMiddleware
# Prometheus指标
API_REQUESTS = Counter('api_requests_total', 'Total API requests', ['method', 'endpoint', 'status'])
API_LATENCY = Histogram('api_request_latency_seconds', 'API request latency', ['endpoint'])
# 日志配置
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("api")
class MonitoringMiddleware(BaseHTTPMiddleware):
async def dispatch(self, request, call_next):
start_time = time.time()
try:
response = await call_next(request)
latency = time.time() - start_time
# 记录指标
API_REQUESTS.labels(
method=request.method,
endpoint=request.url.path,
status=response.status_code
).inc()
API_LATENCY.labels(endpoint=request.url.path).observe(latency)
# 记录访问日志
logger.info(
f"{request.method} {request.url.path} - {response.status_code} "
f"- {latency:.3f}s"
)
return response
except Exception as e:
latency = time.time() - start_time
logger.error(f"Error in {request.url.path}: {str(e)} - {latency:.3f}s")
raise
# 应用中间件
app.add_middleware(MonitoringMiddleware)
# 健康检查端点
@app.get("/api/health")
async def health_check():
"""系统健康检查API"""
return {
"status": "healthy",
"timestamp": datetime.utcnow().isoformat(),
"version": "1.0.0"
}
# 指标端点
@app.get("/api/metrics")
async def metrics():
"""Prometheus指标端点"""
from prometheus_client import generate_latest
return Response(generate_latest(), media_type="text/plain")
🚀 部署与扩展
Docker容器化部署
FROM python:3.9-slim
WORKDIR /app
# 安装系统依赖
RUN apt-get update && apt-get install -y \
gcc \
g++ \
&& rm -rf /var/lib/apt/lists/*
# 复制依赖文件
COPY requirements.txt .
RUN pip install -r requirements.txt
# 复制应用代码
COPY . .
# 创建非root用户
RUN useradd -m -u 1000 apiuser && chown -R apiuser:apiuser /app
USER apiuser
# 暴露端口
EXPOSE 8000
# 启动命令
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"]
Kubernetes部署配置
apiVersion: apps/v1
kind: Deployment
metadata:
name: ai-data-api
spec:
replicas: 3
selector:
matchLabels:
app: ai-data-api
template:
metadata:
labels:
app: ai-data-api
spec:
containers:
- name: api
image: ai-engineering-hub/data-api:latest
ports:
- containerPort: 8000
env:
- name: GROQ_API_KEY
valueFrom:
secretKeyRef:
name: api-secrets
key: groq-api-key
resources:
requests:
memory: "512Mi"
cpu: "250m"
limits:
memory: "1Gi"
cpu: "500m"
livenessProbe:
httpGet:
path: /api/health
port: 8000
initialDelaySeconds: 30
periodSeconds: 10
---
apiVersion: v1
kind: Service
metadata:
name: ai-data-api-service
spec:
selector:
app: ai-data-api
ports:
- port: 80
targetPort: 8000
type: LoadBalancer
🎯 总结与展望
AI Engineering Hub的数据API提供了完整的数据访问解决方案，从文档处理到向量搜索，从实时数据流到安全认证，覆盖了AI项目开发中的各种数据需求。通过本文的详细介绍和代码示例，你应该能够：
- 快速集成：五分钟内搭建起完整的数据API服务
- 灵活扩展：根据项目需求选择合适的数据处理模块
- 性能优化：利用缓存、并发和监控确保API高性能
- 安全保障：通过认证和速率限制保护数据安全
- 生产部署：使用容器化和云原生技术进行规模部署
未来，AI Engineering Hub将继续扩展数据API的能力，包括：
- 🔄 更多数据源连接器（数据库、云存储、消息队列）
- 🤖 智能数据预处理和增强功能
- 📊 高级数据分析和可视化接口
- 🌐 分布式数据缓存和同步机制
立即行动：开始使用AI Engineering Hub数据API，让你的AI项目开发效率提升300%！记得点赞、收藏、关注三连，下期我们将深入探讨AI模型部署和推理优化。
创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考
