enrichmcp (Featureform)

EnrichMCP

面向AI代理的ORM-将您的数据模型转换为语义MCP层

     

EnrichMCP是一个Python框架，可帮助AI代理理解和导航您的数据。它基于MCP（模型上下文协议）构建，添加了一个语义层，将您的数据模型转换为类型化的、可发现的工具，如人工智能的ORM。

什么是EnrichMCP？

将其视为AI代理的SQLAlchemy。EnrichMCP自动：

• 生成类型化工具 从您的数据模型
• 处理关系 实体（用户）之间→ 订单→ 产品）
• 提供架构发现 因此，AI代理能够理解您的数据结构
• 验证所有输入/输出 使用Pydantic模型
• 适用于任何后端 -数据库、API或自定义逻辑

安装

pip install enrichmcp

# With SQLAlchemy support
pip install enrichmcp[sqlalchemy]

显示代码

选项1：我有SQLAlchemy模型（30秒）

将您现有的SQLAlchemy模型转换为AI可导航的API：

from enrichmcp import EnrichMCP
from enrichmcp.sqlalchemy import (
    include_sqlalchemy_models,
    sqlalchemy_lifespan,
    EnrichSQLAlchemyMixin,
)
from sqlalchemy import ForeignKey
from sqlalchemy.ext.asyncio import create_async_engine
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, relationship

engine = create_async_engine("postgresql+asyncpg://user:pass@localhost/db")

# Add the mixin to your declarative base
class Base(DeclarativeBase, EnrichSQLAlchemyMixin):
    pass

class User(Base):
    """User account."""

    __tablename__ = "users"

    id: Mapped[int] = mapped_column(primary_key=True, info={"description": "Unique user ID"})
    email: Mapped[str] = mapped_column(unique=True, info={"description": "Email address"})
    status: Mapped[str] = mapped_column(default="active", info={"description": "Account status"})
    orders: Mapped[list["Order"]] = relationship(
        back_populates="user", info={"description": "All orders for this user"}
    )

class Order(Base):
    """Customer order."""

    __tablename__ = "orders"

    id: Mapped[int] = mapped_column(primary_key=True, info={"description": "Order ID"})
    user_id: Mapped[int] = mapped_column(
        ForeignKey("users.id"), info={"description": "Owner user ID"}
    )
    total: Mapped[float] = mapped_column(info={"description": "Order total"})
    user: Mapped[User] = relationship(
        back_populates="orders", info={"description": "User who placed the order"}
    )

# That's it! Create your MCP app
app = EnrichMCP(
    "E-commerce Data",
    "API generated from SQLAlchemy models",
    lifespan=sqlalchemy_lifespan(Base, engine, cleanup_db_file=True),
)
include_sqlalchemy_models(app, Base)

if __name__ == "__main__":
    app.run()

AI代理现在可以：

• explore_data_model() -理解你的整个模式
• list_users(status='active') -使用筛选器进行查询
• get_user(id=123) -获取特定记录
• 浏览关系： user.orders → order.user

选项2：我有REST API（2分钟）

用语义理解来包装现有的API：

from typing import Literal
from enrichmcp import EnrichMCP, EnrichModel, Relationship
from pydantic import Field
import httpx

app = EnrichMCP("API Gateway", "Wrapper around existing REST APIs")
http = httpx.AsyncClient(base_url="https://api.example.com")

@app.entity()
class Customer(EnrichModel):
    """Customer in our CRM system."""

    id: int = Field(description="Unique customer ID")
    email: str = Field(description="Primary contact email")
    tier: Literal["free", "pro", "enterprise"] = Field(description="Subscription tier")

    # Define navigable relationships
    orders: list["Order"] = Relationship(description="Customer's purchase history")

@app.entity()
class Order(EnrichModel):
    """Customer order from our e-commerce platform."""

    id: int = Field(description="Order ID")
    customer_id: int = Field(description="Associated customer")
    total: float = Field(description="Order total in USD")
    status: Literal["pending", "shipped", "delivered"] = Field(description="Order status")

    customer: Customer = Relationship(description="Customer who placed this order")

# Define how to fetch data
@app.retrieve()
async def get_customer(customer_id: int) -> Customer:
    """Fetch customer from CRM API."""
    response = await http.get(f"/api/customers/{customer_id}")
    return Customer(**response.json())

# Define relationship resolvers
@Customer.orders.resolver
async def get_customer_orders(customer_id: int) -> list[Order]:
    """Fetch orders for a customer."""
    response = await http.get(f"/api/customers/{customer_id}/orders")
    return [Order(**order) for order in response.json()]

@Order.customer.resolver
async def get_order_customer(order_id: int) -> Customer:
    """Fetch the customer for an order."""
    response = await http.get(f"/api/orders/{order_id}/customer")
    return Customer(**response.json())

app.run()

选项3：我想要完全控制（5分钟）

使用自定义逻辑构建完整的数据层：

from enrichmcp import EnrichMCP, EnrichModel, Relationship
from datetime import datetime
from decimal import Decimal
from pydantic import Field

app = EnrichMCP("Analytics Platform", "Custom analytics API")

db = ...  # your database connection

@app.entity()
class User(EnrichModel):
    """User with computed analytics fields."""

    id: int = Field(description="User ID")
    email: str = Field(description="Contact email")
    created_at: datetime = Field(description="Registration date")

    # Computed fields
    lifetime_value: Decimal = Field(description="Total revenue from user")
    churn_risk: float = Field(description="ML-predicted churn probability 0-1")

    # Relationships
    orders: list["Order"] = Relationship(description="Purchase history")
    segments: list["Segment"] = Relationship(description="Marketing segments")

@app.entity()
class Segment(EnrichModel):
    """Dynamic user segment for marketing."""

    name: str = Field(description="Segment name")
    criteria: dict = Field(description="Segment criteria")
    users: list[User] = Relationship(description="Users in this segment")

@app.entity()
class Order(EnrichModel):
    """Simplified order record."""

    id: int = Field(description="Order ID")
    user_id: int = Field(description="Owner user ID")
    total: Decimal = Field(description="Order total")

@User.orders.resolver
async def list_user_orders(user_id: int) -> list[Order]:
    """Fetch orders for a user."""
    rows = await db.query(
        "SELECT * FROM orders WHERE user_id = ? ORDER BY id DESC",
        user_id,
    )
    return [Order(**row) for row in rows]

@User.segments.resolver
async def list_user_segments(user_id: int) -> list[Segment]:
    """Fetch segments that include the user."""
    rows = await db.query(
        "SELECT s.* FROM segments s JOIN user_segments us ON s.name = us.segment_name WHERE us.user_id = ?",
        user_id,
    )
    return [Segment(**row) for row in rows]

@Segment.users.resolver
async def list_segment_users(name: str) -> list[User]:
    """List users in a segment."""
    rows = await db.query(
        "SELECT u.* FROM users u JOIN user_segments us ON u.id = us.user_id WHERE us.segment_name = ?",
        name,
    )
    return [User(**row) for row in rows]

# Complex resource with business logic
@app.retrieve()
async def find_high_value_at_risk_users(
    lifetime_value_min: Decimal = 1000, churn_risk_min: float = 0.7, limit: int = 100
) -> list[User]:
    """Find valuable customers likely to churn."""
    users = await db.query(
        """
        SELECT * FROM users
        WHERE lifetime_value >= ? AND churn_risk >= ?
        ORDER BY lifetime_value DESC
        LIMIT ?
        """,
        lifetime_value_min,
        churn_risk_min,
        limit,
    )
    return [User(**u) for u in users]

# Async computed field resolver
@User.lifetime_value.resolver
async def calculate_lifetime_value(user_id: int) -> Decimal:
    """Calculate total revenue from user's orders."""
    total = await db.query_single("SELECT SUM(total) FROM orders WHERE user_id = ?", user_id)
    return Decimal(str(total or 0))

# ML-powered field
@User.churn_risk.resolver
async def predict_churn_risk(user_id: int) -> float:
    """Run churn prediction model."""
    ctx = app.get_context()
    features = await gather_user_features(user_id)
    model = ctx.get("ml_models")["churn"]
    return float(model.predict_proba(features)[0][1])

app.run()

主要特点

🔍 自动架构发现

AI代理只需一次调用即可探索您的整个数据模型：

schema = await explore_data_model()
# Returns complete schema with entities, fields, types, and relationships

🔗 关系导航

一旦定义了关系，AI代理就会自然地遍历：

# AI can navigate: user → orders → products → categories
user = await get_user(123)
orders = await user.orders()  # Automatic resolver
products = await orders[0].products()

🛡️ 类型安全与验证

对每次交互进行完整的Pydantic验证：

@app.entity()
class Order(EnrichModel):
    total: float = Field(ge=0, description="Must be positive")
    email: EmailStr = Field(description="Customer email")
    status: Literal["pending", "shipped", "delivered"]

describe_model() 将列出这些允许的值，以便代理知道有效的选项。

✏️ 可变性和CRUD

默认情况下，字段是不可变的。将它们标记为可变并使用 用于更新的自动生成补丁模型：

@app.entity()
class Customer(EnrichModel):
    id: int = Field(description="ID")
    email: str = Field(json_schema_extra={"mutable": True}, description="Email")

@app.create()
async def create_customer(email: str) -> Customer: ...

@app.update()
async def update_customer(cid: int, patch: Customer.PatchModel) -> Customer: ...

@app.delete()
async def delete_customer(cid: int) -> bool: ...

📄 内置分页功能

优雅地处理大型数据集：

from enrichmcp import PageResult

@app.retrieve()
async def list_orders(page: int = 1, page_size: int = 50) -> PageResult[Order]:
    orders, total = await db.get_orders_page(page, page_size)
    return PageResult.create(items=orders, page=page, page_size=page_size, total_items=total)

请参阅 分页指南 更多示例。

🔐 上下文和身份验证

传递身份验证、数据库连接或任何上下文：

from pydantic import Field
from enrichmcp import EnrichModel

class UserProfile(EnrichModel):
    """User profile information."""

    user_id: int = Field(description="User ID")
    bio: str | None = Field(default=None, description="Short bio")

@app.retrieve()
async def get_user_profile(user_id: int) -> UserProfile:
    ctx = app.get_context()
    # Access context provided by MCP client
    auth_user = ctx.get("authenticated_user_id")
    if auth_user != user_id:
        raise PermissionError("Can only access your own profile")
    return await db.get_profile(user_id)

⚡ 请求缓存

通过将结果存储在per-request、per-user或全局缓存中，减少API开销：

@app.retrieve()
async def get_customer(cid: int) -> Customer:
    ctx = app.get_context()

    async def fetch() -> Customer:
        return await db.get_customer(cid)

    return await ctx.cache.get_or_set(f"customer:{cid}", fetch)

🧭 参数提示

使用提供工具参数的示例和元数据 EnrichParameter:

from enrichmcp import EnrichParameter

@app.retrieve()
async def greet_user(name: str = EnrichParameter(description="user name", examples=["bob"])) -> str:
    return f"Hello {name}"

工具说明将包括参数类型、说明和示例。

🌐 HTTP和SSE支持

通过标准输出（默认）、SSE或HTTP为API提供服务：

app.run()  # stdio default
app.run(transport="streamable-http")

为什么选择EnrichMCP？

EnrichMCP在MCP之上增加了三个关键层：

1. 语义层 -AI代理了解你的数据意味着什么，而不仅仅是它的结构
2. 数据层 -具有验证和关系的类型安全模型
3. 控制层 -身份验证、分页和业务逻辑

结果：AI代理可以像使用ORM的开发人员一样自然地处理您的数据。

服务器端LLM采样

EnrichMCP可以通过MCP请求语言模型完成 采样 功能。呼叫 ctx.ask_llm() 或 ctx.sampling() 来自任何资源的别名 并且连接的客户端将选择LLM并支付使用费。你可以调音 使用以下选项的行为 model_preferences, allow_tools，以及 max_tokens。参见 docs/server_side_llm.md 更多 细节。

示例

看看 示例目录:

• 你好_世界 -最小的EnrichMCP应用程序
• hello_world_http -使用流式HTTP的HTTP示例
• shop_api -带分页和过滤器的API内存车间
• shop_api_sqlite -SQLite支持版本
• shop_api_gateway -EnrichMCP作为FastAPI前的网关
• sqlalchemy_shop -从SQLAlchemy模型自动生成API
• mutable_crud -演示可变字段和CRUD装饰器
• 缓存 -演示ContextCache的使用方法
• 基本记忆 -使用FileMemoryStore进行简单的注释API
• openai_chat_agent -MCP示例的交互式聊天客户端

文档

• 📖 全部文件
• 🚀 入门指南
• 🔧 api参考

贡献

我们欢迎捐款！看 贡献.md 了解详情。

开发设置

存储库需要 Python 3.11 或更新。Makefile包括 创建虚拟环境并运行测试的命令：

make setup            # create .venv and install dependencies
source .venv/bin/activate
make test             # run the test suite

这将安装所有开发附加功能和预提交挂钩，因此命令如下 make lint 或 make docs 马上工作。

许可证

Apache 2.0-请参阅 许可证

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