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将推理 SDK 与 Foundry Local 集成

2025-06-24

重要

Foundry Local 以预览版形式提供。通过公共预览版，可以提前访问正处于开发状态的功能。
正式发布 (GA) 之前，功能、方法和流程可能会发生更改或功能受限。

Foundry Local 与各种推理 SDK（例如 OpenAI、Azure OpenAI、Langchain 等）集成。本指南介绍如何使用常用 SDK 将应用程序连接到本地运行的 AI 模型。

先决条件

已安装 Foundry Local。有关安装说明，请参阅 Foundry Local 入门文章。

安装 pip 包

安装以下 Python 包：

pip install openai
pip install foundry-local-sdk

小窍门

建议使用虚拟环境来避免包冲突。可以使用 venv 或 conda 创建虚拟环境。

将 OpenAI SDK 与 Foundry Local 配合使用

以下示例演示如何将 OpenAI SDK 与 Foundry Local 配合使用。该代码初始化 Foundry Local 服务，加载模型，并使用 OpenAI SDK 生成响应。

将以下代码复制并粘贴到名为 app.py“Python”的 Python 文件中：

import openai
from foundry_local import FoundryLocalManager

# By using an alias, the most suitable model will be downloaded 
# to your end-user's device. 
alias = "phi-3.5-mini"

# Create a FoundryLocalManager instance. This will start the Foundry
# Local service if it is not already running and load the specified model.
manager = FoundryLocalManager(alias)
# The remaining code uses the OpenAI Python SDK to interact with the local model.
# Configure the client to use the local Foundry service
client = openai.OpenAI(
    base_url=manager.endpoint,
    api_key=manager.api_key  # API key is not required for local usage
)
# Set the model to use and generate a response
response = client.chat.completions.create(
    model=manager.get_model_info(alias).id,
    messages=[{"role": "user", "content": "What is the golden ratio?"}]
)
print(response.choices[0].message.content)

使用以下命令运行代码：

python app.py

流式处理响应

如果要接收流式处理响应，可以按如下所示修改代码：

import openai
from foundry_local import FoundryLocalManager

# By using an alias, the most suitable model will be downloaded 
# to your end-user's device.
alias = "phi-3.5-mini"

# Create a FoundryLocalManager instance. This will start the Foundry 
# Local service if it is not already running and load the specified model.
manager = FoundryLocalManager(alias)

# The remaining code us es the OpenAI Python SDK to interact with the local model.

# Configure the client to use the local Foundry service
client = openai.OpenAI(
    base_url=manager.endpoint,
    api_key=manager.api_key  # API key is not required for local usage
)

# Set the model to use and generate a streaming response
stream = client.chat.completions.create(
    model=manager.get_model_info(alias).id,
    messages=[{"role": "user", "content": "What is the golden ratio?"}],
    stream=True
)

# Print the streaming response
for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="", flush=True)

可以使用与之前相同的命令运行代码：

python app.py

将 `requests` 与 Foundry Local 配合使用

# Install with: pip install requests
import requests
import json
from foundry_local import FoundryLocalManager

# By using an alias, the most suitable model will be downloaded 
# to your end-user's device. 
alias = "phi-3.5-mini"

# Create a FoundryLocalManager instance. This will start the Foundry
# Local service if it is not already running and load the specified model.
manager = FoundryLocalManager(alias)

url = manager.endpoint + "/chat/completions"

payload = {
    "model": manager.get_model_info(alias).id,
    "messages": [
        {"role": "user", "content": "What is the golden ratio?"}
    ]
}

headers = {
    "Content-Type": "application/json"
}

response = requests.post(url, headers=headers, data=json.dumps(payload))
print(response.json()["choices"][0]["message"]["content"])

安装 Node.js 包

需要安装以下 Node.js 包：

npm install openai
npm install foundry-local-sdk

Foundry Local SDK 允许管理 Foundry 本地服务和模型。

将 OpenAI SDK 与 Foundry Local 配合使用

以下示例演示如何将 OpenAI SDK 与 Foundry Local 配合使用。该代码初始化 Foundry Local 服务，加载模型，并使用 OpenAI SDK 生成响应。

将以下代码复制并粘贴到名为 app.js 的 JavaScript 文件中：

import { OpenAI } from "openai";
import { FoundryLocalManager } from "foundry-local-sdk";

// By using an alias, the most suitable model will be downloaded 
// to your end-user's device.
// TIP: You can find a list of available models by running the 
// following command in your terminal: `foundry model list`.
const alias = "phi-3.5-mini";

// Create a FoundryLocalManager instance. This will start the Foundry 
// Local service if it is not already running.
const foundryLocalManager = new FoundryLocalManager()

// Initialize the manager with a model. This will download the model 
// if it is not already present on the user's device.
const modelInfo = await foundryLocalManager.init(alias)
console.log("Model Info:", modelInfo)

const openai = new OpenAI({
  baseURL: foundryLocalManager.endpoint,
  apiKey: foundryLocalManager.apiKey,
});

async function generateText() {
  const response = await openai.chat.completions.create({
    model: modelInfo.id,
    messages: [
      {
        role: "user",
        content: "What is the golden ratio?",
      },
    ],
  });

  console.log(response.choices[0].message.content);
}

generateText();

使用以下命令运行代码：

node app.js

流式处理响应

如果要接收流式处理响应，可以按如下所示修改代码：

import { OpenAI } from "openai";
import { FoundryLocalManager } from "foundry-local-sdk";

// By using an alias, the most suitable model will be downloaded 
// to your end-user's device.
// TIP: You can find a list of available models by running the 
// following command in your terminal: `foundry model list`.
const alias = "phi-3.5-mini";

// Create a FoundryLocalManager instance. This will start the Foundry 
// Local service if it is not already running.
const foundryLocalManager = new FoundryLocalManager()

// Initialize the manager with a model. This will download the model 
// if it is not already present on the user's device.
const modelInfo = await foundryLocalManager.init(alias)
console.log("Model Info:", modelInfo)

const openai = new OpenAI({
  baseURL: foundryLocalManager.endpoint,
  apiKey: foundryLocalManager.apiKey,
});

async function streamCompletion() {
    const stream = await openai.chat.completions.create({
      model: modelInfo.id,
      messages: [{ role: "user", content: "What is the golden ratio?" }],
      stream: true,
    });
  
    for await (const chunk of stream) {
      if (chunk.choices[0]?.delta?.content) {
        process.stdout.write(chunk.choices[0].delta.content);
      }
    }
}
  
streamCompletion();

使用以下命令运行代码：

node app.js

将提取 API 与 Foundry Local 配合使用

如果您偏好使用像 fetch 这样的 HTTP 客户端，可以按如下方式进行。

import { FoundryLocalManager } from "foundry-local-sdk";

// By using an alias, the most suitable model will be downloaded 
// to your end-user's device.
// TIP: You can find a list of available models by running the 
// following command in your terminal: `foundry model list`.
const alias = "phi-3.5-mini";

// Create a FoundryLocalManager instance. This will start the Foundry 
// Local service if it is not already running.
const foundryLocalManager = new FoundryLocalManager()

// Initialize the manager with a model. This will download the model 
// if it is not already present on the user's device.
const modelInfo = await foundryLocalManager.init(alias)
console.log("Model Info:", modelInfo)

async function queryModel() {
    const response = await fetch(foundryLocalManager.endpoint + "/chat/completions", {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
        },
        body: JSON.stringify({
            model: modelInfo.id,
            messages: [
                { role: "user", content: "What is the golden ratio?" },
            ],
        }),
    });

    const data = await response.json();
    console.log(data.choices[0].message.content);
}

queryModel();

流式处理响应

如果要使用提取 API 接收流式处理响应，可以按如下所示修改代码：

import { FoundryLocalManager } from "foundry-local-sdk";

// By using an alias, the most suitable model will be downloaded 
// to your end-user's device.
// TIP: You can find a list of available models by running the 
// following command in your terminal: `foundry model list`.
const alias = "phi-3.5-mini";

// Create a FoundryLocalManager instance. This will start the Foundry 
// Local service if it is not already running.
const foundryLocalManager = new FoundryLocalManager()

// Initialize the manager with a model. This will download the model 
// if it is not already present on the user's device.
const modelInfo = await foundryLocalManager.init(alias)
console.log("Model Info:", modelInfo)

async function streamWithFetch() {
    const response = await fetch(foundryLocalManager.endpoint + "/chat/completions", {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            Accept: "text/event-stream",
        },
        body: JSON.stringify({
            model: modelInfo.id,
            messages: [{ role: "user", content: "what is the golden ratio?" }],
            stream: true,
        }),
    });

    const reader = response.body.getReader();
    const decoder = new TextDecoder();

    while (true) {
        const { done, value } = await reader.read();
        if (done) break;

        const chunk = decoder.decode(value);
        const lines = chunk.split("\n").filter((line) => line.trim() !== "");

        for (const line of lines) {
            if (line.startsWith("data: ")) {
                const data = line.substring(6);
                if (data === "[DONE]") continue;

                try {
                    const json = JSON.parse(data);
                    const content = json.choices[0]?.delta?.content || "";
                    if (content) {
                        // Print to console without line breaks, similar to process.stdout.write
                        process.stdout.write(content);
                    }
                } catch (e) {
                    console.error("Error parsing JSON:", e);
                }
            }
        }
    }
}

// Call the function to start streaming
streamWithFetch();

创建项目

创建新的 C# 项目并导航到它：

dotnet new console -n hello-foundry-local
cd hello-foundry-local

安装 NuGet 包

将以下 NuGet 包安装到项目文件夹中：

dotnet add package Microsoft.AI.Foundry.Local --version 0.1.0
dotnet add package OpenAI --version 2.2.0-beta.4

将 OpenAI SDK 与 Foundry Local 配合使用

以下示例演示如何将 OpenAI SDK 与 Foundry Local 配合使用。该代码初始化 Foundry Local 服务，加载模型，并使用 OpenAI SDK 生成响应。

将以下代码复制并粘贴到名为 Program.cs 的 C# 文件中。

using Microsoft.AI.Foundry.Local;
using OpenAI;
using OpenAI.Chat;
using System.ClientModel;
using System.Diagnostics.Metrics;

var alias = "phi-3.5-mini";

var manager = await FoundryLocalManager.StartModelAsync(aliasOrModelId: alias);

var model = await manager.GetModelInfoAsync(aliasOrModelId: alias);
ApiKeyCredential key = new ApiKeyCredential(manager.ApiKey);
OpenAIClient client = new OpenAIClient(key, new OpenAIClientOptions
{
    Endpoint = manager.Endpoint
});

var chatClient = client.GetChatClient(model?.ModelId);

var completionUpdates = chatClient.CompleteChatStreaming("Why is the sky blue'");

Console.Write($"[ASSISTANT]: ");
foreach (var completionUpdate in completionUpdates)
{
    if (completionUpdate.ContentUpdate.Count > 0)
    {
        Console.Write(completionUpdate.ContentUpdate[0].Text);
    }
}

使用以下命令运行代码：

dotnet run

创建项目

创建新的 Rust 项目并导航到它：

cargo new hello-foundry-local
cd hello-foundry-local

安装 crate

使用 Cargo 安装以下 Rust 库：

cargo add foundry-local anyhow env_logger serde_json
cargo add reqwest --features json
cargo add tokio --features full

更新`main.rs`文件

以下示例演示如何使用对 Foundry Local 服务的请求进行推理。该代码初始化 Foundry Local 服务，加载模型，并使用库生成响应 reqwest 。

将以下代码复制并粘贴到名为 main.rsRust 文件中：

use foundry_local::FoundryLocalManager;
use anyhow::Result;

#[tokio::main]
async fn main() -> Result<()> {
    // Create a FoundryLocalManager instance with default options
    let mut manager = FoundryLocalManager::builder()
        .alias_or_model_id("qwen2.5-0.5b") // Specify the model to use   
        .bootstrap(true) // Start the service if not running
        .build()
        .await?;
    
    // Use the OpenAI compatible API to interact with the model
    let client = reqwest::Client::new();
    let endpoint = manager.endpoint()?;
    let response = client.post(format!("{}/chat/completions", endpoint))
        .header("Content-Type", "application/json")
        .header("Authorization", format!("Bearer {}", manager.api_key()))
        .json(&serde_json::json!({
            "model": manager.get_model_info("qwen2.5-0.5b", true).await?.id,
            "messages": [{"role": "user", "content": "What is the golden ratio?"}],
        }))
        .send()
        .await?;

    let result = response.json::<serde_json::Value>().await?;
    println!("{}", result["choices"][0]["message"]["content"]);
    
    Ok(())
}

使用以下命令运行代码：

cargo run

通过

将推理 SDK 与 Foundry Local 集成

先决条件

安装 pip 包

将 OpenAI SDK 与 Foundry Local 配合使用

流式处理响应

将 requests 与 Foundry Local 配合使用

安装 Node.js 包

将 OpenAI SDK 与 Foundry Local 配合使用

流式处理响应

将提取 API 与 Foundry Local 配合使用

流式处理响应

创建项目

安装 NuGet 包

将 OpenAI SDK 与 Foundry Local 配合使用

创建项目

安装 crate

更新main.rs文件

后续步骤

反馈

其他资源

将 `requests` 与 Foundry Local 配合使用

更新`main.rs`文件