LMStudio

npm install @aigne/lmstudio

pnpm add @aigne/lmstudio

yarn add @aigne/lmstudio

import { LMStudioChatModel } from "@aigne/lmstudio";

// 1. モデルをインスタンス化します
// モデル名が LM Studio にロードされたものと一致することを確認してください。
const model = new LMStudioChatModel({
  model: "llama-3.2-3b-instruct",
});

// 2. モデルを呼び出します
async function main() {
  try {
    const response = await model.invoke({
      messages: [
        { role: "user", content: "What is the capital of France?" }
      ],
    });

    console.log(response.text);
  } catch (error) {
    console.error("Error invoking model:", error);
  }
}

main();

フランスの首都はパリです。

import { LMStudioChatModel } from "@aigne/lmstudio";

const model = new LMStudioChatModel({
  baseURL: "http://localhost:1234/v1",
  model: "Mistral-7B-Instruct-v0.2-GGUF",
  modelOptions: {
    temperature: 0.8,
    maxTokens: 4096,
  },
});

LM_STUDIO_BASE_URL=http://localhost:1234/v1
# LM_STUDIO_API_KEY=必要に応じてキーを入力

import { LMStudioChatModel } from "@aigne/lmstudio";

const model = new LMStudioChatModel({
  model: "llama-3.2-3b-instruct",
});

async function streamStory() {
  const stream = await model.invoke(
    {
      messages: [{ role: "user", content: "Tell me a short story about a robot who discovers music." }],
    },
    { streaming: true }
  );

  for await (const chunk of stream) {
    if (chunk.type === "delta" && chunk.delta.text) {
      process.stdout.write(chunk.delta.text.text);
    }
  }
}

streamStory();

import { LMStudioChatModel } from "@aigne/lmstudio";

const model = new LMStudioChatModel({
  model: "llama-3.2-3b-instruct",
});

const tools = [
  {
    type: "function" as const,
    function: {
      name: "get_weather",
      description: "Get the current weather for a specified location.",
      parameters: {
        type: "object",
        properties: {
          location: {
            type: "string",
            description: "The city and state, e.g., San Francisco, CA",
          },
        },
        required: ["location"],
      },
    },
  },
];

async function checkWeather() {
  const response = await model.invoke({
    messages: [
      { role: "user", content: "What's the weather like in New York?" }
    ],
    tools,
  });

  if (response.toolCalls?.length) {
    console.log("Tool calls:", JSON.stringify(response.toolCalls, null, 2));
  }
}

checkWeather();

import { LMStudioChatModel } from "@aigne/lmstudio";

const model = new LMStudioChatModel({
  model: "llama-3.2-3b-instruct",
});

const responseFormat = {
  type: "json_schema" as const,
  json_schema: {
    name: "weather_response",
    schema: {
      type: "object",
      properties: {
        location: { type: "string" },
        temperature: { type: "number" },
        description: { type: "string" },
      },
      required: ["location", "temperature", "description"],
    },
  },
};

async function getWeatherAsJson() {
  const response = await model.invoke({
    messages: [
      { role: "user", content: "What is the weather in Paris? Respond in the requested JSON format." }
    ],
    responseFormat,
  });

  console.log(response.json);
}

getWeatherAsJson();

import { LMStudioChatModel } from "@aigne/lmstudio";

const model = new LMStudioChatModel();

async function safeInvoke() {
  try {
    const response = await model.invoke({
      messages: [{ role: "user", content: "Hello!" }],
    });
    console.log(response.text);
  } catch (error) {
    if (error.code === "ECONNREFUSED") {
      console.error("Connection failed. Please ensure the LM Studio server is running.");
    } else {
      console.error("An unexpected error occurred:", error.message);
    }
  }
}

safeInvoke();