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ChatOllama

Ollama 允许您在本地运行开源的大语言模型，例如 Llama 3.1。

Ollama 将模型权重、配置和数据打包成一个单独的包，由 Modelfile 定义。它优化了包括 GPU 使用在内的设置和配置细节。

本指南将帮助您开始使用 ChatOllama 聊天模型。有关所有 ChatOllama 功能和配置的详细文档，请参阅 API 参考。

概述​

集成细节​

Ollama 使您可以使用各种具有不同功能的模型。下面详细信息表中的一些字段仅适用于 Ollama 提供的一部分模型。

有关支持的模型和模型变体的完整列表，请参阅 Ollama 模型库 并按标签搜索。

类别包本地支持可序列化Python 支持包下载量最新包
ChatOllama@langchain/ollama✅beta✅NPM - 下载量NPM - 版本

模型功能​

有关如何使用特定功能的指南，请参见下表标题中的链接。

工具调用结构化输出JSON 模式图像输入音频输入视频输入逐令牌流式传输令牌使用情况对数概率
✅✅✅✅❌❌✅✅❌

安装配置​

请按照 这些说明 设置并运行本地 Ollama 实例。然后下载 @langchain/ollama 包。

凭据​

如果您希望自动追踪模型调用，也可以通过取消注释以下命令设置您的 LangSmith API 密钥：

# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

安装​

LangChain ChatOllama 集成位于 @langchain/ollama 包中：

:::提示 请参阅安装集成包的一般说明部分。 :::

yarn add @langchain/ollama @langchain/core

实例化​

现在我们可以实例化我们的模型对象并生成聊天补全结果：

import { ChatOllama } from "@langchain/ollama";

const llm = new ChatOllama({
  model: "llama3",
  temperature: 0,
  maxRetries: 2,
  // other params...
});

调用​

const aiMsg = await llm.invoke([
  [
    "system",
    "You are a helpful assistant that translates English to French. Translate the user sentence.",
  ],
  ["human", "I love programming."],
]);
aiMsg;
AIMessage {
  "content": "Je adore le programmation.\n\n(Note: \"programmation\" is the feminine form of the noun in French, but if you want to use the masculine form, it would be \"le programme\" instead.)",
  "additional_kwargs": {},
  "response_metadata": {
    "model": "llama3",
    "created_at": "2024-08-01T16:59:17.359302Z",
    "done_reason": "stop",
    "done": true,
    "total_duration": 6399311167,
    "load_duration": 5575776417,
    "prompt_eval_count": 35,
    "prompt_eval_duration": 110053000,
    "eval_count": 43,
    "eval_duration": 711744000
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 35,
    "output_tokens": 43,
    "total_tokens": 78
  }
}
console.log(aiMsg.content);
Je adore le programmation.

(Note: "programmation" is the feminine form of the noun in French, but if you want to use the masculine form, it would be "le programme" instead.)

链式调用​

我们可以像这样将模型与提示模板链式调用：

import { ChatPromptTemplate } from "@langchain/core/prompts";

const prompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    "You are a helpful assistant that translates {input_language} to {output_language}.",
  ],
  ["human", "{input}"],
]);

const chain = prompt.pipe(llm);
await chain.invoke({
  input_language: "English",
  output_language: "German",
  input: "I love programming.",
});
AIMessage {
  "content": "Ich liebe Programmieren!\n\n(Note: \"Ich liebe\" means \"I love\", \"Programmieren\" is the verb for \"programming\")",
  "additional_kwargs": {},
  "response_metadata": {
    "model": "llama3",
    "created_at": "2024-08-01T16:59:18.088423Z",
    "done_reason": "stop",
    "done": true,
    "total_duration": 585146125,
    "load_duration": 27557166,
    "prompt_eval_count": 30,
    "prompt_eval_duration": 74241000,
    "eval_count": 29,
    "eval_duration": 481195000
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 30,
    "output_tokens": 29,
    "total_tokens": 59
  }
}

工具​

Ollama 现在为其部分可用模型提供了原生工具调用的支持 (查看支持工具调用的模型)。以下示例演示了如何从 Ollama 模型中调用工具。

import { tool } from "@langchain/core/tools";
import { ChatOllama } from "@langchain/ollama";
import { z } from "zod";

const weatherTool = tool((_) => "Da weather is weatherin", {
  name: "get_current_weather",
  description: "Get the current weather in a given location",
  schema: z.object({
    location: z.string().describe("The city and state, e.g. San Francisco, CA"),
  }),
});

// Define the model
const llmForTool = new ChatOllama({
  model: "llama3-groq-tool-use",
});

// Bind the tool to the model
const llmWithTools = llmForTool.bindTools([weatherTool]);

const resultFromTool = await llmWithTools.invoke(
  "What's the weather like today in San Francisco? Ensure you use the 'get_current_weather' tool."
);

console.log(resultFromTool);
AIMessage {
  "content": "",
  "additional_kwargs": {},
  "response_metadata": {
    "model": "llama3-groq-tool-use",
    "created_at": "2024-08-01T18:43:13.2181Z",
    "done_reason": "stop",
    "done": true,
    "total_duration": 2311023875,
    "load_duration": 1560670292,
    "prompt_eval_count": 177,
    "prompt_eval_duration": 263603000,
    "eval_count": 30,
    "eval_duration": 485582000
  },
  "tool_calls": [
    {
      "name": "get_current_weather",
      "args": {
        "location": "San Francisco, CA"
      },
      "id": "c7a9d590-99ad-42af-9996-41b90efcf827",
      "type": "tool_call"
    }
  ],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 177,
    "output_tokens": 30,
    "total_tokens": 207
  }
}

.withStructuredOutput​

对于支持工具调用的模型，您还可以调用 .withStructuredOutput() 以从工具获取结构化输出。

import { ChatOllama } from "@langchain/ollama";
import { z } from "zod";

// Define the model
const llmForWSO = new ChatOllama({
  model: "llama3-groq-tool-use",
});

// Define the tool schema you'd like the model to use.
const schemaForWSO = z.object({
  location: z.string().describe("The city and state, e.g. San Francisco, CA"),
});

// Pass the schema to the withStructuredOutput method to bind it to the model.
const llmWithStructuredOutput = llmForWSO.withStructuredOutput(schemaForWSO, {
  name: "get_current_weather",
});

const resultFromWSO = await llmWithStructuredOutput.invoke(
  "What's the weather like today in San Francisco? Ensure you use the 'get_current_weather' tool."
);
console.log(resultFromWSO);
{ location: 'San Francisco, CA' }

JSON 模式​

Ollama 还为所有聊天模型支持一种 JSON 模式，该模式会强制模型输出仅返回 JSON。以下是一个如何利用此功能进行信息提取的示例：

import { ChatOllama } from "@langchain/ollama";
import { ChatPromptTemplate } from "@langchain/core/prompts";

const promptForJsonMode = ChatPromptTemplate.fromMessages([
  [
    "system",
    `You are an expert translator. Format all responses as JSON objects with two keys: "original" and "translated".`,
  ],
  ["human", `Translate "{input}" into {language}.`],
]);

const llmJsonMode = new ChatOllama({
  baseUrl: "http://localhost:11434", // Default value
  model: "llama3",
  format: "json",
});

const chainForJsonMode = promptForJsonMode.pipe(llmJsonMode);

const resultFromJsonMode = await chainForJsonMode.invoke({
  input: "I love programming",
  language: "German",
});

console.log(resultFromJsonMode);
AIMessage {
  "content": "{\n\"original\": \"I love programming\",\n\"translated\": \"Ich liebe Programmierung\"\n}",
  "additional_kwargs": {},
  "response_metadata": {
    "model": "llama3",
    "created_at": "2024-08-01T17:24:54.35568Z",
    "done_reason": "stop",
    "done": true,
    "total_duration": 1754811583,
    "load_duration": 1297200208,
    "prompt_eval_count": 47,
    "prompt_eval_duration": 128532000,
    "eval_count": 20,
    "eval_duration": 318519000
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 47,
    "output_tokens": 20,
    "total_tokens": 67
  }
}

多模态模型​

Ollama 支持开源多模态模型，例如 LLaVA，在版本 0.1.15 及以上中。 您可以将图像作为消息 content 字段的一部分传递给如下所示的支持多模态输入模型：

import { ChatOllama } from "@langchain/ollama";
import { HumanMessage } from "@langchain/core/messages";
import * as fs from "node:fs/promises";

const imageData = await fs.readFile("../../../../../examples/hotdog.jpg");
const llmForMultiModal = new ChatOllama({
  model: "llava",
  baseUrl: "http://127.0.0.1:11434",
});
const multiModalRes = await llmForMultiModal.invoke([
  new HumanMessage({
    content: [
      {
        type: "text",
        text: "What is in this image?",
      },
      {
        type: "image_url",
        image_url: `data:image/jpeg;base64,${imageData.toString("base64")}`,
      },
    ],
  }),
]);
console.log(multiModalRes);
AIMessage {
  "content": " The image shows a hot dog in a bun, which appears to be a footlong. It has been cooked or grilled to the point where it's browned and possibly has some blackened edges, indicating it might be slightly overcooked. Accompanying the hot dog is a bun that looks toasted as well. There are visible char marks on both the hot dog and the bun, suggesting they have been cooked directly over a source of heat, such as a grill or broiler. The background is white, which puts the focus entirely on the hot dog and its bun. ",
  "additional_kwargs": {},
  "response_metadata": {
    "model": "llava",
    "created_at": "2024-08-01T17:25:02.169957Z",
    "done_reason": "stop",
    "done": true,
    "total_duration": 5700249458,
    "load_duration": 2543040666,
    "prompt_eval_count": 1,
    "prompt_eval_duration": 1032591000,
    "eval_count": 127,
    "eval_duration": 2114201000
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 1,
    "output_tokens": 127,
    "total_tokens": 128
  }
}

API 参考文档​

如需了解 ChatOllama 所有功能和配置的详细文档，请访问 API 参考页面: https://api.js.langchain.com/classes/langchain_ollama.ChatOllama.html

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