Ollama allows you to run open-source large language models, such as Llama 3.1, locally. Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. It optimizes setup and configuration details, including GPU usage. This guide will help you getting started with ChatOllama chat models. For detailed documentation of all ChatOllama features and configurations head to the API reference.

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Overview

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Integration details

Ollama allows you to use a wide range of models with different capabilities. Some of the fields in the details table below only apply to a subset of models that Ollama offers. For a complete list of supported models and model variants, see the Ollama model library and search by tag.
ClassPackageLocalSerializablePY supportDownloadsVersion
ChatOllama@langchain/ollama✅beta✅NPM - DownloadsNPM - Version

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Model features

See the links in the table headers below for guides on how to use specific features.
Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingToken usageLogprobs
✅✅✅✅❌❌✅✅❌

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Setup

Follow these instructions to set up and run a local Ollama instance. Then, download the @langchain/ollama package.

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Credentials

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below: 
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

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Installation

The LangChain ChatOllama integration lives in the @langchain/ollama package: 
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  @langchain/ollama @langchain/core
</Npm2Yarn>

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Instantiation

Now we can instantiate our model object and generate chat completions: 
import { ChatOllama } from "@langchain/ollama"

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

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Invocation

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.)

​
Chaining

We can chain our model with a prompt template like so: 
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
  }
}

​
Tools

Ollama now offers support for native tool calling for a subset of their available models. The example below demonstrates how you can invoke a tool from an Ollama model. 
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

For models that support tool calling, you can also call .withStructuredOutput() to get a structured output from the tool. 
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 mode

Ollama also supports a JSON mode for all chat models that coerces model outputs to only return JSON. Here’s an example of how this can be useful for extraction: 
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
  }
}

​
Multimodal models

Ollama supports open source multimodal models like LLaVA in versions 0.1.15 and up. You can pass images as part of a message’s content field to multimodal-capable models like this: 
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 reference

For detailed documentation of all ChatOllama features and configurations head to the API reference.