> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# ChatOllama integration

> Integrate with the ChatOllama chat model using LangChain JavaScript.

[Ollama](https://ollama.ai/) allows you to run open-source Large Language Models (LLMs), 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](/oss/javascript/langchain/models). For detailed documentation of all `ChatOllama` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-ollama/ChatOllama).

## Overview

### 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](https://ollama.com/search) and search by tag.

| Class                                                                                  | Package                                                                | Serializable | [PY support](https://python.langchain.com/docs/integrations/chat/ollama) |                                             Downloads                                             |                                             Version                                            |
| :------------------------------------------------------------------------------------- | :--------------------------------------------------------------------- | :----------: | :----------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: |
| [`ChatOllama`](https://reference.langchain.com/javascript/langchain-ollama/ChatOllama) | [`@langchain/ollama`](https://www.npmjs.com/package/@langchain/ollama) |     beta     |                                     ✅                                    | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/ollama?style=flat-square\&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/ollama?style=flat-square\&label=%20&) |

### Model features

See the links in the table headers below for guides on how to use specific features.

| [Tool calling](/oss/javascript/langchain/tools) | [Structured output](/oss/javascript/langchain/structured-output) | [Image input](/oss/javascript/langchain/messages#multimodal) | Audio input | Video input | [Token-level streaming](/oss/javascript/langchain/streaming/) | [Token usage](/oss/javascript/langchain/models#token-usage) | [Logprobs](/oss/javascript/langchain/models#log-probabilities) |
| :---------------------------------------------: | :--------------------------------------------------------------: | :----------------------------------------------------------: | :---------: | :---------: | :-----------------------------------------------------------: | :---------------------------------------------------------: | :------------------------------------------------------------: |
|                        ✅                        |                                 ✅                                |                               ✅                              |      ❌      |      ❌      |                               ✅                               |                              ✅                              |                                ❌                               |

## Setup

Follow [these instructions](https://github.com/ollama/ollama) to set up and run a local Ollama instance. Then, download the `@langchain/ollama` package.

### Credentials

If you want to get automated tracing of your model calls you can also set your [LangSmith](/langsmith/observability) API key by uncommenting below:

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"
```

### Installation

The LangChain ChatOllama integration lives in the `@langchain/ollama` package:

<CodeGroup>
  ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  npm install @langchain/ollama @langchain/core
  ```

  ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  yarn add @langchain/ollama @langchain/core
  ```

  ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pnpm add @langchain/ollama @langchain/core
  ```
</CodeGroup>

## Instantiation

Now we can instantiate our model object and generate chat completions:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatOllama } from "@langchain/ollama"

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

## Invocation

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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
  }
}
```

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
console.log(aiMsg.content)
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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.)
```

## Tools

Ollama now offers support for native tool calling [for a subset of their available models](https://ollama.com/search?c=tools). The example below demonstrates how you can invoke a tool from an Ollama model.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { tool } from "@langchain/core/tools";
import { ChatOllama } from "@langchain/ollama";
import * as 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);
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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
  }
}
```

## Structured output

Ollama natively supports [structured output](https://docs.ollama.com/capabilities/structured-outputs) for all models, allowing you to force the model to return a specific format by calling `.withStructuredOutput()`.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatOllama } from "@langchain/ollama";
import { z } from "zod";

// Define the schema
const Country = z.object({
  name: z.string(),
  capital: z.string(),
  languages: z.array(z.string()),
});

// Define the model
const llm = new ChatOllama({
  model: "llama3.1",
  temperature: 0,
});

// Pass the schema to enforce a specific output format
const structuredLlm = llm.withStructuredOutput(Country);

const result = await structuredLlm.invoke("Tell me about Canada.");
console.log(result);
```

```output theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
{
  name: 'Canada',
  capital: 'Ottawa',
  languages: [ 'English', 'French' ]
}
```

If you prefer to use structured output via tool calling, pass the `method: "functionCalling"` option:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatOllama } from "@langchain/ollama";
import { z } from "zod";

// Define the schema
const Sentence = z.object({
  nouns: z.array(z.string()),
});

// Define the model
const llm = new ChatOllama({
  model: "llama3.1",
  temperature: 0,
});

// Use structured output via tool calling
const structuredLlm = llm.withStructuredOutput(Sentence, { method: "functionCalling" });

const result = await structuredLlm.invoke("Extract all nouns: A cat named Luna who is 5 years old and loves playing with yarn. She has grey fur");
console.log(result);
```

```output theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
{ nouns: [ 'cat', 'Luna', 'years', 'yarn', 'fur' ] }
```

## Multimodal models

Ollama supports open source multimodal models like [LLaVA](https://ollama.ai/library/llava) in versions 0.1.15 and up.
You can pass images as part of a message's `content` field to [multimodal-capable](/oss/javascript/langchain/messages#multimodal) models like this:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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);
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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](https://reference.langchain.com/javascript/langchain-ollama/ChatOllama).

***

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