---
title: OpenAI compatibility
---

Ollama provides compatibility with parts of the [OpenAI API](https://platform.openai.com/docs/api-reference) to help connect existing applications to Ollama.

## Usage

### OpenAI Python library

```python
from openai import OpenAI

client = OpenAI(
    base_url='http://localhost:11434/v1/',

    # required but ignored
    api_key='ollama',
)

chat_completion = client.chat.completions.create(
    messages=[
        {
            'role': 'user',
            'content': 'Say this is a test',
        }
    ],
    model='llama3.2',
)

response = client.chat.completions.create(
    model="llava",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": "data:image/png;base64,[encoded data, 4864 characters]",
                },
            ],
        }
    ],
    max_tokens=300,
)

completion = client.completions.create(
    model="llama3.2",
    prompt="Say this is a test",
)

list_completion = client.models.list()

model = client.models.retrieve("llama3.2")

embeddings = client.embeddings.create(
    model="all-minilm",
    input=["why is the sky blue?", "why is the grass green?"],
)
```

#### Structured outputs

```python
from pydantic import BaseModel
from openai import OpenAI

client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")

# Define the schema for the response
class FriendInfo(BaseModel):
    name: str
    age: int
    is_available: bool

class FriendList(BaseModel):
    friends: list[FriendInfo]

try:
    completion = client.beta.chat.completions.parse(
        temperature=0,
        model="llama3.1:8b",
        messages=[
            {"role": "user", "content": "I have two friends. The first is Ollama 22 years old busy saving the world, and the second is Alonso 23 years old and wants to hang out. Return a list of friends in JSON format"}
        ],
        response_format=FriendList,
    )

    friends_response = completion.choices[0].message
    if friends_response.parsed:
        print(friends_response.parsed)
    elif friends_response.refusal:
        print(friends_response.refusal)
except Exception as e:
    print(f"Error: {e}")
```

### OpenAI JavaScript library

```javascript
import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: "http://localhost:11434/v1/",

  // required but ignored
  apiKey: "ollama",
});

const chatCompletion = await openai.chat.completions.create({
  messages: [{ role: "user", content: "Say this is a test" }],
  model: "llama3.2",
});

const response = await openai.chat.completions.create({
  model: "llava",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "What's in this image?" },
        {
          type: "image_url",
          image_url:
            "data:image/png;base64,[encoded data, 4864 characters]",
        },
      ],
    },
  ],
});

const completion = await openai.completions.create({
  model: "llama3.2",
  prompt: "Say this is a test.",
});

const listCompletion = await openai.models.list();

const model = await openai.models.retrieve("llama3.2");

const embedding = await openai.embeddings.create({
  model: "all-minilm",
  input: ["why is the sky blue?", "why is the grass green?"],
});
```

### `curl`

```shell
curl http://localhost:11434/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "llama3.2",
        "messages": [
            {
                "role": "system",
                "content": "You are a helpful assistant."
            },
            {
                "role": "user",
                "content": "Hello!"
            }
        ]
    }'

curl http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llava",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What'\''s in this image?"
          },
          {
            "type": "image_url",
            "image_url": {
               "url": "data:image/png;base64,[encoded data, 4864 characters]"
            }
          }
        ]
      }
    ],
    "max_tokens": 300
  }'

curl http://localhost:11434/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "llama3.2",
        "prompt": "Say this is a test"
    }'

curl http://localhost:11434/v1/models

curl http://localhost:11434/v1/models/llama3.2

curl http://localhost:11434/v1/embeddings \
    -H "Content-Type: application/json" \
    -d '{
        "model": "all-minilm",
        "input": ["why is the sky blue?", "why is the grass green?"]
    }'
```

## Endpoints

### `/v1/chat/completions`

#### Supported features

- [x] Chat completions
- [x] Streaming
- [x] JSON mode
- [x] Reproducible outputs
- [x] Vision
- [x] Tools
- [ ] Logprobs

#### Supported request fields

- [x] `model`
- [x] `messages`
  - [x] Text `content`
  - [x] Image `content`
    - [x] Base64 encoded image
    - [ ] Image URL
  - [x] Array of `content` parts
- [x] `frequency_penalty`
- [x] `presence_penalty`
- [x] `response_format`
- [x] `seed`
- [x] `stop`
- [x] `stream`
- [x] `stream_options`
  - [x] `include_usage`
- [x] `temperature`
- [x] `top_p`
- [x] `max_tokens`
- [x] `tools`
- [ ] `tool_choice`
- [ ] `logit_bias`
- [ ] `user`
- [ ] `n`

### `/v1/completions`

#### Supported features

- [x] Completions
- [x] Streaming
- [x] JSON mode
- [x] Reproducible outputs
- [ ] Logprobs

#### Supported request fields

- [x] `model`
- [x] `prompt`
- [x] `frequency_penalty`
- [x] `presence_penalty`
- [x] `seed`
- [x] `stop`
- [x] `stream`
- [x] `stream_options`
  - [x] `include_usage`
- [x] `temperature`
- [x] `top_p`
- [x] `max_tokens`
- [x] `suffix`
- [ ] `best_of`
- [ ] `echo`
- [ ] `logit_bias`
- [ ] `user`
- [ ] `n`

#### Notes

- `prompt` currently only accepts a string

### `/v1/models`

#### Notes

- `created` corresponds to when the model was last modified
- `owned_by` corresponds to the ollama username, defaulting to `"library"`

### `/v1/models/{model}`

#### Notes

- `created` corresponds to when the model was last modified
- `owned_by` corresponds to the ollama username, defaulting to `"library"`

### `/v1/embeddings`

#### Supported request fields

- [x] `model`
- [x] `input`
  - [x] string
  - [x] array of strings
  - [ ] array of tokens
  - [ ] array of token arrays
- [x] `encoding format`
- [x] `dimensions`
- [ ] `user`

## Models

Before using a model, pull it locally `ollama pull`:

```shell
ollama pull llama3.2
```

### Default model names

For tooling that relies on default OpenAI model names such as `gpt-3.5-turbo`, use `ollama cp` to copy an existing model name to a temporary name:

```shell
ollama cp llama3.2 gpt-3.5-turbo
```

Afterwards, this new model name can be specified the `model` field:

```shell
curl http://localhost:11434/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "gpt-3.5-turbo",
        "messages": [
            {
                "role": "user",
                "content": "Hello!"
            }
        ]
    }'
```

### Setting the context size

The OpenAI API does not have a way of setting the context size for a model. If you need to change the context size, create a `Modelfile` which looks like:

```
FROM <some model>
PARAMETER num_ctx <context size>
```

Use the `ollama create mymodel` command to create a new model with the updated context size. Call the API with the updated model name:

```shell
curl http://localhost:11434/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "mymodel",
        "messages": [
            {
                "role": "user",
                "content": "Hello!"
            }
        ]
    }'
```