# OpenAI compatibility

> **Note:** OpenAI compatibility is experimental and is subject to major adjustments including breaking changes. For fully-featured access to the Ollama API, see the Ollama [Python library](https://github.com/ollama/ollama-python), [JavaScript library](https://github.com/ollama/ollama-js) and [REST API](https://github.com/ollama/ollama/blob/main/docs/api.md).

Ollama provides experimental 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',
)

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

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

list_completion = client.models.list()

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

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

### 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',
})

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: "[encoded data, 4864 characters]",
            },
        ],
        },
    ],
})

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

const listCompletion = await openai.models.list()

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

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",
        "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": "[encoded data, 4864 characters]"
            }
          }
        ]
      }
    ],
    "max_tokens": 300
  }'

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

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

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

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 (streaming support coming soon)
- [ ] 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] `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] `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
- [ ] `encoding format`
- [ ] `dimensions`
- [ ] `user`

## Models

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

```shell
ollama pull llama3
```

### 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:

```
ollama cp llama3 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!"
            }
        ]
    }'
```
