996bb1b85e
* OpenAI v1 models
* Refactor Writers
* Add Test
Co-Authored-By: Attila Kerekes
* Credit Co-Author
Co-Authored-By: Attila Kerekes <439392+keriati@users.noreply.github.com>
* Empty List Testing
* Use Namespace for Ownedby
* Update Test
* Add back envconfig
* v1/models docs
* Use ModelName Parser
* Test Names
* Remove Docs
* Clean Up
* Test name
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
* Add Middleware for Chat and List
* Testing Cleanup
* Test with Fatal
* Add functionality to chat test
* OpenAI: /v1/models/{model} compatibility (#5028)
* Retrieve Model
* OpenAI Delete Model
* Retrieve Middleware
* Remove Delete from Branch
* Update Test
* Middleware Test File
* Function name
* Cleanup
* Test Update
* Test Update
---------
Co-authored-by: Attila Kerekes <439392+keriati@users.noreply.github.com>
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
140 lines
2.9 KiB
Markdown
140 lines
2.9 KiB
Markdown
# OpenAI compatibility
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> **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).
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Ollama provides experimental compatibility with parts of the [OpenAI API](https://platform.openai.com/docs/api-reference) to help connect existing applications to Ollama.
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## Usage
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### OpenAI Python library
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```python
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from openai import OpenAI
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client = OpenAI(
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base_url='http://localhost:11434/v1/',
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# required but ignored
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api_key='ollama',
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)
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chat_completion = client.chat.completions.create(
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messages=[
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{
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'role': 'user',
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'content': 'Say this is a test',
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}
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],
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model='llama3',
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)
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```
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### OpenAI JavaScript library
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```javascript
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import OpenAI from 'openai'
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const openai = new OpenAI({
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baseURL: 'http://localhost:11434/v1/',
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// required but ignored
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apiKey: 'ollama',
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})
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const chatCompletion = await openai.chat.completions.create({
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messages: [{ role: 'user', content: 'Say this is a test' }],
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model: 'llama3',
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})
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```
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### `curl`
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```
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curl http://localhost:11434/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "llama3",
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"messages": [
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{
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"role": "system",
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"content": "You are a helpful assistant."
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},
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{
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"role": "user",
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"content": "Hello!"
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}
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]
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}'
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```
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## Endpoints
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### `/v1/chat/completions`
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#### Supported features
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- [x] Chat completions
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- [x] Streaming
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- [x] JSON mode
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- [x] Reproducible outputs
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- [ ] Vision
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- [ ] Function calling
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- [ ] Logprobs
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#### Supported request fields
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- [x] `model`
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- [x] `messages`
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- [x] Text `content`
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- [ ] Array of `content` parts
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- [x] `frequency_penalty`
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- [x] `presence_penalty`
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- [x] `response_format`
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- [x] `seed`
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- [x] `stop`
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- [x] `stream`
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- [x] `temperature`
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- [x] `top_p`
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- [x] `max_tokens`
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- [ ] `logit_bias`
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- [ ] `tools`
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- [ ] `tool_choice`
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- [ ] `user`
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- [ ] `n`
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#### Notes
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- `usage.prompt_tokens` will be 0 for completions where prompt evaluation is cached
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## Models
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Before using a model, pull it locally `ollama pull`:
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```shell
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ollama pull llama3
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```
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### Default model names
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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:
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```
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ollama cp llama3 gpt-3.5-turbo
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```
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Afterwards, this new model name can be specified the `model` field:
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```shell
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curl http://localhost:11434/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "gpt-3.5-turbo",
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"messages": [
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{
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"role": "user",
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"content": "Hello!"
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}
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]
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}'
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```
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