docs: Add OpenAI API compatibility page

This adds some initial content documenting our OpenAI compatible APIs
- Responses, Chat Completions, Completions, and Models - along with
instructions on how to use them via OpenAI or Llama Stack clients and
some simple examples for each.

It's not a lot of content, but it's a start so that users have some
idea how to get going as we continue to work on these APIs.

Signed-off-by: Ben Browning <bbrownin@redhat.com>
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Ben Browning 2025-05-29 20:06:03 -04:00
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@ -103,6 +103,7 @@ getting_started/index
getting_started/detailed_tutorial getting_started/detailed_tutorial
introduction/index introduction/index
concepts/index concepts/index
openai/index
providers/index providers/index
distributions/index distributions/index
building_applications/index building_applications/index

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# OpenAI API Compatibility
## Server path
Llama Stack exposes an OpenAI-compatible API endpoint at `/v1/openai/v1`. So, for a Llama Stack server running on locally on port 8321, the full url to the OpenAI-compatible API endpoint is `http://localhost:8321/v1/openai/v1`.
## Clients
You should be able to use any client that speaks OpenAI APIs with Llama Stack. We regularly test with the official Llama Stack clients as well as OpenAI's official Python client.
### Llama Stack Client
When using the Llama Stack client, set the `base_url` to the root of your Llama Stack server. It will automatically route OpenAI-compatible requests to the right server endpoint for you.
```python
from llama_stack_client import LlamaStackClient
client = LlamaStackClient(base_url="http://localhost:8321")
```
### OpenAI Client
When using an OpenAI client, set the `base_url` to the `/v1/openai/v1` path on your Llama Stack server.
```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8321/v1/openai/v1", api_key="none")
```
Regardless of the client you choose, the following code examples should all work the same.
## APIs implemented
### Responses
```{note}
The Responses API implementation is still in active development. While it is quite usable, there are still unimplemented parts of the API. We'd love feedback on any use-cases you try that do not work to help prioritize the pieces left to implement. Please open issues in the [meta-llama/llama-stack](https://github.com/meta-llama/llama-stack) GitHub repository with details of anything that does not work.
```
#### Simple inference
Request:
```
response = client.responses.create(
model="meta-llama/Llama-3.2-3B-Instruct",
input="Write a haiku about coding."
)
print(response.output_text)
```
Example output:
```text
Pixels dancing slow
Syntax whispers secrets sweet
Code's gentle silence
```
#### Structured Output
```{warning}
Structured outputs are not yet implemented for the Responses API as of Llama Stack 0.2.8. Below is an example of how this should work, once it is implemented.
```
Request:
```python
response = client.responses.create(
model="meta-llama/Llama-3.2-3B-Instruct",
input=[
{
"role": "system",
"content": "Extract the participants from the event information."
},
{
"role": "user",
"content": "Alice and Bob are going to a science fair on Friday."
}
],
text={
"format": {
"type": "json_schema",
"name": "participants",
"schema": {
"type": "object",
"properties": {
"participants": {
"type": "array",
"items": {
"type": "string"
}
}
},
"required": [
"participants"
],
}
}
}
)
print(response.output_text)
```
Example output:
Omitted - see warning above.
### Chat Completions
#### Simple inference
Request:
```python
chat_completion = client.chat.completions.create(
model="meta-llama/Llama-3.2-3B-Instruct",
messages=[
{
"role": "user",
"content": "Write a haiku about coding."
}
]
)
print(chat_completion.choices[0].message.content)
```
Example output:
```text
Lines of code unfold
Logic flows like a river
Code's gentle beauty
```
#### Structured Output
Request:
```python
chat_completion = client.chat.completions.create(
model="meta-llama/Llama-3.2-3B-Instruct",
messages=[
{
"role": "system",
"content": "Extract the participants from the event information."
},
{
"role": "user",
"content": "Alice and Bob are going to a science fair on Friday."
}
],
response_format={
"type": "json_schema",
"json_schema": {
"name": "participants",
"schema": {
"type": "object",
"properties": {
"participants": {
"type": "array",
"items": {
"type": "string"
}
}
},
"required": [
"participants"
],
}
}
}
)
print(chat_completion.choices[0].message.content)
```
Example output:
```text
{ "participants": ["Alice", "Bob"] }
```
### Completions
#### Simple inference
Request:
```python
completion = client.completions.create(
model="meta-llama/Llama-3.2-3B-Instruct",
prompt="Write a haiku about coding."
)
print(completion.choices[0].text)
```
Example output:
```text
Lines of code unfurl
Logic whispers in the dark
Art in hidden form
```
### Models
```python
models = client.models.list()
```