docs: Reorganize documentation on the webpage (#2651)
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# What does this PR do?
Reorganizes the Llama stack webpage into more concise index pages,
introduce more of a workflow, and reduce repetition of content.

New nav structure so far based on #2637 

Further discussions in
https://github.com/meta-llama/llama-stack/discussions/2585

**Preview:**
![Screenshot 2025-07-09 at 2 31
53 PM](https://github.com/user-attachments/assets/4c1f3845-b328-4f12-9f20-3f09375007af)

You can also build a full local preview locally 

 **Feedback**
Looking for feedback on page titles and general feedback on the new
structure

**Follow up documentation**
I plan on reducing some sections and standardizing some terminology in a
follow up PR.
More discussions on that in
https://github.com/meta-llama/llama-stack/discussions/2585
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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 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
### Models
Many of the APIs require you to pass in a model parameter. To see the list of models available in your Llama Stack server:
```python
models = client.models.list()
```
### 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
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:
```text
{ "participants": ["Alice", "Bob"] }
```
### 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
```