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## Summary
Adds OpenAI-compatible usage tracking types to enable reporting token
consumption for both streaming and non-streaming responses.
## Type Definitions
**Chat Completion Usage** (inference API):
```python
class OpenAIChatCompletionUsage(BaseModel):
prompt_tokens: int
completion_tokens: int
total_tokens: int
prompt_tokens_details: OpenAIChatCompletionUsagePromptTokensDetails | None
completion_tokens_details: OpenAIChatCompletionUsageCompletionTokensDetails | None
```
**Response Usage** (responses API):
```python
class OpenAIResponseUsage(BaseModel):
input_tokens: int
output_tokens: int
total_tokens: int
input_tokens_details: OpenAIResponseUsageInputTokensDetails | None
output_tokens_details: OpenAIResponseUsageOutputTokensDetails | None
```
This matches OpenAI's usage reporting format and enables PR #3766 to
implement usage tracking in streaming responses.
Co-authored-by: Claude <noreply@anthropic.com>
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| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
| getting_started_llama_api.ipynb | ||
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| README.md | ||
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Llama Stack Documentation
Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our Github page.
Render locally
From the llama-stack docs/ directory, run the following commands to render the docs locally:
npm install
npm run gen-api-docs all
npm run build
npm run serve
You can open up the docs in your browser at http://localhost:3000
Content
Try out Llama Stack's capabilities through our detailed Jupyter notebooks:
- Building AI Applications Notebook - A comprehensive guide to building production-ready AI applications using Llama Stack
- Benchmark Evaluations Notebook - Detailed performance evaluations and benchmarking results
- Zero-to-Hero Guide - Step-by-step guide for getting started with Llama Stack