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This commit introduces a new FastAPI router-based system for defining API endpoints, enabling a migration path away from the legacy @webmethod decorator system. The implementation includes router infrastructure, migration of the Batches API as the first example, and updates to server, OpenAPI generation, and inspection systems to support both routing approaches. The router infrastructure consists of a router registry system that allows APIs to register FastAPI router factories, which are then automatically discovered and included in the server application. Standard error responses are centralized in router_utils to ensure consistent OpenAPI specification generation with proper $ref references to component responses. The Batches API has been migrated to demonstrate the new pattern. The protocol definition and models remain in llama_stack_api/batches, maintaining clear separation between API contracts and server implementation. The FastAPI router implementation lives in llama_stack/core/server/routers/batches, following the established pattern where API contracts are defined in llama_stack_api and server routing logic lives in llama_stack/core/server. The server now checks for registered routers before falling back to the legacy webmethod-based route discovery, ensuring backward compatibility during the migration period. The OpenAPI generator has been updated to handle both router-based and webmethod-based routes, correctly extracting metadata from FastAPI route decorators and Pydantic Field descriptions. The inspect endpoint now includes routes from both systems, with proper filtering for deprecated routes and API levels. Response descriptions are now explicitly defined in router decorators, ensuring the generated OpenAPI specification matches the previous format. Error responses use $ref references to component responses (BadRequest400, TooManyRequests429, etc.) as required by the specification. This is neat and will allow us to remove a lot of boiler plate code from our generator once the migration is done. This implementation provides a foundation for incrementally migrating other APIs to the router system while maintaining full backward compatibility with existing webmethod-based APIs. Closes: https://github.com/llamastack/llama-stack/issues/4188 Signed-off-by: Sébastien Han <seb@redhat.com> |
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| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
| getting_started_llama_api.ipynb | ||
| license_header.txt | ||
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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
File Import System
This documentation uses remark-code-import to import files directly from the repository, eliminating copy-paste maintenance. Files are automatically embedded during build time.
Importing Code Files
To import Python code (or any code files) with syntax highlighting, use this syntax in .mdx files:
```python file=./demo_script.py title="demo_script.py"
This automatically imports the file content and displays it as a formatted code block with Python syntax highlighting.
**Note:** Paths are relative to the current `.mdx` file location, not the repository root.
### Importing Markdown Files as Content
For importing and rendering markdown files (like CONTRIBUTING.md), use the raw-loader approach:
```jsx
import Contributing from '!!raw-loader!../../../CONTRIBUTING.md';
import ReactMarkdown from 'react-markdown';
<ReactMarkdown>{Contributing}</ReactMarkdown>
Requirements:
- Install dependencies:
npm install --save-dev raw-loader react-markdown
Path Resolution:
- For
remark-code-import: Paths are relative to the current.mdxfile location - For
raw-loader: Paths are relative to the current.mdxfile location - Use
../to navigate up directories as needed
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