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# What does this PR do? - remove auto-download of ollama embedding models - add embedding model metadata to dynamic listing w/ unit test - add support and tests for allowed_models - removed inference provider models.py files where dynamic listing is enabled - store embedding metadata in embedding_model_metadata field on inference providers - make model_entries optional on ModelRegistryHelper and LiteLLMOpenAIMixin - make OpenAIMixin a ModelRegistryHelper - skip base64 embedding test for remote::ollama, always returns floats - only use OpenAI client for ollama model listing - remove unused build_model_entry function - remove unused get_huggingface_repo function ## Test Plan ci w/ new tests |
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docs | ||
notebooks | ||
openapi_generator | ||
resources | ||
src | ||
static | ||
zero_to_hero_guide | ||
docusaurus.config.ts | ||
dog.jpg | ||
getting_started.ipynb | ||
getting_started_llama4.ipynb | ||
getting_started_llama_api.ipynb | ||
license_header.txt | ||
original_rfc.md | ||
package-lock.json | ||
package.json | ||
quick_start.ipynb | ||
README.md | ||
sidebars.ts | ||
tsconfig.json |
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