forked from phoenix-oss/llama-stack-mirror
047303e339
# What does this PR do? This PR introduces APIs to retrieve past chat completion requests, which will be used in the LS UI. Our current `Telemetry` is ill-suited for this purpose as it's untyped so we'd need to filter by obscure attribute names, making it brittle. Since these APIs are 'provided by stack' and don't need to be implemented by inference providers, we introduce a new InferenceProvider class, containing the existing inference protocol, which is implemented by inference providers. The APIs are OpenAI-compliant, with an additional `input_messages` field. ## Test Plan This PR just adds the API and marks them provided_by_stack. S tart stack server -> doesn't crash |
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| _static | ||
| notebooks | ||
| openapi_generator | ||
| resources | ||
| source | ||
| zero_to_hero_guide | ||
| conftest.py | ||
| contbuild.sh | ||
| dog.jpg | ||
| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
| getting_started_llama_api.ipynb | ||
| license_header.txt | ||
| make.bat | ||
| Makefile | ||
| readme.md | ||
| requirements.txt | ||
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 ReadTheDocs page.
Render locally
pip install -r requirements.txt
cd docs
python -m sphinx_autobuild source _build
You can open up the docs in your browser at http://localhost:8000
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