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query_metrics currently has no implementation, meaning once a metric is emitted there is no way in llama stack to query it from the store. implement query_metrics for the meta_reference provider which follows a similar style to `query_traces`, using the trace_store to format an SQL query and execute it in this case the parameters for the query are `metric.METRIC_NAME, start_time, and end_time`. this required client side changes since the client had no `query_metrics` or any associated resources, so any tests here will fail but I will provider manual execution logs for the new tests I am adding order the metrics by timestamp. Additionally add `unit` to the `MetricDataPoint` class since this adds much more context to the metric being queried. these metrics can also be aggregated via a `granularity` parameter. This was pre-defined as a string like: `1m, 1h, 1d` where metrics occuring in same timespan specified are aggregated together. Signed-off-by: Charlie Doern <cdoern@redhat.com> |
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| notebooks | ||
| openapi_generator | ||
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| 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 | ||
| original_rfc.md | ||
| quick_start.ipynb | ||
| README.md | ||
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
From the llama-stack root directory, run the following command to render the docs locally:
uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all
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