mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-10-04 12:07:34 +00:00
d73955a41e
Some checks failed
Integration Auth Tests / test-matrix (oauth2_token) (push) Failing after 1s
Vector IO Integration Tests / test-matrix (push) Failing after 2s
Test Llama Stack Build / build-ubi9-container-distribution (push) Failing after 1s
Test External Providers Installed via Module / test-external-providers-from-module (venv) (push) Has been skipped
Pre-commit / pre-commit (push) Failing after 3s
Test Llama Stack Build / generate-matrix (push) Failing after 3s
Integration Tests (Replay) / Integration Tests (, , , client=, vision=) (push) Failing after 5s
Test Llama Stack Build / build-custom-container-distribution (push) Failing after 3s
Test Llama Stack Build / build (push) Has been skipped
Unit Tests / unit-tests (3.12) (push) Failing after 1s
Python Package Build Test / build (3.13) (push) Failing after 2s
Test Llama Stack Build / build-single-provider (push) Failing after 5s
Python Package Build Test / build (3.12) (push) Failing after 4s
SqlStore Integration Tests / test-postgres (3.13) (push) Failing after 7s
Unit Tests / unit-tests (3.13) (push) Failing after 2s
UI Tests / ui-tests (22) (push) Failing after 4s
Test External API and Providers / test-external (venv) (push) Failing after 4s
Update ReadTheDocs / update-readthedocs (push) Failing after 3s
SqlStore Integration Tests / test-postgres (3.12) (push) Failing after 12s
# What does this PR do? Finding these issues while moving to github pages. ## Test Plan uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all
1.1 KiB
1.1 KiB
Using Llama Stack as a Library
Setup Llama Stack without a Server
If you are planning to use an external service for Inference (even Ollama or TGI counts as external), it is often easier to use Llama Stack as a library. This avoids the overhead of setting up a server.
# setup
uv pip install llama-stack
llama stack build --distro starter --image-type venv
from llama_stack.core.library_client import LlamaStackAsLibraryClient
client = LlamaStackAsLibraryClient(
"starter",
# provider_data is optional, but if you need to pass in any provider specific data, you can do so here.
provider_data={"tavily_search_api_key": os.environ["TAVILY_SEARCH_API_KEY"]},
)
This will parse your config and set up any inline implementations and remote clients needed for your implementation.
Then, you can access the APIs like models and inference on the client and call their methods directly:
response = client.models.list()
If you've created a custom distribution, you can also use the run.yaml configuration file directly:
client = LlamaStackAsLibraryClient(config_path)