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34ab7a3b6c
Lint check in main branch is failing. This fixes the lint check after we moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We need to move to a `ruff.toml` file as well as fixing and ignoring some additional checks. Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
1.2 KiB
1.2 KiB
Using Llama Stack as a Library
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 --template together --image-type venv
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
client = LlamaStackAsLibraryClient(
"ollama",
# 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"]},
)
await client.initialize()
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)
client.initialize()