forked from phoenix-oss/llama-stack-mirror
34 lines
1.2 KiB
Markdown
34 lines
1.2 KiB
Markdown
# Using Llama Stack as a Library
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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.
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```python
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# setup
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pip install llama-stack
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llama stack build --template together --image-type venv
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```
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```python
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from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
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client = LlamaStackAsLibraryClient(
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"ollama",
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# provider_data is optional, but if you need to pass in any provider specific data, you can do so here.
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provider_data = {"tavily_search_api_key": os.environ['TAVILY_SEARCH_API_KEY']}
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)
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await client.initialize()
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```
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This will parse your config and set up any inline implementations and remote clients needed for your implementation.
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Then, you can access the APIs like `models` and `inference` on the client and call their methods directly:
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```python
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response = client.models.list()
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```
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If you've created a [custom distribution](https://llama-stack.readthedocs.io/en/latest/distributions/building_distro.html), you can also use the run.yaml configuration file directly:
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```python
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client = LlamaStackAsLibraryClient(config_path)
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client.initialize()
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```
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