cdac705165
This fixes two tests that fail for me locally:
- tests/unit/providers/vector_io/test_sqlite_vec.py
- tests/unit/rag/test_vector_store.py
The error with test_sqlite_vec.py was:
```
@pytest.mark.asyncio
async def test_add_chunks(sqlite_vec_index, sample_chunks, sample_embeddings):
> await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings, batch_size=2)
E AttributeError: 'coroutine' object has no attribute 'add_chunks'
tests/unit/providers/vector_io/test_sqlite_vec.py:76: AttributeError
```
The reason for that error is the sqlite_vec_index fixture was not
marked as `@pytest_asyncio.fixture` and instead was marked as
`@pytest.fixture`. Thus, it was returning a coroutine from the fixture
instead of the actual fixture.
The error with test_vector_store.py was:
```
> assert content == "Dumm y PDF file"
E AssertionError: assert 'Dummy PDF file' == 'Dumm y PDF file'
E
E - Dumm y PDF file
E ? -
E + Dummy PDF file
tests/unit/rag/test_vector_store.py:76: AssertionError
```
The reason for this error is that, on my machine, the dummy PDF file
is properly extracting the text as "Dummy PDF file". However, it looks
like from the commit history that on other's machines this was
returning "Dumm y PDF file". So, the test now allows either of those
options as valid parsing of that PDF file for the purposes of the test
passing.
With both of these changes, the entire unit test suite passes for me
locally (assuming all necessary test dependencies are installed) with:
```
python -m pytest -v tests/unit/
```
Signed-off-by: Ben Browning <bbrownin@redhat.com>
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| .python-version | ||
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| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| MANIFEST.in | ||
| pyproject.toml | ||
| README.md | ||
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| SECURITY.md | ||
| uv.lock | ||
Llama Stack
Quick Start | Documentation | Colab Notebook
Llama Stack standardizes the core building blocks that simplify AI application development. It codifies best practices across the Llama ecosystem. More specifically, it provides
- Unified API layer for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry.
- Plugin architecture to support the rich ecosystem of different API implementations in various environments, including local development, on-premises, cloud, and mobile.
- Prepackaged verified distributions which offer a one-stop solution for developers to get started quickly and reliably in any environment.
- Multiple developer interfaces like CLI and SDKs for Python, Typescript, iOS, and Android.
- Standalone applications as examples for how to build production-grade AI applications with Llama Stack.
Llama Stack Benefits
- Flexible Options: Developers can choose their preferred infrastructure without changing APIs and enjoy flexible deployment choices.
- Consistent Experience: With its unified APIs, Llama Stack makes it easier to build, test, and deploy AI applications with consistent application behavior.
- Robust Ecosystem: Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies) that offer tailored infrastructure, software, and services for deploying Llama models.
By reducing friction and complexity, Llama Stack empowers developers to focus on what they do best: building transformative generative AI applications.
API Providers
Here is a list of the various API providers and available distributions that can help developers get started easily with Llama Stack.
| API Provider Builder | Environments | Agents | Inference | Memory | Safety | Telemetry |
|---|---|---|---|---|---|---|
| Meta Reference | Single Node | ✅ | ✅ | ✅ | ✅ | ✅ |
| SambaNova | Hosted | ✅ | ||||
| Cerebras | Hosted | ✅ | ||||
| Fireworks | Hosted | ✅ | ✅ | ✅ | ||
| AWS Bedrock | Hosted | ✅ | ✅ | |||
| Together | Hosted | ✅ | ✅ | ✅ | ||
| Groq | Hosted | ✅ | ||||
| Ollama | Single Node | ✅ | ||||
| TGI | Hosted and Single Node | ✅ | ||||
| NVIDIA NIM | Hosted and Single Node | ✅ | ||||
| Chroma | Single Node | ✅ | ||||
| PG Vector | Single Node | ✅ | ||||
| PyTorch ExecuTorch | On-device iOS | ✅ | ✅ | |||
| vLLM | Hosted and Single Node | ✅ |
Distributions
A Llama Stack Distribution (or "distro") is a pre-configured bundle of provider implementations for each API component. Distributions make it easy to get started with a specific deployment scenario - you can begin with a local development setup (eg. ollama) and seamlessly transition to production (eg. Fireworks) without changing your application code. Here are some of the distributions we support:
| Distribution | Llama Stack Docker | Start This Distribution |
|---|---|---|
| Meta Reference | llamastack/distribution-meta-reference-gpu | Guide |
| Meta Reference Quantized | llamastack/distribution-meta-reference-quantized-gpu | Guide |
| SambaNova | llamastack/distribution-sambanova | Guide |
| Cerebras | llamastack/distribution-cerebras | Guide |
| Ollama | llamastack/distribution-ollama | Guide |
| TGI | llamastack/distribution-tgi | Guide |
| Together | llamastack/distribution-together | Guide |
| Fireworks | llamastack/distribution-fireworks | Guide |
| vLLM | llamastack/distribution-remote-vllm | Guide |
Installation
You have two ways to install this repository:
-
Install as a package: You can install the repository directly from PyPI by running the following command:
pip install llama-stack -
Install from source: If you prefer to install from the source code, we recommend using uv. Then, run the following commands:
git clone git@github.com:meta-llama/llama-stack.git cd llama-stack uv sync uv pip install -e .
Documentation
Please checkout our Documentation page for more details.
- CLI references
- llama (server-side) CLI Reference: Guide for using the
llamaCLI to work with Llama models (download, study prompts), and building/starting a Llama Stack distribution. - llama (client-side) CLI Reference: Guide for using the
llama-stack-clientCLI, which allows you to query information about the distribution.
- llama (server-side) CLI Reference: Guide for using the
- Getting Started
- Quick guide to start a Llama Stack server.
- Jupyter notebook to walk-through how to use simple text and vision inference llama_stack_client APIs
- The complete Llama Stack lesson Colab notebook of the new Llama 3.2 course on Deeplearning.ai.
- A Zero-to-Hero Guide that guide you through all the key components of llama stack with code samples.
- Contributing
- Adding a new API Provider to walk-through how to add a new API provider.
Llama Stack Client SDKs
| Language | Client SDK | Package |
|---|---|---|
| Python | llama-stack-client-python |  |
| Swift | llama-stack-client-swift |  |
| Typescript | llama-stack-client-typescript |  |
| Kotlin | llama-stack-client-kotlin |  |
Check out our client SDKs for connecting to a Llama Stack server in your preferred language, you can choose from python, typescript, swift, and kotlin programming languages to quickly build your applications.
You can find more example scripts with client SDKs to talk with the Llama Stack server in our llama-stack-apps repo.