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Composable building blocks to build Llama Apps

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Francisco Arceo 74a2584cdb chore: Updating Milvus Client calls to be non-blocking (#1830 ) # What does this PR do? This PR converts blocking Milvus Client calls to non-blocking. Another one for https://github.com/meta-llama/llama-stack/issues/1489 ## Test Plan I ran the integration tests from https://github.com/meta-llama/llama-stack/pull/1467 with: ```python pytest -s -v tests/integration/vector_io/test_vector_io.py \ --stack-config inference=sentence-transformers,vector_io=inline::milvus \ --embedding-model all-miniLM-L6-V2 --env MILVUS_DB_PATH=/tmp/moo.db INFO 2025-03-28 21:35:22,726 tests.integration.conftest:41 tests: Setting DISABLE_CODE_SANDBOX=1 for macOS /Users/farceo/dev/llama-stack/.venv/lib/python3.10/site-packages/pytest_asyncio/plugin.py:207: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset. The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session" warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET)) =============================================================================================================================================================================================================================================================== test session starts =============================================================================================================================================================================================================================================================== platform darwin -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0 -- /Users/farceo/dev/llama-stack/.venv/bin/python3 cachedir: .pytest_cache metadata: {'Python': '3.10.16', 'Platform': 'macOS-15.3.1-arm64-arm-64bit', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'cov': '6.0.0', 'html': '4.1.1', 'metadata': '3.1.1', 'asyncio': '0.25.3', 'anyio': '4.8.0', 'nbval': '0.11.0'}} rootdir: /Users/farceo/dev/llama-stack configfile: pyproject.toml plugins: cov-6.0.0, html-4.1.1, metadata-3.1.1, asyncio-0.25.3, anyio-4.8.0, nbval-0.11.0 asyncio: mode=strict, asyncio_default_fixture_loop_scope=None collected 7 items tests/integration/vector_io/test_vector_io.py::test_vector_db_retrieve[emb=all-miniLM-L6-V2] PASSED tests/integration/vector_io/test_vector_io.py::test_vector_db_register[emb=all-miniLM-L6-V2] PASSED tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=all-miniLM-L6-V2-test_case0] PASSED tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=all-miniLM-L6-V2-test_case1] PASSED tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=all-miniLM-L6-V2-test_case2] PASSED tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=all-miniLM-L6-V2-test_case3] PASSED tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=all-miniLM-L6-V2-test_case4] PASSED ========================================================================================================================================================================================================================================================= 7 passed, 2 warnings in 40.33s ========================================================================================================================================================================================================================================================== ``` [//]: # (## Documentation) Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>		2025-03-28 22:14:07 -04:00
.github	ci: add myself to CODEOWNERS (#1823 )	2025-03-28 09:37:42 -07:00
docs	docs: Document sqlite-vec faiss comparison (#1821 )	2025-03-28 17:41:33 +01:00
llama_stack	chore: Updating Milvus Client calls to be non-blocking (#1830 )	2025-03-28 22:14:07 -04:00
rfcs	chore: remove straggler references to llama-models (#1345 )	2025-03-01 14:26:03 -08:00
scripts	chore: remove distributions dir (#1809 )	2025-03-27 09:03:39 -04:00
tests	fix: skip code interp (#1827 )	2025-03-28 12:58:08 -07:00
.gitignore	build: remove .python-version (#1513 )	2025-03-12 20:08:24 -07:00
.pre-commit-config.yaml	fix: only invoke openapi generator if APIs or API generator changes (#1744 )	2025-03-21 10:25:18 -04:00
.readthedocs.yaml	first version of readthedocs (#278 )	2024-10-22 10:15:58 +05:30
CHANGELOG.md	docs: Add changelog for v0.1.7 and v0.1.8 (#1780 )	2025-03-25 14:40:55 -04:00
CODE_OF_CONDUCT.md	Initial commit	2024-07-23 08:32:33 -07:00
CONTRIBUTING.md	docs: Add more env vars in dotenv instructions (#1791 )	2025-03-25 20:03:21 -07:00
LICENSE	Update LICENSE (#47 )	2024-08-29 07:39:50 -07:00
MANIFEST.in	chore: remove distributions dir (#1809 )	2025-03-27 09:03:39 -04:00
pyproject.toml	build: Bump version to 0.1.9	2025-03-29 00:22:35 +00:00
README.md	docs: remove redundant installation instructions (#1138 )	2025-03-18 14:52:21 -07:00
requirements.txt	build: Bump version to 0.1.19	2025-03-29 00:18:38 +00:00
SECURITY.md	Create SECURITY.md	2024-10-08 13:30:40 -04:00
uv.lock	build: Bump version to 0.1.9	2025-03-29 00:22:35 +00:00

README.md

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		✅
OpenAI	Hosted		✅
Anthropic	Hosted		✅
Gemini	Hosted		✅

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

Documentation

Please checkout our Documentation page for more details.

CLI references
- llama (server-side) CLI Reference: Guide for using the llama CLI 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-client CLI, which allows you to query information about the distribution.
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.