mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-06-28 02:53:30 +00:00
2b2db5fbda
# What does this PR do? This stubs in some OpenAI server-side compatibility with three new endpoints: /v1/openai/v1/models /v1/openai/v1/completions /v1/openai/v1/chat/completions This gives common inference apps using OpenAI clients the ability to talk to Llama Stack using an endpoint like http://localhost:8321/v1/openai/v1 . The two "v1" instances in there isn't awesome, but the thinking is that Llama Stack's API is v1 and then our OpenAI compatibility layer is compatible with OpenAI V1. And, some OpenAI clients implicitly assume the URL ends with "v1", so this gives maximum compatibility. The openai models endpoint is implemented in the routing layer, and just returns all the models Llama Stack knows about. The following providers should be working with the new OpenAI completions and chat/completions API: * remote::anthropic (untested) * remote::cerebras-openai-compat (untested) * remote::fireworks (tested) * remote::fireworks-openai-compat (untested) * remote::gemini (untested) * remote::groq-openai-compat (untested) * remote::nvidia (tested) * remote::ollama (tested) * remote::openai (untested) * remote::passthrough (untested) * remote::sambanova-openai-compat (untested) * remote::together (tested) * remote::together-openai-compat (untested) * remote::vllm (tested) The goal to support this for every inference provider - proxying directly to the provider's OpenAI endpoint for OpenAI-compatible providers. For providers that don't have an OpenAI-compatible API, we'll add a mixin to translate incoming OpenAI requests to Llama Stack inference requests and translate the Llama Stack inference responses to OpenAI responses. This is related to #1817 but is a bit larger in scope than just chat completions, as I have real use-cases that need the older completions API as well. ## Test Plan ### vLLM ``` VLLM_URL="http://localhost:8000/v1" INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" llama stack build --template remote-vllm --image-type venv --run LLAMA_STACK_CONFIG=http://localhost:8321 INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" python -m pytest -v tests/integration/inference/test_openai_completion.py --text-model "meta-llama/Llama-3.2-3B-Instruct" ``` ### ollama ``` INFERENCE_MODEL="llama3.2:3b-instruct-q8_0" llama stack build --template ollama --image-type venv --run LLAMA_STACK_CONFIG=http://localhost:8321 INFERENCE_MODEL="llama3.2:3b-instruct-q8_0" python -m pytest -v tests/integration/inference/test_openai_completion.py --text-model "llama3.2:3b-instruct-q8_0" ``` ## Documentation Run a Llama Stack distribution that uses one of the providers mentioned in the list above. Then, use your favorite OpenAI client to send completion or chat completion requests with the base_url set to http://localhost:8321/v1/openai/v1 . Replace "localhost:8321" with the host and port of your Llama Stack server, if different. --------- Signed-off-by: Ben Browning <bbrownin@redhat.com> |
||
|---|---|---|
| .. | ||
| _static | ||
| notebooks | ||
| openapi_generator | ||
| resources | ||
| source | ||
| zero_to_hero_guide | ||
| conftest.py | ||
| contbuild.sh | ||
| dog.jpg | ||
| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
| license_header.txt | ||
| make.bat | ||
| Makefile | ||
| readme.md | ||
| requirements.txt | ||
Llama Stack Documentation
Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our ReadTheDocs page.
Render locally
pip install -r requirements.txt
cd docs
python -m sphinx_autobuild source _build
You can open up the docs in your browser at http://localhost:8000
Content
Try out Llama Stack's capabilities through our detailed Jupyter notebooks:
- Building AI Applications Notebook - A comprehensive guide to building production-ready AI applications using Llama Stack
- Benchmark Evaluations Notebook - Detailed performance evaluations and benchmarking results
- Zero-to-Hero Guide - Step-by-step guide for getting started with Llama Stack