13 commits

Author	SHA1	Message	Date
IAN MILLER	b57db11bed	feat: create dynamic model registration for OpenAI and Llama compat remote inference providers (#2745) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> The purpose of this task is to create a solution that can automatically detect when new models are added, deprecated, or removed by OpenAI and Llama API providers, and automatically update the list of supported models in LLamaStack. This feature is vitally important in order to avoid missing new models and editing the entries manually hence I created automation allowing users to dynamically register: - any models from OpenAI provider available at [https://api.openai.com/v1/models](https://api.openai.com/v1/models) that are not in [https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/openai/models.py](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/openai/models.py) - any models from Llama API provider available at [https://api.llama.com/v1/models](https://api.llama.com/v1/models) that are not in [https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/llama_openai_compat/models.py](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/llama_openai_compat/models.py) <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> Closes #2504 this PR is dependant on #2710 ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> 1. Create venv at root llamastack directory: `uv venv .venv --python 3.12 --seed` 2. Activate venv: `source .venv/bin/activate` 3. `uv pip install -e .` 4. Create OpenAI distro modifying run.yaml 5. Build distro: `llama stack build --template starter --image-type venv` 6. Then run LlamaStack, but before navigate to templates/starter folder: `llama stack run run.yaml --image-type venv OPENAI_API_KEY=<YOUR_KEY> ENABLE_OPENAI=openai` 7. Then try to register dummy llm that doesn't exist in OpenAI provider: ` llama-stack-client models register ianm/ianllm --provider-model-id=ianllm --provider-id=openai ` You should receive this output - combined list of static config + fetched available models from OpenAI: <img width="1380" height="474" alt="Screenshot 2025-07-14 at 12 48 50" src="https://github.com/user-attachments/assets/d26aad18-6b15-49ee-9c49-b01b2d33f883" /> 8. Then register real llm from OpenAI: llama-stack-client models register openai/gpt-4-turbo-preview --provider-model-id=gpt-4-turbo-preview --provider-id=openai <img width="1253" height="613" alt="Screenshot 2025-07-14 at 13 43 02" src="https://github.com/user-attachments/assets/60a5c9b1-3468-4eb9-9e92-cd7d21de3ca0" /> <img width="1288" height="655" alt="Screenshot 2025-07-14 at 13 43 11" src="https://github.com/user-attachments/assets/c1e48871-0e24-4bd9-a0b8-8c95552a51ee" /> We correctly fetched all available models from OpenAI As for Llama API, as a non-US person I don't have access to Llama API Key but I joined wait list. The implementation for Llama is the same as for OpenAI since Llama is openai compatible. So, the response from GET endpoint has the same structure as OpenAI https://llama.developer.meta.com/docs/api/models	2025-07-16 12:49:38 -04:00
Ben Browning	51d9fd4808	fix: Don't cache clients for passthrough auth providers (#2728) ... # What does this PR do? Some of our inference providers support passthrough authentication via `x-llamastack-provider-data` header values. This fixes the providers that support passthrough auth to not cache their clients to the backend providers (mostly OpenAI client instances) so that the client connecting to Llama Stack has to provide those auth values on each and every request. ## Test Plan I added some unit tests to ensure we're not caching clients across requests for all the fixed providers in this PR. ``` uv run pytest -sv tests/unit/providers/inference/test_inference_client_caching.py ``` I also ran some of our OpenAI compatible API integration tests for each of the changed providers, just to ensure they still work. Note that these providers don't actually pass all these tests (for unrelated reasons due to quirks of the Groq and Together SaaS services), but enough of the tests passed to confirm the clients are still working as intended. ### Together ``` ENABLE_TOGETHER="together" \ uv run llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ uv run pytest -sv \ tests/integration/inference/test_openai_completion.py \ --text-model "together/meta-llama/Llama-3.1-8B-Instruct" ``` ### OpenAI ``` ENABLE_OPENAI="openai" \ uv run llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ uv run pytest -sv \ tests/integration/inference/test_openai_completion.py \ --text-model "openai/gpt-4o-mini" ``` ### Groq ``` ENABLE_GROQ="groq" \ uv run llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ uv run pytest -sv \ tests/integration/inference/test_openai_completion.py \ --text-model "groq/meta-llama/Llama-3.1-8B-Instruct" ``` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-07-11 13:38:27 -07:00
Sébastien Han	9b7eecebcf	ci: test safety with starter (#2628) ... # What does this PR do? We are now testing the safety capability with the starter image. This includes a few changes: * Enable the safety integration test * Relax the shield model requirements from llama-guard to make it work with llama-guard3:8b coming from Ollama * Expose a shield for each inference provider in the starter distro. The shield will only be registered if the provider is enabled. Closes: https://github.com/meta-llama/llama-stack/issues/2528 Signed-off-by: Sébastien Han <seb@redhat.com>	2025-07-09 16:53:50 +02:00
Sébastien Han	ac5fd57387	chore: remove nested imports (#2515) ... # What does this PR do? * Given that our API packages use "import *" in `__init.py__` we don't need to do `from llama_stack.apis.models.models` but simply from llama_stack.apis.models. The decision to use `import *` is debatable and should probably be revisited at one point. * Remove unneeded Ruff F401 rule * Consolidate Ruff F403 rule in the pyprojectfrom llama_stack.apis.models.models Signed-off-by: Sébastien Han <seb@redhat.com>	2025-06-26 08:01:05 +05:30
Hardik Shah	985d0b156c	feat: Add suffix to openai_completions (#2449) ... For code completion apps need "fill in the middle" capabilities. Added option of `suffix` to `openai_completion` to enable this. Updated ollama provider to showcase the same. ### Test Plan ``` pytest -sv --stack-config="inference=ollama" tests/integration/inference/test_openai_completion.py --text-model qwen2.5-coder:1.5b -k test_openai_completion_non_streaming_suffix ``` ### OpenAI Sample script ``` from openai import OpenAI client = OpenAI(base_url="http://localhost:8321/v1/openai/v1") response = client.completions.create( model="qwen2.5-coder:1.5b", prompt="The capital of ", suffix="is Paris.", max_tokens=10, ) print(response.choices[0].text) ``` ### Output ``` France is ____. To answer this question, we ```	2025-06-13 16:06:06 -07:00
Hardik Shah	b21050935e	feat: New OpenAI compat embeddings API (#2314) ... # What does this PR do? Adds a new endpoint that is compatible with OpenAI for embeddings api. `/openai/v1/embeddings` Added providers for OpenAI, LiteLLM and SentenceTransformer. ## Test Plan ``` LLAMA_STACK_CONFIG=http://localhost:8321 pytest -sv tests/integration/inference/test_openai_embeddings.py --embedding-model all-MiniLM-L6-v2,text-embedding-3-small,gemini/text-embedding-004 ```	2025-05-31 22:11:47 -07:00
ehhuang	8feb1827c8	fix: openai provider model id (#2229) ... # What does this PR do? Since https://github.com/meta-llama/llama-stack/pull/2193 switched to openai sdk, we need to strip 'openai/' from the model_id ## Test Plan start server with openai provider and send a chat completion call	2025-05-22 14:51:01 -07:00
Matthew Farrellee	64f8d4c3ad	feat: use openai-python for openai inference provider (#2193) ... # What does this PR do? fixes #2121 this implementation splits reponsibility between litellm and openai libraries - | Inference Method | Implementation Source | |----------------------------|--------------------------| | completion | LiteLLMOpenAIMixin | | chat_completion | LiteLLMOpenAIMixin | | embedding | LiteLLMOpenAIMixin | | batch_completion | LiteLLMOpenAIMixin | | batch_chat_completion | LiteLLMOpenAIMixin | | openai_completion | AsyncOpenAI | | openai_chat_completion | AsyncOpenAI | ## Test Plan smoke test with - ``` $ OPENAI_API_KEY=$LLAMA_API_KEY OPENAI_BASE_URL=https://api.llama.com/compat/v1 llama stack build --image-type conda --image-name openai --providers inference=remote::openai --run $ llama-stack-client models register Llama-4-Scout-17B-16E-Instruct-FP8 $ curl "http://localhost:8321/v1/openai/v1/chat/completions" -H "Content-Type: application/json" \ -d '{ "model": "Llama-4-Scout-17B-16E-Instruct-FP8", "messages": [ {"role": "user", "content": "Hello Llama! Can you give me a quick intro?"} ] }' {"id":"AmPwrrkc5JgVjejPdIPrpT2","choices":[{"finish_reason":"stop","index":0,"logprobs":{"content":null,"refusal":null},"message":{"content":"Hello! I'm Llama, a Meta-designed model that adapts to your conversational style. Whether you need quick answers, deep dives into ideas, or just want to vent, joke, or brainstorm—I'm here for it. What’s on your mind?","refusal":"","role":"assistant","annotations":null,"audio":null,"function_call":null,"tool_calls":null,"id":"AmPwrrkc5JgVjejPdIPrpT2"}}],"created":1747410061,"model":"Llama-4-Scout-17B-16E-Instruct-FP8","object":"chat.completions","service_tier":null,"system_fingerprint":null,"usage":{"completion_tokens":54,"prompt_tokens":22,"total_tokens":76,"completion_tokens_details":null,"prompt_tokens_details":null}} ``` and run full test suite.	2025-05-16 12:57:56 -07:00
Matthew Farrellee	aa5bef8e05	feat: expand set of known openai models, allow using openai canonical model names (#2164) ... note: the openai provider exposes the litellm specific model names to the user. this change is compatible with that. the litellm names should be deprecated.	2025-05-14 13:18:15 -07:00
Ihar Hrachyshka	9e6561a1ec	chore: enable pyupgrade fixes (#1806) ... # What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>	2025-05-01 14:23:50 -07:00
Ashwin Bharambe	928a39d17b	feat(providers): Groq now uses LiteLLM openai-compat (#1303) ... Groq has never supported raw completions anyhow. So this makes it easier to switch it to LiteLLM. All our test suite passes. I also updated all the openai-compat providers so they work with api keys passed from headers. `provider_data` ## Test Plan ```bash LLAMA_STACK_CONFIG=groq \ pytest -s -v tests/client-sdk/inference/test_text_inference.py \ --inference-model=groq/llama-3.3-70b-versatile --vision-inference-model="" ``` Also tested (openai, anthropic, gemini) providers. No regressions.	2025-02-27 13:16:50 -08:00
Ashwin Bharambe	4cf95475e5	fix: make vision and embedding tests pass with openai, anthropic and gemini ... NOTE - Anthropic embeddings do not work due to LiteLLM not supporting them.	2025-02-26 11:24:01 -08:00
Ashwin Bharambe	63e6acd0c3	feat: add (openai, anthropic, gemini) providers via litellm (#1267) ... # What does this PR do? This PR introduces more non-llama model support to llama stack. Providers introduced: openai, anthropic and gemini. All of these providers use essentially the same piece of code -- the implementation works via the `litellm` library. We will expose only specific models for providers we enable making sure they all work well and pass tests. This setup (instead of automatically enabling _all_ providers and models allowed by LiteLLM) ensures we can also perform any needed prompt tuning on a per-model basis as needed (just like we do it for llama models.) ## Test Plan ```bash #!/bin/bash args=("$@") for model in openai/gpt-4o anthropic/claude-3-5-sonnet-latest gemini/gemini-1.5-flash; do LLAMA_STACK_CONFIG=dev pytest -s -v tests/client-sdk/inference/test_text_inference.py \ --embedding-model=all-MiniLM-L6-v2 \ --vision-inference-model="" \ --inference-model=$model "${args[@]}" done ```	2025-02-25 22:07:33 -08:00