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adding logo and favicon
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> chore: Enable keyword search for Milvus inline (#3073) With https://github.com/milvus-io/milvus-lite/pull/294 - Milvus Lite supports keyword search using BM25. While introducing keyword search we had explicitly disabled it for inline milvus. This PR removes the need for the check, and enables `inline::milvus` for tests. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> Run llama stack with `inline::milvus` enabled: ``` pytest tests/integration/vector_io/test_openai_vector_stores.py::test_openai_vector_store_search_modes --stack-config=http://localhost:8321 --embedding-model=all-MiniLM-L6-v2 -v ``` ``` INFO 2025-08-07 17:06:20,932 tests.integration.conftest:64 tests: Setting DISABLE_CODE_SANDBOX=1 for macOS =========================================================================================== test session starts ============================================================================================ platform darwin -- Python 3.12.11, pytest-7.4.4, pluggy-1.5.0 -- /Users/vnarsing/miniconda3/envs/stack-client/bin/python cachedir: .pytest_cache metadata: {'Python': '3.12.11', 'Platform': 'macOS-14.7.6-arm64-arm-64bit', 'Packages': {'pytest': '7.4.4', 'pluggy': '1.5.0'}, 'Plugins': {'asyncio': '0.23.8', 'cov': '6.0.0', 'timeout': '2.2.0', 'socket': '0.7.0', 'html': '3.1.1', 'langsmith': '0.3.39', 'anyio': '4.8.0', 'metadata': '3.0.0'}} rootdir: /Users/vnarsing/go/src/github/meta-llama/llama-stack configfile: pyproject.toml plugins: asyncio-0.23.8, cov-6.0.0, timeout-2.2.0, socket-0.7.0, html-3.1.1, langsmith-0.3.39, anyio-4.8.0, metadata-3.0.0 asyncio: mode=Mode.AUTO collected 3 items tests/integration/vector_io/test_openai_vector_stores.py::test_openai_vector_store_search_modes[None-None-all-MiniLM-L6-v2-None-384-vector] PASSED [ 33%] tests/integration/vector_io/test_openai_vector_stores.py::test_openai_vector_store_search_modes[None-None-all-MiniLM-L6-v2-None-384-keyword] PASSED [ 66%] tests/integration/vector_io/test_openai_vector_stores.py::test_openai_vector_store_search_modes[None-None-all-MiniLM-L6-v2-None-384-hybrid] PASSED [100%] ============================================================================================ 3 passed in 4.75s ============================================================================================= ``` Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com> Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com> chore: Fixup main pre commit (#3204) build: Bump version to 0.2.18 chore: Faster npm pre-commit (#3206) Adds npm to pre-commit.yml installation and caches ui Removes node installation during pre-commit. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> chiecking in for tonight, wip moving to agents api Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> remove log Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> updated Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> fix: disable ui-prettier & ui-eslint (#3207) chore(pre-commit): add pre-commit hook to enforce llama_stack logger usage (#3061) This PR adds a step in pre-commit to enforce using `llama_stack` logger. Currently, various parts of the code base uses different loggers. As a custom `llama_stack` logger exist and used in the codebase, it is better to standardize its utilization. Signed-off-by: Mustafa Elbehery <melbeher@redhat.com> Co-authored-by: Matthew Farrellee <matt@cs.wisc.edu> fix: fix ```openai_embeddings``` for asymmetric embedding NIMs (#3205) NVIDIA asymmetric embedding models (e.g., `nvidia/llama-3.2-nv-embedqa-1b-v2`) require an `input_type` parameter not present in the standard OpenAI embeddings API. This PR adds the `input_type="query"` as default and updates the documentation to suggest using the `embedding` API for passage embeddings. <!-- If resolving an issue, uncomment and update the line below --> Resolves #2892 ``` pytest -s -v tests/integration/inference/test_openai_embeddings.py --stack-config="inference=nvidia" --embedding-model="nvidia/llama-3.2-nv-embedqa-1b-v2" --env NVIDIA_API_KEY={nvidia_api_key} --env NVIDIA_BASE_URL="https://integrate.api.nvidia.com" ``` cleaning up Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> updating session manager to cache messages locally Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> fix linter Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> more cleanup Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo
parent
e7be568d7e
commit
6620b625f1
76 changed files with 2343 additions and 1187 deletions
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@ -4,11 +4,10 @@
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import logging
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import warnings
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from collections.abc import AsyncIterator
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from openai import APIConnectionError, BadRequestError
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from openai import NOT_GIVEN, APIConnectionError, BadRequestError
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from llama_stack.apis.common.content_types import (
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InterleavedContent,
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@ -27,12 +26,16 @@ from llama_stack.apis.inference import (
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Inference,
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LogProbConfig,
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Message,
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OpenAIEmbeddingData,
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OpenAIEmbeddingsResponse,
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OpenAIEmbeddingUsage,
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ResponseFormat,
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SamplingParams,
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TextTruncation,
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ToolChoice,
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ToolConfig,
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)
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from llama_stack.log import get_logger
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from llama_stack.models.llama.datatypes import ToolDefinition, ToolPromptFormat
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from llama_stack.providers.utils.inference.model_registry import (
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ModelRegistryHelper,
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@ -54,7 +57,7 @@ from .openai_utils import (
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)
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from .utils import _is_nvidia_hosted
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logger = logging.getLogger(__name__)
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logger = get_logger(name=__name__, category="inference")
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class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
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@ -210,6 +213,57 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
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#
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return EmbeddingsResponse(embeddings=[embedding.embedding for embedding in response.data])
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async def openai_embeddings(
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self,
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model: str,
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input: str | list[str],
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encoding_format: str | None = "float",
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dimensions: int | None = None,
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user: str | None = None,
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) -> OpenAIEmbeddingsResponse:
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"""
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OpenAI-compatible embeddings for NVIDIA NIM.
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Note: NVIDIA NIM asymmetric embedding models require an "input_type" field not present in the standard OpenAI embeddings API.
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We default this to "query" to ensure requests succeed when using the
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OpenAI-compatible endpoint. For passage embeddings, use the embeddings API with
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`task_type='document'`.
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"""
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extra_body: dict[str, object] = {"input_type": "query"}
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logger.warning(
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"NVIDIA OpenAI-compatible embeddings: defaulting to input_type='query'. "
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"For passage embeddings, use the embeddings API with task_type='document'."
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)
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response = await self.client.embeddings.create(
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model=await self._get_provider_model_id(model),
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input=input,
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encoding_format=encoding_format if encoding_format is not None else NOT_GIVEN,
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dimensions=dimensions if dimensions is not None else NOT_GIVEN,
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user=user if user is not None else NOT_GIVEN,
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extra_body=extra_body,
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)
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data = []
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for i, embedding_data in enumerate(response.data):
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data.append(
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OpenAIEmbeddingData(
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embedding=embedding_data.embedding,
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index=i,
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)
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)
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usage = OpenAIEmbeddingUsage(
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prompt_tokens=response.usage.prompt_tokens,
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total_tokens=response.usage.total_tokens,
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)
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return OpenAIEmbeddingsResponse(
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data=data,
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model=response.model,
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usage=usage,
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)
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async def chat_completion(
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self,
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model_id: str,
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