diff --git a/.github/actions/setup-runner/action.yml b/.github/actions/setup-runner/action.yml index 448fdbbfe..905d6b73a 100644 --- a/.github/actions/setup-runner/action.yml +++ b/.github/actions/setup-runner/action.yml @@ -29,8 +29,8 @@ runs: # Install llama-stack-client-python based on the client-version input if [ "${{ inputs.client-version }}" = "latest" ]; then - echo "Installing latest llama-stack-client-python from next branch" - uv pip install git+https://github.com/llamastack/llama-stack-client-python.git@next + echo "Installing latest llama-stack-client-python from main branch" + uv pip install git+https://github.com/llamastack/llama-stack-client-python.git@main elif [ "${{ inputs.client-version }}" = "published" ]; then echo "Installing published llama-stack-client-python from PyPI" uv pip install llama-stack-client diff --git a/.github/actions/setup-test-environment/action.yml b/.github/actions/setup-test-environment/action.yml index ececca0f6..478e8f598 100644 --- a/.github/actions/setup-test-environment/action.yml +++ b/.github/actions/setup-test-environment/action.yml @@ -44,8 +44,8 @@ runs: run: | # Install llama-stack-client-python based on the client-version input if [ "${{ inputs.client-version }}" = "latest" ]; then - echo "Installing latest llama-stack-client-python from next branch" - export LLAMA_STACK_CLIENT_DIR=git+https://github.com/llamastack/llama-stack-client-python.git@next + echo "Installing latest llama-stack-client-python from main branch" + export LLAMA_STACK_CLIENT_DIR=git+https://github.com/llamastack/llama-stack-client-python.git@main elif [ "${{ inputs.client-version }}" = "published" ]; then echo "Installing published llama-stack-client-python from PyPI" unset LLAMA_STACK_CLIENT_DIR diff --git a/.github/workflows/conformance.yml b/.github/workflows/conformance.yml index 9383476f5..b19b77cce 100644 --- a/.github/workflows/conformance.yml +++ b/.github/workflows/conformance.yml @@ -43,7 +43,7 @@ jobs: # Cache oasdiff to avoid checksum failures and speed up builds - name: Cache oasdiff id: cache-oasdiff - uses: actions/cache@0400d5f644dc74513175e3cd8d07132dd4860809 + uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 with: path: ~/oasdiff key: oasdiff-${{ runner.os }} diff --git a/docs/docs/references/llama_cli_reference/index.md b/docs/docs/references/llama_cli_reference/index.md index fe3aa51ab..9b71a6795 100644 --- a/docs/docs/references/llama_cli_reference/index.md +++ b/docs/docs/references/llama_cli_reference/index.md @@ -261,7 +261,7 @@ You can even run `llama model prompt-format` see all of the templates and their ``` llama model prompt-format -m Llama3.2-3B-Instruct ``` -![alt text](../../../resources/prompt-format.png) +![alt text](/img/prompt-format.png) You will be shown a Markdown formatted description of the model interface and how prompts / messages are formatted for various scenarios. diff --git a/docs/docs/references/python_sdk_reference/index.md b/docs/docs/references/python_sdk_reference/index.md index e0b29363e..bce87e14a 100644 --- a/docs/docs/references/python_sdk_reference/index.md +++ b/docs/docs/references/python_sdk_reference/index.md @@ -217,7 +217,6 @@ from llama_stack_client.types import ( Methods: - client.inference.chat_completion(\*\*params) -> InferenceChatCompletionResponse -- client.inference.completion(\*\*params) -> InferenceCompletionResponse - client.inference.embeddings(\*\*params) -> EmbeddingsResponse ## VectorIo diff --git a/docs/getting_started.ipynb b/docs/getting_started.ipynb index 641cf4224..56aef2b7d 100644 --- a/docs/getting_started.ipynb +++ b/docs/getting_started.ipynb @@ -824,16 +824,10 @@ "\n", "\n", "user_input = \"Michael Jordan was born in 1963. He played basketball for the Chicago Bulls. He retired in 2003. Extract this information into JSON for me. \"\n", - "response = client.inference.completion(\n", - " model_id=\"meta-llama/Llama-3.1-8B-Instruct\",\n", - " content=user_input,\n", - " stream=False,\n", - " sampling_params={\n", - " \"strategy\": {\n", - " \"type\": \"greedy\",\n", - " },\n", - " \"max_tokens\": 50,\n", - " },\n", + "response = client.chat.completions.create(\n", + " model=\"meta-llama/Llama-3.1-8B-Instruct\",\n", + " messages=[{\"role\": \"user\", \"content\": user_input}],\n", + " max_tokens=50,\n", " response_format={\n", " \"type\": \"json_schema\",\n", " \"json_schema\": Output.model_json_schema(),\n", @@ -1013,7 +1007,7 @@ "\n", "\n", "\n", - "\"drawing\"\n", + "\"drawing\"\n", "\n", "\n", "Agents are characterized by having access to\n", diff --git a/docs/notebooks/nvidia/beginner_e2e/Llama_Stack_NVIDIA_E2E_Flow.ipynb b/docs/notebooks/nvidia/beginner_e2e/Llama_Stack_NVIDIA_E2E_Flow.ipynb index d8f29d999..601276526 100644 --- a/docs/notebooks/nvidia/beginner_e2e/Llama_Stack_NVIDIA_E2E_Flow.ipynb +++ b/docs/notebooks/nvidia/beginner_e2e/Llama_Stack_NVIDIA_E2E_Flow.ipynb @@ -706,20 +706,15 @@ " provider_id=\"nvidia\",\n", ")\n", "\n", - "response = client.inference.completion(\n", - " content=\"Complete the sentence using one word: Roses are red, violets are \",\n", + "response = client.completions.create(\n", + " prompt=\"Complete the sentence using one word: Roses are red, violets are \",\n", " stream=False,\n", - " model_id=CUSTOMIZED_MODEL_DIR,\n", - " sampling_params={\n", - " \"strategy\": {\n", - " \"type\": \"top_p\",\n", - " \"temperature\": 0.7,\n", - " \"top_p\": 0.9\n", - " },\n", - " \"max_tokens\": 20,\n", - " },\n", + " model=CUSTOMIZED_MODEL_DIR,\n", + " temperature=0.7,\n", + " top_p=0.9,\n", + " max_tokens=20,\n", ")\n", - "print(f\"Inference response: {response.content}\")" + "print(f\"Inference response: {response.choices[0].text}\")" ] }, { @@ -1233,20 +1228,15 @@ " provider_id=\"nvidia\",\n", ")\n", "\n", - "response = client.inference.completion(\n", - " content=\"Complete the sentence using one word: Roses are red, violets are \",\n", + "response = client.completions.create(\n", + " prompt=\"Complete the sentence using one word: Roses are red, violets are \",\n", " stream=False,\n", - " model_id=customized_chat_model_dir,\n", - " sampling_params={\n", - " \"strategy\": {\n", - " \"type\": \"top_p\",\n", - " \"temperature\": 0.7,\n", - " \"top_p\": 0.9\n", - " },\n", - " \"max_tokens\": 20,\n", - " },\n", + " model=customized_chat_model_dir,\n", + " temperature=0.7,\n", + " top_p=0.9,\n", + " max_tokens=20,\n", ")\n", - "print(f\"Inference response: {response.content}\")" + "print(f\"Inference response: {response.choices[0].text}\")" ] }, { diff --git a/docs/resources/agentic-system.png b/docs/static/img/agentic-system.png similarity index 100% rename from docs/resources/agentic-system.png rename to docs/static/img/agentic-system.png diff --git a/docs/resources/list-templates.png b/docs/static/img/list-templates.png similarity index 100% rename from docs/resources/list-templates.png rename to docs/static/img/list-templates.png diff --git a/docs/resources/llama-stack.png b/docs/static/img/llama-stack.png similarity index 100% rename from docs/resources/llama-stack.png rename to docs/static/img/llama-stack.png diff --git a/docs/resources/model-lifecycle.png b/docs/static/img/model-lifecycle.png similarity index 100% rename from docs/resources/model-lifecycle.png rename to docs/static/img/model-lifecycle.png diff --git a/docs/resources/prompt-format.png b/docs/static/img/prompt-format.png similarity index 100% rename from docs/resources/prompt-format.png rename to docs/static/img/prompt-format.png diff --git a/docs/static/llama-stack-spec.html b/docs/static/llama-stack-spec.html index e3ce6df58..85763f4ee 100644 --- a/docs/static/llama-stack-spec.html +++ b/docs/static/llama-stack-spec.html @@ -1239,50 +1239,6 @@ ] } }, - "/v1/inference/embeddings": { - "post": { - "responses": { - "200": { - "description": "An array of embeddings, one for each content. Each embedding is a list of floats. The dimensionality of the embedding is model-specific; you can check model metadata using /models/{model_id}.", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/EmbeddingsResponse" - } - } - } - }, - "400": { - "$ref": "#/components/responses/BadRequest400" - }, - "429": { - "$ref": "#/components/responses/TooManyRequests429" - }, - "500": { - "$ref": "#/components/responses/InternalServerError500" - }, - "default": { - "$ref": "#/components/responses/DefaultError" - } - }, - "tags": [ - "Inference" - ], - "summary": "Generate embeddings for content pieces using the specified model.", - "description": "Generate embeddings for content pieces using the specified model.", - "parameters": [], - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/EmbeddingsRequest" - } - } - }, - "required": true - } - } - }, "/v1alpha/eval/benchmarks/{benchmark_id}/evaluations": { "post": { "responses": { @@ -6965,7 +6921,7 @@ } } }, - "/v1/inference/rerank": { + "/v1alpha/inference/rerank": { "post": { "responses": { "200": { @@ -12081,80 +12037,6 @@ "title": "OpenAIDeleteResponseObject", "description": "Response object confirming deletion of an OpenAI response." }, - "EmbeddingsRequest": { - "type": "object", - "properties": { - "model_id": { - "type": "string", - "description": "The identifier of the model to use. The model must be an embedding model registered with Llama Stack and available via the /models endpoint." - }, - "contents": { - "oneOf": [ - { - "type": "array", - "items": { - "type": "string" - } - }, - { - "type": "array", - "items": { - "$ref": "#/components/schemas/InterleavedContentItem" - } - } - ], - "description": "List of contents to generate embeddings for. Each content can be a string or an InterleavedContentItem (and hence can be multimodal). The behavior depends on the model and provider. Some models may only support text." - }, - "text_truncation": { - "type": "string", - "enum": [ - "none", - "start", - "end" - ], - "description": "(Optional) Config for how to truncate text for embedding when text is longer than the model's max sequence length." - }, - "output_dimension": { - "type": "integer", - "description": "(Optional) Output dimensionality for the embeddings. Only supported by Matryoshka models." - }, - "task_type": { - "type": "string", - "enum": [ - "query", - "document" - ], - "description": "(Optional) How is the embedding being used? This is only supported by asymmetric embedding models." - } - }, - "additionalProperties": false, - "required": [ - "model_id", - "contents" - ], - "title": "EmbeddingsRequest" - }, - "EmbeddingsResponse": { - "type": "object", - "properties": { - "embeddings": { - "type": "array", - "items": { - "type": "array", - "items": { - "type": "number" - } - }, - "description": "List of embedding vectors, one per input content. Each embedding is a list of floats. The dimensionality of the embedding is model-specific; you can check model metadata using /models/{model_id}" - } - }, - "additionalProperties": false, - "required": [ - "embeddings" - ], - "title": "EmbeddingsResponse", - "description": "Response containing generated embeddings." - }, "AgentCandidate": { "type": "object", "properties": { diff --git a/docs/static/llama-stack-spec.yaml b/docs/static/llama-stack-spec.yaml index ee0240640..e4422b41c 100644 --- a/docs/static/llama-stack-spec.yaml +++ b/docs/static/llama-stack-spec.yaml @@ -861,41 +861,6 @@ paths: required: true schema: type: string - /v1/inference/embeddings: - post: - responses: - '200': - description: >- - An array of embeddings, one for each content. Each embedding is a list - of floats. The dimensionality of the embedding is model-specific; you - can check model metadata using /models/{model_id}. - content: - application/json: - schema: - $ref: '#/components/schemas/EmbeddingsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Inference - summary: >- - Generate embeddings for content pieces using the specified model. - description: >- - Generate embeddings for content pieces using the specified model. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/EmbeddingsRequest' - required: true /v1alpha/eval/benchmarks/{benchmark_id}/evaluations: post: responses: @@ -5040,7 +5005,7 @@ paths: schema: $ref: '#/components/schemas/QueryTracesRequest' required: true - /v1/inference/rerank: + /v1alpha/inference/rerank: post: responses: '200': @@ -8937,72 +8902,6 @@ components: title: OpenAIDeleteResponseObject description: >- Response object confirming deletion of an OpenAI response. - EmbeddingsRequest: - type: object - properties: - model_id: - type: string - description: >- - The identifier of the model to use. The model must be an embedding model - registered with Llama Stack and available via the /models endpoint. - contents: - oneOf: - - type: array - items: - type: string - - type: array - items: - $ref: '#/components/schemas/InterleavedContentItem' - description: >- - List of contents to generate embeddings for. Each content can be a string - or an InterleavedContentItem (and hence can be multimodal). The behavior - depends on the model and provider. Some models may only support text. - text_truncation: - type: string - enum: - - none - - start - - end - description: >- - (Optional) Config for how to truncate text for embedding when text is - longer than the model's max sequence length. - output_dimension: - type: integer - description: >- - (Optional) Output dimensionality for the embeddings. Only supported by - Matryoshka models. - task_type: - type: string - enum: - - query - - document - description: >- - (Optional) How is the embedding being used? This is only supported by - asymmetric embedding models. - additionalProperties: false - required: - - model_id - - contents - title: EmbeddingsRequest - EmbeddingsResponse: - type: object - properties: - embeddings: - type: array - items: - type: array - items: - type: number - description: >- - List of embedding vectors, one per input content. Each embedding is a - list of floats. The dimensionality of the embedding is model-specific; - you can check model metadata using /models/{model_id} - additionalProperties: false - required: - - embeddings - title: EmbeddingsResponse - description: >- - Response containing generated embeddings. AgentCandidate: type: object properties: diff --git a/llama_stack/apis/inference/inference.py b/llama_stack/apis/inference/inference.py index b46ffe925..29b014a11 100644 --- a/llama_stack/apis/inference/inference.py +++ b/llama_stack/apis/inference/inference.py @@ -17,11 +17,11 @@ from typing import ( from pydantic import BaseModel, Field, field_validator from typing_extensions import TypedDict -from llama_stack.apis.common.content_types import ContentDelta, InterleavedContent, InterleavedContentItem +from llama_stack.apis.common.content_types import ContentDelta, InterleavedContent from llama_stack.apis.common.responses import Order from llama_stack.apis.models import Model from llama_stack.apis.telemetry import MetricResponseMixin -from llama_stack.apis.version import LLAMA_STACK_API_V1 +from llama_stack.apis.version import LLAMA_STACK_API_V1, LLAMA_STACK_API_V1ALPHA from llama_stack.models.llama.datatypes import ( BuiltinTool, StopReason, @@ -1070,27 +1070,7 @@ class InferenceProvider(Protocol): """ ... - @webmethod(route="/inference/embeddings", method="POST", level=LLAMA_STACK_API_V1) - async def embeddings( - self, - model_id: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - """Generate embeddings for content pieces using the specified model. - - :param model_id: The identifier of the model to use. The model must be an embedding model registered with Llama Stack and available via the /models endpoint. - :param contents: List of contents to generate embeddings for. Each content can be a string or an InterleavedContentItem (and hence can be multimodal). The behavior depends on the model and provider. Some models may only support text. - :param output_dimension: (Optional) Output dimensionality for the embeddings. Only supported by Matryoshka models. - :param text_truncation: (Optional) Config for how to truncate text for embedding when text is longer than the model's max sequence length. - :param task_type: (Optional) How is the embedding being used? This is only supported by asymmetric embedding models. - :returns: An array of embeddings, one for each content. Each embedding is a list of floats. The dimensionality of the embedding is model-specific; you can check model metadata using /models/{model_id}. - """ - ... - - @webmethod(route="/inference/rerank", method="POST", experimental=True, level=LLAMA_STACK_API_V1) + @webmethod(route="/inference/rerank", method="POST", level=LLAMA_STACK_API_V1ALPHA) async def rerank( self, model: str, diff --git a/llama_stack/core/datatypes.py b/llama_stack/core/datatypes.py index b5558c66f..6a297f012 100644 --- a/llama_stack/core/datatypes.py +++ b/llama_stack/core/datatypes.py @@ -433,6 +433,12 @@ class InferenceStoreConfig(BaseModel): num_writers: int = Field(default=4, description="Number of concurrent background writers") +class ResponsesStoreConfig(BaseModel): + sql_store_config: SqlStoreConfig + max_write_queue_size: int = Field(default=10000, description="Max queued writes for responses store") + num_writers: int = Field(default=4, description="Number of concurrent background writers") + + class StackRunConfig(BaseModel): version: int = LLAMA_STACK_RUN_CONFIG_VERSION diff --git a/llama_stack/core/resolver.py b/llama_stack/core/resolver.py index 373446de6..f421c47ed 100644 --- a/llama_stack/core/resolver.py +++ b/llama_stack/core/resolver.py @@ -29,6 +29,7 @@ from llama_stack.apis.telemetry import Telemetry from llama_stack.apis.tools import ToolGroups, ToolRuntime from llama_stack.apis.vector_dbs import VectorDBs from llama_stack.apis.vector_io import VectorIO +from llama_stack.apis.version import LLAMA_STACK_API_V1ALPHA from llama_stack.core.client import get_client_impl from llama_stack.core.datatypes import ( AccessRule, @@ -412,8 +413,14 @@ def check_protocol_compliance(obj: Any, protocol: Any) -> None: mro = type(obj).__mro__ for name, value in inspect.getmembers(protocol): - if inspect.isfunction(value) and hasattr(value, "__webmethod__"): - if value.__webmethod__.experimental: + if inspect.isfunction(value) and hasattr(value, "__webmethods__"): + has_alpha_api = False + for webmethod in value.__webmethods__: + if webmethod.level == LLAMA_STACK_API_V1ALPHA: + has_alpha_api = True + break + # if this API has multiple webmethods, and one of them is an alpha API, this API should be skipped when checking for missing or not callable routes + if has_alpha_api: continue if not hasattr(obj, name): missing_methods.append((name, "missing")) diff --git a/llama_stack/core/routers/inference.py b/llama_stack/core/routers/inference.py index fcf01a9c4..80f47fb5d 100644 --- a/llama_stack/core/routers/inference.py +++ b/llama_stack/core/routers/inference.py @@ -16,7 +16,6 @@ from pydantic import Field, TypeAdapter from llama_stack.apis.common.content_types import ( InterleavedContent, - InterleavedContentItem, ) from llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError from llama_stack.apis.inference import ( @@ -26,8 +25,6 @@ from llama_stack.apis.inference import ( CompletionMessage, CompletionResponse, CompletionResponseStreamChunk, - EmbeddingsResponse, - EmbeddingTaskType, Inference, ListOpenAIChatCompletionResponse, LogProbConfig, @@ -48,7 +45,6 @@ from llama_stack.apis.inference import ( ResponseFormat, SamplingParams, StopReason, - TextTruncation, ToolChoice, ToolConfig, ToolDefinition, @@ -312,25 +308,6 @@ class InferenceRouter(Inference): return response - async def embeddings( - self, - model_id: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - logger.debug(f"InferenceRouter.embeddings: {model_id}") - await self._get_model(model_id, ModelType.embedding) - provider = await self.routing_table.get_provider_impl(model_id) - return await provider.embeddings( - model_id=model_id, - contents=contents, - text_truncation=text_truncation, - output_dimension=output_dimension, - task_type=task_type, - ) - async def openai_completion( self, model: str, diff --git a/llama_stack/providers/inline/agents/meta_reference/agent_instance.py b/llama_stack/providers/inline/agents/meta_reference/agent_instance.py index dcc08a482..467777b72 100644 --- a/llama_stack/providers/inline/agents/meta_reference/agent_instance.py +++ b/llama_stack/providers/inline/agents/meta_reference/agent_instance.py @@ -924,7 +924,7 @@ async def get_raw_document_text(document: Document) -> str: DeprecationWarning, stacklevel=2, ) - elif not (document.mime_type.startswith("text/") or document.mime_type == "application/yaml"): + elif not (document.mime_type.startswith("text/") or document.mime_type in ("application/yaml", "application/json")): raise ValueError(f"Unexpected document mime type: {document.mime_type}") if isinstance(document.content, URL): diff --git a/llama_stack/providers/inline/eval/meta_reference/eval.py b/llama_stack/providers/inline/eval/meta_reference/eval.py index a03e8951c..0dfe23dca 100644 --- a/llama_stack/providers/inline/eval/meta_reference/eval.py +++ b/llama_stack/providers/inline/eval/meta_reference/eval.py @@ -12,7 +12,7 @@ from llama_stack.apis.agents import Agents, StepType from llama_stack.apis.benchmarks import Benchmark from llama_stack.apis.datasetio import DatasetIO from llama_stack.apis.datasets import Datasets -from llama_stack.apis.inference import Inference, SystemMessage, UserMessage +from llama_stack.apis.inference import Inference, OpenAISystemMessageParam, OpenAIUserMessageParam, UserMessage from llama_stack.apis.scoring import Scoring from llama_stack.providers.datatypes import BenchmarksProtocolPrivate from llama_stack.providers.inline.agents.meta_reference.agent_instance import ( @@ -159,31 +159,40 @@ class MetaReferenceEvalImpl( ) -> list[dict[str, Any]]: candidate = benchmark_config.eval_candidate assert candidate.sampling_params.max_tokens is not None, "SamplingParams.max_tokens must be provided" + sampling_params = {"max_tokens": candidate.sampling_params.max_tokens} generations = [] for x in tqdm(input_rows): if ColumnName.completion_input.value in x: + if candidate.sampling_params.stop: + sampling_params["stop"] = candidate.sampling_params.stop + input_content = json.loads(x[ColumnName.completion_input.value]) - response = await self.inference_api.completion( + response = await self.inference_api.openai_completion( model=candidate.model, - content=input_content, - sampling_params=candidate.sampling_params, + prompt=input_content, + **sampling_params, ) - generations.append({ColumnName.generated_answer.value: response.completion_message.content}) + generations.append({ColumnName.generated_answer.value: response.choices[0].text}) elif ColumnName.chat_completion_input.value in x: chat_completion_input_json = json.loads(x[ColumnName.chat_completion_input.value]) - input_messages = [UserMessage(**x) for x in chat_completion_input_json if x["role"] == "user"] + input_messages = [ + OpenAIUserMessageParam(**x) for x in chat_completion_input_json if x["role"] == "user" + ] + messages = [] if candidate.system_message: messages.append(candidate.system_message) - messages += [SystemMessage(**x) for x in chat_completion_input_json if x["role"] == "system"] + + messages += [OpenAISystemMessageParam(**x) for x in chat_completion_input_json if x["role"] == "system"] + messages += input_messages - response = await self.inference_api.chat_completion( - model_id=candidate.model, + response = await self.inference_api.openai_chat_completion( + model=candidate.model, messages=messages, - sampling_params=candidate.sampling_params, + **sampling_params, ) - generations.append({ColumnName.generated_answer.value: response.completion_message.content}) + generations.append({ColumnName.generated_answer.value: response.choices[0].message.content}) else: raise ValueError("Invalid input row") diff --git a/llama_stack/providers/remote/inference/bedrock/bedrock.py b/llama_stack/providers/remote/inference/bedrock/bedrock.py index 29b935bbd..2206aa641 100644 --- a/llama_stack/providers/remote/inference/bedrock/bedrock.py +++ b/llama_stack/providers/remote/inference/bedrock/bedrock.py @@ -11,21 +11,17 @@ from botocore.client import BaseClient from llama_stack.apis.common.content_types import ( InterleavedContent, - InterleavedContentItem, ) from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, ChatCompletionResponseStreamChunk, - EmbeddingsResponse, - EmbeddingTaskType, Inference, LogProbConfig, Message, OpenAIEmbeddingsResponse, ResponseFormat, SamplingParams, - TextTruncation, ToolChoice, ToolConfig, ToolDefinition, @@ -47,8 +43,6 @@ from llama_stack.providers.utils.inference.openai_compat import ( ) from llama_stack.providers.utils.inference.prompt_adapter import ( chat_completion_request_to_prompt, - content_has_media, - interleaved_content_as_str, ) from .models import MODEL_ENTRIES @@ -218,36 +212,6 @@ class BedrockInferenceAdapter( ), } - async def embeddings( - self, - model_id: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - model = await self.model_store.get_model(model_id) - - # Convert foundation model ID to inference profile ID - region_name = self.client.meta.region_name - inference_profile_id = _to_inference_profile_id(model.provider_resource_id, region_name) - - embeddings = [] - for content in contents: - assert not content_has_media(content), "Bedrock does not support media for embeddings" - input_text = interleaved_content_as_str(content) - input_body = {"inputText": input_text} - body = json.dumps(input_body) - response = self.client.invoke_model( - body=body, - modelId=inference_profile_id, - accept="application/json", - contentType="application/json", - ) - response_body = json.loads(response.get("body").read()) - embeddings.append(response_body.get("embedding")) - return EmbeddingsResponse(embeddings=embeddings) - async def openai_embeddings( self, model: str, diff --git a/llama_stack/providers/remote/inference/cerebras/cerebras.py b/llama_stack/providers/remote/inference/cerebras/cerebras.py index 6662f004d..6be39fa5d 100644 --- a/llama_stack/providers/remote/inference/cerebras/cerebras.py +++ b/llama_stack/providers/remote/inference/cerebras/cerebras.py @@ -11,21 +11,17 @@ from cerebras.cloud.sdk import AsyncCerebras from llama_stack.apis.common.content_types import ( InterleavedContent, - InterleavedContentItem, ) from llama_stack.apis.inference import ( ChatCompletionRequest, CompletionRequest, CompletionResponse, - EmbeddingsResponse, - EmbeddingTaskType, Inference, LogProbConfig, Message, OpenAIEmbeddingsResponse, ResponseFormat, SamplingParams, - TextTruncation, ToolChoice, ToolConfig, ToolDefinition, @@ -187,16 +183,6 @@ class CerebrasInferenceAdapter( **get_sampling_options(request.sampling_params), } - async def embeddings( - self, - model_id: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - raise NotImplementedError() - async def openai_embeddings( self, model: str, diff --git a/llama_stack/providers/remote/inference/databricks/databricks.py b/llama_stack/providers/remote/inference/databricks/databricks.py index 6eac6e4f4..d85b477f5 100644 --- a/llama_stack/providers/remote/inference/databricks/databricks.py +++ b/llama_stack/providers/remote/inference/databricks/databricks.py @@ -11,15 +11,12 @@ from databricks.sdk import WorkspaceClient from llama_stack.apis.common.content_types import ( InterleavedContent, - InterleavedContentItem, ) from llama_stack.apis.inference import ( ChatCompletionResponse, ChatCompletionResponseStreamChunk, CompletionResponse, CompletionResponseStreamChunk, - EmbeddingsResponse, - EmbeddingTaskType, Inference, LogProbConfig, Message, @@ -27,7 +24,6 @@ from llama_stack.apis.inference import ( OpenAICompletion, ResponseFormat, SamplingParams, - TextTruncation, ToolChoice, ToolConfig, ToolDefinition, @@ -118,16 +114,6 @@ class DatabricksInferenceAdapter( ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]: raise NotImplementedError() - async def embeddings( - self, - model_id: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - raise NotImplementedError() - async def list_models(self) -> list[Model] | None: self._model_cache = {} # from OpenAIMixin ws_client = WorkspaceClient(host=self.config.url, token=self.get_api_key()) # TODO: this is not async diff --git a/llama_stack/providers/remote/inference/fireworks/fireworks.py b/llama_stack/providers/remote/inference/fireworks/fireworks.py index 069a0a674..ed4b56fad 100644 --- a/llama_stack/providers/remote/inference/fireworks/fireworks.py +++ b/llama_stack/providers/remote/inference/fireworks/fireworks.py @@ -10,22 +10,18 @@ from fireworks.client import Fireworks from llama_stack.apis.common.content_types import ( InterleavedContent, - InterleavedContentItem, ) from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, CompletionRequest, CompletionResponse, - EmbeddingsResponse, - EmbeddingTaskType, Inference, LogProbConfig, Message, ResponseFormat, ResponseFormatType, SamplingParams, - TextTruncation, ToolChoice, ToolConfig, ToolDefinition, @@ -48,8 +44,6 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( chat_completion_request_to_prompt, completion_request_to_prompt, - content_has_media, - interleaved_content_as_str, request_has_media, ) @@ -259,28 +253,3 @@ class FireworksInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Nee logger.debug(f"params to fireworks: {params}") return params - - async def embeddings( - self, - model_id: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - model = await self.model_store.get_model(model_id) - - kwargs = {} - if model.metadata.get("embedding_dimension"): - kwargs["dimensions"] = model.metadata.get("embedding_dimension") - assert all(not content_has_media(content) for content in contents), ( - "Fireworks does not support media for embeddings" - ) - response = self._get_client().embeddings.create( - model=model.provider_resource_id, - input=[interleaved_content_as_str(content) for content in contents], - **kwargs, - ) - - embeddings = [data.embedding for data in response.data] - return EmbeddingsResponse(embeddings=embeddings) diff --git a/llama_stack/providers/remote/inference/nvidia/NVIDIA.md b/llama_stack/providers/remote/inference/nvidia/NVIDIA.md index d9c18533a..4cb2dc394 100644 --- a/llama_stack/providers/remote/inference/nvidia/NVIDIA.md +++ b/llama_stack/providers/remote/inference/nvidia/NVIDIA.md @@ -39,25 +39,6 @@ client = LlamaStackAsLibraryClient("nvidia") client.initialize() ``` -### Create Completion - -The following example shows how to create a completion for an NVIDIA NIM. - -> [!NOTE] -> The hosted NVIDIA Llama NIMs (for example ```meta-llama/Llama-3.1-8B-Instruct```) that have ```NVIDIA_BASE_URL="https://integrate.api.nvidia.com"``` do not support the ```completion``` method, while locally deployed NIMs do. - -```python -response = client.inference.completion( - model_id="meta-llama/Llama-3.1-8B-Instruct", - content="Complete the sentence using one word: Roses are red, violets are :", - stream=False, - sampling_params={ - "max_tokens": 50, - }, -) -print(f"Response: {response.content}") -``` - ### Create Chat Completion The following example shows how to create a chat completion for an NVIDIA NIM. diff --git a/llama_stack/providers/remote/inference/nvidia/nvidia.py b/llama_stack/providers/remote/inference/nvidia/nvidia.py index 92094a0f3..a31981adb 100644 --- a/llama_stack/providers/remote/inference/nvidia/nvidia.py +++ b/llama_stack/providers/remote/inference/nvidia/nvidia.py @@ -11,8 +11,6 @@ from openai import NOT_GIVEN, APIConnectionError from llama_stack.apis.common.content_types import ( InterleavedContent, - InterleavedContentItem, - TextContentItem, ) from llama_stack.apis.inference import ( ChatCompletionRequest, @@ -21,8 +19,6 @@ from llama_stack.apis.inference import ( CompletionRequest, CompletionResponse, CompletionResponseStreamChunk, - EmbeddingsResponse, - EmbeddingTaskType, Inference, LogProbConfig, Message, @@ -31,7 +27,6 @@ from llama_stack.apis.inference import ( OpenAIEmbeddingUsage, ResponseFormat, SamplingParams, - TextTruncation, ToolChoice, ToolConfig, ) @@ -156,60 +151,6 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference): # we pass n=1 to get only one completion return convert_openai_completion_choice(response.choices[0]) - async def embeddings( - self, - model_id: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - if any(content_has_media(content) for content in contents): - raise NotImplementedError("Media is not supported") - - # - # Llama Stack: contents = list[str] | list[InterleavedContentItem] - # -> - # OpenAI: input = str | list[str] - # - # we can ignore str and always pass list[str] to OpenAI - # - flat_contents = [content.text if isinstance(content, TextContentItem) else content for content in contents] - input = [content.text if isinstance(content, TextContentItem) else content for content in flat_contents] - provider_model_id = await self._get_provider_model_id(model_id) - - extra_body = {} - - if text_truncation is not None: - text_truncation_options = { - TextTruncation.none: "NONE", - TextTruncation.end: "END", - TextTruncation.start: "START", - } - extra_body["truncate"] = text_truncation_options[text_truncation] - - if output_dimension is not None: - extra_body["dimensions"] = output_dimension - - if task_type is not None: - task_type_options = { - EmbeddingTaskType.document: "passage", - EmbeddingTaskType.query: "query", - } - extra_body["input_type"] = task_type_options[task_type] - - response = await self.client.embeddings.create( - model=provider_model_id, - input=input, - extra_body=extra_body, - ) - # - # OpenAI: CreateEmbeddingResponse(data=[Embedding(embedding=list[float], ...)], ...) - # -> - # Llama Stack: EmbeddingsResponse(embeddings=list[list[float]]) - # - return EmbeddingsResponse(embeddings=[embedding.embedding for embedding in response.data]) - async def openai_embeddings( self, model: str, diff --git a/llama_stack/providers/remote/inference/ollama/ollama.py b/llama_stack/providers/remote/inference/ollama/ollama.py index 3fb10445f..16b104fb5 100644 --- a/llama_stack/providers/remote/inference/ollama/ollama.py +++ b/llama_stack/providers/remote/inference/ollama/ollama.py @@ -14,7 +14,6 @@ from ollama import AsyncClient as AsyncOllamaClient from llama_stack.apis.common.content_types import ( ImageContentItem, InterleavedContent, - InterleavedContentItem, TextContentItem, ) from llama_stack.apis.common.errors import UnsupportedModelError @@ -25,8 +24,6 @@ from llama_stack.apis.inference import ( CompletionRequest, CompletionResponse, CompletionResponseStreamChunk, - EmbeddingsResponse, - EmbeddingTaskType, GrammarResponseFormat, InferenceProvider, JsonSchemaResponseFormat, @@ -34,7 +31,6 @@ from llama_stack.apis.inference import ( Message, ResponseFormat, SamplingParams, - TextTruncation, ToolChoice, ToolConfig, ToolDefinition, @@ -66,9 +62,7 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( chat_completion_request_to_prompt, completion_request_to_prompt, - content_has_media, convert_image_content_to_url, - interleaved_content_as_str, request_has_media, ) @@ -363,27 +357,6 @@ class OllamaInferenceAdapter( async for chunk in process_chat_completion_stream_response(stream, request): yield chunk - async def embeddings( - self, - model_id: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - model = await self._get_model(model_id) - - assert all(not content_has_media(content) for content in contents), ( - "Ollama does not support media for embeddings" - ) - response = await self.ollama_client.embed( - model=model.provider_resource_id, - input=[interleaved_content_as_str(content) for content in contents], - ) - embeddings = response["embeddings"] - - return EmbeddingsResponse(embeddings=embeddings) - async def register_model(self, model: Model) -> Model: if await self.check_model_availability(model.provider_model_id): return model diff --git a/llama_stack/providers/remote/inference/passthrough/passthrough.py b/llama_stack/providers/remote/inference/passthrough/passthrough.py index a2bdf0369..ae482b7b0 100644 --- a/llama_stack/providers/remote/inference/passthrough/passthrough.py +++ b/llama_stack/providers/remote/inference/passthrough/passthrough.py @@ -14,8 +14,6 @@ from llama_stack.apis.inference import ( ChatCompletionResponse, ChatCompletionResponseStreamChunk, CompletionMessage, - EmbeddingsResponse, - EmbeddingTaskType, Inference, LogProbConfig, Message, @@ -27,7 +25,6 @@ from llama_stack.apis.inference import ( OpenAIResponseFormatParam, ResponseFormat, SamplingParams, - TextTruncation, ToolChoice, ToolConfig, ToolDefinition, @@ -190,25 +187,6 @@ class PassthroughInferenceAdapter(Inference): chunk = convert_to_pydantic(ChatCompletionResponseStreamChunk, chunk) yield chunk - async def embeddings( - self, - model_id: str, - contents: list[InterleavedContent], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - client = self._get_client() - model = await self.model_store.get_model(model_id) - - return await client.inference.embeddings( - model_id=model.provider_resource_id, - contents=contents, - text_truncation=text_truncation, - output_dimension=output_dimension, - task_type=task_type, - ) - async def openai_embeddings( self, model: str, diff --git a/llama_stack/providers/remote/inference/runpod/runpod.py b/llama_stack/providers/remote/inference/runpod/runpod.py index ff2fe6401..82252b04d 100644 --- a/llama_stack/providers/remote/inference/runpod/runpod.py +++ b/llama_stack/providers/remote/inference/runpod/runpod.py @@ -136,16 +136,6 @@ class RunpodInferenceAdapter( **get_sampling_options(request.sampling_params), } - async def embeddings( - self, - model: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - raise NotImplementedError() - async def openai_embeddings( self, model: str, diff --git a/llama_stack/providers/remote/inference/tgi/tgi.py b/llama_stack/providers/remote/inference/tgi/tgi.py index 27597900f..e1632e4a0 100644 --- a/llama_stack/providers/remote/inference/tgi/tgi.py +++ b/llama_stack/providers/remote/inference/tgi/tgi.py @@ -12,14 +12,11 @@ from pydantic import SecretStr from llama_stack.apis.common.content_types import ( InterleavedContent, - InterleavedContentItem, ) from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, CompletionRequest, - EmbeddingsResponse, - EmbeddingTaskType, Inference, LogProbConfig, Message, @@ -27,7 +24,6 @@ from llama_stack.apis.inference import ( ResponseFormat, ResponseFormatType, SamplingParams, - TextTruncation, ToolChoice, ToolConfig, ToolDefinition, @@ -306,16 +302,6 @@ class _HfAdapter( **self._build_options(request.sampling_params, request.response_format), ) - async def embeddings( - self, - model_id: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - raise NotImplementedError() - async def openai_embeddings( self, model: str, diff --git a/llama_stack/providers/remote/inference/together/together.py b/llama_stack/providers/remote/inference/together/together.py index c199677be..083c528bb 100644 --- a/llama_stack/providers/remote/inference/together/together.py +++ b/llama_stack/providers/remote/inference/together/together.py @@ -12,14 +12,11 @@ from together.constants import BASE_URL from llama_stack.apis.common.content_types import ( InterleavedContent, - InterleavedContentItem, ) from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, CompletionRequest, - EmbeddingsResponse, - EmbeddingTaskType, Inference, LogProbConfig, Message, @@ -27,7 +24,6 @@ from llama_stack.apis.inference import ( ResponseFormat, ResponseFormatType, SamplingParams, - TextTruncation, ToolChoice, ToolConfig, ToolDefinition, @@ -50,8 +46,6 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( chat_completion_request_to_prompt, completion_request_to_prompt, - content_has_media, - interleaved_content_as_str, request_has_media, ) @@ -247,26 +241,6 @@ class TogetherInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Need logger.debug(f"params to together: {params}") return params - async def embeddings( - self, - model_id: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - model = await self.model_store.get_model(model_id) - assert all(not content_has_media(content) for content in contents), ( - "Together does not support media for embeddings" - ) - client = self._get_client() - r = await client.embeddings.create( - model=model.provider_resource_id, - input=[interleaved_content_as_str(content) for content in contents], - ) - embeddings = [item.embedding for item in r.data] - return EmbeddingsResponse(embeddings=embeddings) - async def list_models(self) -> list[Model] | None: self._model_cache = {} # Together's /v1/models is not compatible with OpenAI's /v1/models. Together support ticket #13355 -> will not fix, use Together's own client diff --git a/llama_stack/providers/remote/inference/vllm/vllm.py b/llama_stack/providers/remote/inference/vllm/vllm.py index 8fbb4b815..bef5cbf2c 100644 --- a/llama_stack/providers/remote/inference/vllm/vllm.py +++ b/llama_stack/providers/remote/inference/vllm/vllm.py @@ -16,7 +16,6 @@ from openai.types.chat.chat_completion_chunk import ( from llama_stack.apis.common.content_types import ( InterleavedContent, - InterleavedContentItem, TextDelta, ToolCallDelta, ToolCallParseStatus, @@ -31,8 +30,6 @@ from llama_stack.apis.inference import ( CompletionRequest, CompletionResponse, CompletionResponseStreamChunk, - EmbeddingsResponse, - EmbeddingTaskType, GrammarResponseFormat, Inference, JsonSchemaResponseFormat, @@ -41,7 +38,6 @@ from llama_stack.apis.inference import ( ModelStore, ResponseFormat, SamplingParams, - TextTruncation, ToolChoice, ToolConfig, ToolDefinition, @@ -74,8 +70,6 @@ from llama_stack.providers.utils.inference.openai_compat import ( from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( completion_request_to_prompt, - content_has_media, - interleaved_content_as_str, request_has_media, ) @@ -550,27 +544,3 @@ class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsPro "stream": request.stream, **options, } - - async def embeddings( - self, - model_id: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - model = await self._get_model(model_id) - - kwargs = {} - assert model.model_type == ModelType.embedding - assert model.metadata.get("embedding_dimension") - kwargs["dimensions"] = model.metadata.get("embedding_dimension") - assert all(not content_has_media(content) for content in contents), "VLLM does not support media for embeddings" - response = await self.client.embeddings.create( - model=model.provider_resource_id, - input=[interleaved_content_as_str(content) for content in contents], - **kwargs, - ) - - embeddings = [data.embedding for data in response.data] - return EmbeddingsResponse(embeddings=embeddings) diff --git a/llama_stack/providers/remote/inference/watsonx/watsonx.py b/llama_stack/providers/remote/inference/watsonx/watsonx.py index cb8b45565..00b9acc06 100644 --- a/llama_stack/providers/remote/inference/watsonx/watsonx.py +++ b/llama_stack/providers/remote/inference/watsonx/watsonx.py @@ -11,13 +11,11 @@ from ibm_watsonx_ai.foundation_models import Model from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams from openai import AsyncOpenAI -from llama_stack.apis.common.content_types import InterleavedContent, InterleavedContentItem +from llama_stack.apis.common.content_types import InterleavedContent from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, CompletionRequest, - EmbeddingsResponse, - EmbeddingTaskType, GreedySamplingStrategy, Inference, LogProbConfig, @@ -30,7 +28,6 @@ from llama_stack.apis.inference import ( OpenAIResponseFormatParam, ResponseFormat, SamplingParams, - TextTruncation, ToolChoice, ToolConfig, ToolDefinition, @@ -265,16 +262,6 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper): } return params - async def embeddings( - self, - model_id: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - raise NotImplementedError("embedding is not supported for watsonx") - async def openai_embeddings( self, model: str, diff --git a/llama_stack/providers/remote/post_training/nvidia/README.md b/llama_stack/providers/remote/post_training/nvidia/README.md index 6647316df..9b088a615 100644 --- a/llama_stack/providers/remote/post_training/nvidia/README.md +++ b/llama_stack/providers/remote/post_training/nvidia/README.md @@ -140,13 +140,11 @@ client.models.register( #### 2. Inference with the fine-tuned model ```python -response = client.inference.completion( - content="Complete the sentence using one word: Roses are red, violets are ", +response = client.completions.create( + prompt="Complete the sentence using one word: Roses are red, violets are ", stream=False, - model_id="test-example-model@v1", - sampling_params={ - "max_tokens": 50, - }, + model="test-example-model@v1", + max_tokens=50, ) -print(response.content) +print(response.choices[0].text) ``` diff --git a/llama_stack/providers/utils/inference/embedding_mixin.py b/llama_stack/providers/utils/inference/embedding_mixin.py index 9bd0aa8ce..facc59f65 100644 --- a/llama_stack/providers/utils/inference/embedding_mixin.py +++ b/llama_stack/providers/utils/inference/embedding_mixin.py @@ -15,16 +15,11 @@ if TYPE_CHECKING: from sentence_transformers import SentenceTransformer from llama_stack.apis.inference import ( - EmbeddingsResponse, - EmbeddingTaskType, - InterleavedContentItem, ModelStore, OpenAIEmbeddingData, OpenAIEmbeddingsResponse, OpenAIEmbeddingUsage, - TextTruncation, ) -from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str EMBEDDING_MODELS = {} @@ -35,23 +30,6 @@ log = get_logger(name=__name__, category="providers::utils") class SentenceTransformerEmbeddingMixin: model_store: ModelStore - async def embeddings( - self, - model_id: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - model = await self.model_store.get_model(model_id) - embedding_model = await self._load_sentence_transformer_model(model.provider_resource_id) - embeddings = await asyncio.to_thread( - embedding_model.encode, - [interleaved_content_as_str(content) for content in contents], - show_progress_bar=False, - ) - return EmbeddingsResponse(embeddings=embeddings) - async def openai_embeddings( self, model: str, diff --git a/llama_stack/providers/utils/inference/litellm_openai_mixin.py b/llama_stack/providers/utils/inference/litellm_openai_mixin.py index b1e38f323..966081e9f 100644 --- a/llama_stack/providers/utils/inference/litellm_openai_mixin.py +++ b/llama_stack/providers/utils/inference/litellm_openai_mixin.py @@ -11,14 +11,11 @@ import litellm from llama_stack.apis.common.content_types import ( InterleavedContent, - InterleavedContentItem, ) from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, ChatCompletionResponseStreamChunk, - EmbeddingsResponse, - EmbeddingTaskType, InferenceProvider, JsonSchemaResponseFormat, LogProbConfig, @@ -32,7 +29,6 @@ from llama_stack.apis.inference import ( OpenAIResponseFormatParam, ResponseFormat, SamplingParams, - TextTruncation, ToolChoice, ToolConfig, ToolDefinition, @@ -50,9 +46,6 @@ from llama_stack.providers.utils.inference.openai_compat import ( get_sampling_options, prepare_openai_completion_params, ) -from llama_stack.providers.utils.inference.prompt_adapter import ( - interleaved_content_as_str, -) logger = get_logger(name=__name__, category="providers::utils") @@ -269,24 +262,6 @@ class LiteLLMOpenAIMixin( ) return api_key - async def embeddings( - self, - model_id: str, - contents: list[str] | list[InterleavedContentItem], - text_truncation: TextTruncation | None = TextTruncation.none, - output_dimension: int | None = None, - task_type: EmbeddingTaskType | None = None, - ) -> EmbeddingsResponse: - model = await self.model_store.get_model(model_id) - - response = litellm.embedding( - model=self.get_litellm_model_name(model.provider_resource_id), - input=[interleaved_content_as_str(content) for content in contents], - ) - - embeddings = [data["embedding"] for data in response["data"]] - return EmbeddingsResponse(embeddings=embeddings) - async def openai_embeddings( self, model: str, diff --git a/llama_stack/providers/utils/responses/responses_store.py b/llama_stack/providers/utils/responses/responses_store.py index 829cd8a62..b9fceb1ab 100644 --- a/llama_stack/providers/utils/responses/responses_store.py +++ b/llama_stack/providers/utils/responses/responses_store.py @@ -3,6 +3,9 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import asyncio +from typing import Any + from llama_stack.apis.agents import ( Order, ) @@ -14,24 +17,51 @@ from llama_stack.apis.agents.openai_responses import ( OpenAIResponseObject, OpenAIResponseObjectWithInput, ) -from llama_stack.core.datatypes import AccessRule +from llama_stack.core.datatypes import AccessRule, ResponsesStoreConfig from llama_stack.core.utils.config_dirs import RUNTIME_BASE_DIR +from llama_stack.log import get_logger from ..sqlstore.api import ColumnDefinition, ColumnType from ..sqlstore.authorized_sqlstore import AuthorizedSqlStore -from ..sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig, sqlstore_impl +from ..sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig, SqlStoreType, sqlstore_impl + +logger = get_logger(name=__name__, category="responses_store") class ResponsesStore: - def __init__(self, sql_store_config: SqlStoreConfig, policy: list[AccessRule]): - if not sql_store_config: - sql_store_config = SqliteSqlStoreConfig( + def __init__( + self, + config: ResponsesStoreConfig | SqlStoreConfig, + policy: list[AccessRule], + ): + # Handle backward compatibility + if not isinstance(config, ResponsesStoreConfig): + # Legacy: SqlStoreConfig passed directly as config + config = ResponsesStoreConfig( + sql_store_config=config, + ) + + self.config = config + self.sql_store_config = config.sql_store_config + if not self.sql_store_config: + self.sql_store_config = SqliteSqlStoreConfig( db_path=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(), ) - self.sql_store = AuthorizedSqlStore(sqlstore_impl(sql_store_config), policy) + self.sql_store = None + self.policy = policy + + # Disable write queue for SQLite to avoid concurrency issues + self.enable_write_queue = self.sql_store_config.type != SqlStoreType.sqlite + + # Async write queue and worker control + self._queue: asyncio.Queue[tuple[OpenAIResponseObject, list[OpenAIResponseInput]]] | None = None + self._worker_tasks: list[asyncio.Task[Any]] = [] + self._max_write_queue_size: int = config.max_write_queue_size + self._num_writers: int = max(1, config.num_writers) async def initialize(self): """Create the necessary tables if they don't exist.""" + self.sql_store = AuthorizedSqlStore(sqlstore_impl(self.sql_store_config), self.policy) await self.sql_store.create_table( "openai_responses", { @@ -42,9 +72,68 @@ class ResponsesStore: }, ) + if self.enable_write_queue: + self._queue = asyncio.Queue(maxsize=self._max_write_queue_size) + for _ in range(self._num_writers): + self._worker_tasks.append(asyncio.create_task(self._worker_loop())) + else: + logger.info("Write queue disabled for SQLite to avoid concurrency issues") + + async def shutdown(self) -> None: + if not self._worker_tasks: + return + if self._queue is not None: + await self._queue.join() + for t in self._worker_tasks: + if not t.done(): + t.cancel() + for t in self._worker_tasks: + try: + await t + except asyncio.CancelledError: + pass + self._worker_tasks.clear() + + async def flush(self) -> None: + """Wait for all queued writes to complete. Useful for testing.""" + if self.enable_write_queue and self._queue is not None: + await self._queue.join() + async def store_response_object( self, response_object: OpenAIResponseObject, input: list[OpenAIResponseInput] ) -> None: + if self.enable_write_queue: + if self._queue is None: + raise ValueError("Responses store is not initialized") + try: + self._queue.put_nowait((response_object, input)) + except asyncio.QueueFull: + logger.warning(f"Write queue full; adding response id={getattr(response_object, 'id', '')}") + await self._queue.put((response_object, input)) + else: + await self._write_response_object(response_object, input) + + async def _worker_loop(self) -> None: + assert self._queue is not None + while True: + try: + item = await self._queue.get() + except asyncio.CancelledError: + break + response_object, input = item + try: + await self._write_response_object(response_object, input) + except Exception as e: # noqa: BLE001 + logger.error(f"Error writing response object: {e}") + finally: + self._queue.task_done() + + async def _write_response_object( + self, response_object: OpenAIResponseObject, input: list[OpenAIResponseInput] + ) -> None: + if self.sql_store is None: + raise ValueError("Responses store is not initialized") + data = response_object.model_dump() data["input"] = [input_item.model_dump() for input_item in input] @@ -73,6 +162,9 @@ class ResponsesStore: :param model: The model to filter by. :param order: The order to sort the responses by. """ + if not self.sql_store: + raise ValueError("Responses store is not initialized") + if not order: order = Order.desc @@ -100,6 +192,9 @@ class ResponsesStore: """ Get a response object with automatic access control checking. """ + if not self.sql_store: + raise ValueError("Responses store is not initialized") + row = await self.sql_store.fetch_one( "openai_responses", where={"id": response_id}, @@ -113,6 +208,9 @@ class ResponsesStore: return OpenAIResponseObjectWithInput(**row["response_object"]) async def delete_response_object(self, response_id: str) -> OpenAIDeleteResponseObject: + if not self.sql_store: + raise ValueError("Responses store is not initialized") + row = await self.sql_store.fetch_one("openai_responses", where={"id": response_id}) if not row: raise ValueError(f"Response with id {response_id} not found") diff --git a/llama_stack/schema_utils.py b/llama_stack/schema_utils.py index 4f8b4edff..c58fcdd01 100644 --- a/llama_stack/schema_utils.py +++ b/llama_stack/schema_utils.py @@ -22,7 +22,6 @@ class WebMethod: raw_bytes_request_body: bool | None = False # A descriptive name of the corresponding span created by tracing descriptive_name: str | None = None - experimental: bool | None = False required_scope: str | None = None deprecated: bool | None = False @@ -39,7 +38,6 @@ def webmethod( response_examples: list[Any] | None = None, raw_bytes_request_body: bool | None = False, descriptive_name: str | None = None, - experimental: bool | None = False, required_scope: str | None = None, deprecated: bool | None = False, ) -> Callable[[T], T]: @@ -50,7 +48,6 @@ def webmethod( :param public: True if the operation can be invoked without prior authentication. :param request_examples: Sample requests that the operation might take. Pass a list of objects, not JSON. :param response_examples: Sample responses that the operation might produce. Pass a list of objects, not JSON. - :param experimental: True if the operation is experimental and subject to change. :param required_scope: Required scope for this endpoint (e.g., 'monitoring.viewer'). 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"https://registry.npmjs.org/tw-animate-css/-/tw-animate-css-1.4.0.tgz", + "integrity": "sha512-7bziOlRqH0hJx80h/3mbicLW7o8qLsH5+RaLR2t+OHM3D0JlWGODQKQ4cxbK7WlvmUxpcj6Kgu6EKqjrGFe3QQ==", "dev": true, "license": "MIT", "funding": { diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 70462b534..1e01c347c 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -33,7 +33,7 @@ "react-markdown": "^10.1.0", "remark-gfm": "^4.0.1", "remeda": "^2.32.0", - "shiki": "^1.29.2", + "shiki": "^3.13.0", "sonner": "^2.0.7", "tailwind-merge": "^3.3.1" }, @@ -56,7 +56,7 @@ "prettier": "3.6.2", "tailwindcss": "^4", "ts-node": "^10.9.2", - "tw-animate-css": "^1.2.9", + "tw-animate-css": "^1.4.0", "typescript": "^5" } } diff --git a/tests/integration/README.md b/tests/integration/README.md index 467f97e02..b68526410 100644 --- a/tests/integration/README.md +++ b/tests/integration/README.md @@ -178,10 +178,10 @@ Note that when re-recording tests, you must use a Stack pointing to a server (i. ### Basic Test Pattern ```python -def test_basic_completion(llama_stack_client, text_model_id): - response = llama_stack_client.inference.completion( +def test_basic_chat_completion(llama_stack_client, text_model_id): + response = llama_stack_client.inference.chat_completion( model_id=text_model_id, - content=CompletionMessage(role="user", content="Hello"), + messages=[{"role": "user", "content": "Hello"}], ) # Test structure, not AI output quality diff --git a/tests/integration/fixtures/common.py b/tests/integration/fixtures/common.py index a56da83c3..68aa2b60b 100644 --- a/tests/integration/fixtures/common.py +++ b/tests/integration/fixtures/common.py @@ -166,7 +166,7 @@ def model_providers(llama_stack_client): @pytest.fixture(autouse=True) def skip_if_no_model(request): - model_fixtures = ["text_model_id", "vision_model_id", "embedding_model_id", "judge_model_id"] + model_fixtures = ["text_model_id", "vision_model_id", "embedding_model_id", "judge_model_id", "shield_id"] test_func = request.node.function actual_params = inspect.signature(test_func).parameters.keys() diff --git a/tests/integration/providers/nvidia/test_datastore.py b/tests/integration/providers/nvidia/test_datastore.py index 5cddd7781..ebae02bdd 100644 --- a/tests/integration/providers/nvidia/test_datastore.py +++ b/tests/integration/providers/nvidia/test_datastore.py @@ -14,6 +14,13 @@ from . import skip_in_github_actions # LLAMA_STACK_CONFIG="nvidia" pytest -v tests/integration/providers/nvidia/test_datastore.py +@pytest.fixture(autouse=True) +def skip_if_no_nvidia_provider(llama_stack_client): + provider_types = {p.provider_type for p in llama_stack_client.providers.list() if p.api == "datasetio"} + if "remote::nvidia" not in provider_types: + pytest.skip("datasetio=remote::nvidia provider not configured, skipping") + + # nvidia provider only @skip_in_github_actions @pytest.mark.parametrize( diff --git a/tests/integration/recordings/responses/8d035e153b6f.json b/tests/integration/recordings/responses/8d035e153b6f.json new file mode 100644 index 000000000..18f3ee3cd --- /dev/null +++ b/tests/integration/recordings/responses/8d035e153b6f.json @@ -0,0 +1,56 @@ +{ + "request": { + "method": "POST", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "llama3.2:3b-instruct-fp16", + "messages": [ + { + "role": "user", + "content": "Who is the CEO of Meta?" + } + ], + "max_tokens": 0 + }, + "endpoint": "/v1/chat/completions", + "model": "llama3.2:3b-instruct-fp16" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "chatcmpl-708", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "logprobs": null, + "message": { + "content": "Mark Zuckerberg is the founder, chairman and CEO of Meta, which he originally founded as Facebook in 2004.", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": null + } + } + ], + "created": 1759012142, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": { + "completion_tokens": 24, + "prompt_tokens": 32, + "total_tokens": 56, + "completion_tokens_details": null, + "prompt_tokens_details": null + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/recordings/responses/92a9a916ef02.json b/tests/integration/recordings/responses/92a9a916ef02.json new file mode 100644 index 000000000..5fe294826 --- /dev/null +++ b/tests/integration/recordings/responses/92a9a916ef02.json @@ -0,0 +1,56 @@ +{ + "request": { + "method": "POST", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "llama3.2:3b-instruct-fp16", + "messages": [ + { + "role": "user", + "content": "What is the currency of Japan?" + } + ], + "max_tokens": 0 + }, + "endpoint": "/v1/chat/completions", + "model": "llama3.2:3b-instruct-fp16" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "chatcmpl-343", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "logprobs": null, + "message": { + "content": "The currency of Japan is the Japanese yen (, ry\u014d) and its symbol, \u00a5.", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": null + } + } + ], + "created": 1759012146, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": { + "completion_tokens": 20, + "prompt_tokens": 32, + "total_tokens": 52, + "completion_tokens_details": null, + "prompt_tokens_details": null + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/recordings/responses/c62eb5d7115e.json b/tests/integration/recordings/responses/c62eb5d7115e.json new file mode 100644 index 000000000..fa872ac44 --- /dev/null +++ b/tests/integration/recordings/responses/c62eb5d7115e.json @@ -0,0 +1,56 @@ +{ + "request": { + "method": "POST", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "llama3.2:3b-instruct-fp16", + "messages": [ + { + "role": "user", + "content": "What is the smallest country in the world?" + } + ], + "max_tokens": 0 + }, + "endpoint": "/v1/chat/completions", + "model": "llama3.2:3b-instruct-fp16" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "chatcmpl-842", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "logprobs": null, + "message": { + "content": "The smallest country in the world is the Vatican City, an independent city-state located within Rome, Italy. It has a total area of approximately 0.44 km\u00b2 (0.17 sq mi) and a population of around 800 people.\n\nDespite its tiny size, the Vatican City is a sovereign state with its own government, currency, postal system, and even a small army (the Gendarmeria Romana). It's also home to numerous iconic landmarks, including St. Peter's Basilica, the Sistine Chapel, and the Vatican Museums.\n\nThe Vatican City is so small that it can fit entirely within an average American city park!", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": null + } + } + ], + "created": 1759012145, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": { + "completion_tokens": 133, + "prompt_tokens": 34, + "total_tokens": 167, + "completion_tokens_details": null, + "prompt_tokens_details": null + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/recordings/responses/e25ab43491af.json b/tests/integration/recordings/responses/e25ab43491af.json new file mode 100644 index 000000000..9fb331942 --- /dev/null +++ b/tests/integration/recordings/responses/e25ab43491af.json @@ -0,0 +1,56 @@ +{ + "request": { + "method": "POST", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "llama3.2:3b-instruct-fp16", + "messages": [ + { + "role": "user", + "content": "What is the capital of France?" + } + ], + "max_tokens": 0 + }, + "endpoint": "/v1/chat/completions", + "model": "llama3.2:3b-instruct-fp16" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "chatcmpl-808", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "logprobs": null, + "message": { + "content": "The capital of France is Paris.", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": null + } + } + ], + "created": 1759012142, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": { + "completion_tokens": 8, + "prompt_tokens": 32, + "total_tokens": 40, + "completion_tokens_details": null, + "prompt_tokens_details": null + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/recordings/responses/f28a44c97ea7.json b/tests/integration/recordings/responses/f28a44c97ea7.json new file mode 100644 index 000000000..d50851dfd --- /dev/null +++ b/tests/integration/recordings/responses/f28a44c97ea7.json @@ -0,0 +1,56 @@ +{ + "request": { + "method": "POST", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "llama3.2:3b-instruct-fp16", + "messages": [ + { + "role": "user", + "content": "What is the largest planet in our solar system?" + } + ], + "max_tokens": 0 + }, + "endpoint": "/v1/chat/completions", + "model": "llama3.2:3b-instruct-fp16" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "chatcmpl-282", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "logprobs": null, + "message": { + "content": "The largest planet in our solar system is Jupiter. It is a gas giant, with a diameter of approximately 142,984 kilometers (88,846 miles). This makes it more than 11 times the diameter of the Earth and more than 2.5 times the mass of all the other planets in our solar system combined.", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": null + } + } + ], + "created": 1759012143, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": { + "completion_tokens": 67, + "prompt_tokens": 35, + "total_tokens": 102, + "completion_tokens_details": null, + "prompt_tokens_details": null + } + } + }, + "is_streaming": false + } +} diff --git a/tests/unit/providers/agent/test_get_raw_document_text.py b/tests/unit/providers/agent/test_get_raw_document_text.py index eb481c0d8..302a893b1 100644 --- a/tests/unit/providers/agent/test_get_raw_document_text.py +++ b/tests/unit/providers/agent/test_get_raw_document_text.py @@ -107,14 +107,34 @@ async def test_get_raw_document_text_deprecated_text_yaml_with_text_content_item assert "text/yaml" in str(w[0].message) +async def test_get_raw_document_text_supports_json_mime_type(): + """Test that the function accepts application/json mime type.""" + json_content = '{"name": "test", "version": "1.0", "items": ["item1", "item2"]}' + + document = Document(content=json_content, mime_type="application/json") + + result = await get_raw_document_text(document) + assert result == json_content + + +async def test_get_raw_document_text_with_json_text_content_item(): + """Test that the function handles JSON TextContentItem correctly.""" + json_content = '{"key": "value", "nested": {"array": [1, 2, 3]}}' + + document = Document(content=TextContentItem(text=json_content), mime_type="application/json") + + result = await get_raw_document_text(document) + assert result == json_content + + async def test_get_raw_document_text_rejects_unsupported_mime_types(): """Test that the function rejects unsupported mime types.""" document = Document( content="Some content", - mime_type="application/json", # Not supported + mime_type="application/pdf", # Not supported ) - with pytest.raises(ValueError, match="Unexpected document mime type: application/json"): + with pytest.raises(ValueError, match="Unexpected document mime type: application/pdf"): await get_raw_document_text(document) diff --git a/tests/unit/providers/agents/meta_reference/test_openai_responses.py b/tests/unit/providers/agents/meta_reference/test_openai_responses.py index a964bc219..38ce365c1 100644 --- a/tests/unit/providers/agents/meta_reference/test_openai_responses.py +++ b/tests/unit/providers/agents/meta_reference/test_openai_responses.py @@ -42,10 +42,12 @@ from llama_stack.apis.inference import ( ) from llama_stack.apis.tools.tools import Tool, ToolGroups, ToolInvocationResult, ToolParameter, ToolRuntime from llama_stack.core.access_control.access_control import default_policy +from llama_stack.core.datatypes import ResponsesStoreConfig from llama_stack.providers.inline.agents.meta_reference.responses.openai_responses import ( OpenAIResponsesImpl, ) from llama_stack.providers.utils.responses.responses_store import ResponsesStore +from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig from tests.unit.providers.agents.meta_reference.fixtures import load_chat_completion_fixture @@ -677,7 +679,9 @@ async def test_responses_store_list_input_items_logic(): # Create mock store and response store mock_sql_store = AsyncMock() - responses_store = ResponsesStore(sql_store_config=None, policy=default_policy()) + responses_store = ResponsesStore( + ResponsesStoreConfig(sql_store_config=SqliteSqlStoreConfig(db_path="mock_db_path")), policy=default_policy() + ) responses_store.sql_store = mock_sql_store # Setup test data - multiple input items diff --git a/tests/unit/providers/vector_io/test_faiss.py b/tests/unit/providers/vector_io/test_faiss.py index 90108d7a0..9ee5c82f4 100644 --- a/tests/unit/providers/vector_io/test_faiss.py +++ b/tests/unit/providers/vector_io/test_faiss.py @@ -5,13 +5,12 @@ # the root directory of this source tree. import asyncio -from unittest.mock import AsyncMock, MagicMock, patch +from unittest.mock import MagicMock, patch import numpy as np import pytest from llama_stack.apis.files import Files -from llama_stack.apis.inference import EmbeddingsResponse, Inference from llama_stack.apis.vector_dbs import VectorDB from llama_stack.apis.vector_io import Chunk, QueryChunksResponse from llama_stack.providers.datatypes import HealthStatus @@ -70,13 +69,6 @@ def mock_vector_db(vector_db_id, embedding_dimension) -> MagicMock: return mock_vector_db -@pytest.fixture -def mock_inference_api(sample_embeddings): - mock_api = MagicMock(spec=Inference) - mock_api.embeddings = AsyncMock(return_value=EmbeddingsResponse(embeddings=sample_embeddings)) - return mock_api - - @pytest.fixture def mock_files_api(): mock_api = MagicMock(spec=Files) @@ -96,22 +88,6 @@ async def faiss_index(embedding_dimension): yield index -@pytest.fixture -async def faiss_adapter(faiss_config, mock_inference_api, mock_files_api) -> FaissVectorIOAdapter: - # Create the adapter - adapter = FaissVectorIOAdapter(config=faiss_config, inference_api=mock_inference_api, files_api=mock_files_api) - - # Create a mock KVStore - mock_kvstore = MagicMock() - mock_kvstore.values_in_range = AsyncMock(return_value=[]) - - # Patch the initialize method to avoid the kvstore_impl call - with patch.object(FaissVectorIOAdapter, "initialize"): - # Set the kvstore directly - adapter.kvstore = mock_kvstore - yield adapter - - async def test_faiss_query_vector_returns_infinity_when_query_and_embedding_are_identical( faiss_index, sample_chunks, sample_embeddings, embedding_dimension ): diff --git a/tests/unit/utils/responses/test_responses_store.py b/tests/unit/utils/responses/test_responses_store.py index 44d4b30da..4e5256c1b 100644 --- a/tests/unit/utils/responses/test_responses_store.py +++ b/tests/unit/utils/responses/test_responses_store.py @@ -67,6 +67,9 @@ async def test_responses_store_pagination_basic(): input_list = [create_test_response_input(f"Input for {response_id}", f"input-{response_id}")] await store.store_response_object(response, input_list) + # Wait for all queued writes to complete + await store.flush() + # Test 1: First page with limit=2, descending order (default) result = await store.list_responses(limit=2, order=Order.desc) assert len(result.data) == 2 @@ -110,6 +113,9 @@ async def test_responses_store_pagination_ascending(): input_list = [create_test_response_input(f"Input for {response_id}", f"input-{response_id}")] await store.store_response_object(response, input_list) + # Wait for all queued writes to complete + await store.flush() + # Test ascending order pagination result = await store.list_responses(limit=1, order=Order.asc) assert len(result.data) == 1 @@ -145,6 +151,9 @@ async def test_responses_store_pagination_with_model_filter(): input_list = [create_test_response_input(f"Input for {response_id}", f"input-{response_id}")] await store.store_response_object(response, input_list) + # Wait for all queued writes to complete + await store.flush() + # Test pagination with model filter result = await store.list_responses(limit=1, model="model-a", order=Order.desc) assert len(result.data) == 1 @@ -192,6 +201,9 @@ async def test_responses_store_pagination_no_limit(): input_list = [create_test_response_input(f"Input for {response_id}", f"input-{response_id}")] await store.store_response_object(response, input_list) + # Wait for all queued writes to complete + await store.flush() + # Test without limit (should use default of 50) result = await store.list_responses(order=Order.desc) assert len(result.data) == 2 @@ -212,6 +224,9 @@ async def test_responses_store_get_response_object(): input_list = [create_test_response_input("Test input content", "input-test-resp")] await store.store_response_object(response, input_list) + # Wait for all queued writes to complete + await store.flush() + # Retrieve the response retrieved = await store.get_response_object("test-resp") assert retrieved.id == "test-resp" @@ -242,6 +257,9 @@ async def test_responses_store_input_items_pagination(): ] await store.store_response_object(response, input_list) + # Wait for all queued writes to complete + await store.flush() + # Verify all items are stored correctly with explicit IDs all_items = await store.list_response_input_items("test-resp", order=Order.desc) assert len(all_items.data) == 5 @@ -319,6 +337,9 @@ async def test_responses_store_input_items_before_pagination(): ] await store.store_response_object(response, input_list) + # Wait for all queued writes to complete + await store.flush() + # Test before pagination with descending order # In desc order: [Fifth, Fourth, Third, Second, First] # before="before-3" should return [Fifth, Fourth]