diff --git a/llama_stack/apis/inference/inference.py b/llama_stack/apis/inference/inference.py index c50986813..4627c7851 100644 --- a/llama_stack/apis/inference/inference.py +++ b/llama_stack/apis/inference/inference.py @@ -1008,28 +1008,7 @@ class InferenceProvider(Protocol): model_store: ModelStore | None = None - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]: - """Generate a completion for the given content using the specified model. - - :param model_id: The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint. - :param content: The content to generate a completion for. - :param sampling_params: (Optional) Parameters to control the sampling strategy. - :param response_format: (Optional) Grammar specification for guided (structured) decoding. - :param stream: (Optional) If True, generate an SSE event stream of the response. Defaults to False. - :param logprobs: (Optional) If specified, log probabilities for each token position will be returned. - :returns: If stream=False, returns a CompletionResponse with the full completion. - If stream=True, returns an SSE event stream of CompletionResponseStreamChunk. - """ - ... - + @webmethod(route="/inference/chat-completion", method="POST", level=LLAMA_STACK_API_V1) async def chat_completion( self, model_id: str, diff --git a/llama_stack/core/routers/inference.py b/llama_stack/core/routers/inference.py index 80f47fb5d..4b004a82c 100644 --- a/llama_stack/core/routers/inference.py +++ b/llama_stack/core/routers/inference.py @@ -267,47 +267,6 @@ class InferenceRouter(Inference): ) return response - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> AsyncGenerator: - if sampling_params is None: - sampling_params = SamplingParams() - logger.debug( - f"InferenceRouter.completion: {model_id=}, {stream=}, {content=}, {sampling_params=}, {response_format=}", - ) - model = await self._get_model(model_id, ModelType.llm) - provider = await self.routing_table.get_provider_impl(model_id) - params = dict( - model_id=model_id, - content=content, - sampling_params=sampling_params, - response_format=response_format, - stream=stream, - logprobs=logprobs, - ) - - prompt_tokens = await self._count_tokens(content) - response = await provider.completion(**params) - if stream: - return self.stream_tokens_and_compute_metrics( - response=response, - prompt_tokens=prompt_tokens, - model=model, - ) - - metrics = await self.count_tokens_and_compute_metrics( - response=response, prompt_tokens=prompt_tokens, model=model - ) - response.metrics = metrics if response.metrics is None else response.metrics + metrics - - return response - async def openai_completion( self, model: str, diff --git a/llama_stack/providers/inline/inference/meta_reference/inference.py b/llama_stack/providers/inline/inference/meta_reference/inference.py index f9e295014..db022d65d 100644 --- a/llama_stack/providers/inline/inference/meta_reference/inference.py +++ b/llama_stack/providers/inline/inference/meta_reference/inference.py @@ -24,11 +24,7 @@ from llama_stack.apis.inference import ( ChatCompletionResponseEventType, ChatCompletionResponseStreamChunk, CompletionMessage, - CompletionRequest, - CompletionResponse, - CompletionResponseStreamChunk, InferenceProvider, - InterleavedContent, LogProbConfig, Message, ResponseFormat, @@ -59,10 +55,8 @@ from llama_stack.providers.utils.inference.model_registry import ( ) from llama_stack.providers.utils.inference.openai_compat import ( OpenAIChatCompletionToLlamaStackMixin, - OpenAICompletionToLlamaStackMixin, ) from llama_stack.providers.utils.inference.prompt_adapter import ( - augment_content_with_response_format_prompt, chat_completion_request_to_messages, convert_request_to_raw, ) @@ -82,7 +76,6 @@ def llama_builder_fn(config: MetaReferenceInferenceConfig, model_id: str, llama_ class MetaReferenceInferenceImpl( - OpenAICompletionToLlamaStackMixin, OpenAIChatCompletionToLlamaStackMixin, SentenceTransformerEmbeddingMixin, InferenceProvider, @@ -100,6 +93,9 @@ class MetaReferenceInferenceImpl( if self.config.create_distributed_process_group: self.generator.stop() + async def openai_completion(self, *args, **kwargs): + raise NotImplementedError("OpenAI completion not supported by meta reference provider") + async def should_refresh_models(self) -> bool: return False @@ -165,11 +161,6 @@ class MetaReferenceInferenceImpl( self.llama_model = llama_model log.info("Warming up...") - await self.completion( - model_id=model_id, - content="Hello, world!", - sampling_params=SamplingParams(max_tokens=10), - ) await self.chat_completion( model_id=model_id, messages=[UserMessage(content="Hi how are you?")], @@ -185,137 +176,6 @@ class MetaReferenceInferenceImpl( elif request.model != self.model_id: raise RuntimeError(f"Model mismatch: request model: {request.model} != loaded model: {self.model_id}") - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> CompletionResponse | CompletionResponseStreamChunk: - if sampling_params is None: - sampling_params = SamplingParams() - if logprobs: - assert logprobs.top_k == 1, f"Unexpected top_k={logprobs.top_k}" - - content = augment_content_with_response_format_prompt(response_format, content) - request = CompletionRequest( - model=model_id, - content=content, - sampling_params=sampling_params, - response_format=response_format, - stream=stream, - logprobs=logprobs, - ) - self.check_model(request) - request = await convert_request_to_raw(request) - - if request.stream: - return self._stream_completion(request) - else: - results = await self._nonstream_completion([request]) - return results[0] - - async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator: - tokenizer = self.generator.formatter.tokenizer - - def impl(): - stop_reason = None - - for token_results in self.generator.completion([request]): - token_result = token_results[0] - if token_result.token == tokenizer.eot_id: - stop_reason = StopReason.end_of_turn - text = "" - elif token_result.token == tokenizer.eom_id: - stop_reason = StopReason.end_of_message - text = "" - else: - text = token_result.text - - logprobs = None - if stop_reason is None: - if request.logprobs: - assert len(token_result.logprobs) == 1 - - logprobs = [TokenLogProbs(logprobs_by_token={token_result.text: token_result.logprobs[0]})] - - yield CompletionResponseStreamChunk( - delta=text, - stop_reason=stop_reason, - logprobs=logprobs if request.logprobs else None, - ) - - if stop_reason is None: - yield CompletionResponseStreamChunk( - delta="", - stop_reason=StopReason.out_of_tokens, - ) - - if self.config.create_distributed_process_group: - async with SEMAPHORE: - for x in impl(): - yield x - else: - for x in impl(): - yield x - - async def _nonstream_completion(self, request_batch: list[CompletionRequest]) -> list[CompletionResponse]: - tokenizer = self.generator.formatter.tokenizer - - first_request = request_batch[0] - - class ItemState(BaseModel): - tokens: list[int] = [] - logprobs: list[TokenLogProbs] = [] - stop_reason: StopReason | None = None - finished: bool = False - - def impl(): - states = [ItemState() for _ in request_batch] - - results = [] - for token_results in self.generator.completion(request_batch): - for result in token_results: - idx = result.batch_idx - state = states[idx] - if state.finished or result.ignore_token: - continue - - state.finished = result.finished - if first_request.logprobs: - state.logprobs.append(TokenLogProbs(logprobs_by_token={result.text: result.logprobs[0]})) - - state.tokens.append(result.token) - if result.token == tokenizer.eot_id: - state.stop_reason = StopReason.end_of_turn - elif result.token == tokenizer.eom_id: - state.stop_reason = StopReason.end_of_message - - for state in states: - if state.stop_reason is None: - state.stop_reason = StopReason.out_of_tokens - - if state.tokens[-1] in self.generator.formatter.tokenizer.stop_tokens: - state.tokens = state.tokens[:-1] - content = self.generator.formatter.tokenizer.decode(state.tokens) - results.append( - CompletionResponse( - content=content, - stop_reason=state.stop_reason, - logprobs=state.logprobs if first_request.logprobs else None, - ) - ) - - return results - - if self.config.create_distributed_process_group: - async with SEMAPHORE: - return impl() - else: - return impl() - async def chat_completion( self, model_id: str, diff --git a/llama_stack/providers/inline/inference/meta_reference/model_parallel.py b/llama_stack/providers/inline/inference/meta_reference/model_parallel.py index 9031d36b3..9d0295d65 100644 --- a/llama_stack/providers/inline/inference/meta_reference/model_parallel.py +++ b/llama_stack/providers/inline/inference/meta_reference/model_parallel.py @@ -27,8 +27,6 @@ class ModelRunner: def __call__(self, task: Any): if task[0] == "chat_completion": return self.llama.chat_completion(task[1]) - elif task[0] == "completion": - return self.llama.completion(task[1]) else: raise ValueError(f"Unexpected task type {task[0]}") diff --git a/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py b/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py index 34665b63e..691497033 100644 --- a/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py +++ b/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py @@ -6,8 +6,9 @@ from collections.abc import AsyncGenerator +from pyparsing import Any + from llama_stack.apis.inference import ( - CompletionResponse, InferenceProvider, LogProbConfig, Message, @@ -18,6 +19,7 @@ from llama_stack.apis.inference import ( ToolDefinition, ToolPromptFormat, ) +from llama_stack.apis.inference.inference import OpenAICompletion from llama_stack.apis.models import ModelType from llama_stack.log import get_logger from llama_stack.providers.datatypes import Model, ModelsProtocolPrivate @@ -26,7 +28,6 @@ from llama_stack.providers.utils.inference.embedding_mixin import ( ) from llama_stack.providers.utils.inference.openai_compat import ( OpenAIChatCompletionToLlamaStackMixin, - OpenAICompletionToLlamaStackMixin, ) from .config import SentenceTransformersInferenceConfig @@ -36,7 +37,6 @@ log = get_logger(name=__name__, category="inference") class SentenceTransformersInferenceImpl( OpenAIChatCompletionToLlamaStackMixin, - OpenAICompletionToLlamaStackMixin, SentenceTransformerEmbeddingMixin, InferenceProvider, ModelsProtocolPrivate, @@ -74,17 +74,6 @@ class SentenceTransformersInferenceImpl( async def unregister_model(self, model_id: str) -> None: pass - async def completion( - self, - model_id: str, - content: str, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> CompletionResponse | AsyncGenerator: - raise ValueError("Sentence transformers don't support completion") - async def chat_completion( self, model_id: str, @@ -99,3 +88,31 @@ class SentenceTransformersInferenceImpl( tool_config: ToolConfig | None = None, ) -> AsyncGenerator: raise ValueError("Sentence transformers don't support chat completion") + + async def openai_completion( + self, + # Standard OpenAI completion parameters + model: str, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + user: str | None = None, + # vLLM-specific parameters + guided_choice: list[str] | None = None, + prompt_logprobs: int | None = None, + # for fill-in-the-middle type completion + suffix: str | None = None, + ) -> OpenAICompletion: + raise NotImplementedError("OpenAI completion not supported by sentence transformers provider") diff --git a/llama_stack/providers/remote/inference/bedrock/bedrock.py b/llama_stack/providers/remote/inference/bedrock/bedrock.py index 2206aa641..f87a5b5e2 100644 --- a/llama_stack/providers/remote/inference/bedrock/bedrock.py +++ b/llama_stack/providers/remote/inference/bedrock/bedrock.py @@ -6,12 +6,10 @@ import json from collections.abc import AsyncGenerator, AsyncIterator +from typing import Any from botocore.client import BaseClient -from llama_stack.apis.common.content_types import ( - InterleavedContent, -) from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, @@ -27,6 +25,7 @@ from llama_stack.apis.inference import ( ToolDefinition, ToolPromptFormat, ) +from llama_stack.apis.inference.inference import OpenAICompletion from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig from llama_stack.providers.utils.bedrock.client import create_bedrock_client from llama_stack.providers.utils.inference.model_registry import ( @@ -36,7 +35,6 @@ from llama_stack.providers.utils.inference.openai_compat import ( OpenAIChatCompletionToLlamaStackMixin, OpenAICompatCompletionChoice, OpenAICompatCompletionResponse, - OpenAICompletionToLlamaStackMixin, get_sampling_strategy_options, process_chat_completion_response, process_chat_completion_stream_response, @@ -89,7 +87,6 @@ class BedrockInferenceAdapter( ModelRegistryHelper, Inference, OpenAIChatCompletionToLlamaStackMixin, - OpenAICompletionToLlamaStackMixin, ): def __init__(self, config: BedrockConfig) -> None: ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES) @@ -109,17 +106,6 @@ class BedrockInferenceAdapter( if self._client is not None: self._client.close() - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> AsyncGenerator: - raise NotImplementedError() - async def chat_completion( self, model_id: str, @@ -221,3 +207,31 @@ class BedrockInferenceAdapter( user: str | None = None, ) -> OpenAIEmbeddingsResponse: raise NotImplementedError() + + async def openai_completion( + self, + # Standard OpenAI completion parameters + model: str, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + user: str | None = None, + # vLLM-specific parameters + guided_choice: list[str] | None = None, + prompt_logprobs: int | None = None, + # for fill-in-the-middle type completion + suffix: str | None = None, + ) -> OpenAICompletion: + raise NotImplementedError("OpenAI completion not supported by the Bedrock provider") diff --git a/llama_stack/providers/remote/inference/cerebras/cerebras.py b/llama_stack/providers/remote/inference/cerebras/cerebras.py index 6be39fa5d..95da71de8 100644 --- a/llama_stack/providers/remote/inference/cerebras/cerebras.py +++ b/llama_stack/providers/remote/inference/cerebras/cerebras.py @@ -9,9 +9,6 @@ from urllib.parse import urljoin from cerebras.cloud.sdk import AsyncCerebras -from llama_stack.apis.common.content_types import ( - InterleavedContent, -) from llama_stack.apis.inference import ( ChatCompletionRequest, CompletionRequest, @@ -35,8 +32,6 @@ from llama_stack.providers.utils.inference.openai_compat import ( get_sampling_options, process_chat_completion_response, process_chat_completion_stream_response, - process_completion_response, - process_completion_stream_response, ) from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( @@ -73,48 +68,6 @@ class CerebrasInferenceAdapter( async def shutdown(self) -> None: pass - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> AsyncGenerator: - if sampling_params is None: - sampling_params = SamplingParams() - model = await self.model_store.get_model(model_id) - request = CompletionRequest( - model=model.provider_resource_id, - content=content, - sampling_params=sampling_params, - response_format=response_format, - stream=stream, - logprobs=logprobs, - ) - if stream: - return self._stream_completion( - request, - ) - else: - return await self._nonstream_completion(request) - - async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse: - params = await self._get_params(request) - - r = await self._cerebras_client.completions.create(**params) - - return process_completion_response(r) - - async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator: - params = await self._get_params(request) - - stream = await self._cerebras_client.completions.create(**params) - - async for chunk in process_completion_stream_response(stream): - yield chunk - async def chat_completion( self, model_id: str, diff --git a/llama_stack/providers/remote/inference/databricks/databricks.py b/llama_stack/providers/remote/inference/databricks/databricks.py index d85b477f5..cd5dfb40d 100644 --- a/llama_stack/providers/remote/inference/databricks/databricks.py +++ b/llama_stack/providers/remote/inference/databricks/databricks.py @@ -9,14 +9,9 @@ from typing import Any from databricks.sdk import WorkspaceClient -from llama_stack.apis.common.content_types import ( - InterleavedContent, -) from llama_stack.apis.inference import ( ChatCompletionResponse, ChatCompletionResponseStreamChunk, - CompletionResponse, - CompletionResponseStreamChunk, Inference, LogProbConfig, Message, @@ -63,17 +58,6 @@ class DatabricksInferenceAdapter( async def shutdown(self) -> None: pass - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]: - raise NotImplementedError() - async def openai_completion( self, model: str, diff --git a/llama_stack/providers/remote/inference/fireworks/fireworks.py b/llama_stack/providers/remote/inference/fireworks/fireworks.py index ed4b56fad..dcc9e240b 100644 --- a/llama_stack/providers/remote/inference/fireworks/fireworks.py +++ b/llama_stack/providers/remote/inference/fireworks/fireworks.py @@ -8,14 +8,9 @@ from collections.abc import AsyncGenerator from fireworks.client import Fireworks -from llama_stack.apis.common.content_types import ( - InterleavedContent, -) from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, - CompletionRequest, - CompletionResponse, Inference, LogProbConfig, Message, @@ -37,13 +32,10 @@ from llama_stack.providers.utils.inference.openai_compat import ( get_sampling_options, process_chat_completion_response, process_chat_completion_stream_response, - process_completion_response, - process_completion_stream_response, ) 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, request_has_media, ) @@ -94,79 +86,6 @@ class FireworksInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Nee return prompt[len("<|begin_of_text|>") :] return prompt - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> AsyncGenerator: - if sampling_params is None: - sampling_params = SamplingParams() - model = await self.model_store.get_model(model_id) - request = CompletionRequest( - model=model.provider_resource_id, - content=content, - sampling_params=sampling_params, - response_format=response_format, - stream=stream, - logprobs=logprobs, - ) - if stream: - return self._stream_completion(request) - else: - return await self._nonstream_completion(request) - - async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse: - params = await self._get_params(request) - r = await self._get_client().completion.acreate(**params) - return process_completion_response(r) - - async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator: - params = await self._get_params(request) - - # Wrapper for async generator similar - async def _to_async_generator(): - stream = self._get_client().completion.create(**params) - for chunk in stream: - yield chunk - - stream = _to_async_generator() - async for chunk in process_completion_stream_response(stream): - yield chunk - - def _build_options( - self, - sampling_params: SamplingParams | None, - fmt: ResponseFormat, - logprobs: LogProbConfig | None, - ) -> dict: - options = get_sampling_options(sampling_params) - options.setdefault("max_tokens", 512) - - if fmt: - if fmt.type == ResponseFormatType.json_schema.value: - options["response_format"] = { - "type": "json_object", - "schema": fmt.json_schema, - } - elif fmt.type == ResponseFormatType.grammar.value: - options["response_format"] = { - "type": "grammar", - "grammar": fmt.bnf, - } - else: - raise ValueError(f"Unknown response format {fmt.type}") - - if logprobs and logprobs.top_k: - options["logprobs"] = logprobs.top_k - if options["logprobs"] <= 0 or options["logprobs"] >= 5: - raise ValueError("Required range: 0 < top_k < 5") - - return options - async def chat_completion( self, model_id: str, @@ -222,22 +141,46 @@ class FireworksInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Nee async for chunk in process_chat_completion_stream_response(stream, request): yield chunk - async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict: + def _build_options( + self, + sampling_params: SamplingParams | None, + fmt: ResponseFormat | None, + logprobs: LogProbConfig | None, + ) -> dict: + options = get_sampling_options(sampling_params) + options.setdefault("max_tokens", 512) + + if fmt: + if fmt.type == ResponseFormatType.json_schema.value: + options["response_format"] = { + "type": "json_object", + "schema": fmt.json_schema, + } + elif fmt.type == ResponseFormatType.grammar.value: + options["response_format"] = { + "type": "grammar", + "grammar": fmt.bnf, + } + else: + raise ValueError(f"Unknown response format {fmt.type}") + + if logprobs and logprobs.top_k: + options["logprobs"] = logprobs.top_k + if options["logprobs"] <= 0 or options["logprobs"] >= 5: + raise ValueError("Required range: 0 < top_k < 5") + + return options + + async def _get_params(self, request: ChatCompletionRequest) -> dict: input_dict = {} media_present = request_has_media(request) llama_model = self.get_llama_model(request.model) - if isinstance(request, ChatCompletionRequest): - # TODO: tools are never added to the request, so we need to add them here - if media_present or not llama_model: - input_dict["messages"] = [ - await convert_message_to_openai_dict(m, download=True) for m in request.messages - ] - else: - input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model) + # TODO: tools are never added to the request, so we need to add them here + if media_present or not llama_model: + input_dict["messages"] = [await convert_message_to_openai_dict(m, download=True) for m in request.messages] else: - assert not media_present, "Fireworks does not support media for Completion requests" - input_dict["prompt"] = await completion_request_to_prompt(request) + input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model) # Fireworks always prepends with BOS if "prompt" in input_dict: diff --git a/llama_stack/providers/remote/inference/nvidia/nvidia.py b/llama_stack/providers/remote/inference/nvidia/nvidia.py index a31981adb..8619b6b68 100644 --- a/llama_stack/providers/remote/inference/nvidia/nvidia.py +++ b/llama_stack/providers/remote/inference/nvidia/nvidia.py @@ -9,16 +9,10 @@ from collections.abc import AsyncIterator from openai import NOT_GIVEN, APIConnectionError -from llama_stack.apis.common.content_types import ( - InterleavedContent, -) from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, ChatCompletionResponseStreamChunk, - CompletionRequest, - CompletionResponse, - CompletionResponseStreamChunk, Inference, LogProbConfig, Message, @@ -37,14 +31,10 @@ from llama_stack.providers.utils.inference.openai_compat import ( convert_openai_chat_completion_stream, ) from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin -from llama_stack.providers.utils.inference.prompt_adapter import content_has_media from . import NVIDIAConfig from .openai_utils import ( convert_chat_completion_request, - convert_completion_request, - convert_openai_completion_choice, - convert_openai_completion_stream, ) from .utils import _is_nvidia_hosted @@ -109,48 +99,6 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference): """ return f"{self._config.url}/v1" if self._config.append_api_version else self._config.url - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]: - if sampling_params is None: - sampling_params = SamplingParams() - if content_has_media(content): - raise NotImplementedError("Media is not supported") - - # ToDo: check health of NeMo endpoints and enable this - # removing this health check as NeMo customizer endpoint health check is returning 404 - # await check_health(self._config) # this raises errors - - provider_model_id = await self._get_provider_model_id(model_id) - request = convert_completion_request( - request=CompletionRequest( - model=provider_model_id, - content=content, - sampling_params=sampling_params, - response_format=response_format, - stream=stream, - logprobs=logprobs, - ), - n=1, - ) - - try: - response = await self.client.completions.create(**request) - except APIConnectionError as e: - raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e - - if stream: - return convert_openai_completion_stream(response) - else: - # we pass n=1 to get only one completion - return convert_openai_completion_choice(response.choices[0]) - 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 16b104fb5..85ad62f9a 100644 --- a/llama_stack/providers/remote/inference/ollama/ollama.py +++ b/llama_stack/providers/remote/inference/ollama/ollama.py @@ -13,7 +13,6 @@ from ollama import AsyncClient as AsyncOllamaClient from llama_stack.apis.common.content_types import ( ImageContentItem, - InterleavedContent, TextContentItem, ) from llama_stack.apis.common.errors import UnsupportedModelError @@ -21,9 +20,6 @@ from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, ChatCompletionResponseStreamChunk, - CompletionRequest, - CompletionResponse, - CompletionResponseStreamChunk, GrammarResponseFormat, InferenceProvider, JsonSchemaResponseFormat, @@ -55,13 +51,10 @@ from llama_stack.providers.utils.inference.openai_compat import ( get_sampling_options, process_chat_completion_response, process_chat_completion_stream_response, - process_completion_response, - process_completion_stream_response, ) 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, convert_image_content_to_url, request_has_media, ) @@ -168,67 +161,6 @@ class OllamaInferenceAdapter( raise ValueError("Model store not set") return await self.model_store.get_model(model_id) - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> CompletionResponse | AsyncGenerator[CompletionResponseStreamChunk, None]: - if sampling_params is None: - sampling_params = SamplingParams() - model = await self._get_model(model_id) - if model.provider_resource_id is None: - raise ValueError(f"Model {model_id} has no provider_resource_id set") - request = CompletionRequest( - model=model.provider_resource_id, - content=content, - sampling_params=sampling_params, - response_format=response_format, - stream=stream, - logprobs=logprobs, - ) - if stream: - return self._stream_completion(request) - else: - return await self._nonstream_completion(request) - - async def _stream_completion( - self, request: CompletionRequest - ) -> AsyncGenerator[CompletionResponseStreamChunk, None]: - params = await self._get_params(request) - - async def _generate_and_convert_to_openai_compat(): - s = await self.ollama_client.generate(**params) - async for chunk in s: - choice = OpenAICompatCompletionChoice( - finish_reason=chunk["done_reason"] if chunk["done"] else None, - text=chunk["response"], - ) - yield OpenAICompatCompletionResponse( - choices=[choice], - ) - - stream = _generate_and_convert_to_openai_compat() - async for chunk in process_completion_stream_response(stream): - yield chunk - - async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse: - params = await self._get_params(request) - r = await self.ollama_client.generate(**params) - - choice = OpenAICompatCompletionChoice( - finish_reason=r["done_reason"] if r["done"] else None, - text=r["response"], - ) - response = OpenAICompatCompletionResponse( - choices=[choice], - ) - - return process_completion_response(response) - async def chat_completion( self, model_id: str, @@ -262,7 +194,7 @@ class OllamaInferenceAdapter( else: return await self._nonstream_chat_completion(request) - async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict: + async def _get_params(self, request: ChatCompletionRequest) -> dict: sampling_options = get_sampling_options(request.sampling_params) # This is needed since the Ollama API expects num_predict to be set # for early truncation instead of max_tokens. @@ -272,21 +204,16 @@ class OllamaInferenceAdapter( input_dict: dict[str, Any] = {} media_present = request_has_media(request) llama_model = self.get_llama_model(request.model) - if isinstance(request, ChatCompletionRequest): - if media_present or not llama_model: - contents = [await convert_message_to_openai_dict_for_ollama(m) for m in request.messages] - # flatten the list of lists - input_dict["messages"] = [item for sublist in contents for item in sublist] - else: - input_dict["raw"] = True - input_dict["prompt"] = await chat_completion_request_to_prompt( - request, - llama_model, - ) + if media_present or not llama_model: + contents = [await convert_message_to_openai_dict_for_ollama(m) for m in request.messages] + # flatten the list of lists + input_dict["messages"] = [item for sublist in contents for item in sublist] else: - assert not media_present, "Ollama does not support media for Completion requests" - input_dict["prompt"] = await completion_request_to_prompt(request) input_dict["raw"] = True + input_dict["prompt"] = await chat_completion_request_to_prompt( + request, + llama_model, + ) if fmt := request.response_format: if isinstance(fmt, JsonSchemaResponseFormat): diff --git a/llama_stack/providers/remote/inference/passthrough/passthrough.py b/llama_stack/providers/remote/inference/passthrough/passthrough.py index ae482b7b0..3ac45e949 100644 --- a/llama_stack/providers/remote/inference/passthrough/passthrough.py +++ b/llama_stack/providers/remote/inference/passthrough/passthrough.py @@ -9,7 +9,6 @@ from typing import Any from llama_stack_client import AsyncLlamaStackClient -from llama_stack.apis.common.content_types import InterleavedContent from llama_stack.apis.inference import ( ChatCompletionResponse, ChatCompletionResponseStreamChunk, @@ -86,37 +85,6 @@ class PassthroughInferenceAdapter(Inference): provider_data=provider_data, ) - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> AsyncGenerator: - if sampling_params is None: - sampling_params = SamplingParams() - client = self._get_client() - model = await self.model_store.get_model(model_id) - - request_params = { - "model_id": model.provider_resource_id, - "content": content, - "sampling_params": sampling_params, - "response_format": response_format, - "stream": stream, - "logprobs": logprobs, - } - - request_params = {key: value for key, value in request_params.items() if value is not None} - - # cast everything to json dict - json_params = self.cast_value_to_json_dict(request_params) - - # only pass through the not None params - return await client.inference.completion(**json_params) - async def chat_completion( self, model_id: str, diff --git a/llama_stack/providers/remote/inference/runpod/runpod.py b/llama_stack/providers/remote/inference/runpod/runpod.py index 82252b04d..77c5c7187 100644 --- a/llama_stack/providers/remote/inference/runpod/runpod.py +++ b/llama_stack/providers/remote/inference/runpod/runpod.py @@ -14,7 +14,6 @@ from llama_stack.apis.inference import OpenAIEmbeddingsResponse from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper, build_hf_repo_model_entry from llama_stack.providers.utils.inference.openai_compat import ( OpenAIChatCompletionToLlamaStackMixin, - OpenAICompletionToLlamaStackMixin, get_sampling_options, process_chat_completion_response, process_chat_completion_stream_response, @@ -55,7 +54,6 @@ class RunpodInferenceAdapter( ModelRegistryHelper, Inference, OpenAIChatCompletionToLlamaStackMixin, - OpenAICompletionToLlamaStackMixin, ): def __init__(self, config: RunpodImplConfig) -> None: ModelRegistryHelper.__init__(self, stack_to_provider_models_map=RUNPOD_SUPPORTED_MODELS) @@ -67,17 +65,6 @@ class RunpodInferenceAdapter( async def shutdown(self) -> None: pass - async def completion( - self, - model: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> AsyncGenerator: - raise NotImplementedError() - async def chat_completion( self, model: str, diff --git a/llama_stack/providers/remote/inference/tgi/tgi.py b/llama_stack/providers/remote/inference/tgi/tgi.py index e1632e4a0..27fc263a6 100644 --- a/llama_stack/providers/remote/inference/tgi/tgi.py +++ b/llama_stack/providers/remote/inference/tgi/tgi.py @@ -10,13 +10,9 @@ from collections.abc import AsyncGenerator from huggingface_hub import AsyncInferenceClient, HfApi from pydantic import SecretStr -from llama_stack.apis.common.content_types import ( - InterleavedContent, -) from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, - CompletionRequest, Inference, LogProbConfig, Message, @@ -44,13 +40,10 @@ from llama_stack.providers.utils.inference.openai_compat import ( get_sampling_options, process_chat_completion_response, process_chat_completion_stream_response, - process_completion_response, - process_completion_stream_response, ) from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( chat_completion_request_to_model_input_info, - completion_request_to_prompt_model_input_info, ) from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig @@ -122,31 +115,6 @@ class _HfAdapter( async def unregister_model(self, model_id: str) -> None: pass - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> AsyncGenerator: - if sampling_params is None: - sampling_params = SamplingParams() - model = await self.model_store.get_model(model_id) - request = CompletionRequest( - model=model.provider_resource_id, - content=content, - sampling_params=sampling_params, - response_format=response_format, - stream=stream, - logprobs=logprobs, - ) - if stream: - return self._stream_completion(request) - else: - return await self._nonstream_completion(request) - def _get_max_new_tokens(self, sampling_params, input_tokens): return min( sampling_params.max_tokens or (self.max_tokens - input_tokens), @@ -180,53 +148,6 @@ class _HfAdapter( return options - async def _get_params_for_completion(self, request: CompletionRequest) -> dict: - prompt, input_tokens = await completion_request_to_prompt_model_input_info(request) - - return dict( - prompt=prompt, - stream=request.stream, - details=True, - max_new_tokens=self._get_max_new_tokens(request.sampling_params, input_tokens), - stop_sequences=["<|eom_id|>", "<|eot_id|>"], - **self._build_options(request.sampling_params, request.response_format), - ) - - async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator: - params = await self._get_params_for_completion(request) - - async def _generate_and_convert_to_openai_compat(): - s = await self.hf_client.text_generation(**params) - async for chunk in s: - token_result = chunk.token - finish_reason = None - if chunk.details: - finish_reason = chunk.details.finish_reason - - choice = OpenAICompatCompletionChoice(text=token_result.text, finish_reason=finish_reason) - yield OpenAICompatCompletionResponse( - choices=[choice], - ) - - stream = _generate_and_convert_to_openai_compat() - async for chunk in process_completion_stream_response(stream): - yield chunk - - async def _nonstream_completion(self, request: CompletionRequest) -> AsyncGenerator: - params = await self._get_params_for_completion(request) - r = await self.hf_client.text_generation(**params) - - choice = OpenAICompatCompletionChoice( - finish_reason=r.details.finish_reason, - text="".join(t.text for t in r.details.tokens), - ) - - response = OpenAICompatCompletionResponse( - choices=[choice], - ) - - return process_completion_response(response) - async def chat_completion( self, model_id: str, diff --git a/llama_stack/providers/remote/inference/together/together.py b/llama_stack/providers/remote/inference/together/together.py index 083c528bb..0c8363f6a 100644 --- a/llama_stack/providers/remote/inference/together/together.py +++ b/llama_stack/providers/remote/inference/together/together.py @@ -10,13 +10,9 @@ from openai import AsyncOpenAI from together import AsyncTogether from together.constants import BASE_URL -from llama_stack.apis.common.content_types import ( - InterleavedContent, -) from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, - CompletionRequest, Inference, LogProbConfig, Message, @@ -39,13 +35,10 @@ from llama_stack.providers.utils.inference.openai_compat import ( get_sampling_options, process_chat_completion_response, process_chat_completion_stream_response, - process_completion_response, - process_completion_stream_response, ) 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, request_has_media, ) @@ -81,31 +74,6 @@ class TogetherInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Need async def shutdown(self) -> None: pass - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> AsyncGenerator: - if sampling_params is None: - sampling_params = SamplingParams() - model = await self.model_store.get_model(model_id) - request = CompletionRequest( - model=model.provider_resource_id, - content=content, - sampling_params=sampling_params, - response_format=response_format, - stream=stream, - logprobs=logprobs, - ) - if stream: - return self._stream_completion(request) - else: - return await self._nonstream_completion(request) - def _get_client(self) -> AsyncTogether: together_api_key = None config_api_key = self.config.api_key.get_secret_value() if self.config.api_key else None @@ -127,19 +95,6 @@ class TogetherInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Need api_key=together_client.api_key, ) - async def _nonstream_completion(self, request: CompletionRequest) -> ChatCompletionResponse: - params = await self._get_params(request) - client = self._get_client() - r = await client.completions.create(**params) - return process_completion_response(r) - - async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator: - params = await self._get_params(request) - client = self._get_client() - stream = await client.completions.create(**params) - async for chunk in process_completion_stream_response(stream): - yield chunk - def _build_options( self, sampling_params: SamplingParams | None, @@ -219,18 +174,14 @@ class TogetherInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Need async for chunk in process_chat_completion_stream_response(stream, request): yield chunk - async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict: + async def _get_params(self, request: ChatCompletionRequest) -> dict: input_dict = {} media_present = request_has_media(request) llama_model = self.get_llama_model(request.model) - if isinstance(request, ChatCompletionRequest): - if media_present or not llama_model: - input_dict["messages"] = [await convert_message_to_openai_dict(m) for m in request.messages] - else: - input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model) + if media_present or not llama_model: + input_dict["messages"] = [await convert_message_to_openai_dict(m) for m in request.messages] else: - assert not media_present, "Together does not support media for Completion requests" - input_dict["prompt"] = await completion_request_to_prompt(request) + input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model) params = { "model": request.model, diff --git a/llama_stack/providers/remote/inference/vllm/vllm.py b/llama_stack/providers/remote/inference/vllm/vllm.py index bef5cbf2c..44b3dc3db 100644 --- a/llama_stack/providers/remote/inference/vllm/vllm.py +++ b/llama_stack/providers/remote/inference/vllm/vllm.py @@ -15,7 +15,6 @@ from openai.types.chat.chat_completion_chunk import ( ) from llama_stack.apis.common.content_types import ( - InterleavedContent, TextDelta, ToolCallDelta, ToolCallParseStatus, @@ -27,9 +26,6 @@ from llama_stack.apis.inference import ( ChatCompletionResponseEventType, ChatCompletionResponseStreamChunk, CompletionMessage, - CompletionRequest, - CompletionResponse, - CompletionResponseStreamChunk, GrammarResponseFormat, Inference, JsonSchemaResponseFormat, @@ -64,14 +60,8 @@ from llama_stack.providers.utils.inference.openai_compat import ( convert_tool_call, get_sampling_options, process_chat_completion_stream_response, - process_completion_response, - process_completion_stream_response, ) from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin -from llama_stack.providers.utils.inference.prompt_adapter import ( - completion_request_to_prompt, - request_has_media, -) from .config import VLLMInferenceAdapterConfig @@ -363,33 +353,6 @@ class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsPro def get_extra_client_params(self): return {"http_client": httpx.AsyncClient(verify=self.config.tls_verify)} - async def completion( # type: ignore[override] # Return type more specific than base class which is allows for both streaming and non-streaming responses. - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> CompletionResponse | AsyncGenerator[CompletionResponseStreamChunk, None]: - if sampling_params is None: - sampling_params = SamplingParams() - model = await self._get_model(model_id) - if model.provider_resource_id is None: - raise ValueError(f"Model {model_id} has no provider_resource_id set") - request = CompletionRequest( - model=model.provider_resource_id, - content=content, - sampling_params=sampling_params, - response_format=response_format, - stream=stream, - logprobs=logprobs, - ) - if stream: - return self._stream_completion(request) - else: - return await self._nonstream_completion(request) - async def chat_completion( self, model_id: str, @@ -474,24 +437,6 @@ class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsPro async for chunk in res: yield chunk - async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse: - if self.client is None: - raise RuntimeError("Client is not initialized") - params = await self._get_params(request) - r = await self.client.completions.create(**params) - return process_completion_response(r) - - async def _stream_completion( - self, request: CompletionRequest - ) -> AsyncGenerator[CompletionResponseStreamChunk, None]: - if self.client is None: - raise RuntimeError("Client is not initialized") - params = await self._get_params(request) - - stream = await self.client.completions.create(**params) - async for chunk in process_completion_stream_response(stream): - yield chunk - async def register_model(self, model: Model) -> Model: try: model = await self.register_helper.register_model(model) @@ -511,7 +456,7 @@ class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsPro ) return model - async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict: + async def _get_params(self, request: ChatCompletionRequest) -> dict: options = get_sampling_options(request.sampling_params) if "max_tokens" not in options: options["max_tokens"] = self.config.max_tokens @@ -521,11 +466,7 @@ class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsPro if isinstance(request, ChatCompletionRequest) and request.tools: input_dict = {"tools": _convert_to_vllm_tools_in_request(request.tools)} - if isinstance(request, ChatCompletionRequest): - input_dict["messages"] = [await convert_message_to_openai_dict(m, download=True) for m in request.messages] - else: - assert not request_has_media(request), "vLLM does not support media for Completion requests" - input_dict["prompt"] = await completion_request_to_prompt(request) + input_dict["messages"] = [await convert_message_to_openai_dict(m, download=True) for m in request.messages] if fmt := request.response_format: if isinstance(fmt, JsonSchemaResponseFormat): diff --git a/llama_stack/providers/remote/inference/watsonx/watsonx.py b/llama_stack/providers/remote/inference/watsonx/watsonx.py index 00b9acc06..cb9d61102 100644 --- a/llama_stack/providers/remote/inference/watsonx/watsonx.py +++ b/llama_stack/providers/remote/inference/watsonx/watsonx.py @@ -11,7 +11,6 @@ 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 from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, @@ -43,8 +42,6 @@ from llama_stack.providers.utils.inference.openai_compat import ( prepare_openai_completion_params, process_chat_completion_response, process_chat_completion_stream_response, - process_completion_response, - process_completion_stream_response, ) from llama_stack.providers.utils.inference.prompt_adapter import ( chat_completion_request_to_prompt, @@ -87,31 +84,6 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper): async def shutdown(self) -> None: pass - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> AsyncGenerator: - if sampling_params is None: - sampling_params = SamplingParams() - model = await self.model_store.get_model(model_id) - request = CompletionRequest( - model=model.provider_resource_id, - content=content, - sampling_params=sampling_params, - response_format=response_format, - stream=stream, - logprobs=logprobs, - ) - if stream: - return self._stream_completion(request) - else: - return await self._nonstream_completion(request) - def _get_client(self, model_id) -> Model: config_api_key = self._config.api_key.get_secret_value() if self._config.api_key else None config_url = self._config.url @@ -128,40 +100,6 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper): ) return self._openai_client - async def _nonstream_completion(self, request: CompletionRequest) -> ChatCompletionResponse: - params = await self._get_params(request) - r = self._get_client(request.model).generate(**params) - choices = [] - if "results" in r: - for result in r["results"]: - choice = OpenAICompatCompletionChoice( - finish_reason=result["stop_reason"] if result["stop_reason"] else None, - text=result["generated_text"], - ) - choices.append(choice) - response = OpenAICompatCompletionResponse( - choices=choices, - ) - return process_completion_response(response) - - async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator: - params = await self._get_params(request) - - async def _generate_and_convert_to_openai_compat(): - s = self._get_client(request.model).generate_text_stream(**params) - for chunk in s: - choice = OpenAICompatCompletionChoice( - finish_reason=None, - text=chunk, - ) - yield OpenAICompatCompletionResponse( - choices=[choice], - ) - - stream = _generate_and_convert_to_openai_compat() - async for chunk in process_completion_stream_response(stream): - yield chunk - async def chat_completion( self, model_id: str, diff --git a/llama_stack/providers/utils/inference/litellm_openai_mixin.py b/llama_stack/providers/utils/inference/litellm_openai_mixin.py index 10df664eb..23a72bb3a 100644 --- a/llama_stack/providers/utils/inference/litellm_openai_mixin.py +++ b/llama_stack/providers/utils/inference/litellm_openai_mixin.py @@ -4,14 +4,11 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from collections.abc import AsyncGenerator, AsyncIterator +from collections.abc import AsyncIterator from typing import Any import litellm -from llama_stack.apis.common.content_types import ( - InterleavedContent, -) from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, @@ -108,17 +105,6 @@ class LiteLLMOpenAIMixin( else model_id ) - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, - stream: bool | None = False, - logprobs: LogProbConfig | None = None, - ) -> AsyncGenerator: - raise NotImplementedError("LiteLLM does not support completion requests") - async def chat_completion( self, model_id: str, diff --git a/llama_stack/providers/utils/inference/openai_compat.py b/llama_stack/providers/utils/inference/openai_compat.py index cdd471d5e..da97d7c79 100644 --- a/llama_stack/providers/utils/inference/openai_compat.py +++ b/llama_stack/providers/utils/inference/openai_compat.py @@ -103,8 +103,6 @@ from llama_stack.apis.inference import ( JsonSchemaResponseFormat, Message, OpenAIChatCompletion, - OpenAICompletion, - OpenAICompletionChoice, OpenAIEmbeddingData, OpenAIMessageParam, OpenAIResponseFormatParam, @@ -1281,76 +1279,6 @@ async def prepare_openai_completion_params(**params): return completion_params -class OpenAICompletionToLlamaStackMixin: - async def openai_completion( - self, - model: str, - prompt: str | list[str] | list[int] | list[list[int]], - best_of: int | None = None, - echo: bool | None = None, - frequency_penalty: float | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_tokens: int | None = None, - n: int | None = None, - presence_penalty: float | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - top_p: float | None = None, - user: str | None = None, - guided_choice: list[str] | None = None, - prompt_logprobs: int | None = None, - suffix: str | None = None, - ) -> OpenAICompletion: - if stream: - raise ValueError(f"{self.__class__.__name__} doesn't support streaming openai completions") - - # This is a pretty hacky way to do emulate completions - - # basically just de-batches them... - prompts = [prompt] if not isinstance(prompt, list) else prompt - - sampling_params = _convert_openai_sampling_params( - max_tokens=max_tokens, - temperature=temperature, - top_p=top_p, - ) - - choices = [] - # "n" is the number of completions to generate per prompt - n = n or 1 - for _i in range(0, n): - # and we may have multiple prompts, if batching was used - - for prompt in prompts: - result = self.completion( - model_id=model, - content=prompt, - sampling_params=sampling_params, - ) - - index = len(choices) - text = result.content - finish_reason = _convert_stop_reason_to_openai_finish_reason(result.stop_reason) - - choice = OpenAICompletionChoice( - index=index, - text=text, - finish_reason=finish_reason, - ) - choices.append(choice) - - return OpenAICompletion( - id=f"cmpl-{uuid.uuid4()}", - choices=choices, - created=int(time.time()), - model=model, - object="text_completion", - ) - - class OpenAIChatCompletionToLlamaStackMixin: async def openai_chat_completion( self, diff --git a/llama_stack/providers/utils/inference/prompt_adapter.py b/llama_stack/providers/utils/inference/prompt_adapter.py index ca6fdaf7e..728bbf8c9 100644 --- a/llama_stack/providers/utils/inference/prompt_adapter.py +++ b/llama_stack/providers/utils/inference/prompt_adapter.py @@ -229,28 +229,6 @@ async def convert_image_content_to_url( return base64.b64encode(content).decode("utf-8") -async def completion_request_to_prompt(request: CompletionRequest) -> str: - content = augment_content_with_response_format_prompt(request.response_format, request.content) - request.content = content - request = await convert_request_to_raw(request) - - formatter = ChatFormat(tokenizer=Tokenizer.get_instance()) - model_input = formatter.encode_content(request.content) - return formatter.tokenizer.decode(model_input.tokens) - - -async def completion_request_to_prompt_model_input_info( - request: CompletionRequest, -) -> tuple[str, int]: - content = augment_content_with_response_format_prompt(request.response_format, request.content) - request.content = content - request = await convert_request_to_raw(request) - - formatter = ChatFormat(tokenizer=Tokenizer.get_instance()) - model_input = formatter.encode_content(request.content) - return (formatter.tokenizer.decode(model_input.tokens), len(model_input.tokens)) - - def augment_content_with_response_format_prompt(response_format, content): if fmt_prompt := response_format_prompt(response_format): if isinstance(content, list): diff --git a/tests/integration/recordings/responses/0ff78129bb3a.json b/tests/integration/recordings/responses/0ff78129bb3a.json deleted file mode 100644 index 3a52c789b..000000000 --- a/tests/integration/recordings/responses/0ff78129bb3a.json +++ /dev/null @@ -1,167 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n {\n \"name\": \"greet_everyone\",\n \"description\": \"\",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"url\"],\n \"properties\": {\n \"url\": {\n \"type\": \"string\",\n \"description\": \"\"\n }\n }\n }\n },\n {\n \"name\": \"get_boiling_point\",\n \"description\": \"\nReturns the boiling point of a liquid in Celsius or Fahrenheit.\n\n:param liquid_name: The name of the liquid\n:param celsius: Whether to return the boiling point in Celsius\n:return: The boiling point of the liquid in Celcius or Fahrenheit\n\",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"liquid_name\", \"celsius\"],\n \"properties\": {\n \"liquid_name\": {\n \"type\": \"string\",\n \"description\": \"\"\n },\n \"celsius\": {\n \"type\": \"boolean\",\n \"description\": \"\"\n }\n }\n }\n }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSay hi to the world. Use tools to do so.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n[greet_everyone(url=\"world\")]<|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\nHello, world!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - "options": { - "temperature": 0.0 - }, - "stream": true - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": [ - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:26:18.143606Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "How", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:26:18.186151Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " can", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:26:18.229036Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " I", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:26:18.271516Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " assist", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:26:18.316272Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " you", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:26:18.361005Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " further", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:26:18.404689Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "?", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:26:18.447699Z", - "done": true, - "done_reason": "stop", - "total_duration": 456939083, - "load_duration": 79653292, - "prompt_eval_count": 471, - "prompt_eval_duration": 71724667, - "eval_count": 8, - "eval_duration": 304859000, - "response": "", - "thinking": null, - "context": null - } - } - ], - "is_streaming": true - } -} diff --git a/tests/integration/recordings/responses/168daab89068.json b/tests/integration/recordings/responses/168daab89068.json deleted file mode 100644 index 323b0dfd4..000000000 --- a/tests/integration/recordings/responses/168daab89068.json +++ /dev/null @@ -1,167 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n {\n \"name\": \"greet_everyone\",\n \"description\": \"\",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"url\"],\n \"properties\": {\n \"url\": {\n \"type\": \"string\",\n \"description\": \"\"\n }\n }\n }\n },\n {\n \"name\": \"get_boiling_point\",\n \"description\": \"\n Returns the boiling point of a liquid in Celsius or Fahrenheit.\n\n :param liquid_name: The name of the liquid\n :param celsius: Whether to return the boiling point in Celsius\n :return: The boiling point of the liquid in Celcius or Fahrenheit\n \",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"liquid_name\"],\n \"properties\": {\n \"liquid_name\": {\n \"type\": \"string\",\n \"description\": \"\"\n },\n \"celsius\": {\n \"type\": \"boolean\",\n \"description\": \"\",\n \"default\": \"True\"\n }\n }\n }\n }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSay hi to the world. Use tools to do so.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n[greet_everyone(url=\"world\")]<|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\nHello, world!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - "options": { - "temperature": 0.0 - }, - "stream": true - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": [ - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-27T18:05:56.663224Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "How", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-27T18:05:56.706706Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " can", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-27T18:05:56.751075Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " I", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-27T18:05:56.794187Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " assist", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-27T18:05:56.837831Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " you", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-27T18:05:56.879926Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " further", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-27T18:05:56.92182Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "?", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-27T18:05:56.963339Z", - "done": true, - "done_reason": "stop", - "total_duration": 492973041, - "load_duration": 103979375, - "prompt_eval_count": 482, - "prompt_eval_duration": 87032041, - "eval_count": 8, - "eval_duration": 300586375, - "response": "", - "thinking": null, - "context": null - } - } - ], - "is_streaming": true - } -} diff --git a/tests/integration/recordings/responses/1b8394f90636.json b/tests/integration/recordings/responses/1b8394f90636.json deleted file mode 100644 index 6857c6840..000000000 --- a/tests/integration/recordings/responses/1b8394f90636.json +++ /dev/null @@ -1,41 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "prompt": "<|begin_of_text|>Complete the sentence using one word: Roses are red, violets are ", - "raw": true, - "options": { - "temperature": 0.0, - "max_tokens": 50, - "num_predict": 50 - }, - "stream": false - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:36:13.821929Z", - "done": true, - "done_reason": "stop", - "total_duration": 1907912167, - "load_duration": 90979292, - "prompt_eval_count": 18, - "prompt_eval_duration": 77350291, - "eval_count": 43, - "eval_duration": 1738568334, - "response": " _______.\n\nThe best answer is blue. The traditional nursery rhyme goes like this:\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you! (Or something similar.)", - "thinking": null, - "context": null - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/responses/1b92be674e2a.json b/tests/integration/recordings/responses/1b92be674e2a.json deleted file mode 100644 index e5f05bf54..000000000 --- a/tests/integration/recordings/responses/1b92be674e2a.json +++ /dev/null @@ -1,39 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho is the CEO of Meta?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - "options": { - "temperature": 0.0 - }, - "stream": false - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - 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"object": "list", + "usage": { + "prompt_tokens": 162, + "total_tokens": 162 + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/recordings/responses/390f0c7dac96.json b/tests/integration/recordings/responses/390f0c7dac96.json deleted file mode 100644 index e8c9528fb..000000000 --- a/tests/integration/recordings/responses/390f0c7dac96.json +++ /dev/null @@ -1,39 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest metrics generation 1<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - "options": { - "temperature": 0.0 - }, - "stream": false - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-11T15:51:18.170868Z", - "done": true, - "done_reason": "stop", - "total_duration": 5240614083, - "load_duration": 9823416, - "prompt_eval_count": 21, - "prompt_eval_duration": 21000000, - "eval_count": 310, - "eval_duration": 5209000000, - "response": "This is the start of a test. I'll provide some sample data and you can try to generate metrics based on it.\n\n**Data:**\n\nLet's say we have a dataset of user interactions with an e-commerce website. The data includes:\n\n| User ID | Product Name | Purchase Date | Quantity | Price |\n| --- | --- | --- | --- | --- |\n| 1 | iPhone 13 | 2022-01-01 | 2 | 999.99 |\n| 1 | MacBook Air | 2022-01-05 | 1 | 1299.99 |\n| 2 | Samsung TV | 2022-01-10 | 3 | 899.99 |\n| 3 | iPhone 13 | 2022-01-15 | 1 | 999.99 |\n| 4 | MacBook Pro | 2022-01-20 | 2 | 1799.99 |\n\n**Task:**\n\nYour task is to generate the following metrics based on this data:\n\n1. Average order value (AOV)\n2. Conversion rate\n3. Average revenue per user (ARPU)\n4. Customer lifetime value (CLV)\n\nPlease provide your answers in a format like this:\n\n| Metric | Value |\n| --- | --- |\n| AOV | 1234.56 |\n| Conversion Rate | 0.25 |\n| ARPU | 1000.00 |\n| CLV | 5000.00 |\n\nGo ahead and generate the metrics!", - "thinking": null, - "context": null - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/responses/3b60c09d6c4f.json b/tests/integration/recordings/responses/3b60c09d6c4f.json new file mode 100644 index 000000000..573daa802 --- /dev/null +++ b/tests/integration/recordings/responses/3b60c09d6c4f.json @@ -0,0 +1,806 @@ +{ + "request": { + "method": "POST", + "url": "http://0.0.0.0:11434/v1/v1/embeddings", + "headers": {}, + "body": { + "model": "nomic-embed-text:137m-v1.5-fp16", + "input": [ + "The secret string is foobazbar." + ], + "encoding_format": "float" + }, + "endpoint": "/v1/embeddings", + "model": "nomic-embed-text:137m-v1.5-fp16" + }, + "response": { + "body": { + "__type__": 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"options": { - "temperature": 0.0 - }, - "stream": true - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": [ - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T20:56:51.314693Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "[", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T20:56:51.362989Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "get", - "thinking": null, - "context": null - } - 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"done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "=\"", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T20:56:51.552257Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "San", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T20:56:51.599938Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " Francisco", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T20:56:51.645807Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": ",", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T20:56:51.694632Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " CA", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T20:56:51.743454Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "\")]", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T20:56:51.790525Z", - "done": true, - "done_reason": "stop", - "total_duration": 687242541, - "load_duration": 131028916, - "prompt_eval_count": 324, - "prompt_eval_duration": 76000000, - "eval_count": 11, - "eval_duration": 479000000, - "response": "", - "thinking": null, - "context": null - } - } - ], - "is_streaming": true - } -} diff --git a/tests/integration/recordings/responses/44fb9cf5875f.json b/tests/integration/recordings/responses/44fb9cf5875f.json deleted file mode 100644 index 17c538862..000000000 --- a/tests/integration/recordings/responses/44fb9cf5875f.json +++ /dev/null @@ -1,39 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest trace 1<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - "options": { - "temperature": 0.0 - }, - "stream": false - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:41:49.581065Z", - "done": true, - "done_reason": "stop", - "total_duration": 2391571708, - "load_duration": 182022958, - "prompt_eval_count": 20, - "prompt_eval_duration": 74456583, - "eval_count": 51, - "eval_duration": 2134471458, - "response": "It seems like you're trying to test the system, but I'm not sure what specific functionality or feature you'd like to test. Could you please provide more context or clarify what you're looking for? I'll do my best to assist you!", - "thinking": null, - "context": null - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/responses/4597743bcd2a.json b/tests/integration/recordings/responses/4597743bcd2a.json deleted file mode 100644 index 868d27a0e..000000000 --- a/tests/integration/recordings/responses/4597743bcd2a.json +++ /dev/null @@ -1,185 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n {\n \"name\": \"greet_everyone\",\n \"description\": \"\",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"url\"],\n \"properties\": {\n \"url\": {\n \"type\": \"string\",\n \"description\": \"\"\n }\n }\n }\n },\n {\n \"name\": \"get_boiling_point\",\n \"description\": \"\nReturns the boiling point of a liquid in Celsius or Fahrenheit.\n\n:param liquid_name: The name of the liquid\n:param celsius: Whether to return the boiling point in Celsius\n:return: The boiling point of the liquid in Celcius or Fahrenheit\n\",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"liquid_name\", \"celsius\"],\n \"properties\": {\n \"liquid_name\": {\n \"type\": \"string\",\n \"description\": \"\"\n },\n \"celsius\": {\n \"type\": \"boolean\",\n \"description\": \"\"\n }\n }\n }\n }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSay hi to the world. 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"request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhich planet has rings around it with a name starting with letter S?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - "options": { - "temperature": 0.0 - }, - "stream": false - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:42:17.227488Z", - "done": true, - "done_reason": "stop", - "total_duration": 3003964916, - "load_duration": 111221916, - "prompt_eval_count": 30, - "prompt_eval_duration": 72578583, - "eval_count": 70, - "eval_duration": 2819555375, - "response": "The answer is Saturn! Saturn's ring system is one of the most iconic and well-known in our solar system. The rings are made up of ice particles, rock debris, and dust that orbit around the planet due to its gravitational pull.\n\nWould you like to know more about Saturn's rings or is there something else I can help you with?", - "thinking": null, - "context": null - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/responses/731824c54461.json b/tests/integration/recordings/responses/731824c54461.json deleted file mode 100644 index 2d88c6329..000000000 --- a/tests/integration/recordings/responses/731824c54461.json +++ /dev/null @@ -1,203 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nGive me a sentence that contains the word: hello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - 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"model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-18T19:47:58.646586Z", - "done": true, - "done_reason": "stop", - "total_duration": 1011323917, - "load_duration": 76575458, - "prompt_eval_count": 31, - "prompt_eval_duration": 553259250, - "eval_count": 10, - "eval_duration": 380302792, - "response": "", - "thinking": null, - "context": null - } - } - ], - "is_streaming": true - } -} diff --git a/tests/integration/recordings/responses/7354ec181984.json b/tests/integration/recordings/responses/7354ec181984.json deleted file mode 100644 index b73a7cd50..000000000 --- a/tests/integration/recordings/responses/7354ec181984.json +++ /dev/null @@ -1,39 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the smallest country in the world?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - "options": { - "temperature": 0.0 - }, - "stream": false - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:39:54.374714Z", - "done": true, - "done_reason": "stop", - "total_duration": 6321793333, - "load_duration": 182255958, - "prompt_eval_count": 25, - "prompt_eval_duration": 67964459, - "eval_count": 150, - "eval_duration": 6070867875, - "response": "The smallest country in the world is the Vatican City, which has a total area of approximately 0.44 km\u00b2 (0.17 sq mi). It is an independent city-state located within Rome, Italy, and is home to the Pope and the central government of the Catholic Church.\n\nTo put that into perspective, the Vatican City is smaller than a golf course! Despite its tiny size, it has its own government, currency, postal system, and even its own police force. It's also home to numerous iconic landmarks like St. Peter's Basilica and the Sistine Chapel.\n\nInterestingly, the Vatican City is not only the smallest country in the world but also the most densely populated, with a population of just over 800 people!", - "thinking": null, - "context": null - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/responses/75d0dd9d0fa3.json b/tests/integration/recordings/responses/75d0dd9d0fa3.json deleted file mode 100644 index 561fa1e67..000000000 --- a/tests/integration/recordings/responses/75d0dd9d0fa3.json +++ /dev/null @@ -1,64 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "prompt": "<|begin_of_text|>Michael Jordan was born in 1963. He played basketball for the Chicago Bulls. He retired in 2003.Please respond in JSON format with the schema: {\"properties\": {\"name\": {\"title\": \"Name\", \"type\": \"string\"}, \"year_born\": {\"title\": \"Year Born\", \"type\": \"string\"}, \"year_retired\": {\"title\": \"Year Retired\", \"type\": \"string\"}}, \"required\": [\"name\", \"year_born\", \"year_retired\"], \"title\": \"AnswerFormat\", \"type\": \"object\"}", - "raw": true, - "format": { - "properties": { - "name": { - "title": "Name", - "type": "string" - }, - "year_born": { - "title": "Year Born", - "type": "string" - }, - "year_retired": { - "title": "Year Retired", - "type": "string" - } - }, - "required": [ - "name", - "year_born", - "year_retired" - ], - "title": "AnswerFormat", - "type": "object" - }, - "options": { - "temperature": 0.0, - "max_tokens": 50, - "num_predict": 50 - }, - "stream": false - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:36:17.508028Z", - "done": true, - "done_reason": "stop", - "total_duration": 1529591917, - "load_duration": 84990667, - "prompt_eval_count": 119, - "prompt_eval_duration": 189045583, - "eval_count": 29, - "eval_duration": 1254813583, - "response": "{ \"name\": \"Michael Jordan\", \"year_born\": \"1963\", \"year_retired\": \"2003\"}\n ", - "thinking": null, - "context": null - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/responses/7bcb0f86c91b.json b/tests/integration/recordings/responses/7bcb0f86c91b.json deleted file mode 100644 index 4c9a55153..000000000 --- a/tests/integration/recordings/responses/7bcb0f86c91b.json +++ /dev/null @@ -1,39 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest metrics generation 0<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - "options": { - "temperature": 0.0 - }, - "stream": false - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-11T15:51:12.918723Z", - "done": true, - "done_reason": "stop", - "total_duration": 8868987792, - "load_duration": 2793275292, - "prompt_eval_count": 21, - "prompt_eval_duration": 250000000, - "eval_count": 344, - "eval_duration": 5823000000, - "response": "Here are some common test metrics used to evaluate the performance of a system:\n\n1. **Accuracy**: The proportion of correct predictions or classifications out of total predictions made.\n2. **Precision**: The ratio of true positives 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a/tests/integration/recordings/responses/b91f1fb4aedb.json b/tests/integration/recordings/responses/b91f1fb4aedb.json deleted file mode 100644 index dccb05cce..000000000 --- a/tests/integration/recordings/responses/b91f1fb4aedb.json +++ /dev/null @@ -1,221 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. 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You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. 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- "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " can", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:23.00029Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " I", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:23.043332Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " assist", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:23.085324Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " you", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:23.128181Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": " further", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:23.172026Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "?", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:23.216706Z", - "done": true, - "done_reason": "stop", - "total_duration": 516060000, - "load_duration": 127260334, - "prompt_eval_count": 479, - "prompt_eval_duration": 87107292, - "eval_count": 8, - "eval_duration": 299381042, - "response": "", - "thinking": null, - "context": null - } - } - ], - "is_streaming": true - } -} diff --git a/tests/integration/recordings/responses/bd656a9e3f8f.json b/tests/integration/recordings/responses/bd656a9e3f8f.json new file mode 100644 index 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"method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest metrics generation 0<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - "options": { - "temperature": 0.0 - }, - "stream": false - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b" - }, - "response": { - "body": { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b", - "created_at": "2025-08-11T15:56:06.703788Z", - "done": true, - "done_reason": "stop", - "total_duration": 2722294000, - "load_duration": 9736083, - "prompt_eval_count": 21, - "prompt_eval_duration": 113000000, - "eval_count": 324, - "eval_duration": 2598000000, - "response": "Here are some test metrics that can be used to evaluate the performance of a system:\n\n1. **Accuracy**: The proportion of correct predictions made by the model.\n2. **Precision**: The ratio of true positives (correctly predicted instances) to total positive predictions.\n3. **Recall**: The ratio of true positives to the sum of true positives and false negatives (missed instances).\n4. **F1-score**: The harmonic mean of precision and recall, providing a balanced measure of both.\n5. **Mean Squared Error (MSE)**: The average squared difference between predicted and actual values.\n6. **Mean Absolute Error (MAE)**: The average absolute difference between predicted and actual values.\n7. **Root Mean Squared Percentage Error (RMSPE)**: A variation of MSE that expresses the error as a percentage.\n8. **Coefficient of Determination (R-squared, R2)**: Measures how well the model explains the variance in the data.\n9. **Mean Absolute Percentage Error (MAPE)**: The average absolute percentage difference between predicted and actual values.\n10. **Mean Squared Logarithmic Error (MSLE)**: A variation of MSE that is more suitable for skewed distributions.\n\nThese metrics can be used to evaluate different aspects of a system's performance, such as:\n\n* Classification models: accuracy, precision, recall, F1-score\n* Regression models: MSE, MAE, RMSPE, R2, MSLE\n* Time series forecasting: MAPE, RMSPE\n\nNote that the choice of metric depends on the specific problem and data.", - "thinking": null, - "context": null - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/responses/c7582fa7c2c4.json b/tests/integration/recordings/responses/c7582fa7c2c4.json deleted file mode 100644 index d1edd7336..000000000 --- a/tests/integration/recordings/responses/c7582fa7c2c4.json +++ /dev/null @@ -1,347 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. 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b/tests/integration/recordings/responses/ed9e9b34008d.json deleted file mode 100644 index d0591dbc1..000000000 --- a/tests/integration/recordings/responses/ed9e9b34008d.json +++ /dev/null @@ -1,39 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the largest planet in our solar system?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - "options": { - "temperature": 0.0 - }, - "stream": false - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:39:48.030217Z", - "done": true, - "done_reason": "stop", - "total_duration": 9760536750, - "load_duration": 242188583, - "prompt_eval_count": 26, - "prompt_eval_duration": 83819333, - "eval_count": 232, - "eval_duration": 9434009042, - "response": "The largest planet in our solar system is Jupiter. It is a gas giant, meaning it is primarily composed of hydrogen and helium gases. Jupiter has a diameter of approximately 142,984 kilometers (88,846 miles), which is more than 11 times the diameter of Earth.\n\nJupiter is not only the largest planet in terms of size, but also the most massive planet in our solar system, with a mass that is more than 318 times that of Earth. It has a thick atmosphere and a strong magnetic field, and is known for its distinctive banded appearance, which is caused by strong winds in the upper atmosphere.\n\nJupiter's massive size and gravitational pull have a significant impact on the surrounding space, including the orbits of nearby planets and asteroids. Its moons are also notable, with four large ones: Io, Europa, Ganymede, and Callisto, which are known as the Galilean moons due to their discovery by Galileo Galilei in 1610.\n\nJupiter is a fascinating planet that continues to be studied by astronomers and space agencies around the world, offering insights into the formation and evolution of our solar system.", - "thinking": null, - "context": null - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/responses/ef757a75ed08.json b/tests/integration/recordings/responses/ef757a75ed08.json deleted file mode 100644 index 05860c4bb..000000000 --- a/tests/integration/recordings/responses/ef757a75ed08.json +++ /dev/null @@ -1,185 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n {\n \"name\": \"greet_everyone\",\n \"description\": \"\",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"url\"],\n \"properties\": {\n \"url\": {\n \"type\": \"string\",\n \"description\": \"\"\n }\n }\n }\n },\n {\n \"name\": \"get_boiling_point\",\n \"description\": \"\n Returns the boiling point of a liquid in Celsius or Fahrenheit.\n\n :param liquid_name: The name of the liquid\n :param celsius: Whether to return the boiling point in Celsius\n :return: The boiling point of the liquid in Celcius or Fahrenheit\n \",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"liquid_name\", \"celsius\"],\n \"properties\": {\n \"liquid_name\": {\n \"type\": \"string\",\n \"description\": \"\"\n },\n \"celsius\": {\n \"type\": \"boolean\",\n \"description\": \"\"\n }\n }\n }\n }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSay hi to the world. Use tools to do so.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - "options": { - "temperature": 0.0 - }, - "stream": true - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": [ - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:22.272912Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "[g", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:22.31501Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "reet", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:22.356888Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "_every", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:22.398576Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "one", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:22.440412Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "(url", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:22.482165Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "=\"", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:22.523773Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "world", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:22.565072Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "\")]", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-09-03T17:34:22.607117Z", - "done": true, - "done_reason": "stop", - "total_duration": 1386049708, - "load_duration": 96970583, - "prompt_eval_count": 456, - "prompt_eval_duration": 952471625, - "eval_count": 9, - "eval_duration": 335924459, - "response": "", - "thinking": null, - "context": null - } - } - ], - "is_streaming": true - } -} diff --git a/tests/integration/recordings/responses/f3c3afbd9b7e.json b/tests/integration/recordings/responses/f3c3afbd9b7e.json deleted file mode 100644 index a5aecf06f..000000000 --- a/tests/integration/recordings/responses/f3c3afbd9b7e.json +++ /dev/null @@ -1,59 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:1b", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n {\n \"name\": \"greet_everyone\",\n \"description\": \"\",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"url\"],\n \"properties\": {\n \"url\": {\n \"type\": \"string\",\n \"description\": \"\"\n }\n }\n }\n },\n {\n \"name\": \"get_boiling_point\",\n \"description\": \"\nReturns the boiling point of a liquid in Celsius or Fahrenheit.\n\n:param liquid_name: The name of the liquid\n:param celsius: Whether to return the boiling point in Celsius\n:return: The boiling point of the liquid in Celcius or Fahrenheit\n\",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"liquid_name\", \"celsius\"],\n \"properties\": {\n \"liquid_name\": {\n \"type\": \"string\",\n \"description\": \"\"\n },\n \"celsius\": {\n \"type\": \"boolean\",\n \"description\": \"\"\n }\n }\n }\n }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSay hi to the world. Use tools to do so.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - "options": { - "temperature": 0.0 - }, - "stream": true - }, - "endpoint": "/api/generate", - "model": "llama3.2:1b" - }, - "response": { - "body": [ - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:1b", - "created_at": "2025-07-29T23:23:09.553247Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - "prompt_eval_count": null, - "prompt_eval_duration": null, - "eval_count": null, - "eval_duration": null, - "response": "Hi", - "thinking": null, - "context": null - } - }, - { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:1b", - "created_at": "2025-07-29T23:23:09.564069Z", - "done": true, - "done_reason": "stop", - "total_duration": 2125493250, - "load_duration": 1610279708, - "prompt_eval_count": 448, - "prompt_eval_duration": 502413125, - "eval_count": 2, - "eval_duration": 11573709, - "response": "", - "thinking": null, - "context": null - } - } - ], - "is_streaming": true - } -} diff --git a/tests/integration/recordings/responses/f6857bcea729.json b/tests/integration/recordings/responses/f6857bcea729.json deleted file mode 100644 index 404bfb987..000000000 --- a/tests/integration/recordings/responses/f6857bcea729.json +++ /dev/null @@ -1,39 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest metrics generation 2<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - "options": { - "temperature": 0.0 - }, - "stream": false - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b" - }, - "response": { - "body": { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b", - "created_at": "2025-08-11T15:56:13.082679Z", - "done": true, - "done_reason": "stop", - "total_duration": 2606245291, - "load_duration": 9979708, - "prompt_eval_count": 21, - "prompt_eval_duration": 23000000, - "eval_count": 321, - "eval_duration": 2572000000, - "response": "Here are some test metrics that can be used to evaluate the performance of a system:\n\n1. **Accuracy**: Measures how close the predicted values are to the actual values.\n2. **Precision**: Measures the proportion of true positives among all positive predictions made by the model.\n3. **Recall**: Measures the proportion of true positives among all actual positive instances.\n4. **F1-score**: The harmonic mean of precision and recall, providing a balanced measure of both.\n5. **Mean Squared Error (MSE)**: Measures the average squared difference between predicted and actual values.\n6. **Mean Absolute Error (MAE)**: Measures the average absolute difference between predicted and actual values.\n7. **Root Mean Squared Percentage Error (RMSPE)**: A variation of MSE that expresses errors as a percentage of the actual value.\n8. **Coefficient of Determination (R-squared, R2)**: Measures how well the model explains the variance in the data.\n9. **Mean Absolute Percentage Error (MAPE)**: Measures the average absolute percentage difference between predicted and actual values.\n10. **Mean Squared Logarithmic Error (MSLE)**: A variation of MSE that is more suitable for skewed distributions.\n\nThese metrics can be used to evaluate different aspects of a system's performance, such as:\n\n* Classification models: accuracy, precision, recall, F1-score\n* Regression models: MSE, MAE, RMSPE, R2\n* Time series forecasting: MAPE, MSLE\n\nNote that the choice of metric depends on the specific problem and data.", - "thinking": null, - "context": null - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/responses/f80b99430f7e.json b/tests/integration/recordings/responses/f80b99430f7e.json deleted file mode 100644 index 5b692f4ca..000000000 --- a/tests/integration/recordings/responses/f80b99430f7e.json +++ /dev/null @@ -1,39 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/generate", - "headers": {}, - "body": { - "model": "llama3.2:3b", - "raw": true, - "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest metrics generation 1<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - "options": { - "temperature": 0.0 - }, - "stream": false - }, - "endpoint": "/api/generate", - "model": "llama3.2:3b" - }, - "response": { - "body": { - "__type__": "ollama._types.GenerateResponse", - "__data__": { - "model": "llama3.2:3b", - "created_at": "2025-08-11T15:56:10.465932Z", - "done": true, - "done_reason": "stop", - "total_duration": 3745686709, - "load_duration": 9734584, - "prompt_eval_count": 21, - "prompt_eval_duration": 23000000, - "eval_count": 457, - "eval_duration": 3712000000, - "response": "Here are some test metrics that can be used to evaluate the performance of a system:\n\n**Primary Metrics**\n\n1. **Response Time**: The time it takes for the system to respond to a request.\n2. **Throughput**: The number of requests processed by the system per unit time (e.g., requests per second).\n3. **Error Rate**: The percentage of requests that result in an error.\n\n**Secondary Metrics**\n\n1. **Average Response Time**: The average response time for all requests.\n2. **Median Response Time**: The middle value of the response times, used to detect outliers.\n3. **99th Percentile Response Time**: The response time at which 99% of requests are completed within this time.\n4. **Request Latency**: The difference between the request arrival time and the response time.\n\n**User Experience Metrics**\n\n1. **User Satisfaction (USAT)**: Measured through surveys or feedback forms to gauge user satisfaction with the system's performance.\n2. **First Response Time**: The time it takes for a user to receive their first response from the system.\n3. **Time Spent in System**: The total amount of time a user spends interacting with the system.\n\n**System Resource Metrics**\n\n1. **CPU Utilization**: The percentage of CPU resources being used by the system.\n2. **Memory Usage**: The amount of memory being used by the system.\n3. **Disk I/O Wait Time**: The average time spent waiting for disk I/O operations to complete.\n\n**Security Metrics**\n\n1. **Authentication Success Rate**: The percentage of successful authentication attempts.\n2. **Authorization Success Rate**: The percentage of successful authorization attempts.\n3. **Error Rate (Security)**: The percentage of security-related errors.\n\n**Other Metrics**\n\n1. **Page Load Time**: The time it takes for a page to load.\n2. **Click-Through Rate (CTR)**: The percentage of users who click on a link or button after seeing an ad or notification.\n3. **Conversion Rate**: The percentage of users who complete a desired action (e.g., fill out a form, make a purchase).\n\nThese metrics can be used to evaluate the performance and effectiveness of various aspects of your system, from user experience to security and resource utilization.", - "thinking": null, - "context": null - } - }, - "is_streaming": false - } -}