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feat: add embedding and dynamic model support to Together inference adapter
- updated to use OpenAIMixin - workarounds for Together api quirks - recordings for together suite when subdirs=inference,pattern=openai test with: `TOGETHER_API_KEY=_NONE_ ./scripts/integration-tests.sh --stack-config server:ci-tests --setup together --subdirs inference --pattern openai`
This commit is contained in:
Matthew Farrellee
parent
f4ab154ade
commit
3e02dc5c2f
20 changed files with 9227 additions and 180 deletions
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@ -4,7 +4,6 @@
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from llama_stack.apis.models import ModelType
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from llama_stack.models.llama.sku_types import CoreModelId
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from llama_stack.providers.utils.inference.model_registry import (
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ProviderModelEntry,
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@ -21,57 +20,84 @@ SAFETY_MODELS_ENTRIES = [
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CoreModelId.llama_guard_3_11b_vision.value,
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),
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]
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MODEL_ENTRIES = [
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build_hf_repo_model_entry(
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"meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
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CoreModelId.llama3_1_8b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
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CoreModelId.llama3_1_70b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
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CoreModelId.llama3_1_405b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/Llama-3.2-3B-Instruct-Turbo",
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CoreModelId.llama3_2_3b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo",
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CoreModelId.llama3_2_11b_vision_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo",
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CoreModelId.llama3_2_90b_vision_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/Llama-3.3-70B-Instruct-Turbo",
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CoreModelId.llama3_3_70b_instruct.value,
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),
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ProviderModelEntry(
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provider_model_id="togethercomputer/m2-bert-80M-8k-retrieval",
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model_type=ModelType.embedding,
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metadata={
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"embedding_dimension": 768,
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"context_length": 8192,
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},
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),
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ProviderModelEntry(
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# source: https://docs.together.ai/docs/serverless-models#embedding-models
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EMBEDDING_MODEL_ENTRIES = {
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"togethercomputer/m2-bert-80M-32k-retrieval": ProviderModelEntry(
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provider_model_id="togethercomputer/m2-bert-80M-32k-retrieval",
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model_type=ModelType.embedding,
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metadata={
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"embedding_dimension": 768,
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"context_length": 32768,
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},
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),
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build_hf_repo_model_entry(
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"meta-llama/Llama-4-Scout-17B-16E-Instruct",
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CoreModelId.llama4_scout_17b_16e_instruct.value,
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"BAAI/bge-large-en-v1.5": ProviderModelEntry(
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provider_model_id="BAAI/bge-large-en-v1.5",
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metadata={
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"embedding_dimension": 1024,
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"context_length": 512,
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},
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),
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build_hf_repo_model_entry(
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"meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
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CoreModelId.llama4_maverick_17b_128e_instruct.value,
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"BAAI/bge-base-en-v1.5": ProviderModelEntry(
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provider_model_id="BAAI/bge-base-en-v1.5",
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metadata={
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"embedding_dimension": 768,
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"context_length": 512,
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},
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),
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] + SAFETY_MODELS_ENTRIES
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"Alibaba-NLP/gte-modernbert-base": ProviderModelEntry(
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provider_model_id="Alibaba-NLP/gte-modernbert-base",
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metadata={
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"embedding_dimension": 768,
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"context_length": 8192,
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},
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),
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"intfloat/multilingual-e5-large-instruct": ProviderModelEntry(
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provider_model_id="intfloat/multilingual-e5-large-instruct",
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metadata={
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"embedding_dimension": 1024,
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"context_length": 512,
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},
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),
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}
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MODEL_ENTRIES = (
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[
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build_hf_repo_model_entry(
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"meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
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CoreModelId.llama3_1_8b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
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CoreModelId.llama3_1_70b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
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CoreModelId.llama3_1_405b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/Llama-3.2-3B-Instruct-Turbo",
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CoreModelId.llama3_2_3b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo",
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CoreModelId.llama3_2_11b_vision_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo",
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CoreModelId.llama3_2_90b_vision_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/Llama-3.3-70B-Instruct-Turbo",
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CoreModelId.llama3_3_70b_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/Llama-4-Scout-17B-16E-Instruct",
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CoreModelId.llama4_scout_17b_16e_instruct.value,
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),
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build_hf_repo_model_entry(
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"meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
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CoreModelId.llama4_maverick_17b_128e_instruct.value,
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),
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]
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+ SAFETY_MODELS_ENTRIES
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+ list(EMBEDDING_MODEL_ENTRIES.values())
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)
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@ -4,11 +4,11 @@
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from collections.abc import AsyncGenerator, AsyncIterator
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from typing import Any
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from collections.abc import AsyncGenerator
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from openai import AsyncOpenAI
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from openai import NOT_GIVEN, AsyncOpenAI
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from together import AsyncTogether
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from together.constants import BASE_URL
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from llama_stack.apis.common.content_types import (
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InterleavedContent,
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@ -23,12 +23,7 @@ from llama_stack.apis.inference import (
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Inference,
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LogProbConfig,
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Message,
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OpenAIChatCompletion,
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OpenAIChatCompletionChunk,
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OpenAICompletion,
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OpenAIEmbeddingsResponse,
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OpenAIMessageParam,
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OpenAIResponseFormatParam,
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ResponseFormat,
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ResponseFormatType,
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SamplingParams,
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@ -38,18 +33,20 @@ from llama_stack.apis.inference import (
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ToolDefinition,
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ToolPromptFormat,
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)
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from llama_stack.apis.inference.inference import OpenAIEmbeddingUsage
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from llama_stack.apis.models import Model, ModelType
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from llama_stack.core.request_headers import NeedsRequestProviderData
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from llama_stack.log import get_logger
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from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
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from llama_stack.providers.utils.inference.openai_compat import (
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convert_message_to_openai_dict,
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get_sampling_options,
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prepare_openai_completion_params,
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process_chat_completion_response,
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process_chat_completion_stream_response,
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process_completion_response,
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process_completion_stream_response,
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)
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from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
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from llama_stack.providers.utils.inference.prompt_adapter import (
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chat_completion_request_to_prompt,
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completion_request_to_prompt,
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@ -59,15 +56,22 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
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)
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from .config import TogetherImplConfig
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from .models import MODEL_ENTRIES
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from .models import EMBEDDING_MODEL_ENTRIES, MODEL_ENTRIES
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logger = get_logger(name=__name__, category="inference::together")
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class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):
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class TogetherInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, NeedsRequestProviderData):
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def __init__(self, config: TogetherImplConfig) -> None:
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ModelRegistryHelper.__init__(self, MODEL_ENTRIES, config.allowed_models)
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self.config = config
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self._model_cache: dict[str, Model] = {}
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def get_api_key(self):
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return self.config.api_key.get_secret_value()
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def get_base_url(self):
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return BASE_URL
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async def initialize(self) -> None:
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pass
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@ -255,6 +259,36 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
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embeddings = [item.embedding for item in r.data]
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return EmbeddingsResponse(embeddings=embeddings)
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async def list_models(self) -> list[Model] | None:
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self._model_cache = {}
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for m in await self._get_client().models.list():
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if m.type == "embedding":
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if m.id not in EMBEDDING_MODEL_ENTRIES:
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logger.warning(f"Unknown embedding dimension for model {m.id}, skipping.")
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continue
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self._model_cache[m.id] = Model(
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provider_id=self.__provider_id__,
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provider_resource_id=EMBEDDING_MODEL_ENTRIES[m.id].provider_model_id,
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identifier=m.id,
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model_type=ModelType.embedding,
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metadata=EMBEDDING_MODEL_ENTRIES[m.id].metadata,
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)
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else:
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self._model_cache[m.id] = Model(
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provider_id=self.__provider_id__,
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provider_resource_id=m.id,
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identifier=m.id,
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model_type=ModelType.llm,
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)
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return self._model_cache.values()
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async def should_refresh_models(self) -> bool:
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return True
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async def check_model_availability(self, model):
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return model in self._model_cache
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async def openai_embeddings(
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self,
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model: str,
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@ -263,125 +297,39 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
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dimensions: int | None = None,
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user: str | None = None,
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) -> OpenAIEmbeddingsResponse:
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raise NotImplementedError()
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"""
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Together's OpenAI-compatible embeddings endpoint is not compatible with
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the standard OpenAI embeddings endpoint.
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async def openai_completion(
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self,
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model: str,
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prompt: str | list[str] | list[int] | list[list[int]],
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best_of: int | None = None,
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echo: bool | None = None,
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frequency_penalty: float | None = None,
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logit_bias: dict[str, float] | None = None,
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logprobs: bool | None = None,
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max_tokens: int | None = None,
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n: int | None = None,
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presence_penalty: float | None = None,
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seed: int | None = None,
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stop: str | list[str] | None = None,
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stream: bool | None = None,
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stream_options: dict[str, Any] | None = None,
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temperature: float | None = None,
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top_p: float | None = None,
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user: str | None = None,
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guided_choice: list[str] | None = None,
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prompt_logprobs: int | None = None,
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suffix: str | None = None,
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) -> OpenAICompletion:
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model_obj = await self.model_store.get_model(model)
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params = await prepare_openai_completion_params(
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model=model_obj.provider_resource_id,
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prompt=prompt,
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best_of=best_of,
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echo=echo,
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frequency_penalty=frequency_penalty,
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logit_bias=logit_bias,
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logprobs=logprobs,
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max_tokens=max_tokens,
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n=n,
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presence_penalty=presence_penalty,
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seed=seed,
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stop=stop,
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stream=stream,
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stream_options=stream_options,
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temperature=temperature,
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top_p=top_p,
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user=user,
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The endpoint -
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- does not return usage information
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- does not support user param, returns 400 Unrecognized request arguments supplied: user
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- does not support dimensions param, returns 400 Unrecognized request arguments supplied: dimensions
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- does not support encoding_format param, always returns floats, never base64
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"""
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# Together support ticket #13332 -> will not fix
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if user is not None:
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raise ValueError("Together's embeddings endpoint does not support user param.")
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# Together support ticket #13333 -> escalated
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if dimensions is not None:
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raise ValueError("Together's embeddings endpoint does not support dimensions param.")
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# Together support ticket #13331 -> will not fix, compute client side
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if encoding_format not in (None, NOT_GIVEN, "float"):
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raise ValueError("Together's embeddings endpoint only supports encoding_format='float'.")
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response = await self.client.embeddings.create(
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model=await self._get_provider_model_id(model),
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input=input,
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)
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return await self._get_openai_client().completions.create(**params) # type: ignore
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async def openai_chat_completion(
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self,
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model: str,
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messages: list[OpenAIMessageParam],
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frequency_penalty: float | None = None,
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function_call: str | dict[str, Any] | None = None,
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functions: list[dict[str, Any]] | None = None,
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logit_bias: dict[str, float] | None = None,
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logprobs: bool | None = None,
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max_completion_tokens: int | None = None,
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max_tokens: int | None = None,
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n: int | None = None,
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parallel_tool_calls: bool | None = None,
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presence_penalty: float | None = None,
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response_format: OpenAIResponseFormatParam | None = None,
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seed: int | None = None,
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stop: str | list[str] | None = None,
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stream: bool | None = None,
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stream_options: dict[str, Any] | None = None,
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temperature: float | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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tools: list[dict[str, Any]] | None = None,
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top_logprobs: int | None = None,
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top_p: float | None = None,
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user: str | None = None,
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) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
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model_obj = await self.model_store.get_model(model)
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params = await prepare_openai_completion_params(
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model=model_obj.provider_resource_id,
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messages=messages,
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frequency_penalty=frequency_penalty,
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function_call=function_call,
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functions=functions,
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logit_bias=logit_bias,
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logprobs=logprobs,
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max_completion_tokens=max_completion_tokens,
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max_tokens=max_tokens,
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n=n,
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parallel_tool_calls=parallel_tool_calls,
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presence_penalty=presence_penalty,
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response_format=response_format,
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seed=seed,
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stop=stop,
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stream=stream,
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stream_options=stream_options,
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temperature=temperature,
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tool_choice=tool_choice,
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tools=tools,
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top_logprobs=top_logprobs,
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top_p=top_p,
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user=user,
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)
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if params.get("stream", False):
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return self._stream_openai_chat_completion(params)
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return await self._get_openai_client().chat.completions.create(**params) # type: ignore
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response.model = model # return the user the same model id they provided, avoid exposing the provider model id
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async def _stream_openai_chat_completion(self, params: dict) -> AsyncGenerator:
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# together.ai sometimes adds usage data to the stream, even if include_usage is False
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# This causes an unexpected final chunk with empty choices array to be sent
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# to clients that may not handle it gracefully.
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include_usage = False
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if params.get("stream_options", None):
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include_usage = params["stream_options"].get("include_usage", False)
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stream = await self._get_openai_client().chat.completions.create(**params)
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# Together support ticket #13330 -> escalated
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# - togethercomputer/m2-bert-80M-32k-retrieval *does not* return usage information
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if not hasattr(response, "usage") or response.usage is None:
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logger.warning(
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f"Together's embedding endpoint for {model} did not return usage information, substituting -1s."
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)
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response.usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
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seen_finish_reason = False
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async for chunk in stream:
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# Final usage chunk with no choices that the user didn't request, so discard
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if not include_usage and seen_finish_reason and len(chunk.choices) == 0:
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break
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yield chunk
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for choice in chunk.choices:
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if choice.finish_reason:
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seen_finish_reason = True
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break
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return response
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|
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|
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@ -96,6 +96,7 @@ def skip_if_doesnt_support_n(client_with_models, model_id):
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# Error code: 400 - [{'error': {'code': 400, 'message': 'Unable to submit request because candidateCount must be 1 but
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# the entered value was 2. Update the candidateCount value and try again.', 'status': 'INVALID_ARGUMENT'}
|
||||
"remote::tgi", # TGI ignores n param silently
|
||||
"remote::together", # `n` > 1 is not supported when streaming tokens. Please disable `stream`
|
||||
):
|
||||
pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.")
|
||||
|
||||
|
|
|
|||
|
|
@ -29,9 +29,34 @@ def provider_from_model(client_with_models, model_id):
|
|||
return providers[provider_id]
|
||||
|
||||
|
||||
def skip_if_model_doesnt_support_variable_dimensions(model_id):
|
||||
if "text-embedding-3" not in model_id:
|
||||
pytest.skip("{model_id} does not support variable output embedding dimensions")
|
||||
def skip_if_model_doesnt_support_user_param(client, model_id):
|
||||
provider = provider_from_model(client, model_id)
|
||||
if provider.provider_type in (
|
||||
"remote::together", # service returns 400
|
||||
):
|
||||
pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support user param.")
|
||||
|
||||
|
||||
def skip_if_model_doesnt_support_encoding_format_base64(client, model_id):
|
||||
provider = provider_from_model(client, model_id)
|
||||
if provider.provider_type in (
|
||||
"remote::together", # param silently ignored, always returns floats
|
||||
):
|
||||
pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support encoding_format='base64'.")
|
||||
|
||||
|
||||
def skip_if_model_doesnt_support_variable_dimensions(client_with_models, model_id):
|
||||
provider = provider_from_model(client_with_models, model_id)
|
||||
if provider.provider_type in (
|
||||
"remote::together", # returns 400
|
||||
):
|
||||
pytest.skip(
|
||||
f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions."
|
||||
)
|
||||
if provider.provider_type == "remote::openai" and "text-embedding-3" not in model_id:
|
||||
pytest.skip(
|
||||
f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions."
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture(params=["openai_client", "llama_stack_client"])
|
||||
|
|
@ -92,6 +117,7 @@ def test_openai_embeddings_multiple_strings(compat_client, client_with_models, e
|
|||
response = compat_client.embeddings.create(
|
||||
model=embedding_model_id,
|
||||
input=input_texts,
|
||||
encoding_format="float",
|
||||
)
|
||||
|
||||
assert response.object == "list"
|
||||
|
|
@ -127,7 +153,7 @@ def test_openai_embeddings_with_encoding_format_float(compat_client, client_with
|
|||
def test_openai_embeddings_with_dimensions(compat_client, client_with_models, embedding_model_id):
|
||||
"""Test OpenAI embeddings endpoint with custom dimensions parameter."""
|
||||
skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
|
||||
skip_if_model_doesnt_support_variable_dimensions(embedding_model_id)
|
||||
skip_if_model_doesnt_support_variable_dimensions(client_with_models, embedding_model_id)
|
||||
|
||||
input_text = "Test dimensions parameter"
|
||||
dimensions = 16
|
||||
|
|
@ -148,6 +174,7 @@ def test_openai_embeddings_with_dimensions(compat_client, client_with_models, em
|
|||
def test_openai_embeddings_with_user_parameter(compat_client, client_with_models, embedding_model_id):
|
||||
"""Test OpenAI embeddings endpoint with user parameter."""
|
||||
skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
|
||||
skip_if_model_doesnt_support_user_param(client_with_models, embedding_model_id)
|
||||
|
||||
input_text = "Test user parameter"
|
||||
user_id = "test-user-123"
|
||||
|
|
@ -196,11 +223,13 @@ def test_openai_embeddings_different_inputs_different_outputs(compat_client, cli
|
|||
response1 = compat_client.embeddings.create(
|
||||
model=embedding_model_id,
|
||||
input=input_text1,
|
||||
encoding_format="float",
|
||||
)
|
||||
|
||||
response2 = compat_client.embeddings.create(
|
||||
model=embedding_model_id,
|
||||
input=input_text2,
|
||||
encoding_format="float",
|
||||
)
|
||||
|
||||
embedding1 = response1.data[0].embedding
|
||||
|
|
@ -214,7 +243,8 @@ def test_openai_embeddings_different_inputs_different_outputs(compat_client, cli
|
|||
def test_openai_embeddings_with_encoding_format_base64(compat_client, client_with_models, embedding_model_id):
|
||||
"""Test OpenAI embeddings endpoint with base64 encoding format."""
|
||||
skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
|
||||
skip_if_model_doesnt_support_variable_dimensions(embedding_model_id)
|
||||
skip_if_model_doesnt_support_encoding_format_base64(client_with_models, embedding_model_id)
|
||||
skip_if_model_doesnt_support_variable_dimensions(client_with_models, embedding_model_id)
|
||||
|
||||
input_text = "Test base64 encoding format"
|
||||
dimensions = 12
|
||||
|
|
@ -247,6 +277,7 @@ def test_openai_embeddings_with_encoding_format_base64(compat_client, client_wit
|
|||
def test_openai_embeddings_base64_batch_processing(compat_client, client_with_models, embedding_model_id):
|
||||
"""Test OpenAI embeddings endpoint with base64 encoding for batch processing."""
|
||||
skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
|
||||
skip_if_model_doesnt_support_encoding_format_base64(client_with_models, embedding_model_id)
|
||||
|
||||
input_texts = ["First text for base64", "Second text for base64", "Third text for base64"]
|
||||
|
||||
|
|
|
|||
800
tests/integration/recordings/responses/07c5fa34d9ca.json
Normal file
800
tests/integration/recordings/responses/07c5fa34d9ca.json
Normal file
|
|
@ -0,0 +1,800 @@
|
|||
{
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "https://api.together.xyz/v1/v1/embeddings",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "togethercomputer/m2-bert-80M-32k-retrieval",
|
||||
"input": "Test encoding format"
|
||||
},
|
||||
"endpoint": "/v1/embeddings",
|
||||
"model": "togethercomputer/m2-bert-80M-32k-retrieval"
|
||||
},
|
||||
"response": {
|
||||
"body": {
|
||||
"__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
|
||||
"__data__": {
|
||||
"data": [
|
||||
{
|
||||
"embedding": [
|
||||
-0.011256923,
|
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0.0037174695,
|
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0.047607094,
|
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-0.03605117,
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0.022678856,
|
||||
0.0022196341,
|
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0.008172763,
|
||||
-0.07876377,
|
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-0.012652523,
|
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-0.124776885,
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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-0.026567455,
|
||||
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|
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-0.078372054,
|
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|
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|
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0.07340631,
|
||||
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|
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-0.05765069,
|
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0.025966862,
|
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-0.0045753582,
|
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-0.030865112,
|
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0.039448086,
|
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-0.037273232,
|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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-0.017308103,
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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-0.030472409,
|
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-0.021670958,
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
0.009784897,
|
||||
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|
||||
-0.004051051,
|
||||
-0.014803267,
|
||||
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|
||||
-0.01848677,
|
||||
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|
||||
0.01814009,
|
||||
0.0051442874,
|
||||
-0.033988528,
|
||||
0.0033705293,
|
||||
-0.05515113,
|
||||
-0.023601055,
|
||||
-0.06183089,
|
||||
0.012501645,
|
||||
-0.08027637,
|
||||
0.022573682,
|
||||
0.079796925,
|
||||
-0.00926268,
|
||||
-0.02180816,
|
||||
0.0059841494,
|
||||
-0.018863965,
|
||||
-0.011257763,
|
||||
0.055679787,
|
||||
-0.018714463,
|
||||
-0.04081558,
|
||||
-0.017017504,
|
||||
0.026006198,
|
||||
-0.03687599,
|
||||
-0.05399378,
|
||||
0.042955294,
|
||||
0.00079697353,
|
||||
-0.0015601065,
|
||||
0.026138263,
|
||||
-0.01198548,
|
||||
0.07594801,
|
||||
-0.0049053924,
|
||||
-0.001241132,
|
||||
0.022863775,
|
||||
0.025632044,
|
||||
-0.023908222,
|
||||
-0.02252925,
|
||||
0.042020634,
|
||||
-0.060588334,
|
||||
0.05498828,
|
||||
-0.03466166,
|
||||
0.003202133,
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59
tests/integration/recordings/responses/0c1f45455d3b.json
Normal file
59
tests/integration/recordings/responses/0c1f45455d3b.json
Normal file
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@ -0,0 +1,59 @@
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800
tests/integration/recordings/responses/17030e75309f.json
Normal file
800
tests/integration/recordings/responses/17030e75309f.json
Normal file
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@ -0,0 +1,800 @@
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2352
tests/integration/recordings/responses/432a346b2ed8.json
Normal file
2352
tests/integration/recordings/responses/432a346b2ed8.json
Normal file
File diff suppressed because it is too large
Load diff
59
tests/integration/recordings/responses/4ca6152a0eb8.json
Normal file
59
tests/integration/recordings/responses/4ca6152a0eb8.json
Normal file
|
|
@ -0,0 +1,59 @@
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{
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1278
tests/integration/recordings/responses/511eb1b92e34.json
Normal file
1278
tests/integration/recordings/responses/511eb1b92e34.json
Normal file
File diff suppressed because it is too large
Load diff
46
tests/integration/recordings/responses/565b1072cb9d.json
Normal file
46
tests/integration/recordings/responses/565b1072cb9d.json
Normal file
|
|
@ -0,0 +1,46 @@
|
|||
{
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|
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}
|
||||
756
tests/integration/recordings/responses/6730dcde0b73.json
Normal file
756
tests/integration/recordings/responses/6730dcde0b73.json
Normal file
|
|
@ -0,0 +1,756 @@
|
|||
{
|
||||
"request": {
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
},
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||||
"endpoint": "/v1/chat/completions",
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"choices": [
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||||
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||||
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},
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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87
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59
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800
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"__data__": {
|
||||
"id": "oBUtdGc-62bZhn-9801a2b11e77499b",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null,
|
||||
"token_id": 128009
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"logprobs": null,
|
||||
"text": "",
|
||||
"seed": 10296991816860367000
|
||||
}
|
||||
],
|
||||
"created": 1758039042,
|
||||
"model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": null,
|
||||
"usage": {
|
||||
"completion_tokens": 20,
|
||||
"prompt_tokens": 45,
|
||||
"total_tokens": 65,
|
||||
"completion_tokens_details": null,
|
||||
"prompt_tokens_details": null,
|
||||
"cached_tokens": 0
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
}
|
||||
}
|
||||
|
|
@ -100,6 +100,14 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
|
|||
"text_model": "tgi/Qwen/Qwen3-0.6B",
|
||||
},
|
||||
),
|
||||
"together": Setup(
|
||||
name="together",
|
||||
description="Together computer models",
|
||||
defaults={
|
||||
"text_model": "together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
|
||||
"embedding_model": "together/togethercomputer/m2-bert-80M-32k-retrieval",
|
||||
},
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue