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65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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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 typing import List, Literal, Optional, Protocol, runtime_checkable
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from pydantic import BaseModel
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from llama_stack.apis.common.job_types import BaseJob
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from llama_stack.apis.inference import (
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InterleavedContent,
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LogProbConfig,
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Message,
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ResponseFormat,
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SamplingParams,
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ToolChoice,
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ToolDefinition,
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ToolPromptFormat,
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)
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from llama_stack.schema_utils import webmethod
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class BatchInferenceJob(BaseJob, BaseModel):
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type: Literal["batch_inference"] = "batch_inference"
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class ListBatchInferenceJobsResponse(BaseModel):
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data: list[BatchInferenceJob]
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@runtime_checkable
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class BatchInference(Protocol):
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"""Batch inference API for generating completions and chat completions.
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This is an asynchronous API. If the request is successful, the response will be a job which can be polled for completion.
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NOTE: This API is not yet implemented and is subject to change in concert with other asynchronous APIs
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including (post-training, evals, etc).
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"""
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@webmethod(route="/batch-inference/completion", method="POST")
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async def completion(
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self,
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model: str,
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content_batch: List[InterleavedContent],
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sampling_params: Optional[SamplingParams] = None,
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response_format: Optional[ResponseFormat] = None,
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logprobs: Optional[LogProbConfig] = None,
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) -> BatchInferenceJob: ...
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@webmethod(route="/batch-inference/chat-completion", method="POST")
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async def chat_completion(
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self,
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model: str,
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messages_batch: List[List[Message]],
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sampling_params: Optional[SamplingParams] = None,
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# zero-shot tool definitions as input to the model
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tools: Optional[List[ToolDefinition]] = None,
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tool_choice: Optional[ToolChoice] = ToolChoice.auto,
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tool_prompt_format: Optional[ToolPromptFormat] = None,
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response_format: Optional[ResponseFormat] = None,
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logprobs: Optional[LogProbConfig] = None,
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) -> BatchInferenceJob: ...
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