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9e6561a1ec
# What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
55 lines
1.9 KiB
Python
55 lines
1.9 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 Protocol, runtime_checkable
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from llama_stack.apis.common.job_types import Job
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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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@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: SamplingParams | None = None,
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response_format: ResponseFormat | None = None,
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logprobs: LogProbConfig | None = None,
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) -> Job: ...
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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: SamplingParams | None = None,
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# zero-shot tool definitions as input to the model
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tools: list[ToolDefinition] | None = None,
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tool_choice: ToolChoice | None = ToolChoice.auto,
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tool_prompt_format: ToolPromptFormat | None = None,
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response_format: ResponseFormat | None = None,
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logprobs: LogProbConfig | None = None,
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) -> Job: ...
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