mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-10-04 12:07:34 +00:00
76b354a081
- added providers/registry - cleaned up api/ subdirectories and moved impls away - restructured api/api.py - from llama_stack.apis.<api> import foo should work now - update imports to do llama_stack.apis.<api> - update many other imports - added __init__, fixed some registry imports - updated registry imports - create_agentic_system -> create_agent - AgenticSystem -> Agent
71 lines
2.4 KiB
Python
71 lines
2.4 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, Optional, Protocol
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from llama_models.schema_utils import json_schema_type, webmethod
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from pydantic import BaseModel, Field
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_stack.apis.inference import * # noqa: F403
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@json_schema_type
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class BatchCompletionRequest(BaseModel):
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model: str
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content_batch: List[InterleavedTextMedia]
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sampling_params: Optional[SamplingParams] = SamplingParams()
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logprobs: Optional[LogProbConfig] = None
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@json_schema_type
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class BatchCompletionResponse(BaseModel):
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completion_message_batch: List[CompletionMessage]
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@json_schema_type
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class BatchChatCompletionRequest(BaseModel):
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model: str
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messages_batch: List[List[Message]]
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sampling_params: Optional[SamplingParams] = SamplingParams()
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# zero-shot tool definitions as input to the model
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tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
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tool_choice: Optional[ToolChoice] = Field(default=ToolChoice.auto)
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tool_prompt_format: Optional[ToolPromptFormat] = Field(
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default=ToolPromptFormat.json
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)
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logprobs: Optional[LogProbConfig] = None
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@json_schema_type
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class BatchChatCompletionResponse(BaseModel):
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completion_message_batch: List[CompletionMessage]
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class BatchInference(Protocol):
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@webmethod(route="/batch_inference/completion")
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async def batch_completion(
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self,
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model: str,
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content_batch: List[InterleavedTextMedia],
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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logprobs: Optional[LogProbConfig] = None,
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) -> BatchCompletionResponse: ...
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@webmethod(route="/batch_inference/chat_completion")
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async def batch_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] = SamplingParams(),
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# zero-shot tool definitions as input to the model
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tools: Optional[List[ToolDefinition]] = list,
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tool_choice: Optional[ToolChoice] = ToolChoice.auto,
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tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
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logprobs: Optional[LogProbConfig] = None,
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) -> BatchChatCompletionResponse: ...
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