forked from phoenix-oss/llama-stack-mirror
5712566061
* [1/n] migrate inference/chat_completion * migrate inference/completion * inference/completion * inference regenerate openapi spec * safety api * migrate agentic system * migrate apis without implementations * re-generate openapi spec * remove hack from openapi generator * fix inference * fix inference * openapi generator rerun * Simplified Telemetry API and tying it to logger (#57) * Simplified Telemetry API and tying it to logger * small update which adds a METRIC type * move span events one level down into structured log events --------- Co-authored-by: Ashwin Bharambe <ashwin@meta.com> * fix api to work with openapi generator * fix agentic calling inference * together adapter inference * update inference adapters --------- Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com> Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the terms described in the LICENSE file in
|
|
# the root directory of this source tree.
|
|
|
|
from enum import Enum
|
|
|
|
from typing import Any, Dict, List, Optional, Protocol
|
|
|
|
from llama_models.schema_utils import json_schema_type, webmethod
|
|
|
|
from pydantic import BaseModel
|
|
|
|
from llama_models.llama3.api.datatypes import * # noqa: F403
|
|
from llama_toolchain.reward_scoring.api import * # noqa: F403
|
|
|
|
|
|
class FilteringFunction(Enum):
|
|
"""The type of filtering function."""
|
|
|
|
none = "none"
|
|
random = "random"
|
|
top_k = "top_k"
|
|
top_p = "top_p"
|
|
top_k_top_p = "top_k_top_p"
|
|
sigmoid = "sigmoid"
|
|
|
|
|
|
@json_schema_type
|
|
class SyntheticDataGenerationRequest(BaseModel):
|
|
"""Request to generate synthetic data. A small batch of prompts and a filtering function"""
|
|
|
|
dialogs: List[Message]
|
|
filtering_function: FilteringFunction = FilteringFunction.none
|
|
model: Optional[str] = None
|
|
|
|
|
|
@json_schema_type
|
|
class SyntheticDataGenerationResponse(BaseModel):
|
|
"""Response from the synthetic data generation. Batch of (prompt, response, score) tuples that pass the threshold."""
|
|
|
|
synthetic_data: List[ScoredDialogGenerations]
|
|
statistics: Optional[Dict[str, Any]] = None
|
|
|
|
|
|
class SyntheticDataGeneration(Protocol):
|
|
@webmethod(route="/synthetic_data_generation/generate")
|
|
def synthetic_data_generate(
|
|
self,
|
|
dialogs: List[Message],
|
|
filtering_function: FilteringFunction = FilteringFunction.none,
|
|
model: Optional[str] = None,
|
|
) -> Union[SyntheticDataGenerationResponse]: ...
|