forked from phoenix-oss/llama-stack-mirror
9487ad8294
* API Keys passed from Client instead of distro configuration * delete distribution registry * Rename the "package" word away * Introduce a "Router" layer for providers Some providers need to be factorized and considered as thin routing layers on top of other providers. Consider two examples: - The inference API should be a routing layer over inference providers, routed using the "model" key - The memory banks API is another instance where various memory bank types will be provided by independent providers (e.g., a vector store is served by Chroma while a keyvalue memory can be served by Redis or PGVector) This commit introduces a generalized routing layer for this purpose. * update `apis_to_serve` * llama_toolchain -> llama_stack * Codemod from llama_toolchain -> llama_stack - 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 * Moved some stuff out of common/; re-generated OpenAPI spec * llama-toolchain -> llama-stack (hyphens) * add control plane API * add redis adapter + sqlite provider * move core -> distribution * Some more toolchain -> stack changes * small naming shenanigans * Removing custom tool and agent utilities and moving them client side * Move control plane to distribution server for now * Remove control plane from API list * no codeshield dependency randomly plzzzzz * Add "fire" as a dependency * add back event loggers * stack configure fixes * use brave instead of bing in the example client * add init file so it gets packaged * add init files so it gets packaged * Update MANIFEST * bug fix --------- Co-authored-by: Hardik Shah <hjshah@fb.com> Co-authored-by: Xi Yan <xiyan@meta.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.
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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 enum import Enum
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from typing import Any, Dict, 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
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_stack.apis.reward_scoring import * # noqa: F403
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class FilteringFunction(Enum):
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"""The type of filtering function."""
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none = "none"
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random = "random"
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top_k = "top_k"
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top_p = "top_p"
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top_k_top_p = "top_k_top_p"
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sigmoid = "sigmoid"
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@json_schema_type
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class SyntheticDataGenerationRequest(BaseModel):
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"""Request to generate synthetic data. A small batch of prompts and a filtering function"""
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dialogs: List[Message]
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filtering_function: FilteringFunction = FilteringFunction.none
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model: Optional[str] = None
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@json_schema_type
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class SyntheticDataGenerationResponse(BaseModel):
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"""Response from the synthetic data generation. Batch of (prompt, response, score) tuples that pass the threshold."""
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synthetic_data: List[ScoredDialogGenerations]
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statistics: Optional[Dict[str, Any]] = None
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class SyntheticDataGeneration(Protocol):
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@webmethod(route="/synthetic_data_generation/generate")
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def synthetic_data_generate(
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self,
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dialogs: List[Message],
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filtering_function: FilteringFunction = FilteringFunction.none,
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model: Optional[str] = None,
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) -> Union[SyntheticDataGenerationResponse]: ...
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