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### Motivation and Context This PR significantly reduces the complexity of implementing a new memory store. Previously, memory stores needed to implement EmbeddingIndexBase, DataStoreBase, and MemoryStoreBase. Now, memory stores only need to implement MemoryStoreBase. This PR also brings MemoryStoreBase to parity with the .NET IMemoryStore interface. ### Description - Removed `data_entry.py`, `data_store_base.py`, `volatile_data_store.py`, and `embedding_index_base.py` - Added `with_embedding` parameter to all get/search methods. Passing the embedding between the application and storage is not optional and defaults to False - `MemoryStoreBase` (Python SK) == `IMemoryStore` (.NET SK) - Updated `volatile_memory_store` to implement `MemoryStoreBase` - Removed `ABC` from `MemoryStoreBase` - Added documentation to memory files Closes https://github.com/microsoft/semantic-kernel/issues/670
200 lines
6.0 KiB
Python
200 lines
6.0 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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from abc import ABC, abstractmethod
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from logging import Logger
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from typing import TYPE_CHECKING, Callable, Optional
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from semantic_kernel.connectors.ai.complete_request_settings import (
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CompleteRequestSettings,
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)
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from semantic_kernel.connectors.ai.text_completion_client_base import (
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TextCompletionClientBase,
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)
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from semantic_kernel.memory.semantic_text_memory_base import SemanticTextMemoryBase
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from semantic_kernel.orchestration.context_variables import ContextVariables
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from semantic_kernel.orchestration.sk_context import SKContext
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from semantic_kernel.skill_definition.function_view import FunctionView
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if TYPE_CHECKING:
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from semantic_kernel.skill_definition.read_only_skill_collection_base import (
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ReadOnlySkillCollectionBase,
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)
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class SKFunctionBase(ABC):
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FUNCTION_PARAM_NAME_REGEX = r"^[0-9A-Za-z_]*$"
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FUNCTION_NAME_REGEX = r"^[0-9A-Za-z_]*$"
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SKILL_NAME_REGEX = r"^[0-9A-Za-z_]*$"
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@property
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@abstractmethod
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def name(self) -> str:
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"""
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Name of the function.
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The name is used by the skill collection and in
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prompt templates; e.g., {{skillName.functionName}}
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"""
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pass
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@property
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@abstractmethod
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def skill_name(self) -> str:
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"""
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Name of the skill that contains this function.
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The name is used by the skill collection and in
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prompt templates; e.g., {{skillName.functionName}}"""
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pass
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@property
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@abstractmethod
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def description(self) -> str:
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"""
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Function description.
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The description is used in combination with embeddings
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when searching for relevant functions."""
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pass
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@property
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@abstractmethod
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def is_semantic(self) -> bool:
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"""
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Whether the function is semantic.
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IMPORTANT: native functions might use semantic functions
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internally, so when this property is False, executing
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the function might still involve AI calls.
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"""
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pass
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@property
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@abstractmethod
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def is_native(self) -> bool:
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"""
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Whether the function is native.
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IMPORTANT: native functions might use semantic functions
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internally, so when this property is True, executing
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the function might still involve AI calls.
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"""
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pass
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@property
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@abstractmethod
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def request_settings(self) -> CompleteRequestSettings:
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"""AI service settings"""
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pass
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@abstractmethod
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def describe() -> FunctionView:
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"""
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Returns a description of the function,
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including its parameters
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Returns:
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FunctionView -- The function description.
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"""
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pass
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@abstractmethod
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def invoke(
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self,
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input: Optional[str] = None,
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variables: ContextVariables = None,
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context: Optional[SKContext] = None,
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memory: Optional[SemanticTextMemoryBase] = None,
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settings: Optional[CompleteRequestSettings] = None,
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log: Optional[Logger] = None,
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) -> SKContext:
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"""
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Invokes the function with an explicit string input
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Keyword Arguments:
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input {str} -- The explicit string input (default: {None})
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variables {ContextVariables} -- The custom input
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context {SKContext} -- The context to use
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memory: {SemanticTextMemoryBase} -- The memory to use
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settings {CompleteRequestSettings} -- LLM completion settings
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log {Logger} -- Application logger
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Returns:
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SKContext -- The updated context, potentially a new one if
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context switching is implemented.
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"""
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pass
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@abstractmethod
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async def invoke_async(
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self,
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input: Optional[str] = None,
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variables: ContextVariables = None,
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context: Optional[SKContext] = None,
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memory: Optional[SemanticTextMemoryBase] = None,
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settings: Optional[CompleteRequestSettings] = None,
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log: Optional[Logger] = None,
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) -> SKContext:
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"""
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Invokes the function with an explicit string input
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Keyword Arguments:
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input {str} -- The explicit string input (default: {None})
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variables {ContextVariables} -- The custom input
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context {SKContext} -- The context to use
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memory: {SemanticTextMemoryBase} -- The memory to use
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settings {CompleteRequestSettings} -- LLM completion settings
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log {Logger} -- Application logger
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Returns:
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SKContext -- The updated context, potentially a new one if
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context switching is implemented.
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"""
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pass
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@abstractmethod
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def set_default_skill_collection(
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self,
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skills: "ReadOnlySkillCollectionBase",
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) -> "SKFunctionBase":
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"""
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Sets the skill collection to use when the function is
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invoked without a context or with a context that doesn't have
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a skill collection
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Arguments:
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skills {ReadOnlySkillCollectionBase} -- Kernel's skill collection
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Returns:
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SKFunctionBase -- The function instance
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"""
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pass
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@abstractmethod
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def set_ai_service(
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self, service_factory: Callable[[], TextCompletionClientBase]
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) -> "SKFunctionBase":
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"""
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Sets the AI service used by the semantic function, passing in a factory
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method. The factory allows us to lazily instantiate the client and to
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properly handle its disposal
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Arguments:
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service_factory -- AI service factory
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Returns:
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SKFunctionBase -- The function instance
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"""
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pass
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@abstractmethod
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def set_ai_configuration(
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self, settings: CompleteRequestSettings
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) -> "SKFunctionBase":
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"""
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Sets the AI completion settings used with LLM requests
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Arguments:
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settings {CompleteRequestSettings} -- LLM completion settings
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Returns:
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SKFunctionBase -- The function instance
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"""
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pass
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