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### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> I built support for function calling into SK! Related to/fixes: - #2315 - #2175 - #1450 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> This implementation builds on top of all the existing pieces, but did require some major work, so feel free to comment on where that is or is not appropriate. - Added a `ChatMessage` class to capture the relevant pieces of function calling (name and content) - Added a `complete_chat_with_functions_async` into OpenAIChatCompletions class - Added a `function_call` field to ChatRequestSettings class - Added several helper functions and smaller changes - Added a sample with updated core_skill that uses function calling to demonstrate - Added a second sample that shows how to use function_calling with non-sk functions. ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [x] I didn't break anyone 😄 --------- Co-authored-by: Abby Harrison <[email protected]>
286 lines
9.4 KiB
Python
286 lines
9.4 KiB
Python
import logging
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import typing as t
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import pydantic as pdt
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import pytest
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import typing_extensions as te
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from semantic_kernel import SKFunctionBase
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from semantic_kernel.core_skills.conversation_summary_skill import (
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ConversationSummarySkill,
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)
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from semantic_kernel.core_skills.file_io_skill import FileIOSkill
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from semantic_kernel.core_skills.http_skill import HttpSkill
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from semantic_kernel.core_skills.math_skill import MathSkill
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from semantic_kernel.core_skills.text_memory_skill import TextMemorySkill
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from semantic_kernel.core_skills.text_skill import TextSkill
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from semantic_kernel.core_skills.time_skill import TimeSkill
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from semantic_kernel.core_skills.wait_skill import WaitSkill
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from semantic_kernel.core_skills.web_search_engine_skill import WebSearchEngineSkill
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from semantic_kernel.memory.null_memory import NullMemory
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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.delegate_handlers import DelegateHandlers
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from semantic_kernel.orchestration.delegate_inference import DelegateInference
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from semantic_kernel.orchestration.sk_context import SKContext
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from semantic_kernel.orchestration.sk_function import SKFunction
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from semantic_kernel.sk_pydantic import PydanticField, SKBaseModel
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from semantic_kernel.skill_definition.function_view import FunctionView
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from semantic_kernel.skill_definition.functions_view import FunctionsView
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from semantic_kernel.skill_definition.parameter_view import ParameterView
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from semantic_kernel.skill_definition.read_only_skill_collection import (
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ReadOnlySkillCollection,
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)
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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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from semantic_kernel.skill_definition.sk_function_decorator import sk_function
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from semantic_kernel.skill_definition.skill_collection import SkillCollection
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from semantic_kernel.skill_definition.skill_collection_base import SkillCollectionBase
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from semantic_kernel.template_engine.blocks.block import Block
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from semantic_kernel.template_engine.blocks.block_types import BlockTypes
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from semantic_kernel.template_engine.blocks.code_block import CodeBlock
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from semantic_kernel.template_engine.blocks.function_id_block import FunctionIdBlock
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from semantic_kernel.template_engine.blocks.text_block import TextBlock
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from semantic_kernel.template_engine.blocks.val_block import ValBlock
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from semantic_kernel.template_engine.blocks.var_block import VarBlock
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from semantic_kernel.template_engine.code_tokenizer import CodeTokenizer
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from semantic_kernel.template_engine.prompt_template_engine import PromptTemplateEngine
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from semantic_kernel.template_engine.protocols.code_renderer import CodeRenderer
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from semantic_kernel.template_engine.protocols.prompt_templating_engine import (
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PromptTemplatingEngine,
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)
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from semantic_kernel.template_engine.protocols.text_renderer import TextRenderer
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from semantic_kernel.template_engine.template_tokenizer import TemplateTokenizer
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PydanticFieldT = t.TypeVar("PydanticFieldT", bound=PydanticField)
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class _Serializable(t.Protocol):
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"""A serializable object."""
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def json(self) -> pdt.Json:
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"""Return a JSON representation of the object."""
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raise NotImplementedError
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@classmethod
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def parse_raw(cls: t.Type[te.Self], json: pdt.Json) -> te.Self:
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"""Return the constructed object from a JSON representation."""
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raise NotImplementedError
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@pytest.fixture()
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def sk_factory() -> t.Callable[[t.Type[_Serializable]], _Serializable]:
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"""Return a factory for various objects in semantic-kernel."""
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def create_functions_view() -> FunctionsView:
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"""Return a functions view."""
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result = FunctionsView()
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result.add_function(
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FunctionView(
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name="function1",
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skill_name="skill1",
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description="Native function",
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parameters=[],
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is_semantic=False,
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is_asynchronous=True,
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)
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)
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result.add_function(
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FunctionView(
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name="function1",
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skill_name="skill1",
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description="Semantic function",
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parameters=[],
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is_semantic=True,
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is_asynchronous=True,
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)
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)
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return result
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def create_sk_function() -> SKFunction:
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"""Return an SKFunction."""
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@sk_function(name="function")
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def my_function_async(cx: SKContext) -> str:
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return f"F({cx.variables.input})"
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return SKFunction.from_native_method(my_function_async)
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def create_context_variables() -> ContextVariables:
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"""Return a context variables object."""
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return ContextVariables(
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content="content",
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variables={"foo": "bar"},
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)
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def create_skill_collection() -> SkillCollection:
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"""Return a skill collection."""
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# TODO: Add a few skills to this collection.
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return SkillCollection()
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cls_obj_map = {
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Block: Block("foo"),
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CodeBlock: CodeBlock("foo"),
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FunctionIdBlock: FunctionIdBlock("bar"),
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TextBlock: TextBlock("baz"),
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ValBlock: ValBlock("qux"),
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VarBlock: VarBlock("quux"),
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CodeTokenizer: CodeTokenizer(log=logging.getLogger("test")),
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PromptTemplateEngine: PromptTemplateEngine(logger=logging.getLogger("test")),
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TemplateTokenizer: TemplateTokenizer(log=logging.getLogger("test")),
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ParameterView: ParameterView(
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name="foo",
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description="bar",
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default_value="baz",
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type="string",
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required=True,
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),
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FunctionView: FunctionView(
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"foo",
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"bar",
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"baz",
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[ParameterView(name="qux", description="bar", default_value="baz")],
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True,
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False,
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),
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FunctionsView: create_functions_view(),
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ReadOnlySkillCollection: create_skill_collection().read_only_skill_collection,
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DelegateHandlers: DelegateHandlers(),
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DelegateInference: DelegateInference(),
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ContextVariables: create_context_variables(),
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SkillCollection: create_skill_collection(),
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SKContext[NullMemory]: SKContext(
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# TODO: Test serialization with different types of memories.
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memory=NullMemory(),
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variables=create_context_variables(),
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skill_collection=create_skill_collection().read_only_skill_collection,
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),
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NullMemory: NullMemory(),
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}
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def constructor(cls: t.Type[_Serializable]) -> _Serializable:
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"""Return a serializable object."""
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return cls_obj_map[cls]
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return constructor
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PROTOCOLS = [
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pytest.param(
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ConversationSummarySkill, marks=pytest.mark.xfail(reason="Contains data")
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),
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FileIOSkill,
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HttpSkill,
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MathSkill,
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TextMemorySkill,
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TextSkill,
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TimeSkill,
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WaitSkill,
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pytest.param(WebSearchEngineSkill, marks=pytest.mark.xfail(reason="Contains data")),
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CodeRenderer,
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PromptTemplatingEngine,
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TextRenderer,
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]
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BASE_CLASSES = [
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ReadOnlySkillCollectionBase,
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SkillCollectionBase,
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SemanticTextMemoryBase,
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SKFunctionBase,
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]
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# Classes that don't need serialization
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UNSERIALIZED_CLASSES = [
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ReadOnlySkillCollection,
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]
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STATELESS_CLASSES = [
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CodeTokenizer,
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PromptTemplateEngine,
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TemplateTokenizer,
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DelegateHandlers,
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DelegateInference,
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NullMemory,
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]
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ENUMS = [
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BlockTypes,
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DelegateInference,
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]
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PYDANTIC_MODELS = [
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Block,
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CodeBlock,
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FunctionIdBlock,
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TextBlock,
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ValBlock,
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VarBlock,
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ParameterView,
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FunctionView,
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FunctionsView,
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ReadOnlySkillCollection,
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SkillCollection,
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ContextVariables,
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SKContext[NullMemory],
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pytest.param(
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SKFunction,
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marks=pytest.mark.xfail(reason="Need to implement Pickle serialization."),
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),
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]
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class TestUsageInPydanticFields:
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@pytest.mark.parametrize(
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"sk_type",
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BASE_CLASSES
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+ PROTOCOLS
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+ ENUMS
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+ PYDANTIC_MODELS
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+ STATELESS_CLASSES
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+ UNSERIALIZED_CLASSES,
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)
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def test_usage_as_optional_field(
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self,
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sk_type: t.Type[PydanticFieldT],
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) -> None:
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"""Semantic Kernel objects should be valid Pydantic fields.
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Otherwise, they cannot be used in Pydantic models.
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"""
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class TestModel(SKBaseModel):
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"""A test model."""
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field: t.Optional[sk_type] = None
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assert_serializable(TestModel(), TestModel)
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@pytest.mark.parametrize("sk_type", PYDANTIC_MODELS + STATELESS_CLASSES)
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def test_usage_as_required_field(
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self,
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sk_factory: t.Callable[[t.Type[PydanticFieldT]], PydanticFieldT],
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sk_type: t.Type[PydanticFieldT],
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) -> None:
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"""Semantic Kernel objects should be valid Pydantic fields.
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Otherwise, they cannot be used in Pydantic models.
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"""
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class TestModel(SKBaseModel):
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"""A test model."""
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field: sk_type = pdt.Field(default_factory=lambda: sk_factory(sk_type))
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assert_serializable(TestModel(), TestModel)
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assert_serializable(TestModel(field=sk_factory(sk_type)), TestModel)
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def assert_serializable(obj: _Serializable, obj_type) -> None:
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"""Assert that an object is serializable."""
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assert obj is not None
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serialized = obj.json()
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assert isinstance(serialized, str)
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deserialized = obj_type.parse_raw(serialized)
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assert deserialized == obj
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