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Фикс функций и распределение кода по папкам (#233)
* - Перенес парсеры в папку output_parsers - Перенес преобразователи функций в utils/function_calling.py - Изменил парсеры, добавив поддержку pydantic v2 методов - Пофиксил преобразование тулов, сделав кастомный генератор JSON схемы `pydantic_generator.py`, также добавил проверку на тулы с Union[X,Y] -> теперь кидается exception при встрече такого типа, так как гигачат такое не поддерживает (убрал anyOf с Optional полями, сделал их добавления в required) - Добавил кастомные классы GigaBaseTool и метот giga_tool, позволяющий добавлять return_schema и few_shot_examples - Добавил тесты на преобразования стандартных тулов (нужно будет сделать тесты на giga_tool) - Пофиксил метод with_structured_output. Возможно нужно будет убрать json_mode * Пофиксил линтер * Пофиксил линтер
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libs/langchain_gigachat/langchain_gigachat/output_parsers/gigachat_functions.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,113 @@ | ||
| import copy | ||
| from types import GenericAlias | ||
| from typing import Any, Dict, List, Type, Union | ||
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| from langchain_core.exceptions import OutputParserException | ||
| from langchain_core.output_parsers import BaseGenerationOutputParser | ||
| from langchain_core.outputs import ChatGeneration, Generation | ||
| from pydantic import BaseModel, model_validator | ||
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| class OutputFunctionsParser(BaseGenerationOutputParser[Any]): | ||
| """Parse an output that is one of sets of values.""" | ||
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| args_only: bool = True | ||
| """Whether to only return the arguments to the function call.""" | ||
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| def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any: | ||
| generation = result[0] | ||
| if not isinstance(generation, ChatGeneration): | ||
| raise OutputParserException( | ||
| "This output parser can only be used with a chat generation." | ||
| ) | ||
| message = generation.message | ||
| try: | ||
| func_call = copy.deepcopy(message.additional_kwargs["function_call"]) | ||
| except KeyError as exc: | ||
| raise OutputParserException( | ||
| f"Could not parse function call: {exc}" | ||
| ) from exc | ||
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| if self.args_only: | ||
| return func_call["arguments"] | ||
| return func_call | ||
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| class PydanticOutputFunctionsParser(OutputFunctionsParser): | ||
| """Parse an output as a pydantic object.""" | ||
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| pydantic_schema: Union[Type[BaseModel], Dict[str, Type[BaseModel]]] | ||
| """The pydantic schema to parse the output with. | ||
| If multiple schemas are provided, then the function name will be used to | ||
| determine which schema to use. | ||
| """ | ||
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| @model_validator(mode="before") | ||
| @classmethod | ||
| def validate_schema(cls, values: dict) -> Any: | ||
| """Validate the pydantic schema. | ||
| Args: | ||
| values: The values to validate. | ||
| Returns: | ||
| The validated values. | ||
| Raises: | ||
| ValueError: If the schema is not a pydantic schema. | ||
| """ | ||
| schema = values["pydantic_schema"] | ||
| if "args_only" not in values: | ||
| values["args_only"] = ( | ||
| isinstance(schema, type) | ||
| and not isinstance(schema, GenericAlias) | ||
| and issubclass(schema, BaseModel) | ||
| ) | ||
| elif values["args_only"] and isinstance(schema, dict): | ||
| msg = ( | ||
| "If multiple pydantic schemas are provided then args_only should be" | ||
| " False." | ||
| ) | ||
| raise ValueError(msg) | ||
| return values | ||
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| def parse_result(self, result: list[Generation], *, partial: bool = False) -> Any: | ||
| """Parse the result of an LLM call to a JSON object. | ||
| Args: | ||
| result: The result of the LLM call. | ||
| partial: Whether to parse partial JSON objects. Default is False. | ||
| Returns: | ||
| The parsed JSON object. | ||
| """ | ||
| _result = super().parse_result(result) | ||
| if self.args_only: | ||
| if hasattr(self.pydantic_schema, "model_validate"): | ||
| pydantic_args = self.pydantic_schema.model_validate(_result) | ||
| else: | ||
| pydantic_args = self.pydantic_schema.parse_obj(_result) # type: ignore | ||
| else: | ||
| fn_name = _result["name"] | ||
| _args = _result["arguments"] | ||
| if isinstance(self.pydantic_schema, dict): | ||
| pydantic_schema = self.pydantic_schema[fn_name] | ||
| else: | ||
| pydantic_schema = self.pydantic_schema | ||
| if hasattr(pydantic_schema, "model_validate"): | ||
| pydantic_args = pydantic_schema.model_validate(_args) # type: ignore | ||
| else: | ||
| pydantic_args = pydantic_schema.parse_obj(_args) # type: ignore | ||
| return pydantic_args | ||
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| class PydanticAttrOutputFunctionsParser(PydanticOutputFunctionsParser): | ||
| """Parse an output as an attribute of a pydantic object.""" | ||
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| attr_name: str | ||
| """The name of the attribute to return.""" | ||
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| def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any: | ||
| result = super().parse_result(result) | ||
| return getattr(result, self.attr_name) |
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