英文:
define an output schema for a nested json in langchain
问题
以下是您要翻译的内容：
"it works but I want to know if theres a better way to go about this."
英文:
Whats the recommended way to define an output schema for a nested json, the method I use doesn't feel ideal.
# adding to planner -> from langchain.experimental.plan_and_execute import load_chat_plannerrefinement_response_schemas = [ResponseSchema(name="plan", description="""{'1': {'step': '','tools': [],'data_sources': [],'sub_steps_needed': bool},'2': {'step': '','tools': [<empty list>],'data_sources': [<>], 'sub_steps_needed': bool},}"""),] #define json schema in description, works but doesn't feel properrefinement_output_parser = StructuredOutputParser.from_response_schemas(refinement_response_schemas)refinement_format_instructions = refinement_output_parser.get_format_instructions()refinement_output_parser.parse(output)
gives:
{'plan': {'1': {'step': 'Identify the top 5 strikers in La Liga','tools': [],'data_sources': ['sports websites', 'official league statistics'],'sub_steps_needed': False},'2': {'step': 'Identify the top 5 strikers in the Premier League','tools': [],'data_sources': ['sports websites', 'official league statistics'],'sub_steps_needed': False},...'6': {'step': 'Given the above steps taken, please respond to the users original question','tools': [],'data_sources': [],'sub_steps_needed': False}}}
it works but I want to know if theres a better way to go about this.
答案1
得分: 5
从我所看到的情况来看，建议的方法是使用Pydantic输出解析器，而不是结构化输出解析器... python.langchain.com/docs/modules/model_io/output_parsers/...（有关嵌套处理的说明在这里... youtube.com/watch?v=yD_oDTeObJY）。
例如：
from langchain.output_parsers import PydanticOutputParserfrom pydantic import BaseModel, Field, validatorfrom typing import List, Optional...class PlanItem(BaseModel):step: strtools: Optional[str] = []data_sources: Optional[str] = []sub_steps_needed: strclass Plan(BaseModel):plan: List[PlanItem]parser = PydanticOutputParser(pydantic_object=Plan)parser.get_format_instructions()
英文:
From what I can see the recommended approach is to use the pydantic output parser as opposed to the structured output parser... python.langchain.com/docs/modules/model_io/output_parsers/… (and dealing with nesting explained here... youtube.com/watch?v=yD_oDTeObJY).
e.g.
from langchain.output_parsers import PydanticOutputParserfrom pydantic import BaseModel, Field, validatorfrom typing import List, Optional...class PlanItem(BaseModel):step: strtools: Optional[str] = []data_sources: Optional[str] = []sub_steps_needed: strclass Plan(BaseModel):plan: List[PlanItem]parser = PydanticOutputParser(pydantic_object=Plan)parser.get_format_instructions()
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