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Structured output parser

This output parser can be used when you want to return multiple fields. While the Pydantic/JSON parser is more powerful, this is useful for less powerful models.

from langchain.output_parsers import ResponseSchema, StructuredOutputParser
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
response_schemas = [
ResponseSchema(name="answer", description="answer to the user's question"),
ResponseSchema(
name="source",
description="source used to answer the user's question, should be a website.",
),
]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)

We now get a string that contains instructions for how the response should be formatted, and we then insert that into our prompt.

format_instructions = output_parser.get_format_instructions()
prompt = PromptTemplate(
template="answer the users question as best as possible.\n{format_instructions}\n{question}",
input_variables=["question"],
partial_variables={"format_instructions": format_instructions},
)
model = ChatOpenAI(temperature=0)
chain = prompt | model | output_parser
chain.invoke({"question": "what's the capital of france?"})
{'answer': 'The capital of France is Paris.',
'source': 'https://en.wikipedia.org/wiki/Paris'}
for s in chain.stream({"question": "what's the capital of france?"}):
print(s)
{'answer': 'The capital of France is Paris.', 'source': 'https://en.wikipedia.org/wiki/Paris'}

Find out api documentation for StructuredOutputParser.


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