Retry parser | Документация для разработчиков

Retry parser

Обновлено 24 мая 2024

While in some cases it is possible to fix any parsing mistakes by only looking at the output, in other cases it can't. An example of this is when the output is not just in the incorrect format, but is partially complete. Consider the below example.

from langchain.output_parsers import (
from langchain.prompts import (
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI, OpenAI
template = """Based on the user question, provide an Action and Action Input for what step should be taken.
Question: {query}

class Action(BaseModel):
action: str = Field(description="action to take")
action_input: str = Field(description="input to the action")

parser = PydanticOutputParser(pydantic_object=Action)
prompt = PromptTemplate(
template="Answer the user query.\n{format_instructions}\n{query}\n",
partial_variables={"format_instructions": parser.get_format_instructions()},
prompt_value = prompt.format_prompt(query="who is leo di caprios gf?")
bad_response = '{"action": "search"}'

If we try to parse this response as is, we will get an error


If we try to use the OutputFixingParser to fix this error, it will be confused - namely, it doesn't know what to actually put for action input.

fix_parser = OutputFixingParser.from_llm(parser=parser, llm=ChatOpenAI())
    Action(action='search', action_input='input')

Instead, we can use the RetryOutputParser, which passes in the prompt (as well as the original output) to try again to get a better response.

from langchain.output_parsers import RetryOutputParser
retry_parser = RetryOutputParser.from_llm(parser=parser, llm=OpenAI(temperature=0))
retry_parser.parse_with_prompt(bad_response, prompt_value)
    Action(action='search', action_input='leo di caprio girlfriend')

We can also add the RetryOutputParser easily with a custom chain which transform the raw LLM/ChatModel output into a more workable format.

from langchain_core.runnables import RunnableLambda, RunnableParallel

completion_chain = prompt | OpenAI(temperature=0)

main_chain = RunnableParallel(
completion=completion_chain, prompt_value=prompt
) | RunnableLambda(lambda x: retry_parser.parse_with_prompt(**x))

main_chain.invoke({"query": "who is leo di caprios gf?"})
    Action(action='search', action_input='leo di caprio girlfriend')
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