output_parsers

Create a function that helps retrieve objects from their new locations.

The goal of this function is to help users transition from deprecated imports to new imports.

The function will raise deprecation warning on loops using deprecated_lookups or fallback_module.

Module lookups will import without deprecation warnings (used to speed up imports from large namespaces like llms or chat models).

This function should ideally only be used with deprecated imports not with existing imports that are valid, as in addition to raising deprecation warnings the dynamic imports can create other issues for developers (e.g., loss of type information, IDE support for going to definition etc).

Parse the output of an LLM call to a boolean.

Combine multiple output parsers into one.

Parse the output of an LLM call to a datetime.

Parse an output that is one of a set of values.

Wrap a parser and try to fix parsing errors.

Parse an output using Pandas DataFrame format.

Parse the output of an LLM call using a regex.

Parse the output of an LLM call into a Dictionary using a regex.

Wrap a parser and try to fix parsing errors.

Does this by passing the original prompt and the completion to another LLM, and telling it the completion did not satisfy criteria in the prompt.

Wrap a parser and try to fix parsing errors.

Does this by passing the original prompt, the completion, AND the error that was raised to another language model and telling it that the completion did not work, and raised the given error. Differs from RetryOutputParser in that this implementation provides the error that was raised back to the LLM, which in theory should give it more information on how to fix it.

Schema for a response from a structured output parser.

Parse the output of an LLM call to a structured output.

Parse YAML output using a Pydantic model.