Output Parser

Output Parser is a module provided by LangChain to direct the Large Language Model (LLM) to produce the output in a specified format.

There are many types of parser which can be used to tell the LLM to format in a very specific way, one of the most important features is if the output parser calls a model or not.

In most cases you would use a Parser and get the format instructions to be included in the prompt that is passed to the LLM, so that the LLM sends the output in this format, but the values that are extracted from the answer to the parser output can vary as it is completely dependent on the model.

Ideally one would want to use the parser that does the specific job, but in some cases, the JSON Output Parser didn’t help as much so we had to use a Structured Query Parser

update: 04-05-2024

While I was working on SAiC (Smart AI Chat Bot) Component I had used the pydanticV1 output parser, surprisingly gave me good results as the documentation rightly suggests

This output parser allows users to specify an arbitrary Pydantic Model and query LLMs for outputs that conform to that schema.

Keep in mind that large language models are leaky abstractions! You’ll have to use an LLM with sufficient capacity to generate well-formed JSON. In the OpenAI family, DaVinci can do reliably but Curie’s ability already drops off dramatically.

When this parser was used with OpenAI’s GPT 3.5 Turbo I had received abysmal results albeit I wasn’t much aware of Prompt Engineering and its nuances as much as I am today, when the same parser was used with the GPT 4 Turbo model, the model was able to give proper and valid json every time.

Another interesting parser would be Output Fixing Parser while using the pydantic parser if you get a wrong json schema upon validation, it takes the the wrong json output and the expected output and gives the LLM the prompt to conform the answer to the standards. which works

only the OUTPUT FORMAT!

keep in mind that the output parser only corrects the LLM response and not the generated answer itself