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Output parsers

Language models output text. But many times you may want to get more structured information than just text back. This is where output parsers come in.

Output parsers are classes that help structure language model responses. There are two main methods an output parser must implement:

  • "Get format instructions": A method which returns a string containing instructions for how the output of a language model should be formatted.
  • "Parse": A method which takes in a string (assumed to be the response from a language model) and parses it into some structure.

And then one optional one:

  • "Parse with prompt": A method which takes in a string (assumed to be the response from a language model) and a prompt (assumed to the prompt that generated such a response) and parses it into some structure. The prompt is largely provided in the event the OutputParser wants to retry or fix the output in some way, and needs information from the prompt to do so.

Get started​

Below we go over one useful type of output parser, the StructuredOutputParser.

Structured Output Parser​

This output parser can be used when you want to return multiple fields. If you want complex schema returned (i.e. a JSON object with arrays of strings), use the Zod Schema detailed below.

import { OpenAI } from "langchain/llms/openai";
import { PromptTemplate } from "langchain/prompts";
import { StructuredOutputParser } from "langchain/output_parsers";
import { RunnableSequence } from "langchain/schema/runnable";

const parser = StructuredOutputParser.fromNamesAndDescriptions({
  answer: "answer to the user's question",
  source: "source used to answer the user's question, should be a website.",
});

const chain = RunnableSequence.from([
  PromptTemplate.fromTemplate(
    "Answer the users question as best as possible.\n{format_instructions}\n{question}"
  ),
  new OpenAI({ temperature: 0 }),
  parser,
]);

console.log(parser.getFormatInstructions());

/*
Answer the users question as best as possible.
The output should be formatted as a JSON instance that conforms to the JSON schema below.

As an example, for the schema {{"properties": {{"foo": {{"title": "Foo", "description": "a list of strings", "type": "array", "items": {{"type": "string"}}}}}}, "required": ["foo"]}}}}
the object {{"foo": ["bar", "baz"]}} is a well-formatted instance of the schema. The object {{"properties": {{"foo": ["bar", "baz"]}}}} is not well-formatted.

Here is the output schema:
```
{"type":"object","properties":{"answer":{"type":"string","description":"answer to the user's question"},"sources":{"type":"array","items":{"type":"string"},"description":"sources used to answer the question, should be websites."}},"required":["answer","sources"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}
```

What is the capital of France?
*/

const response = await chain.invoke({
  question: "What is the capital of France?",
  format_instructions: parser.getFormatInstructions(),
});

console.log(response);
// { answer: 'Paris', source: 'https://en.wikipedia.org/wiki/Paris' }

API Reference:

Structured Output Parser with Zod Schema​

This output parser can be also be used when you want to define the output schema using Zod, a TypeScript validation library. The Zod schema passed in needs be parseable from a JSON string, so eg. z.date() is not allowed.

import { z } from "zod";
import { OpenAI } from "langchain/llms/openai";
import { PromptTemplate } from "langchain/prompts";
import { StructuredOutputParser } from "langchain/output_parsers";
import { RunnableSequence } from "langchain/schema/runnable";

// We can use zod to define a schema for the output using the `fromZodSchema` method of `StructuredOutputParser`.
const parser = StructuredOutputParser.fromZodSchema(
  z.object({
    answer: z.string().describe("answer to the user's question"),
    sources: z
      .array(z.string())
      .describe("sources used to answer the question, should be websites."),
  })
);

const chain = RunnableSequence.from([
  PromptTemplate.fromTemplate(
    "Answer the users question as best as possible.\n{format_instructions}\n{question}"
  ),
  new OpenAI({ temperature: 0 }),
  parser,
]);

console.log(parser.getFormatInstructions());

/*
Answer the users question as best as possible.
The output should be formatted as a JSON instance that conforms to the JSON schema below.

As an example, for the schema {{"properties": {{"foo": {{"title": "Foo", "description": "a list of strings", "type": "array", "items": {{"type": "string"}}}}}}, "required": ["foo"]}}}}
the object {{"foo": ["bar", "baz"]}} is a well-formatted instance of the schema. The object {{"properties": {{"foo": ["bar", "baz"]}}}} is not well-formatted.

Here is the output schema:
```
{"type":"object","properties":{"answer":{"type":"string","description":"answer to the user's question"},"sources":{"type":"array","items":{"type":"string"},"description":"sources used to answer the question, should be websites."}},"required":["answer","sources"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}
```

What is the capital of France?
*/

const response = await chain.invoke({
  question: "What is the capital of France?",
  format_instructions: parser.getFormatInstructions(),
});

console.log(response);
/*
{ answer: 'Paris', sources: [ 'https://en.wikipedia.org/wiki/Paris' ] }
*/

API Reference: