gistfile1.txt

import { OpenAI } from 'langchain/llms/openai'
import { PromptTemplate } from 'langchain/prompts'
import { loadQARefineChain } from 'langchain/chains'
import { MemoryVectorStore } from 'langchain/vectorstores/memory'
import { OpenAIEmbeddings } from 'langchain/embeddings/openai'

import {
  StructuredOutputParser,
  OutputFixingParser,
} from 'langchain/output_parsers'
import { Document } from 'langchain/document'
import { z } from 'zod'

const parser = StructuredOutputParser.fromZodSchema(
  z.object({
    mood: z
      .string()
      .describe('the mood of the person who wrote the journal entry.'),
    subject: z.string().describe('the subject of the journal entry.')
  })
)

const getPrompt = async (content) => {
  const format_instructions = parser.getFormatInstructions()

  const prompt = new PromptTemplate({
    template:
      'Analyze the following journal entry. Follow the intrusctions and format your response to match the format instructions, no matter what! \n{format_instructions}\n{entry}',
    inputVariables: ['entry'],
    partialVariables: { format_instructions },
  })

  const input = await prompt.format({
    entry: content,
  })

  return input
}

export const analyzeEntry = async (entry) => {
  const input = await getPrompt(entry.content)
  const model = new OpenAI({ temperature: 0, modelName: 'gpt-3.5-turbo' })
  const output = await model.call(input)

  try {
    return parser.parse(output)
  } catch (e) {
    const fixParser = OutputFixingParser.fromLLM(
      new OpenAI({ temperature: 0, modelName: 'gpt-3.5-turbo' }),
      parser
    )
    const fix = await fixParser.parse(output)
    return fix
  }
}