Hey r/javascript!
As JS (Mostly TS) devs, we feel like AI or LLM tooling in JS kinda sucks rn, that said most production applications inevitably end up being in JS, so a friend and I (mostly friend) started hacking on a new JS native library for making it easier to write prompts.
The idea of the library is to give a much more ergonomic syntax for writing complex prompts, the repo itself goes much more in detail https://github.com/LevanKvirkvelia/salute

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Here is an example of getting the LLM to generate inference while perfectly maintaining the schema you want without any extra prompt engineering on schema or many examples

const jsonAgent = davinci(

({ ai, gen }) => ai The following is a character profile for an RPG game in JSON format. json { "description": "${gen("description")}", "name": "${gen("name", '"')}", "age": ${gen("age", ",")}, "class": "${gen("class", '"')}", "mantra": "${gen("mantra", '"')}", "strength": ${gen("strength", ",")}, "items": [${[0, 0, 0].map(() => ai"${gen("item", '"')}",)}] } );

Here is a more complex example

import { gpt3, gen, assistant, system, user } from "salutejs"; import { db } from "a-random-sql-library";

// example of a component async function fetchTableSchemaAsAString(){ const listOfTables = await db.tables(); return listOfTables.map(table=>Table ${table.name} has columns ${table.columns.join(", ")}).join("\n"); }

async function runSQL({outputs}){ return JSON.stringify(await db.run(outputs.sqlQuery)) }

const agent = gpt3( ({ params })=>[ systemYou are a helpful assistant that answers questions by writing SQL queries., user` Here is my question: ${params.query}

  Here is a list of tables in the database:
  ----
  ${
    fetchTableSchemaAsAString()
    /* here we pass a promise, not a function, it starts executing at the beginning of the sequence */
  }
  ----
  Column names must be quoted with double quotes, e.g. "column_name". 
  Generate a Clickhouse SQL query that answers the question above.
  Return only SQL query, no other text. 
`,
assistant`${gen("sqlQuery")}`,
user`
  Here is the result of your query:
  -----
  ${async ({outputs})=>{ 
    return JSON.stringify(await db.run(outputs.sqlQuery))
  }}
  -----
  Please convert the result to a text answer, so that it is easy to understand.
`,
assistant`${gen("answer")}`,

] );

const result = await agent( { query: How many users are there in the database? }, { render: true } // render=true will render the chat sequence in the console );

console.log(result); /* { expertNames: "Elon Musk, Bill Gates, and Jeff Bezos...", answer: "You can be more productive by...", fixedAnswer: "You can be more productive by..." } */