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Vals using @instructor-ai/instructor

Project homepage@instructor-ai/instructor
Description from the NPM package:
structured outputs for llms

Example copied https://instructor-ai.github.io/instructor-js/#usage into val.town

You will need to fork this and properly set the apiKey and organisation for it to work.

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import Instructor from "https://esm.sh/@instructor-ai/instructor";
import OpenAI from "https://esm.sh/openai";
import { z } from "https://esm.sh/zod";
const openAISecrets = {
apiKey: getApiKey(),
organization: getOrganisationKey(),
};
const oai = new OpenAI(openAISecrets);
const client = Instructor({
client: oai,
mode: "FUNCTIONS",
});
const UserSchema = z.object({
// Description will be used in the prompt
age: z.number().describe("The age of the user"),
name: z.string(),
});
// User will be of type z.infer<typeof UserSchema>
const user = await client.chat.completions.create({
messages: [{ role: "user", content: "Jason Liu is 30 years old" }],
model: "gpt-3.5-turbo",
response_model: {
schema: UserSchema,
name: "User",
},
});
console.log(user);
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Example usage of the add_to_notion_w_ai val

Try with the money database.

Read and watch the demo run here

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/** @jsxImportSource https://esm.sh/preact */
import { blob } from "https://esm.town/v/std/blob";
import process from "node:process";
import Instructor from "npm:@instructor-ai/instructor";
import { Client } from "npm:@notionhq/client";
import OpenAI from "npm:openai";
import { render } from "npm:preact-render-to-string";
import { z } from "npm:zod";
const dbid = "DB_ID_GOES_HERE";
const NOTION_API_KEY = process.env.NOTION_API_KEY;
const notion = new Client({
auth: NOTION_API_KEY,
});
const MAGIC_AI_FILL_SYMB = "✨";
const supported_notion_props = {
"checkbox": "boolean",
"date": "date",
"multi_select": "array_enum",
"number": "number",
"rich_text": "string",
"select": "enum",
"status": "enum",
"title": "string",
"url": "string_url",
"email": "string_email",
};
const oai = new OpenAI({
apiKey: process.env.OPENAI_API_KEY ?? undefined,
});
const client = Instructor({
client: oai,
mode: "TOOLS",
});
function createPrompt(title, description, properties) {
let prompt =
"You are processing content into a database. Based on the title of the database, its properties, their types and options, and any existing descriptions, infer appropriate values for the fields:\n";
prompt += `Database Title: ${title}\n`;
if (description) {
prompt += `Database Description: ${description}\n\n`;
} else {
prompt += "\n";
}
prompt += "Properties (with types and options where applicable.):\n";
Object.keys(properties).forEach(key => {
const prop = properties[key];
prompt += `Name: ${prop.name}, Type: ${prop.type}`;
if (prop.description) {
prompt += `, Description: ${prop.description}`;
}
prompt += "\n";
if (prop.options) {
if (prop.type === "select" || prop.type === "status") {
prompt += "Options (choose one or none of these options):\n";
} else if (prop.type === "multi_select") {
prompt += "Options (choose one, multiple or none of these options):\n";
}
prop.options.forEach(option => {
prompt += ` - ${option.name}`;
if (option.description) {
prompt += `: ${option.description}`;
}
prompt += "\n";
});
}
});
prompt +=
"\nInfer and assign values to these properties based on the provided content and the aforementioned cotext.";
return prompt;
}
function processProperties(jsonObject) {
const properties = jsonObject.properties;
const filteredProps = {};
Object.keys(properties).forEach(key => {
const prop = properties[key];
const supportedType = supported_notion_props[prop.type];
if (supportedType && (prop.description?.startsWith(MAGIC_AI_FILL_SYMB) || prop.type === "title")) {
filteredProps[key] = {
name: prop.name,
type: prop.type,
description: prop.description ? prop.description.replace(MAGIC_AI_FILL_SYMB, "") : "",
};
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Uses instructor and open ai (with gpt-4-turbo) to process any content into a notion database entry.

Use addToNotion with any database id and content.

await addToNotion(
  "DB_ID_GOES_HERE",
  "CONTENT_GOES HERE"//"for example: $43.28 ordered malai kofta and kadhi (doordash) [me and mom] jan 3 2024"
);

Prompts are created based on your database name, database description, property name, property type, property description, and if applicable, property options (and their descriptions).

Supports: checkbox, date, multi_select, number, rich_text, select, status, title, url, email

  • Uses NOTION_API_KEY, OPENAI_API_KEY stored in env variables and uses Valtown blob storage to store information about the database.
  • Use get_notion_db_info to use the stored blob if exists or create one, use get_and_save_notion_db_info to create a new blob (and replace an existing one if exists).
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import { blob } from "https://esm.town/v/std/blob";
import process from "node:process";
import Instructor from "npm:@instructor-ai/instructor";
import { Client } from "npm:@notionhq/client";
import OpenAI from "npm:openai";
import { z } from "npm:zod";
const NOTION_API_KEY = process.env.NOTION_API_KEY;
const notion = new Client({
auth: NOTION_API_KEY,
});
const MAGIC_AI_FILL_SYMB = "✨";
const supported_notion_props = {
"checkbox": "boolean",
"date": "date",
"multi_select": "array_enum",
"number": "number",
"rich_text": "string",
"select": "enum",
"status": "enum",
"title": "string",
"url": "string_url",
"email": "string_email",
};
const oai = new OpenAI({
apiKey: process.env.OPENAI_API_KEY ?? undefined,
});
const client = Instructor({
client: oai,
mode: "TOOLS",
});
function createPrompt(title, description, properties) {
let prompt =
"You are processing content into a database. Based on the title of the database, its properties, their types and options, and any existing descriptions, infer appropriate values for the fields:\n";
prompt += `Database Title: ${title}\n`;
if (description) {
prompt += `Database Description: ${description}\n\n`;
} else {
prompt += "\n";
}
prompt += "Properties (with types and options where applicable.):\n";
Object.keys(properties).forEach(key => {
const prop = properties[key];
prompt += `Name: ${prop.name}, Type: ${prop.type}`;
if (prop.description) {
prompt += `, Description: ${prop.description}`;
}
prompt += "\n";
if (prop.options) {
if (prop.type === "select" || prop.type === "status") {
prompt += "Options (choose one or none of these options):\n";
} else if (prop.type === "multi_select") {
prompt += "Options (choose one, multiple or none of these options):\n";
}
prop.options.forEach(option => {
prompt += ` - ${option.name}`;
if (option.description) {
prompt += `: ${option.description}`;
}
prompt += "\n";
});
}
});
prompt +=
"\nInfer and assign values to these properties based on the provided content and the aforementioned cotext.";
return prompt;
}
function processProperties(jsonObject) {
const properties = jsonObject.properties;
const filteredProps = {};
Object.keys(properties).forEach(key => {
const prop = properties[key];
const supportedType = supported_notion_props[prop.type];
if (supportedType && (prop.description?.startsWith(MAGIC_AI_FILL_SYMB) || prop.type === "title")) {
filteredProps[key] = {
name: prop.name,
type: prop.type,
description: prop.description ? prop.description.replace(MAGIC_AI_FILL_SYMB, "") : "",
};
if (prop.type === "multi_select" || prop.type === "select" || prop.type === "status") {
filteredProps[key].options = prop[prop.type].options.map(option => ({
name: option.name,
description: option.description || "",
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