Search

 * * A GET request to this Val will display a UI to view and search stored tags.
 * * It utilizes the LLM-Safe Fork of @std/sqlite (adjust import if necessary).
 * Assumes 'openai' secret is set in Val Town environment variables.
 * * Last Updated: 2025-05-08 (SQLite refactor and Glassmorphic UI)
 */
export default async function(req: Request) {
  // --- Dynamic Imports (Inside Handler for Val Town) ---
  const { OpenAI } = await import("https://esm.town/v/std/openai");
  const val_fetch = (await import("https://esm.town/v/std/fetch")).fetch;
  const { PDFExtract } = await import("npm:pdf.js-extract");
  }
  // --- Helper Function: Call OpenAI API (Unchanged from previous) ---
  async function callOpenAI(
    openai: OpenAI,
    systemPrompt: string,
    userMessage: string,
  ): Promise<{ role: "assistant" | "system"; content: string | object }> {
    try {
      const response = await openai.chat.completions.create({
        model,
        messages: [{ role: "system", content: systemPrompt }, { role: "user", content: userMessage }],
      return { role: "assistant", content };
    } catch (error) {
      console.error(`OpenAI call failed (ExpectJSON: ${expectJson}):`, error);
      let msg = "Error communicating with AI.";
      if (error.message) msg += ` Details: ${error.message}`;
      if ((error as any).status === 401) msg = "OpenAI Auth Error. Check your 'openai' secret in Val Town.";
      if ((error as any).status === 429) msg = "OpenAI Rate Limit Exceeded.";
      // Consider more specific error handling or re-throwing
      return { role: "system", content: msg }; // Or return an actual Error object / throw
    log: LogEntry[],
  ): Promise<{ finalLog: LogEntry[]; extractedTagsCount: number; documentIdentifier: string | null }> {
    const openai = new OpenAI(); // Ensure your OpenAI secret is set in Val Town
    log.push({ agent: "System", type: "step", message: "Workflow started." });
    log.push({ agent: "System", type: "step", message: "Extracting searchable tags with LLM..." });
    const tagAgent = "Autism Tag Extraction Agent";
    const llmResponse = await callOpenAI(openai, autismTagExtractionSystemPrompt, textToAnalyze, "gpt-4o", true);
    let extractedTags: string[] = [];
        type: "error",
        message: `LLM call failed or returned unexpected format.`,
        details: llmResponse.content, // content here will be the error message from callOpenAI
      });
    }

Note: When changing a SQLite table's schema, change the table's name (e.g., add _2 or _3) to create a fresh table.
### OpenAI
```ts
import { OpenAI } from "https://esm.town/v/std/openai";
const openai = new OpenAI();
const completion = await openai.chat.completions.create({
  messages: [
    { role: "user", content: "Say hello in a creative way" },

Note: When changing a SQLite table's schema, change the table's name (e.g., add _2 or _3) to create a fresh table.
### OpenAI
```ts
import { OpenAI } from "https://esm.town/v/std/openai";
const openai = new OpenAI();
const completion = await openai.chat.completions.create({
  messages: [
    { role: "user", content: "Say hello in a creative way" },

import { email } from "https://esm.town/v/std/email";
import { extractValInfo } from "https://esm.town/v/stevekrouse/extractValInfo";
import { OpenAI } from "npm:openai";
function stripHtmlBackticks(html: string): string {
export default async function(e: Email) {
  const openai = new OpenAI();
  console.log(`from: ${e.from} to: ${e.to} subject: ${e.subject}, cc: ${e.cc}, bcc: ${e.bcc}`);
  }
  const summary = await openai.chat.completions.create({
    messages: [
      {

Note: When changing a SQLite table's schema, change the table's name (e.g., add _2 or _3) to create a fresh table.
### OpenAI
```ts
import { OpenAI } from "https://esm.town/v/std/openai";
const openai = new OpenAI();
const completion = await openai.chat.completions.create({
  messages: [
    { role: "user", content: "Say hello in a creative way" },

Note: When changing a SQLite table's schema, change the table's name (e.g., add _2 or _3) to create a fresh table.
### OpenAI
```ts
import { OpenAI } from "https://esm.town/v/std/openai";
const openai = new OpenAI();
const completion = await openai.chat.completions.create({
  messages: [
    { role: "user", content: "Say hello in a creative way" },

Note: When changing a SQLite table's schema, change the table's name (e.g., add _2 or _3) to create a fresh table.
### OpenAI
```ts
import { OpenAI } from "https://esm.town/v/std/openai";
const openai = new OpenAI();
const completion = await openai.chat.completions.create({
  messages: [
    { role: "user", content: "Say hello in a creative way" },

  },
  {
    "title": "An Introduction to OpenAI fine-tuning",
    "slug": "an-introduction-to-openai-fine-tuning",
    "link": "/blog/an-introduction-to-openai-fine-tuning",
    "description": "How to customize OpenAI to your liking",
    "pubDate": "Fri, 25 Aug 2023 00:00:00 GMT",
    "author": "Steve Krouse",
    "slug": "val-town-newsletter-16",
    "link": "/blog/val-town-newsletter-16",
    "description": "Our seed round, growing team, Codeium completions, @std/openai, and more",
    "pubDate": "Mon, 22 Apr 2024 00:00:00 GMT",
    "author": "Steve Krouse",

/** @jsxImportSource https://esm.sh/react@18.2.0 */
import OpenAI from "https://esm.sh/openai@4.28.4";
import { createRoot } from "https://esm.sh/react-dom@18.2.0/client";
import React, { useEffect, useRef, useState } from "https://esm.sh/react@18.2.0";
  if (request.method === "POST" && new URL(request.url).pathname === "/chat") {
    try {
      const openai = new OpenAI({
        apiKey:
          "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",
      console.log("Received messages:", JSON.stringify(messages, null, 2));
      // Prepare messages for OpenAI API
      const formattedMessages = [
        { role: "system", content: CHARACTER_DESCRIPTION },
      console.log("Formatted messages:", JSON.stringify(formattedMessages, null, 2));
      const completion = await openai.chat.completions.create({
        messages: formattedMessages,
        model: "gpt-3.5-turbo",
      });
      console.log("OpenAI Response:", JSON.stringify(completion.choices[0].message, null, 2));
      return Response.json({

// Store the API key as a constant or use environment variable
const OPENAI_API_KEY =
  "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";
          : "You are an expert guide helping users accelerate their learning and mastery of skills. Provide concise, actionable insights that help people learn faster and more effectively.");
      const response = await fetch("https://api.openai.com/v1/chat/completions", {
        method: "POST",
        headers: {
          "Authorization": `Bearer ${OPENAI_API_KEY}`,
          "Content-Type": "application/json",
        },