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import { email } from "https://esm.town/v/std/email";
import { OpenAI } from "https://esm.town/v/std/openai";
const openai = new OpenAI();
const completion = 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" },

- **Blob Storage**: `import { blob } from "https://esm.town/v/std/blob"`
- **SQLite**: Use Drizzle ORM instead of raw SQL
- **OpenAI**: `import { OpenAI } from "https://esm.town/v/std/openai"`
- **Email**: `import { email } from "https://esm.town/v/std/email"`

// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
import { Hono } from "npm:hono@4.4.12";
import { stream } from "npm:hono@4.4.12/streaming";
app.post("/api", async (c) => {
  const { action } = c.req.query();
  const openai = new OpenAI();
  try {
        }
        const completion = await openai.chat.completions.create({
          model: "gpt-4o",
          messages: [
          );
        const completion = await openai.chat.completions.create({
          model: "gpt-4o-mini", // Use a faster model for chat
          messages: [
        );
        const streamResponse = await openai.chat.completions.create({
          model: "gpt-4o",
          messages: [

// FINAL INTEGRATED VERSION (Parts 1-6)
import { Hono } from "npm:hono@4.4.12";
import { OpenAI } from "https://esm.town/v/std/openai";
import type { Context } from "npm:hono@4.4.12";
import { streamText } from "npm:hono@4.4.12/streaming";
// --- BACKEND SERVICES ---
const services = {
  async callOpenAI(
    systemPrompt: string,
    userContent: string | object,
      c = null,
    } = options;
    const openai = new OpenAI();
    const messages: any[] = [{ role: "system", content: systemPrompt }, {
      role: "user",
    if (isJson) requestPayload.response_format = { type: "json_object" };
    try {
      const completion = await openai.chat.completions.create(requestPayload);
      if (stream && c) {
        return streamText(c, async (s) => {
      return completion;
    } catch (e) {
      console.error(`Error calling OpenAI: ${e.message}`);
      throw new Error("AI service failed.");
    }
  }
  const userContent = `Company Context: ${JSON.stringify(company_context)}`;
  return services.callOpenAI(config.prompts.INDUSTRY_GENERATOR, userContent, {
    c,
    isJson: true,
    );
  try {
    const completion = await services.callOpenAI(
      "You are a helpful assistant following strict output rules.",
      prompt,
    },
  };
  return services.callOpenAI(config.prompts.DYNAMIC_LIST_GENERATOR, payload, {
    c,
    isJson: true,
    `Task: ${task}\n\n` +
    `MANDATE: The refined prompt MUST yield a tangible, industry-standard deliverable typical for this role and domain — e.g., structured reports, specifications, legal documents, marketing assets, or similar professional artifacts. Exclude anything conceptual or partial.`;
  return services.callOpenAI(config.prompts.PROMPT_REFINER, userContent, {
    c,
    isJson: true,
  // in case the userPrompt logic diverges more significantly later.
  if (category === "prompt_building") {
    return services.callOpenAI(systemPrompt, userPrompt, { c, stream: true });
  } else {
    return services.callOpenAI(systemPrompt, userPrompt, { c, stream: true });
  }
});
      briefing,
    );
    const criteriaCompletion = await services.callOpenAI(
      "You are a helpful assistant following strict output rules.",
      criteriaPrompt,
      raw_output: first_draft,
    };
    return services.callOpenAI(config.prompts.EVALUATOR_AGENT, userContent, {
      c,
      isJson: true,
## QA Critique
${critique}`;
  return services.callOpenAI(config.prompts.REFINER_AGENT, userContent, {
    c,
    stream: true,

import { Hono } from "npm:hono@4.4.12";
// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
// --- AI BEHAVIORAL GUIDELINES & PROMPTS ---
const app = new Hono();
const openai = new OpenAI(); // Initialized once
// Middleware to ensure OpenAI key is set
app.use("/api/*", async (c, next) => {
  if (!Deno.env.get("OPENAI_API_KEY")) {
    return c.json(
      { error: "OPENAI_API_KEY environment variable is not set." },
      500,
    );
    }
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini", // Use a fast and efficient model for this task
      messages: [
    }
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      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" },

// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
import { Hono } from "npm:hono@4.4.12";
import { stream } from "npm:hono@4.4.12/streaming";
 * @param occupationTitle - The O*NET title of the expert (e.g., "Software Developers").
 * @param context - The user's company context.
 * @returns A string representing the complete system prompt for the OpenAI API.
 */
function createDocumentPrompt(
  <header class="header">
    <h1>Tribal Knowledge Engine</h1>
    <p>AI-Powered Professional Document Generator • Powered by O*NET + OpenAI</p>
  </header>
app.post("/api", async (c) => {
  const { action } = c.req.query();
  const openai = new OpenAI();
  try {
        }
        const completion = await openai.chat.completions.create({
          model: "gpt-4o-mini",
          messages: [
        );
        const streamResponse = await openai.chat.completions.create({
          model: "gpt-4o",
          messages: [