Search

```
### OpenAI
Do not use val.town std library, import from https://esm.town/v/cricks_unmixed4u/openai-client/main.tsx
TypeScript interface for interacting with OpenAI's chat models, with optional global rate limiting, and uses Val Town's SQLite for persistent rate limit tracking.
Key Components
Message Type: Defines the structure for chat messages (role and content).
ChatOpenAI(model: string): Factory function returning an object with an invoke(messages) method. This method sends an array of messages to the specified OpenAI chat model and returns the assistant's response.
GlobalRateLimitedChatOpenAI(model: string, requestsPerSecond: number): Decorator for ChatOpenAI that enforces a global rate limit (requests per second) using a persistent SQLite table.
GlobalRateLimiter: Class that implements the rate limiting logic. It checks the number of requests in the current time window and throws an error if the limit is exceeded. It uses a table (global_rate_limit_1) in Val Town's SQLite.
ensureGlobalRateLimitTableExists: Ensures the rate limit tracking table exists in the database at startup.
Usage
Use ChatOpenAI(model) for direct, unlimited access to OpenAI chat completions.
Use GlobalRateLimitedChatOpenAI(model, requestsPerSecond) to enforce a global rate limit on chat completions, suitable for shared or public-facing endpoints.
Val Town/Platform Notes
Uses Val Town’s standard SQLite API for persistent storage.
Designed for server-side use (no browser-specific code).
No secrets are hardcoded; OpenAI API keys are managed by the OpenAI SDK/environment.
### Email

---
description: You can use openai-client when integrating vals to an LLM
globs: 
alwaysApply: false
---
TypeScript interface for interacting with OpenAI's chat models, with optional global rate limiting, and uses Val Town's SQLite for persistent rate limit tracking.
Key Components
Message Type: Defines the structure for chat messages (role and content).
ChatOpenAI(model: string): Factory function returning an object with an invoke(messages) method. This method sends an array of messages to the specified OpenAI chat model and returns the assistant's response.
GlobalRateLimitedChatOpenAI(model: string, requestsPerSecond: number): Decorator for ChatOpenAI that enforces a global rate limit (requests per second) using a persistent SQLite table.
GlobalRateLimiter: Class that implements the rate limiting logic. It checks the number of requests in the current time window and throws an error if the limit is exceeded. It uses a table (global_rate_limit_1) in Val Town's SQLite.
ensureGlobalRateLimitTableExists: Ensures the rate limit tracking table exists in the database at startup.
Usage
Use ChatOpenAI(model) for direct, unlimited access to OpenAI chat completions.
Use GlobalRateLimitedChatOpenAI(model, requestsPerSecond) to enforce a global rate limit on chat completions, suitable for shared or public-facing endpoints.
Val Town/Platform Notes
Uses Val Town’s standard SQLite API for persistent storage.
Designed for server-side use (no browser-specific code).
No secrets are hardcoded; OpenAI API keys are managed by the OpenAI SDK/environment.

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 { createClient } from "https://esm.sh/@supabase/supabase-js@2.39.3";
import { Hono } from "https://esm.sh/hono@3.11.7";
import { OpenAI } from "https://esm.sh/openai@4.28.0";
import { Resend } from "https://esm.sh/resend@3.2.0";
import { email } from "https://esm.town/v/std/email";
const supabase = createClient(SUPABASE_URL, SUPABASE_SERVICE_KEY);
// OpenAI configuration
const OPENAI_API_KEY = Deno.env.get("OPENAI_API_KEY")
  || "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";
const openai = new OpenAI({
  apiKey: OPENAI_API_KEY,
});
}
// Function to summarize transcript using OpenAI
async function summarizeTranscript(text: string) {
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [
    const summary = completion.choices[0]?.message?.content;
    if (!summary) {
      throw new Error("No summary generated by OpenAI");
    }
    console.log("Transcript summarized by OpenAI");
    console.log("OpenAI completion ID:", completion.id);
    return {
    };
  } catch (error) {
    console.error("Failed to summarize transcript with OpenAI:", error);
    throw error;
  }
  }
}
async function saveFinalReport(summary: string, email: string, openaiThreadId: string) {
  try {
    const { data, error } = await supabase
          body: summary,
          email: email,
          openai_thread_id: openaiThreadId,
        },
      ])
    }
    // Summarize transcript with OpenAI and save to final_reports
    try {
      console.log("Starting OpenAI summarization...");
      const summaryResult = await summarizeTranscript(body.text);

const html = await fetchText(
  "https://en.wikipedia.org/wiki/OpenAI",
);
const $ = load(html);

import { OpenAI } from "https://esm.town/v/std/openai";
// --- TYPE DEFINITIONS ---
  try {
    if (req.method === "POST") {
      const openai = new OpenAI();
      const body = await req.json();
      switch (action) {
        case "suggestHabit": {
          const completion = await openai.chat.completions.create({
            model: "gpt-4o",
            messages: [
        }
        case "suggestHabitSet": {
          const completion = await openai.chat.completions.create({
            model: "gpt-4o",
            messages: [
        }
        case "suggestIcons": {
          const completion = await openai.chat.completions.create({
            model: "gpt-4o",
            messages: [

- Send transcript content via email to multiple recipients
- Save all transcripts to Supabase database for persistence
- Generate AI-powered summaries using OpenAI GPT-4o-mini
- Save summaries to final reports table
- Generate secure access tokens for each report
1. **Email Delivery** - Sends the transcript to configured recipients
2. **Transcript Storage** - Saves the original transcript to the `transcripts` table
3. **AI Summarization** - Uses OpenAI GPT-4o-mini to generate a professional summary
4. **Final Report Storage** - Saves the AI-generated summary to the `final_reports` table
5. **Token Generation** - Creates a secure access token in the `pricing_wizard_report_tokens` table
3. **Configure environment variables** (optional - falls back to hardcoded keys):
   - `SUPABASE_SERVICE_KEY`
   - `OPENAI_API_KEY`
   - `RESEND_API_KEY`
4. **Test the API** with a sample message
- **Body:** The AI-generated summary of the transcript
- **Email:** The email address of the person who submitted the original transcript
- **OpenAI Thread ID:** Unique identifier from OpenAI for the completion request
- **ID:** UUID primary key (automatically generated)
- **Created At:** Timestamp of summary creation (automatically set by database)
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  email TEXT NOT NULL,
  openai_thread_id TEXT NOT NULL,
  body TEXT NOT NULL,
  created_at TIMESTAMPTZ DEFAULT NOW(),
## AI Summarization
The API uses OpenAI's GPT-4o-mini model to generate professional summaries of transcripts. The AI is prompted to:
- Focus on key points and decisions made
- Identify action items and important details
Each AI-generated summary is associated with:
- The original submitter's email address
- The unique OpenAI completion ID for traceability
### AI Configuration
- **Max Tokens:** 1000
- **Temperature:** 0.3 (for consistent, focused summaries)
- **API Key:** Configured via environment variable `OPENAI_API_KEY`
- **Completion Tracking:** Each summary includes the OpenAI completion ID for audit purposes
### Database Configuration
- **Supabase Project ID:** ffilnpatwtlzjrfbmvxk
- **Supabase Service Role Key:** Configured via environment variable `SUPABASE_SERVICE_KEY`
- **OpenAI API Key:** Configured via environment variable `OPENAI_API_KEY`
### Error Handling

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 { OpenAI } from "https://esm.town/v/std/openai";
// --- TYPE DEFINITIONS ---
}
export default async function(req: Request): Promise<Response> {
  const openai = new OpenAI();
  const url = new URL(req.url);
  const CORS_HEADERS = {
        case "synthesizeProject": {
          const synthesisContent = `Current Date: ${new Date().toISOString().split("T")[0]}\n\nGoal: ${body.goal}`;
          const completion = await openai.chat.completions.create({
            model,
            messages: [{ role: "system", content: PROJECT_SYNTHESIS_PROMPT }, {
            JSON.stringify(body.tasks, null, 2)
          }`;
          const completion = await openai.chat.completions.create({
            model,
            messages: [{ role: "system", content: DAILY_REBALANCE_PROMPT }, {
            conversation.unshift(contextMessage);
          }
          const completion = await openai.chat.completions.create({
            model,
            messages: [{ role: "system", content: CHAT_PROMPT }, ...conversation],

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" },