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import { OpenAI } from "https://esm.town/v/std/openai";
export default async function(req: Request): Promise<Response> {
    });
  }
  const openai = new OpenAI();
  try {
  }
  const stream = await openai.chat.completions.create(body);
  if (!body.stream) {

import { OpenAI } from "https://esm.town/v/std/openai";
export default async function(req: Request): Promise<Response> {
    });
  }
  const openai = new OpenAI();
  try {
  }
  const stream = await openai.chat.completions.create(body);
  if (!body.stream) {

    const openCageKey = Deno.env.get("OPENCAGE_API_KEY");
    const openAIKey = Deno.env.get("OPENAI_API_KEY");
    if (!openCageKey || !openAIKey) {
      return new Response(
        JSON.stringify({ error: "API keys not set in environment variables." }),
    const summary = wikiData.extract || "A place of indescribable intrigue.";
    // Prepare prompt for OpenAI
    const prompt =
      `You are Richard Ayoade from Travel Man. Write a short, dry, funny fact about this place:\n\nLocation: ${placeName}\nFact: ${summary}`;
    // Call OpenAI API for witty fact
    const openaiResp = await fetch("https://api.openai.com/v1/chat/completions", {
      method: "POST",
      headers: {
        Authorization: `Bearer ${openAIKey}`,
        "Content-Type": "application/json",
      },
    });
    if (!openaiResp.ok) {
      const errorText = await openaiResp.text();
      return new Response(
        JSON.stringify({ error: `OpenAI API error: ${errorText}` }),
        { status: 500, headers: { "Content-Type": "application/json" } },
      );
    }
    const aiData = await openaiResp.json();
    const wittyFact = aiData.choices?.[0]?.message?.content || "I couldn’t think of anything witty.";

```
### OpenAI
Do not use val.town std library, import from https://esm.town/v/cricks_unmixed4u/openai-client/main.tsx and use
## GreenPTClient
```tsx
import { GreenPTClient } from "https://esm.town/v/cricks_unmixed4u/openai-client/main.tsx";
const client = GreenPTClient("green-l");

---
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" },

const app = new Hono();
const GPT_WRAPPER_URL = 'https://esm.town/v/cricks_unmixed4u/openai-client/main.tsx';
export default async (req: Request) => {

```
### OpenAI
Do not use val.town std library, import from https://esm.town/v/cricks_unmixed4u/openai-client/main.tsx and use
## GreenPTClient
```tsx
import { GreenPTClient } from "https://esm.town/v/cricks_unmixed4u/openai-client/main.tsx";
const client = GreenPTClient("green-l");

---
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" },