Search

/**
 * OpenAI connection status
 */
export interface OpenAIConnection {
  envVar: string;
  status: ConnectionStatus;
  notion: NotionConnection;
  database: DatabaseConnection;
  openai: OpenAIConnection;
}

│   │   │   ├── blocks.ts   # Block operations
│   │   │   └── search.ts   # Search operations
│   │   ├── aiService.ts    # OpenAI for fuzzy matching
│   │   └── blobService.ts  # Val Town blob storage
│   └── utils/              # Utility functions
### Strategy 4: AI Fuzzy Matching with Date Disambiguation
If strategies 1-3 don't find a match, the system uses OpenAI (via Val Town's `@std/openai`) to match the todo text against project names and client names.
**Initial AI Match**:
- Sends the todo text and list of projects (with client names) to OpenAI (`gpt-4o-mini`)
- AI returns one of:
  - A specific project ID (if confident match to a project name)
4. The relation named in `TODOS_PROPERTIES.projects` also gets AI matching (Strategies 4-5)
**OpenAI Integration**:
- Uses Val Town's built-in OpenAI integration (`@std/openai`)
- Model: `gpt-4o-mini` (fast, cost-effective)
- AI calls per todo (when needed):

  name: {
    displayName: "Name",
    purpose: "What the todo says. May be cleaned up by AI if OpenAI is configured.",
    category: "core",
    required: true,

  }));
  // 6. Check OpenAI connection (just check if env var is set)
  const openaiApiKey = Deno.env.get('OPENAI_API_KEY');
  const openaiStatus: 'connected' | 'not_configured' = openaiApiKey ? 'connected' : 'not_configured';
  return {
        ...(databaseError ? { error: databaseError } : {})
      },
      openai: {
        envVar: 'OPENAI_API_KEY',
        status: openaiStatus
      }
    },

                </dd>
                {/* OpenAI */}
                <dt>OpenAI</dt>
                <dd>
                  <details>
                    <summary>
                      {healthData.connections.openai.status === "connected"
                        ? "OpenAI is connected"
                        : "OpenAI is not configured"}
                    </summary>
                    <article>
                        <dt>Environment variable</dt>
                        <dd>
                          <code>{healthData.connections.openai.envVar}</code>
                        </dd>
                      </dl>
                      {healthData.connections.openai.status !== "connected" && (
                        <p>
                          To enable AI summaries, add to your Val Town
                          <br />
                          <code>
                            {healthData.connections.openai.envVar}=your-api-key
                          </code>
                        </p>

  
  for (const post of search.data.posts) {
    const groqRes = await fetch("https://api.groq.com/openai/v1/chat/completions", {
      method: "POST",
      headers: { "Authorization": `Bearer ${Deno.env.get("GROQ_API_KEY")}`, "Content-Type": "application/json" },

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" },

# PineconeIndex
A simple interface for making and querying Pinecone vector databases. Use OpenAI
embeddings to vectorize and search
Create keys for
[Pinecone](https://docs.pinecone.io/guides/projects/manage-api-keys) and
[OpenAI](https://platform.openai.com/docs/api-reference/project-api-keys), and
store then in your environment variables. Then,
// set up your environment variables
const pineconeKey = Deno.env.get("PINECONE_KEY");
const modelToken = Deno.env.get("OPENAI_KEY");
const index = new PineconeIndex({
PineconeIndex also provides `handleRequest` as a convenience method to access
your indices via HTTP. This is useful when accessing the index from other vals,
especially without sharing Pinecone and OpenAI credentials.
### Server setup

import { Hono } from "npm:hono";
import { sqlite } from "https://esm.town/v/std/sqlite";
import { OpenAI } from "npm:openai";
const app = new Hono();
const TABLE_ID = "PATH_GEN_STABLE_V1";
const openai = new OpenAI();
// --- DATABASE LAYER ---
async function generatePage(path: string) {
  const topic = path.replace(/[^a-zA-Z0-9]/g, " ");
  const completion = await openai.chat.completions.create({
    messages: [
      {

import pg from "npm:pg@8.11.3";
import OpenAI from "npm:openai@4.20.1";
const { Client } = pg;
// Environment variables (set in Val.town secrets)
const OPENAI_API_KEY = Deno.env.get("OPENAI_API_KEY");
const RDS_HOST = Deno.env.get("RDS_HOST");
const RDS_USER = Deno.env.get("RDS_USER");
const EMBEDDING_MODEL = "text-embedding-ada-002";
const openai = new OpenAI({ apiKey: OPENAI_API_KEY });
async function generateQueryEmbedding(query: string): Promise<number[]> {
  const response = await openai.embeddings.create({
    model: EMBEDDING_MODEL,
    input: query,