Search

// OpenAI + Orama Strategy: Semantic search using OpenAI embeddings and Orama
// Faster than JigsawStack (~100-200ms vs ~550ms for query embeddings)
import { create, insertMultiple, search } from "npm:@orama/orama@latest";
// OpenAI embeddings function
export const generateEmbeddings = async (content: string): Promise<number[] | null> => {
  const OPENAI_API_KEY = Deno.env.get("OPENAI_API_KEY");
  const OPENAI_API_URL = "https://api.openai.com/v1/embeddings";
  
  if (!OPENAI_API_KEY) {
    console.warn("OPENAI_API_KEY not found - embeddings disabled");
    return null;
  }
  
  try {
    const response = await fetch(OPENAI_API_URL, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${OPENAI_API_KEY}`,
        "Content-Type": "application/json",
      },
    if (!response.ok) {
      const errorText = await response.text().catch(() => response.statusText);
      throw new Error(`OpenAI API error: ${response.status} ${response.statusText} - ${errorText.substring(0, 200)}`);
    }
    
  } catch (error) {
    const errorMessage = error instanceof Error ? error.message : String(error);
    console.error("OpenAI embeddings failed:", errorMessage);
    return null;
  }
export const searchStrategy: SearchStrategy = {
  name: "openai-orama",
  description: "Semantic search using OpenAI embeddings with Orama vector search (faster than JigsawStack)",
  search: async (query: string, pages: Page[], options: SearchOptions = {}): Promise<SearchResult[]> => {
    const limit = options.limit || 10;
        snippet: page ? generateSnippet(page.content, queryWords, query.toLowerCase()) : hit.document.content.substring(0, 200) + '...',
        metadata: {
          strategy: "openai-orama",
          similarity: hit.score,
          ...(enableTiming && { timings }),

// OpenAI + Cosine Similarity Strategy: Direct cosine similarity calculation
// Fastest for small datasets (<100 pages) - no DB overhead
import { generateSnippet, cosineSimilarity } from "./utils.ts";
// OpenAI embeddings function
export const generateEmbeddings = async (content: string): Promise<number[] | null> => {
  const OPENAI_API_KEY = Deno.env.get("OPENAI_API_KEY");
  const OPENAI_API_URL = "https://api.openai.com/v1/embeddings";
  
  if (!OPENAI_API_KEY) {
    console.warn("OPENAI_API_KEY not found - embeddings disabled");
    return null;
  }
  
  try {
    const response = await fetch(OPENAI_API_URL, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${OPENAI_API_KEY}`,
        "Content-Type": "application/json",
      },
    if (!response.ok) {
      const errorText = await response.text().catch(() => response.statusText);
      throw new Error(`OpenAI API error: ${response.status} ${response.statusText} - ${errorText.substring(0, 200)}`);
    }
    
  } catch (error) {
    const errorMessage = error instanceof Error ? error.message : String(error);
    console.error("OpenAI embeddings failed:", errorMessage);
    return null;
  }
export const searchStrategy: SearchStrategy = {
  name: "openai-cosine",
  description: "Semantic search using OpenAI embeddings with direct cosine similarity (fastest for small datasets)",
  search: async (query: string, pages: Page[], options: SearchOptions = {}): Promise<SearchResult[]> => {
    const limit = options.limit || 10;
      snippet: generateSnippet(page.content, queryWords, query.toLowerCase()),
      metadata: {
        strategy: "openai-cosine",
        similarity,
        ...(enableTiming && { timings }),

// Groq API Configuration
const GROQ_API_KEY = Deno.env.get("GROQ_API_KEY");
const GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions";
// Rate limiter for Groq API: 2 requests per second to avoid rate limits

## Token Counting
Token counts are calculated using [tiktoken](https://github.com/openai/tiktoken) with the `gpt-4` encoding (cl100k_base). This is the same encoding used by:
- GPT-4
- GPT-3.5-turbo
- Many other OpenAI models
Token counts are:

const urls = [
  "https://console.groq.com/docs/quickstart",
  "https://console.groq.com/docs/openai",
  "https://console.groq.com/docs/models",
  "https://console.groq.com/docs/rate-limits",

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" },

// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
// @ts-ignore
import { blob } from "https://esm.town/v/std/blob?v=11";
 *
 * Notes:
 * - Uses std/openai (Val credentials) and std/blob for persistence
 * - UI polls and triggers small "work ticks" to progress jobs
 * - Keep prompts deterministic, temperature low, JSON-only outputs
   ========================= */
const OPENAI_MODEL = "gpt-4o-mini"; // adjust as preferred
const JSON_RESPONSE_FORMAT = { type: "json_object" as const };
const EXTRACTION_CONCURRENCY_PER_TICK = 4; // 24 lenses processed over ~6 ticks
   ========================= */
const client = new OpenAI();
/** Persist and load helpers */
  const resp = await client.chat.completions.create({
    model: OPENAI_MODEL,
    temperature: 0.1,
    response_format: JSON_RESPONSE_FORMAT,
  const resp = await client.chat.completions.create({
    model: OPENAI_MODEL,
    temperature: 0.2,
    response_format: JSON_RESPONSE_FORMAT,
  const resp = await client.chat.completions.create({
    model: OPENAI_MODEL,
    temperature: 0.1,
    response_format: JSON_RESPONSE_FORMAT,
  const resp = await client.chat.completions.create({
    model: OPENAI_MODEL,
    temperature: 0.2,
    response_format: JSON_RESPONSE_FORMAT,

- **Backend**: Hono + MCP Lite + Drizzle ORM + SQLite + Cloudinary
- **Widget**: React 19 + TanStack Router + OpenAI App SDK
## Quick Start
## Message Scoping
Messages are automatically scoped using the `openai/subject` field that ChatGPT includes in request metadata. This provides authless data isolation - each subject gets its own message board.
The scoping happens in tool handlers:
```typescript
const subject = ctx.request.params._meta?.["openai/subject"];
const messages = await getMessages(subject);
```
The exact semantics of `openai/subject` are determined by ChatGPT.
## Where do I go from here?

function widgetMeta(invoking?: string, invoked?: string) {
  return {
    "openai/outputTemplate": WIDGET_URI,
    "openai/toolInvocation/invoking": invoking,
    "openai/toolInvocation/invoked": invoked,
    "openai/widgetAccessible": true,
    "openai/resultCanProduceWidget": true,
  } as const;
}
          _meta: {
            ...widgetMeta(),
            "openai/widgetCSP": {
              connect_domains: [baseUrl, "https://esm.sh", "https://cdn.jsdelivr.net"],
              resource_domains: [baseUrl, "https://esm.sh", "https://cdn.jsdelivr.net"],
          _meta: {
            ...widgetMeta(),
            "openai/widgetCSP": {
              connect_domains: [baseUrl, "https://esm.sh", "https://cdn.jsdelivr.net"],
              resource_domains: [baseUrl, "https://esm.sh", "https://cdn.jsdelivr.net"],
          _meta: {
            ...widgetMeta(),
            "openai/widgetCSP": {
              connect_domains: [baseUrl, "https://esm.sh", "https://cdn.jsdelivr.net"],
              resource_domains: [baseUrl, "https://esm.sh", "https://cdn.jsdelivr.net"],
          _meta: {
            ...widgetMeta(),
            "openai/widgetCSP": {
              connect_domains: [baseUrl, "https://esm.sh", "https://cdn.jsdelivr.net"],
              resource_domains: [baseUrl, "https://esm.sh", "https://cdn.jsdelivr.net"],
  _meta: widgetMeta("Loading messages...", "Messages loaded"),
  handler: async (args, ctx) => {
    const subject = ctx.request.params._meta?.["openai/subject"];
    if (!subject) {
      throw new Error("Missing openai/subject in request metadata");
    }
  _meta: widgetMeta("Loading message...", "Message loaded"),
  handler: async ({ id }, ctx) => {
    const subject = ctx.request.params._meta?.["openai/subject"];
    if (!subject) {
      throw new Error("Missing openai/subject in request metadata");
    }
    }
    const subject = ctx.request.params._meta?.["openai/subject"];
    if (!subject) {
      throw new Error("Missing openai/subject in request metadata");
    }
  _meta: {
    ...widgetMeta("Loading item...", "Item loaded"),
    "openai/outputTemplate": ITEM_CARD_URI,
  },
  handler: async ({ category }) => {
      _meta: {
        ...widgetMeta(),
        "openai/outputTemplate": ITEM_CARD_URI,
      },
    };
  _meta: {
    ...widgetMeta("Loading items...", "Items loaded"),
    "openai/outputTemplate": MULTI_ITEM_CARD_URI,
  },
  handler: async ({ count }) => {
      _meta: {
        ...widgetMeta(),
        "openai/outputTemplate": MULTI_ITEM_CARD_URI,
      },
    };
  _meta: {
    ...widgetMeta("Creating outfit...", "Outfit ready"),
    "openai/outputTemplate": OUTFIT_CARD_URI,
  },
  handler: async () => {
      _meta: {
        ...widgetMeta(),
        "openai/outputTemplate": OUTFIT_CARD_URI,
      },
    };
  outputSchema: CaptureItemsOutput,
  _meta: {
    "openai/toolInvocation/invoking": "Scanning & uploading…",
    "openai/toolInvocation/invoked": "Saved to your closet.",
    "openai/widgetAccessible": true,
    "openai/resultCanProduceWidget": true,
  },
  handler: async (args) => {
      structuredContent: isMultiple ? { items: added } : { item: added[0] },
      _meta: {
        "openai/outputTemplate": isMultiple ? MULTI_ITEM_CARD_URI : ITEM_CARD_URI,
        "openai/toolInvocation/invoking": "Scanning & uploading…",
        "openai/toolInvocation/invoked": "Saved to your closet.",
        "openai/widgetAccessible": true,
        "openai/resultCanProduceWidget": true,
      },
    };
  outputSchema: SuggestOutfitOutput,
  _meta: {
    "openai/outputTemplate": OUTFIT_CARD_URI,
    "openai/toolInvocation/invoking": "Picking pieces…",
    "openai/toolInvocation/invoked": "Outfit ready.",
    "openai/widgetAccessible": true,
    "openai/resultCanProduceWidget": true,
  },
  handler: async () => {
      structuredContent: { top, bottom },
      _meta: {
        "openai/outputTemplate": OUTFIT_CARD_URI,
        "openai/toolInvocation/invoking": "Picking pieces…",
        "openai/toolInvocation/invoked": "Outfit ready.",
        "openai/widgetAccessible": true,
        "openai/resultCanProduceWidget": true,
      },
    };
  }),
  _meta: {
    "openai/outputTemplate": MULTI_ITEM_CARD_URI,
    "openai/toolInvocation/invoking": "Loading closet…",
    "openai/toolInvocation/invoked": "Closet loaded.",
    "openai/widgetAccessible": true,
    "openai/resultCanProduceWidget": true,
  },
  handler: async ({ category }) => {
      structuredContent: { items },
      _meta: {
        "openai/outputTemplate": MULTI_ITEM_CARD_URI,
        "openai/toolInvocation/invoking": "Loading closet…",
        "openai/toolInvocation/invoked": "Closet loaded.",
        "openai/widgetAccessible": true,
        "openai/resultCanProduceWidget": true,
      },
    };