Everything for AI app frontends and backends

import { ai } from "@/gencow/ai";

const reply = await ai.chat({
  model: "llm/economy",
  system: "You are a helpful assistant.",
  messages: chatHistory,
});

// Agent Loop (auto tool calling)
const result = await ai.agent({
  messages, tools, maxSteps: 5,
});

import { rag } from "@/gencow/rag";

// Document indexing (auto chunk + embed)
await rag.ingest(ctx, "manual.pdf", docText);

// Semantic search
const results = await rag.search(ctx,
  "What is the refund policy?",
  { filter: { source: "manual.pdf" } }
);

// Q&A — search + answer in one call
const { answer } = await rag.ask(ctx, query);

import { defineTools } from "@/gencow/tools";

const tools = defineTools(ctx, {
  getOrder: {
    description: "Look up order status",
    parameters: z.object({
      orderId: z.string()
    }),
    handler: async (ctx, args) => {
      return ctx.db.select().from(orders)
        .where(eq(orders.id, args.orderId));
    }
  }
});

await ai.chat({ messages, tools });

import { memory } from "@/gencow/memory";

// Search scoped long-term memory
const { hits } = await memory.search(ctx, {
  namespace: "assistant-memory",
  scope: { conversation: conversationId },
  query,
});

const reply = await ai.chat({
  system: "Known facts: " + JSON.stringify(hits),
  messages: [{ role: "user", content: query }],
});

import { guardrails } from "@/gencow/guardrails";

// PII masking + topic blocking
const safe = await guardrails.validateInput(
  userMsg,
  { maskPII: true, blockTopics: ["politics"] }
);
if (!safe.allowed) return safe.blocked;

// Validate input, call AI, validate output
const { result } = await guardrails.wrap(
  (text) => ai.chat({ messages: [{ role: "user", content: text }] }),
  userMsg,
  { maskPII: true },
  { maxLength: 2000 }
);

// Automatic on Gencow-hosted frontends (no code needed)
// Dashboard → Analytics tab: pageviews, referrers, countries

// Custom event (optional)
window.gencow?.analytics?.track?.("signup_completed");