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@twick/ai-models

v0.15.24

Published

Provider adapters and orchestration types for Twick generative AI integration

Readme

@twick/ai-models

Provider adapters and orchestration primitives for Twick generative AI integrations.

What is included

  1. Provider adapter interface for generation workflows (caption, voice, avatar, media)
  2. Multi-provider orchestration with unified job status and fallback
  3. Job store interface with in-memory implementation
  4. Timeline injection helpers to normalize provider output into patch contracts
  5. Caption normalization helpers for legacy cloud payloads
  6. Types for ModelInfo, AIModelProvider, and patch/result DTOs (apps supply model lists via services)

Core API

import {
  ProviderRegistry,
  GenerationOrchestrator,
  InMemoryJobStore,
  type ProviderAdapter,
  toTimelinePatch,
} from "@twick/ai-models";

const registry = new ProviderRegistry();
const voiceAdapter: ProviderAdapter = /* your implementation */;
const avatarAdapter: ProviderAdapter = /* your implementation */;

registry.registerAdapter(voiceAdapter);
registry.registerAdapter(avatarAdapter);
registry.setProviderConfig({ provider: "your-provider", apiKey: process.env.API_KEY });

const orchestrator = new GenerationOrchestrator(registry, new InMemoryJobStore());

const voiceJob = await orchestrator.createJob({
  type: "voice",
  provider: "your-provider",
  fallbackProviders: [],
  input: {
    text: "Welcome to this lesson.",
    language: "en-US",
    voiceId: "narrator-1",
  },
});

const completed = await orchestrator.dispatch(voiceJob.id);
const patch = toTimelinePatch(completed);

Why this matters

This provides a provider-agnostic backend layer so Twick apps can:

  1. Integrate different caption/voice/avatar/media APIs behind one contract
  2. Add fallback between providers without changing app-level flow
  3. Keep timeline/caption/export pipeline stable while swapping models
  4. Feed normalized patch data into @twick/workflow for project/timeline application

Notes

  • Implement ProviderAdapter for each production provider (e.g. Sora, HeyGen, ElevenLabs).
  • GenerationOrchestrator is backend-oriented and can run in cloud functions or Node services.