AI & Content Workflows

AI workflows provide unified interfaces to text generation and embeddings with automatic provider fallback routing.

Text Generation

Workflow: ai/generate

Unified text generation with automatic fallback: Fabric API, then Gemini, then OpenAI.

fabric run ai/generate \
  --input prompt="Explain quantum computing in simple terms" \
  --input model="gemini-2.5-flash"

Input

Parameter	Type	Default	Description
`prompt`	`string`	required	Input prompt
`model`	`string`	resolved	Model identifier
`provider`	`string`	`"auto"`	Force provider: `auto`, `gemini`, `openai`, `fabric`
`system_prompt`	`string`	`""`	System instruction
`temperature`	`float`	`0.7`	Sampling temperature
`max_tokens`	`int`	model default	Max output tokens

Output

{
  "response": "Quantum computing uses quantum bits (qubits)...",
  "usage": {"input_tokens": 12, "output_tokens": 150},
  "provider_used": "gemini",
  "model_used": "gemini-2.5-flash"
}

Embeddings

Workflow: ai/embeddings

Generate vector embeddings for text. Used internally by problem intelligence clustering, hook clustering, and search.

fabric run ai/embeddings \
  --input texts='["first document", "second document"]'

Input

Parameter	Type	Default	Description
`texts`	`string` or `list[str]`	required	Text(s) to embed
`embedding_model`	`string`	`"auto"`	Model: `auto`, specific model ID

Output

{
  "embeddings": [[0.012, -0.034, ...], [0.045, 0.023, ...]],
  "model_used": "text-embedding-3-small"
}

Fallback chain: Fabric API, then OpenAI, then local sentence-transformers.

Content Generation

Workflow: content/generate

Analyzes a topic, generates structured content, and evaluates quality.

fabric run content/generate \
  --input topic="The future of remote work" \
  --input content_type="blog post" \
  --input audience="tech professionals"

Pipeline

analyze_topic → generate_content → evaluate_quality

Input

Parameter	Type	Default	Description
`topic`	`string`	required	Content topic
`audience`	`string`	`""`	Target audience
`content_type`	`string`	`"blog post"`	Format: `blog post`, `email`, `social`, `video script`

Output

{
  "analysis": {
    "key_points": [...],
    "angles": [...]
  },
  "generated_content": {
    "title": "...",
    "content": "Full article text...",
    "meta_description": "...",
    "tags": ["remote-work", "productivity"]
  },
  "quality_evaluation": {
    "score": 8.5,
    "improvements": [...]
  }
}

YouTube Workflows

Title Generation

Workflow: youtube/titles

Generates 10 viral title options with CTR estimation and style diversity.

fabric run youtube/titles \
  --input topic="AI coding assistants" \
  --input niche="tech"

Thumbnail Generation

Workflow: youtube/thumbnails

Platform-specific thumbnails with native text rendering.

fabric run youtube/thumbnails \
  --input selected_title="Why AI Can't Replace Developers" \
  --input platform="youtube"

Aspect ratios: YouTube (16:9), TikTok/Shorts (9:16), Instagram (1:1, 4:5).

Description Generation

Workflow: youtube/descriptions

Auto-generates YouTube descriptions with chapter timestamps, hashtags, and CTAs from the transcript.

fabric run youtube/descriptions \
  --input selected_title="Why AI Can't Replace Developers" \
  --input full_text="Transcribed text..."