Intelligence Primitives Documentation

The 10 Intelligence Layer primitives provide advanced AI/ML operations for model management, cost optimization, and intelligent automation.

AI Model Management

model_primitive

Purpose: AI model management and routing Features: - Multi-provider model access - Model performance tracking - A/B testing capabilities - Fallback routing

embed_primitive

Purpose: Vector embeddings and similarity Features: - Text embeddings generation - Similarity search - Clustering operations - Semantic matching

token_primitive

Purpose: Token management and optimization Features: - Token counting across models - Cost estimation - Token optimization strategies - Usage analytics

Prompt Engineering

prompt_primitive

Purpose: Prompt engineering and templates Features: - Template management - Variable substitution - Prompt optimization - Version control

context_primitive

Purpose: Context and conversation management Features: - Context window optimization - Conversation history - Context pruning strategies - Memory management

Streaming & Processing

ai_stream_primitive

Purpose: Real-time AI streaming Features: - Streaming responses - Real-time processing - Connection management - Error recovery

ai_batch_primitive

Purpose: Batch AI processing Features: - Bulk processing optimization - Queue management - Resource allocation - Progress tracking

Cost & Performance

cost_primitive

Purpose: AI cost tracking and budget management Features: - Real-time cost tracking - Budget alerts - Cost optimization recommendations - Usage forecasting

rate_primitive

Purpose: Rate limiting and throttling Features: - API rate limiting - Adaptive throttling - Queue management - SLA compliance

ai_metric_primitive

Purpose: AI performance metrics Features: - Response time tracking - Quality metrics - Usage analytics - Performance optimization