# Documentation for Kamiwaza 0.12.0

This page describes the most important building blocks of the Kamiwaza platform, which provides model serving, governed data access, and managed application runtimes.

## Model & Data Handling

### Vector Databases
- **What it is:** A specialized database for storing and retrieving high-dimensional vector data, such as embeddings generated from text or images. Kamiwaza can integrate with vector backends such as **Milvus** and **Qdrant**.
- **Why it matters:** Vector databases are the engine behind similarity search, Retrieval-Augmented Generation (RAG), and other semantic retrieval workflows. Kamiwaza's abstraction layer lets deployments adopt supported backends without changing user-facing workflows.

### Embeddings Management
- **What it is:** The process of generating, storing, and managing vector embeddings for your data. Kamiwaza provides platform services that automate embedding generation using supported models and shared configuration.
- **Why it matters:** Consistent and efficient embedding management is fundamental to vector search quality and retrieval performance.

### Data Catalog
- **What it is:** A centralized inventory of data assets, containers, and secret metadata. Kamiwaza integrates with **DataHub**-backed catalog workflows and uses URN-based references across the platform.
- **Why it matters:** As AI systems grow, so does the data they consume. A data catalog improves discoverability, governance, and access control across models, retrieval flows, connectors, applications, and tools.

## Orchestration & Serving

### Orchestration Engine
- **What it is:** The control and execution layer that manages model launches, runtime coordination, and distributed work. Kamiwaza uses **Ray** and related serving runtimes to handle that workload.
- **Why it matters:** This layer keeps inference, background work, and runtime routing coordinated across cluster resources.

### Model Serving
- **What it is:** The process of taking a model and making it available for real-time inference via a managed API route. Kamiwaza supports multiple serving engines such as **vLLM**, **llama.cpp**, and **MLX**, depending on environment and model type.
- **Why it matters:** Different models have different hardware and runtime requirements. Multi-engine support helps the platform balance compatibility, performance, and cost.

### API Gateway
- **What it is:** A single, unified entry point for platform APIs. Kamiwaza uses **FastAPI** plus ingress routing to expose those services through a consistent customer-facing interface.
- **Why it matters:** A gateway simplifies development by providing one access surface for models, retrieval, apps, tools, logging, and administration. It is also the natural place to enforce authentication, rate limiting, and logging.

## Security & Operations

### Identity & Access Management
- **What it is:** The system that handles user authentication (who you are) and authorization (what you're allowed to do).
- **Why it matters:** Robust security is non-negotiable in an enterprise setting. Kamiwaza's IAM services ensure that only authorized users and applications can access sensitive data and models.

### Caching
- **What it is:** A high-speed storage layer used for session state, temporary runtime coordination, and frequently accessed data.
- **Why it matters:** Caching dramatically improves the performance and responsiveness of AI applications, especially those with high request volumes, leading to a better user experience and lower operational costs.