Core Components & Concepts | Kamiwaza Docs

This is documentation for Kamiwaza 0.7.0, which is no longer actively maintained. For the current GA release, see 1.0.1.

Version: 0.7.0

Model & Data Handling

What it is: A specialized database for storing and retrieving high-dimensional vector data, such as embeddings generated from text or images. Kamiwaza integrates with industry-standard vector databases like Milvus and Qdrant.
Why it matters: Vector databases are the engine behind powerful similarity searches, which are essential for Retrieval-Augmented Generation (RAG), recommendation engines, and other advanced AI applications. Kamiwaza's abstraction layer lets you choose the right database for your needs without changing your application code.

What it is: The process of generating, storing, and managing the vector embeddings for your data. Kamiwaza provides built-in services to automate the creation of embeddings using various open-source or custom models.
Why it matters: Consistent and efficient embedding management is fundamental to the performance of any vector search-based application. By handling this automatically, Kamiwaza reduces a major source of complexity in building RAG pipelines.

What it is: A centralized inventory of all your data assets. Kamiwaza integrates with Acryl DataHub to provide a single place to discover, understand, and govern your data.
Why it matters: As AI systems grow, so does the data they consume. A data catalog provides crucial lineage tracking ("where did this data come from?") and discoverability, which is vital for enterprise governance, security, and scalability.

What it is: The "brain" of the platform that manages the flow of requests and coordinates tasks between different services. Kamiwaza uses frameworks like Ray Serve to handle this complex, distributed workload.
Why it matters: The orchestrator ensures that AI requests are processed efficiently, scaled according to demand, and routed to the correct models and services. This is the key to building resilient, production-grade AI applications.

What it is: The process of taking a trained AI model and making it available for real-time inference via an API. Kamiwaza supports multiple high-performance serving engines like vLLM, llama.cpp, and MLX.
Why it matters: Different models have different hardware needs (CPU vs. GPU). Kamiwaza's multi-engine support ensures you can run a diverse range of models and optimize for both performance and cost.

What it is: A single, unified entry point for all API requests to the platform. Kamiwaza uses FastAPI to create this gateway, which then routes requests to the appropriate internal microservice.
Why it matters: A gateway simplifies development by providing a consistent interface for all platform services. It's also the ideal place to enforce cross-cutting concerns like authentication, rate limiting, and logging.

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.

What it is: A high-speed storage layer (e.g., Redis, Valkey) that keeps frequently accessed data readily available, reducing the need to re-compute or re-fetch it from slower databases.
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.