Weaviate: Architecture and Core Concepts
Welcome to the first part of our exploration into popular vector databases, focusing on Weaviate. Weaviate is an open-source vector database that enables the storage and retrieval of vector embeddings, making it a cornerstone for modern AI applications, particularly those leveraging Retrieval Augmented Generation (RAG).
What is a Vector Database?
At its heart, a vector database is designed to store, index, and search high-dimensional vectors. These vectors are numerical representations (embeddings) of data like text, images, audio, or video, generated by machine learning models. The primary function is to perform efficient similarity searches, finding vectors that are 'close' to a query vector in a high-dimensional space.
Weaviate's Architecture: A High-Level View
Weaviate is built for speed and scalability, utilizing a distributed architecture.
Weaviate employs a distributed, horizontally scalable architecture. It separates compute and storage, allowing for independent scaling of different components. This design ensures high availability and performance even with massive datasets.
Weaviate's architecture is designed for performance and scalability. It is built on a distributed system that allows it to handle large volumes of data and high query loads. Key components include a distributed storage layer, an indexing layer for efficient vector search, and an API layer for interaction. The separation of compute and storage is a critical design choice, enabling independent scaling of resources based on demand. This distributed nature also contributes to its fault tolerance and high availability.
Core Concepts in Weaviate
Understanding Weaviate's core concepts is crucial for effective usage. These concepts define how data is organized, indexed, and queried within the database.
Schema and Classes
In Weaviate, data is organized using a schema. The schema defines 'Classes,' which are analogous to tables in relational databases or collections in NoSQL databases. Each Class has properties, defining the structure of the data it will hold. Properties can include text, numbers, booleans, dates, and importantly, vector embeddings.
Objects and Properties
Within a Class, data is stored as 'Objects.' Each Object is an instance of a Class and contains a set of 'Properties' that hold the actual data values. One of these properties is typically the vector embedding, which represents the semantic meaning of the object.
Vector Embeddings and Vectorization
Weaviate stores vector embeddings, which are numerical representations of data. These embeddings are generated by machine learning models (vectorizers). Weaviate can either store pre-computed embeddings or integrate with vectorization modules to generate them on the fly as data is ingested.
Weaviate's data model is based on a graph-like structure. Classes define the nodes (types of data), and Objects are the individual data points. Properties define the attributes of these Objects. The core of Weaviate's functionality lies in its ability to index and search these Objects based on their vector embeddings, enabling semantic similarity searches.
Text-based content
Library pages focus on text content
Indexing and Search
Weaviate uses advanced indexing techniques, such as Hierarchical Navigable Small Worlds (HNSW), to efficiently perform Approximate Nearest Neighbor (ANN) searches. This allows for rapid retrieval of the most similar vectors to a given query vector, even across millions or billions of data points.
Modules and Integrations
Weaviate supports a modular architecture, allowing for extensions and integrations. This includes modules for vectorization (e.g., OpenAI, Cohere, Hugging Face), generative search, and more. These modules simplify the process of integrating Weaviate into AI pipelines.
Weaviate in RAG Systems
In the context of Retrieval Augmented Generation (RAG), Weaviate plays a critical role as the knowledge retrieval component. When a user asks a question, the query is converted into a vector embedding. Weaviate then searches its indexed data for the most semantically similar content to the query vector. This retrieved content is then passed to a Large Language Model (LLM) to generate a more informed and contextually relevant answer.
Weaviate's ability to store and efficiently search vector embeddings makes it an ideal backend for RAG systems, providing the necessary context for LLMs.
Key Takeaways
To store, index, and search high-dimensional vector embeddings for similarity.
Classes are like tables defining data structure; Objects are individual data instances within a Class.
Hierarchical Navigable Small Worlds (HNSW).
Learning Resources
The official starting point for understanding Weaviate, covering its purpose, features, and core concepts.
A detailed breakdown of Weaviate's distributed architecture, including its components and how they interact.
Explains Weaviate's schema, classes, objects, and properties, which are fundamental to data organization.
Delves into how Weaviate indexes vector data for fast similarity searches, including details on HNSW.
An overview of Weaviate's modular system, highlighting integrations for vectorization and other AI tasks.
While not Weaviate-specific, this article provides excellent context on what vector databases are and why they are important in AI.
A comprehensive guide to RAG systems, explaining how vector databases like Weaviate fit into the architecture.
A video introduction to Weaviate, often covering its architecture and use cases in AI.
A foundational tutorial on word embeddings, which are the core data type stored and searched in vector databases.
Provides a general overview and historical context of Weaviate as an open-source vector database.