Weaviate Flashcards

Posted on by uplatzblog

Curious about Weaviate and why so many AI teams use it? This page provides an easy overview.
You’ll learn how this semantic search database works, its standout features, and where it fits into Retrieval-Augmented Generation (RAG).

In essence, it’s an open-source vector database that supports both structured and unstructured content.
Moreover, it integrates smoothly with embedding models from OpenAI, Cohere, and Hugging Face, making it highly flexible.
With hybrid search, you can combine keyword filters and semantic similarity for better accuracy.

Weaviate vector database architecture with semantic search and hybrid indexing
Weaviate connects embeddings, indexing, and APIs to power semantic search at scale.

For a complete AI stack, you can also integrate it with frameworks like LangChain.
See our LangChain vector store tutorial for examples.
In addition, the official docs show persistence options and cloud deployment.

Hybrid search in Weaviate: combining vector embeddings and keyword filters
Hybrid search blends semantic similarity with keyword filters for precise retrieval.

Scroll down to the flashcards to test yourself on the core concepts.
Then, follow the links at the end to practice with real code.

🧠 Weaviate Flashcards

🔍 What is Weaviate?
An open-source vector database built for semantic search and AI-native apps.
🧠 How does it support semantic search?
It retrieves with embeddings and combines results with keyword filters.
📦 What types of data can it store?
Structured, unstructured, and vectorized content like text, images, and metadata.
⚙️ Does it include vectorization?
Yes—integrates with OpenAI, Cohere, Hugging Face, or built-in modules.
🔧 What is hybrid search?
A mix of semantic similarity and keyword-based filtering.
📡 Which APIs does it offer?
Both REST and GraphQL APIs for flexible queries.
⚡ How fast is it?
It uses HNSW indexing for rapid approximate nearest-neighbour search.
💼 Is it managed or self-hosted?
Both—you can run it via Weaviate Cloud Service or host it yourself.
🔐 How secure is it?
It supports API keys, OIDC, RBAC, and TLS for secure access.
🧪 Does it handle RAG workflows?
Yes—commonly used with LLMs to provide contextual retrieval.

In practice, you can start with a simple collection, test hybrid queries, and later deploy at scale with managed services.
Furthermore, monitoring indexing speed and query recall helps you tune performance.

See our LangChain store integration guide for step-by-step examples.
For official documentation and updates, visit the
Weaviate docs.