Getting Started with Weaviate Vector DB: Store & Search AI Data

Weaviate is an open-source vector database that lets you store, search, and retrieve data using the power of AI embeddings. In this tutorial, you'll learn how to set up Weaviate locally, create collections, import data, and perform both vector and hybrid searches. Whether you're building a semantic search engine, a recommendation system, or a retrieval-augmented generation (RAG) pipeline, Weaviate provides the foundation you need.

By the end of this guide, you'll have a fully functional Weaviate instance running on your machine with real data you can query.

What You'll Learn

• How to install and run Weaviate using Docker
• How to connect to Weaviate with the Python client
• How to create schemas and import data
• How to perform vector similarity search
• How to use hybrid search combining keywords and vectors

Understanding Prerequisites Before You Begin

Before diving in, make sure you have the following tools and knowledge ready:

• Docker Desktop installed and running (v20.10+)
• Python 3.9+ installed on your system
• Basic familiarity with Python programming
• A terminal or command-line interface
• An OpenAI API key (for vectorization; a free-tier key works)

No prior experience with vector databases is required. We'll explain every concept as it appears.

Understanding What a Vector Database Does

A vector database stores data as high-dimensional numerical vectors (embeddings) instead of traditional rows and columns. These embeddings capture the semantic meaning of your data, enabling searches based on similarity rather than exact keyword matches.

For example, searching for "comfortable running shoes" in a vector database would return results about "lightweight athletic footwear" even if those exact words don't appear. This is the core advantage over traditional databases.

Weaviate stands out among vector databases because it offers built-in vectorization modules, hybrid search capabilities, and a developer-friendly API. It supports multiple AI model providers including OpenAI, Cohere, and Hugging Face out of the box.

Installing and Running Weaviate with Docker

The fastest way to get Weaviate running locally is with Docker Compose. Create a new project directory and add a docker-compose.yml file.

mkdir weaviate-tutorial && cd weaviate-tutorial
touch docker-compose.yml

Paste the following configuration into your docker-compose.yml file:

yaml
version: '3.4'
services:
  weaviate:
    image: cr.weaviate.io/semitechnologies/weaviate:1.27.6
    restart: on-failure
    ports:
      - "8080:8080"
      - "50051:50051"
    environment:
      QUERY_DEFAULTS_LIMIT: 25
      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
      PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
      DEFAULT_VECTORIZER_MODULE: 'text2vec-openai'
      ENABLE_MODULES: 'text2vec-openai,generative-openai'
      OPENAI_APIKEY: '${OPENAI_

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📖 **[Read the full tutorial on AI Tutorials →](https://tutorial.gogoai.xin/tutorial/getting-started-with-weaviate-vector-db-store-search-ai-data)**

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