Weaviate

intermediate

Vector database with built-in ML models.

Overview

Weaviate is an open-source vector database that combines the power of traditional databases with modern machine learning capabilities. Developed by SeMI Technologies, Weaviate stores both objects and their vector representations, enabling semantic search and AI-powered applications. Unlike traditional databases that rely on exact matches, Weaviate understands the meaning and context of data through vector embeddings, making it possible to find similar concepts even when exact keywords don't match. This Docker configuration deploys a standalone Weaviate instance with persistence enabled and anonymous access configured for development purposes. The setup exposes both REST and GraphQL APIs on port 8080, along with a high-performance gRPC interface on port 50051 for intensive operations. With the default vectorizer module set to 'none', you have complete control over how your data gets vectorized, whether through external services like OpenAI's API or self-hosted models. This stack is ideal for developers building AI applications, researchers working with semantic search, and organizations looking to implement intelligent knowledge bases. The configuration balances simplicity with functionality, providing a solid Weaviate deployment that can handle everything from prototype development to production workloads. Data scientists and ML engineers will particularly appreciate Weaviate's ability to combine vector similarity search with traditional filtering and its support for multi-modal data types.

Key Features

Built-in ML modules including text2vec-openai, text2vec-huggingface, and img2vec for automatic vectorization
Hybrid search capabilities combining vector similarity with traditional keyword-based filtering
Hierarchical Navigable Small World (HNSW) indexing for fast approximate nearest neighbor search
GraphQL API with automatic schema generation and complex query support
Multi-tenancy support allowing data isolation within a single Weaviate instance
Real-time CRUD operations with immediate vector index updates
Support for multi-modal data including text, images, and custom object types
Modular architecture with pluggable vectorizer and reader modules

Common Use Cases

1Semantic search engines that understand context and meaning rather than just keywords
2Recommendation systems for e-commerce, content platforms, or product catalogs
3Knowledge bases and FAQ systems with intelligent question-answer matching
4Document similarity detection and duplicate content identification
5Multi-modal search applications combining text and image data
6AI-powered chatbots and virtual assistants with contextual understanding
7Research and academic projects requiring semantic analysis of large text corpora

Prerequisites

Minimum 4GB RAM recommended for production use, 1GB sufficient for development
Docker and Docker Compose installed with sufficient disk space for vector indices
Port 8080 available for REST/GraphQL API access and port 50051 for gRPC
Understanding of vector embeddings and semantic search concepts
API keys for external vectorization services if using text2vec-openai or similar modules
Basic knowledge of GraphQL or REST API interaction for data operations

For development & testing. Review security settings, change default credentials, and test thoroughly before production use. See Terms

docker-compose.yml

1services: 
2  weaviate: 
3    image: semitechnologies/weaviate:latest
4    container_name: weaviate
5    restart: unless-stopped
6    environment: 
7      QUERY_DEFAULTS_LIMIT: 25
8      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: "true"
9      PERSISTENCE_DATA_PATH: /var/lib/weaviate
10      DEFAULT_VECTORIZER_MODULE: none
11      CLUSTER_HOSTNAME: node1
12    volumes: 
13      - weaviate_data:/var/lib/weaviate
14    ports: 
15      - "8080:8080"
16      - "50051:50051"
17
18volumes: 
19  weaviate_data:

.env Template

.env

1# Enable modules as needed

Usage Notes

1Docs: https://weaviate.io/developers/weaviate
2REST API at http://localhost:8080/v1, GraphQL at /v1/graphql
3gRPC at localhost:50051 for high-performance operations
4Python client: pip install weaviate-client
5Built-in vectorizers: text2vec-openai, text2vec-huggingface, etc.
6Supports hybrid search (vector + keyword) and multi-tenancy

Quick Start

terminal

1# 1. Create the compose file
2cat > docker-compose.yml << 'EOF'
3services:
4  weaviate:
5    image: semitechnologies/weaviate:latest
6    container_name: weaviate
7    restart: unless-stopped
8    environment:
9      QUERY_DEFAULTS_LIMIT: 25
10      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: "true"
11      PERSISTENCE_DATA_PATH: /var/lib/weaviate
12      DEFAULT_VECTORIZER_MODULE: none
13      CLUSTER_HOSTNAME: node1
14    volumes:
15      - weaviate_data:/var/lib/weaviate
16    ports:
17      - "8080:8080"
18      - "50051:50051"
19
20volumes:
21  weaviate_data:
22EOF
23
24# 2. Create the .env file
25cat > .env << 'EOF'
26# Enable modules as needed
27EOF
28
29# 3. Start the services
30docker compose up -d
31
32# 4. View logs
33docker compose logs -f

One-Liner

Run this command to download and set up the recipe in one step:

terminal

1curl -fsSL https://docker.recipes/api/recipes/weaviate/run | bash

Troubleshooting

Out of memory errors during large imports: Increase Docker memory allocation and consider batch processing data uploads
Slow query performance with large datasets: Check HNSW index parameters and consider increasing efConstruction and maxConnections
Module loading failures: Verify vectorizer module configuration and ensure required API keys are properly set
Connection refused on gRPC port: Ensure port 50051 is properly exposed and not blocked by firewall rules
Schema creation errors: Validate property types and ensure vectorizer module compatibility with your data structure
High memory usage during indexing: Monitor HNSW index size and consider using ef parameter tuning for memory optimization

Community Notes

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