weaviate-examples

GitHub Repository

Examples and resources for Weaviate, an open-source vector database optimized for storing and searching vector embeddings at scale.

Features

• Comprehensive Example Library: Contains a wide array of sample projects and tutorials demonstrating how to use Weaviate for different machine learning and vector search tasks.
• Semantic Search Examples: Includes semantic search through Wikipedia, wine datasets, and other text corpora using various vectorization models (SentenceBERT, Transformers, etc.).
• Multi-Modal Search: Demos for multi-modal text/image search using CLIP, allowing text-based image retrieval.
• Integration with Popular Libraries: Examples integrating Weaviate with BERT, Haystack, PyTorch-BigGraph, and more.
• Image Classification: Projects such as vegetable classification and attendance systems using image2vec-neural and OpenCV.
• Question Answering Systems: Notebooks and code for building QA systems with Weaviate and Haystack.
• NER and Spellcheck: Examples showing how to use Named Entity Recognition and spellcheck modules with Weaviate.
• Custom Vector Usage: Demonstrations on using your own vectors for search and retrieval.
• Monitoring and Operations: Setup guides for Prometheus and Grafana monitoring with Weaviate.
• Web and GUI Applications: Example web apps for movie recommendation, toxic comment classification (with Tkinter GUI), and plant information search using NodeJS and JavaScript.
• Workshops and Tutorials: Jupyter/Colab notebooks from conference workshops to help users get started with vector search and question answering in Weaviate.
• Data Profiling: Example for generating data profiles of data stored in a Weaviate cluster using pandas.
• Docker Compose Configurations: Example configurations for demo datasets and module integrations.

Category

• Curated Resource Lists

Tags

• weaviate
• examples
• resources
• vector-embeddings

Pricing

• Not applicable; this is an open-source resource repository.