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Decision brief
weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications.
Good fit when
- You aim to integrate vector search capabilities into your deep-learning projects and need hands-on examples to understand functionality.
- Specific code snippets or configurations of Weaviate are required but not covered in the formal documentation.
Avoid when
- Your project utilizes a different vector database that aligns better with its specific requirements, such as more customizability in indexing.
- You seek general tutorial material on deep learning without the context of Weaviate's implementation specifics.
Observed Jul 15, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (380d since push)
- As of 2d
- Provenance
- Not a fork · Organization account
- As of 2d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Weaviate. Display-only - separate from trust and ranking.
- Company
- Weaviate·GitHub org profile·1mo
- Funding
- $50,000,000 (2023-04)·GraphCanon curated seed (public press)·1mo
- Commercial model
- Open core·GraphCanon curated seed·1mo
Install
git clone https://github.com/weaviate/weaviate-examplesSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides examples for Weaviate, a comprehensive vector database designed for deep learning applications to facilitate efficient data retrieval through vector search.
Capability facts
- Languages
- html
Source: github.language · Aug 23, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 23, 2026)
| [Google Colab notebook: Getting started with the Python Client](getting-started-with-python-client-colab) | python (Google Colab) | GoogSource link
Tags
README
Weaviate examples
List of examples and tutorials of how to use the Vector Search Engine Weaviate for cool machine-learning related tasks.
Running Weaviate
- Most examples assume you have a Weaviate running. You can run one locally by following this installation guide in the documentation.
- If you need a specific vectorizer module or another ML module, it will be explained in the tutorial.
- Basic links: Documentation – Github - Slack
Examples
| Title | Language | Description |
|---|---|---|
| Semantic search through Wikipedia with the Weaviate vector search engine | GraphQL | Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine |
| PyTorch-BigGraph Wikidata search with the Weaviate vector search engine | GraphQL | Search through Facebook Research's PyTorch BigGraph Wikidata-dataset with the Weaviate vector search engine |
| Multi-Modal Text/Image search using CLIP | Bash, Javascript, React | Use text to search through images using CLIP (multi2vec-clip). Also acts as a demo on how to use Weaviate with React |
| Google Colab notebook: Getting started with the Python Client | python (Google Colab) | Google Colab notebook to learn to get started with the Python client. Contains plenty of example code. |
| Demo dataset News Publications with Contextionary | yaml | Docker-compose configuration file of Weaviate with a News Publications demo dataset. |
| Demo dataset News Publications with Transformers, NER, Spellcheck and Q&A | yaml | Docker-compose configuration file of Weaviate with a News Publications demo dataset. The vectorization is done by a text2vec-transformers module, and the spellcheck, Q&A and Named Entity Recognition module are connected. |
| Weaviate simple schema | Python | Easy example of a schema and how to upload it to Weaviate with the Python client |
| Semantic search through wine dataset | Python | Easy example to get started with Weaviate and semantic search with the Transformers module |
| Unmask Superheroes in 5 steps using the Weaviate NLP module and the Python client | Python | Super simple 5 step guide to get started with the Weaviate NLP modules. This is a basic introduction to semantic search with Weaviate and the Python client. |
| Information Retrieval with BERT (Weaviate without vectorizer module) | Python (Jupyter Notebook) | In this example we are going to use Weaviate without vectorization module, and use it as pure vector database to use a BERT transformer to vectorize text documents, then retrieve the closest ones through Weaviate's Search |
| Text search with weaviate using own vectors | Python | A basic and simple example using our own vectors(obtained using SBERT, but any other model can also be used) in weaviate |
| Harry Potter Question Answering with Haystack & Weaviate | Python (Jupyter/Colab notebook) | A demo notebook showing how to use Weaviate as DocumentStore in Haystack. |
| Vegetable classification using image2vec-neural | Python | An image classification example made using image2vec-neural and flask to classify vegetable images |
| Exploring multi2vec-clip with Python and flask | Python | This example explores the multi2vec-clip module to implem |
For agents
This page has a .md twin and JSON over the API.