| name | weaviate |
| description | Weaviate — open-source vector database with built-in ML. Hybrid search (vector + keyword), generative search, graph connections, multi-modal (text + image), and automatic schema inference. |
| tags | ["vector-database","hybrid-search","rag-retrieval","embedding-indexes","weaviate"] |
Overview
Weaviate is an open-source vector database with built-in vectorization modules (OpenAI, Cohere, HuggingFace, Transformers, CLIP, multi-modal). Supports hybrid search (vector + BM25 keyword), generative search (RAG with LLM integration), and multi-modal data.
Installation
docker run -p 8080:8080 semitechnologies/weaviate:latest
Python Client
import weaviate
import weaviate.classes as wvc
client = weaviate.connect_to_local()
collection = client.collections.create(
name="Documents",
vectorizer_config=wvc.config.Configure.Vectorizer.text2vec_transformers(),
)
collection.data.insert({
"title": "Paris",
"content": "Paris is the capital of France. It is known for the Eiffel Tower.",
})
response = collection.query.hybrid(query="French capital", limit=5)
for obj in response.objects:
print(obj.properties)
References
