Agents IA
milvus
The cloud-native vector database for planet-scale similarity search.
★ 46,3 k étoilesApache-2.0
Voir le guide →weaviate est un projet de agents IA open source, créé par weaviate (weaviate/weaviate sur GitHub). Il compte 16 874 étoiles sur GitHub et 1 423 forks, il est publié sous la licence NOASSERTION et a été mis à jour pour la dernière fois sur GitHub le 8 octobre 2026.
Vector search plus structured filtering in one database.
Be clear about the problem you are solving before installing.
Clone the repository and skim the docs for hardware or key requirements.
Install dependencies, then add API keys, endpoints, or local-model settings.
Launch the example flow and verify the output matches expectations.
Store data and embeddings together.
Vector plus keyword search in one query.
Built-in generative retrieval capabilities.
Scalable, fault-tolerant architecture.
Efficient and concurrency-friendly.
Extensive developer documentation.
Download the source and move into the project folder.
git clone https://github.com/weaviate/weaviate.git
cd weaviateCompile the Go services.
go build ./...Follow the developer docs — Docker is the fastest way to try it.
# see https://weaviate.io/developers/weaviate/It is maintained by the weaviate organization at weaviate/weaviate, with 1,423 forks.
GitHub does not assert a license for this repo, so treat the code as all rights reserved until the author adds one. Check the repository for any LICENSE file.
Self-hosting is the free path; a managed cloud also exists.
Compare with Qdrant, Milvus, and pgvector on filtering, generative features, and operations.
The quickstart in the developer docs — usually Docker-based — is the fastest route.
The cloud-native vector database for planet-scale similarity search.
A high-performance vector search engine for next-gen AI.
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.