Weaviate: open-source rag and vector databases | Signal Check (@SignalCheckRae)

What it does

Weaviate is a vector database that stores your objects alongside their embeddings and supports keyword plus vector (hybrid) search. It can call embedding models for you when you add data.

Good for: Search over your own content that mixes keyword matching with meaning-based matching.

Weaviate on GitHub (opens in a new tab) Project homepage (opens in a new tab)

Useful?

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The signals we checked

Licence

Open core (the core is open source; some enterprise code is under a separate licence; see the note below)

Popularity

Stars: 16,885 (checked 16 Oct 2026)

Last commit

16 Oct 2026

Open issues and PRs

793

Status

Active (not archived)

Self-host

EasyDocker one-liner or Docker Compose.

GitHub topics

approximate-nearest-neighbor-search
generative-search
grpc
hnsw
hybrid-search
image-search
information-retrieval
mlops
nearest-neighbor-search
neural-search

Licence note: BSD-3-Clause outside the "wl" folder; code in "wl" is under a separate enterprise licence.

More in rag and vector databases

RAGFlow A self-hosted knowledge base over company documents, with answers that cite their source.
LlamaIndex Developers building document Q&A or data agents in Python or TypeScript.
Milvus Vector search that may need to grow to very large collections.
Qdrant The search layer behind a "chat with your documents" app.
Chroma Adding document search to a prototype or small app with very little setup.

No affiliate links on this page, and we are not paid by this project. The summary is our own plain-English description; check the repo for current features before you rely on it.

Stars, licences and commit dates come from the GitHub API, checked 16 Oct 2026. "Last commit" is the last push to the repo. Star counts are a rough popularity signal, not a quality score. There are no affiliate links in this section: links go straight to each project.