RAG & Knowledge
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AI-native database for hybrid search across dense, sparse, and tensor vectors plus full text
★ 4.7k starsApache-2.0
View guide →Vespa — the AI search platform: vector, text and tensor search at production scale.
vespa is an open-source rag & knowledge project by vespa-engine (vespa-engine/vespa on GitHub). It has 7,121 GitHub stars and 745 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 8 October 2026.
Decide cloud versus self-hosted, and design your first application package — document schema, indexing and a ranking profile — before writing data.
Clone vespa-engine/vespa from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.
Start from the official getting-started guide: deploy an application package to Vespa Cloud or to your own Docker-based instance.
Deploy the sample application, feed a few documents, and run a query with a vector-plus-text ranking profile to see hybrid retrieval work.
Vector search, classic text search and structured filtering are evaluated together in a single query path.
Ranking uses tensor computation, so ML model inference happens inside the search engine at query time.
Designed for large corpora and high query loads with horizontal scaling and multinode redundancy.
Serving and recommendation are first-class uses alongside search, sharing the same application model.
Run applications on Vespa Cloud (with a free trial) or operate your own Vespa instance from the same open-source code.
A companion sample-apps collection provides fully functional, production-ready starting points.
Get the source from the official GitHub repository for vespa.
git clone https://github.com/vespa-engine/vespa.git
cd vespaCreate and deploy your first Vespa application package to Vespa Cloud or your own instance, following the official guide the README links.
# Follow https://docs.vespa.ai/en/getting-started.html to deploy a sample app — see READMEFor Vespa itself, set up the documented dev environment (including JDK 17) and build the Java modules.
brew install jenv mvnvm openjdk@17 # macOS dev setup example from the README; see its Building sectionvespa-engine (the Vespa team, originally from Yahoo), through the vespa-engine/vespa repository, with the service at vespa.ai.
Yes — the project is open source under the Apache-2.0 license as recorded for this listing. See the license terms for any conditions on redistribution.
No API key is needed to self-host the Apache-2.0 engine. Vespa Cloud is a paid managed service (with a free trial) if you prefer not to operate it.
A vector database does nearest-neighbour lookup. Vespa is a full search platform — text, vector, structured data, tensor ranking and model serving in one distributed system — more powerful for production search, and correspondingly more system to learn and run.
Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — deploy the sample application, feed a few documents, and run a query with a vector-plus-text ranking profile to see hybrid retrieval work.
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