# Weaviate | Open Source Vector Database And Store For Objects And Vectors ✅ **URL:** \ **Category:** Tools & Scripts\ **Tags:** seo, tools, programming, privacy, photo-editing\ **Created:** [May 18, 2025, 7:20pm UTC](https://onehack.st/t/weaviate-open-source-vector-database-and-store-for-objects-and-vectors/305362 "2025-05-18T19:20:07Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![Aina](https://onehack.st/user_avatar/onehack.st/aina/32/167469_2.png) [@Aina](https://onehack.st/u/Aina)\ **Post date:** [May 18, 2025, 7:20pm UTC](https://onehack.st/t/weaviate-open-source-vector-database-and-store-for-objects-and-vectors/305362/1 "2025-05-18T19:20:07Z")
# Weaviate[![Weaviate logo](https://camo.githubusercontent.com/9596f581ae876f09b7cb2f278566f5832e68ee60c65d8e9a5ba48f6a27cd2b55/68747470733a2f2f77656176696174652e696f2f696d672f736974652f77656176696174652d6c6f676f2d6c696768742e706e67)](https://camo.githubusercontent.com/9596f581ae876f09b7cb2f278566f5832e68ee60c65d8e9a5ba48f6a27cd2b55/68747470733a2f2f77656176696174652e696f2f696d672f736974652f77656176696174652d6c6f676f2d6c696768742e706e67) [![Go Reference](https://camo.githubusercontent.com/c9d5df3a3c25c1aaee20b3ef66f323bfd6d0939de1e58d08c4cd7a782ff08a9c/68747470733a2f2f706b672e676f2e6465762f62616467652f6769746875622e636f6d2f77656176696174652f77656176696174652e737667)](https://pkg.go.dev/github.com/weaviate/weaviate) [![Build Status](https://github.com/weaviate/weaviate/actions/workflows/.github/workflows/pull_requests.yaml/badge.svg?branch=main)](https://github.com/weaviate/weaviate/actions/workflows/.github/workflows/pull_requests.yaml) [![Go Report Card](https://camo.githubusercontent.com/ee944df2957c4d3526bfa34ffa6b39174d9ee32e6d51cba7ea0596dd4af00071/68747470733a2f2f676f7265706f7274636172642e636f6d2f62616467652f6769746875622e636f6d2f77656176696174652f7765617669617465)](https://goreportcard.com/report/github.com/weaviate/weaviate) [![Coverage Status](https://camo.githubusercontent.com/89bbba2a636c8fdf46766294bfd1ba2956d6983b9ae7cbd52693fb68ea277e23/68747470733a2f2f636f6465636f762e696f2f67682f77656176696174652f77656176696174652f6272616e63682f6d61696e2f67726170682f62616467652e737667)](https://codecov.io/gh/weaviate/weaviate) [![Slack](https://camo.githubusercontent.com/f7ed7cc41da5e75282eace2e5b06d1e0af46621c3c40c4314ca6374d8b2b0161/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f736c61636b2d2d6368616e6e656c2d626c75653f6c6f676f3d736c61636b)](https://weaviate.io/slack) [![GitHub Tutorials](https://camo.githubusercontent.com/684b84476432e43657230d2790ef00a78619c6c27906ab7534387b658329903e/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f57656176696174655f5475746f7269616c732d677265656e)](https://github.com/weaviate-tutorials/) ## Overview Weaviate is a cloud-native, **open source vector database** that is robust, fast, and scalable. To get started quickly, have a look at one of these pages: - [Quickstart tutorial](https://weaviate.io/developers/weaviate/quickstart) To see Weaviate in action - [Contributor guide](https://weaviate.io/developers/contributor-guide) To contribute to this project For more details, read through the summary on this page or see the system [documentation](https://weaviate.io/developers/weaviate/). * * * ## Why Weaviate? Weaviate uses state-of-the-art machine learning (ML) models to turn your data - text, images, and more - into a searchable vector database. Here are some highlights. ### Speed Weaviate is fast. The core engine can run a 10-NN nearest neighbor search on millions of objects in milliseconds. See [benchmarks](https://weaviate.io/developers/weaviate/benchmarks). ### Flexibility Weaviate can **vectorize your data at import time**. Or, if you have already vectorized your data, you can **upload your own vectors** instead. Modules give you the flexibility to tune Weaviate for your needs. More than two dozen modules connect you to popular services and model hubs such as [OpenAI](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-openai), [Cohere](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-cohere), [VoyageAI](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-voyageai) and [HuggingFace](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-huggingface). Use custom modules to work with your own models or third party services. ### Production-readiness Weaviate is built with [scaling](https://weaviate.io/developers/weaviate/concepts/cluster), [replication](https://weaviate.io/developers/weaviate/concepts/replication-architecture), and [security](https://weaviate.io/developers/weaviate/configuration/authentication) in mind so you can go smoothly from **rapid prototyping** to **production at scale**. ### Beyond search Weaviate doesn’t just power lightning-fast vector searches. Other superpowers include **recommendation** , **summarization** , and **integration with neural search frameworks**. ## Who uses Weaviate? - **Software Engineers** - Weaviate is an ML-first database engine - Out-of-the-box modules for AI-powered searches, automatic classification, and LLM integration - Full CRUD support - Cloud-native, distributed system that runs well on Kubernetes - Scales with your workloads - **Data Engineers** - Weaviate is a fast, flexible vector database - Use your own ML model or third party models - Run locally or with an inference service - **Data Scientists** - Seamless handover of Machine Learning models to engineers and MLOps - Deploy and maintain your ML models in production reliably and efficiently - Easily package custom trained models ## What can you build with Weaviate? A Weaviate vector database can search text, images, or a combination of both. Fast vector search provides a foundation for chatbots, recommendation systems, summarizers, and classification systems. Here are some examples that show how Weaviate integrates with other AI and ML tools: ### Use Weaviate with third party embeddings - [Cohere](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-cohere) ([blogpost](https://txt.cohere.com/embedding-archives-wikipedia/)) - [Hugging Face](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-huggingface) - [OpenAI](https://github.com/openai/openai-cookbook/tree/main/examples/vector_databases/weaviate) ### Use Weaviate as a document store - [DocArray](https://docarray.jina.ai/advanced/document-store/weaviate/) - [Haystack](https://docs.haystack.deepset.ai/reference/integrations-weaviate#weaviatedocumentstore) ([blogpost](https://www.deepset.ai/weaviate-vector-search-engine-integration)) ### Use Weaviate as a memory backend - [Auto-GPT](https://github.com/Significant-Gravitas/Auto-GPT/blob/master/docs/configuration/memory.md#weaviate-setup) ([blogpost](https://weaviate.io/blog/autogpt-and-weaviate)) - [LangChain](https://python.langchain.com/docs/integrations/providers/weaviate) ([blogpost](https://weaviate.io/blog/combining-langchain-and-weaviate)) - [LlamaIndex](https://gpt-index.readthedocs.io/en/latest/how_to/integrations/vector_stores.html) ([blogpost](https://weaviate.io/blog/llamaindex-and-weaviate)) - [OpenAI - ChatGPT retrieval plugin](https://github.com/openai/chatgpt-retrieval-plugin/blob/main/docs/providers/weaviate/setup.md) ### Demos These demos are working applications that highlight some of Weaviate’s capabilities. Their source code is available on GitHub. - [Verba, the Golden RAGtreiver](https://verba.weaviate.io/) ([GitHub](https://github.com/weaviate/verba)) - [Healthsearch](https://healthsearch.weaviate.io/) ([GitHub](https://github.com/weaviate/healthsearch-demo)) - [Awesome-Moviate](https://awesome-moviate.weaviate.io/) ([GitHub](https://github.com/weaviate-tutorials/awesome-moviate)) ## How can you connect to Weaviate? Weaviate exposes a [GraphQL API](https://weaviate.io/developers/weaviate/api/graphql) and a [REST API](https://weaviate.io/developers/weaviate/api/rest). Starting in v1.23, a new [gRPC API](https://weaviate.io/developers/weaviate/api/grpc) provides even faster access to your data. Weaviate provides client libraries for several popular languages: - [Python](https://weaviate.io/developers/weaviate/client-libraries/python) - [JavaScript/TypeScript](https://weaviate.io/developers/weaviate/client-libraries/typescript) - [Go](https://weaviate.io/developers/weaviate/client-libraries/go) - [Java](https://weaviate.io/developers/weaviate/client-libraries/java) There are also [community supported libraries](https://weaviate.io/developers/weaviate/client-libraries/community) for additional languages. ## Where can You learn more? Free, self-paced courses in [Weaviate Academy](https://weaviate.io/developers/academy) teach you how to use Weaviate. The [Tutorials repo](https://github.com/weaviate-tutorials) has code for example projects. The [Recipes repo](https://github.com/weaviate/recipes) has even more project code to get you started. The [Weaviate blog](https://weaviate.io/blog) and [podcast](https://weaviate.io/podcast) regularly post stories on Weaviate and AI. Here are some popular posts: ### Blogs - [What to expect from Weaviate in 2023](https://weaviate.io/blog/what-to-expect-from-weaviate-in-2023) - [Why is vector search so fast?](https://weaviate.io/blog/Why-is-Vector-Search-so-fast) - [Cohere Multilingual ML Models with Weaviate](https://weaviate.io/blog/Cohere-multilingual-with-weaviate) - [Vamana vs. HNSW - Exploring ANN algorithms Part 1](https://weaviate.io/blog/ann-algorithms-vamana-vs-hnsw) - [HNSW+PQ - Exploring ANN algorithms Part 2.1](https://weaviate.io/blog/ann-algorithms-hnsw-pq) - [The Tile Encoder - Exploring ANN algorithms Part 2.2](https://weaviate.io/blog/ann-algorithms-tiles-enocoder) - [How GPT4.0 and other Large Language Models Work](https://weaviate.io/blog/what-are-llms) - [Monitoring Weaviate in Production](https://weaviate.io/blog/monitoring-weaviate-in-production) - [The ChatGPT Retrieval Plugin - Weaviate as a Long-term Memory Store for Generative AI](https://weaviate.io/blog/weaviate-retrieval-plugin) - [Combining LangChain and Weaviate](https://weaviate.io/blog/combining-langchain-and-weaviate) - [How to build an Image Search Application with Weaviate](https://weaviate.io/blog/how-to-build-an-image-search-application-with-weaviate) - [Building Multimodal AI in TypeScript](https://weaviate.io/blog/multimodal-search-in-typescript) - [Giving Auto-GPT Long-Term Memory with Weaviate](https://weaviate.io/blog/autogpt-and-weaviate) ### Podcasts - [Neural Magic in Weaviate](https://www.youtube.com/watch?v=leGgjIQkVYo) - [BERTopic](https://www.youtube.com/watch?v=IwXOaHanfUU) - [Jina AI’s Neural Search Framework](https://www.youtube.com/watch?v=o6MD0tWl0SM) ### Other reading - [Weaviate is an open-source search engine powered by ML, vectors, graphs, and GraphQL (ZDNet)](https://www.zdnet.com/article/weaviate-an-open-source-search-engine-powered-by-machine-learning-vectors-graphs-and-graphql/) - [Weaviate, an ANN Database with CRUD support (DB-Engines.com)](https://db-engines.com/en/blog_post/87) - [A sub-50ms neural search with DistilBERT and Weaviate (Towards Datascience)](https://towardsdatascience.com/a-sub-50ms-neural-search-with-distilbert-and-weaviate-4857ae390154) - [Getting Started with Weaviate Python Library (Towards Datascience)](https://towardsdatascience.com/getting-started-with-weaviate-python-client-e85d14f19e4f) ## GitHub: [https://github.com/weaviate/weaviate](https://github.com/weaviate/weaviate)