Table of contents
Weaviate: An open-source, cloud-native vector database built for scalable and fast vector searches. It's particularly effective for semantic search applications, combining full-text search with vector search for AI-powered insights. - Source: dev.to / 2 months ago
Weaviate is an open-source vector search engine with out-of-the-box support for vectorization, classification, and semantic search. It is designed to make vector search accessible and scalable, supporting use cases such as semantic text search, automatic classification, and more. - Source: dev.to / 3 months ago
Congrats to them! What have your experiences with vector databases been? I've been using https://weaviate.io/ which works great, but just for little tech demos, so I'm not really sure how to compare one versus another or even what to look for really. - Source: Hacker News / 3 months ago
A RAG implementation's quality and performance highly depend on the similarity-based search of embeddings. The challenge arises from the fact that embeddings are usually high-dimensional vectors, and the knowledge base may have many documents. It's not surprising that the popularity of LLM catalyzed the development of specialized vector databases like Pinecone and Weaviate. However, SQL databases are also evolving... - Source: dev.to / 4 months ago
To find semantically similar texts we need to calculate the distance between vectors. While we have just a few short texts we can brute-force it: calculate the distance between our query and each text embedding one by one and see which one is the closest. When we deal with thousands or even millions of entries in our database, however, we need a more efficient way of comparing vectors. Just like for any other way... - Source: dev.to / 6 months ago
For the purposes of this guide, we will be using Weaviate, a popular vector database service, to implement a simple vector database that you can build on for any use case. - Source: dev.to / 9 months ago
As an open-source and self-hosted solution, developers can deploy their own version of the plugin and register it with ChatGPT. The plugin leverages OpenAI embeddings and allows developers to choose a vector database (Milvus, Pinecone, Qdrant, Redis, Weaviate or Zilliz) for indexing and searching documents. Information sources can be synchronized with the database using webhooks. Source: 10 months ago
Https://weaviate.io/ Looks interesting. I was just reading about it. - Source: Hacker News / 12 months ago
Local data is free - be careful about API costs. Check out open-source vector DBs that you can run locally. Source: 12 months ago
Weaviate (https://weaviate.io) | REMOTE (US / Europe) Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. * Software Engineer - Research, Algorithms, and Performance. - Source: Hacker News / about 1 year ago
This is a great question. You might be interested in checking out Weaviate (I'm affiliated); you can use the DB completely open-source. However, with our SaaS and Hybrid-SaaS offerings (aka the Weaviate Cloud Services), we aim to give you a serverless experience (as reflected in the pricing). I hope that's helpful :). Source: about 1 year ago
Have you looked at Weaviate? It's open source and stores objects and embeddings in the same database, which may help your use case if you need filtering combined with vector search. Source: about 1 year ago
Pinecone only has a cloud offering. If you want to host the database locally / on-prem, you can integrate with an open source vector database such as Weaviate. Source: about 1 year ago
In my understanding it's not possible to self host Pinecone, so the data leaks outside your hands. Alternative would be to use open source vector database like Weaviate which can be self hosted. Source: about 1 year ago
Weaviate (4.8k ⭐) → An open-source vector database that allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. - Source: dev.to / about 1 year ago
If preventing runaway costs is important, you may want to take a look at the Weaviate vector database, which is both open-source and has a SaaS offering with well-defined pricing. Source: about 1 year ago
Looks like Weaviate support is coming soon for the memory backend. Will be nice to reduce running costs by using an open-source vector DB instead. Source: about 1 year ago
A good open-source alternative that also offers cloud hosting is Weaviate. Source: about 1 year ago
You should check out Weaviate - it is free and open-source. https://weaviate.io/. Source: about 1 year ago
I would recommend giving Weaviate a try? Source: about 1 year ago
Weaviate is built with Go + Python. It uses Go as a graph based vector database engine and then uses standard python models to generate the vectors. So it’s not like it’s never been done, but generally I dont think it’s common. For your use case it sounds like the python services would be the bottleneck anyways so go soundly really help you. Source: about 1 year ago
Do you know an article comparing Weaviate to other products?
Suggest a link to a post with product alternatives.
This is an informative page about Weaviate. You can review and discuss the product here. The primary details have not been verified within the last quarter, and they might be outdated. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.
