Qdrant

It lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with it pretty much as with a database server.

Search the world's information, including webpages, images, videos and more. Google has many special features to help you find exactly what you're looking for.

It uses the tools you use to make application building a snap. It is built on the battle-tested Apache Zookeeper, it makes it easy to scale up and down.

Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.

It builds completely static HTML sites that you can host on GitHub pages, Amazon S3, or anywhere else you choose. There's a stack of good looking themes available. The built-in dev-server allows you to preview your documentation as you're writing it. It will even auto-reload and refresh your browser whenever you save your changes.

Milvus is an open source vector database. Built with heterogeneous computing architecture for the best cost efficiency. Searches over billion-scale vectors take only milliseconds with minimum computing resources.

It is an open-source embedding database. Chroma makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs.

An open-source, high-performance, distributed SQL database built for resilience and scale. Re-uses the upper half of PostgreSQL to offer advanced RDBMS features, architected to be fully distributed like Google Spanner.

It is an open-source vector search engine. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects.

Searchkick learns what your users are looking for. As more people search, it gets smarter and the results get better. It’s friendly for developers - and magical for your users.

We help your website visitors find what they are looking for. AddSearch is a lightning fast, accurate and customizable site search engine with a Search API. AddSearch works on all devices and is easy to install, customize and tweak.

It organizes your search results into topics. With an instant overview of what's available, you will quickly find what you're looking for.

It is a C++ based full-text search engine including similarity ranking capabilities natively integrated into ArangoDB. It allows users to combine two information retrieval techniques: boolean and generalized ranking retrieval. Search results “approved” by the boolean model can be ranked by relevance to the respective query using the Vector Space Model in conjunction with BM25 or TFIDF weighting schemes.

A fast, lightweight and schema-less search backend. It ingests search texts and identifier tuples that can then be queried against in microseconds.

It is an open-source database for vector search built with persistent storage, which greatly simplifies retrieval, filtering, and management of embeddings.

It is a search engine that does full text indexing. It is a lightweight alternative to Elasticsearch and runs in less than 100 MB of RAM. It uses bluge as the underlying indexing library. It is very simple and easy to operate as opposed to Elasticsearch which requires a couple dozen knobs to understand and tune.

It is an embeddable super fast full text search engine. It can be embedded into MySQL. Mroonga is a storage engine that is based on it.

It is an open-source PostgreSQL database extension to store vector data, generate embeddings, and handle vector search operations. It provides a new index type for vector columns which speeds up ORDER BY ... LIMIT queries.

It is a Postgres extension that automates the transformation and orchestration of text to embeddings and provides hooks into the most popular LLMs. This allows you to do vector search and build LLM applications on existing data with as little as two function calls.

It is an open-source, self-hostable vector database for semantic similarity search that specializes in low query latency. It bridges the gap between information retrieval and memory retention in Large Language Models.

It is an end-to-end vector search engine. Vector generation, storage, and retrieval are handled out of the box through a single API. No need to bring your own embeddings.

It is a powerful agent-first search engine that enables you to run a webscale search engine locally or to connect via remote API. It's ideal for both Large Language Models (LLMs) and human users.

It is a simple, serverless, distributed vector database that can be used as an API. It is designed to handle large amounts of vector text data, making it suitable for projects with high data volumes.