Qdrant ( quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more.
 
The neural search uses semantic embeddings instead of keywords and works best with short texts. With Qdrant and a pre-trained neural network, you can build and deploy semantic neural search on your data in minutes.

Qdrant ( quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more.

Qdrant - Neural Search Engine, Vector Similarity Search Engine with extended filtering support

Lunr.js is a small, full-text search library for use in the browser. It indexes JSON documents and provides a simple search interface for retrieving documents that best match text queries. A bit like Solr, but much smaller and not as bright. Lunr enables you to provide a great search experience without the need for external, server-side, search services. Lunr has no external dependencies and works in your browser or on the server with node.js.
 
For web applications with all their data already sitting in the client, it makes sense to be able to search that data on the client too. A local search index will be quicker, there is no network overhead, and will remain available and usable even without a network connection.

Lunr.js is a small, full-text search library for use in the browser. It indexes JSON documents and provides a simple search interface for retrieving documents that best match text queries. A bit like Solr, but much smaller and not as bright. Lunr enables you to provide a great search experience without the need for external, server-side, search services. Lunr has no external dependencies and works in your browser or on the server with node.js.

LUNR.js - A bit like Solr, but much smaller and not as bright

TNTSearch is a full-text search (FTS) engine written entirely in PHP. A simple configuration allows you to add an amazing search experience in just minutes. Its features include Fuzzy search, Geo-search, Text classification, Stemming, Bm25 ranking algorithm, Result highlighting, Boolean search and lot more.

TNTSearch - A fully featured full text search engine written in PHP

Quickwit is a distributed search engine built from the ground up to offer cost-efficiency and high reliability. By mere mortals for mere mortals, Quickwit's architecture is as simple as possible. Quickwit is written in Rust and built on top of the mighty tantivy library. It designed to index big datasets straight from object storage like AWS S3 in a stateless manner.
 
<ul><li>Scalable distributed search: Host an arbitrary number of indexes on Amazon S3 and answer search queries in less than a second with a small pool of stateless search instances.</li><li>Stream indexing: Ingest TB of data from your favorite distributed event streaming service. As of today, Quickwit supports Apache Kafka natively. The next releases will bring support for more platforms.</li><li>Fault-tolerant architecture that won't lose data: Quickwit achieves exactly-once processing for indexing and safely stores your data on highly reliable object storage services such as Amazon S3.</li><li>Cloud-native, easy to operate: Thanks to true decoupled compute and storage, search instances are stateless, add or remove search nodes within seconds.</li><li>Sub-second full-text search on cloud / distributed storage: Quickwit Search re-designed indexing and index data structure to open it in less than 60ms on Amazon S3.</li><li>Time-based sharding: Quickwit shards data by time when enabled. And you can use a second dimension to shard data thanks to our <a href="/docs/design/querying">tags feature</a>. Time-based queries only access splits (a data piece of the index) that match the time range of the query which leads to significant performance improvements.</li><li>Painless multi-tenant search: Create indexes for each tenant without hurting query performance. Or group tenants into one index and use tagging to prune irrelevant splits for your tenant query to improve significantly performance.</li></ul>

Quickwit is a distributed search engine built from the ground up to offer cost-efficiency and high reliability. By mere mortals for mere mortals, Quickwit's architecture is as simple as possible. Quickwit is written in Rust and built on top of the mighty tantivy library. It designed to index big datasets straight from object storage like AWS S3 in a stateless manner.

Quickwit - Fast and cost-efficient distributed search engine for immutable data

MeiliSearch is a powerful, fast, open-source, easy to use, and deploy search engine. The search and indexation are fully customizable and handles features like typo-tolerance, filters, and synonyms. It features include Search as-you-type experience, Full text search, Supports Synonym, Highly customizable, RESTfull API and lot more.

MeiliSearch - Ultra relevant, instant and typo-tolerant full-text search API

Elasticlunr.js is a lightweight full-text search engine in Javascript for browser search and offline search. Elasticlunr.js is developed based on Lunr.js, but more flexible than lunr.js. Elasticlunr.js provides Query-Time boosting and field search. Elasticlunr.js is a bit like Solr, but much smaller and not as bright, but also provide flexible configuration and query-time boosting.

Elasticlunr.js - Lightweight full-text search engine in Javascript for browser search and offline search

OpenSearch is a community-driven, open source search and analytics suite derived from Apache 2.0 licensed Elasticsearch 7.10.2 & Kibana 7.10.2. It consists of a search engine daemon, OpenSearch, and a visualization and user interface, OpenSearch Dashboards. OpenSearch enables people to easily ingest, secure, search, aggregate, view, and analyze data. These capabilities are popular for use cases such as application search, log analytics, and more.
 
Its features include: 
<ul><li>Log analytics</li><li> Real-time application monitoring</li><li> Clickstream analytics</li><li>Use SQL or a piped processing language to query your data</li><li>Automate index operations</li><li>Monitor and optimize your cluster</li><li>Run search requests in the background</li><li>KNN- Find “nearest neighbors” in your vector data</li><li>Authentication and access control for your cluster</li><li>Anomaly Detection</li></ul>

OpenSearch is a community-driven, open source search and analytics suite derived from Apache 2.0 licensed Elasticsearch 7.10.2 & Kibana 7.10.2. It consists of a search engine daemon, OpenSearch, and a visualization and user interface, OpenSearch Dashboards. OpenSearch enables people to easily ingest, secure, search, aggregate, view, and analyze data. These capabilities are popular for use cases such as application search, log analytics, and more.

OpenSearch - Open source distributed and RESTful search engine

Haystack is an end-to-end framework that enables you to build powerful and production-ready pipelines for different search use cases. Whether you want to perform Question Answering or semantic document search, you can use the State-of-the-Art NLP models in Haystack to provide unique search experiences and allow your users to query in natural language. Haystack is built in a modular fashion so that you can combine the best technology from other open-source projects like Huggingface's Transformers, Elasticsearch, or Milvus.
 
Haystack can perform semantic search and retrieve documents according to meaning, not keywords. It can ask questions in natural language and find granular answers in your documents. It can automate processes by automatically applying a list of questions to new documents and using the extracted answers.

Haystack is an end-to-end framework that enables you to build powerful and production-ready pipelines for different search use cases. Whether you want to perform Question Answering or semantic document search, you can use the State-of-the-Art NLP models in Haystack to provide unique search experiences and allow your users to query in natural language. Haystack is built in a modular fashion so that you can combine the best technology from other open-source projects like Huggingface's Transformers, Elasticsearch, or Milvus.

Haystack - Build a natural language interface for your data

Tantivy is a full text search engine library written in rust. It is closer to Lucene than to Elastic Search and Solr in the sense it is not an off-the-shelf search engine server, but rather a crate that can be used to build such a search engine.
 
Its features include: 
<ul>
<li>Full-text search</li>
<li>Configurable tokenizer (stemming available for 17 Latin languages with third party support for Chinese (<a href="https://crates.io/crates/tantivy-jieba" target="_blank" rel="nofollow">tantivy-jieba</a> and <a href="https://crates.io/crates/cang-jie" rel="nofollow">cang-jie</a>), Japanese (<a href="https://github.com/lindera-morphology/lindera-tantivy">lindera</a> and <a href="https://crates.io/crates/tantivy-tokenizer-tiny-segmenter" rel="nofollow">tantivy-tokenizer-tiny-segmenter</a>) and Korean (<a href="https://github.com/lindera-morphology/lindera-tantivy">lindera</a> + <a href="https://github.com/lindera-morphology/lindera-ko-dic-builder">lindera-ko-dic-builder</a>)</li>
<li>Fast (check out the <g-emoji class="g-emoji" alias="racehorse" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f40e.png">🐎</g-emoji> <g-emoji class="g-emoji" alias="sparkles" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/2728.png">✨</g-emoji> <a href="https://tantivy-search.github.io/bench/" rel="nofollow">benchmark</a> <g-emoji class="g-emoji" alias="sparkles" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/2728.png">✨</g-emoji> <g-emoji class="g-emoji" alias="racehorse" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f40e.png">🐎</g-emoji>)</li>
<li>Tiny startup time (&lt;10ms), perfect for command line tools</li>
<li>BM25 scoring (the same as Lucene)</li>
<li>Natural query language (e.g. <code>(michael AND jackson) OR "king of pop"</code>)</li>
<li>Phrase queries search (e.g. <code>"michael jackson"</code>)</li>
<li>Incremental indexing</li>
<li>Multithreaded indexing (indexing English Wikipedia takes &lt; 3 minutes on my desktop)</li>
<li>Mmap directory</li>
<li>SIMD integer compression when the platform/CPU includes the SSE2 instruction set</li>
<li>Single valued and multivalued u64, i64, and f64 fast fields (equivalent of doc values in Lucene)</li>
<li><code>&amp;[u8]</code> fast fields</li>
<li>Text, i64, u64, f64, dates, and hierarchical facet fields</li>
<li>LZ4 compressed document store</li>
<li>Range queries</li>
<li>Faceted search</li>
<li>Configurable indexing (optional term frequency and position indexing)</li>
<li>Cheesy logo with a horse</li>
</ul>

Tantivy is a full text search engine library written in rust. It is closer to Lucene than to Elastic Search and Solr in the sense it is not an off-the-shelf search engine server, but rather a crate that can be used to build such a search engine.

Tantivy - Full-text search engine library inspired by Lucene and written in Rust

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