Site Search 360
Algolia
ElasticSearch
Google Custom Search
Klevu
Apache Solr
Swiftype
Swiftype Site Search
Qdrant
Milvus
Weaviate
pgvecto.rs
Vespa.ai
Zilliz
ElasticSearch
txtai
Site Search 360 is a smart, ad-free search interface for your website. With a simple drag-and-drop integration, get your search up and running in no time! Let your visitors find exactly what they are looking for, right away.
Features of Site Search 360 include: * Quick and easy visual configuration of your search bar, suggestions and results with our Search Designer * Instant search-as-you-type results, autocomplete and popular searches * Smart categories and Faceted Search * Key information (e.g. brand, price, date) can be automatically extracted and shown directly in your search snippets * Semantic search: built-in dictionaries in 19 languages + the ability to add your custom synonyms * Analytics to help you get the most out of your search: what your visitors look for the most, what results they click on, what queries bring no results, etc. * Full control over search results: boost, reorder, redirect and map * Integration with Google Analytics and Google Tag Manager * Import of Google Custom Search promotions * Low-to-no-code Search Designer to customize your search appearance and results and avoid search abandonment * FAQ page search * Search Secure Content * Awesome support: via live chat, email, or phone
Qdrant is a leading open-source high-performance Vector Database written in Rust with extended metadata filtering support and advanced features. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications. Powering vector similarity search solutions of any scale due to a flexible architecture and low-level optimization. Qdrant is trusted and high-rated by Machine Learning and Data Science teams of top-tier companies worldwide.
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Qdrant's answer:
Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.
Qdrant's answer:
Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.
Qdrant's answer:
Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.
Basic install/setup was done in about an hour. Had some questions on customization and their support staff is quick to respond and very helpful. Very good value for the price!
We have been very happy with SiteSearch 360 as it was an easy search solution to deploy to our website as well as easy to manage. It is also affordable compared to some others out there.
The plugin not only vastly improved the search experience of our clients using it, but the customer service configuring the plugin was spot on! Quick responses and in-depth one-on-one help got the plugin set up exactly how we needed it to be!
Based on our record, Qdrant seems to be a lot more popular than Site Search 360. While we know about 38 links to Qdrant, we've tracked only 1 mention of Site Search 360. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Site Search 360 - https://sitesearch360.com - $9/month. - Source: dev.to / about 1 year ago
Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker. - Source: dev.to / about 6 hours ago
I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours. - Source: dev.to / 7 days ago
I'm currently looking to implement locally, using QDrant [1] for instance. I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2]. [1]. https://qdrant.tech/. - Source: Hacker News / 29 days ago
There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb. - Source: Hacker News / about 2 months ago
Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance. - Source: dev.to / 2 months ago
Algolia - Algolia's Search API makes it easy to deliver a great search experience in your apps & websites. Algolia Search provides hosted full-text, numerical, faceted and geolocalized search.
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
ElasticSearch - Elasticsearch is an open source, distributed, RESTful search engine.
Weaviate - Welcome to Weaviate
Google Custom Search - Google Custom Search enables you to create a search engine for your website, your blog, or a collection of websites.
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs
