for MongoDB Atlas Search

MongoDB Atlas Search is a fully-managed, cloud-based search feature integrated into MongoDB Atlas, enabling developers to add advanced search capabilities to their applications without the need for separate search infrastructure. Built on top of the open-source search engine, Apache Lucene, it provides powerful full-text search, filtering, and indexing features, allowing users to perform complex queries on data stored in MongoDB. With seamless integration, real-time indexing, and scalability, MongoDB Atlas Search simplifies the process of implementing robust search functionality within MongoDB applications.

How Discovery unlocks the full potential of MongoDB Atlas Search:

Ease of Integration: swift, smooth, and seamless integration with the Atlas Search engine.

Content Ingestion & Processing: Discovery ingestion manages the efficient gathering and importing of data from various sources using standard connectors. Content processing pipelines clean, normalize, and enrich data to optimize for search before publishing to Atlas Search.

Retrieval-Augmented Generation (RAG): combines information retrieval and Generative AI, enabling Large Language Models (LLMs) to access data from external sources and generate dynamic, context-aware answers, rather than merely returning links or static documents. Discovery allows RAG solutions to be built with security.

Vector Search: This allows users to perform similarity searches based on vector embeddings, which is useful for use cases like semantic search, recommendations, and image search. Discovery orchestrates the use of AI to create text embeddings for storage as well as query embeddings for content retrieval.

Choice of LLM: Discovery is Large Language Model independent and supports most models including open source.

Enhanced Search Features: Discovery enables the use of advanced search features in Atlas Search such as: facet snapping, content tagging and saved queries.

Recommendations: suggestions for relevant content, products, or information tailored to users based on their behaviour, preferences, and past interactions to enhance their search experience.

Hyper-personalization: Discovery is capable of delivering highly tailored results based on individual user data and real-time signals, to provide a more relevant and engaging search experience.

Search Performance Dashboard: Discovery includes a dashboard tool that you can use to objectively measure and improve search performance. Compatible with Atlas Search, it provides data-driven insights to fine-tune relevance and enhance user experience.

QueryFlow: features a powerful API builder that developers can use to create a fully personalized search experience. Utilizing advanced query parsing and routing, Natural Language Processing (NLP) and other AI services, QueryFlow effectively discerns user intent. QueryFlow can also facilitate the integration of application User Interfaces (UIs) with underlying search engines.

If you’re looking to enhance your existing MongoDB Atlas Search implementation with any of the above functionalities, Discovery offers a more cost-effective, efficient and faster way to do so.