6 posts tagged with "vector-search"

Search technology has undergone an incredible transformation over the past few years. It began with simple, directory-based methods and evolved into sophisticated algorithms capable of interpreting the nuances of human language.

Mostly of the systems until today rely heavily on keyword matching. However, this approach has a lot of limitations, often overlooking the context or the true intent behind a user's query.

So the need for a more intelligent search became apparent. This need led to the rise of artificial intelligence (AI) in search technologies, giving birth to vector search, a method that understands queries and content at a much deeper level. By prioritizing context and semantics, vector search can discern the meaning behind words, providing more accurate and relevant results.

What is Search by Keywords?​

Keyword-based search is the foundation upon which traditional search engines were built. It involves searching through documents to find matches for specified words or phrases. Despite its straightforward nature, this method has drawbacks---it lacks the ability to understand the context or the intent behind the search query. This limitation often results in a list of results that contain the keywords but may be irrelevant to what the user is actually looking for.

Despite the huge surge in popularity in building AI applications with LLMs and vector search, we haven't seen any walkthroughs boil this down to a super-simple, few-command process. With SuperDuperDB together with MongoDB Atlas, it's easier and more flexible than ever before.

Implementing a (RAG) chat application like a question-your-documents service can be a tedious and complex process. There are several steps involved in doing this:

In step-by-step tutorial we will show how to leverage MongoDB Atlas Vector Search with SuperDuperDB, including the generation of vector embeddings. Learn how to connect embedding APIs such as OpenAI or use embedding models for example from HuggingFace with MongoDB Atlas with simple Python commands.

Install superduperdb Python package

Using vector-search with SuperDuperDB on MongoDB requires only one simple python package install:

MongoDB now supports vector-search on Atlas enabling developers to build next-gen AI applications directly on their favourite database. SuperDuperDB now make this process painless by allowing to integrate, train and manage any AI models and APIs directly with your database with simple Python.

Build next-gen AI applications - without the need of complex MLOps pipelines and infrastructure nor data duplication and migration to specialized vector databases:

• (RAG) chat applications on documents hosted in MongoDB Atlas
• semantic-text-search & similiarity-search, using vector embeddings of your data stored in Atlas
• image similarity & image-search on images hosted in or referred to on MongoDB Atlas
• video search including search within videos for key content
• content based recommendation based on content hosted in MongoDB Atlas
• ...and much, much more!

Imagine effortlessly infusing AI into your data repositories—databases, data warehouses, or data lakes—without breaking a sweat. With SuperDuperDB, we aim to make this dream a reality. We want to provide everyone with the tools to build AI applications directly on top of their data stores, with just a pinch of Python magic sprinkled on top! 🐍✨

In this latest blog post we take a dive into one such example - a Retrieval Augmented Generation (RAG) app we built directly on top of our MongoDB store.

In this blog-post we show you how to easily operate vector-search in MongoDB Atlas using SuperDuperDB, leading to many savings and efficiencies in your AI development.

In 2023 vector-databases are hugely popular; they provide the opportunity for developers to connect LLMs, such as OpenAI’s GPT models, with their data, as well as providing the key to deploying “search-by-meaning” on troves of documents.