As marvels of Artificial Intelligence (AI) such as ChatGPT and other large language models (LLMs) surge in popularity, an exciting technological domain is getting an immense boost: Vector Databases.
ChatGPT and other LLMs work within a finite context window - a memory limit. They can "remember" and process conversation details up to a certain extent, beyond which, details are lost. It's like having Iron Man's JARVIS who can't remember your name, let alone manage the Iron Legion; quite counterproductive, isn't it? While newer versions of the ChatGPT API have broadened this context window, there's still a fixed upper limit to its memory capacity.
Source: Google Analytics - June 18th, 2023
We're currently navigating a world filled with distractions. The dopamine-fueled rushes of scrolling through social media often leave us unable to develop thoughtful, deep connections. Imagine if we could rein in this habit, using some of that time to document our lives and learning instead. Many individuals possess immense potential and intellectual capabilities that remain untapped due to the numbing effects of endless scrolling. Documenting knowledge may not provide immediate excitement, but it's crucial for personal growth 🕳️. Consider it a knowledge gathering exercise, a creation of a second brain. This brain could save time on repetitive tasks and facilitate the assimilation of new information into your existing knowledge base, thereby aiding in long-term memory formation.
“Intelligence is not only the ability to reason; it is also the ability to find relevant material in memory and to deploy attention when needed.“ by Daniel Kahneman Thinking, Fast and Slow
Achieving this is possible through cost-effective technologies like vector databases. Imagine documenting everything and then using ChatGPT to sift through all the information and summarize it. The cost in both time and resources could be substantial! However, with vector databases and similarity search, you can significantly reduce the information that needs to be summarized. The search handles finding relevant information, leaving the LLM to read and summarize a much smaller subset of data.
Numerous vector databases, from venture-backed startups to open-source projects, are available for application.
Pinecone is a cloud-based vector database designed for extensive machine learning applications. Its key feature is ease of use, boasting a user-friendly API that allows rapid deployment and querying of vector indexes. Pinecone simplifies the management of large datasets with its dynamic scaling and automatic indexing. It's perfect for recommendation systems, personalized search engines, and anomaly detection in sensor networks.
Milvus is an open-source vector database aimed at machine learning and deep learning applications. Its distributed system allows efficient storage and retrieval of high-dimensional data, making it ideal for natural language processing, recommendation systems, and image/video analysis. Milvus supports diverse data types, offers high scalability and performance, and provides a user-friendly web interface and RESTful API. It can power personalized search engines, content-based recommendation systems, and image and video retrieval.
the AI-native open-source embedding database
Chroma, a cloud-based vector database, is designed for high-performance machine learning and deep learning applications. It's optimized for similarity search, handles both dense and sparse vectors, and can search billions of vectors in milliseconds. Chroma's real-time indexing and dynamic scaling capabilities make it suitable for managing large, growing datasets. It can be used in applications like personalized recommendations, content search and retrieval, and anomaly detection in sensor networks.