Demystifying Vector Databases: The Core of Personalised Large Language Models
With the evolution of artificial intelligence (AI) and machine learning (ML), we've experienced a phenomenal increase in data volume, complexity, and velocity. These new data landscapes call for novel data management techniques. Enter vector databases, an essential tool for powering personalised large language models. This blog post will walk you through what vector databases are, how they're used, and how they work.
What is a Vector Database?
A vector database, also known as a vector memory, is a database management system designed to efficiently handle high-dimensional vector data. It's not your traditional database that deals with scalar data types like integers, text, and dates. Instead, it's engineered to manage, query, and retrieve vector data, where every data point can have hundreds or even thousands of dimensions.
Vector databases play a crucial role in AI applications, including personalised large language models, image and voice recognition systems, and recommendation algorithms, where multi-dimensional data is commonplace.
The essence of a vector database lies in its ability to handle 'similarity search'. That is, given a query vector, it can rapidly identify other vectors in the database that are most similar to the query.
The Role of Vector Databases in Personalised Large Language Models
Personalised large language models, such as GPT-4 or its successors, employ vector databases to store information about individual users' interactions with the model. This helps tailor responses to the user's specific needs and preferences, creating a unique and personalised AI experience.
Every conversation a user has with the AI model generates a 'context vector'. This vector, a high-dimensional representation of the conversation's context, encapsulates the user's queries, responses, and other interaction details.
These context vectors are then stored in the vector database. When a user interacts with the AI model, the system generates a new context vector for the ongoing conversation and compares it with the stored vectors in the database. This allows the model to understand the user's past interactions and adjust its responses accordingly, leading to a more personalised and context-aware conversation.
How Does a Vector Database Work?
Under the hood, vector databases use a technique called 'vector indexing' for efficient storage and retrieval of high-dimensional data. Unlike traditional databases, where data is indexed based on exact values, vector databases create indexes based on the 'distance' or 'similarity' between vectors.
Here's a simplified workflow of a vector database in action:
Vector Ingestion: The database ingests high-dimensional vectors generated by the AI model from user interactions.
Vector Indexing: Each vector is indexed based on its features. The indexing algorithm (like Hierarchical Navigable Small World graphs, Annoy, or Facebook's FAISS) clusters similar vectors together, creating a 'vector space'.
Similarity Search: When a new vector (query) comes in, the system navigates this vector space to find the most similar vectors. It doesn't need to check every vector in the database. Instead, it efficiently traverses through the clusters in the index.
Vector Retrieval: The system retrieves vectors that are most similar to the query vector, helping the AI model to generate personalised responses.
Vector Update: As new interactions occur, vectors can be updated or new vectors can be added to the database, continually refining the user's profile.
The beauty of vector databases lies in their scalability and performance. Even when dealing with billions of vectors, they can still perform similarity search in near real-time, making them perfect for applications that demand instant responses, such as conversational AI.
Conclusion
Vector databases are a cornerstone of modern AI applications, enabling efficient handling of complex, high-dimensional data. As
the world becomes more personalised and data-driven, these databases are set to play an increasingly vital role in shaping our AI experiences.
By transforming how we store and manage data, vector databases not only make our interactions with AI models more personalised but also open new possibilities for the future of AI and machine learning.