Exploring The Need For Vector Databases & Its Relation With LLMs

In one of our recent projects, we were looking to provide an external knowledge base for a Generative AI Assistant to help ensure that it provides trustworthy information.

Vector databases are well-suited for this purpose. It is because vector databases can efficiently handle complex data and enable similarity search, anomaly detection and temporal #data.

In this article, we will focus on the relationship between #vectordatabase and LLMs. We will also have a look at how vector databases help language models in storing, processing and retrieving relevant information from a huge unstructured dataset.
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The Shift From Traditional Databases

Technologies continue to evolve rapidly. And with each technological advancement, we are introduced to transformational tech stacks that support these new-age technologies.

Over sixty years ago, when the web revolution began, databases became a crucial part of it.

Over the years, as #technology changed, these database tools improved their infrastructure for storing, managing, and processing data.

A few years ago, we were quite satisfied with the traditional database systems. This was before the advent of #ai models and applications.

Now in the era of applications backed by LLMs, transformers, and GANs, we can no more depend on traditional databases.

Why?

Unstructured data constitutes a major part of the content that is generated today. So, we cannot ignore these datasets.

Another reason is that LLMs turn text into high-dimensional vectors that capture the meaning of the text.

This transformation into vector embeddings is core to AI applications, as this is the factor that allows the system to process complex operations on the text. This includes finding similar words, sentences, documents, and other relevant data as per the query.

Now such a complex vector embedding requires a specialized database system to store and manage it. Traditional scalar-based #databases cannot handle the complexity of such data.

This is where the vector databases come into play.

Vector databases offer optimized storage and management of unstructured data. It is an advanced database management system that is capable of handling unique structure of vector embeddings and large complex data types.

Vector databases are very crucial in realizing the desired functioning of AI applications built on Large Language Models (LLMs).

You need to understand that the core functioning of LLMs depends heavily on data stored as vectors that allow it to process and understand language.

And you need a specialized database (vector database) to manage these complex vectors.

In a nutshell, vector databases are important for LLMs because they enable efficient and effective representation of textual information.

Now let us dive deeper into vector embeddings and vector databases.
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What Is A Vector Database?

To understand vector databases, you first need to learn about the concept of vector embeddings.

Vector Embedding is a type of data representation that carries the semantic meaning of the data. The semantic information helps AI applications understand the intent, relationship, and true meaning of the #data.

Moreover, it enables the system to process the data according to its semantic value and retrieve relevant data as per the query.

LLMs use a vector model of data representation that enables them to understand different patterns and structures of the data.

So what are #vectors?

Vectors are ordered lists of numerical values and variables. While the elements of the list are called components of the vector.

When we put vectors in the context of data analytics, they could represent data in an n-dimensional space. Here, each component of the vector represents a specific feature or attribute of the vector.

However, when we see vectors from the perspective of #naturallanguageprocessing , they represent a word or a piece of text. In this case, each dimension of the vector will represent the semantic meaning and context of the text as processed by the language model.

For example, a language model will represent the word "bank" in a 300-dimensional vector, where each dimension will represent the meaning, usage, and other aspects of the word. Now that the word is stored as a dimensional vector, we can do a semantic search that can easily differentiate between "river bank" and "loans in the bank”.

Now coming to the vector database.

Vector databases are specialized database management systems (DBMS) that store vector embeddings using advanced storage, indexing, and query processing techniques. They provide essential data management functionalities like create, read, update, and delete operations, and also offer language bindings for popular data science languages such as Python, SQL, Java, and Tensorflow.

Moreover, they incorporate advanced features like fast data ingestion, sharding (dividing data across multiple nodes), and replication (creating redundant copies of data).

The primary purpose of vector databases is to efficiently handle diverse queries and algorithmic patterns observed in tasks such as similarity search, anomaly detection, observability, fraud detection, and analytics of Internet of Things (IoT) sensor data.

These styles of data processing have emerged due to the impact of digital transformation and the increasing prominence of #generativeai techniques.

The importance of vector embeddings and vector databases lies in their capability to process similarity search.

We all know how Google uses similarity search to offer us personalized movies, songs and product recommendations. It filters the content as per your interests, searches history and preferences to offer highly personalized content.

The use of similarity search goes beyond the Google search engine. AI-powered applications use similarity search to detect fraud, recommend products and services, analyze user preferences, detect anomalies, and much more.

We will learn more about vector similarity search and its use cases in the next article in this series.

Why Vector Database is Important for LLMs?

Here are a few reasons why vector database is important for the existence of LLMs.

Semantic Understanding: Vector databases store word embeddings or vector representations of words, phrases, or documents. These embeddings capture semantic relationships and meaning between words. LLMs utilize these vectors to understand the context and meaning of the input text, enabling them to generate coherent and relevant responses.

Efficient Similarity Search: Vector databases allow for efficient similarity search. By indexing the vector representations of textual data, LLMs can quickly retrieve similar or related information. This is particularly useful in tasks like information retrieval, recommendation systems, or content generation, where finding relevant content based on similarity is crucial.

Transfer Learning: Pre-training LLMs on large amounts of text data allows them to learn general language patterns. By leveraging vector databases, LLMs can benefit from pre-trained word embeddings or contextualized embeddings. These embeddings can be used to bootstrap LLMs' understanding of language and aid in transferring knowledge from the vector database to the model.

Multimodal Applications: LLMs are not limited to processing textual data alone. They can also be used in multimodal applications that involve images, audio, or other forms of data. In such cases, vector databases can store embeddings of multimodal data, allowing LLMs to integrate and reason across different modalities for tasks like image captioning, visual question answering, or speech recognition.

Dimensionality Reduction: Vector databases often employ dimensionality reduction techniques to represent high-dimensional textual data in lower-dimensional vector spaces. This reduces computational complexity and memory requirements, making it easier for LLMs to process and work with large amounts of textual information.

How Does Vector Database Work for LLMs?

Traditional databases store data in rows and columns. Whenever you make a query, the system searches the rows and lists. The one that matches your query the most is produced as output.

On the contrary, the working of vector databases is very advanced. Vector database consists of algorithms that help in Approximate Nearest Neighbor (ANN) search.

When you make a query in the vector database it not only does ANN but also a semantic search to retrieve the most relevant data as per the prompt.

A query in the vector database goes through three stages:

Indexing:

When vector embeddings are stored in a vector database, the database utilizes various algorithms to map the embeddings to data structures, facilitating faster and more efficient searching. This indexing process organizes the vectors in a way that enables quick retrieval based on similarity metrics.

Querying:

During querying, the vector database compares the queried vector to the indexed vectors using the defined similarity metric. It searches for the nearest neighbours, which are the vectors most similar to the query, based on the chosen metric. This allows for the effective retrieval of relevant information or data points.

Post Processing:

After finding the nearest neighbours, the vector database may apply post-processing techniques to refine the final output of the query. This can involve additional operations such as re-ranking the nearest neighbours to provide a more accurate or contextually relevant result. The post-processing step aims to enhance the quality of the query output and improve future reference by optimizing the ranking or presentation of the nearest neighbours.

Final Thought

Vector databases provide a structured and efficient way to represent and store contextual data, which is crucial for LLMs to understand language, retrieve relevant information, and perform various language-related tasks effectively.

Though vector database is in its very early stages. Well-established companies with expertise in time series data, such as KX Systems, have entered the market. At the same time, promising startups like Milvus, Pinecone, Weaviate, Vald, Deephaven, and Qdrant are attracting early-stage venture capital investments.

However, as the AI revolution takes its full course, the growth of vector databases is inevitable.

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