What this blog covers:
- Vector databases and their importance
- Differences: Traditional vs. Vector DB
- How vector DBs work
- Vector DB usage with Kyvos for conversational analytics and SQL generation
We’re living in the age of artificial intelligence (AI) and machine learning (ML) with advanced applications like natural language processing becoming increasingly popular today. The backbone of these powerful applications is the massive volumes of unstructured data— videos, images, documents, audio files, etc.—which is processed to identify accurate patterns and correlations for optimal outcomes.
However, this type of data essentially lacks any specific format and has many features and attributes. This makes it impossible to store it in a traditional database which organizes structured data in tabular form as row and columns.
Enter vector databases or ‘vector DB’, a pivotal database solution to store and process unstructured data for tasks like similarity search, recommendation systems, etc. With the usage of unstructured data expanding, enterprises need vector databases to scale, handle and use them efficiently. But before we dig deeper into details, let’s understand what vector DBs are.
What is a Vector DB and How is it Different from Traditional Databases?
Vector databases are a type of database where objects are represented as vectors, which are essentially an array of numbers. These databases are uniquely designed to store and retrieve data in the form of vectors. While vectors or the numerical representations of data points are the building blocks, embeddings help understand the semantic relationships between data points.
The term vector and vector embeddings are often used interchangeably, however, they’re not the same. While a vector is a numerical representation of a piece of data, a vector embedding captures the semantic or structural relationships between data points. Embeddings capture the essential features of data, making them useful for tasks like similarity searches, contextual analysis and generative AI. At its core, all embeddings are vectors but not all vectors are embeddings.
Unlike traditional databases, a vector database maps data based on its semantic meaning, which is derived from the underlying features and properties of the data and stores it in the form of vectors. This approach allows for more accurate comparisons between similar data items and makes it easier for AI to understand and retrieve relevant information faster.
Traditional vs. Vector Databases
Traditional databases have been the backbone of countless applications for decades. From powering online shopping to banking, they’ve been used to handling all the data. However, vector databases have recently garnered attention with their ability to store and process unstructured, complex data types used by AI applications, machine learning, sentiment analysis, time-series analysis and geospatial applications.
To illustrate the differences further, let’s take an example. Imagine traditional databases as filing cabinets where all the documents are neatly separated and kept into labeled drawers. This system works well for storing structured data like customer records or product inventories.
On the other hand, a vector DB is like a digital image gallery where pictures are grouped not just based on their names but also on their semantic similarities. For instance, a vector database could store the image of a flamingo as numerical representation of its visual attributes and features which helps in efficiently searching for similar images. The main differences between the two are highlighted in the table below:
| Feature | Traditional Database | Vector Database |
|---|---|---|
| Data Structure | Structured (numbers, text) | Unstructured (vectors, embeddings) |
| Query Type | Exact matching | Similarity search |
| Strengths | Ensures data integrity, good for querying structured data | Good for complex queries, machine learning |
| Data Type | Customer information, financial records | Images, videos, text documents |
| Use Cases | Majorly used in BI Applications | Majorly used in AI Applications |
How Do Vector DB Work?
A high-dimensional vector DB allows mapping of data points to a multi-dimensional space which enables sophisticated understanding of relationships and similarities. It acts like a high-dimensional library, where books are organized not just by their titles or categories, but also by the content and words they contain. Within such databases, each item, be it a video, image or document, is represented in the form of a vector. These vectors are complex and multi-dimensional, stored in a structure designed for efficient and rapid searching.
When searching for similar items, a vector is created for the target object and the database identifies other vectors that are close to it in multi-dimensional space. The proximity of vectors indicates the similarity of the items they represent. This method allows for quick, accurate and flexible searches across extensive datasets.
Vector DBs are valuable for applications like recommendation systems, search engines and data analysis, as they can uncover subtle similarities between objects that traditional databases might overlook.
The Kyvos Angle
Kyvos is a semantic performance layer that supercharges analytics and AI initiatives. It is a master player for delivering BI or AI on complex data and leverages vector DB for analysis on unstructured data.
The platform offers conversational analytics that enable users to query data in natural language and receive instant insights. It utilizes vector databases to enhance this capability further and deliver highly accurate and relevant answers to user queries even on unstructured data.
How Kyvos Uses Vector DB to Enhance Conversational Analytics
One of the key aspects of building a substantial conversational analytics system in today’s Gen AI era is the quality of metadata, or ‘data about data’. Enhancing metadata in conversational analytics is essential for increasing the accuracy of query responses, democratizing data and making it more accessible.
To do this, Kyvos creates embeddings from semantic model metadata which can include descriptions, tags, data types, levels, attributes, measures, column names, synonyms etc.Kyvos stores these embeddings in the vector database. The platform also uses the vector DB to provide SQL constructs for natural language search based on these embeddings.
When a user asks a question in natural language, Kyvos converts the query into an embedding—a numerical representation. The vector DB then matches the query to relevant semantic models based on similarity scores, identifying columns and other metadata that are most relevant. As a next step, it compares the similarity score of the question embedding with the stored ones. Based on this score, Kyvos identifies the most relevant semantic models and columns to answer the query.
It also holds the capability to pinpoint the specific columns that are most likely to have the information related to the query asked. Kyvos achieves this by using Retrieval Augmented Generation (RAG) process which is used to enhance the accuracy of large language models by incorporating the relevant metadata. This focused search improves the accuracy of the response by narrowing down the data space.
Enhancing SQL Generation with Vector Databases
Kyvos’ integration with vector DB also improves SQL generation. When a user asks a question in their natural language, Kyvos converts it into a query by leveraging LLMs and the embeddings stored in the vector database. The process includes a layer of autocorrection, ensuring the generated SQL query matches the data structure in Kyvos’ semantic model.
This approach minimizes errors and enhances performance, allowing Kyvos to execute complex queries on structured and unstructured data sources.
Conclusion
Kyvos powered by vector databases is a game-changer for AI-driven conversational analytics. By leveraging embeddings and semantic search, Kyvos enhances conversational analytics and allows users to seamlessly get answers in natural language. This fusion of vector databases and Kyvos’ semantic models enables users to intuitively connect with their data while getting faster and accurate responses. With vector database, Kyvos is set to revolutionize how organizations harness AI and BI for smarter and efficient decision-making.