Vector Databases Explained for Enterprise AI Applications

AI applications used by businesses have to access huge amounts of business data in the form of documents, customers' data, product data, pictures, support conversations and internal knowledge. Classic databases are very well suited to make structured queries; however, they are not always designed to comprehend the semantics of unstructured data. To provide a reliable solution to this problem, businesses apply a vector database approach.

A vector database is a type of specialised database which stores and fetches the numerical representation of the data, called the vector embeddings. It allows AI applications to do semantic searches instead of searching using exact keyword matching.

What Is a Vector Database?

Vector databases represent data in the form of numerical vectors in high-dimensional spaces produced by an embedding model.

The vectors carry the semantics of the data, which may include texts, images, audio files, and code.

For example, while a keyword search could miss the similarity between the phrases "How do I reset my password?" and "I cannot log into my account; how do I get back?" due to the different choice of words, a vector search would be able to identify the similarity between the two phrases because the vectors corresponding to the two phrases are close together in the vector space.

This allows the enterprise AI system to consider related data even though users have not used the same words that were originally written in the data.

How Does the Vector Database Work?

The general process of a vector database includes the following steps.

First, the enterprise data is collected from different sources such as documentation, website, database, CRM system, and knowledge base.

The data is then segmented appropriately and fed through the embedding model. The embedding model transforms every document into numerical vectors. These vectors, together with related metadata, are then stored and indexed in a vector database.

Upon making a query, the same is transformed into vector form. The vector database then compares the query with previously generated vectors and finds the best match. These results are then fed to an AI model that provides a response.

This process is popularly used in RAG, where external enterprise knowledge is sourced prior to generating a response using an LLM.

Enterprise AI Applications of Vector Databases

Vector databases can support a wide range of enterprise AI use cases.

Intelligent enterprise search: Employees can use natural language to search their documents and retrieve information based on its semantic meaning rather than by exact keyword matching.

RAG-based AI assistants: Organisations can integrate their knowledge databases with AI assistants and receive results that are grounded in their own data. When organisations need domain-specific LLM behaviour, in that scenario, LLM fine-tuning strategies can play an important role in achieving desired model behaviour.

Recommendations: Vector similarity can be employed for finding items, services or content similar to the user's interests and past actions.

Customer service: The AI systems can extract information from relevant articles on support, product descriptions, and any other available information before generating an answer.

Fraud detection: Vector embeddings can facilitate detecting similarities between suspicious behaviours.

Multimodal search: Vector databases can also be employed for multimodal searches involving images, sound, video and so on if the appropriate embeddings are available.

Advantages for Enterprises

One of the key features of vector databases is semantic search. Instead of relying completely on word matching, AI systems can find information using context-based similarities.

This allows quick similarity search operations on massive amounts of data and enables incorporation into AI systems of today. Depending on the needs of the system in question, organisations can make use of either vector databases specifically designed for such tasks or vector capabilities in already existing data solutions.

Another notable feature of vector databases is that vector search can be combined with metadata filtering and, in some cases, with keyword or structured search as well.

Challenges to Consider

The adoption of a vector database is not limited to embedding. Firms have to take into account the issue of data quality, how to chunk the data, choice of embedding model, indexing, search performance, latency, scalability and updates.

Similarly, security and governance are key considerations. An enterprise’s AI applications will handle sensitive customer, financial or operational information, which requires proper access control, encryption, filtering by metadata and effective governance policies.

Finally, businesses should determine whether they require a stand-alone vector database or whether vector searching is provided by their existing database solution.

Conclusion

Today, the vector database has emerged as one of the key elements of modern AI infrastructure in enterprises. Through the use of semantic similarity search, it enables AI to access information from the data pool.

However, when vector databases are used together with RAG, enterprise search, AI assistants, and recommender systems, they can be helpful for making proprietary information of the company available to AI. Nevertheless, the right approach does not only lie in choosing the technology itself. It is necessary to consider other aspects as well.