How to Use Vector Database in Data Integration for GenAI Projects

Imagine you are searching for the phrase “size of Amazon.” How will the search app know if you meant the company or the river? Or let's say you ask your generative AI (GenAI) tool to create a post about “the properties of bark.” How will the model know if you meant the noise made by a dog or the outer covering of a tree?

What if you ask a chatbot for “a shot of lemons”? How would it know whether you are asking about a photograph or a shot of limoncello liqueur? In other words, how does AI understand the context of a particular task?

Of course, machines only understand values. So, the answer is that each word must be assigned an embedded value, which can help the AI understand the intent of the search or query and provide a contextualized result.

Every single text or visual-based AI model /LLM will need millions or even billions of such embedded vector values. These must be stored in a way that the LLM can access, search and retrieve from them quickly and without undue computing effort. And it all needs to be done before the person at the other end of the screen gets tired of waiting and clicks away.

The sheer volume of embedded values involved makes this sound like a herculean task. This is where a vector database (VectorDB) comes into the picture.

A VectorDB is a specialized database system designed to manage, store and retrieve high-dimensional data, typically represented as vectors. Typically, in ML and AI use cases, vectors are numerical representations of text, images or audio data points.

VectorDBs store the embedded vectors optimally by converting text into high-dimensional vectors. They use advanced indexing techniques to compare, search and retrieve the most relevant information from within massive high-dimensional datasets much faster and more accurately than other traditional search methods. Capturing the semantic meaning of the text in vector embeddings enhances search outcomes and enables more advanced natural language processing tasks.

VectorDBs offer several advantages over traditional relational databases for managing complex data structures and supporting demanding operations. Their efficiency, flexibility, speed and advanced capabilities make them a compelling choice for modern applications, particularly where real-time data processing and personalized user experiences are crucial.

Vector databases are becoming an integral component of modern large language model (LLM) applications because of their ability to retrieve contextually relevant information from massive high-dimensional data sets needed to power GenAI tools.

Their efficiency is particularly vital in the context of ML and AI applications, where they enable fast and more accurate similarity searches and nearest-neighbor queries.

Advantages of VectorDBs

• Handle unstructured and semi-structured data: Traditional storage structures, such as relational or NoSQL databases, are not built to handle unstructured data. Vector databases, on the other hand, excel in handling large datasets with complex unstructured and semi-structured data structures, making them highly suitable for various GenAI applications.
• Drive efficiency: VectorDBs can store billions of embedded values optimally, requiring minimal computing and saving costs.
• Faster search: Linear searches of vast embedded values can take a lot of computation effort and time, unlike the better-organized data structures in VectorDBs.
• Can support flexible schemas: Since each vector can have a different set of attributes, new attributes can be easily added or removed without requiring schema modifications, making it easier to accommodate changes in data requirements. This is particularly useful when new features or data sources are frequently introduced, as it allows the database to adapt without significant disruptions.
• More reliable results: VectorDBs can provide LLMs with the latest context and continuous data streaming to avoid hallucinations ​and improve accuracy. They also help enterprises ingest their domain-specific proprietary data to LLMs so that GenAI apps built on top of LLMs understand the context better and can answer industry-specific prompts more accurately.
• Support Retrieval Augmentation Generation (RAG) framework:​ This helps train company AI models on their own private data to hyper-customize outcomes.

As GenAI itself embeds itself across all industries, the use cases for vector databases are also increasing. They are increasingly implemented for use cases that enhance GenAI app or tool capabilities. For instance:

• Recommendation systems to better identify similar items, suggest related products or display content aligned with the user’s demonstrated interests.
• Semantic search to better understand the intent and contextual meaning of search queries and improve the accuracy of responses.
• Development of tools that use real-time context and insights, such as co-pilots, conversational chatbots or fraud detection systems.
• Enabling sentiment analysis from unstructured data on social media and facilitating contextual text classification.

LLMs and AI models are nothing without high-quality, accessible data that can be stored as embedded vectors. The data needed to train LLMs would be both real-time and historical. Not only would the data reside on different cloud and on-premises systems, but it would also be in diverse formats, from structured to semi-structured and unstructured.

A scalable and successful AI model would be impossible without an efficient data integration strategy. Robust, secure and continuous data integration is critical to handling data complexity and training/retraining AI models on new incoming data sets to avoid hallucinations caused by static, out-of-date or out-of-context data.

Efficient data integration also supports VectorDBs in saving vast datasets in a modular fashion so that it can retrain the AI model only with the relevant new data sets. This drives efficiency and speed.

Data integration for VectorDB involves three key steps:

• Ingesting documents: Data integration engineers ingest various documents (structured and unstructured) into the pipeline.
• Converting text into embeddings: The pipeline processes text data and converts it into vector embeddings (high-dimensional representations).
• Storing embeddings in a VectorDB: The vector embeddings are stored in a vector database, which efficiently manages and retrieves data.

Data integrators, data engineers and data architects are crucial enablers of LLM-based AI projects and initiatives. They automate, streamline and simplify data integration workflows and ensure data governance to ensure that only clean, reliable data is converted into vectors, thereby improving the VectorDB performance. 

As we have seen, enterprise-scale GenAI applications require the effective integration of various data sources in various formats to feed LLMs, all while preserving data security and integrity. That’s no easy task, and it requires a powerful data integration tool to get high-performance data ready for the VectorDB.

Without efficient data integration, the costs of prototyping and scaling complex AI models would spiral out of control. Training and retaining models every day is expensive.

Informatica data integration tools automate the data integration workflow and support the smooth movement of enterprise data in and out of VectorDBs to ensure LLMs are trained on the most accurate, recent, and contextual data. 

If you are running an AI project or want to scale up an existing AI model prototype, try CDI-Free and see how easy it is to get high-integrity data flowing in and out of your Vector DB, such as Pinecone.