Memory for the machine: How vector databases power the next generation of AI assistants

When Aquant Inc. was looking to build its platform — an artificial intelligence service that supports field technicians and agents teams with an AI-powered copilot to provide personalized recommendations and support agents — it needed something that could ground its AI models with real-time knowledge.

The solution: a vector database.

Oded Sagie, vice president of product at Aquant, told SiliconANGLE the company used Pinecone, a popular vector database that allows for production-ready vector infrastructure on serverless architecture, so it could quickly build artificial intelligence applications. “We wanted to introduce generative AI as part of our product suite and we wanted to do it fast,” he said.

Large language models, the engines behind modern artificial intelligence assistants and agents, offer powerful reasoning and language capabilities, but they lack grounded knowledge, memory and real-time adaptability.

Docugami’s Jean Paoli: “Vector search is allowing you to target interesting things.” Photo: LinkedIn

The moment an LLM finishes training, its knowledge is already out of date. General-purpose models also struggle with domain-specific accuracy, making them poorly suited for specialized industry tasks, expert reasoning or, most importantly, proprietary enterprise data.

That’s where vector databases shine. Acting as a kind of semantic memory for AI, they help assistants and agents “remember” and retrieve information based on meaning, not just keywords. Instead of relying on exact matches, a vector database allows AI systems to understand context and intent by making it possible to discover relevant results even when the language varies.

Vector databases also make searching vast enormous collections of data practical by retrieving the most relevant information in real time. This is essential for current AI applications, which rely on interpreting fuzzy human conversation to surface insights out of documents, information and unstructured data.

“Vector search is allowing you to target interesting things,” Jean Paoli, chief executive of Docugami Inc., a document analysis and data extraction firm, told SiliconANGLE. “It lets us reduce this big corpus [of knowledge] into relevant pieces, and now we’re asking point questions of small contexts.”

And they’re about to get an even bigger role. As the broader enterprise tech industry continues to adopt AI agents, vector databases are becoming increasingly critical. They provide the situational awareness and on-demand memory needed for agents to retrieve relevant knowledge, plan across steps and make decisions with greater autonomy.

The early life of and rise of vector databases

Vector databases have their roots in similarity search, a core concept that uses multidimensional data to provide context about subject matter. A vector is a numerical value that represents key features of unstructured data such as text, images or audio. The closer two vectors are to each other in space, the more likely the data they represent are related.

For example, three images representing two dogs and a cat would cluster near one another because they all depict animals, or more specifically, mammals. The dogs would be even closer to each other, since they share even more characteristics. This same kind of numerical representation can be applied to words, paragraphs or entire documents, allowing a system to pull them from a database based on semantic similarity rather than exact matches.

Google’s Andi Gutmans: “Now, vector processing is critical for generative AI and intelligent apps.” Photo: LinkedIn

Although vector databases are decades old, it wasn’t until the early 2000s that significant strides were made in their development, leading to consumer applications outside academic research. E-commerce was one of the earliest adopters. Home Depot, for instance, used the technology on its website to augment keyword search. Instead of depending on exact terms or trying to anticipate every possible typo or synonym, vector search let the company infer intent from context.

With the explosive popularity of OpenAI’s ChatGPT and other large language models starting in late 2022, vector databases quickly became the go-to solution for supplying those models with external data in real time. They’ve become a key component of retrieval-augmented generation or RAG, where relevant information is fetched from a vector store and provided to the model to improve accuracy, reduce hallucinations and personalize results.

This led to the quick rise of vector database vendors, such as Pinecone, a well-known fully managed vector database built for developers to scale high-performance AI applications. Other examples include Weaviate B.V., Qdrant and Redis. Large-scale platform integrations that initially provided traditional databases and infrastructure also began to offer vector capabilities — for example, Google Cloud, MongoDB Atlas, SAP HANA Cloud and Oracle.

The vector database market is experiencing rapid growth, with projections estimating it will reach $10.6 billion by 2032, according to market research firm SNS Insider. This surge is driven by the increasing demand for AI-driven applications across various industries, including finance, healthcare and e-commerce.

“In 2019, vector capabilities were completely uninteresting,” Andi Gutmans, general manager and vice president of engineering and databases at Google Cloud, told SiliconANGLE. “But now, vector processing is critical for generative AI and intelligent apps.”

This evolution presents enterprises with strategic choices: adopting vector functionality through integrated services from cloud providers or using specialized solutions from pure-play vector database vendors.

The decision between integrated and specialized solutions hinges on factors like performance requirements, scalability and specific use cases.

AI assistants and agents rely on vector databases

Although AI models are trained on vast amounts of general data, and can be fine-tuned with expert knowledge, that information can quickly become outdated. Enterprise businesses need a way to provide LLMs, AI assistants and agents with fresh, real-time data so they can deliver accurate, personalized responses.

In many cases, organizations also have proprietary data that lives entirely within their own firewalls. It’s not feasible for an AI model to ingest all of that information up front and simply “remember” it. That’s because LLMs are limited by context windows, essentially the model’s “short-term memory,” or how much context it can consider to generate coherent responses. It needs to search and retrieve small, relevant chunks of data when a query is made. These high-accuracy elements must be passed to the model on demand to produce the best possible answer.

“Even with a 2 million-token context, you don’t want to overload the model,” said Gutmans. “You want to feed it just what it needs.”

This approach is especially critical for AI agents, which go beyond chatbots by reasoning through complex tasks, breaking them down into step-by-step sequences, and executing them with little or no human involvement. An AI agent may include multiple LLMs, each with its own area of expertise, and must maintain a memory of what it has done in order to complete its goals.

Early experiments in agentic AI — notably Auto-GPT and BabyAGI — used Pinecone to store information between steps, providing this kind of long-term memory.

Vector databases aren’t limited to text. Enterprises are also using them to index images, audio and video, enabling multimodal AI assistants. For example, a social media company might store image embeddings to help an assistant fetch the most visually relevant content for posts on X or Instagram.

Vectors can encode not just metadata, but also the essence of an image, such as color, shape or composition. That same capability opens the door for advanced document processing in industries where data comes in many forms, including blueprints, technical charts or image-heavy reports.

Inside the stack: How the enterprise is adopting vector databases

Docugami needed to convert long-form unstructured enterprise documents, such as insurance policies or legal filings, into structured knowledge graphs for intelligent querying in validation. To approach this, the company uses 10 to 15 open-source large language models and the fast, in-memory database store from Redis Inc. to orchestrate them.

Morningstar’s Ben Barrett “We could basically ground our generative AI in our lots of proprietary data and research.” Photo: LinkedIn

Going beyond RAG, Docugami uses vector databases to support its agentic systems by building knowledge graphs, where the AI system can track semantic elements and relationships across hundreds of pages and data points.

“We developed some agentic algorithms that double-check the values and find the ones that might be wrong,” Paoli said. “Our application uses vector databases at every level, not just for RAG, including to support these kinds of agentic systems.”

Heavily regulated industries, such as finance, have also found vector databases useful for AI integration. For example, financial services firm Morningstar Inc. selected AI-native database startup Weaviate to power its vector database infrastructure.

“We landed on vector databases as kind of key toward our retrieval-augmented generation, so that we could basically ground our generative AI in our lots of proprietary data and research. That’s the bread and butter of Morningstar,” Ben Barrett, the company’s head of technology and research products, said in an interview.

Although the company uses vector databases and generative AI for internal purposes, Barrett noted that Morningstar is intentionally cautious about which data gets fed into these systems. Client portfolio data, in particular, presents a complicated regulatory landscape.

Barrett said Morningstar’s AI team works closely with quantitative researchers to improve response quality using a hybrid search strategy, combining keyword search with semantic search. He added that hybrid outputs proved far superior to semantic-only results in answer quality.

The road ahead: from memory to autonomy

The rise of agentic AI is beginning to force an evolution across the AI industry, shifting the focus from chatbots that summarize documents and answer questions to agents that reason over time and make autonomous decisions. Enterprise software engineers are now building multi-agent platforms that orchestrate specialized agents, each with expert skills, to complete complex goals.

Breaking down a task such as predicting if an item’s stock will come up short, identifying a supplier and placing an order requires querying across numerous enterprise systems. This information may span traditional databases, live APIs and documents that include both text and images.

Devin Pratt, a research director at IDC’s AI, automation, data and analytics division, told SiliconANGLE that many enterprise leaders believe that adding vector search will power up AI agents, but they overlook infrastructure and governance.

“Retrieval-augmented generation pipelines are important for anchoring LLM outputs to trusted, context-specific data,” Pratt said. “While vector search finds semantically similar content, RAG frameworks determine which documents actually feed into the model, cutting down on AI hallucinations, and improving answer accuracy.”

IDC’s Devin Pratt: Multi-agent systems are still “largely experimental” but ” beginning to appear in real-world settings.” Photo: LinkedIn

The large language models that drive these agents work best when they can access precisely the information they need, exactly when they need it. Similar to how Docugami orchestrates a dozen LLMs at once, vector databases are emerging as the shared memory layer for multi-agent systems. They also support the creation of dynamic knowledge graphs, which are structured information networks that map the relationships between entities across a business.

For example, an agent might use a knowledge graph for task planning or tracking the memory of prior actions taken by other agents, and a vector database for semantic recall. An insurance agent could log “I emailed the client about Policy X” as a relationship in the graph, then later retrieve similar follow-up examples from the vector database to generate a better response or escalation plan.

These multi-agent systems aren’t just theoretical. Large technology companies such as Google have begun investing in them. It recently released an open-source framework designed to make developing agentic platforms more accessible. Anthropic PBC, a major enterprise AI developer, recently open-sourced its Model Context Protocol, a toolkit for connecting LLMs to external systems that makes it easier for agents to call tools, interact with each other, share context and collaborate.

“Multi-agent systems remain largely in the early experimentation phase,” Pratt said. “They’re beginning to appear in real-world settings as organizations look to automate and coordinate complex tasks.”

In finance, collaborative agents have been put to work analyzing market data to assess risk. In logistics and supply chain management, multi-agent teams can share real-time inventory information to optimize stock levels and coordinate deliveries. And in healthcare, providers are piloting networks to monitor patient health, allocate staff and equipment, and predict shortages.

Looking ahead, vector databases are rapidly evolving, driven by the increasing adoption of AI and the need to efficiently retrieve high-dimensional information from large amounts of data and pair it down into suitably small bites.

Directions vector databases are already taking include into multimodal vectors which can relate not just text but images, audio and video, which is critical for AI applications that can interact across the whole spectrum of human experience. These diverse datasets allow AI applications that can look through personal image portfolios, listen to audio and provide real-time responses based on what they see and hear.

Such vector databases will drive the next-generation of AI applications in smartphones, virtual reality and augmented reality glasses that are already being developed by companies such as Google and Meta Platforms Inc.

Major providers are also integrating hybrid search and integration with existing databases by combining semantic and traditional search. Increasingly there has been integration between existing database technology, such as PostgreSQL and Cassandra with other unified cloud data management from Google, AWS and Oracle.

This trend allows users to simplify data management by reducing the need to manage separate vector and traditional databases. As much as vector databases are changing data management, they’re in for a lot of change themselves.

Image: 3alexd/Getty Images