Why Are Vector Databases So Important in the Era of Large Models? | Yunqi Capital ChatGPT Special

Recently, OpenAI dropped another bombshell on the tech industry — partially lifting ChatGPT's inability to access the internet. By integrating third-party plugins, OpenAI has enabled ChatGPT to go online, giving it "eyes and ears everywhere." This means ChatGPT can now truly retrieve real-time information, search knowledge bases, and execute actions on behalf of users.

"Yunqi Tech π" continues its ChatGPT special series. This installment features Zilliz, a Yunqi Capital angel-round portfolio company, joining the circle of "mega-influencer OpenAI" — as one of the first invited partners, contributing a vector database plugin to ChatGPT that bridges the gap between knowledge retrieval and large language models.

➤➤➤ More good news from Zilliz!

After being name-checked by Jensen Huang during the NVIDIA GTC Keynote, Zilliz has now joined the circle of "mega-influencer OpenAI" — as one of the first invited partners, contributing a vector database plugin to ChatGPT that bridges the gap between knowledge retrieval and large language models!

Zilliz is a pioneer and global leader in vector database systems, developing vector database systems for AI production environments. With a mission to unlock the value of unstructured data, Zilliz is dedicated to building next-generation database technologies for AI applications, helping enterprises develop AI applications with ease. Zilliz's products significantly reduce the cost of managing AI data infrastructure, enabling AI technology to empower more enterprises, organizations, and individuals.

**ChatGPT Goes Online,

Vector Databases Join the "Team Fight"

This morning, OpenAI dropped another bombshell on the tech industry — partially lifting ChatGPT's inability to access the internet. By integrating third-party plugins, OpenAI has enabled ChatGPT to go online, giving it "eyes and ears everywhere." This means ChatGPT can now truly retrieve real-time information, search knowledge bases, and execute actions on behalf of users.

In this context, ensuring ChatGPT extracts the most precise information from data sources becomes particularly critical — and the importance of vector databases speaks for itself. OpenAI explicitly stated in its announcement: the open-source retrieval plugin allows ChatGPT to access personal or organizational information sources with permission. It enables users to ask questions or express needs in natural language, and retrieve the most relevant document snippets from their data sources, such as files, notes, emails, or public documents.

To better support developers, OpenAI provided six vector databases capable of effective retrieval — with both Milvus and Zilliz making the list. Vector databases can effectively help developers or users with indexing and document search.

Why We Matter So Much in the Era of Large Models

As the wave of large models surges forward, vector databases are destined to spark a massive wave in retrieval. If ChatGPT is the processing core of LLMs and prompts are code, then vector databases are the storage that LLMs need.

Milvus is the world's first unstructured data project named a "vector database," operated and supported by commercial company Zilliz, and widely recognized in the industry as "the world's fastest vector database." Currently, Milvus has earned the trust of over a thousand enterprise users globally, including NVIDIA, eBay, Shopee, Walmart, Kuaishou, and IKEA, with maximum deployments exceeding 1 billion vectors. In many application scenarios, Milvus achieves QPS of over 10K.

Typical application domains for Milvus include long-form text, images, and video. In long-form text, it enables translation, Q&A, semantic search, and sentiment analysis — with semantic search and Q&A deployable alongside ChatGPT to improve response accuracy. In images, it supports deduplication, object detection, image retrieval, and multimodal text-image cross-search. In video, it powers recommendations, compliance detection, and classification.

Of course, many emerging application scenarios are also emerging — including in biopharma, where molecular formulas are converted to vectors to determine whether small molecules can tightly bind with proteins; in audio, for deduplication and sentiment analysis; in risk control, for identifying potential risks; and in autonomous driving, where vector retrieval helps identify objects missed during real-time analysis.

Zilliz holds a dual identity. It is not only the commercial company behind Milvus, but also offers Zilliz Cloud, a cloud-native vector database service. Building on Milvus, it effectively helps users solve operational, deployment, and performance challenges — truly enabling them to "forget complexity and focus on business and data itself."