How vector databases can revolutionize our relationship with generative AI

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Generative AI has already received a lot of attention this year in the tech world and beyond. Whether it be ChatGPT prose Or The art of Stable Diffusion2022 provided a glimpse of the potential for AI to disrupt creative industries.

But behind the headlines, 2022 brought an even bigger development in AI: the rise of the vector database.

Although their impacts are less immediately obvious, the adoption of vector databases could completely change the way we interact with our devices, while dramatically improving our productivity in a wide range of administrative and office tasks.

Eventually, vector databases will be essential infrastructure to bring about the societal and economic changes promised by AI.


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But what East a vector database? To understand this, we need to make sense of the underlying problem it addresses: unstructured data.

The database dilemma

Databases are one of the most enduring and resilient verticals in the software industry. Total spend on databases and database management solutions double from $38.6 billion in 2017 to $80 billion in 2021. And since 2020, databases have only solidified their position as one of the fastest growing software categories, due of continued digitization following the massive shift to remote working.

However, the modern database is still constrained by a problem that has persisted for decades: the problem of unstructured data. It is the up to 80% of the data stored in the world that has not been formatted, labeled or structured in a way that allows for quick search or recall.

For a simple analogy between structured and unstructured data, think of a spreadsheet with multiple columns per row. In this case, a row of “structured data” has all the relevant columns populated, while a row of “unstructured data” does not. In the case of unstructured input, the data may have been automatically imported into the first column of the row; someone now needs to break this cell down and fill in the data in the relevant columns.

Why is unstructured data a problem? In short, it makes it harder to sort, find, review, and use information in a database. However, our understanding of unstructured data is relative to how data is generally structured.

Missing tags or misaligned formatting means that unstructured entries can be missed in searches or incorrectly excluded/included from filtering. This introduces error risks in many database operations, which we have to solve by manually structuring the data. This often forces us to manually review unstructured entries. This does not mean that the data itself is not necessarily structured; it simply requires more manual intervention than our usual means of storing data.

We often hear about the burden of manual review with claims such as data scientists spend 80% of their time on data preparation. But in practice, it’s something we all do to some degree, or at least live with the effects. If you’ve had to struggle with a file explorer to find something on your hard drive or spend a lot of time filtering through irrelevant search engine results, you’ve probably been hit with the problem of unstructured data.

This time wasted on manual formatting, reviewing and filtering is not a new or exclusively digital problem. For example, librarians manually organize books according to the Dewey decimal system. The problem of unstructured data is just a digital version of a fundamental challenge with all the record-keeping tasks humans have had since we invented writing: we need to classify information to store it and use them.

This is where vector databases come in particularly handy. Rather than relying on separate categories and lists to organize our records, vector databases instead place them on a map.

Vectors and cartography

Vector databases use a concept in machine learning And deep learning called vector embeddings. Vector embedding is a technique in which words or phrases in a text are mapped onto high-dimensional vectors, also known as word embeddings. These vectors are learned in such a way that semantically similar words are close to each other in the vector space.

This representation allows deep neural networks to process textual data more efficiently and has proven to be very useful in a variety of natural language processing tasks such as text classification, translation, and sentiment analysis.

In the context of the database, vector integration is actually a numerical representation of a group of properties that we want to measure.

To create an integration, we take a trained machine learning model and ask it to monitor these properties in inputs to a dataset.

In the case of a text string, for example, the model can be instructed to record average word length, sentiment analysis scores, or the occurrence of specific words.

The final embedding takes the form of a series of numbers corresponding to the “scores” recorded during the property audit. A vector database takes the scores of vector embeddings and plots them on a graph. Each property that we measure in an integrating vector constitutes a dimension of the graph, so it usually has many more than the three dimensions that we can visualize conventionally.

With all of this information plotted, we can still calculate how far away an integration is from another integration the same way we can in any other chart. Perhaps more importantly, we can engage in a new way of searching for data. By generating an embedding vector of an entered search query, we draw a point on the graph that we want to target. Then we can discover the closest embeddings to our search point.

Embedded vectors aren’t a perfect solution for everything. They are usually learned in an unsupervised way, which makes it difficult to interpret their meaning and how they contribute to the overall performance of the model. Pre-trained embeddings may also contain biases present in the training data, such as gender, racial, or political biases, which can negatively impact model performance.

The potential of vector research

A vector database does not rely on tags, labels, metadata, or other tools typically used to structure data. Instead, because a vector integration can track any property we deem relevant, vector databases allow us to get search results based on overall similarity.

While today’s searches for unstructured data involve manual review and interpretation, vector databases will allow searches to truly reflect the meaning behind our queries rather than superficial properties like keywords.

This change will revolutionize data processing, record keeping and most administrative and clerical tasks. Due to the reduction in “false positive” search results and the reduced need to pre-screen and format queries in a system, vector databases can dramatically increase the productivity and efficiency of almost any job in the industry. knowledge economy.

In addition to administrative productivity gains, these advanced search capabilities will allow us to rely on databases to respond more effectively to creative and open queries.

It is an ideal complement to the rise of generative AI. Since vector databases reduce the need to structure data, we can dramatically speed up training times for generative AI models by automating much of the work around processing unstructured data for training and production.

Therefore, many organizations can simply import their unstructured data into a vector database and tell it what properties they want to measure in their integrations. With these generated embeds, an organization can quickly train and deploy a generative model by simply letting it search the vector database to gather information for tasks.

The vector database is set to dramatically improve our productivity and revolutionize the way we send queries back to computers. Overall, this makes vector databases one of the most important emerging technologies of the next decade.

grinding wheel Hao is a partner of speedinvest.


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