Talk to Your Data: Turn CSV Files into Databases with One Click

Data is only as valuable as its usability. With our new Feature CSV Database in Kaia’s Team, you can convert CSV files into structured databases with a single click—making them searchable, analyzable, and mathematically operable. Instead of storing data as plain text in vectorized documents, you can now filter, compute, and extract precise insights directly from your assistant.

But why is this such a breakthrough?

Why CSV Database is a Game Changer

For a long time, Retrieval-Augmented Generation (RAG) was the go-to solution for integrating knowledge into AI assistants. However, RAG-based systems have significant limitations that hinder their reliability in business applications:

Lack of reliability: AI-generated responses may hallucinate or be unprecise when no direct answer exists.
No mathematical capabilities: RAG systems process numbers as text, with no ability to compute.
Limited control over data handling: Users have little control over how information is stored, embedded, or prioritized.

CSV Database eliminates these issues by enabling you to store data in structured SQL-like tables instead of embedding them into a vector database. This ensures accurate data retrieval, precise calculations, and greater transparency in how data is accessed.

How Do Databases Outperform RAG?

RAG (Vectorized Documents)	CSV Database (Convert CSV into Database)
Stores data as vectorized text, retrievable through AI search.	Stores data as structured tables with defined columns.
Returns answers based on semantic similarity.	Returns exact results based on queries.
No mathematical functions—cannot perform calculations.	Supports complex calculations with an integrated calculator.
No filtering or sorting options within stored data.	Allows filtering, sorting, and aggregating for targeted analysis.
Difficult to update—requires reprocessing of embeddings.	Easily updatable—new data is directly added to the table.

When is Vectorization Still the Better Choice?

Despite its advantages, database storage is not always the right choice. In some cases, vectorized documents offer unique benefits:

Full-text search in large documents: Ideal for manuals, legal texts, and other unstructured information.
Semantic similarity-based retrieval: Useful when users search for related concepts rather than exact matches.
Handling unstructured data: Best suited for long texts, descriptions, or open-ended queries.

For structured data, databases are superior. For conceptual search and long-form text retrieval, vectorization is still useful.

What Can CSV Database Be Used For?

With CSV Database, you can build powerful data-driven applications directly within Kaia’s Team:

Business Intelligence (BI): Analyze sales, revenue, and performance data in real time.
Customer Relationship Management (CRM): Store and retrieve customer contacts, transaction history, and support interactions.
Finance and Inventory Management: Track stock levels, calculate profit margins, and forecast demand.
Geolocation and Airport Databases: Query structured lists of airports, cities, or logistics hubs.
Medical and Pharmaceutical Databases: Store and analyze structured healthcare and medication data.
Product Catalogs and Recommendations: Filter products based on availability, price, and specifications.

The Integrated Calculator: Perform Advanced Computations on Your Data

One of the most powerful features of CSV Database is the built-in calculator, allowing direct mathematical operations within the data table.

Supported Functions

Basic Arithmetic

Addition (+), Subtraction (-), Multiplication (*), Division (/), Modulo (%)
Exponentiation (^) → Example: 2^3 = 8

Trigonometry

sin(x), cos(x), tan(x), asin(x), acos(x), atan(x)

Logarithms & Roots

ln(x) (natural logarithm), log(x) (base 10 logarithm)
sqrt(x) (square root), root(x, n) (nth root)

Statistics & Randomization

random(min, max) → Generates a random number between min and max
round(x), floor(x), ceil(x) → Rounding functions

Examples of Database Calculations

Calculate total revenue: SUM(revenue_column)
Find the average stock level: AVG(stock_column)
Compute logarithmic values: log(1000) → 3
Determine square roots: sqrt(144) → 12

Conclusion: Transforming Data into Actionable Insights

"CSV Database" in Kaia’s Team is not just about storing CSV files - it's a complete Business Intelligence tool. It enables your assistant to retrieve, calculate, and analyze structured data with unparalleled accuracy.

With structured data storage, real-time computations, and powerful filtering, it turns Kaia’s Team into a true data-driven decision-making assistant.

If you want your assistant to deliver precise numbers, handle structured queries, and provide reliable answers, CSV Database is the solution.

Start using Kaia's Team today and unlock the full potential of your data!