Our data and vector search
In today’s digital world, the data generated by businesses or projects are incredibly valuable. It’s more than just numbers and text; it’s a goldmine of insights waiting to be unlocked. Every customer review, comment, or survey response adds a piece to the puzzle of what customers really want and need.
But how do we make sense of all this varied information? That’s where modern technology, such as vector search, comes into play. Special tools can dive deep into this data, understanding not just the words, but also the context and sentiment behind them. This isn’t just about counting keywords; it’s about seeing the bigger picture.
Well…. first, what is vector search??? How can I eat it? It can fly??
Well, Vector search is a modern way to search for information, different from the usual keyword search. It uses machine learning, particularly NLP (natural language processing), to better understand and handle searches and documents.
Here’s how it works:
Turning Text into Vectors: Text, like words or sentences, is converted into vectors. Vectors are just lists of numbers that represent the deeper meaning of the text. This is done using special models trained on lots of data.
Understanding Context: Unlike regular searches that just look for specific words, vector search understands the context and deeper meaning of the text.
Matching Search Queries: When you search for something, your query is also turned into a vector. The search engine then finds documents that have similar vectors (meaning they’re about similar things) using special calculations.
Vector search is useful in many areas:
Finding the Right Information: It helps systems find information that matches the real meaning behind what you’re searching for.
Recommending Content: It can suggest things like articles or products that match your interests.
Searching Images and Audio: It can find similar images or sounds by understanding their content, not just their labels.
Helping with Language Tasks: It’s important for tasks like translating languages or analyzing sentiments where understanding language is crucial.
Improving Chatbots and Customer Support: It makes chatbots and support systems better at giving relevant and accurate answers.
Traditional search vs Semantic search
Traditional Search Example:
Imagine you’re searching for a “lightweight summer jacket” on an online shopping site. In a traditional keyword search, the system looks for items that match the exact words: “lightweight,” “summer,” and “jacket.” This might bring up all jackets described as lightweight or suitable for summer, but the results may not always be relevant. For example, a heavy raincoat listed for summer use might also show up.
Semantic Search Example:
Now, let’s apply vector search to the same scenario. When you type “lightweight summer jacket,” the vector search doesn’t just look for the keywords. Instead, it understands the context and the intended meaning behind your search. It knows you’re looking for something casual, suitable for warm weather, and easy to carry.
Using vector representations, it compares your search query with the vectorized descriptions of products in its database. It recognizes that you’re likely looking for a jacket made of thin material, possibly with a hood, and in styles that are popular for summer wear. As a result, the search results are more accurate and tailored to what you really want, even if the item descriptions don’t explicitly match your search terms. This kind of search is particularly useful in e-commerce, where customers might not know the exact terms for what they’re looking for, or where product descriptions vary widely.
Other interesting uses…
So, imagine a Company with bunch of restaurants getting tons of online reviews. It’s a lot to go through, right? But with vector search, they can quickly figure out what’s working and what’s not.
Here’s how it plays out: Let’s say a customer writes a review like, “Love the tacos, but the service was slow.” Normally, just looking for keywords in reviews could miss the real message. But vector search gets the whole picture — it understands that the tacos are a hit, but the service speed needs work.
This way, the restaurant chain gets the gist of what customers are saying without having to read every single review. They know exactly what to improve — maybe make those tacos even better and speed up the service.
It’s like having a superpower to understand customer feedback instantly. And the best part? It’s not just for big companies. Even smaller businesses can use this to really tune in to what their customers want. Pretty neat, huh?
And what does mongo have to do with it?
Surprisingly for me we can do this using Mongo db and Mongo atlas but that ladies and gentlemen will be history for next time.