Unlocking Possibilities: Real Vector Databases Demystified!
Summary
What separates vector databases and how to choose the best one for your project?
Part 2/3: The fascinating universe of vector databases
Welcome to the wild world of AI, where ChatGPT has made its grand entrance, bringing with it a new era of artificial intelligence!
And guess what? Vector databases are like the cool new kids on the block, becoming the must-have infrastructure for the future.
As AI-powered apps take over the world, vector databases are stepping up to the plate to handle the challenges thrown their way.
Now, in the first part of our epic trilogy, we delved into the mysterious realm of unstructured data and how it’s processed, analyzed, and queried.
Today, get ready to explore the fascinating universe of vector databases — what they are, how they stand out from the standard vector retrieval methods, and most importantly, how to choose the perfect one for your project.
What Exactly Is a Vector Database?
Picture this: Traditional relational databases store data all neatly in tables, behaving like those responsible, precise folks who never miss a beat.
But vector databases? Oh, they are the cool rebels, taking on unstructured data like pictures, sounds, videos, and text.
They store these babies as high-dimensional values known as embeddings, and when it comes to finding the nearest neighbor of a query point in a massive collection of points, they whip out their Approximate Nearest Neighbour (ANN) technique, like a boss!
You know, with the advent of ChatGPT, these vector databases are like the sidekicks that help large language models (LLMs) tackle their challenges.
It’s like having your trusty partner in crime as you take on the world!
Vector Search Libraries vs. Vector Databases
But hold your horses! Before vector databases took center stage, there were these libraries for vector retrieval like FAISS, ScaNN, and HNSW.
They were cool in their own right, but compared to vector databases, they kinda struggled when datasets and user demand got massive.
It’s like trying to fit a watermelon into a tiny backpack — not gonna work!
Vector databases, on the other hand, are the superheroes we need!
They handle massive amounts of data with ease, scaling up to the billions like it’s a walk in the park. CRUD capabilities, disaster recovery, access control — they’ve got it all covered!
Plus, they’re so user-friendly that even a tech newbie can add unstructured data to them with just a few lines of code.
Can you imagine trying to do that with other libraries? Chaos!
Vector Search Plugins vs. Vector Databases
So, here’s the deal: Many traditional databases are feeling the heat and trying to ride the vector database wave.
They’re adding vector search plugins like there’s no tomorrow, but let’s face it, they’re just playing catch-up.
It’s like putting a jet engine on a bicycle and calling it a fighter jet — nice try, but not quite!
```
from pymilvus import Collectioncollection = Collection('book')mr = collection.insert(data)
```
LLM Augmentation Needs Vector Databases
Now, let’s talk about the LLMs. These language models are fantastic at content production, but they’ve got a few quirks.
They might get a bit loopy with hallucinations, and sometimes they can’t handle in-depth queries due to token restrictions.
Enter the vector database, coming to the rescue like a memory bank for LLMs. They store all that extra info outside the LLMs, providing context and making the responses so much more accurate.
It’s like LLMs have upgraded their knowledge base and become supercharged!
And hey, here’s a pro tip for the developers out there — integrating a vector database with GPTCache can save you a whole lot of time and money!
Say goodbye to redundant queries and slow response times.
It’s like having a speed dial to all the answers, ready to deliver them in a flash!
Now you might be wondering, what can’t vector databases do? The answer is not much!
They’re like the Swiss Army knives of the tech world, with applications in recommender systems, image/audio/video/text similarity searches, anomaly detection, question-answering systems, and even molecular similarity searches.
Phew, that’s a mouthful!
How Do You Select the Best Vector Database for Your Project?
But with great power comes great responsibility, and choosing the right vector database can be a tough nut to crack.
Fear not, my friends, for we have the ultimate solution!
VectorDBBench is here to save the day, an open-source benchmarking tool that’ll help you make the most educated choice based on real performance.
Say goodbye to marketing jargon and hello to actual results!
Final words
So, there you have it — the magnificent world of vector databases, a realm of possibilities and power, where AI meets unstructured data in an epic showdown!
With the coolness of ChatGPT and the might of vector databases, you’re all set to conquer the AI landscape! Until next time, happy vectoring!
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Unlocking Possibilities: Real Vector Databases Demystified! was originally published in Artificial Intelligence in Plain English on Medium, where people are continuing the conversation by highlighting and responding to this story.
https://ai.plainenglish.io/unlocking-possibilities-real-vector-databases-demystified-eae353da3d5b?source=rss—-78d064101951—4
By: P. Rehan
Title: Unlocking Possibilities: Real Vector Databases Demystified!
Sourced From: ai.plainenglish.io/unlocking-possibilities-real-vector-databases-demystified-eae353da3d5b?source=rss—-78d064101951—4
Published Date: Wed, 26 Jul 2023 04:17:03 GMT
