Vector Search: The Future of Data Querying Explained

Vector Search: The Future of Data Querying Explained | Semantic Searching

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9 ай бұрын

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** Support for the '$vectorSearch' aggregation pipeline stage is available with MongoDB Atlas 6.0.11 and 7.0.2. **
Welcome to this comprehensive guide on Vector Search, a revolutionary technique that allows you to search data based on meaning rather than just keywords. If you've ever struggled to find exactly what you're looking for in a database, this video is for you. We'll explore how Vector Search works, its benefits, and how to implement it in MongoDB for a seamless and powerful search experience.
🛠 What You'll Learn
What is Vector Search?: Understand the machine learning models behind Vector Search and how they transform text, audio, images, or other types of data into high-dimensional vectors.
Benefits of Vector Search: Discover why semantic understanding, scalability, and flexibility make Vector Search a must-have feature for modern databases.
MongoDB & Vector Search: Learn how to set up Vector Search in MongoDB Atlas, create triggers with OpenAI API, and perform vector search queries. We'll walk you through each step, from setting up your MongoDB Atlas account to writing JavaScript functions for querying.
📚 Resources 📚
✅ Written article & code → mdb.link/semantic-search-mong...
✅ Vector Search Documentation→ mdb.link/vector-search-1ZIYVN...
✅ OpenAI → platform.openai.com/overview
✅ Create your first FREE MongoDB Atlas Cluster → • Get Your Free MongoDB ...
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Пікірлер: 38

@MongoDB 9 ай бұрын

✅ Sign-up for a free cluster at → mdb.link/free-1ZIYVNvRVsY ✅ Vector Search Documentation→ mdb.link/vector-search-1ZIYVNvRVsY

@mzf11125 5 ай бұрын

Linear Algebra wasn't a waste of time after all

@spectralvalkyrie 8 ай бұрын

Wow I really want to learn to do this!

@user-cd1vg8le7f 8 ай бұрын

cool. i will try it

@user-gy9ig3qj3m 6 ай бұрын

Two Questions: 1. I use embedding model from Huggingface, and calculate embeddings myself. Can that be done inside the function that you showed? This requires importing some libs. 2. Are there other supported vector indexing algorithms? KNN has major issues with performance, I use HNSW (Hierarchical Small World Graph).

@KingAtem1000 8 ай бұрын

Thats pretty cool

@emanuelsosa5923 6 ай бұрын

I love that!

@user-xz8bp5eb4d 3 ай бұрын

Love you so much

@isabellesteinhauslin650 4 ай бұрын

Hey there, great content! I code using Pythong but this video provided me direction. Btw, I think there was an up date in the interface of MongoDB and the code for creating a vector search might need to be updated.

@Wooster23 7 ай бұрын

Is there a score or some quantified notion of similarity that can be returned with each result?

@user-ip1wg4sg3w 5 ай бұрын

I am really interested in implementing this in my microservices all written with Go. Can all of this be made programmatically with Mongo client for Go? Indexes can be created with the client, so I imagine that all of this can be done too.

@donalshijan5615 4 ай бұрын

I have a question, In this example it's going through plot which was saved as movie data. right? It then queries that plot for similar result, could it be done in a way where it goes through the actual movie and tries to figure out , oh there it is, a boy and a yellow dog, and then return that movie suggesting it might be the one we are referring to.

@jorgeimperial-sosa9097 3 ай бұрын

Cool t-shirt.

@bobonaqa 8 ай бұрын

So this is only working with dedicated servers and not supported with the serverless option?

@bobuputheeckal2693 4 ай бұрын

This worked for me. But some searches didn’t work as expected. I asked which is the first Bond movie. It gave me a list of bond movies. I understand the vector is only on plot. But can we vectorize the whole document ?

@user-wj3uq3tm8c 5 ай бұрын

What about searching in multiple fields inside a document? Is it possible to embed and search not only the plot of the movie but 20 more properties?

@jainsamyak970 4 ай бұрын

Hello! Did you find the answer to this ? Because I am also looking for this only. But not able to find anything.

@harikrishnansathyan4382 4 ай бұрын

Hii.. Any update on this, did you find any way to do this?

@bobuputheeckal2693 4 ай бұрын

Did u guys think of vectorizing all the properties of the document?

@Konstantin19877 4 ай бұрын

The search output is an exact text from DB or model twicks it the way it wants? Do i understand correct, model is just a search mechanism it does not changes found data it gives it to you as is?

@ViciOuSKiddo 4 ай бұрын

Correct. It's a search mechanism, but it's a "semantic" search engine. The results it returns are exactly what you saved. If you want the models to change it, you have to send it to an LLM to do that and ask it nicely.

@miralirafiyev4646 8 ай бұрын

Is it free to use in VPS server ( out of atlas ) where mongodb installed ?

@MongoDB 8 ай бұрын

This is only available through Atlas currently

@minhkhaitran1447 8 ай бұрын

unfortunately, I just follow the guide from the doc, but I get this error | MongoServerError: $vectorSearch is not allowed or the syntax is incorrect, see the Atlas documentation for more information not sure why is that? anyone know? please help

@SiiKiiN 8 ай бұрын

Are you using the serverless?

@hoangng16 Ай бұрын

Can we create a new vector search index using code instead of going to the MongoDB UI?

@Masyukun 3 ай бұрын

Don't set your Trigger to trigger off updates too, or you'll create an infinite loop! Also... it's responseCode, not response_code.

@JaimeHRozo 8 ай бұрын

It does not work on my Atlas cluster. The trigger log is showing the following error message even when uploading only one record: [ "Processing document with id: 573a1390f29313caabcd4eaf", "Failed to receive embedding. Status code: 429" ] How can it be fixed?

@stevehoward2749 8 ай бұрын

To me, the 429 looks like you exhausted some free resource, probably with openai.

@0xVeeda 6 ай бұрын

Seems you got rate limited

@Masyukun 3 ай бұрын

I recommend re-running the REST call using your favorite client -- cURL, Bruno, Postman, etc. For me, it gave me more information -- I had no credit in my account.