Getting Started with ChromaDB
Welcome to ChromaDB, the lowest learning curve vector database and semantic search engine! ChromaDB is a powerful tool for storing and querying high-dimensional vector data, making it ideal for applications such as image recognition, natural language processing, and recommendation systems.
1. Sign Up for a ChromaDB Account
Before getting started with ChromaDB, you’ll need to sign up for an account. Visit our website and create an account to get access to the dashboard and API documentation.
2. Importing Data
Once you have created your account, you can start importing your vector data into ChromaDB. Data can be imported in various formats, including CSV, JSON, and directly through the API. Simply follow the documentation to learn how to import your data efficiently.
3. Querying Data
With your data imported, you can start querying it using ChromaDB’s powerful search capabilities. Whether you’re searching for similar vectors or exploring relationships between data points, ChromaDB provides a simple and intuitive query interface.
4. Integration with Your Application
ChromaDB can easily be integrated into your existing applications through its API. With support for various programming languages and frameworks, you can seamlessly incorporate ChromaDB’s functionality into your projects.
5. Get Support and Resources
If you ever run into any issues or have questions about using ChromaDB, don’t hesitate to reach out to our support team. Additionally, make sure to check out our documentation and tutorials for helpful tips and tricks on optimizing your use of ChromaDB.
Start exploring the power of ChromaDB today and take your vector database and semantic search capabilities to the next level!
Please help me out with a subscribe if this video helped you 😀 AND I would love to know what you're doing in your ChromaDB project.
Check out my new video: How to vectorize 33K embeddings to ChromaDB in 3 minutes: https://youtu.be/7FvdwwvqrD4
Good Intro / Review. Thank You from a NEW Subscriber !!!
U should really sell ur code everyone's benefiting for free.
The POC use case I want to try is to query vehicle parts based on a user description which can be somewhat vague and keyword search is not ideal most of the time. I'd also want to do search-as-you-type with a vector db. My understanding is that there are embedding models trained on vehicle parts and seeing how easy it so specify a new embedding model although you have to remember the collection, I hope I can prove my POC. In addition, I want to use the persisted collection for other ideas as well. Thanks for making this video it really helps.
great demo and concise
You have a new subscriber. This video was very timely for my proof of concept.
thank you
Thank you so much for an easy-to-follow, practical example!
I’m curious about something… at one point in the video you note illustrate that the default embeddings are returning something unrelated (sesame ball) as the #1 choice. Your solution is to swap it out for another embedding provider.
But how would you go about digging in here and debugging further?
what do you do when all-MiniLM-L6-v2 is not very good at judging whats similar? it gets it wrong a lot!
This helped a lot! 👍
Thank you my guy 🙂
Amazing video thank you so much 🙂❤
Excellent tutorial.
Excellent tutorial. Thank You!
Such an amazing video man, Thanks for this valuable Knowledge
Awesome demo
Thanks for the excellent explanation
Short and Sweet! Thank you very much.
Rad. Thanks.