1. Sign up in docker - https://hub.docker.com/account/signup/ |
2. Login and go to docker hub |
3. You will get an mail from docker, you need to verify your mail id first. |
4. Create a repository in docker hub |
5. login into to your server. (Assume you have already installed docker) |
6. $ sudo docker login |
provide your user id / password / email for docker hub |
7. $ sudo docker ps -- to identify the container, whose image you need to push to docker hub |
8. $ sudo docker commit |
9. $ sudo docker images --- Now you can see new entry in the Repository with your public facing repository and a default TAG named 'latest' |
10. $ sudo docker push userid/repository-name --push the image to your repository created in docker hub |
11. Verify this from docker hub repository. |
Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.
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