Day 2 - RAG - What is Vector DB ?

1. 1Break down private documents into chunks based on specific criteria
2. 2Feed these chunks into an embedding model to generate vector embeddings
3. 3Store the generated vector embeddings in a Vector DB for efficient querying
4. 4Use the Vector DB to retrieve relevant documents based on similarity scores
5. 5Integrate the Vector DB with an LLM to enable RAG functionality

Data scientists and AI engineers can benefit from understanding Vector DB to improve their RAG pipelines and integrate private documents with LLMs

💡 Vector DB enables efficient storage and querying of vector embeddings, making it a crucial component in RAG pipelines

Discover Vector DB and its role in RAG! Store and query vector embeddings for private documents to supercharge your LLM integrations