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with Kumaran Ponnambalam
Learn about the basics of vector databases and how to use them in LLM caching and retrieval-augmented generation.
GenAI with vector databases
Course coverage and prerequisites
What is a vector?
Vectorization in NLP
Vector similarity search
Vector databases
Pros and cons of vector databases
Introduction to Milvus DB
Milvus architecture
Collections in Milvus
Partitions in Milvus
Indexes in Milvus
Managing data in Milvus
Query and search in Milvus
Set up Milvus and exercise files
Create a connection
Create databases and users
Create collections
Insert data into Milvus
Build an index
Query scalar data
Search vector fields
Delete objects and entities
LLMs and caching
Prompt caching workflow
Set up the Milvus cache
Inference process and caching
Cache management
LLMs as a knowledge source
Introduction to retrieval augmented generation
RAG: Knowledge curation process
RAG question-answering process
Applications of RAG
Set up Milvus for RAG
Prepare data for the knowledge base
Populate the Milvus database
Answer questions with RAG
Choose a vector database
Combine vector and scalar data
Distance measure considerations
Tune vector DB performance
Continue with LLMs