In this hands-on tutorial, we’ll introduce LLMs and two main problems they face when it comes to production. First, high cost. Second, lack of domain knowledge. We then introduce vector databases as a solution to this problem. We cover how a vector database can facilitate data injection and caching through the use of vector embeddings.
Then we’ll use this knowledge to build an LLM application using LlamaIndex and Milvus, the world’s most popular vector database.
- What is a vector database
- Why do LLMs face data issues
- How to deal with data issues in an LLM
What you’ll need:
- Python 3.9 or above
- A basic understanding of vectors and databases