Vector Databases

About

Implement text splitters and document loading techniques within a vector database. Utilize these skills to proficiently query a vector database using cosine similarity, enabling the construction of question and answer retrieval chains.

Learning Objectives

You will be able to:

• Describe Retrieval Augmented Generation (RAGs)
• Use text splitters and document loading within a vector database
• Query a vector database using cosine similarity to build question and answer retrieval chains

Content

Topic	Skills
Slides	Introduction to LangChain
Introduction to Vector Databases and Queries	Use text splitters and document loading within a vector database
Retrieval Augmented Generation (RAGs)	Query a vector database using cosine similarity to build question and answer retrieval chains

Prerequisites

• Write and run Python code in a Jupyter notebook.
• Create basic Python functions.
• Import and use pandas and numpy.
• Create data visualizations in a noteboook using Matplotlib and Seaborn

References

📖 Reference Materials