Introduction
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In the previous lesson, you gained an understanding of text embeddings and how large language models (LLMs) leverage them to comprehend text data. Now, it’s time to delve deeper into the practical aspects of building a Retrieval-Augmented Generation (RAG) application.
In this lesson, you’ll learn how to embed text into a vector database, a crucial step in creating a RAG system. You’ll explore the capabilities of vector databases and discuss strategies to optimize your embeddings, ensuring your LLM can efficiently retrieve and process relevant information.
By the end of this lesson, you will have learned to:
- Implement text embedding extraction using a model in LangChain.
- Set up and interact with a vector database.
- Optimize embedding strategies for different types of data.