Hands-On LangChain for LLM Applications Development: Vector Database & Text Embeddings

Once you have loaded your documents and split them up into small, semantically meaningful chunks, it’s time to put these chunks into an index, whereby we can easily retrieve them when it comes time to answer questions about this corpus of data. 

To do so we will use embeddings and vector stores a sophisticated approach that not only facilitates the storage of information but also transforms the way we answer questions about our data corpus. 

In this article, we will first explore what is text embeddings and vector stores. Then we will cover how to create and store text Embeddings in vector stores with LangChain. We will conclude this article with some failure cases of this method.

Hands-On LangChain for LLM Applications Development: Vector Database & Text Embeddings / Image by Author

Table of Contents:

1. What are Text Embeddings?

2. What is a Vector Database?

3. Creating Text Embeddings with LangChain

4. Store Text Embeddings In Vector Database with LangChain

5. Failure Cases

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