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

Last Updated on January 25, 2024 by Editorial Team

Author(s): Youssef Hosni

Originally published on Towards AI.

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.

/ Image by AuthorWhat are Text Embeddings?What is a Vector Database?Creating Text Embeddings with LangChainStore Text Embeddings In Vector Database with LangChainFailure Cases

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