From the course: Level up LLM applications development with LangChain and OpenAI
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Quickstart: Installation and setup
From the course: Level up LLM applications development with LangChain and OpenAI
Quickstart: Installation and setup
- [Instructor] - So what more can we do with chains and with long chain? We could, for example, add a retrieval components to give additional context and information to the language model and allow similarity search. Similarity search is a technique used to retrieve content in a dataset that are similar to a given query item. And this is a technique which is widely used in various fields such as information retrieval, image recognition, recommendation systems, and many natural language processing tasks. In this module, we look at a basic example to create a vector store, then create embeddings to create vector representations of a piece of text to allow similarity search by querying the vector store. And so the definition of embeddings is that embedding models are used to create a vector representation of a piece of texts. So this is the example that we're going to be building in this module. But before that, I'd like to walk you through the setup. So the installation of the packages…
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