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Hands-On LangChain for LLM Applications Development: Information Retrieval
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Hands-On LangChain for LLM Applications Development: Information Retrieval

Last Updated on January 29, 2024 by Editorial Team

Author(s): Youssef Hosni

Originally published on Towards AI.

Effective retrieval becomes crucial during query time when you need to fetch the most relevant information based on a given query. In our previous lesson, we delved into the fundamentals of semantic search, noting its effectiveness across various use cases.

However, we also encountered some nuanced scenarios where challenges arose. In this article, we will conduct a thorough exploration of retrieval, delving into more advanced techniques to address these edge cases.

While our previous discussion touched on semantic similarity search, we will now delve into several more sophisticated methods. Our journey begins with Maximum Marginal Relevance (MMR), a technique designed to retrieve more diverse data.

Following that, we’ll explore LLM-aided retrieval, allowing for self-query and the application of filters to enhance query precision. Finally, we’ll investigate retrieval by comparison, aiming to extract only the most pertinent information from the retrieved passages.

Figure 1. / Image by AuthorMaximum Marginal RelevanceLLM Aided RetrievalRetrieval by ComparisonCombining Various Retrieval Techniques

Most insights I share in Medium have previously been shared in my weekly newsletter, To Data & Beyond.

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