Better RAG with Merger Retriever (LOTR) and Re-ranking Retriever (Long Context Reorder)

In today's tutorial, I dive deep into the world of advanced information retrieval, focusing on two essential concepts:

LOTR (Lord of the Retriever), also known as the Merger Retriever. This intriguing technique utilizes a round-robin approach to merge results from multiple vector databases, ensuring a robust and diverse set of results.
Long Context Reorder: This is all about the reranking of retrievers. Once you've retrieved your documents using multiple models, how do you optimally order them to ensure relevance and precision?
For those dabbling with Retrieval Augmented Generation (RAG), implementing these techniques is pivotal. A more effective retrieval process directly enhances the quality and relevance of the generated content in RAG models.

Throughout the tutorial, I've leveraged LangChain and its high-level abstract classes, which streamline the implementation of these advanced techniques.

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GitHub Here: https://github.com/AIAnytime/How-to-i...
LOTR: https://python.langchain.com/docs/int...
BGE Embedding Model: https://huggingface.co/BAAI/bge-large-en

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