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Building an Advanced Fusion Retriever from Scratch - LlamaIndex 🦙 0.8.49

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Reranking/fusion: fuse results from all queries, and apply a reranking step to “fuse” the top relevant results! Then in the next section we’ll plug this into our response synthesis module. The first step is to generate queries from the original query to better match the query intent, and increase precision/recall of the retrieved results. For instance, we might be able to rewrite the query into smaller queries. We can do this by prompting ChatGPT. Now we run retrieval for each query. This means that we fetch the top-k most relevant results from each vector store. NOTE: We can also have multiple retrievers. Then the total number of queries we run is NM, where N is number of retrievers and M is number of generated queries. Hence there will also be NM retrieved lists. Here we’ll use the retriever provided from our vector store. If you want to see how to build this from scratch please see our tutorial on this. The next step here is to perform fusion: combining the results from several retr

Building an Advanced Fusion Retriever from Scratch | Developer Documentation Skip to content Building an Advanced Fusion Retriever from Scratch In this tutorial, we show you how to build an advanced retriever from scratch. Specifically, we show you how to build our QueryFusionRetriever from scratch. This is heavily inspired from the RAG-fusion repo here: https://github.com/Raudaschl/rag-fusion . Setup Section titled “Setup” We load documents and build a simple vector index. % pip install llama - index - readers - file pymupdf % pip install llama - index - llms - openai % pip install llama - in

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