Summary
In this chapter, we explored various querying strategies and architectures within LlamaIndex with a deep focus on retrievers. Retrievers provide essential capabilities for extracting relevant information from indexes to generate useful responses in RAG systems. Throughout this chapter, we looked at basic retriever types such as VectorIndexRetriever and SummaryIndexRetriever. We also gained an understanding of advanced concepts such as asynchronous retrieval, metadata filters, tools, selectors, and query transformations. These allow us to build more sophisticated retrieval logic.
Additionally, we covered fundamental paradigms such as dense retrieval and sparse retrieval and discussed their strengths and weaknesses. Implementations in LlamaIndex such as BM25Retriever were also introduced.
Overall, this chapter provided an overview of retrieval capabilities in LlamaIndex, laying the foundation for building high-performance and contextually-aware RAG applications.
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