How do I fine-tune the retrieval process in LlamaIndex?

To fine-tune the retrieval process in LlamaIndex, you can adjust several components that influence how data is indexed, retrieved, and ranked. Start by customizing the way documents are split and stored. LlamaIndex uses “nodes” (chunks of text) to represent data, and modifying parameters like chunk size, overlap, or the splitting method directly impacts retrieval quality. For example, increasing chunk size might capture more context but reduce precision, while smaller chunks could miss broader themes. You can also experiment with advanced node parsers, such as the SentenceWindowNodeParser, which groups sentences with surrounding context. For instance, splitting text into windows of three sentences with a one-sentence overlap ensures key ideas are retained without isolating them. Adjusting these settings helps balance granularity and context based on your data type (e.g., technical docs vs. narratives).

Next, optimize the retriever itself by tuning query parameters or switching between retrieval strategies. LlamaIndex supports multiple retrievers, such as the VectorIndexRetriever for semantic search or KeywordTableRetriever for exact keyword matching. If using a vector-based approach, adjust the similarity_top_k parameter to control how many results are fetched. For example, setting similarity_top_k=10 retrieves more candidates, which can then be re-ranked. Hybrid approaches, like combining vector and keyword retrievers with a QueryEngineTool, often yield better results. You might also implement a RecursiveRetriever to traverse hierarchical data (e.g., nested sections in a document). Additionally, tweak the embedding model—switching from a general-purpose model like text-embedding-ada-002 to a domain-specific one—can improve relevance for specialized data.

Finally, refine post-retrieval steps to improve result quality. Use rerankers like Cohere’s or Hugging Face’s CrossEncoder models to rescore retrieved nodes based on their actual relevance to the query. For example, passing the top 20 nodes through a reranker can surface the most accurate 5 results. Metadata filtering is another lever: restrict retrieval to nodes tagged with specific attributes (e.g., date ranges or document sections) using MetadataFilters. If your data includes structured fields, define a VectorStoreIndex with SQLDatabase integration to blend semantic and structured queries. Testing different combinations of these settings—via metrics like hit rate or precision—is critical. For instance, run A/B tests comparing a baseline retriever against a hybrid setup with reranking to quantify improvements.