How do I optimize search performance in LlamaIndex?

To optimize search performance in LlamaIndex, focus on three key areas: index structure, query tuning, and infrastructure optimization. Start by ensuring your data is indexed efficiently. LlamaIndex supports multiple index types (e.g., vector stores, tree-based, keyword-based), and choosing the right one depends on your use case. For example, a VectorStoreIndex with a lower embedding dimension (e.g., 128 instead of 768) reduces memory usage and speeds up similarity searches. Breaking documents into smaller, semantically meaningful chunks (e.g., 512 tokens instead of 2048) also improves retrieval accuracy and reduces computational overhead. If your data includes hierarchical relationships, a HierarchicalKeywordTableIndex can prioritize broader topics before diving into details, reducing unnecessary node traversals.

Next, optimize queries by refining how search operations are executed. Use query transformations like HyDE (Hypothetical Document Embeddings) to generate synthetic answers and match them to relevant nodes, which often improves relevance over raw keyword searches. For hybrid search (combining vector and keyword-based retrieval), set weights to balance precision and recall—for example, 0.7 for vector similarity and 0.3 for keyword matches. Adjust the similarity_top_k parameter to limit the number of nodes processed during retrieval; reducing it from 20 to 10 might cut latency by 30% without sacrificing quality. Additionally, use RouterQueryEngine to direct queries to the most suitable index automatically—like routing fact-based questions to a keyword index and conceptual queries to a vector index.

Finally, optimize infrastructure to handle scale. Use a high-performance vector database (e.g., FAISS, Pinecone) to offload similarity searches, as they’re optimized for fast nearest-neighbor lookups. Enable caching for frequent queries—LlamaIndex’s SimpleDocumentCache stores retrieved nodes in memory, avoiding redundant embedding generation. If latency is critical, precompute embeddings during indexing instead of at query time. For distributed systems, shard indexes across servers (e.g., splitting by date or category) to parallelize searches. Profile performance using tools like cProfile to identify bottlenecks—for example, if tokenization consumes 40% of query time, switch to a faster library like tiktoken. Regularly prune outdated or low-relevance nodes to keep indexes lean.