To optimize LlamaIndex query performance, focus on three key areas: efficient data indexing, query configuration tuning, and leveraging caching or hardware acceleration. Start by ensuring your data is structured and indexed appropriately for your use case. For example, adjust chunk sizes and overlap parameters when splitting documents. Smaller chunks reduce computational overhead during retrieval but may lose context, while larger chunks preserve context at the cost of slower processing. Use metadata filters to narrow search spaces—for instance, tagging documents with timestamps or categories lets you exclude irrelevant data early in the query pipeline. Tools like the SimpleNodeParser or SentenceWindowNodeParser can help balance granularity and context retention based on your data type.
Next, optimize query execution by adjusting LlamaIndex settings. Reduce the similarity_top_k parameter to limit the number of nodes retrieved per query, which speeds up response times. For instance, fetching 3 results instead of 10 reduces vector comparison work. Experiment with hybrid search approaches: combine vector similarity with keyword-based filters (BM25) to improve relevance while avoiding exhaustive vector scans. Configure the ResponseSynthesizer to use compact modes like tree_summarize instead of slower iterative methods. If using OpenAI models, set lower temperature values and shorter max_tokens limits to reduce generation time. Always test different embedding models (e.g., text-embedding-3-small vs. all-mpnet-base-v2) to find the best speed/accuracy tradeoff for your data.
Finally, implement caching and hardware optimizations. Cache frequently used embeddings locally using SimpleCache or integrate Redis for distributed caching. Use GPU acceleration for embedding generation by running models like bge-small-en with CUDA support. For large datasets, offload vector storage to dedicated databases like Pinecone or PGVector instead of in-memory storage. Asynchronous query processing (via async_query) can parallelize tasks like fetching nodes and synthesizing responses. If processing many similar queries, precompute embeddings for static datasets. For example, a support chatbot could pre-embed all documentation articles, reducing runtime work to just query execution. These steps collectively reduce latency and resource usage without sacrificing result quality.