What are the best practices for fine-tuning the retrieval process in LlamaIndex?

Fine-tuning the retrieval process in LlamaIndex involves optimizing how data is organized, indexed, and queried to improve accuracy and efficiency. The key practices focus on data preparation, indexing strategies, and query configuration. By addressing these areas systematically, developers can ensure the system retrieves the most relevant information while minimizing computational overhead.

First, data preparation is critical. Ensure your documents are chunked appropriately for the use case—smaller chunks (e.g., 256 tokens) work for precise answers, while larger chunks (e.g., 512 tokens) retain context for broader queries. Overlapping chunks (e.g., 10-20% overlap) can help avoid missing information at boundaries. Adding metadata (e.g., dates, categories, or source identifiers) allows for filtering during retrieval. For example, tagging articles with “published_year: 2023” lets you prioritize recent content. Clean your data by removing irrelevant sections or redundant text, as noise in the input directly impacts retrieval quality.

Next, indexing strategies depend on the type of data and queries. Choose the right index type: a VectorStoreIndex works well for semantic search, while a KeywordTableIndex suits keyword-heavy tasks. For complex scenarios, combine indexes (e.g., a hybrid of vector and keyword search) using ComposabilityGraph. Optimize embedding models—switching from a general-purpose model (e.g., OpenAI’s text-embedding-ada-002) to a domain-specific one (e.g., sentence-transformers/all-mpnet-base-v2) can improve relevance. Adjust parameters like chunk_size and similarity_top_k based on testing. For example, reducing similarity_top_k from 10 to 5 might speed up retrieval without sacrificing accuracy if the top results are consistently relevant.

Finally, query configuration ensures the system interprets requests effectively. Use query engines like RetrieverQueryEngine to apply post-processing steps, such as reranking results with models like Cohere’s reranker or BAAI’s bge-reranker-large to prioritize better matches. Configure response synthesis modes: refine aggregates multiple chunks for detailed answers, while simple is faster for straightforward queries. Implement metadata filtering in queries (e.g., MetadataFilters to exclude outdated documents). Continuously evaluate performance using metrics like hit rate (percentage of queries where the correct document is retrieved) or Mean Reciprocal Rank (MRR). For instance, if a medical app struggles with rare conditions, test different chunk sizes and rerankers to improve recall.

By iterating on these practices—structuring data thoughtfully, selecting appropriate indexes, and refining query logic—developers can build a retrieval system that balances speed, accuracy, and scalability.