Improving the relevance of LlamaIndex search results requires a combination of data preparation, configuration tuning, and query optimization. Start by refining how your data is indexed, adjusting retrieval parameters, and structuring queries to align with your use case. Focus on the quality of input data, the way documents are processed, and how search algorithms prioritize results. Below are actionable steps to achieve better relevance in your search outcomes.
First, ensure your data is well-structured and preprocessed. LlamaIndex relies on document chunks and metadata to build its index, so inconsistent formatting or noisy data can degrade results. Break large documents into smaller, meaningful chunks (e.g., paragraphs or sections) using a tokenizer or text splitter. Overlapping chunks slightly (e.g., 10% of the chunk size) helps retain context between adjacent sections. Add metadata like titles, section headers, or keywords to provide additional signals for retrieval. For example, indexing research papers with metadata such as “author,” “publication year,” and “keywords” allows the retriever to prioritize documents matching specific criteria. Clean the text by removing irrelevant content (e.g., HTML tags, boilerplate text) to avoid polluting embeddings with noise.
Next, optimize indexing and retrieval settings. LlamaIndex offers flexibility in choosing embedding models, chunk sizes, and retrieval strategies. Experiment with different embedding models (e.g., OpenAI’s text-embedding-3-small vs. open-source alternatives like BAAI/bge-base-en) to see which captures semantic relationships in your data best. Adjust the chunk size based on content type: technical documents may require larger chunks for context, while conversational data might perform better with smaller segments. Use hybrid search, which combines keyword-based (e.g., BM25) and semantic search, to balance precision and recall. For instance, a query like “Python async frameworks” could match exact keywords (“async”) while also retrieving semantically related terms (“asyncio,” “concurrency”). Configure the retriever’s top_k parameter to balance speed and accuracy—start with a higher value (e.g., top_k=20) and use reranking to filter the most relevant results.
Finally, refine query handling and post-processing. Structure queries to include explicit instructions or filters using LlamaIndex’s query engines. For example, use a query like “Find case studies published after 2020 about renewable energy projects in Europe” to leverage metadata filters. Implement query expansion techniques, such as generating synonyms or rephrasing the query, to broaden the search scope. Post-process results with rerankers like Cohere’s or cross-encoders (e.g., BAAI/bge-reranker-base) to rescore retrieved chunks based on their relevance to the query. For instance, after retrieving 20 chunks, a reranker can identify the top 5 most relevant. Additionally, test custom retrievers or node postprocessors to exclude low-confidence matches or apply domain-specific rules. Regularly evaluate results using metrics like hit rate or precision and iterate on your pipeline based on feedback.