To customize the scoring function in LlamaIndex, you need to modify how the system evaluates the relevance of retrieved documents or data nodes for a given query. LlamaIndex uses scoring functions to rank results, typically by comparing semantic similarity between a query and stored data. Customization involves creating a new scoring class that implements your logic, then integrating it into the retrieval pipeline. This allows you to tailor the ranking behavior to your specific use case, such as prioritizing certain metadata fields or combining multiple relevance signals.
For example, you might create a custom scorer that combines semantic similarity with keyword matching. First, subclass the BaseScorer (or equivalent interface in your LlamaIndex version) and override the score method. Within this method, you could compute a hybrid score using both a vector similarity score (from embeddings) and a BM25-based keyword score. You might assign weights to each component, like final_score = 0.7 * semantic_similarity + 0.3 * keyword_score, to balance semantic and exact-match relevance. To integrate this, pass your scorer to the Retriever configuration when initializing the query engine. This approach ensures your custom logic is applied during the retrieval phase.
Practical use cases for customization include handling domain-specific terminology or balancing freshness and relevance in time-sensitive data. For instance, a medical app might prioritize documents containing exact symptom keywords alongside semantic matches. A news aggregator could decay scores for older articles while maintaining relevance. Testing is critical—use a validation set of queries to compare your custom scorer’s performance against default settings. Tools like LlamaIndex’s evaluation modules can help measure metrics like hit rate or MRR (Mean Reciprocal Rank). Remember to document your scoring logic clearly, as future maintainers will need to understand how results are prioritized.