To customize search result rankings in Haystack, you can adjust retrieval strategies, modify relevance scoring, or implement custom ranking logic. Haystack provides flexibility through its pipeline components, allowing developers to control how documents are retrieved and ordered. The key methods involve modifying retrievers, using re-rankers, or creating custom ranking nodes in your pipeline.
First, consider adjusting the retriever’s parameters or switching between different retrievers. For example, if using the EmbeddingRetriever, you could experiment with different embedding models (e.g., switching from sentence-transformers/all-MiniLM-L6-v2 to a larger model) to improve semantic matching. For BM25-based retrieval with Elasticsearch, tweak parameters like k1 and b in the BM25 algorithm via Elasticsearch settings to prioritize term frequency or document length normalization. You could also combine multiple retrievers using a JoinDocuments node to merge results from sparse (BM25) and dense (embedding) retrievers before re-ranking.
Second, implement a re-ranker to refine initial results. Haystack supports transformer-based re-rankers like CrossEncoderRanker, which applies a more computationally intensive but accurate scoring model to the top N initial results. For example, after retrieving 100 documents with BM25, you could re-rank the top 20 using a Cross-Encoder model like cross-encoder/ms-marco-MiniLM-L-6-v2 to better assess relevance. Alternatively, create a custom ranking node by subclassing BaseRanker to apply business-specific logic, such as boosting documents from preferred sources or penalizing outdated content.
Finally, leverage Haystack’s pipeline configurations for advanced control. Use a WeightedRanker to combine scores from multiple retrievers with adjustable weights (e.g., 70% weight to BM25 and 30% to embedding similarity). For hybrid search, normalize scores from different retrievers using document_score_normalization before merging. If using Elasticsearch, directly customize its query DSL in Haystack’s ElasticsearchRetriever to add custom scoring scripts or function_score queries that incorporate metadata like popularity or freshness. Monitor results with Haystack’s evaluation tools to iteratively test and refine your ranking strategy based on precision/recall metrics.