To improve the accuracy of search results in Haystack, focus on optimizing the retriever, refining preprocessing, and tuning the reader or reranker components. Haystack’s pipeline-based architecture allows adjustments at each stage to enhance relevance. Start by ensuring your retriever (e.g., Elasticsearch or BM25) is configured correctly. For example, adjust parameters like analyzer settings to handle synonyms or stemmed words, or use custom mappings to prioritize specific fields. If using dense retrievers like DPR or sentence-transformers, fine-tune the embedding model on domain-specific data to better capture contextual relevance. For instance, training embeddings on medical texts for a healthcare application will yield better results than generic pretrained models.
Post-retrieval processing is another critical area. Implement reranking with cross-encoders (e.g., BERT-based models) to rescore the initial results from the retriever. A common approach is to use Haystack’s TransformersRanker to prioritize documents that better match the query’s intent. Additionally, apply filters to narrow results using metadata (e.g., date ranges, categories) to reduce noise. For example, in a news search system, filtering articles by publication date ensures outdated content isn’t prioritized. You can also experiment with hybrid retrieval (combining sparse and dense methods) to balance recall and precision. Tools like Haystack’s EnsembleRetriever let you weight results from multiple retrievers, which is useful when some queries benefit from keyword matching while others need semantic understanding.
Finally, optimize the reader component if you’re using extractive QA. Choose a reader model (like RoBERTa or MiniLM) pretrained on data similar to your domain. Fine-tune the model on labeled examples from your dataset to improve its ability to extract answers. Adjust hyperparameters such as max_seq_length and doc_stride to balance context retention and computational efficiency. For example, increasing max_seq_length allows the model to process longer passages but may slow inference. If you’re using a generative approach (e.g., with GPT), control output with parameters like temperature to reduce randomness. Regularly evaluate results using metrics like Exact Match (EM) or F1-score, and iterate based on failure cases—such as tweaking the retriever’s top-k values or expanding the training data for the reader.