To tune the performance of Haystack’s retrieval algorithms, start by selecting and optimizing the retriever type and its parameters. Haystack supports sparse retrievers like BM25, dense retrievers like Dense Passage Retrieval (DPR), and hybrid approaches. For BM25, adjust parameters such as k1 (term frequency scaling) and b (document length normalization) to balance keyword matches with document context. For example, increasing b gives more weight to shorter documents, which might improve precision in some datasets. For dense retrievers like DPR, experiment with different pre-trained encoder models (e.g., bert-base-uncased vs. sentence-transformers/all-mpnet-base-v2) and fine-tune them on your domain-specific data. Hybrid retrievers combine sparse and dense methods, often using a ranker (e.g., a cross-encoder) to reorder results—adjust the weighting between BM25 and DPR scores to prioritize recall or precision.
Next, optimize data preprocessing and indexing. Ensure documents are split into logical chunks (e.g., paragraphs or sections) to avoid missing relevant content or including noise. For instance, setting a chunk size of 300-500 tokens often balances context retention and retrieval efficiency. Clean text by removing irrelevant markup, normalizing whitespace, and handling special characters. If using metadata (e.g., dates, categories), leverage it in filters or boost scores for specific fields. For example, prioritize recent documents by adding a metadata-based score boost. Indexing settings also matter: for BM25, ensure the inverted index is built with appropriate tokenization (e.g., using stopwords or stemming), while dense retrievers benefit from efficient vector storage (e.g., FAISS or Milvus for approximate nearest neighbor search).
Finally, use systematic evaluation and iteration. Define metrics like recall@k (how many relevant documents are in the top-k results) or mean reciprocal rank (MRR) to measure performance. Use Haystack’s Pipeline and Evaluator classes to test retrievers on a validation set. For example, compare BM25 with k1=1.2 and b=0.75 against DPR with a larger batch size to see which achieves higher recall. If results are inconsistent, try hybrid retrieval: combine BM25 and DPR outputs, then use a cross-encoder (e.g., cross-encoder/ms-marco-MiniLM-L-6-v2) to rerank the top 100 documents. Adjust the number of candidates passed to the ranker (e.g., top_k=50) to balance speed and accuracy. For scalability, optimize batch processing during inference and consider caching frequent queries or precomputed embeddings. Regularly retrain or fine-tune models as new data becomes available to maintain performance.