How do I handle user feedback and relevance feedback in Haystack?

To handle user feedback and relevance feedback in Haystack, you start by collecting explicit or implicit signals from users about the quality of search results. Haystack provides a Feedback API to log user interactions, such as clicks, thumbs-up/down, or document ratings. For example, when a user clicks on a specific search result, you can log this as positive feedback using document_store.write_feedback(). This data is stored alongside queries and documents, creating a dataset for improving your retrieval or ranking models. Developers typically implement this by adding feedback collection directly into their application’s frontend or API layer, ensuring each interaction is captured and sent to Haystack’s document store.

Relevance feedback is integrated into model improvement through techniques like fine-tuning retrievers (e.g., DensePassageRetriever) or rankers (e.g., SentenceTransformersRanker). For instance, if users consistently mark documents containing code snippets as relevant for queries like “Python error handling,” you can retrain your retriever to prioritize code-heavy content. Haystack supports this by exporting feedback data into training formats like Triplet or Pairwise datasets. You might also use Learning-to-Rank (LTR) approaches, where a model like XGBoost is trained on features such as document click-through rates or keyword matches to predict relevance scores. For active learning, tools like LabelStudio integration allow developers to identify low-confidence predictions and solicit direct user feedback on those cases, optimizing the feedback loop.

Practically, implement feedback pipelines by:

1. Setting up a feedback logging system (e.g., using REST endpoints to capture user actions).
2. Periodically retraining models with updated feedback data (e.g., running retriever.train() on new triplets).
3. Testing updated models in A/B experiments to measure performance gains. For example, after fine-tuning a retriever with feedback data, compare its recall@k against the previous version using a held-out validation set. Store feedback in a database like SQLite or PostgreSQL, and use Haystack’s evaluation metrics (e.g., eval() method) to quantify improvements. Avoid overfitting by validating on diverse queries and using techniques like cross-validation. This structured approach ensures feedback directly translates into measurable improvements in search quality.