How do I implement session-based search in Haystack?

To implement session-based search in Haystack, you need to maintain context across multiple interactions within a user session. This typically involves tracking a user’s search history, filters, or preferences during their session and using that data to refine subsequent queries. Haystack provides tools to handle this through pipelines, session storage, and custom components. The core idea is to store session-specific data (like previous queries or filters) and inject it into search operations dynamically.

First, create a session storage mechanism. You can use a simple in-memory dictionary for development or integrate a persistent store like Redis for production. Each session should have a unique identifier (e.g., a session ID) passed with each request. For example, when a user starts a session, generate an ID and store their initial query parameters. In subsequent requests, retrieve the session data using the ID and update it as needed. In Haystack, you can pass session data through the meta field of a Request object. For instance, a pipeline might process a query, extract the session ID from the request, and fetch stored filters or context to modify the search.

Next, design your pipeline to incorporate session data. Use Haystack’s Pipeline class to chain components like retrievers, filters, or custom nodes. For example, add a custom node that checks the session for prior filters (e.g., a date range) and applies them to the current query. If a user previously searched for “AI research papers,” the session could store a filter for “publication_year >= 2020,” which is automatically added to future queries. You can also use QueryAugmenter to append historical queries from the session to the current query, improving relevance. Ensure your retriever (e.g., BM25Retriever or EmbeddingRetriever) uses this enriched input.

Finally, handle session expiration and data cleanup. Set a timeout for sessions to prevent memory leaks. For in-memory storage, use a background task or a library like cachetools with TTL (time-to-live). In production, rely on your storage system’s expiration features (e.g., Redis’s EXPIRE command). Test session logic thoroughly—simulate multiple sequential queries to ensure filters and context persist correctly. For example, if a user applies a “category: tutorials” filter in one query, verify that subsequent searches within the same session inherit this filter unless explicitly removed. Document how session data impacts search results to avoid unexpected behavior for end users.