To implement a custom Retriever in Haystack, you need to create a class that inherits from the BaseRetriever class and override its core methods. Haystack’s architecture allows developers to define their own retrieval logic while ensuring compatibility with other components like pipelines and document stores. Start by subclassing BaseRetriever and implementing the retrieve() method, which takes a query string and returns a list of relevant Document objects. You’ll also need to define how your retriever interacts with a document store or external data source, such as a database or API.
For example, suppose you want a retriever that filters documents based on a custom scoring function. You might initialize the retriever with a reference to a Haystack DocumentStore and define logic in retrieve() to fetch documents, compute scores, and return the top results. If your retrieval relies on vector similarity, you could integrate a library like sentence-transformers to generate embeddings for the query and documents, then compare them using cosine similarity. Ensure your method returns results in the format Haystack expects—such as a list of Document objects with metadata and scores—to maintain compatibility with downstream components like readers or rerankers.
After defining your retriever, integrate it into a Haystack pipeline by instantiating it and adding it to a Pipeline object. For instance, you might create a RetrievalPipeline that connects your custom retriever to a prompt template or a question-answering model. Test the retriever by running queries through the pipeline and validating the output. If performance is slow, consider optimizing your scoring logic or using Haystack’s caching mechanisms. By following this pattern, you can extend Haystack’s built-in capabilities to support domain-specific retrieval needs, such as combining keyword search with semantic matching or enforcing business rules during document selection.