To handle multi-step retrieval and reasoning tasks in Haystack, you can utilize its modular architecture which allows you to break down complex tasks into manageable components. This process generally involves setting up a pipeline that can handle both retrieving relevant information and reasoning over that information to arrive at an answer. You’ll start by creating a retrieval pipeline that can fetch documents related to a query and then follow that up with a reasoning component, such as a reader model that analyzes those documents to extract or deduce answers.
A practical example of this approach can be seen in applications like question answering systems. First, you would set up a DensePassageRetriever or a BM25Retriever to identify relevant passages from your document store based on user input. Once you have a set of documents, the next step is to use a transformers model (like a pre-trained BERT model) as a reader component to extract specific information or answer questions based on the retrieved context. This can be achieved by configuring your Haystack pipeline to pass the documents through the reader model, allowing it to generate answers based on the insights gleaned from the text.
Additionally, if your multi-step tasks require combining information from multiple sources or passing intermediate results through several reasoning steps, you can extend this pipeline. For instance, you might implement logic to first summarize the relevant documents before answering, or use custom processing functions to handle specific rules or conditions. By chaining these components together and tweaking their settings for your unique use case, you can create a robust system capable of handling complex retrieval and reasoning tasks effectively.