Handling multi-step retrieval and reasoning tasks in Haystack requires a structured approach using its pipeline architecture. Haystack allows developers to chain components like retrievers, readers, and generators in a sequence to process complex queries. For instance, you might first retrieve relevant documents using a retriever, then filter or re-rank them, and finally pass the results to a reader or generator for reasoning. This modular design lets you break down tasks into manageable steps while maintaining flexibility to customize the workflow for specific use cases.
A common pattern involves combining sparse and dense retrievers to improve recall and precision. For example, you could use a BM25Retriever for keyword-based document retrieval, followed by a DensePassageRetriever to capture semantic matches. After merging the results, a reader like TransformersReader could extract answers from the top documents. For tasks requiring synthesis, a Generator component (e.g., leveraging a large language model) might analyze the retrieved context to produce a final answer. Developers can control the flow using Haystack’s Pipeline class, which supports conditional logic, branching, and custom nodes. For instance, you might add a node to validate intermediate results or reroute processing based on confidence scores.
To handle reasoning, Haystack’s PromptNode is useful for guiding language models through multi-step logic. For example, in a fact-checking scenario, you could first retrieve evidence documents, then use a prompt like “Based on [documents], is the claim [X] true?” to generate a reasoned verdict. For more complex workflows, you can create custom nodes to perform calculations, aggregate results, or apply domain-specific rules. Debugging tools like visualization of pipeline execution and intermediate outputs help refine each step. By systematically testing and iterating on individual components, developers can optimize both retrieval accuracy and the quality of reasoning in multi-stage tasks.