Haystack handles complex queries and multi-step reasoning through a modular pipeline architecture that chains specialized components. Each component in the pipeline addresses a specific task—like retrieving documents, filtering results, or generating answers—and passes its output to the next step. For example, a query requiring analysis of multiple documents might first use a retriever to fetch relevant text, then a reader to extract key details, and finally a generator to synthesize an answer. This separation of concerns allows developers to mix and match tools (e.g., Elasticsearch for retrieval, BERT-based models for comprehension) to handle nuanced questions. The pipeline design also enables conditional logic, such as rerouting queries to different components based on confidence scores or context.
A practical example involves multi-hop reasoning, where answering a question requires connecting information from multiple sources. Suppose a user asks, “What were the environmental impacts of Company X’s projects in 2020?” Haystack might first retrieve internal reports using a keyword retriever, then use a dense retriever (like a vector database) to find related scientific studies, and finally pass both sets of documents to a language model to compare and summarize findings. Developers can also implement query decomposition: breaking a complex question like “Compare the GDP growth of Germany and France after 2015” into sub-queries (e.g., “Germany GDP 2016–2023,” “France GDP 2016–2023”) and merging the results programmatically.
Customization is key. Haystack allows developers to insert custom logic between pipeline steps, such as filtering low-confidence retrievals or adding context-aware reranking. For instance, an ensemble retriever could combine keyword and semantic search results to improve coverage, while a summarization step might condense lengthy documents before feeding them to an answer generator. Tools like agents—configurable modules that decide which pipeline steps to execute based on the input—can handle dynamic workflows, like looping back to gather more data if initial results are insufficient. This flexibility ensures that even queries requiring iterative reasoning (e.g., “Explain how Project A influenced Policy B, then identify critics of that policy”) can be addressed systematically by adjusting the pipeline’s structure and components.