How do I create and manage pipelines in Haystack?

To create and manage pipelines in Haystack, you start by defining a sequence of components (called nodes) that process data in a specific order. Haystack pipelines are built using the Pipeline class, which lets you chain together nodes like retrievers, readers, or custom components. You first import the Pipeline class and instantiate it, then add nodes using add_node(), specifying their roles (e.g., a retriever for document search) and how they connect. For example, a basic question-answering pipeline might include a retriever to fetch documents and a reader to extract answers, linked sequentially. You can also configure pipelines using YAML files for better reusability, defining nodes and their connections in a declarative format.

Managing pipelines involves organizing components, handling dependencies, and ensuring efficient execution. Haystack allows you to save pipeline configurations as YAML files, which makes it easier to version-control and modify pipelines without rewriting code. For instance, a YAML file might define a retriever node using Elasticsearch and a reader node using a Hugging Face model, with the pipeline routing inputs from the retriever to the reader. You can load these configurations dynamically using Pipeline.load_from_config(), enabling flexibility in experimentation. Logging and error handling are critical: Haystack provides built-in logging to track data flow, and you can wrap nodes in try-except blocks or use custom error-handling nodes to manage failures gracefully.

Advanced pipeline management includes optimizing performance and scaling components. For example, you might parallelize nodes using Haystack’s JoinDocuments node to merge results from multiple retrievers or use caching for frequent queries. To scale pipelines for production, you can deploy nodes as microservices using Haystack’s REST API or tools like Docker. Monitoring is also key—integrating with tools like Prometheus to track latency or accuracy metrics. If a component becomes a bottleneck (e.g., a slow reader model), you can replace it with a faster alternative or adjust batch sizes. Finally, testing pipelines with validation datasets ensures reliability, and Haystack’s evaluation features help measure performance metrics like answer correctness or retrieval recall.