Haystack pipelines: our agent's secret power
1. Haystack pipelines: our agent's secret power
Agents are a powerful architecture —2. Agents for reasoning
especially when we need a system that can reason under uncertainty, plan the actions, and choose the right tools for the task. This is particularly useful when answering open-ended questions that require depth and context — like research-style queries or tasks that involve multi-step reasoning.3. Pipelines for defined flow
But not every task requires that level of flexibility. In fact, when the execution path is clear, when the steps are fixed and the logic is predictable, using an agent might actually add unnecessary complexity. For simple, deterministic workflows like data processing, retrieval or converting queries into SQL, we don’t need an agent. We need a pipeline.4. Pipeline and Component
So what is a pipeline in Haystack? A pipeline is a directed graph of components connected together in a specific order to form a repeatable, structured process.5. Pipeline and Component
We’ve already seen some of these components, like OpenAIChatGenerator, SerperDevWebSearch, and the Agent itself. Each component has a clearly defined responsibility like, creating embeddings, connecting to an API, or generating text.6. Pipeline and Component
Haystack provides a long list of built-in components, and if we need custom logic or behavior, we can also define our own component.7. Pipelines as agent tools
Here’s the powerful part: once we've built a pipeline that solves a specific task, like translating a question into an SQL query, we can wrap that pipeline as a tool and give it to our agent. That means our agent doesn't have to “figure out” how to do SQL translation or data fetching. It can just call the tool, and the tool, so our text-to-sql pipeline under the hood, can handle the task. This approach gives us the structure and efficiency of pipelines, and the flexibility and reasoning of agents.8. Coming up
In the next exercises, we’ll build a pipeline that translates natural language questions into SQL queries and retrieves answers from a structured database. Then, we’ll wrap that pipeline as a tool and connect it to our agent — getting us closer to a fully functional healthcare agent.9. Let's practice!
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