To visualize the results of a Haystack query, you can use a combination of built-in tools and custom code to display retrieved documents, answers, or metadata in a meaningful way. Haystack, a Python framework for building search systems, provides utilities for processing natural language queries, but visualizing results often requires integrating additional libraries or interfaces. Start by structuring your query output to include relevant details like document text, confidence scores, or answer spans, then map this data to visual formats like tables, graphs, or highlighted text.
One straightforward method is to use Haystack’s REST API alongside a simple web interface. For example, after running a query with a Pipeline (e.g., a ExtractiveQAPipeline), you can extract answers and their metadata (confidence scores, document IDs) and pass them to a frontend tool. Using Python libraries like matplotlib or Plotly, you can create bar charts to compare answer confidence scores or heatmaps to show where answers appear in documents. For text-heavy results, tools like Jupyter Notebook allow you to display answers with highlighted spans using HTML rendering. For instance, you might loop through retrieved documents and use IPython’s display function to show text snippets with answer spans emphasized.
If you need deeper integration, consider building a custom dashboard with frameworks like Streamlit or Dash. For example, Streamlit lets you create a search interface where users input a query, and the results are displayed in a table with columns for answers, source documents, and confidence values. You could also use Elasticsearch’s Kibana for visualizing document relevance scores if Haystack is connected to an Elasticsearch document store. Another approach is to log query results to a tool like Weights & Biases (wandb) to track performance over time, visualizing metrics like answer accuracy or retrieval latency. By combining Haystack’s data structures with visualization libraries, you can tailor outputs to your specific use case—whether debugging retrieval logic or presenting results to end users.