How do I export and visualize search results in Haystack?

To export and visualize search results in Haystack, you can use built-in tools and third-party libraries to convert results into structured formats and create meaningful visualizations. Haystack provides straightforward methods to extract data from its pipelines, and common Python libraries like pandas or Matplotlib can handle the visualization part. Here’s how to approach this in three key steps.

First, to export results, use Haystack’s pipeline output. When you run a query through a pipeline (e.g., a Retriever + Reader setup), the results are returned as a list of Answer or Document objects. These objects contain metadata, scores, and text content. For example, after running pipeline.run(query="your_query"), you can extract relevant fields like text, score, or meta (custom metadata) from each result. Convert these into a Python dictionary or a pandas DataFrame for structured storage. For instance, using pd.DataFrame([doc.to_dict() for doc in results]) creates a DataFrame you can save as CSV/JSON. This allows easy sharing or further analysis.

Next, for visualization, leverage data analysis libraries. For basic exploration, use pandas’ built-in plotting (e.g., bar charts for scores) or Matplotlib/Seaborn for histograms of relevance scores. If you want interactive visualizations, tools like Plotly or Streamlit work well. For example, you could plot document similarity scores in a histogram to analyze result quality or create a heatmap to compare multiple queries. To visualize document relationships, use dimensionality reduction techniques like t-SNE or UMAP on document embeddings (extracted via Haystack’s EmbeddingRetriever) and plot them in 2D space. This helps identify clusters of similar documents.

Finally, consider custom exports for specific use cases. If you need to share results with non-technical teams, export DataFrames to Excel or use Haystack’s REST API to serve results via a web interface. For debugging, logging results in JSON format (using Python’s json module) preserves metadata and scores for inspection. If you’re using Haystack with Elasticsearch, Kibana provides built-in dashboards for visualizing indexed data. Always validate exported data by checking field consistency (e.g., ensuring scores are normalized) and testing visualizations with sample queries to catch formatting issues early.