Visual Tools & Dashboard¶

Embenx provides a suite of interactive tools to help you understand your embeddings, visualize index structures, and test retrieval quality in real-time.

Launching the Explorer¶

The primary dashboard is the Embenx Explorer, a Streamlit-based web UI. You can launch it directly from the CLI:

embenx explorer

The dashboard will automatically detect any .parquet collections in your current directory.

Vector Clusters¶

The Vector Clusters tab provides a high-level view of your data distribution.

Dimensionality Reduction: Choose between PCA (fast, linear) or t-SNE (better for complex clusters) to project your vectors into 2D or 3D space.
Metadata Inspection: Hover over any point in the scatter plot to see its associated metadata fields.
Cluster Identification: Visually identify semantic groups and outliers in your dataset.

HNSW Visualizer 🕸️¶

The HNSW Visualizer tab offers a unique look at the internal architecture of Hierarchical Navigable Small World graphs.

Layer Visualization: See how nodes are distributed across different layers of the hierarchy.
Navigation Paths: Visualize the “hops” the search algorithm takes through the graph to reach the nearest neighbors.
Structure Audit: Understand the connectivity and density of your index, which is critical for tuning parameters like M and efConstruction.

RAG Playground 💬¶

The RAG Playground allows you to test Retrieval-Augmented Generation (RAG) loops without writing any code.

Select a Collection: Choose the data you want to query.
Configure the LLM: Enter an LLM model string (compatible with LiteLLM, e.g., ollama/llama3 or gpt-4o).
Chat: Ask a question. Embenx will: * Embed your query. * Retrieve the top-K relevant documents from your collection. * Inject the context into a prompt. * Generate a response using the selected LLM.
Inspect Context: Expand the “Show Retrieved Context” section to see exactly what information was provided to the model, helping you debug retrieval precision issues.

Interactive Search¶

The Interactive Search tab is a dedicated area for raw retrieval testing.

Real-time Results: Type a query and instantly see the metadata and distance scores for the top results.
Score Tuning: Adjust top_k on the fly to see how it affects the result set.
Filtering: (Upcoming) Apply metadata filters directly in the UI to test subset retrieval.

One-Click Dashboarding¶

Because the Explorer runs on Streamlit, it is easy to deploy as a permanent dashboard for your team. Simply host the explorer.py file on any Python-capable server or container.