EmbeddingGemma 2 Explorer

Tools

• embeddinggemma_2_explorer_cross_modal_search Search a mixed corpus of text, images and audio with a text, image, audio or video query. Every item is embedded into EmbeddingGemma 2's single shared vector space, truncated to `dim` (Matryoshka) and L2-normalized, then ranked by cosine similarity. If a user passes a file as an input, use the upload_file_to_gradio tool, if present, to upload the file to the gradio app and create a Gradio File Input. Then use the returned path as the input to the tool. Returns: HTML listing the corpus ranked by similarity to the query.
• embeddinggemma_2_explorer_embed Return the EmbeddingGemma 2 embedding for a piece of text. Returns: The embedding as a list of floats.
• embeddinggemma_2_explorer_similarity_matrix Pairwise cosine similarity between lines of text (one per line), as a heatmap. Every line gets the same symmetric task prefix. Works across languages. Returns: HTML heatmap of the similarity matrix.
• embeddinggemma_2_explorer_text_search Rank documents for a query at every Matryoshka size (768, 512, 256, 128 dims). The query gets the task prefix for the chosen task; each document is formatted as `title: {title} | text: {content}`. Shows how ranking holds up as vectors shrink. Returns: Rows of [title, document, cosine@768, rank@768, rank@512, rank@256, rank@128].

No tool matches.

As the server's own listing gives them. We never call a tool.