Import a public Hugging Face model's pre-built ONNX file into EdgeGate, registering it as an Artifact. Returns an artifact_id for edgegate_create_pipeline, with optional polling for completion.
Submit a genie compile job for a target device chipset, choosing one of three input formats: HuggingFace repo, ONNX artifacts, or precompiled bundle. Returns a job ID to poll for progress.
Execute a complete clustering pipeline to identify high-CPU SQL queries, extract performance metrics, and group similar queries for optimization prioritization.
Run an ONNX benchmark on any connected edge device (Jetson, Snapdragon, gateway) by name or ID, and get latency/memory results within seconds. Requires admin access.
Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.
Local semantic recall MCP server for citation-integrity workflows. Enables semantic search over a text corpus, returning file paths and page numbers for verification, with 100% local processing using ONNX embeddings and LanceDB.
Generate a depth map from any 2D image or video TOP using Depth Anything v2, removing the need for Kinect or RealSense. Output depth TOP feeds displacement, pop-field, or silhouette tools.
Turn object/person detection into TouchDesigner control channels via WebSocket or CPU ONNX inference, outputting presence, count, and normalized bounding boxes as a Null CHOP ready for binding.
Turn object/person detection into TouchDesigner control channels via WebSocket or ONNX CPU inference, providing presence, count, and bounding boxes as CHOP channels.
Executes the complete SQL query clustering pipeline to identify high-CPU queries for optimization. Extracts, embeds, and clusters queries to surface optimization candidates.
Sets up a TouchDesigner scaffold for YOLO-style object tracking, creating source input, detection DAT, stable track CHOP, and annotated TOP. Routes live detections via WebSocket, ONNX, NDI, or file-watch backends.
Index PDFs into a named knowledge database layer using local embeddings, creating a semantic knowledge base for persistent research memory. Supports rebuilding existing databases.
Execute a complete SQL query clustering pipeline to identify high CPU usage queries for optimization analysis, extracting logs, calculating performance metrics, generating embeddings, and grouping similar queries.