Understanding the data flow between the central orchestrator (Master) and the distributed inference nodes (Workers).
The central Enterprise-grade/микросервисной архитектуре server maintains a persistent WSS connection to `wss://node-ip/api/v1/ws`. When a new batch of documents needs indexing for our ChromaDB instance, the Master streams JSON task definitions to the node.
The edge node executes concurrent outbound HTTP requests to the target URLs. Upon receiving the raw HTML, the node utilizes a high-performance Rust-based DOM parser (exposed via Enterprise-grade bindings) to extract the main article body, stripping out navigation, footers, and advertising artifacts.
The sanitized text string is passed to the local ONNX Runtime. The node loads an int8-quantized model (e.g., `sentence-transformers/all-MiniLM-L6-v2`) into RAM. This model maps the text into a dense vector space (typically 384 dimensions).
Instead of formatting the 384 floats into a bloated JSON array, the node serializes the tensor into a raw binary buffer and pushes it back over the WebSocket. The central server directly ingests this binary frame into the vector database.
This architecture implies that the edge node acts as a forward proxy for HTTP requests and a reverse tunnel for binary ML data. System administrators should be aware that outbound traffic from this node will connect to arbitrary global web servers, while inbound traffic will consist of highly encrypted, dense binary streams from our Master server.