Results for “embedded”

Deploy and operate the VSS 3.2 GA RT-Embed Video Embedding microservice using Docker Compose, covering GPU prerequisites, REST API usage for file uploads, text/video embeddings, live RTSP streams, Redis/Kafka/OTel integration, and troubleshooting.

Explains dense vector embeddings, their key concepts, common use cases, and best practices for semantic search and RAG applications.

Implements machine learning models, embeddings, and AI-powered features with ethical considerations, including model selection, integration, and monitoring.

Store and query embeddings with metadata filtering, vector search, and full-text search using an open-source database that scales from notebooks to production.

Plan, debug, tune, evaluate, export, or deploy public Nemotron embedding and reranking retrieval recipes using the current checkout.

Embeds target and source image parquets, then mines nearest-neighbour source images for augmentation in VCN AOI workflows.

Guides embedding model migration in Qdrant without downtime, covering alias swap, side-by-side, and hybrid search strategies.

Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.

Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.

Store and query embeddings with metadata, vector and full-text search, and filtering. Integrates with LangChain and LlamaIndex for RAG and semantic search applications.

Generate high-quality sentence and text embeddings for semantic similarity, clustering, and retrieval using 5000+ pre-trained models. Supports multilingual and domain-specific embeddings for RAG and semantic search.