Results for “rvg”

Comprehensive guide to vgg. Master the concepts, implementation, best practices, and real-world applications of vgg in professional environments.

Guides downloading and configuring RVC voice conversion models, including HuBERT and index files, and running voice conversion scripts.

Builds a Streamlit and FastAPI RAG application that lets users upload documents and query them with natural language through LM Studio.

RAG with Vllm. building RAG systems.

Voice/audio-enabled AI chat interface forked from HuggingFace Chat UI with RVF document store replacing MongoDB

Optimize SVG files with SVGO — remove unnecessary metadata, minify paths, merge shapes, configure plugins, and integrate into build pipelines. Use when tasks involve reducing SVG file size, cleaning up exported SVGs from design tools, building icon systems, or automating SVG optimization in CI/CD.

Produces auditable, schema-valid JSON decision records with assumptions, risks, recommendations, and review gating for high-stakes choices.

Evaluates RAG pipelines using a filesystem-based benchmark with corpus/ and train.json, running evaluate_rag.py to tune retrieval and generation flags and interpret RAGAS metrics.

Deploy and operate the RTVI-CV-3D microservice for multi-camera 3D detection and tracking, supporting sample datasets, custom videos, and RTSP streams.

Runs chest X-ray reasoning smoke tests using the NV-Reason-CXR-3B model via local inference or a public Hugging Face Space API.

Deploy, debug, and operate the RTVI-CV 2D detection/tracking microservice and call its REST API for stream management, health checks, and metrics.

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.

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.

Create Route Origin Authorizations (ROAs) at RIRs, deploy RPKI validator software, and configure Route Origin Validation (ROV) on Cisco and Juniper routers to prevent BGP route hijacking.

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems.

RAG Builder with Parallel Document Processing Vector database construction with local embeddings (zero cost) Handles PDF download, text extraction, chunking, and vector database creation Absorbed B5 (Parallel Document Processor) capabilities Use when: building RAG, creating vector database, downloading PDFs, embedding documents, batch processing Triggers: build RAG, create vector database, download PDFs, embed documents, batch PDF processing

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

Manage Kafka consumer groups on Redpanda with rpk group: list, describe, seek, delete groups, and delete offsets.

Audit-ready decision artifacts for LLM outputs — assumptions, risks, recommendation, and review gating (schema-valid JSON).

Specialized hardware interfaces and system reliability for Zephyr RTOS. Covers LVGL GUI development, Audio I2S/Codecs, Watchdog timers, and Fault Injection. Trigger when building human-machine interfaces (HMI), audio devices, or high-reliability mission-critical systems.

Generate svg icon generator operations. Auto-activating skill for Visual Content. Triggers on: svg icon generator, svg icon generator Part of the Visual Content skill category. Use when working with svg icon generator functionality. Trigger with phrases like "svg icon generator", "svg generator", "svg".

Generates 3-5 editable SVG logo concepts with design rationale and usage notes, ready for refinement and shipping.

Evaluate RAG pipelines with Ragas — measure faithfulness, answer relevancy, context precision/recall, and noise sensitivity using LLM-as-judge metrics; run automated test suite generation with TestsetGenerator; integrate with LangChain, LlamaIndex, and CI pipelines.

RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.

Serve LLMs and VLMs with structured outputs, prefix caching, and high throughput using RadixAttention.

Generate pixel art SVG illustrations for READMEs, docs, or slides. Use when user says "画像素图", "pixel art", "make an SVG illustration", "README hero image", or wants a cute visual.

Set price, IV rank, unusual activity, earnings, and VRP alerts with contextual notifications and management commands.

Audits trending open-source repos for exploitable vulnerabilities using Semgrep, TruffleHog, osv-scanner, and Slither, then routes findings to responsible disclosure channels (PVR for code flaws, public PRs for dependency CVEs).

Create, inspect, and edit PCG Graph assets — add nodes, configure settings, connect pins, and wire procedural generation graphs (native PCG Python API). Use when the user asks to build a PCG graph, add PCG nodes (Surface Sampler, Static Mesh Spawner, filters), wire PCG pins, place a PCGVolume and generate, or set up procedural scattering.

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

Designs and implements RAG pipelines, covering document chunking, embedding strategies, hybrid search, answer synthesis with source attribution, and evaluation using RAGAS metrics.

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