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Persistent, adaptive vector memory for agents on a ruOS desktop — a Rust-native vector database with local semantic embeddings, HNSW retrieval, and graph relationships. Use to remember and recall across sessions with no database server or API key.
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Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.
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Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
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Utilizes the Vectorworks RAG + MCP server to search and retrieve Python and VectorScript API documentation.
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High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
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High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
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Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
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Converts documents into clean, chunked datasets suitable for embeddings and vector search. Produces chunked JSONL files with metadata, deduplication logic, and quality checks. Use when preparing "training data", "vector datasets", "document processing", or "embedding data".
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使用Qdrant将文本等内容表示为向量，实现相似内容搜索。支持按类别或日期筛选、为AI回答检索资料、实时推荐，以及内存和分布式部署的配置调整。
生成照片风格图像、插画和产品效果图，也可替换现有图像的背景、移除多余物体或制作透明背景，并根据参考图探索不同方案。
使用AgentDB实现按含义查找文档的搜索功能，将文档存储、相似度搜索与属性筛选结合起来，构建检索相关资料并据此回答问题的系统。
打开命名的浏览器会话，保存初始画面、页面结构和每次操作的记录。保留网站探索过程，供后续分析，并为信息提取、表单填写和测试准备会话。
使用AgentDB实现按含义查找文档的搜索功能，将文档存储、相似度搜索与属性筛选结合起来，构建检索相关资料并据此回答问题的系统。
Plan HyperFrames videos so motion flows continuously between scenes. Match movement direction and speed across cuts, connect actions through visible causes, and verify scene boundaries while keeping each scene active through meaningful reveals or interactions.
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Create and edit bitmap images, such as photos, illustrations, product mockups, and game assets. Use reference images for visual variations, replace backgrounds, remove objects, or make transparent cutouts while preserving specified parts of an existing image.
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Manage Ollama models and run text generation, chat, and embeddings, which represent text as numerical vectors, through command-line requests. Check server status, inspect model details, and receive structured JSON output for agent workflows.
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Create and edit vector graphics, images built from shapes and paths, using commands for text, colors, layers, and layout. Save project state, undo edits, and export finished graphics as SVG, PNG, or PDF.
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Design and implement LLM applications, AI agents, and RAG systems that retrieve documents to support answers. Cover model selection, search quality, testing, monitoring, safety measures, and cost controls for applications intended for production use.
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Combine vector search, which finds similar meanings, with keyword search for exact terms. Design retrieval for search engines and RAG systems that supply reference material to AI, using methods to merge and rerank results.
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Review source code for MCP, a protocol connecting AI applications to tools. Check authentication, input validation, unsafe execution, and other security risks, then report findings with file and line references and separate manual follow-ups.
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Set up and manage AlloyDB for PostgreSQL clusters, instances, and backups. Includes guidance for MCP integration, a way for agents to operate databases, plus references for application connections, Terraform, authentication, and AI search capabilities.
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Design RAG systems that retrieve relevant documents to support LLM answers. The skill guides document splitting, meaning-based and keyword search, result reranking, and separate retrieval evaluation to improve the context passed to the model.
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Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.
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Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.
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