Loading...
Loading...
Found 65 Skills
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
Use when "vector database", "embedding storage", "similarity search", "semantic search", "Chroma", "ChromaDB", "FAISS", "Qdrant", "RAG retrieval", "k-NN search", "vector index", "HNSW", "IVF"
Comprehensive guide for managing vector databases including Pinecone, Weaviate, and Chroma for semantic search, RAG systems, and similarity-based applications
Qdrant vector database integration patterns with LangChain4j. Store embeddings, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.
Vector database selection, embedding storage, approximate nearest neighbor (ANN) algorithms, and vector search optimization. Use when choosing vector stores, designing semantic search, or optimizing similarity search performance.
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.
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, context retrieval, knowledge base, LLM with documents, chunking strategy, pinecone, weaviate, chromadb, pgvector, rag, embeddings, vector-database, retrieval, semantic-search, llm, ai, langchain, llamaindex" mentioned.
Pinecone integration. Manage Indexs. Use when the user wants to interact with Pinecone data.
Build modular Agentic RAG systems with LangGraph, featuring hierarchical indexing, conversation memory, and multi-agent query processing
Manage the full lifecycle of Alibaba Cloud managed Milvus instances—creation, scaling, configuration management, network management, and status queries. Use this Skill when users want to create a Milvus instance, view instance status, get connection addresses, scale/change configuration, modify settings, enable/disable public network access, set whitelists, release instances, or troubleshoot creation failures. Also applicable when users say "create a Milvus instance", "view instance details", "what's the connection address", "help me check the instance", "scale CU", "change config", "enable public network", "delete instance", etc.
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
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.