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Found 106 Skills
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.
Search and navigate large codebases efficiently. Use when finding specific code patterns, tracing function calls, understanding code structure, or locating bugs. Handles semantic search, grep patterns, AST analysis.
End-to-end drug discovery platform combining ChEMBL compounds, DrugBank, targets, and FDA labels. Natural language powered by Valyu.
Search FDA drug labels with natural language queries. Official drug information, indications, and safety data via Valyu.
Search ChEMBL bioactive molecules database with natural language queries. Find compounds and assay data with Valyu semantic search.
Search bioRxiv biology preprints with natural language queries. Semantic search powered by Valyu.
Search personal markdown knowledge bases, notes, meeting transcripts, and documentation using QMD - a local hybrid search engine. Combines BM25 keyword search, vector semantic search, and LLM re-ranking. Use when users ask to search notes, find documents, look up information in their knowledge base, retrieve meeting notes, or search documentation. Triggers on "search markdown files", "search my notes", "find in docs", "look up", "what did I write about", "meeting notes about".
Complete biomedical information search combining PubMed, preprints, clinical trials, and FDA drug labels. Powered by Valyu semantic search.
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.
Search global patents with natural language queries. Prior art, patent landscapes, and innovation tracking via Valyu.
Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.
Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.