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Found 126 Skills
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.
PostgreSQL-based semantic and hybrid search with pgvector and ParadeDB. Use when implementing vector search, semantic search, hybrid search, or full-text search in PostgreSQL. Covers pgvector setup, indexing (HNSW, IVFFlat), hybrid search (FTS + BM25 + RRF), ParadeDB as Elasticsearch alternative, and re-ranking with Cohere/cross-encoders. Supports vector(1536) and halfvec(3072) types for OpenAI embeddings. Triggers: pgvector, vector search, semantic search, hybrid search, embedding search, PostgreSQL RAG, BM25, RRF, HNSW index, similarity search, ParadeDB, pg_search, reranking, Cohere rerank, pg_trgm, trigram, fuzzy search, LIKE, ILIKE, autocomplete, typo tolerance, fuzzystrmatch
Search code and text files using semantic similarity and keyword matching. Use when: finding relevant code snippets, searching documentation, or locating files by content. NOT for: binary file search or regex pattern matching.
AST-based semantic code search skill for AI agents. Teaches agents to use sqry's 34 MCP tools for finding symbols by structure (functions, classes, types), tracing relationships (callers, callees, imports, inheritance), analyzing dependencies, and detecting code quality issues. Unlike embedding-based search, sqry parses code like a compiler. Supports 37 languages. Uses tiered discovery: start with Quick Tool Selection below, load reference files only when you need parameter details or advanced workflows.
Semantic code search using Phase 1 vector embeddings and Phase 2 hybrid search.
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.
TOC router for PyGraphistry tasks. Use when a request involves PyGraphistry and you need to choose the right workflow: loading/ETL shaping, visualization/layout/sharing, GFQL queries (Cypher, chain-lists, Let/DAG, GRAPH constructors), AI/UMAP/embed/semantic-search workflows, or connector-specific ingestion.
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.