501 件 ・ 関連度順
概要と使いどころ
Builds AI agents on the Convex agent component: threads, messages, tools that call queries and mutations, streaming, RAG with vector search, and workflows for multi step jobs. Use when adding a chat assistant, tool calling agent, or retrieval feature to a Convex app.
日本語の概要は準備中です。原文の説明を表示しています。
Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.
日本語の概要は準備中です。原文の説明を表示しています。
Retrieval-augmented generation (RAG) skill for the D&D 5e System Reference Document (SRD). Use when answering questions about D&D 5e core rules, spells, combat, equipment, conditions, monsters, and other SRD content. This skill provides agentic search-based access to the SRD split into page-range markdown files.
日本語の概要は準備中です。原文の説明を表示しています。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
日本語の概要は準備中です。原文の説明を表示しています。
Interactive Archon integration for knowledge base and project management via REST API. On first use, asks for Archon host URL. Use when searching documentation, managing projects/tasks, or querying indexed knowledge. Provides RAG-powered semantic search, website crawling, document upload, hierarchical project/task management, and document versioning. Always try Archon first for external documentation and knowledge retrieval before using other sources.
日本語の概要は準備中です。原文の説明を表示しています。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
日本語の概要は準備中です。原文の説明を表示しています。
Query EBI QuickGO REST API for GO terms and protein annotations. Fetch term metadata by ID, search by keyword, walk ancestor/descendant hierarchies, download annotations filtered by taxon, evidence code, aspect. Use for GO resolution, ontology traversal, annotation retrieval before enrichment. Use gseapy-gene-enrichment for enrichment; uniprot-protein-database for proteins.
日本語の概要は準備中です。原文の説明を表示しています。
ENA REST API for sequences, reads, assemblies, and annotations. Portal API search, Browser API retrieval (XML/FASTA/EMBL), file reports for FASTQ/BAM URLs, taxonomy, cross-refs. For multi-DB Python use bioservices; for NCBI-only use pubmed-database or Biopython Entrez.
日本語の概要は準備中です。原文の説明を表示しています。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
日本語の概要は準備中です。原文の説明を表示しています。
Enables interaction with Google NotebookLM for advanced RAG (Retrieval-Augmented Generation) capabilities via the notebooklm-mcp-cli tool. Use when querying project documentation stored in NotebookLM, managing research notebooks and sources, retrieving AI-synthesized information, generating audio podcasts or reports from notebooks, or performing contextual queries against curated knowledge bases. Triggers on "notebooklm", "nlm", "notebook query", "research notebook", "query documentation in notebooklm".
日本語の概要は準備中です。原文の説明を表示しています。
Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.
日本語の概要は準備中です。原文の説明を表示しています。
Provides unit test, integration test, and mock AI patterns for LangChain4j applications. Creates mock LLM responses, tests retrieval chains, validates RAG workflows, and implements Testcontainers-based integration tests for Java AI services. Use when unit testing AI services, integration testing LangChain4j components, mocking AI models, or testing LLM-based Java applications.
日本語の概要は準備中です。原文の説明を表示しています。
Provides configuration patterns for LangChain4J vector stores in 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.
日本語の概要は準備中です。原文の説明を表示しています。
Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.
日本語の概要は準備中です。原文の説明を表示しています。
Provides AWS Secrets Manager patterns for AWS SDK for Java 2.x, including secret retrieval, caching, rotation-aware access, and Spring Boot integration. Use when storing or reading secrets in Java services, replacing hardcoded credentials, or wiring secret-backed configuration into applications.
日本語の概要は準備中です。原文の説明を表示しています。
Build SQL queries for construction databases. Generate optimized SQL queries for construction data retrieval
日本語の概要は準備中です。原文の説明を表示しています。
Build RAG / unstructured-document evaluation datasets and demo documents (e.g. for Knowledge Assistant) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.
日本語の概要は準備中です。原文の説明を表示しています。
MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
日本語の概要は準備中です。原文の説明を表示しています。
Searches and navigates stored knowledge in memory palaces. Use when looking for previously stored information or cross-referencing concepts across palaces.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you need full-text PDFs for a list of DOIs, such as a meta-analysis screening set. Batch-downloads open-access copies via Unpaywall, PMC, OpenAlex and Crossref, lists paywalled papers for manual access, and can convert PDFs to Markdown.
日本語の概要は準備中です。原文の説明を表示しています。
Use FlagEmbedding for embedding, reranking, retrieval evaluation, and fine-tuning workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Use for Feast feature store tasks: feature repositories, definitions, CLI, retrieval, materialization, serving, RAG/vector search, integrations, and Feast contributor workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Guides Gensim topic modelling, document indexing, embeddings, similarity retrieval, downloader, and script workflows for Python NLP and IR tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Use DocArray for multimodal Pydantic-style documents, typed DocList and DocVec batches, serialization and local storage, FastAPI payloads, and vector retrieval indexes.
日本語の概要は準備中です。原文の説明を表示しています。