148건 · 관련도순
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.
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guides Gensim topic modelling, document indexing, embeddings, similarity retrieval, downloader, and script workflows for Python NLP and IR tasks.
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Use DocArray for multimodal Pydantic-style documents, typed DocList and DocVec batches, serialization and local storage, FastAPI payloads, and vector retrieval indexes.
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Institutional-grade equity research skills for AI agents. 80 Claude-type skills across 14 verticals (equity-research-core, models-and-pitches, bio-pharm, scenarios, quantitative-analysis, idea-generation, options-derivatives, business-intelligence, industry-analysis, macro-strategy, portfolio-strategy, technical-analysis, risk-and-psychology, trading-as-business) powered by agentii.ai's agent-use-ready SEC filing data plane — SEC filings and 15K+ earnings call transcripts, 15.99M XBRL facts, and company profiles for 1,146+ US-public-equity tickers. Features the three-layer retrieval protocol (Document Discovery → Page Map → Deep Read, with deep-outline escalation) — measured, not asserted: 97.5% saved against a sequential read, and the page map itself is 10.0% of one (spec 062 T030, 2026-09-23) — server-side parallel multi-period search via search_cross_period, a full Excel/PPT generation pipeline with 3-tier office backend support, and the spec-046 governance layer (constitution, thesis and single-skill modes, mechanical gates, and a template-owned disclaimer on generated reports).
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Cohere API for enterprise NLP — embeddings, reranking, RAG, and text generation. Use when building RAG pipelines, semantic search, document reranking, or enterprise NLP applications. Command R+ excels at tool use and retrieval-augmented generation; Embed v3 and Rerank 3 are best-in-class for search quality.
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Assign items to a CLOSED label vocabulary that is too large to put in a prompt — product taxonomies, category hierarchies, tag vocabularies, routing tables, ICD/SIC-style code lists. A cheap model writes the label it thinks the vocabulary would use, and an embedder snaps that writing onto the nearest legal value, so the schema is never transmitted and the output is always in-vocabulary. Use for "classify these into our taxonomy", "tag these against the existing tag list", "map these queries to categories", "the enum is too big to send", or a Literal/enum that hits a provider cap. NOT for a vocabulary that fits in a prompt — structured output measured 0.701 acc@1 there against this pattern's 0.564. NOT for open-ended labelling with no fixed vocabulary, and not for ranked retrieval over documents (bm25).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Retrieves patent abstract images (drawings) from the Nexscope (Nexscope) patent database by patent ID or publication number. Trigger when the user mentions patent abstract drawings, patent diagrams, patent figures, patent images, abstract drawing retrieval, patent image lookup, patent abstract images, patent drawings, patent illustrations, Nexscope, abstract image lookup. Also trigger when the user needs to view the drawings or figures in patent documents, even without explicitly mentioning Nexscope.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
High-precision semantic search and content retrieval via Exa API. Use when: (1) Deep research requiring semantic understanding, (2) Code documentation and examples lookup, (3) Company/professional research, (4) AI-powered comprehensive research tasks, (5) URL content extraction with structured output. Triggers: "research", "find papers", "code examples", "company info", "LinkedIn profiles", "deep analysis". Differentiator: Exa excels at semantic/neural search while grok-search is better for real-time news and general web content.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Optional power-up — detects oversized codebases/docs and suggests tobi/qmd for local semantic search. Bridges cm-continuity (working memory) with long-term document retrieval. Zero-config detection, non-intrusive suggestion.
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Analyzes web performance using Chrome DevTools MCP. Measures Core Web Vitals (LCP, INP, CLS) and supplementary metrics (FCP, TBT, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge.
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Build data-centric LLM applications with indexing and retrieval frameworks — document ingestion, indices, query engines, and RAG pipelines. Use when the core problem is getting the right data in front of the model.
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Convert unstructured documents into LLM-ready structured data. Supports PDF, Word, PPT, and images; extracts paragraphs, formulas, tables, charts, and other elements in one step; generates up to 8 levels of headings; and outputs Markdown organized in reading order. Useful for field extraction, comparison and validation, knowledge retrieval, and intelligent Q&A.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Openapi utility services - real-time currency exchange rates, HTML-to-PDF conversion (renders JavaScript), .it domain registration/management, and managed RAG (retrieval-augmented generation over your documents). Use for these one-off utility tasks.
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Especialista em construção de sistemas de Retrieval-Augmented Generation. Domina modelos de embedding, bancos de dados vetoriais, estratégias de chunking e otimização de recuperação para aplicações LLM. Use quando: construir RAG, busca vetorial, embeddings, busca semântica, recuperação de documentos.
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Discover, compare, and compose existing open-source GitHub repositories for a user's goal, using GitHub MCP for live discovery and verification plus Context7 semantic retrieval over repository code and official documentation. Use when a user asks for GitHub repo recommendations, open-source alternatives, a multi-repository solution stack, project comparison, or help choosing an implementation to adopt.
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.