Vectorize documents, web pages, and emails for semantic search.
npx skills add https://github.com/gentic-co/agent-skills --skill gentic-knowledge
Please help me install this Agent Skill. Skill: gentic-knowledge Source: https://github.com/gentic-co/agent-skills/tree/main/skills/knowledge Command: npx skills add https://github.com/gentic-co/agent-skills --skill gentic-knowledge
Vectorize and index content to enable fast, semantic search across your organization's documents, web pages, and communications.
Vectorize a sample document and perform a semantic search across your indexed knowledge.
High-intent search queries and answers about installing and using this skill.
To build a searchable knowledge base from PDFs and emails, you vectorize documents and web content to enable semantic search. This process indexes your files and communications so you can retrieve relevant results using natural language queries.
Semantic search across indexed documents works by vectorizing content into mathematical representations and matching natural language queries to those vectors. This retrieves contextually relevant information rather than relying on exact keyword matches.
You can vectorize PDF, TXT, RTF, and DOCX file formats for semantic search. The system also supports vectorizing web content and emails to build a comprehensive, searchable knowledge base.
You can check the indexing status of your knowledge base sources using the list_kb_sources command. This verifies which documents, web pages, and emails have been successfully vectorized and are available for semantic search.
You query indexed content by submitting natural language questions to the vectorized knowledge base. The semantic search matches the meaning of your query against indexed documents and communications to retrieve relevant results with context.
The best way to organize and retrieve team knowledge is vectorizing documents, web pages, and notes to enable semantic search. This creates a centralized knowledge base where teams can find relevant information using natural language queries.
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mikkelkrogsholmTranslate natural language questions into SurrealDB queries for semantic search and graph traversal.
lucifertrjClean, tokenize, and normalize English text with NLTK for NLP pipelines.
steipeteIndex local files and search them with BM25, vector, and hybrid queries.
