Execute pixel commands for semantic search and document management in SEMOSS vector databases.
npx skills add https://github.com/SEMOSS/Template --skill semoss-vector
Please help me install this Agent Skill. Skill: semoss-vector Source: https://github.com/SEMOSS/Template/tree/main/.claude/skills/semoss-vector Command: npx skills add https://github.com/SEMOSS/Template --skill semoss-vector
This skill solves the complexity of managing vector databases and implementing RAG (Retrieval-Augmented Generation) workflows, allowing developers to integrate semantic search and document retrieval into their applications without manual indexing overhead.
Use the semoss-vector skill to query the vector database for information regarding time sheets and return the top five relevant results.
High-intent search queries and answers about installing and using this skill.
To implement semantic search, you can use this skill to perform hybrid-search queries against vector databases, retrieving relevant document chunks without manual indexing overhead. It supports querying raw files and pre-chunked data.
RAG integration works by using the skill to retrieve relevant document chunks from vector databases through hybrid-search, which are then fed into LLM calls to ground AI responses in specific organizational data.
Yes, you need integration with the SEMOSS SDK to execute pixel commands for engine interaction and data retrieval when managing documents and querying the vector database.
Yes, you can easily list, add, and remove documents from vector indices, with support for both raw files and pre-chunked CSV data, eliminating manual indexing overhead.
The skill supports metadata filtering during hybrid-search retrieval, allowing you to narrow down document chunks returned from the vector database based on specific attributes.
Irrelevant results may occur if documents are not properly indexed or if metadata filtering is misconfigured. Ensure documents are correctly added to vector indices and hybrid-search parameters are optimized.
patricio0312revConfigure Pinecone, Chroma, pgvector, and Qdrant vector databases for semantic search.
AI-Foundry-CoreRetrieve relevant documents to ground LLM outputs with citations.
Kingly-AgencyBuild RAG pipelines that ground LLM responses with retrieved external knowledge.
rukikotooQuery the econometrics RAG knowledge base to retrieve relevant document chunks.
