Agent Skills
› open-gitagent/opengap
› knowledge-retrieval
knowledge-retrieval
GitHub基于RAG技术对预摄入文档进行语义搜索，支持LlamaIndex/ChromaDB或NVIDIA Foundational RAG。用于检索内部报告、PDF及技术文档中的领域知识，优先于通用网络搜索，返回带引用的相关文本片段。
Trigger Scenarios
需要查询内部知识库或预摄入文档
在PDF、白皮书或技术文档中查找信息
获取公开网络上不可用的特定领域知识
Install
npx skills add open-gitagent/opengap --skill knowledge-retrieval -g -y
SKILL.md
Frontmatter
{
"name": "knowledge-retrieval",
"description": "Semantic search over ingested documents using RAG (LlamaIndex\/ChromaDB or Foundational RAG)",
"allowed-tools": "knowledge-retrieval"
}
Knowledge Retrieval
Perform semantic search over a pre-ingested document collection using Retrieval-Augmented Generation (RAG). Backed by LlamaIndex with ChromaDB or NVIDIA Foundational RAG.
When to Use
- Searching internal or pre-ingested documents and reports
- Finding information in PDFs, whitepapers, or technical documentation
- Retrieving domain-specific knowledge not available on the open web
- This is the highest priority source — check the knowledge base first before web or paper searches
How to Use
- Formulate a semantic search query describing the information needed
- Call
knowledge_retrievalwith the query - Review returned chunks for relevance
- Note the citation metadata (filename, page number) for sourcing
Result Format
Results are returned as text chunks with citation metadata:
Relevant text passage from the ingested document...
Citation: filename.pdf, p.12
Constraints
- Searches only over documents that have been ingested into the knowledge index
- Returns ranked chunks based on semantic similarity
- Citation format:
Citation: filename.ext, p.X - Each call counts toward the researcher's 8-call limit per task
Backend Options
- LlamaIndex + ChromaDB — Local vector store with LlamaIndex orchestration
- NVIDIA Foundational RAG — NVIDIA-hosted RAG service with NeMo Retriever
Version History
- d7a8e2e Current 2026-07-25 08:13
