knowledge-retrieval
Knowledge Retrieval performs semantic search across your ingested document collection using Retrieval-Augmented Generation. It ranks results by relevance and returns text passages with citation metadata, making it ideal for finding domain-specific information in PDFs, reports, and technical documentation.
Knowledge Retrieval searches pre-ingested documents using semantic similarity and RAG technology.
AI-generated summary based on this skill's SKILL.md
Install
open-gitagent/opengap/knowledge-retrieval · repository language: TypeScript
git clone https://github.com/open-gitagent/opengap
cp -r opengap/examples/nvidia-deep-researcher/skills/knowledge-retrieval ~/.claude/skills/knowledge-retrievalFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How does knowledge-retrieval search documents with semantic search?
Knowledge-retrieval uses Retrieval-Augmented Generation to perform semantic search across your ingested document collection. Rather than matching keywords, it understands the meaning of your query and ranks results by relevance, returning text passages with citation metadata so you can trace answers back to their sources.
Can knowledge-retrieval find information in my ingested PDFs?
Yes. Knowledge-retrieval is designed to search pre-ingested PDFs, reports, and technical documentation. It retrieves relevant passages by semantic similarity, making it ideal for locating domain-specific information across your uploaded document collection without needing external sources.
What backends does knowledge-retrieval support?
Knowledge-retrieval can access NVIDIA and LlamaIndex RAG backends for powering its semantic search capabilities. This flexibility allows you to choose the infrastructure that best fits your deployment and performance requirements.
Does knowledge-retrieval provide citations with results?
Yes. Knowledge-retrieval returns text passages alongside citation metadata, allowing you to verify where each answer came from within your knowledge base. This transparency is essential for domain-specific queries where source attribution matters.
How should I use knowledge-retrieval before external sources?
Knowledge-retrieval is optimized to query your knowledge base first, retrieving relevant passages from your pre-ingested documents before turning to external sources. This approach keeps your answers grounded in your own domain-specific content and reduces reliance on outside information.
What license does knowledge-retrieval use?
Knowledge-retrieval is released under the MIT license, giving you freedom to use, modify, and distribute it in both open-source and commercial projects with minimal restrictions.
SKILL.md
rendered from the published skill — quoted content, verbatim
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
(truncated - see the full file via the links below)
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examples/nvidia-deep-researcher/skills/knowledge-retrieval/SKILL.md