# Knowledge Retrieval

> Semantic search over ingested documents using RAG (LlamaIndex/ChromaDB or Foundational RAG)

- **Type:** Skill
- **Install:** `agentstack add skill-open-gitagent-opengap-knowledge-retrieval`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [open-gitagent](https://agentstack.voostack.com/s/open-gitagent)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [open-gitagent](https://github.com/open-gitagent)
- **Source:** https://github.com/open-gitagent/opengap/tree/main/examples/nvidia-deep-researcher/skills/knowledge-retrieval
- **Website:** https://gitagent.sh

## Install

```sh
agentstack add skill-open-gitagent-opengap-knowledge-retrieval
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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

1. Formulate a semantic search query describing the information needed
2. Call `knowledge_retrieval` with the query
3. Review returned chunks for relevance
4. 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

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [open-gitagent](https://github.com/open-gitagent)
- **Source:** [open-gitagent/opengap](https://github.com/open-gitagent/opengap)
- **License:** MIT
- **Homepage:** https://gitagent.sh

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-open-gitagent-opengap-knowledge-retrieval
- Seller: https://agentstack.voostack.com/s/open-gitagent
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.