# KB RAG

> Use this tool when you need to create a local knowledge base and perform semantic search over stored documents. KB RAG solves problems related to information retrieval and knowledge management by allowing AI assistants to ingest documents and query relevant information. It takes in documents as input and outputs relevant search results, making it ideal for use cases where a centralized knowledge base is required.

Canonical page: https://skillsregistry.net/skills/tjblavakumar-kb-rag  
JSON: https://api.skillsregistry.net/v1/skills/tjblavakumar-kb-rag

## Description

KB RAG creates and queries a local retrieval-augmented generation knowledge base via MCP. Uses ChromaDB for vector storage and FastAPI for the server layer. Allows AI assistants to ingest documents and perform semantic search over stored knowledge.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/tjblavakumar-kb-rag)
- **Repository:** <https://github.com/tjblavakumar/mcp_kb_rag>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "tjblavakumar-kb-rag"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/tjblavakumar-kb-rag` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/tjblavakumar-kb-rag/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt