Knowledge Rag

Name: Knowledge Rag
Author: lyonzin

knowledge-rag · by lyonzin

Local RAG System for Claude Code — Hybrid search + Cross-encoder Reranking + Markdown-aware Chunking + 12 MCP Tools. No external servers, pure ONNX in-process.

85 12 tools GitHub PyPI

No known CVEs

MIT license

Actively maintained

Last commit 3d ago

Works with most clients

Transport: stdio, sse, http

12 tools · ~694 tok

Grade B · 0.3% of 200K ctx

Edit this pageView history

AI / ML Data

Step 1

Install in your client

Config is the same across clients — only the file and path differ.

Supported in Claude Desktopstdio, sse, http · Node 18+

Paste into ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "knowledge-rag": {
      "args": [
        "-m",
        "mcp_server.server"
      ],
      "command": "C:\\Users\\YOUR_USER\\knowledge-rag\\venv\\Scripts\\python.exe"
    }
  }
}

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[![MCPpedia Score](https://mcppedia.org/api/badge/knowledge-rag)](https://mcppedia.org/s/knowledge-rag)

Read me

What Knowledge Rag does

Drop your PDFs, markdown, code, notebooks — 1800+ files, 39K chunks, indexed in under 3 minutes. Hybrid search (BM25 + semantic vectors + cross-encoder reranking) through 12 MCP tools. Everything runs locally via ONNX. No Docker, no Ollama, no API keys, no data leaves your machine.

Test This Server

Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.

uvx 'knowledge-rag' 2>&1 | head -1 && echo "✓ Server started successfully"

After testing, let us know if it worked:

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Scored, not listed

Why this score

Five weighted categories — click any category to see the underlying evidence.

Score breakdown

84/100across 5 weighted dimensions

How we score →

0255075100

−16

Security

Maintenance

Efficiency

Documentation

Compatibility

Categoriesclick a row to see evidence

Security

OSV.dev

No known CVEs.

Checked knowledge-rag against OSV.dev.

Inventory

Tools (12)

Click any tool to inspect its schema.

~694 tokens total

Community

Reviews

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How easy was setup?Did it work reliably?How was the documentation?

Frequently Asked Questions

Is Knowledge Rag safe to use?: Knowledge Rag has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments. Licensed under MIT.
How do I install Knowledge Rag?: Knowledge Rag supports copy-paste install configs on its MCPpedia page for Claude Desktop, Cursor, and Claude Code. Scroll to the Quick Install section and select your client.
What can Knowledge Rag do?: Knowledge Rag provides 12 tools: search_knowledge, get_document, add_document, update_document, remove_document and 7 more. See the full tools list on the server page for descriptions and parameters.
What AI clients work with Knowledge Rag?: Knowledge Rag is compatible with claude-desktop, cursor, claude-code. It uses stdio and sse and http transport.
Is Knowledge Rag actively maintained?: Knowledge Rag is actively maintained — last commit was 3 days ago. It has 85 GitHub stars.

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{ "mcpServers": { "knowledge-rag": { "args": [ "-m", "mcp_server.server" ], "command": "C:\\Users\\YOUR_USER\\knowledge-rag\\venv\\Scripts\\python.exe" } } }

Knowledge RAG

Your docs, your machine, zero cloud. Claude Code searches them natively.

Drop your PDFs, markdown, code, notebooks — 1800+ files, 39K chunks, indexed in under 3 minutes.
Hybrid search (BM25 + semantic vectors + cross-encoder reranking) through 12 MCP tools.
Everything runs locally via ONNX. No Docker, no Ollama, no API keys, no data leaves your machine.

pip install knowledge-rag → restart Claude Code → search_knowledge("your query")

12 MCP Tools | Hybrid Search + Reranking | 20 File Formats | Optional NVIDIA GPU | 100% Local

What's New in v3.9.0

Quality Gate — 7-Pillar PR Validation

Every PR (including dependabot bumps and one-line fixes) is now evaluated against 35+ automated checks spread across 7 pillars before any human review:

Pillar

What it enforces

Tools

1 Security

SAST, secrets, CVEs, supply chain

bandit, semgrep, gitleaks, pip-audit, dependency-review, Snyk, CodeQL, Socket

2 Stability

Flake detection, coverage trend, test count, deterministic runs

pytest-rerunfailures, codecov ±0.5pp, test-count guard

3 Memory Leak

RSS bounded under 1000-query load, no idle bloat

psutil-based baseline tests + nightly 50K-iteration soak

4 Versatility

9 OS×Python combos, 14 format parsers, 4 config presets, locale tolerance, property-based fuzzing

matrix CI on Linux+Windows+macOS × 3.11+3.12+3.13, Hypothesis

5 Scalability

Performance regression > 10% blocks merge, public bench dashboard

pytest-benchmark, GH Pages chart

6 Versioning

Atomic version sync, API surface diff, conventional commits, CHANGELOG enforcement, backwards compat

griffe-style AST diff, custom guards

7 Quality

Frequently Asked Questions

Is Knowledge Rag safe to use?

Knowledge Rag has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments. Licensed under MIT.

How do I install Knowledge Rag?

Knowledge Rag supports copy-paste install configs on its MCPpedia page for Claude Desktop, Cursor, and Claude Code. Scroll to the Quick Install section and select your client.

What can Knowledge Rag do?

Knowledge Rag provides 12 tools: search_knowledge, get_document, add_document, update_document, remove_document and 7 more. See the full tools list on the server page for descriptions and parameters.

What AI clients work with Knowledge Rag?

Knowledge Rag is compatible with claude-desktop, cursor, claude-code. It uses stdio and sse and http transport.

Is Knowledge Rag actively maintained?

Knowledge Rag is actively maintained — last commit was 3 days ago. It has 85 GitHub stars.

Knowledge RAG

Your docs, your machine, zero cloud. Claude Code searches them natively.

pip install knowledge-rag → restart Claude Code → search_knowledge("your query")

12 MCP Tools | Hybrid Search + Reranking | 20 File Formats | Optional NVIDIA GPU | 100% Local

Star History

What's New in v3.9.0

Quality Gate — 7-Pillar PR Validation

Every PR (including dependabot bumps and one-line fixes) is now evaluated against 35+ automated checks spread across 7 pillars before any human review:

Pillar	What it enforces	Tools
1 Security	SAST, secrets, CVEs, supply chain	bandit, semgrep, gitleaks, pip-audit, dependency-review, Snyk, CodeQL, Socket
2 Stability	Flake detection, coverage trend, test count, deterministic runs	pytest-rerunfailures, codecov ±0.5pp, test-count guard
3 Memory Leak	RSS bounded under 1000-query load, no idle bloat	psutil-based baseline tests + nightly 50K-iteration soak
4 Versatility	9 OS×Python combos, 14 format parsers, 4 config presets, locale tolerance, property-based fuzzing	matrix CI on Linux+Windows+macOS × 3.11+3.12+3.13, Hypothesis
5 Scalability	Performance regression > 10% blocks merge, public bench dashboard	pytest-benchmark, GH Pages chart
6 Versioning	Atomic version sync, API surface diff, conventional commits, CHANGELOG enforcement, backwards compat	griffe-style AST diff, custom guards
7 Quality

... View full README on GitHub