Skillget

RustRAG

mcp
Guvenlik Denetimi
Gecti
Health Gecti
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 22 GitHub stars
Code Gecti
  • Code scan — Scanned 12 files during light audit, no dangerous patterns found
Permissions Gecti
  • Permissions — No dangerous permissions requested
Purpose
This MCP server provides a one-click knowledge system for documents, internal bots, and AI agents. It ingests files, links, and images, converting them into searchable text, vector embeddings, and graph relations accessible via an operator UI and MCP.

Security Assessment
The overall risk is rated as Medium. The codebase scan did not find any hardcoded secrets, dangerous execution patterns (like running shell commands), or requests for overly broad permissions. However, the tool inherently interacts with sensitive data by design. It acts as a comprehensive document pipeline, meaning it will ingest, parse, and store whatever files or links are provided to it. Additionally, the system requires connecting to multiple backend databases (ArangoDB, Postgres, Redis) and runs a web-accessible edge proxy via nginx. While the light audit found no malicious code, deploying this requires careful network configuration to ensure your knowledge base and infrastructure are properly secured.

Quality Assessment
The project appears to be of good quality and is actively maintained. It utilizes Rust for a robust backend, is covered by a standard MIT license, and saw recent repository activity. With 22 GitHub stars, it has a small but present level of community validation.

Verdict
Safe to use, provided you configure your network securely and understand that the tool will process and store any data explicitly fed into it.
SUMMARY

One-click knowledge system for documents, internal bots, and AI agents

README.md

RustRAG demo: dashboard, documents, grounded assistant, and graph exploration

RustRAG

One-click knowledge system for documents, internal bots, and AI agents

Stars Release Docker Pulls License

README-RU • MCP • MCP-RU

Load files, links, and images into one knowledge base, turn them into searchable text, embeddings, and graph relations, then expose the same memory in the operator UI and over MCP.

Architecture

One port on nginx. / serves the React + Vite SPA; /v1/* routes to the Rust / Axum backend (REST + MCP). The worker is the same image running as a queue consumer.

                         ┌─────────────────────────┐
                         │   nginx (edge proxy)    │
                         └───────────┬─────────────┘
               ┌─────────────────────┴─────────────────────┐
        GET /* (SPA)                                 /v1/* (API + MCP)
      ┌────────▼─────────┐                       ┌─────────▼──────────┐
      │    frontend      │                       │      backend       │
      │  React + Vite    │                       │   Rust / Axum      │
      └──────────────────┘                       └─────────┬──────────┘
                         ┌─────────────────────────────────┼────────────────────┐
                  ┌──────▼──────┐                  ┌───────▼───────┐    ┌───────▼───────┐
                  │  ArangoDB   │                  │   Postgres    │    │    Redis      │
                  │ graph+vector│                  │ IAM + control │    │ worker queue  │
                  └─────────────┘                  └───────────────┘    └───────┬───────┘
                                                                        ┌───────▼───────┐
                                                                        │    worker     │
                                                                        └───────────────┘

Pipeline

upload / URL → extract text → structured blocks → boilerplate filter
  → semantic chunking (2800 chars, 10% overlap, heading-aware)
  → embed chunks → graph extraction (v6: 10 entity types, 88 relation types)
  → entity resolution (alias/acronym merge) → document summary
  → quality scoring → hybrid index (BM25 + vector) → UI + MCP + API

Quick Start

Prerequisite: Docker with Compose v2.

# Install without cloning
curl -fsSL https://raw.githubusercontent.com/mlimarenko/RustRAG/master/install.sh | bash

# Or from a cloned repo
cp .env.example .env
docker compose up -d          # prebuilt images
# docker compose -f docker-compose-local.yml up --build -d  # build from source

After startup: http://127.0.0.1:19000. First visit runs bootstrap — set admin login and password.

Different port: RUSTRAG_PORT=8080 docker compose up -d

Features

  • Document ingestion -- text, code (50+ extensions), PDF, DOCX, PPTX, HTML, images, and web links with boilerplate filtering and quality scoring
  • Typed knowledge graph -- 10 universal entity types (person, organization, location, event, artifact, natural, process, concept, attribute, entity), 88 relation types, entity resolution, and document summaries
  • Hybrid search -- BM25 + vector cosine via Reciprocal Rank Fusion, field-weighted scoring (heading matches boosted 1.5x)
  • Grounded assistant -- built-in chat UI with answer verification and evidence panel
  • 21 MCP tools -- Q&A (ask), search, read, upload, graph exploration, web crawl, and admin
  • Smart chunking -- 2800-char semantic chunks with 10% overlap, heading-aware splitting, code-aware boundaries, boilerplate detection
  • Access control -- API tokens, grants, library scoping, and ready-made MCP client snippets
  • Spending tracking -- per-document and per-library cost visibility
  • Model selection -- configurable providers and models per pipeline stage

MCP

21 tools out of the box. Create a token in Admin > Access, attach grants, copy the snippet from Admin > MCP.

Category Tools
Q&A ask -- grounded question answering
Documents search_documents, read_document, list_documents, upload_documents, update_document, delete_document
Graph search_entities, get_graph_topology, list_relations
Web Crawl submit_web_ingest_run, get_web_ingest_run, cancel_web_ingest_run
Discovery list_workspaces, list_libraries

Search and read responses default to includeReferences=false to minimize token usage. Full guide: MCP.md

Tech Stack

Layer Technology
API + Worker Rust, Axum, SQLx
Frontend React, Vite, Tailwind, shadcn/ui
Graph + Vector ArangoDB 3.12
Control Plane PostgreSQL 18
Worker Queue Redis 8
Reverse Proxy nginx 1.28
Deployment Docker Compose, Ansible

Configuration

All variables use RUSTRAG_* prefix. Key files:

File Purpose
.env.example Compose variables
apps/api/.env.example Full runtime config reference
apps/api/src/app/config.rs Built-in defaults

Benchmarks

Two golden datasets: Wikipedia corpus (30 questions) and code corpus (20 questions across Go/TS/Python/Rust/Terraform/React/K8s/Docker).

export RUSTRAG_SESSION_COOKIE="..."
export RUSTRAG_BENCHMARK_WORKSPACE_ID="workspace-uuid"
make benchmark-grounded-seed   # upload corpus
make benchmark-grounded-all    # run QA matrix
make benchmark-golden          # golden dataset

Roadmap

0.2.0 -- Quality & Performance (done)

  • Hybrid search (BM25 + vector RRF fusion)
  • Graph extraction v6 (few-shot, 10 entity types, 88 relation types)
  • Semantic chunking (2800 chars, overlap, heading-aware, code-aware)
  • Boilerplate detection, quality scoring, entity resolution
  • 21 MCP tools including ask and graph navigation
  • Typed entity coloring and edge labels in graph UI
  • Parallel graph extraction (up to 8 concurrent chunks)
  • SSE streaming for query answers
  • Conversation context in multi-turn queries
  • Incremental re-processing (diff-aware ingest)
  • Export/import libraries
  • Ollama/local model support
  • Confluence, Notion, Google Drive connectors

Star History

Star History Chart

Contributing

PRs welcome. Prefer the one canonical path over compatibility layers.

License

MIT

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