Building an Enterprise RAG & Knowledge Graph Engine with Governed AI Workflows

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3. Human-in-the-Loop Review & Hardened Auditing

Enterprise AI requires high precision. Nexus-KB includes a Review Queue workflow. If an ingestion run or an AI extraction outputs low-confidence scores, chunks are routed to a human review queue supporting approve/reject/modify flows via mock reviewer RBAC.

Every single operation — from ingestion and document reads to search queries and review actions — generates immutable audit events.

4. Model Context Protocol (MCP) Boundaries

To securely ingest third-party enterprise platforms, Nexus-KB leverages an MCP Source Connector Scaffold (specifically a confluence-bridge). It enforces:

Strict user-context authorization.
Disabled mutating tools by default.
Redacted error messages to prevent internal leakage.

5. Knowledge Graph Construction

The workers/graph-builder asynchronous worker extracts entities and relationships from approved data chunks, merges duplicates, stores confidence/provenance metrics, and injects graph context fields directly into the hybrid search results for a richer LLM context window.

🛠️ The Tech Stack

Nexus-KB is built with a modern, highly efficient Python ecosystem:

Backend Framework: FastAPI (0.115)
ORM & Migrations: SQLAlchemy (2.0) & Alembic (1.14)
Vector Engine: Qdrant Client (v1.13)
Testing: Pytest (8.3) with support for full Docker-backed live integration tests.

⚡ Getting Started (Local Quickstart)

Want to explore the codebase or test it locally? Here’s the fast track using a local Docker-backed stack:

1. Spin Up Local Infrastructure

# Clone the repository and boot PostgreSQL + Qdrant
docker compose up -d

2. Run Database Migrations

alembic -c infrastructure/alembic.ini upgrade head

3. Launch the FastAPI Gateway

export PYTHONPATH="packages/shared-contracts:packages/vector-client:workers/document-parser:workers/graph-builder:services/nexus-api"
uvicorn nexus_api.main:app --reload

Head over to http://127.0.0.1:8000/docs to explore the interactive Swagger documentation.

4. Execute a Governed Hybrid Search

curl -X POST "[http://127.0.0.1:8000/api/v1/search](http://127.0.0.1:8000/api/v1/search)" \
 -H "Content-Type: application/json" \
 -d '{"query":"governed retrieval","limit":5,"tags":["rag"]}'

🗺️ What’s Next on the Roadmap?

The core architecture is solid, but there's always more to build. Future work includes:

[ ] Production-ready authentication/OIDC adapters.
[ ] A full Web Admin console for managing review queues and graph entities visually.
[ ] Enterprise connector adapters for live Confluence and Sharepoint environments.
[ ] Production API Gateway hardening and OpenTelemetry export setups.

🤝 Open Source & Contributions

Nexus-KB is licensed under the MIT License and is fully open-source.

If you are passionate about AI Engineering, Knowledge Graphs, and RAG architectures, I'd love to hear your thoughts on this design! Check out the project layout, drop a comment below, or let's connect to discuss how you approach data governance in your LLM workflows.

View all: https://khaitrang1995.github.io/nexus-kb/

Happy coding! If you find this architecture interesting, don't forget to ❤️ and bookmark this post!