Privacy Copilot is an open-source, privacy-first platform that empowers individuals and organizations with a secure, local or cloud-deployable AI assistant for document Q&A, retrieval-augmented generation (RAG), and private LLM fine-tuning—without your data ever leaving your control.
This project is also a comprehensive showcase, demonstrating proficiency in:
- Hybrid backend & AI engineering (Go + Python) with best practices.
- Microservice and distributed systems architecture.
- Integration of modern AI (LLMs, RAG, vector databases, fine-tuning).
- MLOps, DevOps, cloud-native patterns, and CI/CD pipelines.
- End-to-end privacy engineering and compliance-aware design.
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✅ Private Document Q&A & Retrieval-Augmented Generation
Upload documents and ask natural language questions—contextual answers are generated using Retrieval-Augmented Generation (RAG), all running locally or in your private cloud. -
✅ Per-User Data Isolation & End-to-End Encryption
All data, embeddings, and models are encrypted at rest and in transit. Each user’s data is fully siloed for maximum privacy. -
✅ Personalized LLM Fine-Tuning
(Optional) Fine-tune LLMs using your own documents or chat history, with all computation happening privately in your environment. Models are versioned and rollback-supported. -
✅ Bring Your Own Model (BYOM)
Pluggable LLM support—use open-source models (Llama 3, Mistral, GPT-Neo, etc.) or connect your own model endpoints. -
✅ Multi-Modal Search
Securely upload and search both text and images, powered by state-of-the-art embedding models. -
✅ Privacy Controls Dashboard
Manage, export, or delete your data, review audit logs, and control your models—right from the UI. -
✅ Modern MLOps & DevOps
Automated CI/CD, experiment tracking (MLflow), containerized deployment (Docker Compose, Kubernetes), and monitoring tools for both local and cloud setups. -
✅ Compliance & Security by Design
Follows privacy-by-design principles (GDPR-aware), audit logging, and secure API access. No telemetry or external calls by default.
This project demonstrates advanced engineering in:
- Hybrid Go + Python Architecture:
Go for performant backend API, user management, and privacy enforcement. Python for AI pipelines (LLMs, RAG, embeddings, fine-tuning). - Modular Microservices:
Clean separation between backend, AI services, vector DB, and front end—ready for local or cloud scaling. - Cloud-Native & On-Prem Deployments:
Easily run locally (for full privacy) or scale in your own cloud with Kubernetes/Terraform. - AI & MLOps Best Practices:
From RAG pipelines to model versioning, MLflow tracking, and experiment management. - Privacy & Security Engineering:
User isolation, encryption, detailed audit trails, BYOM for ultimate control. - Enterprise-Ready Patterns:
Role-based access (planned), OAuth2/OIDC support (planned), and compliance-aware architecture.
| Category | Technologies Used |
|---|---|
| Architecture | Microservices, REST APIs |
| Backend Languages | Go (API Gateway, user/session management), Python (AI/RAG, LLMs, embeddings) |
| AI/ML | HuggingFace, LangChain, FastAPI, MLflow, ChromaDB/FAISS/Qdrant (vector DB) |
| Frontend | React or Streamlit (privacy dashboard & chat UI) |
| Databases | PostgreSQL (user data/audit logs), Vector DB (per-user embeddings), Encrypted Storage |
| Containerization | Docker, Docker Compose |
| Orchestration | Kubernetes/Helm (Cloud), Terraform (Infra as Code, planned) |
| CI/CD | GitHub Actions |
| Observability | Prometheus, Grafana, Jaeger |
| Security | End-to-end encryption, OAuth2/OIDC (planned), audit logging |
- Git: Install Git.
- Docker: Install Docker Desktop. Docker Compose required.
- Python (optional): For development/debugging AI service outside Docker.
- Go: Install Go (for backend development).
- (Optional) Ollama: For running certain open-source LLMs locally, see Ollama.
- (Optional) jq: JSON CLI tool for testing API responses.
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Clone the Repository:
git clone https://github.com/<your-username>/privacy-copilot.git cd privacy-copilot
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Copy & Edit Environment Variables:
cp .env.example .env # Edit .env as needed for DB, AI model paths, ports, etc. -
Start the Stack:
docker-compose up --build -d
- This starts the Go API gateway, Python AI service, database(s), and vector DB.
- Default UI at:
http://localhost:8080
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(Optional) Run Ollama and Download a Model:
ollama pull llama3
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Access Logs and Monitor:
docker-compose logs -f
Try out the REST API (see API docs) or use the web UI:
- Upload documents
- Ask questions (“What is the main idea of this document?”)
- Export or delete your data from the privacy dashboard
- (Advanced) Launch a model fine-tuning job from the dashboard or via API
- Contribution Guidelines
- API Reference
- Architecture & Security
- Deployment (Local/Cloud)
- BYOM: Bring Your Own Model
We welcome community contributions! Please review CONTRIBUTING.md and link all PRs to relevant issues.
privacy-copilot/
├── backend/ # Go API gateway
├── ai_service/ # Python FastAPI RAG/LLM service
├── frontend/ # React or Streamlit UI
├── infra/ # Helm charts, Terraform scripts
├── mlops/ # MLflow configs, pipelines, experiment tracking
├── docs/ # Documentation & API specs
├── docker-compose.yaml # Local stack orchestration
└── ... # Standard configs (LICENSE, .gitignore, etc.)
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📫 Contact & Commercial Use
Personal, research, and educational use is free. Commercial use requires a separate license—please contact: evana.blanche.privacycopilot@gmail.com
For questions, suggestions, or support: • Open an issue
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⚖️ License
This project is licensed for non-commercial use only. Commercial, SaaS, or enterprise deployments require written permission. See LICENSE for full terms.
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🙌 Acknowledgments • Thanks to the open-source and privacy communities for inspiration and support.
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Built for privacy, flexibility, and as a modern AI/ML engineering showcase.