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https://github.com/mihaibc/PrivacyCopilot

🛡️ PrivacyPilot – A privacy-centric backend API leveraging AI (LLaMA, Mistral, Ollama) to anonymize and moderate sensitive data in real-time. Built with Go, Node.js, and Perl. GDPR-compliant and infrastructure-ready.
https://github.com/mihaibc/PrivacyCopilot

ai anonymization azure-ai backend devops gdpr go microservices nodejs ollama perl5 privacy stable-diffusion

Last synced: 4 months ago
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🛡️ PrivacyPilot – A privacy-centric backend API leveraging AI (LLaMA, Mistral, Ollama) to anonymize and moderate sensitive data in real-time. Built with Go, Node.js, and Perl. GDPR-compliant and infrastructure-ready.

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README 

          
# 🛡️ Privacy Copilot – Private AI, RAG & Backend Showcase



**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.



---



## 🌟 Key Features



- âś… **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.



- âś… **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.



- âś… **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.



- âś… **Bring Your Own Model (BYOM)**  

  Pluggable LLM support—use open-source models (Llama 3, Mistral, GPT-Neo, etc.) or connect your own model endpoints.



- âś… **Multi-Modal Search**  

  Securely upload and search both text and images, powered by state-of-the-art embedding models.



- âś… **Privacy Controls Dashboard**  

  Manage, export, or delete your data, review audit logs, and control your models—right from the UI.



- âś… **Modern MLOps & DevOps**  

  Automated CI/CD, experiment tracking (MLflow), containerized deployment (Docker Compose, Kubernetes), and monitoring tools for both local and cloud setups.



- âś… **Compliance & Security by Design**  

  Follows privacy-by-design principles (GDPR-aware), audit logging, and secure API access. No telemetry or external calls by default.



---



## 🎯 Showcase Goals



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.



---



## 🛠️ Tech Stack



| 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                                 |



---



## 🚀 Getting Started (Local Development)



### đź“‹ Prerequisites



1.  **Git:** [Install Git](https://git-scm.com/downloads).

2.  **Docker:** [Install Docker Desktop](https://docs.docker.com/get-docker/). Docker Compose required.

3.  **Python (optional):** For development/debugging AI service outside Docker.

4.  **Go:** [Install Go](https://go.dev/doc/install) (for backend development).

5.  **(Optional) Ollama:** For running certain open-source LLMs locally, see [Ollama](https://ollama.com/).

6.  **(Optional) jq:** JSON CLI tool for testing API responses.



### ⚙️ Installation & Setup



1.  **Clone the Repository:**

    ```bash

    git clone https://github.com//privacy-copilot.git

    cd privacy-copilot

    ```



2.  **Copy & Edit Environment Variables:**

    ```bash

    cp .env.example .env

    # Edit .env as needed for DB, AI model paths, ports, etc.

    ```



3.  **Start the Stack:**

    ```bash

    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`



4.  **(Optional) Run Ollama and Download a Model:**

    ```bash

    ollama pull llama3

    ```



5.  **Access Logs and Monitor:**

    ```bash

    docker-compose logs -f

    ```



---



## 🧪 Testing the Platform



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**



---



## đź“š Project Documentation



- [Contribution Guidelines](CONTRIBUTING.md)

- [API Reference](docs/api.md)

- [Architecture & Security](docs/architecture.md)

- [Deployment (Local/Cloud)](docs/deployment.md)

- [BYOM: Bring Your Own Model](docs/models.md)



---



## 🤝 Contributing



We welcome community contributions! Please review [CONTRIBUTING.md](CONTRIBUTING.md) and link all PRs to relevant issues.



---



## 🏗️ Project Structure Overview



```text

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.)

```



⸻



đź“« 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



⸻



⚖️ License



This project is licensed for non-commercial use only.

Commercial, SaaS, or enterprise deployments require written permission.

See LICENSE for full terms.



⸻



🙌 Acknowledgments

  •	Thanks to the open-source and privacy communities for inspiration and support.



⸻



Built for privacy, flexibility, and as a modern AI/ML engineering showcase.