Private GPT
Ask questions over your private documents with a fully local LLM and embeddings stack. No data leaves your machine.
External Tool
This open-source tool is maintained externally. View the source on GitHub to learn more or run it yourself.
Browse text & tools tools →What's next with Private GPT?
Choose how you want to get started.
Use it free
Open the official tool or demo — no account needed.
Self-host it
Run the open-source version on your own infrastructure.
What is Private GPT?
PrivateGPT is an open-source API layer designed to transform local machine learning models into scalable, production-ready AI applications. Its primary purpose is to provide developers with a framework for building private AI systems that leverage local models while maintaining data confidentiality. This tool is particularly useful for organizations requiring strict data privacy, such as financial institutions, healthcare providers, and research labs. It addresses the challenge of deploying local models in enterprise environments by offering a secure, customizable API layer that supports advanced features like retrieval-augmented generation (RAG) and tool integration. By abstracting the complexity of model deployment, PrivateGPT enables teams to focus on application development rather than infrastructure management.
How it works
PrivateGPT serves as a middleware solution that bridges local machine learning models with external applications. It acts as a backend service, allowing developers to expose model capabilities via RESTful APIs without exposing sensitive data. The tool is built on Python and leverages modern frameworks to support features like text-to-SQL query parsing, multi-turn conversation management, and integration with OpenAI-compatible inference servers. Its modular architecture enables customization for specific use cases. PrivateGPT supports retrieval-augmented generation (RAG) for contextual responses, skill-based task execution, and tool integration for external APIs. It includes a multi-turn conversation protocol (MCP) for handling complex dialogues. The text-to-SQL feature allows natural language queries to be translated into database commands, while its compatibility with OpenAI servers enables integration with existing infrastructure.
How to use it
- 1Clone the repository from GitHub and install dependencies using the provided Dockerfile or manual setup. 2. Configure the settings.yaml file to specify model paths, API endpoints, and security parameters. 3. Deploy the service using Docker or a production-ready server. 4. Integrate the API into your application via HTTP requests, using the documented endpoints for query processing. Practical tips include using the settings-mock.yaml for testing without external models, leveraging the included scripts for rapid prototyping, and monitoring performance via the provided metrics endpoints.
What it can do
- private document Q&A
Use cases
Assumptions and limitations
Assumptions
- source: https://github.com/zylon-ai/private-gpt
- license: Apache-2.0 — free to use
- privacy: Self-hosted — you control your data
Limitations
- Requires local infrastructure for model hosting
- Learning curve for configuring advanced features
- Dependence on OpenAI-compatible servers for external integrations
- Limited built-in support for non-English languages
- Manual setup for production deployment
Understanding the result
Ask questions over your private documents with a fully local LLM and embeddings stack. No data leaves your machine.
Tool details
- Clearly flagged when a network request is needed.
- No account, no sign-up, and no tracking of your content.
- Powered by (Apache-2.0).
- Built with
- (zylon-ai/private-gpt)
- License
- Apache-2.0
- Runs locally
- No — requires a network request
- Verification
- Not yet verified
- Input
- Query
- Output
- Text
Built with zylon-ai/private-gpt. OpenToolVault provides the discovery and browser interface while crediting the original project maintainers.
- Built with
- License
- Apache-2.0
Open-source project
OpenToolVault is an independent directory. We are not affiliated with or endorsed by this project.
References
- / — GitHub Repository
Upstream project · GitHub
- Apache-2.0 License
Upstream project
Frequently asked
What is PrivateGPT and how does it differ from standard model deployment?
PrivateGPT is an API layer that abstracts local model deployment, enabling secure, scalable AI applications. Unlike direct model usage, it provides built-in features like RAG, MCP, and text-to-SQL parsing. It also enforces data isolation by preventing direct model exposure to external systems.
How does PrivateGPT handle model inference security?
The tool ensures security through isolated model execution, data encryption in transit, and access controls defined in settings.yaml. Sensitive data is never transmitted to external servers, and all processing occurs within the local environment. It also supports private model training pipelines for full data control.
How do I deploy PrivateGPT with a custom model?
First, place your model files in the designated directory specified in settings.yaml. Then, modify the configuration to point to your model's weights and tokenizer. Use Docker to containerize the service, ensuring environment variables match your setup. Test with the mock settings file before deploying to production.
How does PrivateGPT compare to alternatives like Llama.cpp or vLLM?
Unlike Llama.cpp (a model optimizer) or vLLM (a serving library), PrivateGPT provides a complete API framework with built-in RAG and conversation management. It integrates with any OpenAI-compatible server, offering more flexibility than vLLM's native model serving. However, it lacks Llama.cpp's lightweight optimization for resource-constrained environments.
What should I do if the API returns '503 Service Unavailable'?
Check if the model files are correctly placed and accessible. Verify the Docker container is running and the port mappings in docker-compose.yml are correct. Ensure the inference server (e.g., vLLM) is operational and the model is loaded. Review logs in /var/log/privategpt for specific error details.