Mastering PrivateGPT: Tailoring GenAI for your unique applications


PrivateGPT, a well-recognized open-source project with 48K Github stars and a Discord community composed by more than 3K supporters, offers a robust framework for developing Private Context-aware GenAI applications. Tailored to support real-world production scenarios, it provides a default set of functions that efficiently handle common tasks like ingestion of documents, contextual chat and completions, as well as embeddings generation. However, its true strength lies in its adaptability, enabling customization for specific applications.

This tutorial guides you through various configuration options and extensions of PrivateGPT. You'll begin by gaining hands-on experience with its default API and functionalities using its Python SDK. Subsequently, you'll explore tweaking settings to adapt it to different setups, ranging from fully local where everything runs in your computer to multi-service where the LLM, embedding model or vector database can be served by different services. The final segment of the tutorial will lead you through PrivateGPT's internal AI logic and architecture to learn how to extend its basic RAG functionalities.

Upon completing this tutorial, you'll acquire the skills to customize PrivateGPT for any scenario, whether it be for personal use, intra-company initiatives, or as part of innovative commercial production setups.

Session Outline:

Module 1: Exploring PrivateGPT's API & Python SDK

Dive into PrivateGPT's functional API right out of the box. Familiarize yourself by testing it through interactive documentation and writing simple scripts using its Python SDK. Develop the skills to construct private AI applications covering generic use cases such as the popular “chat with your documents”.

Module 2: Configuring PrivateGPT for diverse setups

Master the available configuration options within PrivateGPT and learn how different setups cater to various real-world scenarios. Explore examples, ranging from fully local and self-contained setups on your laptop to production environments utilizing technologies like Ollama or Qdrant.

Module 3: Bring your own RAG

While PrivateGPT comes with a default RAG implementation powering its out-of-the-box features, custom applications demand specific business logic. This module guides you in transitioning from the default setup to a tailored experience by implementing new functions and extending the API to expose them.

Learning objectives:
- Effective API usage: Develop practical skills in using PrivateGPT's API and Python SDK, allowing you to build functional private AI applications, like interactive document-based conversations.
- Versatile configurations: Acquire the ability to adeptly configure PrivateGPT for various setups. Explore scenarios, from local development environments to more complex production deployments.
- Strategic deployment knowledge: Learn to strategically deploy PrivateGPT in real-world situations, adapting configurations for optimal performance in personal, intra-company, or commercial production setups.
- Customized logic implementation: Gain hands-on experience in tailoring PrivateGPT by implementing customized functions and extending the API beyond default RAG features. Enhance your AI applications by seamlessly integrating personalized logic into PrivateGPT's robust framework.

Open Source Tools used:
- PrivateGPT
- LlamaIndex
- Ollama
- Mistral
- Qdrant

Background Knowledge:
- Working installation of PrivateGPT
- Basic understanding of RAG pipelines
- Basic python skills


Daniel is the Co-Founder of two notable ventures: PrivateGPT, an open-source GenAI project; and Zylon, a B2B SaaS product built on the foundation of PrivateGPT, where he currently serves as the Chief Product and Technology Officer (CPTO). In the earlier phase of his professional journey, Daniel spearheaded the development of consumer-oriented products spanning mobile devices, IoT, robotics, 3D printing, and wearables. Later on, he assumed a leadership role in the Amazon Business Delivery Experience space where he successfully formed and guided software engineering teams, steering the creation and delivery of impactful products on a global scale. Daniel's academic accomplishments include both Bachelor’s and Master's degrees in Telecommunications Engineering, as well as holding a PhD in Proactive Context-Aware Recommender Systems from the Technical University of Madrid.

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