PrivateGPT 2.0 - FULLY LOCAL Chat With Docs (PDF, TXT, HTML, PPTX, DOCX, and more)

Introduction and Overview

In this section, the speaker introduces Private GPT, a project that allows users to chat with various types of documents. The speaker mentions that Private GPT has gained popularity and has undergone significant updates since its initial release.

Introduction to Private GPT

• Private GPT is an open-source project that enables users to chat with their documents, including text files, PDFs, CSVs, and Excel files.

• The project has gained popularity and received updates from developers, introducing new functionalities.

• Private GPT can be used as a developer product or by end-users who want to interact with their documents.

Features and Installation Process

This section focuses on the features of Private GPT and provides an updated installation process. The speaker also mentions the availability of an easy-to-use API for extended functionality.

Features of Private GPT

• Private GPT is flexible and can be used alongside ChatGPT or as a standalone tool.

• It offers additional functionalities such as retrieval augmented generation.

• Many projects are using the OpenAI API as a standard and building upon it, making it easy to integrate Private GPT into existing projects.

Installation Process

• The original version of Private GPT (primordial version) is still active but the updated version will be discussed in this video.

• The speaker introduces ServiceNow as the sponsor of the video, highlighting its automation capabilities for businesses.

• Detailed installation steps are available in the documentation provided by Private GPT on GitHub.

Local Installation Steps

This section provides step-by-step instructions for installing Private GPT locally using Python's conda package manager.

Cloning the Repository

• Clone the Private GPT repository using the command git clone followed by the repository URL.

Setting Up the Environment

• Navigate to the cloned directory using cd private-GPT.

• Create a new Python environment using conda with the command conda create -n private-GPT python=3.11.

• Activate the newly created environment using conda activate private-GPT.

Installing Dependencies

• Install Poetry, a dependency management tool, if not already installed (e.g., using Homebrew on macOS).

• Use Poetry to install the user interface and local version of Private GPT with the command poetry install -d --no-root.

Customizing Settings

• The setup script contains various settings that can be customized for Private GPT.

• The settings.yaml file allows users to change models, such as BLS mistl 7B or Llama 2, and experiment with different options.

Conclusion and Additional Information

This section concludes the installation process and provides additional information about customizations in Private GPT.

Final Steps and Running Scripts

• After completing all installation steps, run the setup script provided by Poetry to finalize the installation.

• The setup script also includes settings that can be modified according to user preferences.

Additional Information

• Private GPT's documentation is comprehensive and provides detailed instructions for customization and usage.

• Users can explore different models, such as BLS mistl 7B or Llama 2, based on their requirements.

Setting up the Models

In this section, the speaker discusses the process of downloading the necessary models for the project. The embedding model is highlighted as it converts text into vector storage.

Downloading Models

• The speaker mentions that they are downloading the "mistal instruct" model, which is approximately 4GB in size.

• The "mistol" model is recommended due to its small size and excellent performance on their machine.

Additional Information about Private GPT

Here, the speaker provides additional details about Private GPT, including its compatibility requirements and default settings.

Compatibility and Default Settings

• Private GPT requires GG UF format for models.

• Chroma DB is used as the default local vector storage.

• For Windows users with an Nvidia GPU, specific instructions are provided in the documentation.

Setting Values for Mac Users

This section focuses on setting values specific to Mac users. Instructions for Windows users are mentioned to refer to the documentation.

Setting Values for Mac Users

• Mac users need to run a specific code snippet using cmake args and pip install commands.

• Some errors related to other projects may appear but can be ignored if they are not relevant to this project.

• A crucial step is setting the variable pgp profiles to "local make run".

Testing Private GPT

The speaker demonstrates how Private GPT works by using different modes and uploading files for processing.

Trying Different Modes

• Private GPT uses Gradio for its UI but can be integrated into any UI desired.

• Different modes available include query documents, llm chat (standard chatting with an llm), and context chunks (viewing data from the vector database).

• The speaker selects the query documents mode and uploads a file for processing.

Interacting with Private GPT

In this section, the speaker interacts with Private GPT by asking questions and receiving responses.

Asking Questions

• The speaker asks Private GPT to summarize the uploaded research paper, which it successfully does.

• Different queries can be asked to obtain specific information from the document.

• Switching to context chunks mode allows viewing of data from the vector database.

• Chatting with Private GPT in llm chat mode is also possible.

Using Private GPT API

The speaker discusses using the Private GPT API and highlights different settings available.

Using the API

• Different settings can be used, including running locally or testing a different model locally.

• The ingest endpoint allows uploading files for processing and retrieving a list of ingested documents.

• The completions endpoint functions similarly to OpenAI's API.

Conclusion

Private GPT is a customizable tool that allows users to interact with text-based models. It provides various modes for querying documents, chatting, and viewing data from the vector database. Users can upload files for processing and ask questions based on their content. The tool also offers an API for more advanced usage scenarios.

New Section

In this section, the speaker discusses the birth of Private GPT and its focus on privacy concerns. They also mention their involvement in the open-source community and their knowledge of projects like L chain, Lama index, prb open source Vector database, and GPT.

The Birth of Private GPT

• The speaker realized that privacy was a significant problem when they approached various technology companies who expressed concerns about privacy.

• They were active in the open-source community and knew about projects like L chain, Lama index, prb open source Vector database.

• They combined these smaller elements to create Private GPT.

• The initial version allowed users to chat with their documents locally without an internet connection.

New Section

In this section, the speaker discusses future plans for Private GPT, including adding more tools to the API, integrating access to the internet and databases as data sources, and introducing high-level tools like summarization and data extraction. They also mention focusing on observing pipeline activities, running evaluations for accuracy, and sharing different setup possibilities with the community.

Future Plans for Private GPT

• Adding more tools to the API.

• Integrating access to the internet and databases as additional data sources.

• Introducing high-level tools or APIs such as summarization or data extraction in upcoming weeks/months.

• Focusing on observing pipeline activities and running evaluations for maintaining high accuracy in production setups.

• Sharing different setup possibilities with the community to accommodate various use cases.

New Section

In this section, the speaker highlights that Private GPT can be set up in different ways depending on user requirements. They mention options such as fully local setup, single instance deployment on platforms like GCP, or distributed setups with different components hosted on various platforms.

Different Setup Possibilities for Private GPT

• Private GPT can be set up fully locally, allowing users to have complete control over their data and privacy.

• It can also be deployed as a single instance on platforms like Google Cloud Platform (GCP).

• Distributed setups are possible, where the Private GPT API is hosted on one platform while other components like LLM and Vector database are hosted elsewhere.

• The speaker mentions that they will share different setup possibilities with the community to cater to diverse use cases.

New Section

In this section, the speaker expresses excitement about the usage of Private GPT in production. They thank the audience for joining them and sharing in their enthusiasm.

Conclusion

• The speaker hopes that the audience finds the concept of Private GPT as exciting as they do.

• They express gratitude for everyone's participation and interest in Private GPT.