In the field of artificial intelligence, large language models such as OpenAI's ChatGPT have been trained by taking large amounts of data from the internet through the Laion dataset, allowing them to understand and respond in natural language.
However, if you want to utilize these language models to process your own private data, concerns about potential data leaks may make you hesitate.
But there is an interesting idea:
If you can communicate with documents without an internet connection, use the power of artificial intelligence. 100 private, no data leaves your local machine at any time.PrivateGPT open source address: PrivateGPT.
What is PrivateGPT?
PrivateGPT is a revolutionary technological solution to this problem. It makes it possible to ingest your own private data using AI chatbots without having to make it public.
In this post, I will give you a detailed description of the process of setting up and running PrivateGPT on your local machine.
What you'll need
To get started, there are a few prerequisites that need to be installed on your system.
git - latest source ** version 240.1
python - latest 311.3
Step 1: Set up the project
The first step is to clone the privategpt project from the github project. After cloning, you should see a list of files and folders.
Step 2: Required dependencies
With the cloned project in place, the next step is to install the necessary dependencies. To do this, open the terminal in the project directory and run the following command:
pip install -r requirements.txt
This will install all the dependencies and requirements.
Step 3: Language model
PrivateGPT is designed to work with a variety of open-source language models. Some of the options available include:
Vicuna 13B parameters.
KOALA 7b parameters.
gpt4all
Although all of these models are effective, I recommend starting with the Vicuna 13B model because of its robustness and versatility.
*Once you have the model, copy and paste it into the privategpt project folder. You'll also need to update. ENV file to specify the path and other related settings of the Vikuna model.
persist_directory=dbmodel_type=llamacppmodel_path=models/ggml-vic13b-q5_1.binembeddings_model_name=all-minilm-l6-v2model_n_ctx=1000
Step 4: Add your documents
Copy any documents you want into the Source Documents folder. The supported file extensions are as follows:
csv：csv
docx: Word document.
doc: Word document.
enex：evernote
eml: Email.
epub：epub
html: html file.
md：markdown
msg: Outlook messages.
ODT: Open Document Text.
PDF: Portable Document Format (PDF).
pptx: PowerPoint document.
ppt: PowerPoint document.
txt: a text file (UTF-8).
To ingest data from a document file, open the terminal and run the following command:
python ingest.pyIf the ingestion is successful, you should see the following message in the terminal.
Step 5: Run the application
Now that you've done all the preparations, it's time to start chatting!
Run the following command in the terminal:
python privategpt.pyThis will load the LLM model and get you started chatting.
Perfect!Final thoughts
Overall, PrivateGPT is not just an innovative tool, but a transformative tool that aims to revolutionize the way we interact with AI, addressing key elements of privacy protection.
Imagine you're a business owner who recognizes the huge potential of AI chatbots to enhance customer service, streamline internal communication, and improve overall operational efficiency. However, you've been reluctant to implement these technologies due to concerns about the risk of leaking sensitive business data. This is where PrivateGPT comes into play.