Unlocking the Power of PrivateGPT: Your Free Offline AI Assistant
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Chapter 1: Introduction to PrivateGPT
In today's digital landscape, I’m excited to introduce you to PrivateGPT, a free alternative to ChatGPT that allows for efficient document engagement similar to its more famous counterpart. This solution not only prioritizes your privacy but also operates without an internet connection, alleviating concerns about data security.
PrivateGPT accommodates various document formats, including .csv, .docx, .doc, .epub, .ppt, and .txt. Although it may not match ChatGPT's speed on every device, its offline capabilities and cost-free nature make it a worthwhile option to consider.
Section 1.1: System Requirements for PrivateGPT
To get started with PrivateGPT, it's essential to meet certain system requirements to avoid potential hiccups:
- Python Version: Ensure that Python 3.10 or a later version is installed, as earlier versions will not compile.
- C++ Compiler: If you encounter errors while trying to install via pip, a C++ compiler may be necessary.
For Windows 10/11 users, follow these steps to install a C++ compiler:
- Download and install Visual Studio 2022.
- During installation, select:
- Universal Windows Platform development
- C++ CMake tools for Windows
- Additionally, download the MinGW installer from the MinGW website and select the gcc component during installation.
Section 1.2: Obtaining PrivateGPT
To obtain PrivateGPT, navigate to the GitHub repository. Click on the green "Code" button to copy the repository link. For those comfortable with terminal commands, execute the following:
After cloning, change your directory:
cd privateGPT/
Inside the privateGPT folder, locate the requirements.txt file and install the necessary dependencies:
pip install -r requirements.txt
Once installed, rename the file example.env to .env, and update its contents to reflect:
PERSIST_DIRECTORY=db
MODEL_TYPE=GPT4All
MODEL_PATH=models/ggml-gpt4all-j-v1.3-groovy.bin
EMBEDDINGS_MODEL_NAME=all-MiniLM-L6-v2
MODEL_N_CTX=1000
Next, you'll need to download the LLM (Large Language Model). This is crucial for enhancing the data you use for training and inference. Visit the repository and download the LLM model listed in the environment setup section.
After downloading, create a "models" folder within the privateGPT directory and place the LLM files there. Note that the first run of the script may require an internet connection to download necessary embeddings.
Now, gather the files you wish to analyze, ensuring they are in the supported formats (.csv, .docx, .doc, etc.). Then, execute the command to ingest your data:
python ingest.py
The output will provide feedback on the ingestion process, indicating whether new documents were loaded.
Chapter 2: Engaging with Your Documents
Now that your documents are ingested, you can begin querying them using the command:
python privateGPT.py
You will be prompted to enter a question, and the system will provide responses based on your local document database.
Understanding how this operates can feel like magic. For many, tools like ChatGPT remain a bit enigmatic. PrivateGPT leverages local models and the capabilities of LangChain to ensure that all processing occurs within your environment, maintaining data confidentiality.
The ingest.py script utilizes LangChain to parse documents and create embeddings locally using HuggingFaceEmbeddings (SentenceTransformers), with results stored in a local vector database. Meanwhile, privateGPT.py employs a local LLM based on GPT4All-J or LlamaCpp to interpret questions and generate answers, sourcing context from your local database through similarity searches.
The first video titled "100% Offline ChatGPT Alternative?" dives into the functionalities of PrivateGPT, demonstrating how it serves as an effective ChatGPT substitute while ensuring user privacy.
The second video, "PrivateGPT Step by Step: Chat with your private documents without internet connection," provides a detailed, step-by-step guide on using PrivateGPT with your documents offline.