privateGPT-PLUS

This project was inspired by the original privateGPT (https://github.com/imartinez/privateGPT). Most of the description here is inspired by the original privateGPT.

In this model, I have replaced the GPT4ALL model with Vicuna-7B model and we are using the InstructorEmbeddings instead of LlamaEmbeddings as used in the original privateGPT.

Ask questions to your documents without an internet connection, using the power of LLMs. 100% private, no data leaves your execution environment at any point. You can ingest documents and ask questions without an internet connection!

Built with LangChain and Vicuna-7B and InstructorEmbeddings

Environment Setup

In order to set your environment up to run the code here, first install all requirements:

pip install -r requirements.txt

Rename example.env to .env and edit the variables appropriately.

PERSIST_DIRECTORY: is the folder you want your vectorstore in

Test dataset

This repo uses a state of the union transcript as an example.

Instructions for ingesting your own dataset

Put any and all of your .txt, .pdf, or .csv files into the source_documents directory

Run the following command to ingest all the data.

python ingest.py

It will create a db folder containing the local vectorstore. Will take time, depending on the size of your documents. You can ingest as many documents as you want, and all will be accumulated in the local embeddings database. If you want to start from an empty database, delete the db folder.

Note: When you run this for the first time, it will download take time as it has to download the embedding model. In the subseqeunt runs, no data will leave your local enviroment and can be run without internet connection.

Ask questions to your documents, locally!

In order to ask a question, run a command like:

python privateGPTPLUS.py

And wait for the script to require your input.

> Enter a query:

Hit enter. You'll need to wait 20-30 seconds (depending on your machine) while the LLM model consumes the prompt and prepares the answer. Once done, it will print the answer and the 4 sources it used as context from your documents; you can then ask another question without re-running the script, just wait for the prompt again.

Note: When you run this for the first time, it will need internet connection to download the vicuna-7B model. After that you can turn off your internet connection, and the script inference would still work. No data gets out of your local environment.

Type exit to finish the script.

How does it work?

Selecting the right local models and the power of LangChain you can run the entire pipeline locally, without any data leaving your environment, and with reasonable performance.

• ingest.py uses LangChain tools to parse the document and create embeddings locally using InstructorEmbeddings. It then stores the result in a local vector database using Chroma vector store.
• privateGPTPLUS.py uses a local LLM (Vicuna-7B in this case) to understand questions and create answers. The context for the answers is extracted from the local vector store using a similarity search to locate the right piece of context from the docs.
• You can replace this local LLM with any other LLM from the HuggingFace. Make sure whatever LLM you select is in the HF format.

System Requirements

Python Version

To use this software, you must have Python 3.10 or later installed. Earlier versions of Python will not compile.

C++ Compiler

If you encounter an error while building a wheel during the pip install process, you may need to install a C++ compiler on your computer.

For Windows 10/11

To install a C++ compiler on Windows 10/11, follow these steps:

1. Install Visual Studio 2022.
2. Make sure the following components are selected:
   • Universal Windows Platform development
   • C++ CMake tools for Windows
3. Download the MinGW installer from the MinGW website.
4. Run the installer and select the "gcc" component.

Disclaimer

This is a test project to validate the feasibility of a fully private solution for question answering using LLMs and Vector embeddings. It is not production ready, and it is not meant to be used in production. The models selection is not optimized for performance, but for privacy; but it is possible to use different models and vectorstores to improve performance.