privateChatGPT for Google Colab

The aim of this project is to interact privately with your documents using the power of GPT, 100% privately, no data leaks - Modified for Google Colab /Cloud Notebooks.

Why Google Colab?

LLM, large language models have become an interesting area of tech lately. While working on a project, I faced one issue, storage space in my local disk. I had to figure out an alternative. Google Colab worked well for me.

I realised other folks might face this same issue. This repo will guide you on how to; re-create a private LLM using the power of GPT. Explainer Video.

In the original version by Imartinez, you could 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! But this version uses Google Colab.

Built with LangChain, GPT4All, LlamaCpp, Chroma and SentenceTransformers.

Environment Setup

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

!pip install -r /content/privateGPT/requirements.txt

Then, download the LLM model and place it in a directory of your choice (In your google colab temp space- See my notebook for details):

LLM: default to ggml-gpt4all-j-v1.3-groovy.bin. If you prefer a different GPT4All-J compatible model, just download it and reference it in your .env file.

Please note that the .env will be hidden in your Google Colab after creating it. If you clone this repo into your personal computer, you'd be able to see it though.

Copy the example.env template into .env First create the file, after creating it move it into the main folder of the project in Google Colab, in my case privateGPT.

!touch env.txt #rename to .env

# Rename the file to .env

import os
os.rename('/content/privateGPT/env.txt', '.env')

and edit the variables appropriately in the .env file.

MODEL_TYPE: supports LlamaCpp or GPT4All
PERSIST_DIRECTORY: is the folder you want your vectorstore in
MODEL_PATH: Path to your GPT4All or LlamaCpp supported LLM
MODEL_N_CTX: Maximum token limit for the LLM model
MODEL_N_BATCH: Number of tokens in the prompt that are fed into the model at a time. Optimal value differs a lot depending on the model (8 works well for GPT4All, and 1024 is better for LlamaCpp)
EMBEDDINGS_MODEL_NAME: SentenceTransformers embeddings model name (see https://www.sbert.net/docs/pretrained_models.html)
TARGET_SOURCE_CHUNKS: The amount of chunks (sources) that will be used to answer a question

Note: because of the way langchain loads the SentenceTransformers embeddings, the first time you run the script it will require internet connection to download the embeddings model itself.

Test dataset

This repo uses a Data Visualisation guides as an example.

Additional test dataset

I added this dataset on "Business Analytics textbook" to my project and it worked fine, though cleaning is needed on the output like in the case of ChatGPT.


> Question:
explain data visualisation to me

> Answer (took 7163.81 s.):
 Data visualization refers to a way for people and machines to communicate information through graphical representations, such as charts or graphs that show trends, patterns, relationships between variables, and other important insights. It is an essential tool in many fields of science, engineering, business, economics, medicine, education, government, military, law enforcement, sports, entertainment...

Visualizations can be used to communicate complex information quickly and easily, making it easier for people with different backgrounds or levels of expertise to understand the same concept...

Visualizations can be created using a variety of tools and techniques, including bar charts, line graphs, scatter plots... k

> /content/privateGPT/source_documents/Essentials-of-Business-Analytics (1).pdf:
5
Summary and Further Reading
As stated above, a single chapter is far too little space to describe the intricacies of
data visualization. Few (2009) and Tufte and Graves-Morris (1983) are good sources
with which to broaden your knowledge of the “whys” of data visualization.
Exercises
Ex. 5.1 Answer the following conceptual questions:
(a) What is the key issue with using 3D graphs?
(b) When displaying differences in a single data dimension, explain why using

> /content/privateGPT/source_documents/Essentials-of-Business-Analytics (1).pdf:
Chapter 5
Data Visualization
John F. Tripp
1
Introduction
Data analytics is a burgeoning ﬁeld—with methods emerging quickly to explore
and make sense of the huge amount of information that is being created every
day. However, with any data set or analysis result, the primary concern is in
communicating the results to the reader. Unfortunately, human perception is not
optimized to understand interrelationships between large (or even moderately

> /content/privateGPT/source_documents/Essentials-of-Business-Analytics (1).pdf:
4
S. Seshadri
Data Visualization: This chapter discusses how data is visualized and the way
that visualization can be used to aid in analysis. It starts by explaining that humans
use visuals to understand information, and that using visualizations incorrectly can
lead to mistaken conclusions. It discusses the importance of visualization as a
cognitive aid and the importance of working memory in the brain. It emphasizes
the role of data visualization in reducing the load on the reader.


Enter a query:

Instructions for ingesting your own dataset

Put any and all your files into the source_documents directory

The supported extensions are:

.csv: CSV,
.docx: Word Document,
.doc: Word Document,
.enex: EverNote,
.eml: Email,
.epub: EPub,
.html: HTML File,
.md: Markdown,
.msg: Outlook Message,
.odt: Open Document Text,
.pdf: Portable Document Format (PDF),
.pptx : PowerPoint Document,
.ppt : PowerPoint Document,
.txt: Text file (UTF-8),

Run the following command to ingest all the data.

!python /content/privateGPT/ingest.py

Output should look like this:

Downloading (…)e9125/.gitattributes: 100% 1.18k/1.18k [00:00<00:00, 3.88MB/s]
Downloading (…)_Pooling/config.json: 100% 190/190 [00:00<00:00, 953kB/s]
Downloading (…)7e55de9125/README.md: 100% 10.6k/10.6k [00:00<00:00, 27.9MB/s]
Downloading (…)55de9125/config.json: 100% 612/612 [00:00<00:00, 2.18MB/s]
Downloading (…)ce_transformers.json: 100% 116/116 [00:00<00:00, 439kB/s]
Downloading (…)125/data_config.json: 100% 39.3k/39.3k [00:00<00:00, 9.70MB/s]
Downloading pytorch_model.bin: 100% 90.9M/90.9M [00:00<00:00, 92.2MB/s]
Downloading (…)nce_bert_config.json: 100% 53.0/53.0 [00:00<00:00, 216kB/s]
Downloading (…)cial_tokens_map.json: 100% 112/112 [00:00<00:00, 341kB/s]
Downloading (…)e9125/tokenizer.json: 100% 466k/466k [00:00<00:00, 7.92MB/s]
Downloading (…)okenizer_config.json: 100% 350/350 [00:00<00:00, 1.34MB/s]
Downloading (…)9125/train_script.py: 100% 13.2k/13.2k [00:00<00:00, 35.7MB/s]
Downloading (…)7e55de9125/vocab.txt: 100% 232k/232k [00:00<00:00, 8.82MB/s]
Downloading (…)5de9125/modules.json: 100% 349/349 [00:00<00:00, 1.22MB/s]
Creating new vectorstore
Loading documents from /content/privateGPT/source_documents
Loading new documents: 100%|█████████████████████| 1/1 [00:00<00:00, 135.57it/s]
Loaded 1 new documents from /content/privateGPT/source_documents
Split into 91 chunks of text (max. 500 tokens each)
Creating embeddings. May take some minutes...
Ingestion complete! You can now run privateGPT.py to query your documents

It will create a db folder containing the local vectorstore. Will take 20-30 seconds per document, depending on the size of the document. 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: during the ingest process no data leaves your local environment. You could ingest without an internet connection, except for the first time you run the ingest script, when the embeddings model is downloaded.

Ask questions to your documents, locally!

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

!python /content/privateGPT/privateGPT.py

And wait for the script to require your input.

> Enter a query:

Hit enter. You'll need to wait for seconds-minutes (mine took 2670.53 s) 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.

Type exit to finish the script or simply interrupt in the case of Google Colab.

CLI

The script also supports optional command-line arguments to modify its behavior. You can see a full list of these arguments by running the command python privateGPT.py --help in your terminal.

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 HuggingFaceEmbeddings (SentenceTransformers). It then stores the result in a local vector database using Chroma vector store.
privateGPT.py uses a local LLM based on GPT4All-J or LlamaCpp 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.
GPT4All-J wrapper was introduced in LangChain 0.0.162.

System Requirements

Python Version

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

Credits

Inspired by the works of imartinez

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.

nattive/privateGPT