Securing Your Conversations: Creating Local Chatbots with GPT4All and LangChain for Data Privacy and Security

In today’s digital age, where data privacy and security concerns loom large, organizations are increasingly seeking innovative solutions to protect sensitive information while still harnessing the power of AI-driven interactions.

In this blog, we delve into a fascinating synergy of two powerful technologies — GPT4All and LangChain — to create local chatbots that not only enhance user experiences but also fortify the walls guarding your data.

Join us on this journey as we uncover the myriad advantages of employing local chatbots as sentinels for your digital conversations, ensuring the utmost in data privacy and security.

Local chatbots, sometimes referred to as on-premises or offline chatbots, can offer several benefits with regard to privacy and security of data compared to cloud-based chatbots.

Here are some of the advantages:

Data Sovereignty

One of the most significant benefits of local chatbots is data sovereignty. With local chatbots, all user data remains within the organization’s own infrastructure or premises.

This can be crucial for businesses or entities that need to comply with strict data privacy regulations, such as GDPR in Europe, as it ensures that sensitive user data doesn’t leave the organization’s jurisdiction.

Reduced Data Exposure

When using a cloud-based chatbot, data is transmitted over the internet to remote servers for processing. This data transmission can be vulnerable to interception or hacking during transit.

In contrast, local chatbots process data on-site, reducing the risk of exposure during transmission.

Enhanced Control

Organizations have greater control over the security measures and protocols in place when data is processed locally. They can implement their own security policies, encryption standards, and access controls to protect user data.

Customized Security Measures

Organizations can tailor security measures to their specific needs and the sensitivity of the data they handle.

This customization can include firewalls, intrusion detection systems, and encryption methods that align with the organization’s security policies.

Offline Operation

Local chatbots can continue to operate even when there is no internet connection. This can be crucial in scenarios where internet connectivity is intermittent or unreliable, ensuring uninterrupted service.

Isolation from Cloud Vulnerabilities

Cloud-based chatbots may be susceptible to cloud-specific vulnerabilities and attacks. Local chatbots are generally less exposed to these types of risks since they don’t rely on external cloud infrastructure.

Compliance with Industry Regulations

Certain industries, such as healthcare and finance, have strict regulatory requirements for data security and privacy.

Local chatbots can help organizations in these sectors maintain compliance by keeping data within their controlled environments.

Let’s dive into the practical aspects of creating a chatbot using GPT4All and LangChain.

Installation & Setup

Create a virtual environment and activate it.

python -m venv <venv>
<venv>\Scripts\Activate.ps1

There are many ways to set this up. We will test with GPT4All and PyGPT4All libraries. PyGPT4All is the Python CPU inference for GPT4All language models.

Installing gpt4all

pip install gpt4all

Test it out! In a Python script or console:

from gpt4all import GPT4All
model = GPT4All('D:\gpt4all_langchain_chatbots\models\gpt4all-converted.bin')
for token in model.generate("Once upon a time", n_predict=55):
    print(token, end='', flush=True)

Similarly, pygpt4all can be installed using pip. However, this project has been archived and merged into gpt4all

Pygpt4all Code:

from pygpt4all.models.gpt4all import GPT4All

def new_text_callback(text):
    print(text, end="")

model = GPT4All('D:\gpt4all_langchain_chatbots\models\gpt4all-converted.bin')
model.generate("Once upon a time, ", n_predict=55, new_text_callback=new_text_callback)

Output:

I have downloaded the model from here because of latency and size constraints. GPT4All has the best-performing state-of-the-art models to replace it. I will provide a comparison later in the post.

Models can be downloaded from here.

# Installing langchain
      pip install langchain

Let’s test a sample code of langchain to confirm the installation

from langchain import PromptTemplate, LLMChain
from langchain.llms import GPT4All
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

local_path = 'D:\gpt4all_langchain_chatbots\models\gpt4all-converted.bin' 
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])

template = """Question: {question}

Answer: Let's think step by step.

"""

prompt = PromptTemplate(template=template, input_variables=["question"])
llm = GPT4All(model=local_path, callback_manager=callback_manager, verbose=True)
llm_chain = LLMChain(prompt=prompt, llm=llm)

question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
# question = input("Enter your question: ")

llm_chain.run(question)

Output:

Up until this point, we have exclusively utilized the pre-existing gpt4all models. Moving forward, we will feed custom data to the model by embedding and indexing.

Data Collection & Preparation

We have collected the McDonald’s restaurant location data from ScrapeHero.

We need to convert this data into embeddings using the llama model. Using these embeddings we will create an index that will be used in a similarity match between the question and index documents.

Note: Visual Studio 2022, cpp, cmake installations are a must to prompt the question to langchain prompt template.

We have a privateGPT package that effectively addresses our challenges. It is developed using LangChain, GPT4All, LlamaCpp, Chroma, and SentenceTransformers.

Implementation & Testing

In the implementation part, we will be comparing two GPT4All-J models i.e., ggml-gpt4all-j-v1.3-groovy.bin and wizardlm-13b-v1.1-superhot-8k.ggmlv3.q4_0.bin

Clone PrivateGPT repo and download the models into the ‘models’ directory. Modify the .env file to specify the model path. The McDonald’s restaurant data will be located in the ‘source_documents’ folder, while the custom CSV data will be converted into a VectorStore database.

Run the following command to ingest the custom data as indexes into the db.

python ingest.py

It will create a db folder containing the local vectorstore.

Note: Throughout the ingest process, data remains within our local environment. We can perform ingestion without an internet connection, except for the initial setup, where we run the ingest script to download the embedding model.

The embedding model in our setup is all-MiniLM-L6-v2.

Test the model with questions, locally! Run this command

python privateGPT.py

Question 1: How recent is the available McDonald’s data scraped?

Output from ggml

Output from wizardlm

Question 2: how many stores available in the city “TORONTO”?

Output from ggml

Output from wizardlm

The Wizardlm model outperforms the ggml model. It is the latest and best-performing gpt4all model. The model performs well with more data and a better embedding model.

Conclusion

We now have experience in constructing local chatbots capable of running without internet connectivity to enhance data security and privacy using LangChain, GPT4All, and PrivateGPT. These models can achieve superior performance with larger datasets and improved embeddings.

However, it’s essential to note that local chatbots also come with their own set of challenges. These include higher initial setup costs, the need for dedicated infrastructure and maintenance, and potential limitations in terms of scalability compared to cloud-based solutions.

Organizations should carefully assess their specific requirements and consider the trade-offs between local and cloud-based chatbots when it comes to privacy and security.

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