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Integrating GPTs Into Your Work Environment

artificial assistant

Get Your Own Artificial Intelligence Assistant

So often now, I find myself talking about the need to create work environments that support rather than undermine gpts. I’ve often wondered how I could get my own artificial intelligence assistant.
In this discussion, I want to lay my cards on the table to let you see what that looks like and how you too can get your own artificial assistant.
To date, I like to think that everyone has indulged in something AI-related, if only to chat with an AI chatbot for fun.
More and more, that will shift to a need for better familiarity – one that quickly upscales from using AIs such as ChatGPT from the site of a provider into using it from a desktop client on your own computer.
Why it is significant now is because when you’re in the vapour of privacy and safety, it hurts you if you’re going to these other people’s servers and typing, in private, something that’s intellectual property information or something to do with [a non-vetted, non-trademarked product] not released yet.

Mining Your AI

Thus spoke Zarathustra, so now we’ll look at some tactics you can put into place to help add publicly cited, curated and tagged information into a more densely footnoted arena your LLM AI will ‘mine’.
Some of these connection processes might be short-cuts, depending on the system that you are using.
Notice: This guide is just an exemplar framework, and since most users are familiar with ChatGPT, we will be using this as a base model LLM.
artificial intelligence assistant

It's Not Just About ChatGPT

So in my office at the moment, I’m not simply using ChatGPT as the only LLM model I’m working with. For better or worse, if you have an AI that cannot or will not give you an output based trained – well, then you’re done.
Unless you have another LLM that doesn’t have those restrictions – or that has other restrictions you’re not yet aware of, because they haven’t been raised yet.
For instance, just the other day, I asked Gemini which were the most important covid-19 mandates and rules ordered by the Biden administration since Joe Biden became president in 2021; it said that it was not able to find this information. ChatGPT, on the other hand, had no problem with this task.
Hence, it is now possible for most of us who own a computer to download a desktop version of ChatGPT onto our PC or Mac desktop and use ChatGPT as a desktop client.
Also, soon enough, within the coming days, we will start doing web searching with SearchGPT specifically as a search (as ChatGPT utilizes the web anyway), this will be more direct. But this is not all. The real centre where the rubber meets the road on privacy is PrivateGPT.

PrivateGPT

PrivateGPT can be downloaded to run your local instance of GPT-3 or GPT-4 in conjunction with your data, securing and never sharing it with external servers. At least for me, this is my end game of Private AI. Upgrades for the future though…absolutely! What a way to get your own artificial intelligence assistant!
Again, here I’m not going to get into the technical details for how to install PrivateGPT (does ‘Google it’ sound ironic yet for you? That’s why Google got into this too, of course), as there is plenty of video content out there that will provide step-by-step guidance, but the payoff is substantial.

Benefits

  • Complete control over data privacy.
  • Customization to fit specific business needs.
  • Custom Knowledge Base.
  • Organized and easily accessible data.
  • High customization for specific queries.
  • API integration – to create an API that is your private brain, the AI querying for the right information.
  • Real-time data access.
  • Centralized data management.

Implementation Options

When deciding on the best implementation option for your privateGPT, consider the following factors:
Not 100 per cent technically inclined? No worries. The first place you should go to help implement PrivateGPT is GitHub. No computer science degree required.
After all, is privacy a big deal for you? If not, use the desktop or browser version of ChatGPT.
Scalability is probably the next biggest thing to privacy next to that, and it might be for a small business that isn’t expanding their budget for this.
Now take into consideration what you want to utilize it for, as well as how your company is growing along with employees who are using it, and how to keep business information secure if you’re using a CRM.
artificial assistant

Next Steps

1. Evaluate Needs: Assess your specific needs and constraints.
2. Choose Method: Select the method(s) that best fit your requirements.
3. Plan Implementation: Create a detailed implementation plan.
4. Execute and Monitor: Implement the chosen solution(s) and monitor performance.
Consider what each tool can do for you, judging each one in the context of your research needs, your wallet, your technical infrastructure and what you aim to achieve.
It is up to you to choose according to your precise needs. If you prefer cloud solutions, they are easier to implement and are cost-efficient for scale, while local servers are preferred if you want maximum control over your data, and/or if data privacy concerns are a priority.

On-Premises Deployment

Dispatch a local GPT-3 or GPT-4 engine onto your desktop or within the enterprise.

Implementation Step 1:

1. Hardware Setup: Ensure you have the necessary hardware, such as servers with GPUs.
2. Software Setup: Install required software for running GPT models.
3. Model Deployment: Deploy the model on your local infrastructure.
4. Security Measures: Implement robust security measures to protect data.

Benefits

  • Enhanced data security.
  • Full control over deployment and management.
  • Database Integration.
  • Overview: Store your curated information in a structured database and query it as needed.

Implementation Step 2:

1. Database Setup: Choose a suitable database (e.g., PostgreSQL, MySQL, MongoDB).
2. Data Injestion: Load your curated data into the database.
3. Query Integration: Implement mechanisms for querying the database and retrieving data.

Benefits

  • Efficient data storage and retrieval.
  • Scalability.
  • Document Indexing and Retrieval.
  • Overview: Use document indexing and retrieval systems like Elasticsearch or Solr.

Implementation Step 3:

1. Indexing Setup: Set up Elasticsearch or Solr on your server.
2. Data Indexing: Index your documents for fast retrieval.
3. Data Integration: Integrate the indexing system with your AI model for querying.

Benefits

  • Fast search capabilities.
  • Handling large volumes of text.

Final Thoughts

Thus, by completion of these steps, you will provide yourself with a diverse, safe, efficient environment to use private, curated knowledge in your AITasks.
If you need help with any of these techniques, then once again … go to ChatGPT and have it explain it to you, and use these github sources, and the gurus who will no doubt start posting videos on how to implement your PrivateGPT framework. If none of these steps work, contact this author and I can point you in the right direction.

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