Use Open AI (ChatGPT) On Your Own Large Data -Part 1

Use Open AI (ChatGPT) On Your Own Large Data - Part 1

Introduction

We’ve all heard the buzz around Chat GPT and other Open AI models like GPT-3 and GPT-4. But if you’ve tried using such models to analyze your own data, you might have found them less useful or even downright unfit for the job. The primary reason here is that these models haven't seen your specific data during their training phases, and due to token limitations, you can't fit all your large documents into a single prompt.

The Token Limit Problem

When you are a researcher or someone who deals with massive amounts of text and PDF files, one question comes to mind: How can I leverage state-of-the-art AI models to gain insights from my own data? The problem is, these models have token limits in their inputs. This means you can't just throw all your documents into a single prompt—they’ll exceed the token limits and won't process correctly.

Obvious But Incorrect Solution: Fine-Tuning

One might think that the solution lies in fine-tuning these models to understand your specific data. However, this isn't the optimal route. Fine-tuning is generally used when you want to introduce new patterns or extremely niche information, which is a rare necessity.

The Better Solution: Word Embeddings

A more straightforward way to resolve this issue is by converting your documents into word embeddings. Word embeddings are numerical representations of text—captured in a way that the machine can easily understand. This method allows you to retrieve the relevant text from your entire document dataset.

Let’s say the model converts "How are you?" and "How are you feeling?" into nearly identical word embeddings because they mean almost the same thing. By contrast, "The color is black," will have entirely different embeddings because its meaning diverges entirely.

Using these embeddings, we can perform a similarity check to see which chunks of text in your documents closely match the query you pose. Once you identify the relevant chunks, you can insert them into the prompt and let the model generate a response. This circumvents the token limit issues because you are only bringing in the relevant sections of your documents instead of the whole dataset.

Implementation with Azure

Here's a practical application of this method using Azure and its associated services:

1. Azure Open AI Service: This is where your Open AI models run.
2. Azure Form Recognizer: Converts your PDF files into raw text.
3. Redis: Stores your word embeddings for quick retrieval.

Deployment Steps

1. Log into Azure: Navigate to the deployment page and select the subscription and resource group.
2. Specify Details: Fill in details like the resource prefix, Redis container name, password, etc.
3. Open AI Configuration: Provide the name and key for your Open AI resource.
4. Form Recognizer and Translator: Provide keys and endpoints for these services if you need them to handle non-English text.

After following these deployment steps, a collection of resources (including a web application) will be created in your specified Resource Group on Azure.

Using the Web Application

• Add Documents: Upload your files. These could be PDFs, text files, images, etc. Form Recognizer will convert these into text.
• Query Processing: When a user poses a question, the system will:
  1. Convert the user query into word embeddings.
  2. Match these embeddings with your document embeddings stored in Redis.
  3. Retrieve the chunks of text that closely match the query.
  4. Use these chunks to form the prompt and generate a response via Open AI models.

Practical Examples

You can ask specific questions such as:

• Research Paper: "What are the features used for this prediction model?"
• Annual Report: "What is responsible growth related to in this annual report?"

The web application retrieves the relevant data and provides accurate answers, circumventing the token limit problem.

Microsoft Azure Setup

Azure's services like Form Recognizer, Redis, and Open AI enhance the functionality of this implementation. Detailed instructions and example queries showcase how effectively you can turn your domain-specific data into actionable insights using these technologies.

Conclusion

This approach provides a streamlined, effective way of making Open AI models work with your own large datasets. Through word embeddings and intelligent retrieval, you can gain meaningful insights without running afoul of token limits.

Keywords

• Chat GPT
• Open AI
• Word Embeddings
• Azure
• Form Recognizer
• Redis
• Token Limit
• Large Documents
• Fine-Tuning

FAQ

Q1: What is the primary challenge with using Chat GPT for my large datasets? A1: The main issue is the token limit for input in these models, making it impossible to fit all your data into one prompt.

Q2: Why isn't fine-tuning the best solution for this problem? A2: Fine-tuning is designed for introducing new patterns not covered during the model's original training, which is rarely necessary for analyzing existing data.

Q3: What is a better method than fine-tuning for using large datasets with Open AI models? A3: The more efficient method involves converting your documents into word embeddings and using similarity measurements to fetch relevant sections.

Q4: What services on Azure can be used to implement this solution? A4: You can use Azure Open AI Service, Azure Form Recognizer, and Redis to create, store, and query word embeddings.

Q5: How does using word embeddings resolve the token limit issue? A5: Word embeddings allow you to fetch and process only relevant sections of your documents, limiting the number of tokens used in any single prompt.

Q6: Can this method handle non-English text? A6: Yes, using Azure Translator, you can convert non-English text into English before processing it with other services.

Q7: How do I deploy this solution on Azure? A7: Follow the steps to set up the necessary services on Azure, then upload your documents through the web application provided.

This markdown format details the entire process of leveraging Open AI models for analyzing large, domain-specific datasets by converting them into word embeddings and using Azure's array of services to handle various functionalities.