Multi-Model GPT System for Diverse Data Inputs

Multi-Model GPT System operates similarly to ChatGPT but with a significant distinction, it integrates multiple AI models into one cohesive system. These include cloud models, Allama Index, OpenAI models, Google Pro, and various other large language models (LLMs). Some models are open-source and deployed on custom GPUs, while others are accessed via APIs. This integration allows the system to draw on multiple sources of intelligence, providing robust and versatile responses.


The client faced several challenges in building a unified solution that integrates various AI models into one system:


  1. Seamless Integration: Ensuring different AI models could work together smoothly was challenging due to the varied nature of the models and their data.
  2. Token Limits: Each API request had token limits, which posed problems during long chat conversations, leading to errors and increased costs.
  3. Document Handling: Managing and interacting with multiple document types, such as DOC, DOCX, Excel, PDF, SRT, and EML files, was crucial for the system’s effectiveness.


To address these challenges, we developed a comprehensive system that integrated multiple AI models under one umbrella. This included cloud models, Allama Index, OpenAI models, and Google Pro, among others. Some models were open-source and deployed on custom GPUs, while most were accessed via APIs. Key features of the solution included:


  1. Robust Backend Mechanism: A backend system was built to manage token limits and ensure smooth chat experiences. Before processing each request, the system checks if the message size will exceed token limits. If the chat reaches its token limit, older messages are removed from the stack to maintain the conversation’s context.
  2. Serverless Models for Embedding Tasks: Serverless models were deployed for embedding tasks, ensuring efficient and scalable processing.
  3. Image Generation and Upscaling: For image generation, models like DALL-E and Defusion were used, allowing users to create and upscale images of different sizes.

Features & Benefits

Effective Request Management

The backend mechanism efficiently manages requests, preventing token limit exceedance and ensuring smooth operation of chat conversations and document processing.

Chat Context Maintenance

The system maintains chat context by storing old messages and inserting new ones, allowing for seamless interactions without losing context.

Scalability and Flexibility

The platform offers scalability and flexibility, allowing users to easily integrate various models and services under one umbrella and customize their usage according to specific needs.

Image Generation and Processing

Serverless models enable the generation of custom-sized images and support image upscaling, enhancing the platform’s capabilities for image-related tasks.

Chat with Voice

Users can engage in chat conversations with voice capabilities, expanding the platform’s functionality and user engagement.

Technologies Used to Build the Solution

Cloud-based models, Open-source frameworks, Serverless deployment, Custom GPUs for model deployment, OpenAI, API integration for interaction with backend services