Multimodal Reasoning Application

Gradio UI using the OpenAI SDK with GPT-4.1 model.

Overview

The diagram illustrates the architecture of a context-aware multimodal reasoning application that leverages large language models (LLMs) and retrieval-augmented generation (RAG). It is divided into two main sections:

  • Real-time User Interactions (left side)
  • Batch ETL Processes (right side)

Main Components & Flow

1. Real-time User Interactions (Left Side)

  • User: Initiates interaction by submitting a prompt (text, audio, image, or video).
  • Chatbot/Voicebot (Gradio): Receives user input.
  • RAG Platform (LangChain): Handles retrieval and generation tasks.
  • Guardrail Platform: Applies rule-based or LLM-based guardrails for safety and compliance.
  • Governance Platform: Logs artifacts for evaluation and tracks metrics.

Process Steps (Numbered 1-9):

  1. Submit prompt (text, audio, image, or video).
  2. Input control (guardrails).
  3. Convert audio to text, video to images (optional).
  4. Embed prompt.
  5. Retrieve document chunks/images from vector store.
  6. Submit prompt template with context.
  7. Output control (guardrails).
  8. Convert text to other modality (optional).
  9. Log artifacts for evaluation.

2. Batch ETL Processes (Right Side)

  • Multimodal Data: Source of structured, semi-structured, and unstructured data.
  • RAG Platform (Ingestion): Ingests and processes data.
  • AI Platform (OpenAI): Provides various AI services (speech-to-text, embeddings, image processing, etc.).
  • Vector Store (MongoDB Atlas): Stores document chunks/images and their embeddings.

Process Steps (Lettered A-F): A. Load documents (text, audio, image, or video). B. Convert audio/video (optional). C. Split documents into chunks. D. Embed document chunks/images. E. Store chunks/images and embeddings in vector store. F. Log artifacts for evaluation.

AI Platform (Center)

  • OpenAI Services:
    • Speech-to-Text (whisper-1)
    • Text Embedding (text-embedding-3-small)
    • Image Embedding (clip)
    • Text-and-Image-to-Text (gpt-4o)
    • Text-to-Speech (tts-1)
    • Text-to-Image (dalle-3)

Data Flow

  • User input flows from left to right through chatbot, RAG, and guardrail platforms, interacting with the AI platform and vector store as needed.
  • Batch ETL flows from right to left, starting with multimodal data ingestion, processing via the AI platform, and storage in the vector store.
  • Governance and evaluation are continuous, with logging and metrics tracked throughout.

Legend

  • Numbers (1-9): Real-time user interaction steps.
  • Letters (A-F): Batch ETL process steps.

Summary

This architecture enables a robust, context-aware, and multimodal reasoning system that supports both real-time user interactions and batch data processing, leveraging advanced AI models and retrieval-augmented generation for enhanced performance and governance.