Dr. Farshid Pirahansiah

https://www.pirahansiah.com/farshid/portfolio/publications/Books/AI/ComputerVisionMeetLLM

AI computer vision locally LLMs on device

Computer Vision Meets LLM: Multi-Agent Swarm with RAG for Images and Videos**

Introduction

The convergence of Computer Vision (CV) and Large Language Models (LLMs) marks a significant advancement in artificial intelligence, enabling more comprehensive and intelligent systems capable of understanding and interacting with the world in multimodal ways. By integrating multi-agent swarms with Retrieval-Augmented Generation (RAG), developers can create sophisticated applications that process and analyze images and videos alongside textual data. This synergy enhances capabilities in areas such as image recognition, video analysis, document processing, and interactive user experiences.

1. Integration of Computer Vision and Large Language Models

Combining CV and LLMs leverages the strengths of both modalities:

• Computer Vision excels in interpreting and analyzing visual data, identifying patterns, objects, and actions within images and videos.
• Large Language Models (e.g., GPT-4) are proficient in understanding and generating human-like text, enabling nuanced interactions and contextual understanding.

Together, they enable applications that require both visual and textual comprehension, such as automated content creation, intelligent video summarization, and enhanced accessibility features.

2. Multi-Agent Swarm with Retrieval-Augmented Generation (RAG)

Multi-Agent Swarms involve multiple AI agents working collaboratively to perform complex tasks. When combined with Retrieval-Augmented Generation (RAG), these swarms can efficiently retrieve relevant information from diverse sources and generate contextually appropriate responses or actions.

• RAG Mechanism: Enhances LLMs by integrating a retrieval component that fetches relevant data from external sources (e.g., databases, documents, images) to inform the generation process.
• Multi-Agent Coordination: Different agents specialize in various aspects of data processing (e.g., one handles image analysis, another manages textual data), ensuring comprehensive and efficient task execution.

3. Token Costs and Pricing for Image Processing with GPT-4 Turbo Vision

OpenAI’s GPT-4 Turbo with Vision capabilities offers powerful tools for processing images and videos. Understanding the token costs associated with these operations is crucial for budgeting and optimizing usage.

a. Low Mode

• Description: Processes an image at a fixed cost, suitable for straightforward image analysis tasks.
• Cost: 85 tokens per image.

b. High Mode

• Description: Handles more complex images by resizing and partitioning them into manageable segments before processing.
• Procedure:
  1. Scaling: The image is scaled to fit within a 2048 x 2048 pixel square.
  2. Resizing: The shortest side of the image is resized to 768 pixels.
  3. Partitioning: The resized image is divided into multiple 512-pixel squares.
  4. Cost Calculation:
     • Base Cost: 85 tokens.
     • Additional Cost: 170 tokens per 512-pixel square.
     • Total Cost: (Number of squares * 170) + 85 tokens.
• Example: A 1080x1080 pixel image processed in vision mode costs approximately $0.00765 per image.

c. Token Cost Management

• Tools: Utilize AI Model Cost Calculators to estimate and manage token costs effectively.
  • AI Model Cost Calculator

4. Implementing Retrieval-Augmented Generation (RAG) Systems with Multimodal Data

Building and implementing RAG systems using multimodal data involves several key techniques and considerations:

a. Contrastive Learning

• Purpose: Trains models to distinguish between similar and dissimilar data points across different modalities.
• Application: Enhances the model’s ability to retrieve relevant information by understanding the relationships between text, images, audio, and video.

b. Any-to-Any Search Systems

• Functionality: Allows users to input data in one form (e.g., an image) and retrieve related data in another format (e.g., text or video).
• Use Cases:
  • Document Analysis: Extracting and summarizing information from scanned invoices.
  • Multimodal Recommender Systems: Providing recommendations based on a combination of user preferences and visual data.

c. Training Multimodal Models

• Techniques:
  • Data Integration: Combining datasets from various modalities to train comprehensive models.
  • Model Architecture: Designing architectures that can handle and process multiple data types simultaneously.

5. Real-World Applications

a. Analyzing Documents and Invoices

• Process:
  1. OCR Processing: Extract text from scanned documents using CV.
  2. Data Retrieval: Use RAG to fetch relevant financial data.
  3. Automated Calculation: Compute costs and generate summaries.
• Outcome: Streamlined invoice processing with reduced manual intervention.

b. Multimodal Recommender Systems

• Functionality: Combines user behavior data (text) with visual preferences (images) to provide personalized recommendations.
• Benefit: Enhanced user experience through more accurate and relevant suggestions.

c. Defect Detection in Manufacturing

• Method:
  • Image Analysis: Detect defects in products using CV.
  • Data Augmentation: Use RAG to correlate defects with production data.
• Result: Improved quality control and reduced defect rates.

d. Graphs to Code Conversion

• Description: Transform visual graph representations into executable code using LLMs.
• Application: Facilitates rapid prototyping and development based on visual designs.

e. PowerBI Integration

• Use Case: Enhance data visualization and analysis by integrating AI-driven insights into PowerBI dashboards.
• Advantage: More dynamic and insightful business intelligence solutions.

6. Prompting Strategies

Effective prompting strategies are essential for maximizing the performance of AI systems in multimodal environments.

a. Zero-Shot Prompting

• Definition: Instructing the model to perform a task without providing specific examples.
• Usage: Suitable for straightforward tasks where the model’s general knowledge suffices.

b. Few-Shot Prompting

• Definition: Providing a few examples to guide the model in performing a task.
• Usage: Enhances performance in more complex or nuanced tasks by offering contextual guidance.

7. Context Window Management

Managing the context window is crucial for maintaining coherence and relevance in AI-generated responses, especially when dealing with large amounts of data from multiple modalities.

• Techniques:
  • Chunking Data: Breaking down large datasets into smaller, manageable chunks.
  • Summarization: Condensing information to fit within the context window without losing essential details.
  • Prioritization: Focusing on the most relevant information to maximize the effectiveness of the model’s responses.

8. Tools and Platforms

a. OpenAI’s Vision API

• Capabilities: Enables image and video processing with GPT-4 Turbo, supporting tasks like OCR and detailed analysis.
• Documentation: OpenAI Vision Guide

b. Azure AI Studio

• Features:
  • Data Integration: Upload and manage documents and images.
  • Visual Search: Implement GPT-4 for image-based search queries.
• Access: Azure AI Studio

9. Token Costs Associated with Processing Images Using GPT-4 Turbo with Vision

Understanding token costs is vital for optimizing the use of GPT-4 Turbo’s vision capabilities. Here’s a breakdown of how token costs are calculated:

a. Low Mode

• Fixed Cost: 85 tokens per image.
• Use Case: Suitable for simple image processing tasks where extensive analysis is not required.

b. High Mode

• Procedure:
  1. Scaling: Fit the image within a 2048 x 2048 pixel square.
  2. Resizing: Adjust the shortest side to 768 pixels.
  3. Partitioning: Divide the image into 512-pixel squares.
  4. Cost Calculation: (Number of squares * 170) + 85 tokens.
• Example:
  • 1080x1080 Pixel Image:
    • Number of 512-pixel squares: Calculated based on the resized dimensions.
    • Total Cost: Approximately $0.00765 per image in vision mode.

10. Best Practices for Building and Implementing Multimodal RAG Systems

a. Optimize Data Processing

• Efficient Scaling and Resizing: Ensure images are processed optimally to balance quality and token costs.
• Batch Processing: Handle multiple images or videos simultaneously to reduce processing time and costs.

b. Enhance Retrieval Mechanisms

• Diverse Data Sources: Incorporate a wide range of data sources to improve the relevance and accuracy of retrieved information.
• Continuous Learning: Update retrieval models regularly to adapt to new data and evolving requirements.

c. Ensure Ethical AI Usage

• Bias Mitigation: Continuously monitor and address biases in both CV and LLM components.
• Transparency: Maintain clear documentation and explainability for AI-driven decisions and processes.

11. Resources and Further Reading

a. Official Documentation

• OpenAI Vision Documentation: OpenAI Vision Guide
• Azure AI Studio Documentation: Azure AI Studio

b. GitHub Repositories

• Hugging Face Transformers: huggingface/transformers
• OpenAI Gym: openai/gym
• Ray: ray-project/ray

c. Online Courses

• Deep Learning Specialization (Coursera): Coursera - Deep Learning
• AI for Everyone (Coursera): Coursera - AI for Everyone

d. Books

• “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron: O’Reilly Media
• “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning Book

e. Communities and Forums

• OpenAI Community: OpenAI Community Forum
• Reddit - r/MachineLearning: r/MachineLearning
• Stack Overflow: Stack Overflow AI

12. Conclusion

The integration of Computer Vision with Large Language Models through multi-agent swarms and Retrieval-Augmented Generation represents a frontier in AI development. This powerful combination enables the creation of intelligent systems capable of understanding and interacting with the world in rich, multimodal ways. By effectively managing token costs, employing robust implementation strategies, and adhering to ethical practices, developers can harness the full potential of these technologies to build innovative applications that address complex real-world challenges.

As AI continues to evolve, the symbiosis between visual and textual data processing will unlock new possibilities, driving advancements in industries ranging from healthcare and education to marketing and manufacturing. Embracing these technologies while maintaining the human advantage—creativity, emotional intelligence, and ethical judgment—will be key to fostering a future where AI serves as a catalyst for positive and sustainable progress.

Computer Vision Meet LLM: Multi agent swarm with RAG for image and videos https://www.pirahansiah.com/farshid/portfolio/projects/AI_Model_Cost_Calculator.html https://community.openai.com/t/how-do-i-calculate-image-tokens-in-gpt4-vision/492318 https://platform.openai.com/docs/guides/vision Multi agent swarm with RAG for image and videos token costs associated with processing images using GPT-4 Turbo with Vision capabilities

1.	Low Mode:
The image is processed for a fixed cost of 85 tokens.
2.	High Mode:
•	The image is first scaled to fit within a 2048 x 2048 pixel square.
•	Then, the shortest side is resized to 768 pixels.
•	The model calculates how many 512-pixel squares the image consists of.
•	The total cost is calculated by multiplying the number of squares by 170 tokens and adding 85 tokens as the base. token costs associated with processing images using GPT-4 Turbo with Vision capabilities

OpenAI’s GPT-4 Turbo with Vision capabilities enables various applications, including optical character recognition (OCR) and detailed image analysis. The pricing for these image-based interactions varies based on image size. For instance, a 1080x1080 pixel image costs $0.00765 per image to process in vision mode. This feature is part of the multimodal capabilities added to GPT-4 Turbo, making it a powerful tool for developers to integrate both text and image inputs into their applications https://cookbook.openai.com/examples/gpt_with_vision_for_video_understanding

how to build and implement Retrieval-Augmented Generation (RAG) systems using multimodal data such as text, images, audio, and video. It covers various techniques like contrastive learning to train models that can retrieve information across multiple modalities. The course guides learners through creating any-to-any search systems, where you can input data in one form (e.g., an image) and retrieve related data in other formats (e.g., text or video).

Some of the key takeaways include:

Building multimodal RAG systems that retrieve and reason over multimodal data to generate contextually relevant responses. Training models to process multimodal inputs, allowing for seamless retrieval and reasoning. Implementing real-world applications, such as analyzing documents like invoices and creating multimodal recommender systems. Image Context is everything PowerBI Graphs to code Defect detection

Zero-shot prompting Few-shot prompting

Context window

https://portal.azure.com/#browse/Microsoft.MachineLearningServices%2Faistudio doc+images+content>add your data>visual search>gpt4 Image inputs are metered and charged in tokens