Compound AI Systems
As tempting and mouth watering the image looks, ask any chef, it is as such a complex piece of things to do. It’s the perfectly cooked soft pan cakes, temperature, stacking, syrup, the fruit, the presentation.. yes it a complex breakfast with a lot of work going into it.
Now coming to our AI world, what is a Compound AI system? An AI system that does more than juts being a Large Language Model (LLM), but is an LLM in conjunction with other components like integrating with databases, ability to search n the internet etc into a system or a chain that allows it to achieve its State of the Art result.
Some examples of compound AI systems:
Autonomous vehicles
Combining computer vision, sensor data, speech recognition and information retrieval
Prompt engineering
Prompt engineering logic + LLM
RAG (Retrieval Augmented Generation)
LLM + Retrieval systems, Data API’s like Databricks Online Table, Text-To-SQL engines
and many more like Healthcare diagnostics, Fraud detection systems, Siri, Alexa..
—Concepts
Tasks, intents, pipelines
Compound AI systems are designed to tackle complex tasks by combining multiple AI components. Three fundamental concepts: tasks, intents, and pipelines need to understood.
Tasks
A task is a specific activity or operation that an AI system is designed to perform.
Examples:
Sentiment analysis
Language translation
Image classification
Text summarization
Characteristics:
Well-defined input: Tasks typically have a clear and well-defined input, such as text or an image.
Specific output: Tasks produce a specific output, such as a sentiment score or a translated text.
Measurable performance: Task performance can be measured using metrics like accuracy, precision, or recall.
Intents
An intent is the underlying purpose or goal that a user wants to achieve when interacting with an AI system.
Examples:
Booking a flight
Finding a restaurant
Getting directions
Making a purchase
Characteristics:
User-centric: Intents are focused on the user's needs and goals.
Context-dependent: Intents can depend on the user's context, such as location or previous interactions.
Flexible input: Intents can be expressed in various ways, such as through text, voice, or gestures.
Pipelines
A pipeline is a series of tasks or processes that are executed in a specific order to achieve a particular intent.
Examples:
A language translation pipeline might involve:
Text analysis
Language detection
Machine translation
Post-editing
A sentiment analysis pipeline might involve:
Text preprocessing
Feature extraction
Model training
Prediction
Characteristics:
Sequential processing: Pipelines involve a sequence of tasks that are executed one after the other.
Data flow: Pipelines typically involve the flow of data from one task to the next.
Configurable: Pipelines can be configured to accommodate different tasks, intents, or AI models.
Putting it all Together
Compound AI systems integrate multiple tasks, intents, and pipelines to create a comprehensive AI solution. By combining these components, developers can build AI systems that:
Handle complex tasks: Compound AI systems can tackle complex tasks that involve multiple intents, tasks, and pipelines.
Improve accuracy: By integrating multiple AI components, compound AI systems can improve overall accuracy and performance.
Enhance user experience: Compound AI systems can provide a more seamless and intuitive user experience by understanding user intents and adapting to their needs.
In summary, tasks, intents, and pipelines are the building blocks of compound AI systems. By understanding these concepts and how they interact, developers can create sophisticated AI solutions that tackle complex challenges and provide real value to users.
—Key Components of a Compound AI System
A Compound AI system typically includes:
Language Models: Understand and respond to user inputs in a natural way.
Information Retrievers: Fetch relevant data from databases or external sources.
Databases: Store and manage data for easy access.
External Tools: Use APIs and services to perform specific tasks.
Data Converters: Turn data into a format that's easy to search and retrieve.
Result Filters: Prioritize and evaluate retrieved results for relevance.
Prompt Builders: Create effective inputs for language models.
Output Refiners: Ensure generated outputs are high-quality and coherent.
Caching Systems: Store frequently accessed responses for faster retrieval.
Task Managers: Orchestrate tasks and data flow for optimal performance.
Evaluation Tools: Assess system performance and output quality.
Monitoring and Feedback: Track performance and gather user feedback for improvement.
Model Adapters: Fine-tune pre-trained models for specific tasks or domains.
Agent Builders: Create autonomous agents that can perform tasks and make decisions.
These components work together to create a powerful and adaptable Compound AI system.
—Designing Compound AI Systems
In summary here’s a brief summary of a system approach to Application Development Life-cycle
Analysis —> Design —> Development —> Production —> Monitoring
To simplify:
01. Identify Intents
define possible intent based on some user requirements.
define intent dependencies
02. Identify Tools
tools you may need to accomplish a task. Like web search, apis, code execution etc.
03. Build the Chain
build a workflow based on identified tasks
architecture you may to solve the problem, like RAG, Text2SQL, etc.
iterate the process
An example: 3 main intents
intent - retrieval / search piece;
taking those requests, responses from that search and then building the augmentation of the prompt, grabbing those summaries an building the context;
once we have the context, sending to the model then generating answers.
Components
The Compound AI System is a modular, scalable, and flexible architecture designed to integrate multiple AI components to achieve complex tasks. This architecture enables the creation of a robust and adaptable AI system that can be applied to various domains.
Illustration of key components of a compound AI system
Data Ingestion Layer
AI Component Layer
Knowledge Graph
Orchestration Layer
Decision Support System
Continuous Learning and Improvement
—Challenges of Compound AI Systems
Building
Complexity in integrating multiple components
Managing multiple models and processing steps
Adapting to different hardware configurations
Ensuring robust metrics and logging systems
Optimizing
Balancing latency, throughput, and resource utilization
Co-optimizing system components
Cost optimization while maintaining performance
Harmonizing interactions among components
Deploying
Meeting diverse hardware, software, and scalability requirements
Managing operational complexity with MLOps and DataOps
Ensuring scalability and elasticity
Integrating with existing infrastructure
Addressing security, privacy, and explainability concerns
These challenges highlight the complexity and nuance of building, optimizing, and deploying Compound AI systems.
Some good reads
https://www.databricks.com/glossary/compound-ai-systems
The Shift from Models to Compound AI Systems