Comprehensive Guide to AI Act Technical Documentation
Explore detailed requirements for AI Act documentation, focusing on high-risk AI systems and simplified options for SMEs.
Executive Summary
The EU AI Act, effective from 2024, introduces stringent technical documentation requirements, particularly for high-risk AI systems. This article delves into these requirements, emphasizing the importance for developers to ensure compliance and security through comprehensive documentation. High-risk systems must adhere to Article 11, requiring documentation that is clear, comprehensible, and complete as outlined in Annex IV. This documentation is crucial not only for regulatory compliance but also for building trust and minimizing security risks.
The AI Act acknowledges the challenges faced by SMEs and offers accommodations to ease their compliance burden. Developers must leverage frameworks like LangChain, AutoGen, and CrewAI for effective implementation. Key technical topics such as memory management, tool calling, and agent orchestration are discussed with practical examples, including vector database integrations with Pinecone and Weaviate, and MCP protocol implementation. Here is a sample code snippet for conversation memory management:
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
By following the guidelines and examples provided, developers can confidently navigate the AI Act's requirements, ensuring their systems are both compliant and robust.
Introduction
The European Union's AI Act introduces a groundbreaking regulatory framework aimed at ensuring the safe and effective deployment of artificial intelligence technologies. As part of this framework, the Act mandates comprehensive technical documentation for AI systems, a requirement that becomes increasingly vital as we approach the enforcement date for General-Purpose AI (GPAI) models in August 2025. This documentation is crucial for several reasons: it must satisfy regulatory bodies, mitigate security vulnerabilities, and foster trust by making AI systems transparent to operators. In this article, we delve into the technical documentation requirements stipulated by the AI Act, particularly focusing on high-risk systems as outlined in Article 11 and Annex IV.
Developers and engineers tasked with implementing these requirements must create documentation that is not only extensive but also comprehensible and up-to-date. This involves utilizing modern frameworks and tools to ensure compliance and facilitate integration with existing technologies. For instance, using LangChain for memory management and Pinecone for vector database integration can streamline the documentation process and enhance system functionalities.
Code Snippets and Implementation Examples
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
agent_executor = AgentExecutor(memory=memory)
# Handle multi-turn conversations efficiently
Incorporating these elements is critical. For instance, integrating a vector database like Pinecone requires specific code implementations to handle data effectively:
from pinecone import PineconeClient
client = PineconeClient(api_key='your_api_key')
index = client.Index('your_index')
# Code to interact with vector embeddings
Architecture diagrams, though not included directly here, would typically describe the flow of data between AI components and external systems. These diagrams play a crucial role in illustrating how AI systems meet the EU AI Act's standards, especially in the context of data flow and processing.
In conclusion, as developers navigate these regulatory requirements, technical documentation serves as both a roadmap and a compliance tool, facilitating the safe adoption of AI technologies across various domains.
Background
The European Union's AI Act is a pivotal legislative measure designed to regulate the deployment and management of artificial intelligence systems within the EU. Drafted with the intent to create a framework that ensures the safe and ethical use of AI technologies, the AI Act establishes comprehensive technical documentation requirements to enhance transparency, safety, and trust in AI applications. This initiative is part of a broader effort to position Europe as a leader in AI governance while protecting the rights and safety of its citizens.
The historical journey of the AI Act began with discussions initiated by the European Commission in 2020, focusing on addressing the risks associated with AI. The proposal for the AI Act was officially unveiled in April 2021. After extensive consultations, the regulation was adopted in 2023, marking a significant milestone in AI policy. The Act's implementation is phased, with general requirements taking effect in 2024. However, specific obligations for General-Purpose AI (GPAI) models will commence on August 2, 2025, highlighting the importance of technical documentation in ensuring compliance with regulatory standards and managing AI risk.
Key dates in the AI Act's implementation include the entry into force of the regulation in 2023 and the subsequent start of obligations in 2024 for high-risk AI systems. This timeline allows organizations to adapt and prepare their internal processes to meet the documentation standards required under the AI Act. The regulation divides AI systems into risk categories, with high-risk systems subject to stringent documentation mandates as outlined in Article 11 and Annex IV.
For developers and organizations working with AI, this means preparing technical documentation that demonstrates compliance with the AI Act's requirements before placing systems on the market. Such documentation must be clear, comprehensive, and continually updated to address regulatory, security, and operational concerns. Below are some practical implementations to manage these requirements using modern AI frameworks and tools.
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
agent_executor = AgentExecutor(
memory=memory,
# Add additional configurations here
)
In the context of AI Act compliance, LangChain's memory management features, such as ConversationBufferMemory, are instrumental in logging interactions to maintain comprehensive records. This can be integrated with vector databases like Pinecone or Weaviate to ensure scalable and efficient data retrieval.
from langchain.vectorstores import Pinecone
from langchain.embeddings import OpenAIEmbeddings
vectorstore = Pinecone(
embedding_function=OpenAIEmbeddings()
)
By leveraging these tools, developers can efficiently manage compliance documentation, which is critical for high-risk AI systems as per the AI Act. Furthermore, the use of Multi-Modal Conversation Protocol (MCP) with frameworks like AutoGen can facilitate seamless multi-turn conversations, ensuring that AI interactions remain coherent and aligned with user needs while adhering to regulatory standards.
Methodology
The approach to developing technical documentation for AI systems under the EU AI Act is critically influenced by a risk-based classification system. This system dictates the level of detail and specific requirements that need to be addressed, particularly for high-risk AI systems. Our methodology provides developers with a structured way to ensure compliance by leveraging modern frameworks, integrating vector databases, and implementing robust memory management techniques.
1. Risk-Based Classification System
The EU AI Act categorizes AI systems into risk levels, with high-risk systems requiring more comprehensive documentation. The methodology involves assessing the AI system's intended purpose, its operational environment, and potential impacts. This assessment forms the basis for documentation that addresses the functional requirements outlined in Annex IV of the Act.
2. Technical Documentation Approach
To create actionable and compliant documentation, the following key areas are emphasized:
- Framework Usage: We utilize frameworks such as LangChain and AutoGen for developing AI systems. These frameworks facilitate the seamless integration of modular components necessary for robust AI functionalities.
- Vector Database Integration: Integrating vector databases like Pinecone is essential for efficient data management and retrieval. Below is an example of how to integrate Pinecone using Python:
import pinecone
from langchain.vectorstores import Pinecone
pinecone.init(api_key='your-api-key', environment='us-west1-gcp')
vector_store = Pinecone(api_key='your-api-key')
import { MCPClient } from 'langgraph';
const client = new MCPClient();
client.connect('ws://localhost:8080/mcp');
{
"toolName": "dataAnalyzer",
"parameters": {
"dataFormat": "CSV",
"outputType": "JSON"
}
}
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
from langchain.agents import AgentExecutor
executor = AgentExecutor(agent=agent, memory=memory)
response = executor.run(input="Start conversation")
By adhering to these methodologies, developers can ensure that their AI systems not only comply with regulatory requirements but also operate safely and effectively, thereby instilling trust among operators and stakeholders.
Implementation
The implementation of technical documentation for AI systems, especially those deemed high-risk under the EU AI Act, requires a structured approach to ensure compliance and transparency. Below, we outline the steps for developing comprehensive documentation, focusing on the role of Annex IV and integrating critical code examples for AI systems.
Steps for Developing Documentation
- Understand the Requirements: Begin by thoroughly reviewing Annex IV of the EU AI Act, which outlines the necessary elements for technical documentation. This includes descriptions of the system, its intended purpose, design and development processes, and risk management strategies.
- System Architecture Documentation: Create detailed diagrams and descriptions of the system architecture. For instance, a diagram illustrating the data flow between components, such as AI agents, memory management modules, and vector databases, is crucial.
-
Code Documentation: Include code snippets and examples that demonstrate the system's functionality. Below is an example using Python and LangChain for memory management and agent orchestration:
from langchain.memory import ConversationBufferMemory from langchain.agents import AgentExecutor memory = ConversationBufferMemory( memory_key="chat_history", return_messages=True ) agent_executor = AgentExecutor(memory=memory) -
Tool Integration: Provide examples of tool calling patterns and schemas. For instance, integrating a vector database like Pinecone for memory management is essential for scalable AI systems.
import pinecone pinecone.init(api_key="YOUR_API_KEY") index = pinecone.Index('example-index') # Example tool calling pattern def call_tool(input_data): return index.upsert(items=input_data) - Compliance and Risk Management: Document the risk management processes, including how the system handles vulnerabilities and ensures data security. This is vital for high-risk AI systems.
- Continuous Updates: Ensure the documentation is updated regularly to reflect changes in the system or regulatory requirements.
Role of Annex IV for High-Risk Systems
Annex IV of the EU AI Act is pivotal for high-risk systems as it specifies the documentation elements that demonstrate compliance. This includes:
- System Description: Detailed information about the system's architecture, purpose, and operational conditions.
- Design and Development: Documentation of the methodologies and tools used during the development process.
- Risk Management: Strategies and measures implemented to identify and mitigate risks associated with the AI system.
- Compliance Demonstration: Evidence and rationale showing how the system meets regulatory requirements.
For developers, understanding these requirements is crucial for creating compliant and trustworthy AI systems. The integration of frameworks such as LangChain or CrewAI and vector databases like Pinecone or Weaviate can facilitate this process, providing robust tools for managing memory, handling multi-turn conversations, and orchestrating AI agents effectively.
Implementation Examples
Here is an example of implementing a multi-turn conversation handling with memory management:
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
agent_executor = AgentExecutor(memory=memory)
# Example of a multi-turn conversation
def handle_conversation(input_text):
response = agent_executor.run(input_text)
return response
print(handle_conversation("Hello, how can you assist me today?"))
In conclusion, the implementation of technical documentation for high-risk AI systems under the EU AI Act involves a comprehensive approach that combines detailed system descriptions, code documentation, and compliance strategies. By adhering to the guidelines set out in Annex IV and leveraging advanced frameworks and tools, developers can ensure their AI systems are both compliant and effective.
Case Studies
The European Union's AI Act has introduced rigorous technical documentation requirements that affect diverse industries, compelling them to adopt robust compliance strategies. This section explores examples of compliance from different sectors, highlighting lessons learned by early adopters.
Finance Industry: Implementing AI with LangChain and Pinecone
The finance industry, classified as high-risk due to potential impacts on economic stability, has embraced compliance through detailed technical documentation. A notable example is a financial firm leveraging LangChain for agent orchestration and Pinecone for vector database integration. Here's a snippet demonstrating compliance with AI Act requirements through technical documentation:
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
from langchain.vectorstores import Pinecone
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
vector_store = Pinecone(api_key="your-api-key")
agent_executor = AgentExecutor(memory=memory)
# Documentation ensures clear descriptions of memory management and vector integration
This implementation ensures that the financial firm's AI system maintains comprehensive records of multi-turn conversations, a critical aspect under the AI Act.
Healthcare Sector: CrewAI and Chroma for Data Handling
Healthcare providers are early adopters of the AI Act's documentation mandates, utilizing CrewAI and Chroma to manage sensitive patient data effectively. A healthcare startup documented their use of these frameworks to comply with the act by focusing on memory management and data security:
import { MemoryBuffer } from "crewai";
import { Chroma } from "chroma";
const memory = new MemoryBuffer({ key: "patient_records" });
const chroma = new Chroma({ apiKey: "secure-key" });
// Clear documentation on tool calling patterns ensures transparency and safety
This approach underscores the importance of securely handling patient data while maintaining transparency for regulatory bodies.
Manufacturing: AutoGen with Weaviate for Risk Management
In manufacturing, AutoGen and Weaviate have been pivotal in addressing AI Act compliance, especially concerning risk management in automated systems. A manufacturing company documented its implementation of these tools to ensure safe and compliant operations:
import { Agent } from "autogen";
import { WeaviateClient } from "weaviate";
const agent = new Agent({ memoryManagement: true });
const client = new WeaviateClient({ apiKey: "manufacturing-key" });
// MCP protocol implementation ensures robust risk management documentation
The adherence to MCP protocol standards in their documentation exemplifies how manufacturing companies can manage risks effectively.
Lessons Learned from Early Adopters
Early adopters across industries have identified key lessons in complying with the AI Act's documentation requirements. First, integrating vector databases like Pinecone and Weaviate enhances data retrieval efficiency, a necessity highlighted in documentation. Second, frameworks such as LangChain and CrewAI facilitate comprehensive memory management, crucial for maintaining compliance. Lastly, adopting clear tool calling patterns and schemas ensures transparency and accountability, pivotal for building trust with stakeholders.
Metrics
In the context of the EU AI Act, the technical documentation for high-risk AI systems must adhere to specific criteria to ensure compliance and functionality. Here, we outline key performance indicators (KPIs) and evaluation criteria that developers should focus on when preparing documentation.
Key Performance Indicators for Documentation
- Completeness: Documentation should include all specified elements in Annex IV, such as system architecture, intended purpose, and risk management measures.
- Clarity and Comprehensibility: Use clear language and visual aids like architecture diagrams to convey complex information effectively. For instance, a diagram illustrating data flow and decision-making processes can enhance understanding.
- Traceability: Each component of the AI system should be traceable from documentation to implementation, ensuring accountability and ease of updates.
- Maintainability: The documentation must be easily updatable to reflect system changes and continuously meet compliance requirements.
Evaluation Criteria for Compliance
Compliance evaluation should focus on the accuracy of technical descriptions and the practical usability of the documentation for developers and regulatory bodies.
- System Architecture: Include detailed architecture diagrams. For example, a diagram depicting how a LangChain-based agent interacts with a vector database like Pinecone can clarify the AI's operational structure.
- Implementation Details: Provide working code snippets demonstrating core functionalities. Below is a sample code snippet for managing conversation history in a LangChain environment:
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
agent_executor = AgentExecutor(
memory=memory,
tools=[...]
)
By focusing on these metrics and criteria, developers can create robust technical documentation that not only meets regulatory demands but also facilitates effective AI system management and user comprehension.
Best Practices for AI Act Technical Documentation Compliance
Adhering to the EU AI Act's technical documentation requirements can be intricate, especially for high-risk AI systems. This section outlines effective strategies for maintaining compliance, highlights common pitfalls, and provides actionable advice for developers.
Effective Strategies for Maintaining Compliance
To maintain compliance, it is crucial to adopt a structured approach to documentation. Here are effective strategies:
- Regular Updates: Continuously update documentation to reflect system changes and improvements. This ensures alignment with Article 11 mandates.
- Clear and Comprehensible Documentation: Use straightforward language and well-organized content to make documentation accessible to all stakeholders.
- Comprehensive Coverage: Include all elements specified in Annex IV to demonstrate compliance effectively.
Implementation Example: Multi-turn Conversation Handling
from langchain.agents import AgentExecutor
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
agent_executor = AgentExecutor(memory=memory)
# Example: Handling multi-turn conversation
def handle_conversation(input_text):
response = agent_executor.run(input_text)
print(f"Response: {response}")
Vector Database Integration Example
import pinecone
from langchain.embeddings import OpenAIEmbeddings
pinecone.init(api_key='YOUR_API_KEY', environment='YOUR_ENV')
index = pinecone.Index("ai-compliance")
embeddings = OpenAIEmbeddings()
vector = embeddings.embed(["ai act documentation"])
index.upsert([("doc_id", vector)])
Common Pitfalls and How to Avoid Them
Awareness of common pitfalls can help in avoiding compliance issues:
- Incomplete Documentation: Ensure that all required elements are included and clearly documented. Avoid omitting details that demonstrate compliance.
- Lack of Updates: Regularly review and update documentation to reflect system changes. Employ automated tools to track changes and update records accordingly.
- Ignoring Security Vulnerabilities: Conduct regular security audits and document findings to minimize risks and demonstrate proactive management.
Tool Calling Patterns and Memory Management
import { ToolRegistry, ToolExecutor } from 'langgraph';
const registry = new ToolRegistry();
const executor = new ToolExecutor(registry);
// Define tool schema and execution pattern
registry.register('data-processor', { schema: { input: 'string', output: 'number' } });
executor.execute('data-processor', { input: "sample data" }).then(result => {
console.log(result.output);
});
By following these best practices, developers can create robust, compliant technical documentation that meets the EU AI Act's requirements. This not only facilitates regulatory compliance but also contributes to the trustworthiness and transparency of AI systems.
Advanced Techniques
Developers working with high-risk AI systems under the EU AI Act must employ advanced techniques to meet stringent documentation requirements. This section explores in-depth strategies for documenting complex AI systems and how AI can be leveraged to assist in creating this documentation.
In-depth Strategies for Complex Systems
Creating technical documentation for complex AI systems involves understanding various components, including the Model, Controller, and Presentation (MCP) protocol and memory management. Here's how developers can structure their documentation:
- Architecture Diagrams: Use diagrams to map the interactions between different components. For instance, a flowchart illustrating data flow through the MCP architecture helps clarify system operations.
- Agent Orchestration Patterns: Implementing patterns like the Observer or Strategy can facilitate the documentation of agent interactions. Describing these patterns aids in understanding system dynamics and enhancing modularity.
Leveraging AI to Aid Documentation
AI tools can automate and enhance the documentation process. By integrating AI capabilities, developers can generate dynamic documentation that updates with system changes.
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
from langchain.vectorstores import Pinecone
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
agent = AgentExecutor(memory=memory)
# Implementing a vector database for knowledge retrieval
vector_store = Pinecone()
vector_store.add_documents(documents)
# Using AI to generate documentation
def generate_documentation():
# Retrieve relevant information from the vector store
related_docs = vector_store.similarity_search("compliance requirements")
# Generate documentation snippets from retrieved information
documentation = "Documentation includes: " + ", ".join(doc.title for doc in related_docs)
return documentation
doc_snippet = generate_documentation()
print(doc_snippet)
In the code snippet above, we utilize LangChain to manage memory and Pinecone as a vector database to facilitate information retrieval for documentation. By maintaining a dynamic interaction with memory and the database, the system can efficiently update documentation based on the latest compliance requirements.
This approach ensures that documentation is not only comprehensive and up-to-date but also easily accessible for stakeholders needing to understand the AI system's compliance with the EU AI Act's requirements.
Future Outlook
The European Union's AI Act, with stringent technical documentation requirements for AI systems, particularly for high-risk applications, is set to impact the landscape significantly. As we approach 2025, the predicted changes in AI Act requirements will likely result in even more detailed technical documentation mandates. This future shift aims to ensure transparency, accountability, and safety in AI systems.
One anticipated change is the increased emphasis on real-time documentation updates to keep pace with the dynamic nature of AI development. This requirement will necessitate more robust version control systems and dynamic documentation tools integrated into the development lifecycle. Developers will likely need to incorporate continuous integration and continuous deployment (CI/CD) pipelines to automate documentation updates in line with system changes.
The impact on AI development and deployment will be substantial. As documentation becomes more integral to the development process, tools like LangChain and integration with vector databases such as Pinecone and Weaviate will be crucial. For instance, developers can leverage these tools to enhance the traceability and audibility of model decisions.
Here's a practical implementation example using LangChain for a memory management task:
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
from langchain.vectorstores import Pinecone
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
# Initialize Pinecone for vector database integration
vectorstore = Pinecone(api_key="your-api-key", environment="production")
agent_executor = AgentExecutor(
memory=memory,
vectorstore=vectorstore
)
Moreover, developers will need to focus on implementing Multi-Party Computation (MCP) protocols to secure sensitive data during tool calling. Here's a basic MCP implementation snippet:
def secure_mcp_protocol(input_data):
# Implement secure computation logic
result = perform_secure_computation(input_data)
return result
These transformations in technical documentation are likely to foster a more collaborative and transparent environment, benefiting developers and end-users alike. As AI systems become more complex, the ability to produce clear, detailed documentation will be a differentiator in the marketplace.
In conclusion, while these requirements may initially seem burdensome, they represent a step towards more reliable and trustworthy AI systems, aligning development practices with regulatory standards and enhancing system transparency.
Conclusion
In summary, the EU AI Act's technical documentation requirements are pivotal for ensuring the safe and effective deployment of AI systems. Key elements include comprehensive documentation prior to market placement and continuous updates, particularly for high-risk AI systems. This ensures compliance with regulatory standards, reduces security vulnerabilities, and fosters operator trust through transparency.
For developers, adhering to these requirements is critical. Implementing robust technical documentation facilitates smoother compliance and enhances AI system reliability and usability. Below is a practical code implementation that illustrates some of these requirements using LangChain and Pinecone for vector database integration, crucial for managing AI agent operations and memory:
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
from langchain.connectors.pinecone import PineconeVectorStore
# Initialize memory for multi-turn conversation handling
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
# Configure vector database for AI agent tool calling
vector_store = PineconeVectorStore(api_key="YOUR_API_KEY", environment="production")
# Implementing agent execution with memory management
agent_executor = AgentExecutor(
vector_store=vector_store,
memory=memory
)
# MCP protocol implementation for agent orchestration
class MyAgent:
def __init__(self, executor):
self.executor = executor
def execute_task(self, task):
return self.executor.execute(task)
my_agent = MyAgent(agent_executor)
response = my_agent.execute_task("Process data efficiently")
print(response)
Ultimately, by using frameworks like LangChain and integrating vector databases such as Pinecone, developers can not only comply with the EU AI Act but also enhance the functional capabilities of AI systems, ensuring they are secure, transparent, and efficient in the market.
FAQ: AI Act Technical Documentation Requirements
This FAQ section addresses common questions and clarifies complex requirements regarding the technical documentation mandated by the EU AI Act for AI systems, particularly high-risk ones.
What are the core documentation requirements for high-risk AI systems?
The documentation must be prepared before market placement and kept updated. It should include clear, comprehensible, and complete information as specified in Annex IV, demonstrating compliance with regulatory requirements, minimizing security risks, and fostering trust.
How do I implement memory management for multi-turn conversations?
Using LangChain, you can manage conversation history effectively:
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
How can I integrate a vector database with my AI system?
Consider using Pinecone or Chroma for seamless vector database integration. Here's how you can connect with Pinecone:
import pinecone
pinecone.init(api_key='your-api-key', environment='us-west1-gcp')
index = pinecone.Index('your-index-name')
What are the patterns for tool calling and schemas?
For tool calling in AI agents, use a proper schema structure to maintain clarity:
interface ToolSchema {
name: string;
input: string;
output: string;
}
const tool: ToolSchema = {
name: 'translate',
input: 'text',
output: 'translatedText'
}
What is the MCP protocol and how is it implemented?
The MCP (Model Communication Protocol) ensures secure and efficient communication between AI models. Below is a basic implementation:
import { MCP } from 'some-mcp-library';
const mcp = new MCP('model-endpoint', {
securityKey: 'secure-key'
});
mcp.send('Hello, model!').then(response => {
console.log(response);
});
How do I orchestrate multiple AI agents effectively?
Agent orchestration can be achieved using frameworks like CrewAI. Here's a simple setup:
from crewai.orchestration import AgentOrchestrator
orchestrator = AgentOrchestrator(agent_list)
orchestrator.run()
Can you provide an architecture diagram for AI system integration?
An architecture diagram typically includes components such as AI models, vector databases, memory modules, and communication protocols. Picture these elements connected in a modular fashion, with arrows indicating data flow between models and databases.
