Context-Aware AI agent: Memory Management and state Tracking

Context awareness has become a crucial feature that distinguishes sophisticated systems from basic chatbots. This article I will guide you through creating a context-aware AI agent using Large Language Models (LLMs), combining theoretical understanding with practical pseudocode implementation.

Table of Contents

1. Understanding Context Awareness
2. System Architecture
3. Implementation Guide
4. Best Practices
5. Advanced Techniques
6. Testing and Optimization

Understanding Context Awareness

Let me give you some context about about what I meant about context awareness in AI agents. Context awareness mainly refers to the Agent's ability to:

• Maintain conversation history
• Remember user preferences (e.g., likes, dislikes, event etc)
• Understand temporal and situational events
• Track and update conversation state
• Handle context switches gracefully

The following illustration shows the visual representation of how context flows in an AI agent:

The above diagram shows the flow of context in an AI agent system. The user input is processed by the NLU agent, which then interacts with the context manager, memory system, and state tracker. The core agent processes the input based on the context, and the output is generated accordingly.

Think like this process is like two people talking to each other, where one person remembers the previous conversation and uses that information to respond appropriately.

Design an Agent system

Let's build a context-aware system using modern LLM technologies. Here's our basic architecture:

from typing import List, Dict, Optional
import openai
from datetime import datetime

class ContextManager:
    def __init__(self):
        self.conversation_history: List[Dict] = []
        self.user_preferences: Dict = {}
        self.current_context: Dict = {
            'timestamp': None,
            'topic': None,
            'state': None
        }

Conversation History Management

Memory management is crucial for maintaining context. There are different types of memory systems that can be used:

1. short-term memory: This type of memory is used to store information for a short period of time.
2. long-term memory: This type of memory is used to store information for a long period of time.
3. episodic memory: This type of memory is used to store information about events and experiences.
4. semantic memory: This type of memory is used to store general knowledge, facts, and concepts.

Here's a simple implementation of a conversation manager:

class ConversationManager:
    def __init__(self, max_history: int = 10):
        self.max_history = max_history
        self.history = []

    def add_interaction(self, user_input: str, agent_response: str):
        interaction = {
            'timestamp': datetime.now(),
            'user_input': user_input,
            'agent_response': agent_response
        }
        self.history.append(interaction)

        # Maintain history size
        if len(self.history) > self.max_history:
            self.history = self.history[-self.max_history:]

    def get_context_window(self, window_size: int = 3) -> List[Dict]:
        """Return recent conversations for context"""
        return self.history[-window_size:]

Context-Aware Prompt Generation

To generate context-aware prompts for the LLM, we need to combine the conversation history, user preferences, and current user input. Here's a simple function to generate a contextual prompt:

def generate_contextual_prompt(
    user_input: str,
    conversation_history: List[Dict],
    user_preferences: Dict
) -> str:
    # Format conversation history
    history_text = '
'.join([
        f"User: {interaction['user_input']}
"
        f"agent: {interaction['agent_response']}"
        for interaction in conversation_history
    ])

    # Create system prompt with context
    prompt = f"""
    Previous conversation:
    {history_text}

    User preferences:
    {user_preferences}

    Current user input:
    {user_input}

    Provide a contextually relevant response that takes into account
    the conversation history and user preferences.
    """

    return prompt

Implementation Guide

Now let's combine all the components described above. The following pseudocode outlines the implementation of a context-aware AI agent using LLMs:

class ContextAwareagent:
    def __init__(self, api_key: str):
        self.conversation_manager = ConversationManager()
        self.context_manager = ContextManager()
        openai.api_key = api_key

    async def process_input(self, user_input: str) -> str:
        # Get relevant context
        recent_history = self.conversation_manager.get_context_window()
        user_prefs = self.context_manager.user_preferences

        # Generate contextual prompt
        prompt = generate_contextual_prompt(
            user_input,
            recent_history,
            user_prefs
        )

        # Get response from LLM
        response = await self.get_llm_response(prompt)

        # Update conversation history
        self.conversation_manager.add_interaction(user_input, response)

        return response

    async def get_llm_response(self, prompt: str) -> str:
        try:
            response = await openai.ChatCompletion.create(
                model="gpt-4",
                messages=[
                    {"role": "system", "content": "You are a helpful agent."},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.7,
                max_tokens=150
            )
            return response.choices[0].message.content
        except Exception as e:
            return f"Error: {str(e)}"

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Conclusion

Creating a context-aware AI agent requires careful consideration of various components:

• Robust context management
• Efficient memory systems
• Dynamic context weighting
• Error handling and recovery
• Performance optimization

By following the implementation guidelines and best practices outlined in this article, you can create a sophisticated AI agent that maintains context effectively and provides more natural, coherent interactions.