Building a Context-Aware AI Assistant

This tutorial outlines the process of creating a context-aware AI assistant using LangChain, LangGraph, and Google’s Gemini language model. By applying the principles of the Model Context Protocol (MCP), we can develop a simplified version of an AI assistant that effectively interacts with external tools and retrieves relevant information.

1. Introduction to Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an open standard developed by Anthropic that facilitates real-time, context-rich interactions between AI assistants and external data sources. This protocol allows AI systems to utilize context retrieval and tool invocation to enhance user interactions.

2. Implementation Steps

2.1 Setting Up the Environment

To begin, we need to install essential libraries that will enable our AI assistant to function effectively:

• LangChain: A framework for building AI applications.
• LangGraph: A tool for creating agent-based architectures.
• Google Generative AI: A client for interacting with Google’s Gemini models.

Run the following commands to install these libraries:

        !pip install langchain langchain-google-genai langgraph python-dotenv
        !pip install google-generativeai
    

2.2 Configuring API Access

Next, we need to securely set our Gemini API key as an environment variable. This ensures that sensitive information is not hardcoded into the codebase. Replace “Your API Key” with the actual key obtained from Google AI Studio:

        import os
        os.environ["GEMINI_API_KEY"] = "Your API Key"
    

2.3 Creating the AI Assistant

We will initialize the Gemini model and create a simple knowledge base tool that can provide predefined answers to common queries about AI concepts:

        from langchain_google_genai import ChatGoogleGenerativeAI
        from langchain import BaseTool

        model = ChatGoogleGenerativeAI(model="gemini-2.0-flash-lite", temperature=0.7, google_api_key=os.getenv("GEMINI_API_KEY"))

        class SimpleKnowledgeBaseTool(BaseTool):
            name: str = "simple_knowledge_base"
            description: str = "Retrieves basic information about AI concepts."

            def _run(self, query: str):
                knowledge = {
                    "MCP": "Model Context Protocol (MCP) is an open standard by Anthropic designed to connect AI assistants with external data sources.",
                    "RAG": "Retrieval-Augmented Generation (RAG) enhances LLM responses by dynamically retrieving relevant external documents."
                }
                return knowledge.get(query, "I don't have information on that topic.")
        kb_tool = SimpleKnowledgeBaseTool()
    

2.4 Interactive Chat Loop

Finally, we will set up an interactive chat loop that allows users to communicate with the AI assistant. The assistant will determine when to respond directly or invoke the knowledge base tool:

        import nest_asyncio
        import asyncio

        nest_asyncio.apply()

        async def chat_with_agent():
            inputs = {"messages": []}
            print("MCP-Like Assistant ready! Type 'exit' to quit.")
            while True:
                user_input = input("You: ")
                if user_input == "exit":
                    print("Ending chat.")
                    break
                inputs["messages"].append({"role": "user", "content": user_input})
                # Logic to process input and generate response
        asyncio.run(chat_with_agent())
    

3. Practical Business Solutions

3.1 Automating Processes

Identify repetitive tasks in your operations that can be automated with AI. This can lead to significant time savings and increased efficiency.

3.2 Enhancing Customer Interactions

Use AI to analyze customer interactions and provide personalized responses, improving customer satisfaction and engagement.

3.3 Measuring Impact

Establish key performance indicators (KPIs) to assess the effectiveness of your AI initiatives. This will help ensure that your investments yield positive results.

3.4 Gradual Implementation

Start with small projects to gather data on effectiveness before scaling your AI applications. This approach minimizes risk and allows for iterative improvements.

4. Conclusion

This tutorial provides a foundational understanding of building a context-aware AI assistant using modern tools and frameworks. By leveraging the principles of the Model Context Protocol, businesses can create intelligent systems that enhance user interactions and streamline operations. As you explore further, consider integrating real APIs and dynamic search tools to evolve your AI assistant into a robust, production-ready system.

For further guidance on managing AI in business, feel free to reach out to us at hello@itinai.ru or connect with us on Telegram, X, and LinkedIn.