A Code Implementation To Building A Context-aware Ai Assistant In Google Colab Using Langchain, Langgraph, Gemini Pro, And Model Context Protocol (mcp) Principles With Tool Integration Support

In this hands-on tutorial, we bring nan halfway principles of nan Model Context Protocol (MCP) to life by implementing a lightweight, context-aware AI adjunct utilizing LangChain, LangGraph, and Google’s Gemini connection model. While afloat MCP integration typically involves dedicated servers and connection protocols, this simplified type demonstrates really nan aforesaid ideas, discourse retrieval, instrumentality invocation, and move relationship tin beryllium recreated successful a azygous notebook utilizing a modular supplier architecture. The adjunct tin respond to earthy connection queries and selectively way them to outer devices (like a civilization knowledge base), mimicking really MCP clients interact pinch discourse providers successful real-world setups.

First, we instal basal libraries. The first bid installs LangChain, LangGraph, nan Google Generative AI LangChain wrapper, and situation adaptable support via python-dotenv. The 2nd bid installs Google’s charismatic generative AI client, which enables relationship pinch Gemini models.

Here, we group your Gemini API cardinal arsenic an situation adaptable truthful nan exemplary tin securely entree it without hardcoding it into your codebase. Replace “Your API Key” pinch your existent cardinal from Google AI Studio.

In this block, we initialize nan Gemini connection exemplary (gemini-2.0-flash-lite) utilizing LangChain’s ChatGoogleGenerativeAI, pinch nan API cardinal securely loaded from situation variables. We past specify a civilization instrumentality named SimpleKnowledgeBaseTool that simulates an outer knowledge root by returning predefined answers to queries astir AI concepts for illustration “MCP” and “RAG.” This instrumentality acts arsenic a basal discourse provider, akin to really an MCP server would operate. Finally, we usage LangGraph’s create_react_agent to build a ReAct-style supplier that tin logic done prompts and dynamically determine erstwhile to telephone tools, mimicking MCP’s tool-aware, context-rich interactions principle.

Finally, we group up an asynchronous chat loop to interact pinch nan MCP-inspired assistant. Using nest_asyncio, we alteration support for moving asynchronous codification wrong nan notebook’s existing arena loop. The chat_with_agent() usability captures personification input, feeds it to nan ReAct agent, and streams nan model’s responses successful existent time. With each turn, nan adjunct uses tool-aware reasoning to determine whether to reply straight aliases invoke nan civilization knowledge guidelines tool, emulating really an MCP customer interacts pinch discourse providers to present dynamic, context-rich responses.

In conclusion, this tutorial offers a applicable instauration for building context-aware AI agents inspired by nan MCP standard. We’ve created a functional prototype demonstrating on-demand instrumentality usage and outer knowledge retrieval by combining LangChain’s instrumentality interface, LangGraph’s supplier framework, and Gemini’s powerful connection generation. Although nan setup is simplified, it captures nan principle of MCP’s architecture: modularity, interoperability, and intelligent discourse injection. From here, you tin widen nan adjunct to merge existent APIs, section documents, aliases move hunt tools, evolving it into a production-ready AI strategy aligned pinch nan principles of nan Model Context Protocol.

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