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AI Learning Notes #01 — How LLM Tool Calling Works

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Without @tool , this is simply a Python function.  The important thing is that @tool turns it into a LangChain tool . @ tool def get_stock_price ( ticker : str ) -> str :   """Fetches the mock current stock price for a given ticker symbol (e.g., AAPL, GOOGL)."""   mock_prices = { "AAPL" : 220.50 , "GOOGL" : 175.30 , "MSFT" : 415.00 }   price = mock_prices . get ( ticker . upper (), 100.00 )   return json . dumps ({ "ticker" : ticker . upper (), "price_usd" : price }) This is loading environment vairables  # Load environment variables (GOOGLE_API_KEY / GEMINI_API_KEY) load_dotenv ()   After loading the environment variable you can call ChatGoogleGenerativeAI ( model, temperature) it will by default get the value environment of  " GOOGLE_API_KEY "  variable. You need not to care too much.  llm = ChatGoogleGenerativeAI (     model = "gemini-2.5-flash" ,     temperature = 0.0 ...
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LangChain with Google Gemini in Python: Your First Program Explained Line by Line

# Building Your First LangChain Application with Google Gemini ## What is LangChain? LangChain is an open-source Python framework that helps developers build applications powered by Large Language Models (LLMs) such as Google Gemini, OpenAI GPT, Anthropic Claude, and others. Instead of writing low-level API calls, LangChain provides reusable components for prompts, message handling, chains, document retrieval, memory, tools, and agents, making it easier to build intelligent AI applications. In this article, we will start with a simple LangChain program that communicates with Google's Gemini model. The purpose is to understand the basic building blocks of a LangChain application before moving on to more advanced concepts. --- # Installation ## Install Required Packages Create a Python virtual environment (recommended), then install the required packages: pip install langchain langchain-google-genai python-dotenv The packages are used for the following purposes: | Package | Purpo...
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Building a Retrieval-Augmented Generation (RAG) Application with MCP, Gemini, and ChromaDB in Node.js

Building a Retrieval-Augmented Generation (RAG) Application with MCP, Gemini, and ChromaDB in Node.js Introduction Large Language Models (LLMs) such as Gemini, GPT, and Claude are powerful, but they have one important limitation—they only know what they were trained on and the information provided in the prompt. Suppose your company has an internal document containing leave policies, HR rules, medical policies, or confidential project documentation. If you ask an LLM a question about those documents, it may not know the answer because that information was never part of its training data. This is where Retrieval-Augmented Generation (RAG) becomes useful. Instead of training a new model, RAG retrieves the most relevant information from your own documents and supplies it to the LLM before generating the final answer. In this project, we build a complete RAG application using: Node.js Google Gemini API ChromaDB (Vector Database) Model Context Protocol (MCP) By the end of ...
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