Build Your First AI Assistant: A Step-by-Step Tutorial with LangChain and OpenAI
1. Introduction to AI Assistants and Why LangChain
- Understand what makes an AI assistant “smart” — context, memory, and tool use.
- Learn why LangChain is the go‑to framework for chaining LLM calls and integrating external data.
- Overview of the final assistant: a chatbot that remembers conversations and can query a live API.
2. Setting Up Your Development Environment
- Install Python 3.10+ and create a virtual environment for dependency isolation.
- Install required packages: `langchain`, `openai`, `python-dotenv`, and `streamlit`.
- Set up your OpenAI API key as an environment variable and test connectivity with a simple prompt.
3. Connecting to OpenAI API and Creating a Basic Prompt
- Initialize a `ChatOpenAI` model with temperature and max tokens parameters.
- Write your first chain: a `PromptTemplate` that takes user input and returns an assistant response.
- Run a few test queries to verify the model is responding correctly.
4. Adding Memory and Context to Your Assistant
- Implement `ConversationBufferMemory` to store the chat history.
- Use `ConversationChain` to automatically inject past messages into new prompts.
- Test multi‑turn conversations and observe how the assistant “remembers” earlier context.
5. Implementing Tools and Custom Functions
- Define a custom tool (e.g., `get_current_time` or a simple calculator) using the `@tool` decorator.
- Create an `Agent` that
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