Build a Custom AI Assistant: Step-by-Step Tutorial Using OpenAI & LangChain
1. Introduction to AI Assistants
- Understand what makes an AI assistant “custom” and why businesses are adopting them.
- Explore common use cases: customer support, personal productivity, and content generation.
- Brief overview of the tools we'll use: OpenAI API for language models and LangChain for orchestration.
2. Setting Up Your Development Environment
- Install Python 3.9+ and create a virtual environment to manage dependencies.
- Install required libraries: `openai`, `langchain`, `python-dotenv` for API key management.
- Set up your OpenAI API key and store it securely in a `.env` file.
3. Understanding the OpenAI API
- Learn the difference between Chat Completions and Completions endpoints.
- Write your first API call: send a prompt and receive a text response.
- Experiment with parameters: `temperature`, `max_tokens`, and `top_p` to control output.
4. Integrating LangChain for Structured Workflows
- Introduce LangChain’s `LLMChain` and `PromptTemplate` to build reusable prompts.
- Create a simple question-answering chain that combines user input with context.
- Use LangChain's `SimpleSequentialChain` to connect multiple steps (e.g., summarize then translate).
5. Adding Memory and Context
- Understand why memory is crucial for conversational assistants.
- Implement `ConversationBufferMemory` to maintain chat history across turns.
- Test your assistant by asking follow-up questions and verifying it remembers context.
6. Deploying Your Assistant as a Web App
- Use FastAPI or Streamlit
AI Automation Playbook
Step-by-step workflows for automating content, email, social media, and research with AI agents.
