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From Zero to AI: Build a Custom Chatbot with Python in Under an Hour

1. What You’ll Build and Why It Matters

• Understand the final output: a conversational chatbot that answers questions based on your own data.
• Why this skillset is essential for automating customer support, internal knowledge bases, or personal assistants.
• Overview of tools: Python, OpenAI API, and a lightweight framework (LangChain or plain requests).

2. Setting Up Your Development Environment

• Install Python 3.10+ and create a virtual environment to isolate dependencies.
• Obtain an OpenAI API key and store it securely using environment variables (dotenv).
• Install required packages: openai, python-dotenv, and optionally langchain.

3. Designing the Core Chatbot Logic

• Write a simple function that sends a user message to the OpenAI Chat Completion endpoint.
• Handle the response: extract the assistant’s reply and return it to the user.
• Add basic error handling for API failures, rate limits, and invalid inputs.

4. Adding Memory and Context

• Implement a conversation history list that stores user and assistant messages in order.
• Pass the full history with each API call to maintain context across turns.
• Set a maximum token limit to prevent the history from growing too large (e.g., last 10 exchanges).

5. Making Your Chatbot Know Your Data

• Load a custom document (PDF, CSV, or text file) and split it into chunks.
• Generate embeddings for each chunk using OpenAI’s embedding model and store them in a simple vector store (FAISS or in-memory list).
• Retrieve relevant chunks for each user query and inject them into the prompt as context.

6. Deploying Your Chatbot (Local or Cloud)

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