Скачать или смотреть How to Create an AI Chatbot Using Python

Creating an AI chatbot involves accepting natural language input, processing it using models like GPT, and returning conversational output. This project uses GPT-4 to extract insights from Apple's SEC 10-K filings by downloading and parsing documents with BeautifulSoup. The filings are split into smaller chunks to stay within token limits and avoid exceeding GPT-4’s 8192-token context window. Different transformer models are recommended depending on the task, such as MiniLM for embeddings, FinBERT for financial Q&A, and BART or Falcon for summarization. Embeddings represent text as numerical vectors, while transformers are the neural networks powering models like GPT and BERT. The chatbot loads and splits the filings, then runs a loop where users can input questions, and GPT-4 analyzes and summarizes responses from selected text chunks. When context is too long, the script trims or skips those chunks to avoid API errors. In practice, this approach helps summarize filings, compare risk sections year-over-year, and extract themes like cybersecurity or supply chain issues. The chatbot highlights competitive pressures, supply chain disruptions, and COVID-related uncertainties as top risks facing Apple. Overall, this system demonstrates how NLP, transformer models, and a conversational interface can automate and scale SEC document analysis.