LLM-powered chatbots have transformed how businesses interact with website visitors. Building one with Python gives you full control over data sources, response quality, and integration points.
Architecture Overview
A production chatbot typically includes: a web crawler or document loader, a text chunking and embedding pipeline, a vector store for semantic search, and an LLM for generating contextual responses.
Website-Crawling Chatbots
For site-specific chatbots, crawl and index your content, embed chunks using OpenAI embeddings, and retrieve relevant context before generating answers. This RAG (Retrieval-Augmented Generation) approach reduces hallucinations significantly.
Deployment with Flask
Wrap your chatbot logic in Flask endpoints that accept user queries and return JSON responses. This makes integration into Laravel, React, or mobile apps straightforward via standard HTTP calls.
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