Web Scraping with Kimi: Step-by-Step Guide
Blog post from Bright Data
Kimi K3 is presented as an LLM and vision model for web scraping that can extract structured information from page content or screenshots through prompts, reducing reliance on fragile CSS selectors and XPath rules. The approach still faces conventional scraping barriers such as JavaScript rendering, CAPTCHAs, rate limits, fingerprinting, and anti-bot systems, as well as LLM token costs, which the article addresses using Bright Data’s Web Unlocker API to retrieve protected pages as LLM-optimized Markdown or screenshots. Its Python tutorial demonstrates collecting an Amazon product page through the API, securely loading Kimi and Bright Data credentials from environment variables, defining a typed Pydantic product schema, sending Markdown to Kimi through its OpenAI-compatible API, and saving the schema-conforming JSON result to disk. A parallel visual workflow retrieves a page screenshot, encodes it in Base64, and provides it to Kimi K3 with a revised schema, enabling extraction of visible image text while sacrificing access to underlying fields such as image URLs.
No tracked trend matches for this post yet.
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.