WaterCrawl is innovative and optimized in several technical dimensions:
- Optimized for LLM: Data cleansing and formatting processes specifically designed for the needs of large language models
- High Performance Architecture: Using Scrapy+Celery combination to support distributed crawling and parallel processing
- Multi-language support: Provide Node.js/Go/PHP/Python and other mainstream language SDKs
- Enterprise Features: Integrated MinIO storage, task queue management, and other features required by production environments
- Highly scalable: Plugin architecture supports customized crawling and processing logic
Compared with ordinary crawlers, WaterCrawl not only solves the data acquisition problem, but also focuses on the subsequent data application scenarios, which is especially suitable for projects that need to convert web content into AI training data. Its API-friendly design and containerized deployment also greatly reduces the threshold of use.
This answer comes from the articleWaterCrawl: transforming web content into data usable for large modelsThe
May not be reproduced without permission:AI productivity tools " What are the technical advantages of WaterCrawl over traditional crawling tools?
