GPT-Crawler provides three professional deployment options to meet the technical requirements of different usage scenarios:
- Local Node Environment: Based on the npm ecosystem to run fast, suitable for developers to test the validation of the
- Docker containers: Deployment through standardized containers to ensure environmental consistency, suitable for production environment batch collection
- REST API services: a standard interface based on Swagger documentation that can be integrated into existing workflows
In particular, its Docker implementation achieves data persistence through the volume mapping mechanism (v -v $(pwd)/data:/app/data), while the API service supports triggering asynchronous crawling tasks through POST requests. This architectural design makes the tool suitable for both individual developers to start quickly, but also to meet the needs of enterprise-level system integration.
The technical evaluation shows that the containerized deployment approach reduces resource consumption by about 301 TP3T compared to local operation when processing large-scale collection tasks.
This answer comes from the articleGPT-Crawler: Automatically Crawling Website Content to Generate Knowledge Base DocumentsThe
