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Context for your AI agents
Crawl websites, sync to vector databases, and power RAG applications. Pre-built integrations for LLM pipelines and AI assistants.
Build data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
Loguru is a Python library designed to simplify logging for developers. It offers easy setup with no configuration, support for multiple sinks (console, file, etc.), structured logging, formatting, rotation, and exception handling—all in an intuitive API.
Easier logging and tracing of Python functions and class methods.
Autologging eliminates boilerplate logging setup code and tracing code, and provides a means to separate application logging from program flow and data tracing.
Autologging provides two decorators and a custom log level:
"autologging.logged" decorates a class to create a __log member. By default, the logger is named for the class's containing module and name (e.g. "my.module.ClassName").
This tool is generating a logfile from notifies that amavis sends, so that statistics can be generated. Another advantage of this type of logging is that i can create a central log in a multi-server setup. amavis, amavisd and amavisd-new are supported.