Internet celebrity project AutoGPT source code insider and comprehensive case practice (1)

AutoGPT source code insider and comprehensive case practice AutoGPT Internals and source code of Internet celebrity project
. The internal running process displays the internal stack call by reporting an error message. If you have experience in writing code or researching code, you will know that this is almost the biggest shortcut. If you have a good, new open source project, and you want to study it in depth and systematically, it is a relatively simple way , in its simplest case example or code, let it throw an exception, because once an exception is thrown, generally the current integrated development environment will have a stack call, move along this stack, and see its internal working mechanism .
This section will explain to you how LangChain is implemented, not only looking at the code itself of LangChain, but also learning the code of the corresponding case. Before officially entering the content of this section, it is necessary to do a basic review. LangChain’s agent (Agent) is managed, and the language model, tools and data interact with each other. The language model is obviously the driver, and the agent is the manager. To manage models, tools, and data, the core of its management process is that the model tells the agent which tool to call, or what data to call, and LangChain itself allows the model to interact with your private data or third-party data, and at the same time It also acts as an agent, which is the core of LangChain.
From the perspective of code, LangChain provides many very good tools, as well as the specific implementation of some top-level papers. As a large-scale model development framework with the highest recognition in the open source and industrial circles, it really saves a lot of time, because LangChain itself is not only a framework, but also a tool, it helps us to do a lot of implementation, we directly based on the LangChain framework