Summary

In this chapter, we have dived deeper into LangChain, looking at some more advanced topics. We started by looking at debugging techniques and introduced LangSmith, the go-to tool for advanced logging and monitoring for LangChain applications. We also looked at LangChain agents and understood how to use them to provide different functionalities to our LLM project, as well as how they tie in with OpenAI function calling. We focused on the out-of-the-box tools provided by LangChain and covered creating custom tools by looking at practical examples to help our ChatGPT application answer questions about real-time news and weather. Finally, we covered the concept of providing memory to our agents while looking at the different types of memory, the challenges, and the techniques involved in providing memory in LangChain.

In the next chapter, we’ll look at RAG. You’ll understand what it is, the concepts and processes involved, and how to implement retrieval in LangChain...