Yes also the part I don't understand is that how langchain can be used to make the agents smarter for a particular domain? I did not see any interface to provide feedback of feed data. Maybe I am missing something?
It's usually either providing data to the LLM, or doing back-and-forth with the LLM (usually a mix of both).
For instance, if you want it to answer questions about your code-base, the model doesn't know your code base. You can't feed the entire code-base into a prompt. So, you'd use langchain to:
- preprocess your code-base, by chunking it and embedding it in some vector space
- when you get a question, see where it is in the vector space and find the "k nearest neighbors"
- pass those nearest neighbors, along with your question, to the LLM (because those neighbors are the contextually relevant pieces, and they'd fit in the prompt)
For instance, if you want it to answer questions about your code-base, the model doesn't know your code base. You can't feed the entire code-base into a prompt. So, you'd use langchain to: - preprocess your code-base, by chunking it and embedding it in some vector space - when you get a question, see where it is in the vector space and find the "k nearest neighbors" - pass those nearest neighbors, along with your question, to the LLM (because those neighbors are the contextually relevant pieces, and they'd fit in the prompt)