4 ms·

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 provid

• Ozzie_osman 4y ago
  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)
  • delijati 4y ago
    i would really love to give it a try but with an offline LLM :/