Optimize docs search hyper parameters

[creatorrr]

Let's run an automated evaluation on the RAG dataset (using a local model or something) and then tune the doc search hyperparameters based on this. Parameters are:

• num docs k_docs
• confidence docs_confidence

https://github.com/julep-ai/julep/blob/dev/agents-api/agents_api/models/entry/proc_mem_context.py#L13

rag dataset: rag-12000
contains three columns: context, question, answer

evaluation recipe:

• create an agent

• add all the documents from the context column as agent docs

• for every row in the dataset (use the train split only)

  • create a session with the agent
  • ask the question from question column (you can set max_tokens to 1 since we dont care about the returned answer)
  • note the document-ids returned from session.chat
  • get all documents using the document ids
  • check if context (value of that row) is in the fetched documents

cool thing: optuna: https://optuna.org/