I agree.. So, here's what I see: When they switched to using LiteLLM: there's this:

Which seems to do something different from what I remember dspy.TypedPredictor doing..

Once upon a time: at least at commit hash b32b2ab:

When it would get a TypeError: it would

1. Capture the error
2. Make an example
3. Send that back to the LLM

I agree that this was useful behaviour...

In looking into it tho, I found dspy.predict.retry.py... Which is totally commented out. I was all set to work on adding back the retrying in, till I saw that.. Can someone explain what the plan for retrying Pydantic validation failures?

Below is a message that can be useful.

While we prepare a short tutorial on this: Folks looking to migrate from 2.5-style Assertions, you can now use dspy.BestOfN or dspy.Refine which replace the assertions functionality with streamlined modules instead.

module = dspy.ChainOfThought(...) # or a complex multi-step dspy.Module
module = dspy.BestOfN(module, N=5, reward_fn=reward_fn, threshold=1.0)

module(...) # at most 5 retries, picking best reward, but stopping if `threshold` is reached

where, reward functions can return scalar values like float or bool, e.g.

def reward_fn(input_kwargs, prediction):
    return len(prediction.field1) == len(prediction.field1)