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Hey @Ryzhtus, thanks a lot for the contribution. Since the original issue is quite open about how the integration should be structured, I'd prefer to align on the high-level design before implementing it:
Feel free to share your thoughts in #1635. I haven't reviewed the code, but one early note: if possible, let's avoid introducing new Generators. We are switching to Chat Generators only, which are more flexible, and we may eventually deprecate legacy Generators. |
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Related Issues
Proposed Changes:
This DSPy integration provides generation components:
DSPyGenerator is the internal base class that holds all the core logic: DSPy LM initialization, module creation (Predict/ChainOfThought/ReAct), serialization, and the run(prompt: str) -> {"replies": List[str]} execution path
DSPyChatGenerator — a Haystack @component for quick prototyping with DSPy signatures. Accepts List[ChatMessage], returns List[ChatMessage]. Wraps DSPy modules (Predict, ChainOfThought, ReAct) behind Haystack's chat generator and generator interface.
DSPyChatGenerator just simply extends DSPyGenerator to follow Haystack's ChatGenerator.
I also want to add in either this PR or the next one another component which I would like to call DSPyProgramRunner. The difference will be that DSPyProgramRunner @component loads and executes pre-compiled/optimized DSPy programs in production pipelines. So it can accept dspy.Module for initialization
How did you test it?
Checklist
fix:,feat:,build:,chore:,ci:,docs:,style:,refactor:,perf:,test:.