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Magnus Hedemark
95046675bc
Greenfield SkillOpt: 3 epochs for a Stanford DSPy compiler skill. DSPy is a fundamentally different paradigm from chain/RAG frameworks. Epoch 1 — Prominence: - Hard-gate blockquote: 'DSPy is NOT a chain framework' - Core Paradigm section with runnable code example early Epoch 2 — Decision Guidance: - Framework Routing Guide (DSPy vs LlamaIndex vs LangChain vs LangGraph) - Where to Start table mapping entry points - Troubleshooting table with reference links Epoch 3 — Pattern Expansion: - Optimizer selection cheat sheet from official docs - Caching, compilation cost management, save/load - FAQ covering paradigm confusion, errors, deployment 12 files: SKILL.md, 7 references, 3 templates, 1 script. v1.0.0 -> v1.0.3 across 3 epochs. All API surfaces validated against dspy.ai official docs — optimizer selection guide, caching, core modules, FAQ. Signed-off-by: Jasper <jasper@montcastle.bitches>
31 lines
918 B
Python
31 lines
918 B
Python
#!/usr/bin/env python3
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"""RAG program with DSPy using ColBERT retrieval and ChainOfThought."""
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import dspy
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lm = dspy.LM("openai/gpt-4o-mini")
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dspy.configure(lm=lm)
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class RAG(dspy.Module):
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def __init__(self, k=5):
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self.retrieve = dspy.Retrieve(k=k)
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self.generate = dspy.ChainOfThought("context, question -> answer")
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def forward(self, question):
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context = "\n".join(self.retrieve(question).passages)
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return self.generate(question=question, context=context)
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def correct(example, pred, trace=None):
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return example.answer in pred.answer
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trainset = [
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dspy.Example(question="What is DSPy?", answer="A compiler for prompt programs").with_inputs("question"),
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]
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program = RAG()
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optimizer = dspy.BootstrapFewShot(metric=correct)
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compiled = optimizer.compile(program, trainset=trainset)
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result = compiled(question="What is DSPy used for?")
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print(f"Answer: {result.answer}")
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