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Open In Colab DSPy is a framework for algorithmically optimizing LM prompts and weights, especially when LMs are used one or more times within a pipeline. Weave automatically tracks and logs calls made using DSPy modules and functions.

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Tracing

It’s important to store traces of language model applications in a central location, both during development and in production. These traces can be useful for debugging, and as a dataset that will help you improve your application. Weave will automatically capture traces for DSPy. To start tracking, calling weave.init(project_name="<YOUR-WANDB-PROJECT-NAME>") and use the library as normal. 
import os
import dspy
import weave

os.environ["OPENAI_API_KEY"] = "<YOUR-OPENAI-API-KEY>"

weave.init(project_name="<YOUR-WANDB-PROJECT-NAME>")

lm = dspy.LM('openai/gpt-4o-mini')
dspy.configure(lm=lm)
classify = dspy.Predict("sentence -> sentiment")
classify(sentence="it's a charming and often affecting journey.")
dspy_trace.png Weave logs all LM calls in your DSPy program, providing details about inputs, outputs, and metadata.

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Track your own DSPy Modules and Signatures

A Module is the building block with learnable parameters for DSPy programs that abstracts a prompting technique. A Signature is a declarative specification of input/output behavior of a DSPy Module. Weave automatically tracks all in-built and cutom Signatures and Modules in your DSPy programs. 
import os
import dspy
import weave

os.environ["OPENAI_API_KEY"] = "<YOUR-OPENAI-API-KEY>"

weave.init(project_name="<YOUR-WANDB-PROJECT-NAME>")

class Outline(dspy.Signature):
    """Outline a thorough overview of a topic."""

    topic: str = dspy.InputField()
    title: str = dspy.OutputField()
    sections: list[str] = dspy.OutputField()
    section_subheadings: dict[str, list[str]] = dspy.OutputField(
        desc="mapping from section headings to subheadings"
    )


class DraftSection(dspy.Signature):
    """Draft a top-level section of an article."""

    topic: str = dspy.InputField()
    section_heading: str = dspy.InputField()
    section_subheadings: list[str] = dspy.InputField()
    content: str = dspy.OutputField(desc="markdown-formatted section")


class DraftArticle(dspy.Module):
    def __init__(self):
        self.build_outline = dspy.ChainOfThought(Outline)
        self.draft_section = dspy.ChainOfThought(DraftSection)

    def forward(self, topic):
        outline = self.build_outline(topic=topic)
        sections = []
        for heading, subheadings in outline.section_subheadings.items():
            section, subheadings = (
                f"## {heading}",
                [f"### {subheading}" for subheading in subheadings],
            )
            section = self.draft_section(
                topic=outline.title,
                section_heading=section,
                section_subheadings=subheadings,
            )
            sections.append(section.content)
        return dspy.Prediction(title=outline.title, sections=sections)


draft_article = DraftArticle()
article = draft_article(topic="World Cup 2002")

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Optimization and Evaluation of your DSPy Program

Weave also automatically captures traces for DSPy optimizers and Evaluation calls which you can use to improve and evaulate your DSPy program’s performance on a development set. 
import os
import dspy
import weave

os.environ["OPENAI_API_KEY"] = "<YOUR-OPENAI-API-KEY>"
weave.init(project_name="<YOUR-WANDB-PROJECT-NAME>")

def accuracy_metric(answer, output, trace=None):
    predicted_answer = output["answer"].lower()
    return answer["answer"].lower() == predicted_answer

module = dspy.ChainOfThought("question -> answer: str, explanation: str")
optimizer = dspy.BootstrapFewShot(metric=accuracy_metric)
optimized_module = optimizer.compile(
    module, trainset=SAMPLE_EVAL_DATASET, valset=SAMPLE_EVAL_DATASET
)
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