> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# DSPy
> Use Weave to automatically track and log calls made using DSPy modules and functions
export const GitHubLink = ({url}) =>
GitHub source
;
export const ColabLink = ({url}) =>
Try in Colab
;
[DSPy](https://dspy.ai) 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.
## 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](https://dspy.ai). To start tracking, calling `weave.init(project_name="")` and use the library as normal.
```python lines theme={null}
import os
import dspy
import weave
os.environ["OPENAI_API_KEY"] = ""
weave.init(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.")
```
[](https://wandb.ai/geekyrakshit/dspy-project/weave/calls)
Weave logs all LM calls in your DSPy program, providing details about inputs, outputs, and metadata.
## 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.
```python lines theme={null}
import os
import dspy
import weave
os.environ["OPENAI_API_KEY"] = ""
weave.init(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")
```
[](https://wandb.ai/geekyrakshit/dspy-project/weave/calls)
## 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.
```python lines theme={null}
import os
import dspy
import weave
os.environ["OPENAI_API_KEY"] = ""
weave.init(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
)
```
[](https://wandb.ai/geekyrakshit/dspy-project/weave/calls)
](https://wandb.ai/geekyrakshit/dspy-project/weave/calls)
Weave logs all LM calls in your DSPy program, providing details about inputs, outputs, and metadata.
## 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.
```python lines theme={null}
import os
import dspy
import weave
os.environ["OPENAI_API_KEY"] = "
](https://wandb.ai/geekyrakshit/dspy-project/weave/calls)
## 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.
```python lines theme={null}
import os
import dspy
import weave
os.environ["OPENAI_API_KEY"] = "
](https://wandb.ai/geekyrakshit/dspy-project/weave/calls)