dspy-auto-signature 0.1.3

Generate DSPy Signatures from any prompt using a DSPy meta-program

AutoSignature

Generate typed dspy.Signature classes with dspy.RLM.
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Table of Contents

1. About
2. Quick Start
3. SDK Message Formats
4. DataFrame Example
5. API
6. Contributing
7. License

About

AutoSignature uses dspy.RLM to inspect a prompt or dataset and generate a complete dspy.Signature subclass.

• Automatic signature design — Infers instructions, inputs, outputs, field descriptions, and types
• Dataset-aware generation — Profiles DataFrame columns, distributions, and representative rows
• Ready to use — Returns signatures compatible with dspy.Predict, dspy.ChainOfThought, and other DSPy modules
• Exportable — Renders generated signatures as Python source with to_source()

Requires Python 3.12+, DSPy 3.2+, and Deno for the RLM sandbox.

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Quick Start

Install

Install AutoSignature with uv (recommended):

uv add dspy-auto-signature

Or with pip:

pip install dspy-auto-signature

Basic Usage

import dspy
import dspy_auto_signature as das

das.configure(
    lm=dspy.LM("openai/gpt-4o"),
    sub_lm=dspy.LM("openai/gpt-4o-mini"),
)

signature = das.generate(
    "Given an article, produce a concise summary and three key takeaways."
)

print(signature.to_source())

dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))
summarize = dspy.ChainOfThought(signature.to_signature())
result = summarize(article="...")

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SDK Message Formats

AutoSignature accepts message arrays from popular LLM SDKs. Pass your existing conversation history directly — no conversion needed.

OpenAI SDK

import dspy
import dspy_auto_signature as das

das.configure(
    lm=dspy.LM("openai/gpt-4o"),
    sub_lm=dspy.LM("openai/gpt-4o-mini"),
)

messages = [
    {"role": "system", "content": "You are a technical writer who produces clear documentation."},
    {"role": "user", "content": "Write a README for a Python CLI tool that converts CSV to JSON."},
]

signature = das.generate(messages)
print(signature.to_source())

Anthropic SDK

messages = [
    {"role": "user", "content": "Analyze the sentiment of customer reviews."},
    {"role": "assistant", "content": "I'll classify each review as positive, negative, or neutral."},
]

signature = das.generate(messages)

Google Gemini SDK

contents = [
    {"role": "user", "parts": [{"text": "Extract key entities from this legal contract."}]},
    {"role": "model", "parts": [{"text": "I'll identify parties, dates, and obligations."}]},
]

signature = das.generate(contents)

LiteLLM / OpenAI-Compatible

Any SDK that produces OpenAI-style [{"role": "...", "content": "..."}] arrays works out of the box — including LiteLLM, Azure OpenAI, Ollama, and vLLM.

See example_sdk.py for a complete runnable example.

DataFrame Example

Install with the pandas extra to get pandas:

uv add "dspy-auto-signature[pandas]"

Or with pip:

pip install "dspy-auto-signature[pandas]"

Datasets are profiled before generation so the RLM can use column names, types, distributions, and representative rows when designing the signature.

import dspy
import pandas as pd
import dspy_auto_signature as das

das.configure(
    lm=dspy.LM("openai/gpt-4o"),
    sub_lm=dspy.LM("openai/gpt-4o-mini"),
)

tickets = pd.DataFrame(
    [
        {"message": "Server is down", "urgency": "high"},
        {"message": "Please update my profile", "urgency": "low"},
        {"message": "Payment failed", "urgency": "high"},
    ]
)

signature = das.generate(
    tickets,
    task_hint="Classify support ticket urgency from the message",
)

print(signature.to_source())

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API

generate(source, task_hint=None, *, input_hints=None, output_hints=None)

Generates a dspy.Signature subclass from prompt material or tabular data.

Parameter	Type	Description
source	Any	Prompt string, SDK message array (OpenAI, Anthropic, Google, LiteLLM), DataFrame, list of dictionaries, or list of dspy.Example objects
task_hint	str | None	Optional task description, especially useful for identifying dataset targets
input_hints	dict[str, str] | None	Input field descriptions to supplement or override generated descriptions
output_hints	dict[str, str] | None	Output field descriptions to supplement or override generated descriptions

Supported input formats:

• Raw strings (system prompts, task descriptions)
• OpenAI SDK message arrays: [{"role": "system", "content": "..."}, {"role": "user", "content": "..."}]
• Anthropic SDK message arrays: [{"role": "user", "content": "..."}]
• Google Gemini SDK contents: [{"role": "user", "parts": [{"text": "..."}]}]
• LiteLLM / Azure OpenAI / Ollama / vLLM message arrays
• pandas DataFrames, polars DataFrames / LazyFrames
• list[dict], list[dspy.Example], single dspy.Example

configure(lm=None, dataset_lm=None, sub_lm=None)

Configures the models used during signature generation.

Parameter	Type	Description
lm	dspy.LM | None	Default RLM model. Falls back to the model configured through dspy.configure
dataset_lm	dspy.LM | None	Optional RLM model override for dataset sources
sub_lm	dspy.LM | None	Optional model used for recursive sub-queries

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Contributing

Quick workflow:

1. Fork and branch: git checkout -b feature/name
2. Make changes
3. Commit and push
4. Open a Pull Request

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License

MIT (as declared in pyproject.toml).

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