DSPy

DSPy is a Python framework for composing language-model programs and optimizing their instructions and examples against defined metrics

Texto original de la fuente · EN

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Ejemplo de uso

DSPy: see a signature produce the author’s haiku

Build the first haiku program from the author’s complete LM setup and signature example. The printed poem is the tutorial’s illustrative output; its exact generation model is unspecified. The copied recipe preserves the setup model string and API-key placeholder.

Entrada de ejemplo

import dspy

# Pass the key explicitly...
lm = dspy.LM("openai/gpt-5-nano", api_key="YOUR_OPENAI_API_KEY")

dspy.configure(lm=lm)

haiku_signature = "subject -> haiku"
haiku_generator = dspy.Predict(haiku_signature)
result = haiku_generator(subject="computer science")
print(result.haiku)

Resultado de ejemplo

Silent code unfolds
Logic threads through hidden paths
Bugs bloom, then resolve
Licencia

Complete original license and any source copyright retained. Source-authored examples are separate from site-reproduced results; no original scripts, installations, model/API requests, account creation, memory writes, graph queries or tests were executed. Complete pinned source blocks are assembled in documented order, with their separate file bindings retained in the review record. The SDK setup and printed illustrative poem come from separate source sections; no exact generation-model binding is claimed.

MIT License

Copyright (c) 2023 Stanford Future Data Systems

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Ejemplo de uso

DSPy: specify setting, mood and season

The full source signature adds setting, mood and a Literal season before calling the haiku program for Bodega Bay. The author’s printed poem stays alongside the recipe. Setup and sample output come from separate documented sections, so the poem’s exact model is unspecified.

Entrada de ejemplo

import dspy

# Pass the key explicitly...
lm = dspy.LM("openai/gpt-5-nano", api_key="YOUR_OPENAI_API_KEY")

dspy.configure(lm=lm)

from typing import Literal

Season = Literal[
    "spring", "summer", "autumn", "winter",
]

class HaikuBot(dspy.Signature):
    """
    Write a classical haiku given the provided inputs.
    """
    location: str = dspy.InputField()
    mood: str = dspy.InputField()
    season: Season = dspy.InputField()
    haiku: str = dspy.OutputField()

haiku_bot = dspy.Predict(HaikuBot)
result = haiku_bot(location="Bodega Bay", mood="mysterious", season="autumn")
print(result.haiku)

Resultado de ejemplo

Fog drifts over waves,  
Crimson leaves swirl by the pier—  
Night whispers secrets.
Licencia

Complete original license and any source copyright retained. Source-authored examples are separate from site-reproduced results; no original scripts, installations, model/API requests, account creation, memory writes, graph queries or tests were executed. Complete pinned source blocks are assembled in documented order, with their separate file bindings retained in the review record. The SDK setup and printed illustrative poem come from separate source sections; no exact generation-model binding is claimed.

MIT License

Copyright (c) 2023 Stanford Future Data Systems

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
FUNCIONES INDICADAS POR LA FUENTE

Qué ofrece DSPy

  1. 01Define model tasks with typed signatures
  2. 02Compose reusable modules in Python
  3. 03Optimize prompts using examples and evaluation metrics
Texto original de la fuente · EN
Qué lo diferencia

Qué lo diferencia

DSPy treats model calls as parts of a program that can be evaluated and optimized. Useful results depend on representative examples and the metric supplied by the developer.

Texto original de la fuente · EN
Para quién es adecuado

Para quién es adecuado

  • Developers evaluating language-model pipelines
  • Teams tuning repeatable AI tasks
Texto original de la fuente · EN
ANTES DE ELEGIR

Qué comprobar

  • Prueba una tarea real y compara el resultado con tus necesidades.
  • Comprueba qué datos se guardan, comparten o usan para entrenar.
  • Confirma precios, límites de uso y condiciones de cancelación en el sitio oficial.
Última verificación

Enlace oficial comprobado el 20 oct 2026. Las funciones no se han probado de forma independiente.