DSPy

DSPy is a fantastic framework for LLMs that introduces an automatic compiler that teaches LMs how to conduct the declarative steps in your program. Specifically, the DSPy compiler will internally trace your program and then craft high-quality prompts for large LMs (or train automatic finetunes for small LMs) to teach them the steps of your task.

感谢 Omar Khattab 的贡献，我们已经实现了集成！它可以在对任何 LCEL 链进行一些小修改后正常工作。

这个简短的教程演示了此概念验证功能的工作原理。这还不会给您带来DSPy或LangChain的全部功能，但如果需求很高，我们将对其进行扩展。

注意：此版本是对Omar为DSPy编写的原始示例进行了一些修改。如果你对LangChain <> DSPy感兴趣，但来自DSPy背景，我建议你查看那个版本。你可以在这里找到它：这里。

让我们来看一个例子。在这个例子中，我们将构建一个简单的 RAG 流水线。我们将使用 DSPy 来“编译”我们的程序，并学习一个优化的提示。

此示例使用 ColBERTv2 模型。参见 ColBERTv2：通过轻量级后期交互实现高效检索论文。

安装依赖项

!pip install -U dspy-ai !pip install -U openai jinja2 !pip install -U langchain langchain-community langchain-openai langchain-core

设置

我们将使用 OpenAI，因此应设置 API 密钥

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

我们现在可以设置我们的检索器。对于检索器，我们将通过 DSPy 使用 ColBERT 检索器，尽管这适用于任何检索器。

import dspy

colbertv2 = dspy.ColBERTv2(url="http://20.102.90.50:2017/wiki17_abstracts")

from langchain.globals import set_llm_cache
from langchain_community.cache import SQLiteCache
from langchain_openai import OpenAI

set_llm_cache(SQLiteCache(database_path="cache.db"))

llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0)


def retrieve(inputs):
    return [doc["text"] for doc in colbertv2(inputs["question"], k=5)]

API 参考：set_llm_cache | SQLiteCache | OpenAI

colbertv2("cycling")

[{'text': 'Cycling | Cycling, also called bicycling or biking, is the use of bicycles for transport, recreation, exercise or sport. Persons engaged in cycling are referred to as "cyclists", "bikers", or less commonly, as "bicyclists". Apart from two-wheeled bicycles, "cycling" also includes the riding of unicycles, tricycles, quadracycles, recumbent and similar human-powered vehicles (HPVs).',
  'pid': 2201868,
  'rank': 1,
  'score': 27.078739166259766,
  'prob': 0.3544841299722533,
  'long_text': 'Cycling | Cycling, also called bicycling or biking, is the use of bicycles for transport, recreation, exercise or sport. Persons engaged in cycling are referred to as "cyclists", "bikers", or less commonly, as "bicyclists". Apart from two-wheeled bicycles, "cycling" also includes the riding of unicycles, tricycles, quadracycles, recumbent and similar human-powered vehicles (HPVs).'},
 {'text': 'Cycling (ice hockey) | In ice hockey, cycling is an offensive strategy that moves the puck along the boards in the offensive zone to create a scoring chance by making defenders tired or moving them out of position.',
  'pid': 312153,
  'rank': 2,
  'score': 26.109302520751953,
  'prob': 0.13445464524590262,
  'long_text': 'Cycling (ice hockey) | In ice hockey, cycling is an offensive strategy that moves the puck along the boards in the offensive zone to create a scoring chance by making defenders tired or moving them out of position.'},
 {'text': 'Bicycle | A bicycle, also called a cycle or bike, is a human-powered, pedal-driven, single-track vehicle, having two wheels attached to a frame, one behind the other. A is called a cyclist, or bicyclist.',
  'pid': 2197695,
  'rank': 3,
  'score': 25.849220275878906,
  'prob': 0.10366294133944996,
  'long_text': 'Bicycle | A bicycle, also called a cycle or bike, is a human-powered, pedal-driven, single-track vehicle, having two wheels attached to a frame, one behind the other. A is called a cyclist, or bicyclist.'},
 {'text': 'USA Cycling | USA Cycling or USAC, based in Colorado Springs, Colorado, is the national governing body for bicycle racing in the United States. It covers the disciplines of road, track, mountain bike, cyclo-cross, and BMX across all ages and ability levels. In 2015, USAC had a membership of 61,631 individual members.',
  'pid': 3821927,
  'rank': 4,
  'score': 25.61395263671875,
  'prob': 0.08193096873942958,
  'long_text': 'USA Cycling | USA Cycling or USAC, based in Colorado Springs, Colorado, is the national governing body for bicycle racing in the United States. It covers the disciplines of road, track, mountain bike, cyclo-cross, and BMX across all ages and ability levels. In 2015, USAC had a membership of 61,631 individual members.'},
 {'text': 'Vehicular cycling | Vehicular cycling (also known as bicycle driving) is the practice of riding bicycles on roads in a manner that is in accordance with the principles for driving in traffic.',
  'pid': 3058888,
  'rank': 5,
  'score': 25.35515785217285,
  'prob': 0.06324918635213703,
  'long_text': 'Vehicular cycling | Vehicular cycling (also known as bicycle driving) is the practice of riding bicycles on roads in a manner that is in accordance with the principles for driving in traffic.'},
 {'text': 'Road cycling | Road cycling is the most widespread form of cycling. It includes recreational, racing, and utility cycling. Road cyclists are generally expected to obey the same rules and laws as other vehicle drivers or riders and may also be vehicular cyclists.',
  'pid': 3392359,
  'rank': 6,
  'score': 25.274639129638672,
  'prob': 0.058356079351563846,
  'long_text': 'Road cycling | Road cycling is the most widespread form of cycling. It includes recreational, racing, and utility cycling. Road cyclists are generally expected to obey the same rules and laws as other vehicle drivers or riders and may also be vehicular cyclists.'},
 {'text': 'Cycling South Africa | Cycling South Africa or Cycling SA is the national governing body of cycle racing in South Africa. Cycling SA is a member of the "Confédération Africaine de Cyclisme" and the "Union Cycliste Internationale" (UCI). It is affiliated to the South African Sports Confederation and Olympic Committee (SASCOC) as well as the Department of Sport and Recreation SA. Cycling South Africa regulates the five major disciplines within the sport, both amateur and professional, which include: road cycling, mountain biking, BMX biking, track cycling and para-cycling.',
  'pid': 2508026,
  'rank': 7,
  'score': 25.24260711669922,
  'prob': 0.05651643767006817,
  'long_text': 'Cycling South Africa | Cycling South Africa or Cycling SA is the national governing body of cycle racing in South Africa. Cycling SA is a member of the "Confédération Africaine de Cyclisme" and the "Union Cycliste Internationale" (UCI). It is affiliated to the South African Sports Confederation and Olympic Committee (SASCOC) as well as the Department of Sport and Recreation SA. Cycling South Africa regulates the five major disciplines within the sport, both amateur and professional, which include: road cycling, mountain biking, BMX biking, track cycling and para-cycling.'},
 {'text': 'Cycle sport | Cycle sport is competitive physical activity using bicycles. There are several categories of bicycle racing including road bicycle racing, time trialling, cyclo-cross, mountain bike racing, track cycling, BMX, and cycle speedway. Non-racing cycling sports include artistic cycling, cycle polo, freestyle BMX and mountain bike trials. The Union Cycliste Internationale (UCI) is the world governing body for cycling and international competitive cycling events. The International Human Powered Vehicle Association is the governing body for human-powered vehicles that imposes far fewer restrictions on their design than does the UCI. The UltraMarathon Cycling Association is the governing body for many ultra-distance cycling races.',
  'pid': 3394121,
  'rank': 8,
  'score': 25.170495986938477,
  'prob': 0.05258444735141742,
  'long_text': 'Cycle sport | Cycle sport is competitive physical activity using bicycles. There are several categories of bicycle racing including road bicycle racing, time trialling, cyclo-cross, mountain bike racing, track cycling, BMX, and cycle speedway. Non-racing cycling sports include artistic cycling, cycle polo, freestyle BMX and mountain bike trials. The Union Cycliste Internationale (UCI) is the world governing body for cycling and international competitive cycling events. The International Human Powered Vehicle Association is the governing body for human-powered vehicles that imposes far fewer restrictions on their design than does the UCI. The UltraMarathon Cycling Association is the governing body for many ultra-distance cycling races.'},
 {'text': "Cycling UK | Cycling UK is the brand name of the Cyclists' Touring Club or CTC. It is a charitable membership organisation supporting cyclists and promoting bicycle use. Cycling UK is registered at Companies House (as “Cyclists’ Touring Club”), and covered by company law; it is the largest such organisation in the UK. It works at a national and local level to lobby for cyclists' needs and wants, provides services to members, and organises local groups for local activism and those interested in recreational cycling. The original Cyclists' Touring Club began in the nineteenth century with a focus on amateur road cycling but these days has a much broader sphere of interest encompassing everyday transport, commuting and many forms of recreational cycling. Prior to April 2016, Cycling UK operated under the brand CTC, the national cycling charity. As of January 2007, the organisation's president was the newsreader Jon Snow.",
  'pid': 1841483,
  'rank': 9,
  'score': 25.166988372802734,
  'prob': 0.05240032450529368,
  'long_text': "Cycling UK | Cycling UK is the brand name of the Cyclists' Touring Club or CTC. It is a charitable membership organisation supporting cyclists and promoting bicycle use. Cycling UK is registered at Companies House (as “Cyclists’ Touring Club”), and covered by company law; it is the largest such organisation in the UK. It works at a national and local level to lobby for cyclists' needs and wants, provides services to members, and organises local groups for local activism and those interested in recreational cycling. The original Cyclists' Touring Club began in the nineteenth century with a focus on amateur road cycling but these days has a much broader sphere of interest encompassing everyday transport, commuting and many forms of recreational cycling. Prior to April 2016, Cycling UK operated under the brand CTC, the national cycling charity. As of January 2007, the organisation's president was the newsreader Jon Snow."},
 {'text': 'Cycling in the Netherlands | Cycling is a ubiquitous mode of transport in the Netherlands, with 36% of the people listing the bicycle as their most frequent mode of transport on a typical day as opposed to the car by 45% and public transport by 11%. Cycling has a modal share of 27% of all trips (urban and rural) nationwide. In cities this is even higher, such as Amsterdam which has 38%, though the smaller Dutch cities well exceed that: for instance Zwolle (pop. ~123,000) has 46% and the university town of Groningen (pop. ~198,000) has 31%. This high modal share for bicycle travel is enabled by excellent cycling infrastructure such as cycle paths, cycle tracks, protected intersections, ubiquitous bicycle parking and by making cycling routes shorter, quicker and more direct than car routes.',
  'pid': 1196118,
  'rank': 10,
  'score': 24.954299926757812,
  'prob': 0.0423608394724844,
  'long_text': 'Cycling in the Netherlands | Cycling is a ubiquitous mode of transport in the Netherlands, with 36% of the people listing the bicycle as their most frequent mode of transport on a typical day as opposed to the car by 45% and public transport by 11%. Cycling has a modal share of 27% of all trips (urban and rural) nationwide. In cities this is even higher, such as Amsterdam which has 38%, though the smaller Dutch cities well exceed that: for instance Zwolle (pop. ~123,000) has 46% and the university town of Groningen (pop. ~198,000) has 31%. This high modal share for bicycle travel is enabled by excellent cycling infrastructure such as cycle paths, cycle tracks, protected intersections, ubiquitous bicycle parking and by making cycling routes shorter, quicker and more direct than car routes.'}]

普通 LCEL

首先，让我们像平常一样，用 LCEL 创建一个简单的 RAG 流水线。

为了便于说明，我们来解决以下任务。

任务：构建一个用于生成信息性推文的RAG系统。

输入：一个事实性问题，可能相当复杂。
输出： 一条吸引人的推文，正确回答了从检索到的信息中提出的问题。

让我们使用 LangChain 的表达式语言（LCEL）来说明这一点。这里的任何提示词都可以，我们将用 DSPy 来优化最终的提示词。

考虑到这一点，我们只需保留最基本的内容：根据{context}，将{question}作为一条推文来回答。

# From LangChain, import standard modules for prompting.
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePassthrough

# Just a simple prompt for this task. It's fine if it's complex too.
prompt = PromptTemplate.from_template(
    "Given {context}, answer the question `{question}` as a tweet."
)

# This is how you'd normally build a chain with LCEL. This chain does retrieval then generation (RAG).
vanilla_chain = (
    RunnablePassthrough.assign(context=retrieve) | prompt | llm | StrOutputParser()
)

API 参考：StrOutputParser | PromptTemplate | RunnablePassthrough

LCEL <> DSPy

为了在 DSPy 中使用 LangChain，你需要进行两项小的修改。

LangChainPredict

你需要从执行 prompt | llm 改为使用 LangChainPredict(prompt, llm) 来自 dspy。

这是一个包装器，它会将您的提示和语言模型绑定在一起，以便您可以对其进行优化。

LangChainModule

这是一个包装器，用于包装您最终的 LCEL 链，以便 DSPy 可以优化整个链。

# From DSPy, import the modules that know how to interact with LangChain LCEL.
from dspy.predict.langchain import LangChainModule, LangChainPredict

# This is how to wrap it so it behaves like a DSPy program.
# Just Replace every pattern like `prompt | llm` with `LangChainPredict(prompt, llm)`.
zeroshot_chain = (
    RunnablePassthrough.assign(context=retrieve)
    | LangChainPredict(prompt, llm)
    | StrOutputParser()
)
# Now we wrap it in LangChainModule
zeroshot_chain = LangChainModule(
    zeroshot_chain
)  # then wrap the chain in a DSPy module.

尝试该模块

之后，我们可以将其同时用作 LangChain 可运行对象和 DSPy 模块！

question = "In what region was Eddy Mazzoleni born?"

zeroshot_chain.invoke({"question": question})

' Eddy Mazzoleni, born in Bergamo, Italy, is a professional road cyclist who rode for UCI ProTour Astana Team. #cyclist #Italy'

啊，这听起来差不多对了！（严格来说并不完美：我们要求的是区域而不是城市。下面我们可以做得更好。）

手动检查问题和答案对于了解你的系统非常重要。然而，一个好的系统设计者总是会不断对工作进行基准测试，以量化进展！

为此，我们需要两样东西：我们想要最大化的指标，以及一个（极小的）示例数据集供我们的系统使用。

好的推文是否有预定义的指标？我需要手动标注十万条推文吗？可能不需要。不过，在你开始获得生产环境数据之前，我们可以轻松做一些合理的事情！

加载数据

为了编译我们的链，我们需要一个数据集来使用。这个数据集只需要包含原始的输入和输出。为了我们的目的，我们将使用 HotPotQA 数据集。

注意：请注意，我们的数据集实际上并不包含任何推文！它只包含问题和答案。这没关系，我们的评估指标会处理以推文形式输出的结果的评估。

import dspy
from dspy.datasets import HotPotQA

# Load the dataset.
dataset = HotPotQA(
    train_seed=1,
    train_size=200,
    eval_seed=2023,
    dev_size=200,
    test_size=0,
    keep_details=True,
)

# Tell DSPy that the 'question' field is the input. Any other fields are labels and/or metadata.
trainset = [x.without("id", "type").with_inputs("question") for x in dataset.train]
devset = [x.without("id", "type").with_inputs("question") for x in dataset.dev]
valset, devset = devset[:50], devset[50:]

/Users/harrisonchase/.pyenv/versions/3.11.1/envs/langchain-3-11/lib/python3.11/site-packages/datasets/table.py:1421: FutureWarning: promote has been superseded by mode='default'.
  table = cls._concat_blocks(blocks, axis=0)

定义一个指标

我们现在需要定义一个指标。这将用于确定哪些运行是成功的，我们可以从中学习。这里我们将使用 DSPy 的指标，当然你也可以自己编写。

# Define the signature for autoamtic assessments.
class Assess(dspy.Signature):
    """Assess the quality of a tweet along the specified dimension."""

    context = dspy.InputField(desc="ignore if N/A")
    assessed_text = dspy.InputField()
    assessment_question = dspy.InputField()
    assessment_answer = dspy.OutputField(desc="Yes or No")


gpt4T = dspy.OpenAI(model="gpt-4-1106-preview", max_tokens=1000, model_type="chat")
METRIC = None


def metric(gold, pred, trace=None):
    question, answer, tweet = gold.question, gold.answer, pred.output
    context = colbertv2(question, k=5)

    engaging = "Does the assessed text make for a self-contained, engaging tweet?"
    faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context."
    correct = (
        f"The text above is should answer `{question}`. The gold answer is `{answer}`."
    )
    correct = f"{correct} Does the assessed text above contain the gold answer?"

    with dspy.context(lm=gpt4T):
        faithful = dspy.Predict(Assess)(
            context=context, assessed_text=tweet, assessment_question=faithful
        )
        correct = dspy.Predict(Assess)(
            context="N/A", assessed_text=tweet, assessment_question=correct
        )
        engaging = dspy.Predict(Assess)(
            context="N/A", assessed_text=tweet, assessment_question=engaging
        )

    correct, engaging, faithful = [
        m.assessment_answer.split()[0].lower() == "yes"
        for m in [correct, engaging, faithful]
    ]
    score = (correct + engaging + faithful) if correct and (len(tweet) <= 280) else 0

    if METRIC is not None:
        if METRIC == "correct":
            return correct
        if METRIC == "engaging":
            return engaging
        if METRIC == "faithful":
            return faithful

    if trace is not None:
        return score >= 3
    return score / 3.0

评估基线

好的，让我们评估一下从我们的 LangChain LCEL 对象转换而来的未优化“零样本”版本的链。

from dspy.evaluate.evaluate import Evaluate

evaluate = Evaluate(
    metric=metric, devset=devset, num_threads=8, display_progress=True, display_table=5
)
evaluate(zeroshot_chain)

Average Metric: 62.99999999999998 / 150  (42.0): 100%|██| 150/150 [01:14<00:00,  2.02it/s]
``````output
Average Metric: 62.99999999999998 / 150  (42.0%)
``````output

/Users/harrisonchase/.pyenv/versions/3.11.1/envs/langchain-3-11/lib/python3.11/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  df = df.applymap(truncate_cell)

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<table id="T_390d8">
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      <th id="T_390d8_level0_col1" class="col_heading level0 col1" >answer</th>
      <th id="T_390d8_level0_col2" class="col_heading level0 col2" >gold_titles</th>
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      <th id="T_390d8_level0_col5" class="col_heading level0 col5" >metric</th>
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  <tbody>
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      <th id="T_390d8_level0_row0" class="row_heading level0 row0" >0</th>
      <td id="T_390d8_row0_col0" class="data row0 col0" >Who was a producer who produced albums for both rock bands Juke Karten and Thirty Seconds to Mars?</td>
      <td id="T_390d8_row0_col1" class="data row0 col1" >Brian Virtue</td>
      <td id="T_390d8_row0_col2" class="data row0 col2" >{'Thirty Seconds to Mars', 'Levolution (album)'}</td>
      <td id="T_390d8_row0_col3" class="data row0 col3" >Brian Virtue, who has worked with bands like Jane's Addiction and Velvet Revolver, produced albums for both Juke Kartel and Thirty Seconds to Mars. #BrianVirtue...</td>
      <td id="T_390d8_row0_col4" class="data row0 col4" >Brian Virtue, who has worked with bands like Jane's Addiction and Velvet Revolver, produced albums for both Juke Kartel and Thirty Seconds to Mars. #BrianVirtue...</td>
      <td id="T_390d8_row0_col5" class="data row0 col5" >1.0</td>
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    <tr>
      <th id="T_390d8_level0_row1" class="row_heading level0 row1" >1</th>
      <td id="T_390d8_row1_col0" class="data row1 col0" >Are both the University of Chicago and Syracuse University public universities? </td>
      <td id="T_390d8_row1_col1" class="data row1 col1" >no</td>
      <td id="T_390d8_row1_col2" class="data row1 col2" >{'Syracuse University', 'University of Chicago'}</td>
      <td id="T_390d8_row1_col3" class="data row1 col3" > No, only Syracuse University is a public university. The University of Chicago is a private research university. #university #publicvsprivate</td>
      <td id="T_390d8_row1_col4" class="data row1 col4" > No, only Syracuse University is a public university. The University of Chicago is a private research university. #university #publicvsprivate</td>
      <td id="T_390d8_row1_col5" class="data row1 col5" >0.3333333333333333</td>
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      <td id="T_390d8_row2_col0" class="data row2 col0" >In what region was Eddy Mazzoleni born?</td>
      <td id="T_390d8_row2_col1" class="data row2 col1" >Lombardy, northern Italy</td>
      <td id="T_390d8_row2_col2" class="data row2 col2" >{'Eddy Mazzoleni', 'Bergamo'}</td>
      <td id="T_390d8_row2_col3" class="data row2 col3" > Eddy Mazzoleni, born in Bergamo, Italy, is a professional road cyclist who rode for UCI ProTour Astana Team. #cyclist #Italy</td>
      <td id="T_390d8_row2_col4" class="data row2 col4" > Eddy Mazzoleni, born in Bergamo, Italy, is a professional road cyclist who rode for UCI ProTour Astana Team. #cyclist #Italy</td>
      <td id="T_390d8_row2_col5" class="data row2 col5" >0.0</td>
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      <th id="T_390d8_level0_row3" class="row_heading level0 row3" >3</th>
      <td id="T_390d8_row3_col0" class="data row3 col0" >Who edited the 1990 American romantic comedy film directed by Garry Marshall?</td>
      <td id="T_390d8_row3_col1" class="data row3 col1" >Raja Raymond Gosnell</td>
      <td id="T_390d8_row3_col2" class="data row3 col2" >{'Raja Gosnell', 'Pretty Woman'}</td>
      <td id="T_390d8_row3_col3" class="data row3 col3" > J. F. Lawton wrote the screenplay for Pretty Woman, the 1990 American romantic comedy film directed by Garry Marshall. #PrettyWoman #GarryMarshall #JFLawton</td>
      <td id="T_390d8_row3_col4" class="data row3 col4" > J. F. Lawton wrote the screenplay for Pretty Woman, the 1990 American romantic comedy film directed by Garry Marshall. #PrettyWoman #GarryMarshall #JFLawton</td>
      <td id="T_390d8_row3_col5" class="data row3 col5" >0.0</td>
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    <tr>
      <th id="T_390d8_level0_row4" class="row_heading level0 row4" >4</th>
      <td id="T_390d8_row4_col0" class="data row4 col0" >Burrs Country Park railway station is what stop on the railway line that runs between Heywood and Rawtenstall</td>
      <td id="T_390d8_row4_col1" class="data row4 col1" >seventh</td>
      <td id="T_390d8_row4_col2" class="data row4 col2" >{'Burrs Country Park railway station', 'East Lancashire Railway'}</td>
      <td id="T_390d8_row4_col3" class="data row4 col3" > Burrs Country Park railway station is the seventh stop on the East Lancashire Railway line that runs between Heywood and Rawtenstall.</td>
      <td id="T_390d8_row4_col4" class="data row4 col4" > Burrs Country Park railway station is the seventh stop on the East Lancashire Railway line that runs between Heywood and Rawtenstall.</td>
      <td id="T_390d8_row4_col5" class="data row4 col5" >1.0</td>
    </tr>
  </tbody>
</table>
 

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42.0

好的，酷。我们的零样本链在来自开发集的150个问题上获得了约42.00%的准确率。

上表显示了一些示例。例如：

问题：哪位制作人曾为摇滚乐队Juke Karten和Thirty Seconds to Mars制作过专辑？
推文：曾与Jane's Addiction和Velvet Revolver等乐队合作的Brian Virtue，为Juke Kartel和Thirty Seconds to Mars制作了专辑，展示了……[已截断]
指标：1.0（一条正确、忠实且引人入胜的推文！*）

脚注：* 至少根据我们的指标，而我们的指标只是一个 DSPy 程序，所以如果你想的话也可以优化它！不过这是另一个笔记本的主题了。

优化

现在，让我们优化性能

from dspy.teleprompt import BootstrapFewShotWithRandomSearch

# Set up the optimizer. We'll use very minimal hyperparameters for this example.
# Just do random search with ~3 attempts, and in each attempt, bootstrap <= 3 traces.
optimizer = BootstrapFewShotWithRandomSearch(
    metric=metric, max_bootstrapped_demos=3, num_candidate_programs=3
)

# Now use the optimizer to *compile* the chain. This could take 5-10 minutes, unless it's cached.
optimized_chain = optimizer.compile(zeroshot_chain, trainset=trainset, valset=valset)

Going to sample between 1 and 3 traces per predictor.
Will attempt to train 3 candidate sets.
``````output
Average Metric: 22.33333333333334 / 50  (44.7): 100%|█████| 50/50 [00:26<00:00,  1.87it/s]
/Users/harrisonchase/.pyenv/versions/3.11.1/envs/langchain-3-11/lib/python3.11/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  df = df.applymap(truncate_cell)
``````output
Average Metric: 22.33333333333334 / 50  (44.7%)
Score: 44.67 for set: [0]
New best score: 44.67 for seed -3
Scores so far: [44.67]
Best score: 44.67
``````output
Average Metric: 22.33333333333334 / 50  (44.7): 100%|█████| 50/50 [00:00<00:00, 79.51it/s]
/Users/harrisonchase/.pyenv/versions/3.11.1/envs/langchain-3-11/lib/python3.11/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  df = df.applymap(truncate_cell)
``````output
Average Metric: 22.33333333333334 / 50  (44.7%)
Score: 44.67 for set: [16]
Scores so far: [44.67, 44.67]
Best score: 44.67
``````output
  4%|██                                                   | 8/200 [00:33<13:21,  4.18s/it]
``````output
Bootstrapped 3 full traces after 9 examples in round 0.
``````output
Average Metric: 24.666666666666668 / 50  (49.3): 100%|████| 50/50 [00:28<00:00,  1.77it/s]
/Users/harrisonchase/.pyenv/versions/3.11.1/envs/langchain-3-11/lib/python3.11/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  df = df.applymap(truncate_cell)
``````output
Average Metric: 24.666666666666668 / 50  (49.3%)
Score: 49.33 for set: [16]
New best score: 49.33 for seed -1
Scores so far: [44.67, 44.67, 49.33]
Best score: 49.33
Average of max per entry across top 1 scores: 0.49333333333333335
Average of max per entry across top 2 scores: 0.5533333333333335
Average of max per entry across top 3 scores: 0.5533333333333335
Average of max per entry across top 5 scores: 0.5533333333333335
Average of max per entry across top 8 scores: 0.5533333333333335
Average of max per entry across top 9999 scores: 0.5533333333333335
``````output
  6%|███                                                 | 12/200 [00:31<08:16,  2.64s/it]
``````output
Bootstrapped 2 full traces after 13 examples in round 0.
``````output
Average Metric: 25.66666666666667 / 50  (51.3): 100%|█████| 50/50 [00:25<00:00,  1.92it/s]
/Users/harrisonchase/.pyenv/versions/3.11.1/envs/langchain-3-11/lib/python3.11/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  df = df.applymap(truncate_cell)
``````output
Average Metric: 25.66666666666667 / 50  (51.3%)
Score: 51.33 for set: [16]
New best score: 51.33 for seed 0
Scores so far: [44.67, 44.67, 49.33, 51.33]
Best score: 51.33
Average of max per entry across top 1 scores: 0.5133333333333334
Average of max per entry across top 2 scores: 0.5666666666666668
Average of max per entry across top 3 scores: 0.6000000000000001
Average of max per entry across top 5 scores: 0.6000000000000001
Average of max per entry across top 8 scores: 0.6000000000000001
Average of max per entry across top 9999 scores: 0.6000000000000001
``````output
  0%|▎                                                    | 1/200 [00:02<08:37,  2.60s/it]
``````output
Bootstrapped 1 full traces after 2 examples in round 0.
``````output
Average Metric: 26.33333333333334 / 50  (52.7): 100%|█████| 50/50 [00:23<00:00,  2.11it/s]
/Users/harrisonchase/.pyenv/versions/3.11.1/envs/langchain-3-11/lib/python3.11/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  df = df.applymap(truncate_cell)
``````output
Average Metric: 26.33333333333334 / 50  (52.7%)
Score: 52.67 for set: [16]
New best score: 52.67 for seed 1
Scores so far: [44.67, 44.67, 49.33, 51.33, 52.67]
Best score: 52.67
Average of max per entry across top 1 scores: 0.5266666666666667
Average of max per entry across top 2 scores: 0.56
Average of max per entry across top 3 scores: 0.5666666666666668
Average of max per entry across top 5 scores: 0.6000000000000001
Average of max per entry across top 8 scores: 0.6000000000000001
Average of max per entry across top 9999 scores: 0.6000000000000001
``````output
  0%|▎                                                    | 1/200 [00:02<07:11,  2.17s/it]
``````output
Bootstrapped 1 full traces after 2 examples in round 0.
``````output
Average Metric: 25.666666666666668 / 50  (51.3): 100%|████| 50/50 [00:21<00:00,  2.29it/s]
``````output
Average Metric: 25.666666666666668 / 50  (51.3%)
Score: 51.33 for set: [16]
Scores so far: [44.67, 44.67, 49.33, 51.33, 52.67, 51.33]
Best score: 52.67
Average of max per entry across top 1 scores: 0.5266666666666667
Average of max per entry across top 2 scores: 0.56
Average of max per entry across top 3 scores: 0.6000000000000001
Average of max per entry across top 5 scores: 0.6133333333333334
Average of max per entry across top 8 scores: 0.6133333333333334
Average of max per entry across top 9999 scores: 0.6133333333333334
6 candidate programs found.
``````output

/Users/harrisonchase/.pyenv/versions/3.11.1/envs/langchain-3-11/lib/python3.11/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  df = df.applymap(truncate_cell)

评估优化后的链

嗯，这效果怎么样呢？让我们来做一些正式的评估吧！

evaluate(optimized_chain)

Average Metric: 74.66666666666666 / 150  (49.8): 100%|██| 150/150 [00:54<00:00,  2.74it/s]
``````output
Average Metric: 74.66666666666666 / 150  (49.8%)
``````output

/Users/harrisonchase/.pyenv/versions/3.11.1/envs/langchain-3-11/lib/python3.11/site-packages/dspy/evaluate/evaluate.py:126: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  df = df.applymap(truncate_cell)

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      <th id="T_b4366_level0_col1" class="col_heading level0 col1" >answer</th>
      <th id="T_b4366_level0_col2" class="col_heading level0 col2" >gold_titles</th>
      <th id="T_b4366_level0_col3" class="col_heading level0 col3" >output</th>
      <th id="T_b4366_level0_col4" class="col_heading level0 col4" >tweet_response</th>
      <th id="T_b4366_level0_col5" class="col_heading level0 col5" >metric</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th id="T_b4366_level0_row0" class="row_heading level0 row0" >0</th>
      <td id="T_b4366_row0_col0" class="data row0 col0" >Who was a producer who produced albums for both rock bands Juke Karten and Thirty Seconds to Mars?</td>
      <td id="T_b4366_row0_col1" class="data row0 col1" >Brian Virtue</td>
      <td id="T_b4366_row0_col2" class="data row0 col2" >{'Thirty Seconds to Mars', 'Levolution (album)'}</td>
      <td id="T_b4366_row0_col3" class="data row0 col3" >Brian Virtue, known for his work with Jane's Addiction and Velvet Revolver, produced albums for both Juke Kartel and Thirty Seconds to Mars. #BrianVirtue #Producer...</td>
      <td id="T_b4366_row0_col4" class="data row0 col4" >Brian Virtue, known for his work with Jane's Addiction and Velvet Revolver, produced albums for both Juke Kartel and Thirty Seconds to Mars. #BrianVirtue #Producer...</td>
      <td id="T_b4366_row0_col5" class="data row0 col5" >1.0</td>
    </tr>
    <tr>
      <th id="T_b4366_level0_row1" class="row_heading level0 row1" >1</th>
      <td id="T_b4366_row1_col0" class="data row1 col0" >Are both the University of Chicago and Syracuse University public universities? </td>
      <td id="T_b4366_row1_col1" class="data row1 col1" >no</td>
      <td id="T_b4366_row1_col2" class="data row1 col2" >{'Syracuse University', 'University of Chicago'}</td>
      <td id="T_b4366_row1_col3" class="data row1 col3" > No, only Northeastern Illinois University is a public state university. Syracuse University is a private research university. #University #PublicPrivate #HigherEd</td>
      <td id="T_b4366_row1_col4" class="data row1 col4" > No, only Northeastern Illinois University is a public state university. Syracuse University is a private research university. #University #PublicPrivate #HigherEd</td>
      <td id="T_b4366_row1_col5" class="data row1 col5" >0.0</td>
    </tr>
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      <th id="T_b4366_level0_row2" class="row_heading level0 row2" >2</th>
      <td id="T_b4366_row2_col0" class="data row2 col0" >In what region was Eddy Mazzoleni born?</td>
      <td id="T_b4366_row2_col1" class="data row2 col1" >Lombardy, northern Italy</td>
      <td id="T_b4366_row2_col2" class="data row2 col2" >{'Eddy Mazzoleni', 'Bergamo'}</td>
      <td id="T_b4366_row2_col3" class="data row2 col3" > Eddy Mazzoleni, the Italian professional road cyclist, was born in Bergamo, Italy. #EddyMazzoleni #Cycling #Italy</td>
      <td id="T_b4366_row2_col4" class="data row2 col4" > Eddy Mazzoleni, the Italian professional road cyclist, was born in Bergamo, Italy. #EddyMazzoleni #Cycling #Italy</td>
      <td id="T_b4366_row2_col5" class="data row2 col5" >0.0</td>
    </tr>
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      <th id="T_b4366_level0_row3" class="row_heading level0 row3" >3</th>
      <td id="T_b4366_row3_col0" class="data row3 col0" >Who edited the 1990 American romantic comedy film directed by Garry Marshall?</td>
      <td id="T_b4366_row3_col1" class="data row3 col1" >Raja Raymond Gosnell</td>
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      <td id="T_b4366_row3_col3" class="data row3 col3" > J. F. Lawton wrote the screenplay for Pretty Woman, the 1990 romantic comedy directed by Garry Marshall. #PrettyWoman #GarryMarshall #RomanticComedy</td>
      <td id="T_b4366_row3_col4" class="data row3 col4" > J. F. Lawton wrote the screenplay for Pretty Woman, the 1990 romantic comedy directed by Garry Marshall. #PrettyWoman #GarryMarshall #RomanticComedy</td>
      <td id="T_b4366_row3_col5" class="data row3 col5" >0.0</td>
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      <td id="T_b4366_row4_col0" class="data row4 col0" >Burrs Country Park railway station is what stop on the railway line that runs between Heywood and Rawtenstall</td>
      <td id="T_b4366_row4_col1" class="data row4 col1" >seventh</td>
      <td id="T_b4366_row4_col2" class="data row4 col2" >{'Burrs Country Park railway station', 'East Lancashire Railway'}</td>
      <td id="T_b4366_row4_col3" class="data row4 col3" > Burrs Country Park railway station is the seventh stop on the East Lancashire Railway, which runs between Heywood and Rawtenstall. #EastLancashireRailway #BurrsCountryPark #RailwayStation</td>
      <td id="T_b4366_row4_col4" class="data row4 col4" > Burrs Country Park railway station is the seventh stop on the East Lancashire Railway, which runs between Heywood and Rawtenstall. #EastLancashireRailway #BurrsCountryPark #RailwayStation</td>
      <td id="T_b4366_row4_col5" class="data row4 col5" >1.0</td>
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49.78

好了！我们的链从42%提升到了将近50%！

检查优化后的链

那么，实际上发生了什么来改进这一点呢？我们可以通过查看优化后的链来了解。我们可以通过两种方式实现这一点

查看使用的提示

我们可以查看实际使用的提示。我们可以通过查看 dspy.settings 来实现这一点。

prompt_used, output = dspy.settings.langchain_history[-1]

print(prompt_used)

Essential Instructions: Respond to the provided question based on the given context in the style of a tweet, ensuring the response is concise and within the character limit of a tweet (up to 280 characters).

---

Follow the following format.

Context: ${context}
Question: ${question}
Tweet Response: ${tweet_response}

---

Context:
[1] «Brutus (Funny Car) | Brutus is a pioneering funny car driven by Jim Liberman and prepared by crew chief Lew Arrington in the middle 1960s.»
[2] «USS Brutus (AC-15) | USS "Brutus", formerly the steamer "Peter Jebsen", was a collier in the United States Navy. She was built in 1894 at South Shields-on-Tyne, England, by John Readhead & Sons and was acquired by the U.S. Navy early in 1898 from L. F. Chapman & Company. She was renamed "Brutus" and commissioned at the Mare Island Navy Yard on 27 May 1898, with Lieutenant Vincendon L. Cottman, commanding officer and Lieutenant Randolph H. Miner, executive officer.»
[3] «Brutus Beefcake | Ed Leslie is an American semi-retired professional wrestler, best known for his work in the World Wrestling Federation (WWF) under the ring name Brutus "The Barber" Beefcake. He later worked for World Championship Wrestling (WCW) under a variety of names.»
[4] «Brutus Hamilton | Brutus Kerr Hamilton (July 19, 1900 – December 28, 1970) was an American track and field athlete, coach and athletics administrator.»
[5] «Big Brutus | Big Brutus is the nickname of the Bucyrus-Erie model 1850B electric shovel, which was the second largest of its type in operation in the 1960s and 1970s. Big Brutus is the centerpiece of a mining museum in West Mineral, Kansas where it was used in coal strip mining operations. The shovel was designed to dig from 20 to in relatively shallow coal seams.»
Question: What is the nickname for this United States drag racer who drove Brutus?
Tweet Response: Jim Liberman, also known as "Jungle Jim", drove the pioneering funny car Brutus in the 1960s. #Brutus #FunnyCar #DragRacing

---

Context:
[1] «Philip Markoff | Philip Haynes Markoff (February 12, 1986 – August 15, 2010) was an American medical student who was charged with the armed robbery and murder of Julissa Brisman in a Boston, Massachusetts, hotel on April 14, 2009, and two other armed robberies.»
[2] «Antonia Brenner | Antonia Brenner, better known as Mother Antonia (Spanish: Madre Antonia ), (December 1, 1926 – October 17, 2013) was an American Roman Catholic Religious Sister and activist who chose to reside and care for inmates at the notorious maximum-security La Mesa Prison in Tijuana, Mexico. As a result of her work, she founded a new religious institute called the Eudist Servants of the 11th Hour.»
[3] «Luzira Maximum Security Prison | Luzira Maximum Security Prison is a maximum security prison for both men and women in Uganda. As at July 2016, it is the only maximum security prison in the country and houses Uganda's death row inmates.»
[4] «Pleasant Valley State Prison | Pleasant Valley State Prison (PVSP) is a 640 acres minimum-to-maximum security state prison in Coalinga, Fresno County, California. The facility has housed convicted murderers Sirhan Sirhan, Erik Menendez, X-Raided, and Hans Reiser, among others.»
[5] «Jon-Adrian Velazquez | Jon-Adrian Velazquez is an inmate in the maximum security Sing-Sing prison in New York who is serving a 25-year sentence after being convicted of the 1998 murder of a retired police officer. His case garnered considerable attention from the media ten years after his conviction, due to a visit and support from Martin Sheen and a long-term investigation by Dateline NBC producer Dan Slepian.»
Question: Which maximum security jail housed the killer of Julissa brisman?
Tweet Response:

查看演示

优化的方式是，我们收集了一些示例（或称“演示”）放入提示中。我们可以检查 optimized_chain，以了解这些示例具体是什么。

demos = [
    eg
    for eg in optimized_chain.modules[0].demos
    if hasattr(eg, "augmented") and eg.augmented
]

demos

[Example({'augmented': True, 'question': 'What is the nickname for this United States drag racer who drove Brutus?', 'context': ['Brutus (Funny Car) | Brutus is a pioneering funny car driven by Jim Liberman and prepared by crew chief Lew Arrington in the middle 1960s.', 'USS Brutus (AC-15) | USS "Brutus", formerly the steamer "Peter Jebsen", was a collier in the United States Navy. She was built in 1894 at South Shields-on-Tyne, England, by John Readhead & Sons and was acquired by the U.S. Navy early in 1898 from L. F. Chapman & Company. She was renamed "Brutus" and commissioned at the Mare Island Navy Yard on 27 May 1898, with Lieutenant Vincendon L. Cottman, commanding officer and Lieutenant Randolph H. Miner, executive officer.', 'Brutus Beefcake | Ed Leslie is an American semi-retired professional wrestler, best known for his work in the World Wrestling Federation (WWF) under the ring name Brutus "The Barber" Beefcake. He later worked for World Championship Wrestling (WCW) under a variety of names.', 'Brutus Hamilton | Brutus Kerr Hamilton (July 19, 1900 – December 28, 1970) was an American track and field athlete, coach and athletics administrator.', 'Big Brutus | Big Brutus is the nickname of the Bucyrus-Erie model 1850B electric shovel, which was the second largest of its type in operation in the 1960s and 1970s. Big Brutus is the centerpiece of a mining museum in West Mineral, Kansas where it was used in coal strip mining operations. The shovel was designed to dig from 20 to in relatively shallow coal seams.'], 'tweet_response': ' Jim Liberman, also known as "Jungle Jim", drove the pioneering funny car Brutus in the 1960s. #Brutus #FunnyCar #DragRacing'}) (input_keys=None)]

安装依赖项​

设置​

普通 LCEL​

LCEL <> DSPy​

尝试该模块​

加载数据​

定义一个指标​

评估基线​

优化​

评估优化后的链​

检查优化后的链​

查看使用的提示​

查看演示​