使用 Sentence Transformers 和 MLflow 进行高级语义搜索
踏上使用 Sentence Transformers 和 MLflow 探索高级语义搜索的实践之旅。
Download this Notebook你将学到什么
使用
sentence-transformers实现高级语义搜索。根据独特项目需求自定义 MLflow 的
PythonModel。在 MLflow 的生态系统中管理和记录模型。
使用 MLflow 部署复杂模型以进行实际应用。
理解语义搜索
语义搜索超越了关键词匹配，利用语言的细微差别和上下文来找到相关结果。这种先进的方法反映了人类对语言的理解，考虑了词语在不同场景中的多种含义。
利用句子转换器进行搜索的力量
Sentence Transformers，专为上下文丰富的句子嵌入设计，将搜索查询和文本语料库转换为语义向量。这使得识别语义相似的条目成为可能，这是语义搜索的基石。
MLflow：模型管理和部署的先锋
MLflow 通过高效的实验记录和可定制的模型环境增强了 NLP 项目。它为实验跟踪带来了效率，并增加了一层定制化，这对于独特的 NLP 任务至关重要。
加入我们这个教程，掌握高级语义搜索技术，并发现MLflow如何革新你在NLP模型部署和管理中的方法。
[1]:
import warnings
# Disable a few less-than-useful UserWarnings from setuptools and pydantic
warnings.filterwarnings("ignore", category=UserWarning)
理解使用 MLflow 和 Sentence Transformers 的语义搜索模型
深入探讨 SemanticSearchModel 的复杂性，这是一个使用 MLflow 和 Sentence Transformers 进行语义搜索的自定义实现。
MLflow 和自定义 PyFunc 模型
MLflow 的自定义 Python 函数 (pyfunc) 模型提供了一个灵活且可部署的解决方案，用于集成复杂逻辑，非常适合我们的 SemanticSearchModel。
模型的核心功能
上下文加载：对于初始化 Sentence Transformer 模型并准备语料库进行语义比较至关重要。
预测方法: 语义搜索的核心功能，包括输入验证、查询编码和相似度计算。
预测方法的详细分解
输入验证: 确保查询句子的正确格式和提取。
查询编码：将查询转换为嵌入以进行比较。
余弦相似度计算：确定每个语料库条目与查询的相关性。
顶部结果提取：根据相似度分数识别最相关的条目。
相关性过滤：根据最小相关性阈值过滤结果，增强实际可用性。
警告机制: 如果所有顶级结果的相关性都低于阈值，则会发出警告，确保始终提供结果。
结论
这个语义搜索模型展示了NLP与MLflow的集成，展示了灵活性、用户友好性以及在现代机器学习工作流程中的实际应用。
[2]:
import warnings
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer, util
import mlflow
from mlflow.models.signature import infer_signature
from mlflow.pyfunc import PythonModel
class SemanticSearchModel(PythonModel):
def load_context(self, context):
"""Load the model context for inference, including the corpus from a file."""
try:
# Load the pre-trained sentence transformer model
self.model = SentenceTransformer.load(context.artifacts["model_path"])
# Load the corpus from the specified file
corpus_file = context.artifacts["corpus_file"]
with open(corpus_file) as file:
self.corpus = file.read().splitlines()
# Encode the corpus and convert it to a tensor
self.corpus_embeddings = self.model.encode(self.corpus, convert_to_tensor=True)
except Exception as e:
raise ValueError(f"Error loading model and corpus: {e}")
def predict(self, context, model_input, params=None):
"""Predict method to perform semantic search over the corpus."""
if isinstance(model_input, pd.DataFrame):
if model_input.shape[1] != 1:
raise ValueError("DataFrame input must have exactly one column.")
model_input = model_input.iloc[0, 0]
elif isinstance(model_input, dict):
model_input = model_input.get("sentence")
if model_input is None:
raise ValueError("The input dictionary must have a key named 'sentence'.")
else:
raise TypeError(
f"Unexpected type for model_input: {type(model_input)}. Must be either a Dict or a DataFrame."
)
# Encode the query
query_embedding = self.model.encode(model_input, convert_to_tensor=True)
# Compute cosine similarity scores
cos_scores = util.cos_sim(query_embedding, self.corpus_embeddings)[0]
# Determine the number of top results to return
top_k = params.get("top_k", 3) if params else 3 # Default to 3 if not specified
minimum_relevancy = (
params.get("minimum_relevancy", 0.2) if params else 0.2
) # Default to 0.2 if not specified
# Get the top_k most similar sentences from the corpus
top_results = np.argsort(cos_scores, axis=0)[-top_k:]
# Prepare the initial results list
initial_results = [
(self.corpus[idx], cos_scores[idx].item()) for idx in reversed(top_results)
]
# Filter the results based on the minimum relevancy threshold
filtered_results = [result for result in initial_results if result[1] >= minimum_relevancy]
# If all results are below the threshold, issue a warning and return the top result
if not filtered_results:
warnings.warn(
"All top results are below the minimum relevancy threshold. "
"Returning the highest match instead.",
RuntimeWarning,
)
return [initial_results[0]]
else:
return filtered_results
构建和准备语义搜索语料库
探索构建和准备语义搜索模型的语料库，这是搜索功能的关键组成部分。
模拟真实世界的使用案例
我们创建了一个简化的合成博客文章语料库，以展示模型的核心功能，复制了一个缩小版的典型现实世界场景。
语料库准备的关键步骤
语料库创建：形成一个代表各个博客文章条目的列表。
写入文件: 将语料保存到文本文件中，模拟实际应用中的数据提取和预处理过程。
高效数据处理以实现可扩展性
我们的模型将语料库编码为嵌入向量以进行快速比较，展示了一种适用于扩展到更大数据集的高效方法。
生产考虑
存储嵌入向量：讨论了在大规模应用中，高效存储和检索嵌入向量的选项，这是至关重要的。
可扩展性：强调了可扩展存储系统在处理大量数据集和复杂查询中的重要性。
更新语料库：概述了在动态、不断发展的使用案例中管理和更新语料库的策略。
实现语义搜索概念
虽然简化了，但这种设置反映了开发一个健壮且可扩展的语义搜索系统的基本步骤，结合了NLP技术和高效的数据管理。在实际生产用例中，语料库的处理（创建嵌入）将是一个独立于运行语义搜索的过程。下面的语料库示例仅用于展示目的。
[3]:
corpus = [
"Perfecting a Sourdough Bread Recipe: The Joy of Baking. Baking sourdough bread "
"requires patience, skill, and a good understanding of yeast fermentation. Each "
"loaf is unique, telling its own story of the baker's journey.",
"The Mars Rover's Discoveries: Unveiling the Red Planet. NASA's Mars rover has "
"sent back stunning images and data, revealing the planet's secrets. These "
"discoveries may hold the key to understanding Mars' history.",
"The Art of Growing Herbs: Enhancing Your Culinary Skills. Growing your own "
"herbs can transform your cooking, adding fresh and vibrant flavors. Whether it's "
"basil, thyme, or rosemary, each herb has its own unique characteristics.",
"AI in Software Development: Transforming the Tech Landscape. The rapid "
"advancements in artificial intelligence are reshaping how we approach software "
"development. From automation to machine learning, the possibilities are endless.",
"Backpacking Through Europe: A Journey of Discovery. Traveling across Europe by "
"backpack allows one to immerse in diverse cultures and landscapes. It's an "
"adventure that combines the thrill of exploration with personal growth.",
"Shakespeare's Timeless Influence: Reshaping Modern Storytelling. The works of "
"William Shakespeare continue to inspire and influence contemporary literature. "
"His mastery of language and deep understanding of human nature are unparalleled.",
"The Rise of Renewable Energy: A Sustainable Future. Embracing renewable energy "
"is crucial for achieving a sustainable and environmentally friendly lifestyle. "
"Solar, wind, and hydro power are leading the way in this green revolution.",
"The Magic of Jazz: An Exploration of Sound and Harmony. Jazz music, known for "
"its improvisation and complex harmonies, has a rich and diverse history. It "
"evokes a range of emotions, often reflecting the soul of the musician.",
"Yoga for Mind and Body: The Benefits of Regular Practice. Engaging in regular "
"yoga practice can significantly improve flexibility, strength, and mental "
"well-being. It's a holistic approach to health, combining physical and spiritual "
"aspects.",
"The Egyptian Pyramids: Monuments of Ancient Majesty. The ancient Egyptian "
"pyramids, monumental tombs for pharaohs, are marvels of architectural "
"ingenuity. They stand as a testament to the advanced skills of ancient builders.",
"Vegan Cuisine: A World of Flavor. Exploring vegan cuisine reveals a world of "
"nutritious and delicious possibilities. From hearty soups to delectable desserts, "
"plant-based dishes are diverse and satisfying.",
"Extraterrestrial Life: The Endless Search. The quest to find life beyond Earth "
"continues to captivate scientists and the public alike. Advances in space "
"technology are bringing us closer to answering this age-old question.",
"The Art of Plant Pruning: Promoting Healthy Growth. Regular pruning is essential "
"for maintaining healthy and vibrant plants. It's not just about cutting back, but "
"understanding each plant's growth patterns and needs.",
"Cybersecurity in the Digital Age: Protecting Our Data. With the rise of digital "
"technology, cybersecurity has become a critical concern. Protecting sensitive "
"information from cyber threats is an ongoing challenge for individuals and "
"businesses alike.",
"The Great Wall of China: A Historical Journey. Visiting the Great Wall offers "
"more than just breathtaking views; it's a journey through history. This ancient "
"structure tells stories of empires, invasions, and human resilience.",
"Mystery Novels: Crafting Suspense and Intrigue. A great mystery novel captivates "
"the reader with intricate plots and unexpected twists. It's a genre that combines "
"intellectual challenge with entertainment.",
"Conserving Endangered Species: A Global Effort. Protecting endangered species "
"is a critical task that requires international collaboration. From rainforests to "
"oceans, every effort counts in preserving our planet's biodiversity.",
"Emotions in Classical Music: A Symphony of Feelings. Classical music is not just "
"an auditory experience; it's an emotional journey. Each composition tells a story, "
"conveying feelings from joy to sorrow, tranquility to excitement.",
"CrossFit: A Test of Strength and Endurance. CrossFit is more than just a fitness "
"regimen; it's a lifestyle that challenges your physical and mental limits. It "
"combines various disciplines to create a comprehensive workout.",
"The Renaissance: An Era of Artistic Genius. The Renaissance marked a period of "
"extraordinary artistic and scientific achievements. It was a time when creativity "
"and innovation flourished, reshaping the course of history.",
"Exploring International Cuisines: A Culinary Adventure. Discovering international "
"cuisines is an adventure for the palate. Each dish offers a glimpse into the "
"culture and traditions of its origin.",
"Astronaut Training: Preparing for the Unknown. Becoming an astronaut involves "
"rigorous training to prepare for the extreme conditions of space. It's a journey "
"that tests both physical endurance and mental resilience.",
"Sustainable Gardening: Nurturing the Environment. Sustainable gardening is not "
"just about growing plants; it's about cultivating an ecosystem. By embracing "
"environmentally friendly practices, gardeners can have a positive impact on the "
"planet.",
"The Smartphone Revolution: Changing Communication. Smartphones have transformed "
"how we communicate, offering unprecedented connectivity and convenience. This "
"technology continues to evolve, shaping our daily interactions.",
"Experiencing African Safaris: Wildlife and Wilderness. An African safari is an "
"unforgettable experience that brings you face-to-face with the wonders of "
"wildlife. It's a journey that connects you with the raw beauty of nature.",
"Graphic Novels: A Blend of Art and Story. Graphic novels offer a unique medium "
"where art and narrative intertwine to tell compelling stories. They challenge "
"traditional forms of storytelling, offering visual and textual richness.",
"Addressing Ocean Pollution: A Call to Action. The increasing levels of pollution "
"in our oceans are a pressing environmental concern. Protecting marine life and "
"ecosystems requires concerted global efforts.",
"The Origins of Hip Hop: A Cultural Movement. Hip hop music, originating from the "
"streets of New York, has grown into a powerful cultural movement. Its beats and "
"lyrics reflect the experiences and voices of a community.",
"Swimming: A Comprehensive Workout. Swimming offers a full-body workout that is "
"both challenging and refreshing. It's an exercise that enhances cardiovascular "
"health, builds muscle, and improves endurance.",
"The Fall of the Berlin Wall: A Historical Turning Point. The fall of the Berlin "
"Wall was not just a physical demolition; it was a symbol of political and social "
"change. This historic event marked the end of an era and the beginning of a new "
"chapter in world history.",
]
# Write the corpus to a file
corpus_file = "/tmp/search_corpus.txt"
with open(corpus_file, "w") as file:
for sentence in corpus:
file.write(sentence + "\n")
MLflow 中的模型准备和配置
探索为与MLflow集成准备和配置Sentence Transformer模型的步骤，这对于部署准备至关重要。
加载和保存句子转换器模型
模型初始化：加载
"all-MiniLM-L6-v2"模型，该模型以其性能和速度的平衡著称，适合用于语义搜索任务。模型存储：将模型保存到一个目录中，这对于通过 MLflow 进行后续部署是必不可少的。选择
/tmp/search_model是为了方便教程，这样您的当前工作目录就不会被模型文件填满。您可以将其更改为您选择的任何位置。
准备模型工件和签名
Artifacts 字典: 创建一个包含模型和语料库文件路径的字典，指导 MLflow 找到初始化自定义模型对象所需的部分。
输入示例和测试输出: 定义示例输入和输出以说明模型预期的数据格式。
模型签名：使用
infer_signature进行自动签名生成，涵盖输入、输出和操作参数。
模型签名的重要性
签名确保了训练和部署之间的数据一致性，增强了模型的可用性并减少了错误的可能性。指定签名确保在推理时进行类型验证，防止由于无效类型转换导致的意外行为，这些行为可能导致不正确或令人困惑的推理结果。
结论
这一全面的准备过程确保模型已准备好部署，所有依赖项和操作要求都已明确界定。
[4]:
# Load a pre-trained sentence transformer model
model = SentenceTransformer("all-MiniLM-L6-v2")
# Create an input example DataFrame
input_example = ["Something I want to find matches for."]
# Save the model in the /tmp directory
model_directory = "/tmp/search_model"
model.save(model_directory)
artifacts = {"model_path": model_directory, "corpus_file": corpus_file}
# Generate test output for signature
test_output = ["match 1", "match 2", "match 3"]
# Define the signature associated with the model
signature = infer_signature(
input_example, test_output, params={"top_k": 3, "minimum_relevancy": 0.2}
)
# Visualize the signature
signature
[4]:
inputs:
[string]
outputs:
[string]
params:
['top_k': long (default: 3), 'minimum_relevancy': double (default: 0.2)]
创建一个实验
我们创建一个新的 MLflow 实验，这样我们将要记录模型的运行不会记录到默认实验中，而是拥有其上下文相关条目。
[5]:
# If you are running this tutorial in local mode, leave the next line commented out.
# Otherwise, uncomment the following line and set your tracking uri to your local or remote tracking server.
# mlflow.set_tracking_uri("http://127.0.0.1:8080")
mlflow.set_experiment("Semantic Similarity")
[5]:
<Experiment: artifact_location='file:///Users/benjamin.wilson/repos/mlflow-fork/mlflow/docs/source/llms/sentence-transformers/tutorials/semantic-search/mlruns/405641275158666585', creation_time=1701278766302, experiment_id='405641275158666585', last_update_time=1701278766302, lifecycle_stage='active', name='Semantic Similarity', tags={}>
使用 MLflow 记录模型
了解在 MLflow 中记录模型的过程，这是在 MLflow 框架内管理和部署模型的关键步骤。
开始一个 MLflow 运行
上下文管理：使用
with mlflow.start_run()启动 MLflow 运行，这对于跟踪和管理与模型相关的操作至关重要。
记录模型
模型日志记录：使用
mlflow.pyfunc.log_model记录自定义的SemanticSearchModel，包括模型名称、实例、输入示例、签名、工件和需求等关键参数。
模型日志的结果
模型注册：确保模型在 MLflow 中与所有必要的组件一起注册，准备部署。
可重复性和可追溯性：促进一致的模型部署，并跟踪版本和相关数据。
结论
完成这一关键步骤将模型从开发阶段过渡到部署就绪状态，封装在 MLflow 生态系统中。
[6]:
with mlflow.start_run() as run:
model_info = mlflow.pyfunc.log_model(
"semantic_search",
python_model=SemanticSearchModel(),
input_example=input_example,
signature=signature,
artifacts=artifacts,
pip_requirements=["sentence_transformers", "numpy"],
)
2023/11/30 15:57:53 INFO mlflow.store.artifact.artifact_repo: The progress bar can be disabled by setting the environment variable MLFLOW_ENABLE_ARTIFACTS_PROGRESS_BAR to false
模型推理与预测演示
观察我们的语义搜索模型的实际应用，展示其根据用户查询提供相关预测的能力。
加载模型以进行推理
模型加载: 使用
mlflow.pyfunc.load_model加载模型，准备处理语义搜索查询。
做出预测
运行查询：将示例查询传递给加载的模型，展示其语义搜索能力。
理解预测输出
输出格式: 预测输出的分析，通过相关性分数展示模型的语义理解。
示例结果：展示模型的结果，包括与各种查询相关条目的相关性分数。
结论
这一演示突显了模型在语义搜索中的有效性，强调了其在推荐和知识检索应用中的潜力。
[7]:
# Load our model as a PyFuncModel.
# Note that unlike the example shown in the Introductory Tutorial, there is no 'native' flavor for PyFunc models.
# This model cannot be loaded with `mlflow.sentence_transformers.load_model()` because it is not in the native model format.
loaded_dynamic = mlflow.pyfunc.load_model(model_info.model_uri)
# Make sure that it generates a reasonable output
loaded_dynamic.predict(["I'd like some ideas for a meal to cook."])
[7]:
[('Exploring International Cuisines: A Culinary Adventure. Discovering international cuisines is an adventure for the palate. Each dish offers a glimpse into the culture and traditions of its origin.',
0.43857115507125854),
('Vegan Cuisine: A World of Flavor. Exploring vegan cuisine reveals a world of nutritious and delicious possibilities. From hearty soups to delectable desserts, plant-based dishes are diverse and satisfying.',
0.34688490629196167),
("The Art of Growing Herbs: Enhancing Your Culinary Skills. Growing your own herbs can transform your cooking, adding fresh and vibrant flavors. Whether it's basil, thyme, or rosemary, each herb has its own unique characteristics.",
0.22686949372291565)]
使用可定制参数和警告机制的高级查询处理
探索模型的先进功能，包括可定制的搜索参数和独特的警告机制，以实现最佳用户体验。
执行带有警告的自定义预测
自定义查询与挑战性参数: 测试模型在高度相关性阈值查询下辨别高度相关内容的能力。
触发警告：当搜索条件过于严格时，提醒用户的机制，增强用户反馈。
理解模型的响应
在挑战场景中的结果：分析模型对严格搜索条件的响应，包括相关性阈值未达到的情况。
影响与最佳实践
平衡相关性和覆盖率: 讨论设置适当的相关性阈值以确保精度和结果覆盖率之间平衡的重要性。
语料库改进的用户反馈：利用警告作为反馈来精炼语料库并增强搜索系统。
结论
这个高级功能集展示了模型的适应性以及微调搜索参数对于实现动态和响应式搜索体验的重要性。
[8]:
# Verify that the fallback logic works correctly by returning the 'best, closest' result, even though the parameters submitted should return no results.
# We are also validating that the warning is issued, alerting us to the fact that this behavior is occurring.
loaded_dynamic.predict(
["Latest stories on computing"], params={"top_k": 10, "minimum_relevancy": 0.4}
)
/var/folders/cd/n8n0rm2x53l_s0xv_j_xklb00000gp/T/ipykernel_55915/1325605132.py:71: RuntimeWarning: All top results are below the minimum relevancy threshold. Returning the highest match instead.
warnings.warn(
[8]:
[('AI in Software Development: Transforming the Tech Landscape. The rapid advancements in artificial intelligence are reshaping how we approach software development. From automation to machine learning, the possibilities are endless.',
0.2533860206604004)]
结论：使用 MLflow 的 PythonModel 构建自定义逻辑
随着本教程的结束，让我们回顾一下关键的学习内容和MLflow的``PythonModel``在为现实世界应用定制逻辑时的强大能力，特别是在集成像``sentence-transformers``这样的高级库时。
关键要点
PythonModel 的灵活性：
MLflow 中的
PythonModel提供了无与伦比的灵活性，用于定义自定义逻辑。在本教程中，我们利用这一点构建了一个符合我们特定需求的语义搜索模型。这种灵活性在处理超出标准模型实现的复杂用例时，被证明是无价的。
与Sentence Transformers的集成：
我们无缝集成了
sentence-transformers库到我们的 MLflow 模型中。这展示了如何将先进的 NLP 能力嵌入到自定义模型中，以处理诸如语义搜索等复杂任务。在生成嵌入向量中使用转换器模型展示了如何将尖端的自然语言处理技术应用于实际场景。
自定义和用户体验：
我们的模型不仅执行了语义搜索的核心任务，还允许自定义搜索参数（
top_k和minimum_relevancy）。这种级别的自定义对于使模型的输出与不同的用户需求相匹配至关重要。警告机制的引入进一步丰富了模型，通过提供有价值的反馈，增强了用户体验。
实际应用与可扩展性：
虽然我们的教程专注于一个受控的数据集，但这些原则和方法论适用于更大规模的现实世界数据集。关于使用向量数据库和内存数据库（如Redis或Elasticsearch）以实现可扩展性的讨论，突显了模型如何适应大规模应用。
赋能现实世界应用
MLflow 的
PythonModel与sentence-transformers等高级库的结合，简化了复杂、真实世界应用的创建。封装复杂逻辑、管理依赖关系以及确保模型可移植性的能力，使得 MLflow 成为现代数据科学家工具箱中不可或缺的工具。
继续前进
总结时，请记住，旅程并未在此结束。本教程中探讨的概念和技术为在NLP领域及更广泛的领域中进一步探索和创新奠定了基础。
我们鼓励您利用这些学习成果，尝试使用您的数据集，并继续推动MLflow和先进NLP技术的可能性边界。
感谢您加入我们，一起在这段关于使用 Sentence Transformers 和 MLflow 进行语义搜索的启发性旅程中！