Elasticsearch：语义搜索快速入门

这个交互式 jupyter notebook 将使用官方 Elasticsearch Python 客户端向你介绍 Elasticsearch 的一些基本操作。你将使用 Sentence Transformers 进行文本嵌入的语义搜索。了解如何将传统的基于文本的搜索与语义搜索集成，形成混合搜索系统。在本示例中，我们将使用模型自带的功能对文本进行向量化，而不借助 Elasticsearch 所提供的 ingest pipeline 来进行矢量化。这样的好处是完全免费，不需要购买版权。如果你想使用 ingest pipeline 来进行向量化，请参阅文章 ”Elasticsearch：使用 huggingface 模型的 NLP 文本搜索“。

安装

Elasticsearch 及 Kibana

如果你还没有安装好自己的 Elasticsearch 及 Kibana，请参考如下的链接来进行安装：

如何在 Linux，MacOS 及 Windows 上进行安装 Elasticsearch
Kibana：如何在 Linux，MacOS 及 Windows 上安装 Elastic 栈中的 Kibana

在安装的时候，我们可以选择 Elastic Stack 8.x 的安装指南来进行安装。在本博文中，我将使用最新的 Elastic Stack 8.10 来进行展示。

在安装 Elasticsearch 的过程中，我们需要记下如下的信息：

Python 安装包

在本演示中，我们将使用 Python 来进行展示。我们需要安装访问 Elasticsearch 相应的安装包 elasticsearch：

python3 -m pip install -qU sentence-transformers elasticsearch transformers

我们将使用 Jupyter Notebook 来进行展示。

$ pwd
/Users/liuxg/python/elser
$ jupyter notebook

创建应用并展示

设置嵌入模型

在此示例中，我们使用 all-MiniLM-L6-v2，它是 sentence_transformers 库的一部分。你可以在 Huggingface 上阅读有关此模型的更多信息。

from sentence_transformers import SentenceTransformer
import torch

device = 'cuda' if torch.cuda.is_available() else 'cpu'

model = SentenceTransformer('all-MiniLM-L6-v2', device=device)
model

初始化 Elasticsearch 客户端

现在我们可以实例化 Elasticsearch python 客户端。我们必须提高响应的账号及证书信息：

from elasticsearch import Elasticsearch

ELASTCSEARCH_CERT_PATH = "/Users/liuxg/elastic/elasticsearch-8.10.0/config/certs/http_ca.crt"
 
client = Elasticsearch(  ['https://localhost:9200'],
    basic_auth = ('elastic', 'vXDWYtL*my3vnKY9zCfL'),
    ca_certs = ELASTCSEARCH_CERT_PATH,
    verify_certs = True)
print(client.info())

摄入一些数据

我们的客户端已设置并连接到我们的 Elasticsearch 部署。现在我们需要一些数据来测试 Elasticsearch 查询的基础知识。我们将使用包含以下字段的小型书籍索引：

title
authors
publish_date
num_reviews
publisher

我们在当前项目的根目录下创建 books.json 文件：

books.json

[
  {
    "title": "The Pragmatic Programmer: Your Journey to Mastery",
    "authors": ["andrew hunt", "david thomas"],
    "summary": "A guide to pragmatic programming for software engineers and developers",
    "publish_date": "2019-10-29",
    "num_reviews": 30,
    "publisher": "addison-wesley"
  },
  {
    "title": "Python Crash Course",
    "authors": ["eric matthes"],
    "summary": "A fast-paced, no-nonsense guide to programming in Python",
    "publish_date": "2019-05-03",
    "num_reviews": 42,
    "publisher": "no starch press"
  },
  {
    "title": "Artificial Intelligence: A Modern Approach",
    "authors": ["stuart russell", "peter norvig"],
    "summary": "Comprehensive introduction to the theory and practice of artificial intelligence",
    "publish_date": "2020-04-06",
    "num_reviews": 39,
    "publisher": "pearson"
  },
  {
    "title": "Clean Code: A Handbook of Agile Software Craftsmanship",
    "authors": ["robert c. martin"],
    "summary": "A guide to writing code that is easy to read, understand and maintain",
    "publish_date": "2008-08-11",
    "num_reviews": 55,
    "publisher": "prentice hall"
  },
  {
    "title": "You Don't Know JS: Up & Going",
    "authors": ["kyle simpson"],
    "summary": "Introduction to JavaScript and programming as a whole",
    "publish_date": "2015-03-27",
    "num_reviews": 36,
    "publisher": "oreilly"
  },
  {
    "title": "Eloquent JavaScript",
    "authors": ["marijn haverbeke"],
    "summary": "A modern introduction to programming",
    "publish_date": "2018-12-04",
    "num_reviews": 38,
    "publisher": "no starch press"
  },
  {
    "title": "Design Patterns: Elements of Reusable Object-Oriented Software",
    "authors": [
      "erich gamma",
      "richard helm",
      "ralph johnson",
      "john vlissides"
    ],
    "summary": "Guide to design patterns that can be used in any object-oriented language",
    "publish_date": "1994-10-31",
    "num_reviews": 45,
    "publisher": "addison-wesley"
  },
  {
    "title": "The Clean Coder: A Code of Conduct for Professional Programmers",
    "authors": ["robert c. martin"],
    "summary": "A guide to professional conduct in the field of software engineering",
    "publish_date": "2011-05-13",
    "num_reviews": 20,
    "publisher": "prentice hall"
  },
  {
    "title": "JavaScript: The Good Parts",
    "authors": ["douglas crockford"],
    "summary": "A deep dive into the parts of JavaScript that are essential to writing maintainable code",
    "publish_date": "2008-05-15",
    "num_reviews": 51,
    "publisher": "oreilly"
  },
  {
    "title": "Introduction to the Theory of Computation",
    "authors": ["michael sipser"],
    "summary": "Introduction to the theory of computation and complexity theory",
    "publish_date": "2012-06-27",
    "num_reviews": 33,
    "publisher": "cengage learning"
  }
]

$ pwd
/Users/liuxg/python/elser
$ ls
Multilingual semantic search.ipynb
NLP text search using hugging face transformer model.ipynb
Semantic search - ELSER.ipynb
Semantic search quick start.ipynb
books.json
data.json

创建索引

让我们使用测试数据的正确映射创建一个 Elasticsearch 索引。

INDEX_NAME = "book_index"

if es.indices.exists(index=INDEX_NAME):
    print("Deleting existing %s" % INDEX_NAME)
    client.options(ignore_status=[400, 404]).indices.delete(index=INDEX_NAME)

# Define the mapping
mappings = {
    "properties": {
        "title_vector": {
            "type": "dense_vector",
            "dims": 384,
            "index": "true",
            "similarity": "cosine"
        }
    }
}


# Create the index
client.indices.create(index = INDEX_NAME, mappings = mappings)

索引测试数据

运行以下命令上传一些测试数据，其中包含该数据集中的 10 本流行编程书籍的信息。 model.encode 将使用我们之前初始化的模型将文本动态编码为向量。

import json

# Load data into a JSON object
with open('books.json') as f:
   data_json = json.load(f)
 
print(data_json)

actions = []
for book in data_json:
    actions.append({"index": {"_index": INDEX_NAME}})
    # Transforming the title into an embedding using the model
    book["title_vector"] = model.encode(book["title"]).tolist()
    actions.append(book)
client.bulk(index=INDEX_NAME, operations=actions)

我们可以在 Kibana 中进行查看：

GET book_index/_search

漂亮地打印 Elasticsearch 响应

你的 API 调用将返回难以阅读的嵌套 JSON。我们将创建一个名为 Pretty_response 的小函数，以从示例中返回漂亮的、人类可读的输出。

def pretty_response(response):
    for hit in response['hits']['hits']:
        id = hit['_id']
        publication_date = hit['_source']['publish_date']
        score = hit['_score']
        title = hit['_source']['title']
        summary = hit['_source']['summary']
        publisher = hit["_source"]["publisher"]
        num_reviews = hit["_source"]["num_reviews"]
        authors = hit["_source"]["authors"]
        pretty_output = (f"\nID: {id}\nPublication date: {publication_date}\nTitle: {title}\nSummary: {summary}\nPublisher: {publisher}\nReviews: {num_reviews}\nAuthors: {authors}\nScore: {score}")
        print(pretty_output)

查询

现在我们已经对书籍进行了索引，我们想要对与给定查询相似的书籍执行语义搜索。我们嵌入查询并执行搜索。

response = client.search(index="book_index", body={
    "knn": {
      "field": "title_vector",
      "query_vector": model.encode("Best javascript books?"),
      "k": 10,
      "num_candidates": 100
    }
})

pretty_response(response)

过滤

过滤器上下文主要用于过滤结构化数据。例如，使用过滤器上下文来回答以下问题：

该时间戳是否在 2015 年至 2016 年范围内？
状态字段是否设置为 “published”？

每当查询子句传递给过滤器参数（例如布尔查询中的 filter 或 must_not 参数）时，过滤器上下文就会生效。

在 Elasticsearch 文档中了解有关过滤器上下文的更多信息。

示例：关键字过滤

这是向查询添加关键字过滤器的示例。

它通过仅包含 “publisher” 字段等于 “addison-wesley” 的文档来缩小结果范围。

该代码检索类似于 “Best javascript books?” 的热门书籍。基于他们的 title 向量，并以 “addison-wesley” 作为出版商。

response = client.search(index=INDEX_NAME, body={
    "knn": {
      "field": "title_vector",
      "query_vector": model.encode("Best javascript books?"),
      "k": 10,
      "num_candidates": 100,
      "filter": {
          "term": {
              "publisher.keyword": "addison-wesley"
          }
      }
    }
})

pretty_response(response)

示例：高级过滤

Elasticsearch 中的高级过滤允许通过应用条件来精确细化搜索结果。它支持多种运算符，可用于根据特定字段、范围或条件过滤结果，从而提高搜索结果的精度和相关性。在此查询和过滤上下文示例中了解更多信息。

response = client.search(index="book_index", body={
    "knn": {
      "field": "title_vector",
      "query_vector": model.encode("Best javascript books?"),
      "k": 10,
      "num_candidates": 100,
      "filter": {
          "bool": {
              "should": [
                  {
                    "term": {
                        "publisher.keyword": "addison-wesley"
                    }
                  },
                  {
                    "term": {
                        "authors.keyword": "robert c. martin"
                    }
                  }
              ],

          }
      }
    }
})

pretty_response(response)

Hybrid search

在此示例中，我们正在研究两种搜索算法的组合：用于文本搜索的 BM25 和用于最近邻搜索的 HNSW。通过结合多种排名方法，例如 BM25 和生成密集向量嵌入的 ML 模型，我们可以获得最佳排名结果。这种方法使我们能够利用每种算法的优势并提高整体搜索性能。

倒数排名融合 (RRF) 是一种最先进的排名算法，用于组合来自不同信息检索策略的结果。 RRF 在未经校准的情况下优于所有其他排名算法。简而言之，它支持开箱即用的一流混合搜索。

response = client.search(index="book_index", body={
    "query": {
        "match": {
            "summary": "python"
        }
    },
    "knn": {
        "field": "title_vector",
        # generate embedding for query so it can be compared to `title_vector`
        "query_vector" : model.encode("python programming").tolist(),
        "k": 5,
        "num_candidates": 10
    },
    "rank": {
        "rrf": {
            "window_size": 100,
            "rank_constant": 20
        }
    }
})

pretty_response(response)