Elasticsearch

Elasticsearch 是一个分布式的、RESTful 的搜索和分析引擎。 它提供了一个分布式的、多租户的全文本搜索引擎，具有 HTTP Web 接口和无模式的 JSON 文档。

在本笔记本中，我们将演示使用 Elasticsearch 向量存储的 SelfQueryRetriever。

创建 Elasticsearch 向量存储

首先，我们需要创建一个 Elasticsearch 向量存储，并用一些数据进行初始化。我们创建了一小组包含电影摘要的演示文档。

注意： 自查询检索器需要安装 lark（pip install lark）。我们还需要 elasticsearch 包。

%pip install --upgrade --quiet  U lark langchain langchain-elasticsearch

WARNING: You are using pip version 22.0.4; however, version 23.3 is available.
You should consider upgrading via the '/Users/joe/projects/elastic/langchain/libs/langchain/.venv/bin/python3 -m pip install --upgrade pip' command.


import getpass
import os

from langchain_core.documents import Document
from langchain_elasticsearch import ElasticsearchStore
from langchain_openai import OpenAIEmbeddings

os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

embeddings = OpenAIEmbeddings()

docs = [
    Document(
        page_content="一群科学家带回恐龙，混乱随之而来",
        metadata={"year": 1993, "rating": 7.7, "genre": "科幻"},
    ),
    Document(
        page_content="莱昂纳多·迪卡普里奥在梦中迷失，梦中又有梦...",
        metadata={"year": 2010, "director": "克里斯托弗·诺兰", "rating": 8.2},
    ),
    Document(
        page_content="一名心理学家/侦探在一系列梦中迷失，《盗梦空间》重用了这个概念",
        metadata={"year": 2006, "director": "今敏", "rating": 8.6},
    ),
    Document(
        page_content="一群普通身材的女性非常健康，一些男性对她们心存向往",
        metadata={"year": 2019, "director": "格蕾塔·葛韦格", "rating": 8.3},
    ),
    Document(
        page_content="玩具复活并乐在其中",
        metadata={"year": 1995, "genre": "动画"},
    ),
    Document(
        page_content="三名男子走进区域，三名男子走出区域",
        metadata={
            "year": 1979,
            "director": "安德烈·塔尔科夫斯基",
            "genre": "科幻",
            "rating": 9.9,
        },
    ),
]
vectorstore = ElasticsearchStore.from_documents(
    docs,
    embeddings,
    index_name="elasticsearch-self-query-demo",
    es_url="http://localhost:9200",
)

创建自查询检索器

现在我们可以实例化我们的检索器。为此，我们需要提前提供一些关于我们的文档支持的元数据字段的信息，以及文档内容的简短描述。

from langchain.chains.query_constructor.base import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain_openai import OpenAI

metadata_field_info = [
    AttributeInfo(
        name="genre",
        description="The genre of the movie",
        type="string or list[string]",
    ),
    AttributeInfo(
        name="year",
        description="The year the movie was released",
        type="integer",
    ),
    AttributeInfo(
        name="director",
        description="The name of the movie director",
        type="string",
    ),
    AttributeInfo(
        name="rating", description="A 1-10 rating for the movie", type="float"
    ),
]
document_content_description = "Brief summary of a movie"
llm = OpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(
    llm, vectorstore, document_content_description, metadata_field_info, verbose=True
)

测试一下

现在我们可以尝试实际使用我们的检索器了！

# This example only specifies a relevant query
retriever.invoke("What are some movies about dinosaurs")

[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),
 Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),
 Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'}),
 Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6})]

# This example specifies a query and a filter
retriever.invoke("Has Greta Gerwig directed any movies about women")

[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3})]

过滤 k

我们还可以使用自查询检索器来指定 k：要获取的文档数量。

我们可以通过将 enable_limit=True 传递给构造函数来实现这一点。

retriever = SelfQueryRetriever.from_llm(
    llm,
    vectorstore,
    document_content_description,
    metadata_field_info,
    enable_limit=True,
    verbose=True,
)

# 这个示例仅指定了一个相关查询
retriever.invoke("what are two movies about dinosaurs")

[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),
 Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]

复杂查询的实际应用！

我们已经尝试了一些简单的查询，但更复杂的查询呢？让我们尝试一些更复杂的查询，充分利用Elasticsearch的强大功能。

retriever.invoke(
    "what animated or comedy movies have been released in the last 30 years about animated toys?"
)

[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]

vectorstore.client.indices.delete(index="elasticsearch-self-query-demo")