Skip to main content
Open In ColabOpen on GitHub

Elasticsearch

Elasticsearch 是一个分布式的、RESTful 风格的搜索和分析引擎。 它提供了一个分布式的、支持多租户的全文搜索引擎，具有 HTTP 网络接口和无模式的 JSON 文档。

在这个笔记本中，我们将演示如何使用SelfQueryRetriever与Elasticsearch向量存储。

创建 Elasticsearch 向量存储​

首先，我们需要创建一个Elasticsearch向量存储，并用一些数据填充它。我们已经创建了一个包含电影摘要的小型演示文档集。

注意： 自查询检索器需要你安装 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

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

embeddings = OpenAIEmbeddings()
docs = [
    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="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",
        metadata={"year": 2010, "director": "Christopher Nolan", "rating": 8.2},
    ),
    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},
    ),
    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},
    ),
    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,
            "director": "Andrei Tarkovsky",
            "genre": "science fiction",
            "rating": 9.9,
        },
    ),
]
vectorstore = ElasticsearchStore.from_documents(
    docs,
    embeddings,
    index_name="elasticsearch-self-query-demo",
    es_url="http://localhost:9200",
)

创建我们的自查询检索器​

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.schema 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})]

筛选 k​

我们也可以使用自我查询检索器来指定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,
)
# This example only specifies a relevant query
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'})]

复杂查询实战！​

我们已经尝试了一些简单的查询，但是更复杂的查询呢？让我们尝试一些更复杂的查询，这些查询利用了Elasticsearch的全部功能。

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")

这个页面有帮助吗？