Elasticsearch

Elasticsearch 是一個分散式 RESTful 搜尋和分析引擎。它提供了一個分散式、支援多租戶的全文字搜尋引擎，具有 HTTP 網頁介面和無結構描述的 JSON 文件。

在本筆記本中，我們將示範搭配 Elasticsearch 向量儲存庫使用的 SelfQueryRetriever。

建立 Elasticsearch 向量儲存庫

首先，我們會想要建立一個 Elasticsearch 向量儲存庫，並使用一些資料來初始化它。我們建立了一個包含電影摘要的小型示範文件集。

注意： 自我查詢檢索器需要您安裝 lark (pip install lark)。我們也需要 elasticsearch 套件。

%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()

API 參考：Document | ElasticsearchStore | 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="https://127.0.0.1:9200",
)

建立我們的自我查詢檢索器

現在我們可以實例化我們的檢索器。為此，我們需要預先提供一些關於文件支援的中繼資料欄位以及文件內容的簡短描述資訊。

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
)

API 參考：AttributeInfo | SelfQueryRetriever | OpenAI

測試一下

現在我們可以嘗試實際使用我們的檢索器！

# 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})]

篩選 k

我們也可以使用自我查詢檢索器來指定 k：要提取的文件數量。

我們可以透過將 enable_limit=True 傳遞給建構函式來做到這一點。

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'})]

實際運作中的複雜查詢！

我們已經嘗試了一些簡單的查詢，但是更複雜的查詢呢？讓我們嘗試一些更複雜的查詢，以充分利用 Elasticsearch 的強大功能。

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")