使用 Weaviate 进行自查询

创建 Weaviate 向量存储

首先，我们需要创建一个 Weaviate 向量存储，并使用一些数据进行填充。我们已经创建了一个包含电影摘要的小型演示数据集。

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

#!pip install lark weaviate-client

from langchain.schema import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Weaviate
import os

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, "rating": 9.9, "director": "Andrei Tarkovsky", "genre": "science fiction", "rating": 9.9})
]
vectorstore = Weaviate.from_documents(
    docs, embeddings, weaviate_url="http://127.0.0.1:8080"
)

创建自查询检索器

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

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

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！

# 这个示例只指定了一个相关的查询
retriever.get_relevant_documents("有关恐龙的一些电影")

query='dinosaur' filter=None limit=None

[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': 'science fiction', 'rating': 7.7, 'year': 1993}),
 Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'rating': None, 'year': 1995}),
 Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'genre': 'science fiction', 'rating': 9.9, 'year': 1979}),
 Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'genre': None, 'rating': 8.6, 'year': 2006})]

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

query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None

[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'genre': None, 'rating': 8.3, 'year': 2019})]

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
)

# This example only specifies a relevant query
retriever.get_relevant_documents("what are two movies about dinosaurs")

query='dinosaur' filter=None limit=2

[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': 'science fiction', 'rating': 7.7, 'year': 1993}),
 Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'rating': None, 'year': 1995})]