自查询 self_query

LangChain

自查询检索器是指具有自我查询能力的检索器。具体而言，给定任何自然语言查询，检索器使用查询构造的 LLM 链来编写结构化查询，然后将该结构化查询应用于其底层的 VectorStore。这使得检索器不仅可以将用户输入的查询用于与存储文档内容的语义相似性比较，还可以从用户查询中提取存储文档的元数据过滤器并执行这些过滤器。

入门

在这个示例中，我们将使用 Pinecone 向量存储。

首先，我们需要创建一个 Pinecone VectorStore，并使用一些数据进行初始化。我们创建了一个包含电影摘要的小型演示文档集。

要使用 Pinecone，您需要安装 pinecone 包，并拥有 API 密钥和环境。请参阅安装说明。

注意：自查询检索器要求您安装了 lark 包。

! pip install lark pinecone-client

import os

import pinecone

pinecone.init(api_key=os.environ["PINECONE_API_KEY"], environment=os.environ["PINECONE_ENV"])

from langchain.schema import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Pinecone

embeddings = OpenAIEmbeddings()
# create new index
pinecone.create_index("langchain-self-retriever-demo", dimension=1536)

docs = [
    Document(page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose", metadata={"year": 1993, "rating": 7.7, "genre": ["action", "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", "thriller"], "rating": 9.9})
]
vectorstore = Pinecone.from_documents(
    docs, embeddings, index_name="langchain-self-retriever-demo"
)

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，我们需要提前提供有关文档支持的元数据字段和文档内容的简短描述。

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

现在，我们可以尝试使用我们的检索器！

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

    query='dinosaur' filter=None


    [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': ['action', 'science fiction'], 'rating': 7.7, 'year': 1993.0}),
     Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995.0}),
     Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2006.0}),
     Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'director': 'Christopher Nolan', 'rating': 8.2, 'year': 2010.0})]

This example only specifies a filter
retriever.get_relevant_documents("I want to watch a movie rated higher than 8.5")

    query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)


    [Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2006.0}),
     Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'director': 'Andrei Tarkovsky', 'genre': ['science fiction', 'thriller'], 'rating': 9.9, 'year': 1979.0})]

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')

    [Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'director': 'Greta Gerwig', 'rating': 8.3, 'year': 2019.0})]

This example specifies a composite filter
retriever.get_relevant_documents("What's a highly rated (above 8.5) science fiction film?")

    query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction'), Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)])

    [Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'director': 'Andrei Tarkovsky', 'genre': ['science fiction', 'thriller'], 'rating': 9.9, 'year': 1979.0})]

This example specifies a query and composite filter
retriever.get_relevant_documents("What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated")

    query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990.0), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005.0), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')])

    [Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995.0})]

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")

自查询 self_query

入门​

创建自查询检索器​

测试​

过滤 k​

入门

创建自查询检索器

测试

过滤 k