Pinecone

Pinecone 是一个功能广泛的向量数据库。

在本演示中，我们将展示使用 Pinecone 向量存储的 SelfQueryRetriever。

创建 Pinecone 索引

首先，我们需要创建一个 Pinecone 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。

要使用 Pinecone，您必须安装 pinecone 包，并且必须拥有 API 密钥和环境。以下是安装说明。

注意： 自查询检索器需要您安装 lark 包。

%pip install --upgrade --quiet  lark

%pip install --upgrade --quiet pinecone-notebooks pinecone-client==3.2.2

# 连接到 Pinecone 并获取 API 密钥。
from pinecone_notebooks.colab import Authenticate

Authenticate()

import os

api_key = os.environ["PINECONE_API_KEY"]

/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
  from tqdm.autonotebook import tqdm

我们想要使用 OpenAIEmbeddings，所以我们必须获取 OpenAI API 密钥。

import getpass

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

from pinecone import Pinecone, ServerlessSpec

api_key = os.getenv("PINECONE_API_KEY") or "PINECONE_API_KEY"

index_name = "langchain-self-retriever-demo"

pc = Pinecone(api_key=api_key)

from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
from langchain_pinecone import PineconeVectorStore

embeddings = OpenAIEmbeddings()

# 创建新索引
if index_name not in pc.list_indexes().names():
    pc.create_index(
        name=index_name,
        dimension=1536,
        metric="cosine",
        spec=ServerlessSpec(cloud="aws", region="us-east-1"),
    )

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": ["动作", "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，一些男性对她们心生向往",
        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="三个男人走进区域，三个男人走出区域",
        metadata={
            "year": 1979,
            "director": "安德烈·塔尔科夫斯基",
            "genre": ["科幻", "惊悚"],
            "rating": 9.9,
        },
    ),
]
vectorstore = PineconeVectorStore.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，我们需要提前提供一些关于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")

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.invoke("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.invoke("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.invoke("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.invoke(
    "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,
)

# 此示例仅指定一个相关查询
retriever.invoke("What are two movies about dinosaurs")