Chroma

Chroma 是一个用于构建具有嵌入的 AI 应用程序的向量数据库。

在笔记本中，我们将演示围绕 Chroma 向量存储的 SelfQueryRetriever。

创建 Chroma 向量存储

首先，我们需要创建一个 Chroma 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。

注意： 自查询检索器需要您安装 lark（pip install lark）。我们还需要 langchain-chroma 包。

%pip install --upgrade --quiet  lark

%pip install --upgrade --quiet  langchain-chroma

我们想使用 OpenAIEmbeddings，因此必须获取 OpenAI API 密钥。

import getpass
import os

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

OpenAI API Key: ········

from langchain_chroma import Chroma
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

docs = [
    Document(
        page_content="一群科学家复活了恐龙，随之而来的是混乱",
        metadata={"year": 1993, "rating": 7.7, "genre": "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="一位心理学家/侦探在一系列梦中迷失，而《盗梦空间》重用了这个概念",
        metadata={"year": 2006, "director": "今敏", "rating": 8.6},
    ),
    Document(
        page_content="一群正常身材的女性极其美好，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="三个男人走进禁区，三个男人走出禁区",
        metadata={
            "year": 1979,
            "director": "安德烈·塔尔科夫斯基",
            "genre": "科幻",
            "rating": 9.9,
        },
    ),
]
vectorstore = Chroma.from_documents(docs, embeddings)

使用嵌入的 DuckDB，无需持久性：数据将是暂时的

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.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！

# 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={'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='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='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'year': 2010, 'director': 'Christopher Nolan', 'rating': 8.2})]

# 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={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}),
 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'})]

# 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={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3})]

# 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={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'})]

# 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), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')])

[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]

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")

query='dinosaur' filter=None

[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='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='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'year': 2010, 'director': 'Christopher Nolan', 'rating': 8.2})]