Deep Lake
Deep Lake 是一个用于构建 AI 应用程序的多模态数据库 Deep Lake 是一个面向 AI 的数据库。 存储 Vectors、Images、Texts、Videos 等。可与 LLMs/LangChain 配合使用。存储、查询、管理版本 和可视化任何 AI 数据。实时流式传输数据到 PyTorch/TensorFlow。
在本 Notebook 中，我们将演示围绕 Deep Lake 向量存储包装的 SelfQueryRetriever。
创建 Deep Lake 向量存储
首先，我们将创建一个 Deep Lake 向量存储并为其填充数据。我们创建了一组包含电影摘要的小型演示文档。
注意： 自查询检索器要求你安装 lark（pip install lark）。我们还需要 deeplake 包。
%pip install --upgrade --quiet lark
# in case if some queries fail consider installing libdeeplake manually
%pip install --upgrade --quiet libdeeplake
我们想使用 OpenAIEmbeddings，所以我们需要获取 OpenAI API 密钥。
import getpass
import os
if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
if "ACTIVELOOP_TOKEN" not in os.environ:
os.environ["ACTIVELOOP_TOKEN"] = getpass.getpass("Activeloop token:")
from langchain_community.vectorstores import DeepLake
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
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,
"director": "Andrei Tarkovsky",
"genre": "science fiction",
"rating": 9.9,
},
),
]
username_or_org = "<USERNAME_OR_ORG>"
vectorstore = DeepLake.from_documents(
docs,
embeddings,
dataset_path=f"hub://{username_or_org}/self_queery",
overwrite=True,
)
Your Deep Lake dataset has been successfully created!
``````output
/
``````output
Dataset(path='hub://adilkhan/self_queery', tensors=['embedding', 'id', 'metadata', 'text'])
tensor htype shape dtype compression
------- ------- ------- ------- -------
embedding embedding (6, 1536) float32 None
id text (6, 1) str None
metadata json (6, 1) str None
text text (6, 1) str None
创建我们的自查询检索器
现在我们可以实例化我们的检索器了。为此，我们需要预先提供一些关于我们的文档所支持的元数据字段的信息，以及对文档内容的简短描述。
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
)
测试一下
现在我们可以真正尝试使用我们的检索器了！
# This example only specifies a relevant query
retriever.invoke("What are some movies about dinosaurs")
/home/ubuntu/langchain_activeloop/langchain/libs/langchain/langchain/chains/llm.py:279: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.
warnings.warn(
``````output
query='dinosaur' filter=None limit=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='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 only specifies a filter
retriever.invoke("I want to watch a movie rated higher than 8.5")
# in case if this example errored out, consider installing libdeeplake manually: `pip install libdeeplake`, and then restart notebook.
query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5) limit=None
[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') limit=None
[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.GTE: 'gte'>, attribute='rating', value=8.5), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction')]) limit=None
[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')]) limit=None
[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]
Filter k
我们还可以使用 self query retriever 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此目的。
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")
query='dinosaur' filter=None limit=2
[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'})]