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使用 MyScale 进行自查询

MyScale 是一个集成的向量数据库。您可以通过 SQL 访问您的数据库，并且还可以通过 LangChain 来访问。MyScale 可以利用各种数据类型和过滤器函数。无论是扩展数据还是将系统扩展到更广泛的应用程序，都可以提升您的 LLM 应用程序的性能。

在笔记本中，我们将演示 SelfQueryRetriever 在包装了一个 MyScale 向量存储库的 LangChain 上的使用，其中包含了我们为 LangChain 提供的一些额外功能。简而言之，这可以总结为以下四点：

  1. 添加了 contain 比较器，以匹配列表中的任何一个元素
  2. 添加了 timestamp 数据类型，用于日期时间匹配（ISO 格式或 YYYY-MM-DD）
  3. 添加了 like 比较器，用于字符串模式搜索
  4. 添加了任意函数的能力

创建 MyScale 向量存储库​

MyScale 已经在 LangChain 中进行了集成一段时间。因此，您可以按照此笔记本创建自己的向量存储库供自查询检索器使用。

注意：所有自查询检索器都需要您已经安装了 lark（pip install lark）。我们使用 lark 进行语法定义。在继续下一步之前，我们还想提醒您，与 MyScale 后端进行交互还需要安装 clickhouse-connect。

! pip install lark clickhouse-connect

In this tutorial we follow other example's setting and use OpenAIEmbeddings. Remember to get a OpenAI API Key for valid accesss to LLMs.

import os
import getpass

os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
os.environ['MYSCALE_HOST'] = getpass.getpass('MyScale URL:')
os.environ['MYSCALE_PORT'] = getpass.getpass('MyScale Port:')
os.environ['MYSCALE_USERNAME'] = getpass.getpass('MyScale Username:')
os.environ['MYSCALE_PASSWORD'] = getpass.getpass('MyScale Password:')
from langchain.schema import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import MyScale

embeddings = OpenAIEmbeddings()

Create some sample data​

As you can see, the data we created has some difference to other self-query retrievers. We replaced keyword year to date which gives you a finer control on timestamps. We also altered the type of keyword gerne to list of strings, where LLM can use a new contain comparator to construct filters. We also provides comparator like and arbitrary function support to filters, which will be introduced in next few cells.

Now let's look at the data first.

docs = [
    Document(page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose", metadata={"date": "1993-07-02", "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={"date": "2010-12-30", "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={"date": "2006-04-23", "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={"date": "2019-08-22", "director": "Greta Gerwig", "rating": 8.3}),
    Document(page_content="Toys come alive and have a blast doing so", metadata={"date": "1995-02-11", "genre": ["animated"]}),
    Document(page_content="Three men walk into the Zone, three men walk out of the Zone", metadata={"date": "1979-09-10", "rating": 9.9, "director": "Andrei Tarkovsky", "genre": ["science fiction", "adventure"], "rating": 9.9})
]
vectorstore = MyScale.from_documents(
    docs, 
    embeddings, 
)

Creating our self-querying retriever​

Just like other retrievers... Simple and nice.

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 genres of the movie", 
        type="list[string]", 
    ),
    # If you want to include length of a list, just define it as a new column
    # This will teach the LLM to use it as a column when constructing filter.
    AttributeInfo(
        name="length(genre)",
        description="The lenth of genres of the movie", 
        type="integer", 
    ),
    # Now you can define a column as timestamp. By simply set the type to timestamp.
    AttributeInfo(
        name="date",
        description="The date the movie was released", 
        type="timestamp", 
    ),
    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)

Testing it out with self-query retriever's existing functionalities​

And now we can try actually using our retriever!

# This example only specifies a relevant query
retriever.get_relevant_documents("What are some movies about dinosaurs")
# This example only specifies a filter
retriever.get_relevant_documents("I want to watch a movie rated higher than 8.5")
# This example specifies a query and a filter
retriever.get_relevant_documents("Has Greta Gerwig directed any movies about women")
# This example specifies a composite filter
retriever.get_relevant_documents("What's a highly rated (above 8.5) science fiction film?")
# 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")

Wait a second... What else?

Self-query retriever with MyScale can do more! Let's find out.

# You can use length(genres) to do anything you want
retriever.get_relevant_documents("What's a movie that have more than 1 genres?")
# Fine-grained datetime? You got it already.
retriever.get_relevant_documents("What's a movie that release after feb 1995?")
# Don't know what your exact filter should be? Use string pattern match!
retriever.get_relevant_documents("What's a movie whose name is like Andrei?")
# Contain works for lists: so you can match a list with contain comparator!
retriever.get_relevant_documents("What's a movie who has genres science fiction and adventure?")

Filter k​

We can also use the self query retriever to specify k: the number of documents to fetch.

We can do this by passing enable_limit=True to the constructor.

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")