如何进行“自查询”检索
请访问集成文档,了解内置支持自查询的向量存储。
顾名思义,自查询检索器是一种能够自我查询的检索器。具体来说,给定任何自然语言查询,检索器使用查询构建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向量存储。这使得检索器不仅可以将用户输入的查询与存储文档的内容进行语义相似性比较,还可以从用户查询中提取存储文档元数据的过滤器并执行这些过滤器。
开始使用
为了演示,我们将使用 Chroma
向量存储。我们创建了一个包含电影摘要的小型演示文档集。
注意:自查询检索器需要您安装 lark
包。
%pip install --upgrade --quiet lark langchain-chroma
from langchain_chroma import Chroma
from langchain_core.documents import Document
from langchain_openai import 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": "thriller",
"rating": 9.9,
},
),
]
vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())
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。为此,我们需要预先提供一些关于我们的文档支持的元数据字段以及文档内容的简短描述的信息。
from langchain.chains.query_constructor.schema import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain_openai import ChatOpenAI
metadata_field_info = [
AttributeInfo(
name="genre",
description="The genre of the movie. One of ['science fiction', 'comedy', 'drama', 'thriller', 'romance', 'action', 'animated']",
type="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 = ChatOpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(
llm,
vectorstore,
document_content_description,
metadata_field_info,
)
测试一下
现在我们实际上可以尝试使用我们的检索器了!
# This example only specifies a filter
retriever.invoke("I want to watch a movie rated higher than 8.5")
[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'director': 'Andrei Tarkovsky', 'genre': 'thriller', 'rating': 9.9, 'year': 1979}),
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})]
# This example specifies a query and a filter
retriever.invoke("Has Greta Gerwig directed any movies about women")
[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})]
# This example specifies a composite filter
retriever.invoke("What's a highly rated (above 8.5) science fiction film?")
[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}),
Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'director': 'Andrei Tarkovsky', 'genre': 'thriller', 'rating': 9.9, 'year': 1979})]
# 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"
)
[Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995})]
过滤器 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,
)
# This example only specifies a relevant query
retriever.invoke("What are two movies about dinosaurs")
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': 'science fiction', 'rating': 7.7, 'year': 1993}),
Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995})]
使用 LCEL 从头开始构建
为了了解底层发生了什么,并获得更多自定义控制,我们可以从头开始重建我们的检索器。
首先,我们需要创建一个查询构建链。此链将接收用户查询并生成一个 StructuredQuery
对象,该对象捕获用户指定的过滤器。我们提供了一些辅助函数来创建提示和输出解析器。这些函数具有许多可调整的参数,为了简单起见,我们在这里忽略这些参数。
from langchain.chains.query_constructor.base import (
StructuredQueryOutputParser,
get_query_constructor_prompt,
)
prompt = get_query_constructor_prompt(
document_content_description,
metadata_field_info,
)
output_parser = StructuredQueryOutputParser.from_components()
query_constructor = prompt | llm | output_parser
让我们看一下我们的提示
print(prompt.format(query="dummy question"))
Your goal is to structure the user's query to match the request schema provided below.
<< Structured Request Schema >>
When responding use a markdown code snippet with a JSON object formatted in the following schema:
\`\`\`json
{
"query": string \ text string to compare to document contents
"filter": string \ logical condition statement for filtering documents
}
\`\`\`
The query string should contain only text that is expected to match the contents of documents. Any conditions in the filter should not be mentioned in the query as well.
A logical condition statement is composed of one or more comparison and logical operation statements.
A comparison statement takes the form: `comp(attr, val)`:
- `comp` (eq | ne | gt | gte | lt | lte | contain | like | in | nin): comparator
- `attr` (string): name of attribute to apply the comparison to
- `val` (string): is the comparison value
A logical operation statement takes the form `op(statement1, statement2, ...)`:
- `op` (and | or | not): logical operator
- `statement1`, `statement2`, ... (comparison statements or logical operation statements): one or more statements to apply the operation to
Make sure that you only use the comparators and logical operators listed above and no others.
Make sure that filters only refer to attributes that exist in the data source.
Make sure that filters only use the attributed names with its function names if there are functions applied on them.
Make sure that filters only use format `YYYY-MM-DD` when handling date data typed values.
Make sure that filters take into account the descriptions of attributes and only make comparisons that are feasible given the type of data being stored.
Make sure that filters are only used as needed. If there are no filters that should be applied return "NO_FILTER" for the filter value.
<< Example 1. >>
Data Source:
\`\`\`json
{
"content": "Lyrics of a song",
"attributes": {
"artist": {
"type": "string",
"description": "Name of the song artist"
},
"length": {
"type": "integer",
"description": "Length of the song in seconds"
},
"genre": {
"type": "string",
"description": "The song genre, one of "pop", "rock" or "rap""
}
}
}
\`\`\`
User Query:
What are songs by Taylor Swift or Katy Perry about teenage romance under 3 minutes long in the dance pop genre
Structured Request:
\`\`\`json
{
"query": "teenager love",
"filter": "and(or(eq(\"artist\", \"Taylor Swift\"), eq(\"artist\", \"Katy Perry\")), lt(\"length\", 180), eq(\"genre\", \"pop\"))"
}
\`\`\`
<< Example 2. >>
Data Source:
\`\`\`json
{
"content": "Lyrics of a song",
"attributes": {
"artist": {
"type": "string",
"description": "Name of the song artist"
},
"length": {
"type": "integer",
"description": "Length of the song in seconds"
},
"genre": {
"type": "string",
"description": "The song genre, one of "pop", "rock" or "rap""
}
}
}
\`\`\`
User Query:
What are songs that were not published on Spotify
Structured Request:
\`\`\`json
{
"query": "",
"filter": "NO_FILTER"
}
\`\`\`
<< Example 3. >>
Data Source:
\`\`\`json
{
"content": "Brief summary of a movie",
"attributes": {
"genre": {
"description": "The genre of the movie. One of ['science fiction', 'comedy', 'drama', 'thriller', 'romance', 'action', 'animated']",
"type": "string"
},
"year": {
"description": "The year the movie was released",
"type": "integer"
},
"director": {
"description": "The name of the movie director",
"type": "string"
},
"rating": {
"description": "A 1-10 rating for the movie",
"type": "float"
}
}
}
\`\`\`
User Query:
dummy question
Structured Request:
以及我们完整的链产生的结果
query_constructor.invoke(
{
"query": "What are some sci-fi movies from the 90's directed by Luc Besson about taxi drivers"
}
)
StructuredQuery(query='taxi driver', filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction'), Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2000)]), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Luc Besson')]), limit=None)
查询构造器是自查询检索器的关键要素。为了创建一个好的检索系统,您需要确保查询构造器工作良好。通常,这需要调整提示、提示中的示例、属性描述等。有关如何改进某些酒店库存数据上的查询构造器的示例,请查看此 Cookbook。
下一个关键要素是结构化查询转换器。该对象负责将通用的 StructuredQuery
对象转换为您正在使用的向量存储语法中的元数据过滤器。LangChain 附带了许多内置的转换器。要查看所有转换器,请访问集成部分。
from langchain_community.query_constructors.chroma import ChromaTranslator
retriever = SelfQueryRetriever(
query_constructor=query_constructor,
vectorstore=vectorstore,
structured_query_translator=ChromaTranslator(),
)
retriever.invoke(
"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated"
)
[Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995})]