如何执行 “self-querying” 检索
信息
前往 集成 获取有关内置自查询支持的 vector store 的文档。
顾名思义，自查询检索器是一种能够查询自身的检索器。具体来说，给定任何自然语言查询，检索器使用构建查询的 LLM 链编写结构化查询，然后将该结构化查询应用于其底层向量存储。这样，检索器不仅可以使用用户输入查询与存储文档的内容进行语义相似性比较，还可以从用户对存储文档元数据的查询中提取过滤器并执行这些过滤器。
开始使用
出于演示目的，我们将使用Chroma向量存储。我们创建了一组小型演示文档，其中包含电影摘要。
注意：自查询检索器要求您具有lark包已安装。
%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 retriever。为此，我们需要预先提供有关文档支持的元数据字段的一些信息，以及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 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
现在我们实际上可以尝试使用我们的检索器！
# 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 self query retriever 来指定k：要获取的文档数。
我们可以通过将enable_limit=True添加到构造函数中。
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
要查看后台发生了什么，并有更多的自定义控制，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造链。此链将接受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。我们提供了一些帮助程序函数来创建 prompt 和 output 解析器。这些有许多可调参数，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
让我们看看我们的提示符：
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:
我们的全链条生产什么：
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)
查询构造函数是 self-query retriever 的关键元素。要制作一个出色的检索系统，您需要确保您的查询构造函数运行良好。这通常需要调整提示、提示中的示例、属性描述等。有关演练如何根据某些酒店库存数据优化查询构造函数的示例，请查看此说明书。
下一个关键元素是结构化查询转换器。这是负责翻译泛型StructuredQueryobject 添加到元数据过滤器中。LangChain 带有许多内置转换器。要查看它们，请前往 集成 部分。
from langchain_community.query_constructors.chroma import ChromaTranslator
retriever = SelfQueryRetriever(
query_constructor=query_constructor,
vectorstore=vectorstore,
structured_query_translator=ChromaTranslator(),
)
API 参考：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})]