如何進行「自我查詢」檢索
資訊
前往 整合 以取得關於具有內建自我查詢支援的向量儲存庫的文件。
自我查詢檢索器顧名思義，是一種具有查詢自身能力的檢索器。具體來說，給定任何自然語言查詢，檢索器會使用查詢建構 LLM 鏈來編寫結構化查詢，然後將該結構化查詢應用於其底層的向量儲存庫。這使檢索器不僅可以使用使用者輸入的查詢與儲存文件內容進行語義相似度比較，還可以從使用者查詢中提取有關儲存文件元數據的篩選器，並執行這些篩選器。
開始使用
為了示範目的，我們將使用 Chroma 向量儲存庫。我們建立了一個小型示範文件集，其中包含電影摘要。
注意： 自我查詢檢索器需要您安裝 lark 套件。
%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())
API 參考：Document | OpenAIEmbeddings
建立我們的自我查詢檢索器
現在我們可以實例化我們的檢索器。為此，我們需要預先提供一些關於文件支援的元數據欄位以及文件內容簡短描述的資訊。
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,
)
測試一下
現在我們可以實際嘗試使用我們的檢索器了！
# 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})]
篩選器 k
我們也可以使用自我查詢檢索器來指定 k：要提取的文件數量。
我們可以透過將 enable_limit=True 傳遞給建構子來做到這一點。
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 從頭開始建構
為了了解底層運作原理，並獲得更多自訂控制，我們可以從頭開始重建我們的檢索器。
首先，我們需要建立查詢建構鏈。此鏈將接收使用者查詢並產生 StructuredQuery 物件，該物件捕獲使用者指定的篩選器。我們提供了一些輔助函式來建立提示和輸出解析器。這些函式有許多可調整的參數，為了簡單起見，我們將在此處忽略這些參數。
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
讓我們看看我們的提示
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:
以及我們的完整鏈產生的結果
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)
查詢建構子是自我查詢檢索器的關鍵元素。為了建立出色的檢索系統，您需要確保您的查詢建構子運作良好。通常，這需要調整提示、提示中的範例、屬性描述等。有關透過改進飯店庫存資料的查詢建構子的範例，請查看此 cookbook。
下一個關鍵元素是結構化查詢翻譯器。此物件負責將通用 StructuredQuery 物件翻譯成您正在使用的向量儲存庫語法中的元數據篩選器。LangChain 隨附了許多內建翻譯器。若要查看所有翻譯器，請前往整合章節。
from langchain_community.query_constructors.chroma import ChromaTranslator
retriever = SelfQueryRetriever(
query_constructor=query_constructor,
vectorstore=vectorstore,
structured_query_translator=ChromaTranslator(),
)
API 參考：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})]