Milvus
Milvus 是一個資料庫，用於儲存、索引和管理由深度神經網路和其他機器學習 (ML) 模型產生的大量嵌入向量。
在本逐步解說中，我們將示範搭配 Milvus 向量儲存庫的 SelfQueryRetriever。
建立 Milvus 向量儲存庫
首先，我們會想要建立 Milvus VectorStore 並使用一些資料來初始化它。我們建立了一個小型的示範文件集，其中包含電影摘要。
我使用了 Milvus 的雲端版本，因此我需要 uri 和 token。
注意：自我查詢檢索器需要您安裝 lark (pip install lark)。我們也需要 langchain_milvus 套件。
%pip install --upgrade --quiet lark langchain_milvus
我們想要使用 OpenAIEmbeddings，因此我們必須取得 OpenAI API 金鑰。
import os
OPENAI_API_KEY = "Use your OpenAI key:)"
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
from langchain_core.documents import Document
from langchain_milvus.vectorstores import Milvus
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": "action"},
),
Document(
page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",
metadata={"year": 2010, "genre": "thriller", "rating": 8.2},
),
Document(
page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",
metadata={"year": 2019, "rating": 8.3, "genre": "drama"},
),
Document(
page_content="Three men walk into the Zone, three men walk out of the Zone",
metadata={"year": 1979, "rating": 9.9, "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, "genre": "thriller", "rating": 9.0},
),
Document(
page_content="Toys come alive and have a blast doing so",
metadata={"year": 1995, "genre": "animated", "rating": 9.3},
),
]
vector_store = Milvus.from_documents(
docs,
embedding=embeddings,
connection_args={"uri": "Use your uri:)", "token": "Use your token:)"},
)
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我們的文件支援的中繼資料欄位以及文件內容的簡短描述的資訊。
from langchain.chains.query_constructor.schema import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain_openai import OpenAI
metadata_field_info = [
AttributeInfo(
name="genre",
description="The genre of the movie",
type="string",
),
AttributeInfo(
name="year",
description="The year the movie was released",
type="integer",
),
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, vector_store, document_content_description, metadata_field_info, verbose=True
)
測試一下
現在我們可以嘗試實際使用我們的檢索器！
# This example only specifies a relevant query
retriever.invoke("What are some movies about dinosaurs")
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': 'action'}),
Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),
Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, '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, 'rating': 9.0, 'genre': 'thriller'})]
# This example specifies a filter
retriever.invoke("What are some highly rated movies (above 9)?")
query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=9) limit=None
[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),
Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'genre': 'science fiction'})]
# This example only specifies a query and a filter
retriever.invoke("I want to watch a movie about toys rated higher than 9")
query='toys' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=9) limit=None
[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),
Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'genre': 'science fiction'})]
# This example specifies a composite filter
retriever.invoke("What's a highly rated (above or equal 9) thriller film?")
query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='thriller'), Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=9)]) 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, 'rating': 9.0, 'genre': 'thriller'})]
# This example specifies a query and composite filter
retriever.invoke(
"What's a movie after 1990 but before 2005 that's all about dinosaurs, \
and preferably has a lot of action"
)
query='dinosaur' 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='action')]) limit=None
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'action'})]
篩選 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 傳遞給建構函式來做到這一點。
retriever = SelfQueryRetriever.from_llm(
llm,
vector_store,
document_content_description,
metadata_field_info,
verbose=True,
enable_limit=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': 'action'}),
Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'})]