PGVector (Postgres)

PGVector 是一個用於 Postgres 資料庫的向量相似性搜尋套件。

在筆記本中，我們將示範圍繞 PGVector 向量儲存庫的 SelfQueryRetriever。

建立 PGVector 向量儲存庫

首先，我們會想要建立一個 PGVector 向量儲存庫，並用一些資料來初始化它。我們建立了一個小型示範文件集，其中包含電影摘要。

注意： 自我查詢檢索器要求您安裝 lark (pip install lark)。我們也需要 `` 套件。

%pip install --upgrade --quiet  lark pgvector psycopg2-binary

我們想要使用 OpenAIEmbeddings，因此我們必須取得 OpenAI API 金鑰。

import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

from langchain_community.vectorstores import PGVector
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings

collection = "Name of your collection"
embeddings = OpenAIEmbeddings()

API 參考：PGVector | Document | 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": "science fiction",
            "rating": 9.9,
        },
    ),
]
vectorstore = PGVector.from_documents(
    docs,
    embeddings,
    collection_name=collection,
)

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。

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 or list[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 = OpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(
    llm, vectorstore, document_content_description, metadata_field_info, verbose=True
)

API 參考：AttributeInfo | SelfQueryRetriever | OpenAI

測試它

現在我們可以嘗試實際使用我們的檢索器了！

# This example only specifies a relevant query
retriever.invoke("What are some movies about dinosaurs")

# This example only specifies a filter
retriever.invoke("I want to watch a movie rated higher than 8.5")

# This example specifies a query and a filter
retriever.invoke("Has Greta Gerwig directed any movies about women")

# This example specifies a composite filter
retriever.invoke("What's a highly rated (above 8.5) science fiction film?")

# 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"
)

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。

我們可以通過將 enable_limit=True 傳遞給建構函式來做到這一點。

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.invoke("what are two movies about dinosaurs")