PGVecto.rs
本笔记本展示了如何使用与Postgres向量数据库（pgvecto.rs）相关的功能。
%pip install "pgvecto_rs[sdk]" langchain-community
from typing import List
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores.pgvecto_rs import PGVecto_rs
from langchain_core.documents import Document
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = FakeEmbeddings(size=3)
使用官方演示docker镜像启动数据库。
! docker run --name pgvecto-rs-demo -e POSTGRES_PASSWORD=mysecretpassword -p 5432:5432 -d tensorchord/pgvecto-rs:latest
然后构建数据库URL
## PGVecto.rs需要连接字符串来连接数据库。
## 我们将从环境变量中加载它。
import os
PORT = os.getenv("DB_PORT", 5432)
HOST = os.getenv("DB_HOST", "localhost")
USER = os.getenv("DB_USER", "postgres")
PASS = os.getenv("DB_PASS", "mysecretpassword")
DB_NAME = os.getenv("DB_NAME", "postgres")
# 通过shell运行测试：
URL = "postgresql+psycopg://{username}:{password}@{host}:{port}/{db_name}".format(
port=PORT,
host=HOST,
username=USER,
password=PASS,
db_name=DB_NAME,
)
最后，从文档中创建VectorStore：
db1 = PGVecto_rs.from_documents(
documents=docs,
embedding=embeddings,
db_url=URL,
# 表名为f"collection_{collection_name}"，因此应该是唯一的。
collection_name="state_of_the_union",
)
您可以稍后通过以下方式连接到表：
# 创建新的空向量存储，使用collection_name。
# 或者如果存在，则连接到数据库中现有的向量存储。
# 参数应与创建向量存储时相同。
db1 = PGVecto_rs.from_collection_name(
embedding=embeddings,
db_url=URL,
collection_name="state_of_the_union",
)
确保用户被允许创建表。
带分数的相似性搜索
使用欧几里得距离的相似性搜索（默认）
query = "What did the president say about Ketanji Brown Jackson"
docs: List[Document] = db1.similarity_search(query, k=4)
for doc in docs:
print(doc.page_content)
print("======================")
相似性搜索与过滤器
from pgvecto_rs.sdk.filters import meta_contains
query = "What did the president say about Ketanji Brown Jackson"
docs: List[Document] = db1.similarity_search(
query, k=4, filter=meta_contains({"source": "../../how_to/state_of_the_union.txt"})
)
for doc in docs:
print(doc.page_content)
print("======================")
或者:
query = "What did the president say about Ketanji Brown Jackson"
docs: List[Document] = db1.similarity_search(
query, k=4, filter={"source": "../../how_to/state_of_the_union.txt"}
)
for doc in docs:
print(doc.page_content)
print("======================")