EmbedInDB

Embedin-授权AI具有持久记忆

Embedin是一个开源矢量数据库和有效的库，它无缝将流行数据库（如MySQL，PostgreSQL和MS SQL Server）转换为零努力的矢量数据库。

Embedin是适用于自然语言处理，图像识别和推荐系统等AI应用程序的理想解决方案，提供快速的索引和检索。其简单的API和查询语言可确保易于使用和无缝集成。

最低要求

Python 3.7或更高。

安装

pip install embedin

快速开始

将嵌入在内存中

 from embedin import Embedin

client = Embedin ( collection_name = "test_collection" , texts = [ "This is a test" , "Hello world!" ])
result = client . query ( "These are tests" , top_k = 1 )  # Query the most similar text from the collection
print ( result )

将嵌入在sqlite中

 from embedin import Embedin

url = 'sqlite:///test.db'
client = Embedin ( collection_name = "test_collection" , texts = [ "This is a test" , "Hello world!" ], url = url )
result = client . query ( "These are tests" , top_k = 1 )

将嵌入在Postgresql中

启动Postgresql Docker容器

 cd docker
docker-compose up embedin-postgres

例子

 import os

from embedin import Embedin

url = os . getenv ( 'EMBEDIN_POSGRES_URL' , "postgresql+psycopg2://embedin:embedin@localhost/embedin_db" )
client = Embedin ( collection_name = "test_collection" , texts = [ "This is a test" , "Hello world!" ], url = url )
result = client . query ( "These are tests" , top_k = 1 )

将嵌入在mysql中

启动MySQL Docker容器

 cd docker
docker-compose up embedin-mysql

例子

 import os

from embedin import Embedin

url = os . getenv ( 'EMBEDIN_MYSQL_URL' , "mysql+pymysql://embedin:embedin@localhost/embedin_db" )
client = Embedin ( collection_name = "test_collection" , texts = [ "This is a test" , "Hello world!" ], url = url )
result = client . query ( "These are tests" , top_k = 1 )

将嵌入在SQL Server中

启动SQL Server Docker容器

 cd docker
docker-compose up embedin-mssql

例子

 import os

from embedin import Embedin

url = os . getenv ( 'EMBEDIN_MSSQL_URL' , "mssql+pymssql://sa:StrongPassword123@localhost/tempdb" )
client = Embedin ( collection_name = "test_collection" , url = url )
client . add_data ( texts = [ "This is a test" ], meta_data = [{ "source" : "abc4" }])
result = client . query ( "These are tests" , top_k = 1 )

更多参考

• API参考

贡献

请参考贡献者指南

托多

• 单位测试失败FAISS索引（分割故障）