vecdb

VECDB

一个非常简单的矢量嵌入数据库，您可以说这是一个刊物桌子，可让您找到类似于您要搜索的项目的项目。

为什么！

我是数据库爱好者，这是一个可以在生产中使用的乐趣和学习项目；）。

PS ：我喜欢在空闲时间重新发明轮子，因为这是我的空闲时间！

数据模型

我正在使用{key => value}模型，

• key应该是代表项目的唯一值。
• value应为矢量本身（浮子列表）。

配置

默认情况下， vecdb在当前工作目录中搜索config.yml 。但是，您可以通过提供自己的自定义文件路径来使用--config /path/to/config.yml path/to/config.yml标志对其进行覆盖。

 # http server related configs
server :
  # the address to listen on in the form of '[host]:port'
  listen : " 0.0.0.0:3000 "

# storage related configs
store :
  # the driver you want to use
  # currently vecdb supports "bolt" which is based on boltdb the in process embedded the database
  driver : " bolt "
  # the arguments required by the driver
  # for bolt, it requires a key called `database` points to the path you want to store the data in.
  args :
    database : " ./vec.db "

# embeddings related configs
embedder :
  # whether to enable the embedder and all endpoints using it or not
  enabled : true
  # the driver you want to use, currently vecdb supports gemini
  driver : gemini
  # the arguments required by the driver
  # currently gemini driver requires `api_key` and `text_embedding_model`
  args :
    # by default vecdb will replace anything between ${..} with the actual value from the ENV var
    api_key : " ${GEMINI_API_KEY} "
    text_embedding_model : " text-embedding-004 "

成分

• 原始向量层（低级）
  • 将vectorwriterequest发送到POST /v1/vectors/write并要将其存储在某个地方时。
  • 当您拥有向量时，将vectorearchrequest发送到POST /v1/vectors/search并希望列出所有按余弦相似性以降序顺序订购的所有相似向量的键/ID。
• 嵌入层（可选）
  • 将textembeddingwriterequest发送到POST /v1/embeddings/text/write文本时，并希望vecdb使用配置的嵌入式（gemini）为您构建和存储向量。
  • 将textembeddingsearchrequest发送到POST /v1/embeddings/text/search时，您有文本并希望vecdb构建矢量并搜索相似的向量键，以供您按降低顺序订购。

请求

VectorWritereQuest

 {
  "bucket" : "BUCKET_NAME" , // consider it a collection or a table
  "key" : "product-id-1" , // should be unique and represents a valid value in your main data store (example: the row id in your mysql/postgres ... etc)
  "vector" : [ 1.929292 , 0.3848484 , - 1.9383838383 , ... ] // the vector you want to store 
}

VectorsearchRequest

 {
  "bucket" : "BUCKET_NAME" , // consider it a collection or a table
  "vector" : [ 1.929292 , 0.3848484 , - 1.9383838383 , ... ] , // you will get a list ordered by cosine-similarity in descending order
  "min_cosine_similarity" : 0.0 , // the more you increase, the fewer data you will get
  "max_result_count" : 10 // max vectors to return (vecdb will first order by cosine similarity then apply the limit)
}

textembeddingwriterequest

如果将embedder.enabled设置为true 。

 {
  "bucket" : "BUCKET_NAME" , // consider it a collection or a table
  "key" : "product-id-1" , // should be unique and represents a valid value in your main data store (example: the row id in your mysql/postgres ... etc)
  "content" : "This is some text representing the product" // this will be converted to a vector using the configured embedder 
}

textembeddingsearchrequest

如果将embedder.enabled设置为true 。

 {
  "bucket" : "BUCKET_NAME" , // consider it a collection or a table
  "content" : "A Product Text" , // you will get a list ordered by cosine-similarity in descending order
  "min_cosine_similarity" : 0.0 , // the more you increase, the fewer data you will get
  "max_result_count" : 10 // max vectors to return (vecdb will first order by cosine similarity then apply the limit)
}

下载/安装

• 二进制
• Docker图像