vecdb

VECDB

一個非常簡單的矢量嵌入數據庫，您可以說這是一個刊物桌子，可讓您找到類似於您要搜索的項目的項目。

為什麼！

我是數據庫愛好者，這是一個可以在生產中使用的樂趣和學習項目；）。

PS ：我喜歡在空閒時間重新發明輪子，因為這是我的空閒時間！

數據模型

我正在使用{key => value}模型，

• key應該是代表項目的唯一值。
• value應為矢量本身（浮子列表）。

配置

默認情況下， vecdb在當前工作目錄中搜索config.yml 。但是，您可以通過提供自己的自定義文件路徑來使用--config /path/to/config.yml path/to/config.yml標誌對其進行覆蓋。

 # 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

• 原始向量層（低級）
  • 將vectorwriterequest發送到POST /v1/vectors/write並要將其存儲在某個地方時。
  • 當您擁有向量時，將vectorearchrequest發送到POST /v1/vectors/search並希望列出所有按餘弦相似性以降序順序訂購的所有相似向量的鍵/ID。
• 嵌入層（可選）
  • 將textembeddingwriterequest發送到POST /v1/embeddings/text/write文本時，並希望vecdb使用配置的嵌入式（gemini）為您構建和存儲向量。
  • 將textembeddingsearchrequest發送到POST /v1/embeddings/text/search時，您有文本並希望vecdb構建矢量並蒐索相似的向量鍵，以供您按降低順序訂購。

請求

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

如果將embedder.enabled設置為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

如果將embedder.enabled設置為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)
}

下載/安裝

• 二進制
• Docker圖像