pinecone scala下載 - pinecone scala源代碼下載

Pinecone Scala客戶端

這是一個直觀的異步Scala客戶端，用於Pinecone API，支持所有可用索引，矢量，收集，推理和助理操作/端點，以兩種便利服務提供，稱為PineconeVectorService和PineconeIndexSservice。受支持的電話是：

向量操作：描述IndexStats，查詢，刪除，獲取，更新和UPSERT
收集操作：ListCollections，CreateCollection，DeficteCollection和DeleteCollection
索引操作：ListIndexes，creatIndex，DifcitionIndex，DeleteIndex和ConfigureIndex
推理操作：Embeddata，Rerank和評估
助理操作：listAssistants，createAssistant，Defcitdersistant，deleteassistant，listfiles，uploadfile，Defcitdefile，decretefile，deletefile，chatwithAssistant
- 這些操作由兩種服務提供： PineconeAssistantService和PineconeAssistantFileService

請注意，為了與Pinecone API命名保持一致，服務函數名稱與API端點標題/描述與Camelcase完全匹配。另外，我們的目標是通過最少的依賴關係進行自我限制，因此我們最終僅使用兩個libs play-ahc-ws-standalone和play-ws-standalone-json （位於頂級）。

✔️重要：這是一個“社區維護”的圖書館，因此，與Pinecone公司無關。

在媒體上查看有關LIB/客戶端的文章。另外，如果您想立即查看動手示例，請轉到Pinecone示例或OpenAI + Pinecone示例模塊。

安裝

當前支持的Scala版本為2.12、2.13和3 。

要拉庫，您必須將以下依賴關係添加到構建中。

 "io.cequence" %% "pinecone-scala-client" % "1.2.2"

或to pom.xml （如果使用maven）

 <dependency>
    <groupId>io.cequence</groupId>
    <artifactId>pinecone-scala-client_2.12</artifactId>
    <version>1.2.2</version>
</dependency>

config

env。變量： PINECONE_SCALA_CLIENT_API_KEY ，如果使用了基於pod的服務， PINECONE_SCALA_CLIENT_ENV
文件配置（默認）：Pinecone-Scala-client.conf

用法？

ia。獲得PineconeIndexService

首先，您需要提供隱式執行上下文以及Akka材料器，例如

  implicit val ec = ExecutionContext .global
  implicit val materializer = Materializer ( ActorSystem ())

然後，您可以通過以下方式之一獲得服務（基於POD或無服務器）。

默認配置（期望將ENV。變量設置為Config部分中定義）

  import io . cequence . pineconescala . service . PineconeIndexServiceFactory . FactoryImplicits

  val service = PineconeIndexServiceFactory ().asOne

自定義配置

  val config = ConfigFactory .load( " path_to_my_custom_config " )
  val service = PineconeIndexServiceFactory (config).asOne

沒有配置用於基於POD的服務（With Env） - 創建PineconePodBasedIndexService的實例

  val service  = PineconeIndexServiceFactory (
    apiKey = " your_api_key " ,
    environment = " your_env " // e.g. "northamerica-northeast1-gcp
  )

沒有配置用於無服務器服務 - 創建PineconeServerlessIndexService的實例

  val service = PineconeIndexServiceFactory (
    apiKey = " your_api_key "
  )

IB。獲得PineconeVectorService

與PineconeIndexService相同，您需要首先提供隱式執行上下文和Akka Materializer。然後，您可以通過以下方式之一獲得服務。

默認配置（期望Env。variable（s）設置為Config部分中的定義）。請注意，如果沒有給定名稱的索引，則工廠將None返回。

  PineconeVectorServiceFactory ( " index_name " ).map { serviceOption =>
    val service = serviceOption.getOrElse(
      throw new Exception ( s " Index with a given name does not exist. " )
    )
    // do something with the service
  }

我知道了。獲得PineconeInferenceService

與PineconeIndexService相同，您需要首先提供隱式執行上下文和Akka Materializer。然後，您可以通過以下方式之一獲得服務。

默認配置

  val service = PineconeInferenceServiceFactory ()

自定義配置

  val config = ConfigFactory .load( " path_to_my_custom_config " )
  val service = PineconeInferenceServiceFactory (config)

直接與Api-Key

  val service = PineconeInferenceServiceFactory (
    apiKey = " your_api_key "
  )

ID。獲得PineconeAssistantService

默認配置

  val service = PineconeAssistantServiceFactory ()

自定義配置

  val config = ConfigFactory .load( " path_to_my_custom_config " )
  val service = PineconeAssistantServiceFactory (config)

直接與Api-Key

  val service = PineconeAssistantServiceFactory (
    apiKey = " your_api "
  )

IE。獲取PineconeAssistantFileService

默認配置

  val service = PineconeAssistantFileServiceFactory ()

自定義配置

  val config = ConfigFactory .load( " path_to_my_custom_config " )
  val service = PineconeAssistantFileServiceFactory (config)

直接與Api-Key

  val service = PineconeAssistantFileServiceFactory (
    apiKey = " your_api "
  )

ii。調用功能

PineconeVectorService和PineconeIndexService提供了每個呼叫及其各自的輸入和設置的完整文檔。由於所有呼叫都是異步的，因此他們返回Future包含的響應。

示例：

索引操作

列表索引

  pineconeIndexService.listIndexes.map(indexes =>
    indexes.foreach(println)
  )

創建索引（默認設置）

  import io . cequence . pineconescala . domain . response . CreateResponse

  pineconeIndexService.createIndex(
    name = " auto-gpt-test " ,
    dimension = 1536
  ).map {
    case CreateResponse . Created => println( " Index successfully created. " )
    case CreateResponse . BadRequest => println( " Index creation failed. Request exceeds quota or an invalid index name. " )
    case CreateResponse . AlreadyExists => println( " Index with a given name already exists. " )
  }

描述索引

  pineconeIndexService.describeIndex( " index_name " ).map(indexInfo =>
    // if not found, indexInfo will be None    
    println(indexInfo)
  )

刪除索引

  import io . cequence . pineconescala . domain . response . DeleteResponse

  pineconeIndexService.deleteIndex( " index_name " ).map {
    case DeleteResponse . Deleted => println( " Index successfully deleted. " )
    case DeleteResponse . NotFound => println( " Index with a given name not found. " )
  }

配置索引

  import io . cequence . pineconescala . domain . response . ConfigureIndexResponse

  pineconeIndexService.configureIndex(
    name = " index_name " ,
    replicas = Some ( 2 ),
    pod_type = Some ( PodType .p1_x2)
  ).map { 
    case ConfigureIndexResponse . Updated => println( " Index successfully updated. " )
    case ConfigureIndexResponse . BadRequestNotEnoughQuota => println( " Index update failed. Not enough quota. " )
    case ConfigureIndexResponse . NotFound => println( " Index with a given name not found. " )
  }

收集操作

列表集合

  pineconeIndexService.listCollections.map(collectionNames =>
    println(collectionNames.mkString( " , " ))
  )

創建集合

  import io . cequence . pineconescala . domain . response . CreateResponse

  pineconeIndexService.createCollection(
    name = " collection_name " ,
    source = " index_name "
  ).map {
    case CreateResponse . Created => println( " Collection successfully created. " )
    case CreateResponse . BadRequest => println( " Collection creation failed. Request exceeds quota or an invalid collection name. " )
    case CreateResponse . AlreadyExists => println( " Collection with a given name already exists. " )
  }

描述集合

  pineconeIndexService.describeCollection( " collection_name " ).map(collectionInfo =>
    // if not found, collectionInfo will be None
    println(collectionInfo)
  )

刪除集合

  import io . cequence . pineconescala . domain . response . DeleteResponse

  pineconeIndexService.deleteCollection( " collection_name " ).map {
    case DeleteResponse . Deleted => println( " Collection successfully deleted. " )
    case DeleteResponse . NotFound => println( " Collection with a given name not found. " )
  }

向量操作

UPSERT

  val dimension = 1536

  pineconeVectorService.upsert(
    vectors = Seq (
      PVector (
        id = " 666 " ,
        values = Seq .fill(dimension)( Random .nextDouble),
        metadata = Map (
          " is_relevant " -> " not really but for testing it's ok, you know " ,
          " food_quality " -> " brunches are perfect but don't go there before closing time "
        )
      ),
      PVector (
        id = " 777 " ,
        values = Seq .fill(dimension)( Random .nextDouble),
        metadata = Map (
          " is_relevant " -> " very much so " ,
          " food_quality " -> " burritos are the best! "
        )
      )
    ),
    namespace = " my_namespace " ,
  ).map(vectorUpsertedCount =>
    println( s " Upserted $vectorUpsertedCount vectors. " )
  )

更新

  val fetchedValues = ... // vectors fetched from somewhere 

  pineconeVectorService.update(
    id = " 777 " ,
    namespace = " my_namespace " ,
    values = fetchedValues.map(_ / 100 ), // divide fetched values by 100
    sparseValues = Some ( SparseVector (
      indices = Seq ( 1 , 2 , 3 ),
      values = Seq ( 8.8 , 7.7 , 2.2 )
    )),
    setMetaData = Map (
      " solid_info " -> " this is the source of the truth "
    )
  ).map(_ =>
    println( s " Vectors updated. " )
  )

使用默認設置查詢

  pineconeVectorService.query(
    vector = Seq .fill( 1536 )( Random .nextDouble), // some values/embeddings
    namespace = " my_namespace "
  ).map { queryResponse =>
    queryResponse.matches.foreach { matchInfo =>
      println( s " Matched vector id: ${matchInfo.id} " )
      println( s " Matched vector values: ${matchInfo.values.take( 20 ).mkString( " , " )} .. " )
      println( s " Matched vector score: ${matchInfo.score} " )
      println( s " Matched vector metadata: ${matchInfo.metadata} " )
    }
  }

使用自定義設置查詢

  pineconeVectorService.query(
    vector = Seq .fill( 1536 )( Random .nextDouble), // some values/embeddings
    namespace = " my_namespace " ,
    settings = QuerySettings (
      topK = 5 ,
      includeValues = true ,
      includeMetadata = true
    )
  ).map { queryResponse =>
    queryResponse.matches.foreach { matchInfo =>
      println( s " Matched vector id: ${matchInfo.id} " )
      println( s " Matched vector values: ${matchInfo.values.take( 20 ).mkString( " , " )} .. " )
      println( s " Matched vector score: ${matchInfo.score} " )
      println( s " Matched vector metadata: ${matchInfo.metadata} " )
    }
  }

拿來

  pineconeVectorService.fetch(
    ids = Seq ( " 666 " , " 777 " ),
    namespace = " my_namespace "
  ).map { fetchResponse =>
    fetchResponse.vectors.values.map { pVector =>
      println( s " Fetched vector id: ${pVector.id} " )
      println( s " Fetched vector values: ${pVector.values.take( 20 ).mkString( " , " )} .. " )
      println( s " Fetched vector metadata: ${pVector.metadata} " )
   }
}

由ID刪除（S）

  pineconeVectorService.delete(
    ids = Seq ( " 666 " , " 777 " ),
    namespace = " my_namespace "
  ).map(_ =>
    println( " Vectors deleted " )
  )

刪除全部

  pineconeVectorService.deleteAll(
    namespace = " my_namespace "
  ).map(_ =>
    println( " All vectors deleted " )
  )

描述索引統計數據

  pineconeVectorService.describeIndexStats.map(stats =>
    println(stats)      
  )

推理操作

生成嵌入

  pineconeInferenceService.createEmbeddings(
    Seq ( " The quick brown fox jumped over the lazy dog " )
  ).map { embeddings =>
    println(embeddings.data.mkString( " n " ))
  }

重讀文件 /塊

  pineconeInferenceService.rerank(
    query = " The tech company Apple is known for its innovative products like the iPhone. " ,
    documents = Seq (...)  
  ).map(
    _.data.foreach(println)
  )

評估問答

  pineconeInferenceService.evaluate(
    question = " What are the capital cities of France, England and Spain? " ,
    answer = " Paris is a city of France and Barcelona of Spain " ,
    groundTruthAnswer = " Paris is the capital city of France, London of England and Madrid of Spain "
  ).map { response =>
    println(response)
  }

**助理行動**

列出助手

  pineconeAssistantService.listAssistants.map(assistants =>
    println(assistants.mkString( " , " ))
  )

創建助手

  import io . cequence . pineconescala . domain . response . CreateResponse

  pineconeAssistantService.createAssistant(
    name = " assistant_name " ,
    description = " assistant_description " ,
    assistantType = " assistant_type "
  ).map {
    case CreateResponse . Created => println( " Assistant successfully created. " )
    case CreateResponse . BadRequest => println( " Assistant creation failed. Request exceeds quota or an invalid assistant name. " )
    case CreateResponse . AlreadyExists => println( " Assistant with a given name already exists. " )
  }

描述助手

  pineconeAssistantService.describeAssistant( " assistant_name " ).map(assistant =>
    // if not found, assistant will be None
    println(assistant)
  )

刪除助手

  import io . cequence . pineconescala . domain . response . DeleteResponse

  pineconeAssistantService.deleteAssistant( " assistant_name " ).map {
    case DeleteResponse . Deleted => println( " Assistant successfully deleted. " )
    case DeleteResponse . NotFound => println( " Assistant with a given name not found. " )
  }

列出助手文件

  pineconeAssistantService.listFiles( " assistant_name " ).map(files =>
    println(files.mkString( " , " ))
  )

上傳助手文件

  import io . cequence . pineconescala . domain . response . CreateResponse

  pineconeAssistantService.uploadFile(
    assistantName = " assistant_name " ,
    filePath = " path_to_file "
  ).map {
    case CreateResponse . Created => println( " File successfully uploaded. " )
    case CreateResponse . BadRequest => println( " File upload failed. Request exceeds quota or an invalid file path. " )
    case CreateResponse . AlreadyExists => println( " File with a given name already exists. " )
  }

描述助手文件

  pineconeAssistantService.describeFile( " assistant_name " , " file_name " ).map(file =>
    // if not found, file will be None
    println(file)
  )

與助手聊天

  pineconeAssistantService.chatWithAssistant(
    " assistant_name " ,
    " What is the maximum height of a red pine? "
  ).map(response =>
    println(response)
  )

演示

對於即將運行的演示請求。請參閱單獨的模塊：

Pinecone Scala演示 - 顯示如何使用Pinecone矢量，索引和收集操作
Pinecone + Openai Scala演示 - 顯示如何生成和存儲OpenAi嵌入到Pinecone中並之後查詢它們

常問問題？

我有一個超時例外。如何更改超時設置？
您可以通過將timeouts傳遞給Pinecone{Vector,Index}ServiceFactory來做到這一點，或者，如果您使用自己的配置文件，則可以簡單地將其添加為：

 pinecone-scala-client {
    timeouts {
        requestTimeoutSec = 200
        readTimeoutSec = 200
        connectTimeoutSec = 5
        pooledConnectionIdleTimeoutSec = 60
    }
}

我得到了一個例外，例如com.typesafe.config.ConfigException$UnresolvedSubstitution: pinecone-scala-client.conf @ jar:file:.../io/cequence/pinecone-scala-client_2.13/1.2.2/pinecone-scala-client_2.13-1.2.2.jar!/pinecone-scala-client.conf: 4: Could not resolve substitution to a value: ${PINECONE_SCALA_CLIENT_API_KEY} 。我應該怎麼辦？
設置Env。變量PINECONE_SCALA_CLIENT_API_KEY 。如果您在這裡沒有一個登記冊。
一切看起來都很酷。我想與您聊天有關您的研發？
只需通過[email protected]向我們發送電子郵件。

許可證⚖️

根據MIT許可條款，該庫可用並以開源為開源。

貢獻者

該項目是開源的，歡迎任何貢獻或反饋（在此）。

該圖書館的開發得到了 - cequence.io的支持 - The future of contracting

由彼得·班達（Peter Banda）創建和維護。

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pinecone scala

Pinecone Scala客戶端

安裝

config

用法？

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常問問題？

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pinecone scala

Pinecone Scala客戶端

安裝

config

用法？

演示

常問問題 ？

許可證⚖️

貢獻者

常問問題？