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    Analyzing Entities

    Entity Analysisinspects the given text for known entities (proper nouns such as public figures, landmarks, etc.), and returns information about those entities. Entity analysis is performed with the analyzeEntities method. For information about the types of entities Natural Language identifies, see the Entity documentation. For information on which languages are supported by the Natural Language API, see Language Support .

    This section demonstrates a few ways to detect entities in a document. For each document, you must submit a separate request.

    Analyzing Entities in a String

    Here is an example of performing entity analysis on a text string sent directly to the Natural Language API:

    Protocol

    To analyze entities in a document, make a POST request to the documents:analyzeEntities REST method and provide the appropriate request body as shown in the following example.

    The example uses the gcloud auth application-default print-access-token command to obtain an access token for a service account set up for the project using the Google Cloud Platform gcloud CLI . For instructions on installing the gcloud CLI, setting up a project with a service account see the Quickstart .

    curl  
-X  
POST  
 \ 
  
-H  
 "Authorization: Bearer " 
 $( 
gcloud  
auth  
application-default  
print-access-token ) 
  
 \ 
  
-H  
 "Content-Type: application/json; charset=utf-8" 
  
 \ 
  
--data  
 "{ 
 'encodingType': 'UTF8', 
 'document': { 
 'type': 'PLAIN_TEXT', 
 'content': 'President Trump will speak from the White House, located 
 at 1600 Pennsylvania Ave NW, Washington, DC, on October 7.' 
 } 
 }" 
  
 "https://language.googleapis.com/v2/documents:analyzeEntities"

    If you don't specify document.language_code , then the language will be automatically detected. For information on which languages are supported by the Natural Language API, see Language Support . See the Document reference documentation for more information on configuring the request body.

    If the request is successful, the server returns a 200 OK HTTP status code and the response in JSON format:

     { 
  
 "entities" 
 : 
  
 [ 
  
 { 
  
 "name" 
 : 
  
 "October 7" 
 , 
  
 "type" 
 : 
  
 "DATE" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "month" 
 : 
  
 "10" 
 , 
  
 "day" 
 : 
  
 "7" 
  
 }, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "October 7" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
 , 
  
 "probability" 
 : 
  
 1 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "1600" 
 , 
  
 "type" 
 : 
  
 "NUMBER" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "value" 
 : 
  
 "1600" 
  
 }, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "1600" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
 , 
  
 "probability" 
 : 
  
 1 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "7" 
 , 
  
 "type" 
 : 
  
 "NUMBER" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "value" 
 : 
  
 "7" 
  
 }, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "7" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
 , 
  
 "probability" 
 : 
  
 1 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "1600 Pennsylvania Ave NW, Washington, DC" 
 , 
  
 "type" 
 : 
  
 "ADDRESS" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "locality" 
 : 
  
 "Washington" 
 , 
  
 "narrow_region" 
 : 
  
 "District of Columbia" 
 , 
  
 "street_name" 
 : 
  
 "Pennsylvania Avenue Northwest" 
 , 
  
 "street_number" 
 : 
  
 "1600" 
 , 
  
 "broad_region" 
 : 
  
 "District of Columbia" 
 , 
  
 "country" 
 : 
  
 "US" 
  
 }, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "1600 Pennsylvania Ave NW, Washington, DC" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
 , 
  
 "probability" 
 : 
  
 1 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "1600 Pennsylvania Ave NW" 
 , 
  
 "type" 
 : 
  
 "LOCATION" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "1600 Pennsylvania Ave NW" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
 , 
  
 "probability" 
 : 
  
 0.901 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "President" 
 , 
  
 "type" 
 : 
  
 "PERSON" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "President" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "COMMON" 
 , 
  
 "probability" 
 : 
  
 0.941 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "Trump" 
 , 
  
 "type" 
 : 
  
 "PERSON" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "Trump" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
 , 
  
 "probability" 
 : 
  
 0.948 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "Washington, DC" 
 , 
  
 "type" 
 : 
  
 "LOCATION" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "Washington, DC" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
 , 
  
 "probability" 
 : 
  
 0.92 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "White House" 
 , 
  
 "type" 
 : 
  
 "LOCATION" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "White House" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
 , 
  
 "probability" 
 : 
  
 0.785 
  
 } 
  
 ] 
  
 } 
  
 ], 
  
 "languageCode" 
 : 
  
 "en" 
 , 
  
 "languageSupported" 
 : 
  
 true 
 }

    The entities array contains Entity objects representing the detected entities, which include information such as the entity name and type.

    gcloud

    Refer to the analyze-entities command for complete details.

    To perform entity analysis, use the gcloud CLI and use the --content flag to identify the content to analyze:

    gcloud ml language analyze-entities --content="President Trump will speak from the White House, located
  at 1600 Pennsylvania Ave NW, Washington, DC, on October 7."

    If the request is successful, the server returns a response in JSON format:

     { 
  
 "entities" 
 : 
  
 [ 
  
 { 
  
 "name" 
 : 
  
 "Trump" 
 , 
  
 "type" 
 : 
  
 "PERSON" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "mid" 
 : 
  
 "/m/0cqt90" 
 , 
  
 "wikipedia_url" 
 : 
  
 "https://en.wikipedia.org/wiki/Donald_Trump" 
  
 }, 
  
 "salience" 
 : 
  
 0.7936003 
 , 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "Trump" 
 , 
  
 "beginOffset" 
 : 
  
 10 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
  
 }, 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "President" 
 , 
  
 "beginOffset" 
 : 
  
 0 
  
 }, 
  
 "type" 
 : 
  
 "COMMON" 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "White House" 
 , 
  
 "type" 
 : 
  
 "LOCATION" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "mid" 
 : 
  
 "/m/081sq" 
 , 
  
 "wikipedia_url" 
 : 
  
 "https://en.wikipedia.org/wiki/White_House" 
  
 }, 
  
 "salience" 
 : 
  
 0.09172433 
 , 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "White House" 
 , 
  
 "beginOffset" 
 : 
  
 36 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "Pennsylvania Ave NW" 
 , 
  
 "type" 
 : 
  
 "LOCATION" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "mid" 
 : 
  
 "/g/1tgb87cq" 
  
 }, 
  
 "salience" 
 : 
  
 0.085507184 
 , 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "Pennsylvania Ave NW" 
 , 
  
 "beginOffset" 
 : 
  
 65 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "Washington, DC" 
 , 
  
 "type" 
 : 
  
 "LOCATION" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "mid" 
 : 
  
 "/m/0rh6k" 
 , 
  
 "wikipedia_url" 
 : 
  
 "https://en.wikipedia.org/wiki/Washington,_D.C." 
  
 }, 
  
 "salience" 
 : 
  
 0.029168168 
 , 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "Washington, DC" 
 , 
  
 "beginOffset" 
 : 
  
 86 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
  
 } 
  
 ] 
  
 } 
  
 { 
  
 "name" 
 : 
  
 "1600 Pennsylvania Ave NW, Washington, DC" 
 , 
  
 "type" 
 : 
  
 "ADDRESS" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "country" 
 : 
  
 "US" 
 , 
  
 "sublocality" 
 : 
  
 "Fort Lesley J. McNair" 
 , 
  
 "locality" 
 : 
  
 "Washington" 
 , 
  
 "street_name" 
 : 
  
 "Pennsylvania Avenue Northwest" 
 , 
  
 "broad_region" 
 : 
  
 "District of Columbia" 
 , 
  
 "narrow_region" 
 : 
  
 "District of Columbia" 
 , 
  
 "street_number" 
 : 
  
 "1600" 
  
 }, 
  
 "salience" 
 : 
  
 0 
 , 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "1600 Pennsylvania Ave NW, Washington, DC" 
 , 
  
 "beginOffset" 
 : 
  
 60 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
  
 } 
  
 ] 
  
 } 
  
 } 
  
 { 
  
 "name" 
 : 
  
 "1600" 
 , 
  
 "type" 
 : 
  
 "NUMBER" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "value" 
 : 
  
 "1600" 
  
 }, 
  
 "salience" 
 : 
  
 0 
 , 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "1600" 
 , 
  
 "beginOffset" 
 : 
  
 60 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "October 7" 
 , 
  
 "type" 
 : 
  
 "DATE" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "day" 
 : 
  
 "7" 
 , 
  
 "month" 
 : 
  
 "10" 
  
 }, 
  
 "salience" 
 : 
  
 0 
 , 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "October 7" 
 , 
  
 "beginOffset" 
 : 
  
 105 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
  
 } 
  
 ] 
  
 } 
  
 { 
  
 "name" 
 : 
  
 "7" 
 , 
  
 "type" 
 : 
  
 "NUMBER" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "value" 
 : 
  
 "7" 
  
 }, 
  
 "salience" 
 : 
  
 0 
 , 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "7" 
 , 
  
 "beginOffset" 
 : 
  
 113 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
  
 } 
  
 ] 
  
 } 
  
 ], 
  
 "language" 
 : 
  
 "en" 
 }

    The entities array contains Entity objects representing the detected entities, which include information such as the entity name and type.

    Go

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Go API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "io" 
  
 language 
  
 "cloud.google.com/go/language/apiv2" 
  
 "cloud.google.com/go/language/apiv2/languagepb" 
 ) 
 // analyzeEntities sends a string of text to the Cloud Natural Language API to 
 // detect the entities of the text. 
 func 
  
 analyzeEntities 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 text 
  
 string 
 ) 
  
 error 
  
 { 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 // Initialize client. 
  
 client 
 , 
  
 err 
  
 := 
  
 language 
 . 
  NewClient 
 
 ( 
 ctx 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 defer 
  
 client 
 . 
  Close 
 
 () 
  
 resp 
 , 
  
 err 
  
 := 
  
 client 
 . 
 AnalyzeEntities 
 ( 
 ctx 
 , 
  
& languagepb 
 . 
 AnalyzeEntitiesRequest 
 { 
  
 Document 
 : 
  
& languagepb 
 . 
 Document 
 { 
  
 Source 
 : 
  
& languagepb 
 . 
 Document_Content 
 { 
  
 Content 
 : 
  
 text 
 , 
  
 }, 
  
 Type 
 : 
  
 languagepb 
 . 
  Document_PLAIN_TEXT 
 
 , 
  
 }, 
  
 EncodingType 
 : 
  
 languagepb 
 . 
  EncodingType_UTF8 
 
 , 
  
 }) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "AnalyzeEntities: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Response: %q\n" 
 , 
  
 resp 
 ) 
  
 return 
  
 nil 
 }

    Java

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Java API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      // Instantiate the Language client com.google.cloud.language.v2.LanguageServiceClient 
 try 
  
 ( 
 LanguageServiceClient 
  
 language 
  
 = 
  
 LanguageServiceClient 
 . 
 create 
 ()) 
  
 { 
  
 Document 
  
 doc 
  
 = 
  
 Document 
 . 
 newBuilder 
 (). 
 setContent 
 ( 
 text 
 ). 
 setType 
 ( 
 Type 
 . 
 PLAIN_TEXT 
 ). 
 build 
 (); 
  
 AnalyzeEntitiesRequest 
  
 request 
  
 = 
  
 AnalyzeEntitiesRequest 
 . 
 newBuilder 
 () 
  
 . 
 setDocument 
 ( 
 doc 
 ) 
  
 . 
 setEncodingType 
 ( 
 EncodingType 
 . 
 UTF16 
 ) 
  
 . 
 build 
 (); 
  
 AnalyzeEntitiesResponse 
  
 response 
  
 = 
  
 language 
 . 
 analyzeEntities 
 ( 
 request 
 ); 
  
 // Print the response 
  
 for 
  
 ( 
 Entity 
  
 entity 
  
 : 
  
 response 
 . 
 getEntitiesList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
 "Entity: %s" 
 , 
  
 entity 
 . 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Metadata: " 
 ); 
  
 for 
  
 ( 
 Map 
 . 
 Entry<String 
 , 
  
 String 
>  
 entry 
  
 : 
  
 entity 
 . 
 getMetadataMap 
 (). 
 entrySet 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
 "%s : %s" 
 , 
  
 entry 
 . 
 getKey 
 (), 
  
 entry 
 . 
 getValue 
 ()); 
  
 } 
  
 for 
  
 ( 
 EntityMention 
  
 mention 
  
 : 
  
 entity 
 . 
 getMentionsList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
 "Begin offset: %d\n" 
 , 
  
 mention 
 . 
 getText 
 (). 
 getBeginOffset 
 ()); 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
 "Content: %s\n" 
 , 
  
 mention 
 . 
 getText 
 (). 
 getContent 
 ()); 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
 "Type: %s\n\n" 
 , 
  
 mention 
 . 
 getType 
 ()); 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
 "Probability: %s\n\n" 
 , 
  
 mention 
 . 
 getProbability 
 ()); 
  
 } 
  
 } 
 }

    Node.js

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Node.js API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      // Imports the Google Cloud client library 
 const 
  
 language 
  
 = 
  
 require 
 ( 
 ' @google-cloud/language 
' 
 ). 
 v2 
 ; 
 // Creates a client 
 const 
  
 client 
  
 = 
  
 new 
  
 language 
 . 
  LanguageServiceClient 
 
 (); 
 /** 
 * TODO(developer): Uncomment the following line to run this code. 
 */ 
 // const text = 'Your text to analyze, e.g. Hello, world!'; 
 // Prepares a document, representing the provided text 
 const 
  
 document 
  
 = 
  
 { 
  
 content 
 : 
  
 text 
 , 
  
 type 
 : 
  
 'PLAIN_TEXT' 
 , 
 }; 
 // Detects entities in the document 
 const 
  
 [ 
 result 
 ] 
  
 = 
  
 await 
  
 client 
 . 
 analyzeEntities 
 ({ 
 document 
 }); 
 const 
  
 entities 
  
 = 
  
 result 
 . 
 entities 
 ; 
 console 
 . 
 log 
 ( 
 'Entities:' 
 ); 
 entities 
 . 
 forEach 
 ( 
 entity 
  
 = 
>  
 { 
  
 console 
 . 
 log 
 ( 
 entity 
 . 
 name 
 ); 
  
 console 
 . 
 log 
 ( 
 ` - Type: 
 ${ 
 entity 
 . 
 type 
 } 
 ` 
 ); 
  
 if 
  
 ( 
 entity 
 . 
 metadata 
 ) 
  
 { 
  
 console 
 . 
 log 
 ( 
 ` - Metadata: 
 ${ 
 entity 
 . 
 metadata 
 } 
 ` 
 ); 
  
 } 
 });

    Python

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Python API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      from 
  
 google.cloud 
  
 import 
 language_v2 
 def 
  
 sample_analyze_entities 
 ( 
 text_content 
 : 
 str 
 = 
 "California is a state." 
 ) 
 - 
> None 
 : 
  
 """ 
 Analyzes Entities in a string. 
 Args: 
 text_content: The text content to analyze 
 """ 
 client 
 = 
 language_v2 
 . 
 LanguageServiceClient 
 () 
 # Available types: PLAIN_TEXT, HTML 
 document_type_in_plain_text 
 = 
 language_v2 
 . 
 Document 
 . 
 Type 
 . 
 PLAIN_TEXT 
 # Optional. If not specified, the language is automatically detected. 
 # For list of supported languages: 
 # https://cloud.google.com/natural-language/docs/languages 
 language_code 
 = 
 "en" 
 document 
 = 
 { 
 "content" 
 : 
 text_content 
 , 
 "type_" 
 : 
 document_type_in_plain_text 
 , 
 "language_code" 
 : 
 language_code 
 , 
 } 
 # Available values: NONE, UTF8, UTF16, UTF32. 
 # See https://cloud.google.com/natural-language/docs/reference/rest/v2/EncodingType. 
 encoding_type 
 = 
 language_v2 
 . 
 EncodingType 
 . 
 UTF8 
 response 
 = 
 client 
 . 
  analyze_entities 
 
 ( 
 request 
 = 
 { 
 "document" 
 : 
 document 
 , 
 "encoding_type" 
 : 
 encoding_type 
 } 
 ) 
 for 
 entity 
 in 
 response 
 . 
 entities 
 : 
 print 
 ( 
 f 
 "Representative name for the entity: 
 { 
 entity 
 . 
 name 
 } 
 " 
 ) 
 # Get entity type, e.g. PERSON, LOCATION, ADDRESS, NUMBER, et al. 
 # See https://cloud.google.com/natural-language/docs/reference/rest/v2/Entity#type. 
 print 
 ( 
 f 
 "Entity type: 
 { 
 language_v2 
 . 
 Entity 
 . 
 Type 
 ( 
 entity 
 . 
 type_ 
 ) 
 . 
 name 
 } 
 " 
 ) 
 # Loop over the metadata associated with entity. 
 # Some entity types may have additional metadata, e.g. ADDRESS entities 
 # may have metadata for the address street_name, postal_code, et al. 
 for 
 metadata_name 
 , 
 metadata_value 
 in 
 entity 
 . 
 metadata 
 . 
 items 
 (): 
 print 
 ( 
 f 
 " 
 { 
 metadata_name 
 } 
 : 
 { 
 metadata_value 
 } 
 " 
 ) 
 # Loop over the mentions of this entity in the input document. 
 # The API currently supports proper noun mentions. 
 for 
 mention 
 in 
 entity 
 . 
 mentions 
 : 
 print 
 ( 
 f 
 "Mention text: 
 { 
 mention 
 . 
 text 
 . 
 content 
 } 
 " 
 ) 
 # Get the mention type, e.g. PROPER for proper noun 
 print 
 ( 
 f 
 "Mention type: 
 { 
 language_v2 
 . 
 EntityMention 
 . 
 Type 
 ( 
 mention 
 . 
 type_ 
 ) 
 . 
 name 
 } 
 " 
 ) 
 # Get the probability score associated with the first mention of the entity in the (0, 1.0] range. 
 print 
 ( 
 f 
 "Probability score: 
 { 
 mention 
 . 
 probability 
 } 
 " 
 ) 
 # Get the language of the text, which will be the same as 
 # the language specified in the request or, if not specified, 
 # the automatically-detected language. 
 print 
 ( 
 f 
 "Language of the text: 
 { 
 response 
 . 
 language_code 
 } 
 " 
 )

    Additional languages

    C#: Please follow the C# setup instructions on the client libraries page and then visit the Natural Language reference documentation for .NET.

    PHP: Please follow the PHP setup instructions on the client libraries page and then visit the Natural Language reference documentation for PHP.

    Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the Natural Language reference documentation for Ruby.

    Analyzing Entities from Cloud Storage

    For your convenience, the Natural Language API can perform entity analysis directly on a file located in Cloud Storage, without the need to send the contents of the file in the body of your request.

    Here is an example of performing entity analysis on a file located in Cloud Storage.

    Protocol

    To analyze entities from a document stored in Cloud Storage, make a POST request to the documents:analyzeEntities REST method and provide the appropriate request body with the path to the document as shown in the following example.

    curl  
-X  
POST  
 \ 
  
-H  
 "Authorization: Bearer " 
 $( 
gcloud  
auth  
application-default  
print-access-token ) 
  
 \ 
  
-H  
 "Content-Type: application/json; charset=utf-8" 
  
 \ 
  
--data  
 "{ 
 'document':{ 
 'type':'PLAIN_TEXT', 
 'gcsContentUri':'gs:// <bucket-name> 
/ <object-name> 
' 
 } 
 }" 
  
 "https://language.googleapis.com/v2/documents:analyzeEntities"

    If you don't specify document.language_code , then the language will be automatically detected. For information on which languages are supported by the Natural Language API, see Language Support . See the Document reference documentation for more information on configuring the request body.

    If the request is successful, the server returns a 200 OK HTTP status code and the response in JSON format:

     { 
  
 "entities" 
 : 
  
 [ 
  
 { 
  
 "name" 
 : 
  
 "October 7" 
 , 
  
 "type" 
 : 
  
 "DATE" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "month" 
 : 
  
 "10" 
 , 
  
 "day" 
 : 
  
 "7" 
  
 }, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "October 7" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
 , 
  
 "probability" 
 : 
  
 1 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "1600" 
 , 
  
 "type" 
 : 
  
 "NUMBER" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "value" 
 : 
  
 "1600" 
  
 }, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "1600" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
 , 
  
 "probability" 
 : 
  
 1 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "7" 
 , 
  
 "type" 
 : 
  
 "NUMBER" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "value" 
 : 
  
 "7" 
  
 }, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "7" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
 , 
  
 "probability" 
 : 
  
 1 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "1600 Pennsylvania Ave NW, Washington, DC" 
 , 
  
 "type" 
 : 
  
 "ADDRESS" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "locality" 
 : 
  
 "Washington" 
 , 
  
 "narrow_region" 
 : 
  
 "District of Columbia" 
 , 
  
 "street_name" 
 : 
  
 "Pennsylvania Avenue Northwest" 
 , 
  
 "street_number" 
 : 
  
 "1600" 
 , 
  
 "broad_region" 
 : 
  
 "District of Columbia" 
 , 
  
 "country" 
 : 
  
 "US" 
  
 }, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "1600 Pennsylvania Ave NW, Washington, DC" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
 , 
  
 "probability" 
 : 
  
 1 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "1600 Pennsylvania Ave NW" 
 , 
  
 "type" 
 : 
  
 "LOCATION" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "1600 Pennsylvania Ave NW" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
 , 
  
 "probability" 
 : 
  
 0.901 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "President" 
 , 
  
 "type" 
 : 
  
 "PERSON" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "President" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "COMMON" 
 , 
  
 "probability" 
 : 
  
 0.941 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "Trump" 
 , 
  
 "type" 
 : 
  
 "PERSON" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "Trump" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
 , 
  
 "probability" 
 : 
  
 0.948 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "Washington, DC" 
 , 
  
 "type" 
 : 
  
 "LOCATION" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "Washington, DC" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
 , 
  
 "probability" 
 : 
  
 0.92 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "White House" 
 , 
  
 "type" 
 : 
  
 "LOCATION" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "White House" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
 , 
  
 "probability" 
 : 
  
 0.785 
  
 } 
  
 ] 
  
 } 
  
 ], 
  
 "languageCode" 
 : 
  
 "en" 
 , 
  
 "languageSupported" 
 : 
  
 true 
 }

    The entities array contains Entity objects representing the detected entities, which include information such as the entity name and type.

    gcloud

    Refer to the analyze-entities command for complete details.

    To perform entity analysis on a file in Cloud Storage, use the gcloud command line tool and use the --content-file flag to identify the file path that contains the content to analyze:

    gcloud ml language analyze-entities --content-file=gs:// YOUR_BUCKET_NAME 
/ YOUR_FILE_NAME

    If the request is successful, the server returns a response in JSON format:

     { 
  
 "entities" 
 : 
  
 [ 
  
 { 
  
 "name" 
 : 
  
 "October 7" 
 , 
  
 "type" 
 : 
  
 "DATE" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "month" 
 : 
  
 "10" 
 , 
  
 "day" 
 : 
  
 "7" 
  
 }, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "October 7" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
 , 
  
 "probability" 
 : 
  
 1 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "1600" 
 , 
  
 "type" 
 : 
  
 "NUMBER" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "value" 
 : 
  
 "1600" 
  
 }, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "1600" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
 , 
  
 "probability" 
 : 
  
 1 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "7" 
 , 
  
 "type" 
 : 
  
 "NUMBER" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "value" 
 : 
  
 "7" 
  
 }, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "7" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
 , 
  
 "probability" 
 : 
  
 1 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "1600 Pennsylvania Ave NW, Washington, DC" 
 , 
  
 "type" 
 : 
  
 "ADDRESS" 
 , 
  
 "metadata" 
 : 
  
 { 
  
 "locality" 
 : 
  
 "Washington" 
 , 
  
 "narrow_region" 
 : 
  
 "District of Columbia" 
 , 
  
 "street_name" 
 : 
  
 "Pennsylvania Avenue Northwest" 
 , 
  
 "street_number" 
 : 
  
 "1600" 
 , 
  
 "broad_region" 
 : 
  
 "District of Columbia" 
 , 
  
 "country" 
 : 
  
 "US" 
  
 }, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "1600 Pennsylvania Ave NW, Washington, DC" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "TYPE_UNKNOWN" 
 , 
  
 "probability" 
 : 
  
 1 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "1600 Pennsylvania Ave NW" 
 , 
  
 "type" 
 : 
  
 "LOCATION" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "1600 Pennsylvania Ave NW" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
 , 
  
 "probability" 
 : 
  
 0.901 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "President" 
 , 
  
 "type" 
 : 
  
 "PERSON" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "President" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "COMMON" 
 , 
  
 "probability" 
 : 
  
 0.941 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "Trump" 
 , 
  
 "type" 
 : 
  
 "PERSON" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "Trump" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
 , 
  
 "probability" 
 : 
  
 0.948 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "Washington, DC" 
 , 
  
 "type" 
 : 
  
 "LOCATION" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "Washington, DC" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
 , 
  
 "probability" 
 : 
  
 0.92 
  
 } 
  
 ] 
  
 }, 
  
 { 
  
 "name" 
 : 
  
 "White House" 
 , 
  
 "type" 
 : 
  
 "LOCATION" 
 , 
  
 "metadata" 
 : 
  
 {}, 
  
 "mentions" 
 : 
  
 [ 
  
 { 
  
 "text" 
 : 
  
 { 
  
 "content" 
 : 
  
 "White House" 
 , 
  
 "beginOffset" 
 : 
  
 - 
 1 
  
 }, 
  
 "type" 
 : 
  
 "PROPER" 
 , 
  
 "probability" 
 : 
  
 0.785 
  
 } 
  
 ] 
  
 } 
  
 ], 
  
 "languageCode" 
 : 
  
 "en" 
 , 
  
 "languageSupported" 
 : 
  
 true 
 }

    The entities array contains Entity objects representing the detected entities, which include information such as the entity name and type.

    Go

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Go API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      func 
  
 analyzeEntitiesFromGCS 
 ( 
 ctx 
  
 context 
 . 
 Context 
 , 
  
 gcsURI 
  
 string 
 ) 
  
 ( 
 * 
 languagepb 
 . 
 AnalyzeEntitiesResponse 
 , 
  
 error 
 ) 
  
 { 
  
 return 
  
 client 
 . 
 AnalyzeEntities 
 ( 
 ctx 
 , 
  
& languagepb 
 . 
 AnalyzeEntitiesRequest 
 { 
  
 Document 
 : 
  
& languagepb 
 . 
 Document 
 { 
  
 Source 
 : 
  
& languagepb 
 . 
 Document_GcsContentUri 
 { 
  
 GcsContentUri 
 : 
  
 gcsURI 
 , 
  
 }, 
  
 Type 
 : 
  
 languagepb 
 . 
 Document_PLAIN_TEXT 
 , 
  
 }, 
  
 EncodingType 
 : 
  
 languagepb 
 . 
 EncodingType_UTF8 
 , 
  
 }) 
 }

    Java

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Java API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      // Instantiate the Language client com.google.cloud.language.v2.LanguageServiceClient 
 try 
  
 ( 
 LanguageServiceClient 
  
 language 
  
 = 
  
 LanguageServiceClient 
 . 
 create 
 ()) 
  
 { 
  
 // Set the GCS Content URI path to the file to be analyzed 
  
 Document 
  
 doc 
  
 = 
  
 Document 
 . 
 newBuilder 
 (). 
 setGcsContentUri 
 ( 
 gcsUri 
 ). 
 setType 
 ( 
 Type 
 . 
 PLAIN_TEXT 
 ). 
 build 
 (); 
  
 AnalyzeEntitiesRequest 
  
 request 
  
 = 
  
 AnalyzeEntitiesRequest 
 . 
 newBuilder 
 () 
  
 . 
 setDocument 
 ( 
 doc 
 ) 
  
 . 
 setEncodingType 
 ( 
 EncodingType 
 . 
 UTF16 
 ) 
  
 . 
 build 
 (); 
  
 AnalyzeEntitiesResponse 
  
 response 
  
 = 
  
 language 
 . 
 analyzeEntities 
 ( 
 request 
 ); 
  
 // Print the response 
  
 for 
  
 ( 
 Entity 
  
 entity 
  
 : 
  
 response 
 . 
 getEntitiesList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
 "Entity: %s\n" 
 , 
  
 entity 
 . 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Metadata: " 
 ); 
  
 for 
  
 ( 
 Map 
 . 
 Entry<String 
 , 
  
 String 
>  
 entry 
  
 : 
  
 entity 
 . 
 getMetadataMap 
 (). 
 entrySet 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
 "%s : %s" 
 , 
  
 entry 
 . 
 getKey 
 (), 
  
 entry 
 . 
 getValue 
 ()); 
  
 } 
  
 for 
  
 ( 
 EntityMention 
  
 mention 
  
 : 
  
 entity 
 . 
 getMentionsList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
 "Begin offset: %d\n" 
 , 
  
 mention 
 . 
 getText 
 (). 
 getBeginOffset 
 ()); 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
 "Content: %s\n" 
 , 
  
 mention 
 . 
 getText 
 (). 
 getContent 
 ()); 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
 "Type: %s\n\n" 
 , 
  
 mention 
 . 
 getType 
 ()); 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
 "Probability: %s\n\n" 
 , 
  
 mention 
 . 
 getProbability 
 ()); 
  
 } 
  
 } 
 }

    Node.js

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Node.js API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      // Imports the Google Cloud client library 
 const 
  
 language 
  
 = 
  
 require 
 ( 
 ' @google-cloud/language 
' 
 ). 
 v2 
 ; 
 // Creates a client 
 const 
  
 client 
  
 = 
  
 new 
  
 language 
 . 
  LanguageServiceClient 
 
 (); 
 /** 
 * TODO(developer): Uncomment the following lines to run this code 
 */ 
 // const bucketName = 'Your bucket name, e.g. my-bucket'; 
 // const fileName = 'Your file name, e.g. my-file.txt'; 
 // Prepares a document, representing a text file in Cloud Storage 
 const 
  
 document 
  
 = 
  
 { 
  
 gcsContentUri 
 : 
  
 `gs:// 
 ${ 
 bucketName 
 } 
 / 
 ${ 
 fileName 
 } 
 ` 
 , 
  
 type 
 : 
  
 'PLAIN_TEXT' 
 , 
 }; 
 // Detects entities in the document 
 const 
  
 [ 
 result 
 ] 
  
 = 
  
 await 
  
 client 
 . 
 analyzeEntities 
 ({ 
 document 
 }); 
 const 
  
 entities 
  
 = 
  
 result 
 . 
 entities 
 ; 
 console 
 . 
 log 
 ( 
 'Entities:' 
 ); 
 entities 
 . 
 forEach 
 ( 
 entity 
  
 = 
>  
 { 
  
 console 
 . 
 log 
 ( 
 entity 
 . 
 name 
 ); 
  
 console 
 . 
 log 
 ( 
 ` - Type: 
 ${ 
 entity 
 . 
 type 
 } 
 ` 
 ); 
  
 if 
  
 ( 
 entity 
 . 
 metadata 
 ) 
  
 { 
  
 console 
 . 
 log 
 ( 
 ` - Metadata: 
 ${ 
 entity 
 . 
 metadata 
 } 
 ` 
 ); 
  
 } 
 });

    Python

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Python API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      from 
  
 google.cloud 
  
 import 
 language_v2 
 def 
  
 sample_analyze_entities 
 ( 
 gcs_content_uri 
 : 
 str 
 = 
 "gs://cloud-samples-data/language/entity.txt" 
 , 
 ) 
 - 
> None 
 : 
  
 """ 
 Analyzes Entities in text file stored in Cloud Storage. 
 Args: 
 gcs_content_uri: Google Cloud Storage URI where the file content is located. 
 e.g. gs://[Your Bucket]/[Path to File] 
 """ 
 client 
 = 
 language_v2 
 . 
 LanguageServiceClient 
 () 
 # Available types: PLAIN_TEXT, HTML 
 document_type_in_plain_text 
 = 
 language_v2 
 . 
 Document 
 . 
 Type 
 . 
 PLAIN_TEXT 
 # Optional. If not specified, the language is automatically detected. 
 # For list of supported languages: 
 # https://cloud.google.com/natural-language/docs/languages 
 language_code 
 = 
 "en" 
 document 
 = 
 { 
 "gcs_content_uri" 
 : 
 gcs_content_uri 
 , 
 "type_" 
 : 
 document_type_in_plain_text 
 , 
 "language_code" 
 : 
 language_code 
 , 
 } 
 # Available values: NONE, UTF8, UTF16, UTF32. 
 # See https://cloud.google.com/natural-language/docs/reference/rest/v2/EncodingType. 
 encoding_type 
 = 
 language_v2 
 . 
 EncodingType 
 . 
 UTF8 
 response 
 = 
 client 
 . 
  analyze_entities 
 
 ( 
 request 
 = 
 { 
 "document" 
 : 
 document 
 , 
 "encoding_type" 
 : 
 encoding_type 
 } 
 ) 
 for 
 entity 
 in 
 response 
 . 
 entities 
 : 
 print 
 ( 
 f 
 "Representative name for the entity: 
 { 
 entity 
 . 
 name 
 } 
 " 
 ) 
 # Get entity type, e.g. PERSON, LOCATION, ADDRESS, NUMBER, et al. 
 # See https://cloud.google.com/natural-language/docs/reference/rest/v2/Entity#type. 
 print 
 ( 
 f 
 "Entity type: 
 { 
 language_v2 
 . 
 Entity 
 . 
 Type 
 ( 
 entity 
 . 
 type_ 
 ) 
 . 
 name 
 } 
 " 
 ) 
 # Loop over the metadata associated with entity. 
 # Some entity types may have additional metadata, e.g. ADDRESS entities 
 # may have metadata for the address street_name, postal_code, et al. 
 for 
 metadata_name 
 , 
 metadata_value 
 in 
 entity 
 . 
 metadata 
 . 
 items 
 (): 
 print 
 ( 
 f 
 " 
 { 
 metadata_name 
 } 
 : 
 { 
 metadata_value 
 } 
 " 
 ) 
 # Loop over the mentions of this entity in the input document. 
 # The API currently supports proper noun mentions. 
 for 
 mention 
 in 
 entity 
 . 
 mentions 
 : 
 print 
 ( 
 f 
 "Mention text: 
 { 
 mention 
 . 
 text 
 . 
 content 
 } 
 " 
 ) 
 # Get the mention type, e.g. PROPER for proper noun 
 print 
 ( 
 "Mention type:" 
 f 
 " 
 { 
 language_v2 
 . 
 EntityMention 
 . 
 Type 
 ( 
 mention 
 . 
 type_ 
 ) 
 . 
 name 
 } 
 " 
 ) 
 # Get the probability score associated with the first mention of the entity in the (0, 1.0] range. 
 print 
 ( 
 f 
 "Probability score: 
 { 
 mention 
 . 
 probability 
 } 
 " 
 ) 
 # Get the language of the text, which will be the same as 
 # the language specified in the request or, if not specified, 
 # the automatically-detected language. 
 print 
 ( 
 f 
 "Language of the text: 
 { 
 response 
 . 
 language_code 
 } 
 " 
 )

    Additional languages

    C#: Please follow the C# setup instructions on the client libraries page and then visit the Natural Language reference documentation for .NET.

    PHP: Please follow the PHP setup instructions on the client libraries page and then visit the Natural Language reference documentation for PHP.

    Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the Natural Language reference documentation for Ruby.

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