Process a PDF file with Gemini

  import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "io" 
  
 "google.golang.org/genai" 
 ) 
 // generateTextWithPDF shows how to generate text using a PDF file input. 
 func 
  
 generateTextWithPDF 
 ( 
 w 
  
 io 
 . 
 Writer 
 ) 
  
 error 
  
 { 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 genai 
 . 
 NewClient 
 ( 
 ctx 
 , 
  
& genai 
 . 
 ClientConfig 
 { 
  
 HTTPOptions 
 : 
  
 genai 
 . 
 HTTPOptions 
 { 
 APIVersion 
 : 
  
 "v1" 
 }, 
  
 }) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "failed to create genai client: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 modelName 
  
 := 
  
 "gemini-2.5-flash" 
  
 contents 
  
 := 
  
 [] 
 * 
 genai 
 . 
 Content 
 { 
  
 { 
 Parts 
 : 
  
 [] 
 * 
 genai 
 . 
 Part 
 { 
  
 { 
 Text 
 : 
  
 `You are a highly skilled document summarization specialist. 
 Your task is to provide a concise executive summary of no more than 300 words. 
 Please summarize the given document for a general audience.` 
 }, 
  
 { 
 FileData 
 : 
  
& genai 
 . 
 FileData 
 { 
  
 FileURI 
 : 
  
 "gs://cloud-samples-data/generative-ai/pdf/1706.03762v7.pdf" 
 , 
  
 MIMEType 
 : 
  
 "application/pdf" 
 , 
  
 }}, 
  
 }, 
  
 Role 
 : 
  
 "user" 
 }, 
  
 } 
  
 resp 
 , 
  
 err 
  
 := 
  
 client 
 . 
 Models 
 . 
 GenerateContent 
 ( 
 ctx 
 , 
  
 modelName 
 , 
  
 contents 
 , 
  
 nil 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "failed to generate content: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 respText 
  
 := 
  
 resp 
 . 
 Text 
 () 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 respText 
 ) 
  
 // Example response: 
  
 // "Attention Is All You Need" introduces the Transformer, 
  
 // a groundbreaking neural network architecture designed for... 
  
 // ... 
  
 return 
  
 nil 
 } 
 

  import 
  
 com.google.genai.Client 
 ; 
 import 
  
 com.google.genai.types.Content 
 ; 
 import 
  
 com.google.genai.types.GenerateContentResponse 
 ; 
 import 
  
 com.google.genai.types.HttpOptions 
 ; 
 import 
  
 com.google.genai.types.Part 
 ; 
 public 
  
 class 
 TextGenerationWithPdf 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 modelId 
  
 = 
  
 "gemini-2.5-flash" 
 ; 
  
 generateContent 
 ( 
 modelId 
 ); 
  
 } 
  
 // Generates text with PDF file input 
  
 public 
  
 static 
  
 String 
  
 generateContent 
 ( 
 String 
  
 modelId 
 ) 
  
 { 
  
 // Initialize client that will be used to send requests. This client only needs to be created 
  
 // once, and can be reused for multiple requests. 
  
 try 
  
 ( 
 Client 
  
 client 
  
 = 
  
 Client 
 . 
 builder 
 () 
  
 . 
 location 
 ( 
 "global" 
 ) 
  
 . 
 vertexAI 
 ( 
 true 
 ) 
  
 . 
 httpOptions 
 ( 
 HttpOptions 
 . 
 builder 
 (). 
 apiVersion 
 ( 
 "v1" 
 ). 
 build 
 ()) 
  
 . 
 build 
 ()) 
  
 { 
  
 String 
  
 prompt 
  
 = 
  
 "You are a highly skilled document summarization specialist.\n" 
  
 + 
  
 " Your task is to provide a concise executive summary of no more than 300 words.\n" 
  
 + 
  
 " Please summarize the given document for a general audience" 
 ; 
  
 GenerateContentResponse 
  
 response 
  
 = 
  
 client 
 . 
 models 
 . 
 generateContent 
 ( 
  
 modelId 
 , 
  
 Content 
 . 
 fromParts 
 ( 
  
 Part 
 . 
 fromUri 
 ( 
  
 "gs://cloud-samples-data/generative-ai/pdf/1706.03762v7.pdf" 
 , 
  
 "application/pdf" 
 ), 
  
 Part 
 . 
 fromText 
 ( 
 prompt 
 )), 
  
 null 
 ); 
  
 System 
 . 
 out 
 . 
 print 
 ( 
 response 
 . 
 text 
 ()); 
  
 // Example response: 
  
 // The document introduces the Transformer, a novel neural network architecture designed for 
  
 // sequence transduction tasks, such as machine translation. Unlike previous dominant models 
  
 // that rely on complex recurrent or convolutional neural networks, the Transformer proposes a 
  
 // simpler, more parallelizable design based *solely* on attention mechanisms, entirely 
  
 // dispensing with recurrence and convolutions... 
  
 return 
  
 response 
 . 
 text 
 (); 
  
 } 
  
 } 
 } 
 

  from 
  
 google 
  
 import 
 genai 
 from 
  
 google.genai.types 
  
 import 
 HttpOptions 
 , 
 Part 
 client 
 = 
 genai 
 . 
 Client 
 ( 
 http_options 
 = 
 HttpOptions 
 ( 
 api_version 
 = 
 "v1" 
 )) 
 model_id 
 = 
 "gemini-2.5-flash" 
 prompt 
 = 
 """ 
 You are a highly skilled document summarization specialist. 
 Your task is to provide a concise executive summary of no more than 300 words. 
 Please summarize the given document for a general audience. 
 """ 
 pdf_file 
 = 
 Part 
 . 
 from_uri 
 ( 
 file_uri 
 = 
 "gs://cloud-samples-data/generative-ai/pdf/1706.03762v7.pdf" 
 , 
 mime_type 
 = 
 "application/pdf" 
 , 
 ) 
 response 
 = 
 client 
 . 
 models 
 . 
 generate_content 
 ( 
 model 
 = 
 model_id 
 , 
 contents 
 = 
 [ 
 pdf_file 
 , 
 prompt 
 ], 
 ) 
 print 
 ( 
 response 
 . 
 text 
 ) 
 # Example response: 
 # Here is a summary of the document in 300 words. 
 # 
 # The paper introduces the Transformer, a novel neural network architecture for 
 # sequence transduction tasks like machine translation. Unlike existing models that rely on recurrent or 
 # convolutional layers, the Transformer is based entirely on attention mechanisms. 
 # ...