使用 Gemini API 分析文档（例如 PDF 文件）

 import FirebaseAILogic // Initialize the Gemini Developer API backend service let ai = FirebaseAI.firebaseAI(backend: .googleAI()) // Create a `GenerativeModel` instance with a model that supports your use case let model = ai.generativeModel(modelName: "gemini-2.5-flash") // Provide the PDF as `Data` with the appropriate MIME type let pdf = try InlineDataPart(data: Data(contentsOf: pdfURL), mimeType: "application/pdf") // Provide a text prompt to include with the PDF file let prompt = "Summarize the important results in this report." // To stream generated text output, call `generateContentStream` with the PDF file and text prompt let contentStream = try model.generateContentStream(pdf, prompt) // Print the generated text, handling the case where it might be nil for try await chunk in contentStream {  if let text = chunk.text {  print(text)  } } 

 // Initialize the Gemini Developer API backend service // Create a `GenerativeModel` instance with a model that supports your use case val model = Firebase.ai(backend = GenerativeBackend.googleAI())  .generativeModel("gemini-2.5-flash") val contentResolver = applicationContext.contentResolver // Provide the URI for the PDF you want to send to the model val inputStream = contentResolver.openInputStream(pdfUri) if (inputStream != null) { // Check if the PDF file loaded successfully  inputStream.use { stream ->  // Provide a prompt that includes the PDF file specified above and text  val prompt = content {  inlineData(  bytes = stream.readBytes(),  mimeType = "application/pdf" // Specify the appropriate PDF file MIME type  )  text("Summarize the important results in this report.")  }  // To stream generated text output, call `generateContentStream` with the prompt  var fullResponse = ""  model.generateContentStream(prompt).collect { chunk ->  // Log the generated text, handling the case where it might be null  val chunkText = chunk.text ?: ""  Log.d(TAG, chunkText)  fullResponse += chunkText  }  } } else {  Log.e(TAG, "Error getting input stream for file.")  // Handle the error appropriately } 

 // Initialize the Gemini Developer API backend service // Create a `GenerativeModel` instance with a model that supports your use case GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())  .generativeModel("gemini-2.5-flash"); // Use the GenerativeModelFutures Java compatibility layer which offers // support for ListenableFuture and Publisher APIs GenerativeModelFutures model = GenerativeModelFutures.from(ai); ContentResolver resolver = getApplicationContext().getContentResolver(); // Provide the URI for the PDF file you want to send to the model try (InputStream stream = resolver.openInputStream(pdfUri)) {  if (stream != null) {  byte[] audioBytes = stream.readAllBytes();  stream.close();  // Provide a prompt that includes the PDF file specified above and text  Content prompt = new Content.Builder()  .addInlineData(audioBytes, "application/pdf") // Specify the appropriate PDF file MIME type  .addText("Summarize the important results in this report.")  .build();  // To stream generated text output, call `generateContentStream` with the prompt  Publisher<GenerateContentResponse> streamingResponse =  model.generateContentStream(prompt);  StringBuilder fullResponse = new StringBuilder();  streamingResponse.subscribe(new Subscriber<GenerateContentResponse>() {  @Override  public void onNext(GenerateContentResponse generateContentResponse) {  String chunk = generateContentResponse.getText();  String text = (chunk == null) ? "" : chunk;  Log.d(TAG, text);  fullResponse.append(text);  }  @Override  public void onComplete() {  Log.d(TAG, fullResponse.toString());  }  @Override  public void onError(Throwable t) {  Log.e(TAG, "Failed to generate a response", t);  }  @Override  public void onSubscribe(Subscription s) {  }  });  } else {  Log.e(TAG, "Error getting input stream for file.");  // Handle the error appropriately  } } catch (IOException e) {  Log.e(TAG, "Failed to read the pdf file", e); } catch (URISyntaxException e) {  Log.e(TAG, "Invalid pdf file", e); } 

 import { initializeApp } from "firebase/app"; import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai"; // TODO(developer) Replace the following with your app's Firebase configuration // See: https://firebase.google.com/docs/web/learn-more#config-object const firebaseConfig = {  // ... }; // Initialize FirebaseApp const firebaseApp = initializeApp(firebaseConfig); // Initialize the Gemini Developer API backend service const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() }); // Create a `GenerativeModel` instance with a model that supports your use case const model = getGenerativeModel(ai, { model: "gemini-2.5-flash" }); // Converts a File object to a Part object. async function fileToGenerativePart(file) {  const base64EncodedDataPromise = new Promise((resolve) => {  const reader = new FileReader();  reader.onloadend = () => resolve(reader.result.split(','));  reader.readAsDataURL(file);  });  return {  inlineData: { data: await base64EncodedDataPromise, mimeType: file.type },  }; } async function run() {  // Provide a text prompt to include with the PDF file  const prompt = "Summarize the important results in this report.";  // Prepare PDF file for input  const fileInputEl = document.querySelector("input[type=file]");  const pdfPart = await fileToGenerativePart(fileInputEl.files);  // To stream generated text output, call `generateContentStream` with the text and PDF file  const result = await model.generateContentStream([prompt, pdfPart]);  // Log the generated text  for await (const chunk of result.stream) {  const chunkText = chunk.text();  console.log(chunkText);  } } run(); 

 import 'package:firebase_ai/firebase_ai.dart'; import 'package:firebase_core/firebase_core.dart'; import 'firebase_options.dart'; // Initialize FirebaseApp await Firebase.initializeApp(  options: DefaultFirebaseOptions.currentPlatform, ); // Initialize the Gemini Developer API backend service // Create a `GenerativeModel` instance with a model that supports your use case final model =  FirebaseAI.googleAI().generativeModel(model: 'gemini-2.5-flash'); // Provide a text prompt to include with the PDF file final prompt = TextPart("Summarize the important results in this report."); // Prepare the PDF file for input final doc = await File('document0.pdf').readAsBytes(); // Provide the PDF file as `Data` with the appropriate PDF file MIME type final docPart = InlineDataPart('application/pdf', doc); // To generate text output, call `generateContentStream` with the text and PDF file final response = await model.generateContentStream([  Content.multi([prompt,docPart]) ]); // Print the generated text await for (final chunk in response) {  print(chunk.text); } 

 using Firebase; using Firebase.AI; // Initialize the Gemini Developer API backend service var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()); // Create a `GenerativeModel` instance with a model that supports your use case var model = ai.GetGenerativeModel(modelName: "gemini-2.5-flash"); // Provide a text prompt to include with the PDF file var prompt = ModelContent.Text("Summarize the important results in this report."); // Provide the PDF file as `data` with the appropriate PDF file MIME type var doc = ModelContent.InlineData("application/pdf",  System.IO.File.ReadAllBytes(System.IO.Path.Combine(  UnityEngine.Application.streamingAssetsPath, "document0.pdf"))); // To stream generated text output, call `GenerateContentStreamAsync` with the text and PDF file var responseStream = model.GenerateContentStreamAsync(new [] { prompt, doc }); // Print the generated text await foreach (var response in responseStream) {  if (!string.IsNullOrWhiteSpace(response.Text)) {  UnityEngine.Debug.Log(response.Text);  } }