Google AI & Vertex AI Gemini API Dart Client #

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Unofficial Dart client for the Google AI Gemini Developer API and Vertex AI Gemini API with unified interface.

Note

The official google_generative_ai Dart package has been deprecated in favor of firebase_ai. However, since firebase_ai is a Flutter package rather than a pure Dart package, this unofficial client bridges the gap by providing a pure Dart, fully type-safe API client for both Google AI and Vertex AI.

Ideal for server-side Dart, CLI tools, and backend services needing a type-safe Gemini API client without Flutter dependencies.

Table of Contents

Features
Why choose this client?
Quickstart
Installation
API Versions
Vertex AI Support
Advanced Configuration
Usage
Migrating from Google AI to Vertex AI
Examples
API Coverage
Development
License

Features #

🌍 Core Features (Both Google AI & Vertex AI) #

Generation & Streaming

✅ Content generation (generateContent)
✅ Streaming support (streamGenerateContent) with SSE
✅ Request abortion (cancelable requests via abortTrigger)
✅ Token counting (countTokens)
✅ Function calling support (tools & function declarations)
✅ Safety settings support

Embeddings

✅ Content embeddings (embedContent)
✅ Batch embeddings with automatic fallback

Models

✅ Model listing & discovery (listModels, getModel)
✅ Content caching (full CRUD operations)

🔵 Google AI Only (Not available on Vertex AI) #

Files API

✅ File management (upload, list, get, download, delete)
✅ Multiple upload methods: file path (IO), bytes (all platforms), streaming (IO)
ℹ️ Vertex AI: Use Cloud Storage URIs or base64

Tuned Models

✅ Tuned models (create, list, get, patch, delete, listOperations)
✅ Generation with tuned models
ℹ️ Vertex AI: Use Vertex AI Tuning API

Corpora

✅ Corpus management (create, list, get, update, delete)
⚠️ Document/Chunk/RAG features are only available in Vertex AI
ℹ️ Vertex AI: Use RAG Stores & Vector Search for full RAG capabilities

Note: Google AI only supports basic corpus CRUD operations. The Semantic Retriever API (documents, chunks, semantic search) has been succeeded by Vertex AI Vector Search and RAG Stores.

Permissions

✅ Permission management (create, list, get, update, delete)
✅ Ownership transfer (transferOwnership)
✅ Grantee types (user, group, everyone)
✅ Role-based access (owner, writer, reader)

Prediction (Video Generation)

✅ Synchronous prediction (predict)
✅ Long-running prediction (predictLongRunning) for video generation with Veo
✅ Operation status polling for async predictions
✅ RAI (Responsible AI) filtering support

Batch Operations

✅ Batch content generation (batchGenerateContent)
✅ Synchronous batch embeddings (batchEmbedContents)
✅ Asynchronous batch embeddings (asyncBatchEmbedContent)
✅ Batch management (list, get, update, delete, cancel)
✅ LRO polling for async batch jobs

Quick Comparison #

Aspect	Google AI	Vertex AI
Auth	API Key	OAuth 2.0
Core Features	✅ Full support	✅ Full support
Files API	✅ Supported	❌ Use Cloud Storage URIs
Tuned Models	✅ Supported	❌ Different tuning API
Corpora/RAG	⚠️ Corpus CRUD only	✅ Full RAG support
Enterprise	❌ None	✅ VPC, CMEK, HIPAA

Why choose this client? #

✅ Type-safe with sealed classes
✅ Multiple auth methods (API key, OAuth)
✅ Minimal dependencies (http, logging only)
✅ Works on all compilation targets (native, web, WASM)
✅ Interceptor-driven architecture
✅ Comprehensive error handling
✅ Automatic retry with exponential backoff
✅ Long-running operations (LRO polling)
✅ Pagination support (Paginator utility)
✅ 560+ tests

Quickstart #

import 'package:googleai_dart/googleai_dart.dart';

final client = GoogleAIClient(
  config: GoogleAIConfig.googleAI(
    authProvider: ApiKeyProvider('YOUR_API_KEY'),
  ),
);

final response = await client.models.generateContent(
  model: 'gemini-2.5-flash',
  request: GenerateContentRequest(
    contents: [
      Content(
        parts: [TextPart('Hello Gemini!')], 
        role: 'user',
      ),
    ],
  ),
);

print(response.text);
client.close();

Installation #

dependencies:
  googleai_dart: {version}

API Versions #

This client supports both stable and beta versions of the API:

Version	Stability	Use Case	Features
v1	Stable	Production apps	Guaranteed stability, breaking changes trigger new major version
v1beta	Beta	Testing & development	Early-access features, subject to rapid/breaking changes

Default: The client defaults to v1beta for broadest feature access.

API Version Configuration

import 'package:googleai_dart/googleai_dart.dart';

// Use stable v1
final client = GoogleAIClient(
  config: GoogleAIConfig.googleAI(
    apiVersion: ApiVersion.v1,
    authProvider: ApiKeyProvider('YOUR_API_KEY'),
  ),
);

// Use beta v1beta
final betaClient = GoogleAIClient(
  config: GoogleAIConfig.googleAI(
    apiVersion: ApiVersion.v1beta,
    authProvider: ApiKeyProvider('YOUR_API_KEY'),
  ),
);

Vertex AI Support #

To use Vertex AI, you need:

A Google Cloud Platform (GCP) project
Vertex AI Gemini API enabled
Service account with roles/aiplatform.user role
OAuth 2.0 credentials

Complete Vertex AI Setup

import 'package:googleai_dart/googleai_dart.dart';

// Create an OAuth provider (implement token refresh logic)
class MyOAuthProvider implements AuthProvider {
  @override
  Future<AuthCredentials> getCredentials() async {
    // Your OAuth token refresh logic here
    final token = await getAccessToken(); // Implement this
    // You can use https://pub.dev/packages/googleapis_auth
    return BearerTokenCredentials(token);
  }
}

// Configure for Vertex AI
final vertexClient = GoogleAIClient(
  config: GoogleAIConfig.vertexAI(
    projectId: 'your-gcp-project-id',
    location: 'us-central1', // Or 'global', 'europe-west1', etc.
    apiVersion: ApiVersion.v1, // Stable version
    authProvider: MyOAuthProvider(),
  ),
);

// Use the same API as Google AI
final response = await vertexClient.models.generateContent(
  model: 'gemini-2.5-flash',
  request: GenerateContentRequest(
    contents: [
      Content(
        parts: [TextPart('Explain quantum computing')],
        role: 'user',
      ),
    ],
  ),
);

Advanced Configuration #

Custom Configuration Options

For advanced use cases, you can use the main GoogleAIConfig constructor with full control over all parameters:

import 'package:googleai_dart/googleai_dart.dart';

final client = GoogleAIClient(
  config: GoogleAIConfig(
    baseUrl: 'https://my-custom-proxy.example.com',
    apiMode: ApiMode.googleAI,
    apiVersion: ApiVersion.v1,
    authProvider: ApiKeyProvider('YOUR_API_KEY'),
    timeout: Duration(minutes: 5),
    retryPolicy: RetryPolicy(maxRetries: 5),
    // ... other parameters
  ),
);

Use cases for custom base URLs:

Proxy servers: Route requests through a corporate proxy
Testing: Point to a mock server for integration tests
Regional endpoints: Use region-specific URLs if needed
Custom deployments: Self-hosted or specialized endpoints

The factory constructors (GoogleAIConfig.googleAI() and GoogleAIConfig.vertexAI()) are convenience methods that set sensible defaults, but you can always use the main constructor for full control.

Usage #

Authentication #

All Authentication Methods

The client uses an AuthProvider pattern for flexible authentication:

import 'package:googleai_dart/googleai_dart.dart';

// API Key authentication (query parameter)
final client = GoogleAIClient(
  config: GoogleAIConfig(
    authProvider: ApiKeyProvider('YOUR_API_KEY'),
  ),
);

// API Key as header
final clientWithHeader = GoogleAIClient(
  config: GoogleAIConfig(
    authProvider: ApiKeyProvider(
      'YOUR_API_KEY',
      placement: AuthPlacement.header,
    ),
  ),
);

// Bearer token (for OAuth)
final clientWithBearer = GoogleAIClient(
  config: GoogleAIConfig(
    authProvider: BearerTokenProvider('YOUR_BEARER_TOKEN'),
  ),
);

// Custom OAuth with automatic token refresh
class CustomOAuthProvider implements AuthProvider {
  @override
  Future<AuthCredentials> getCredentials() async {
    // Your token refresh logic here
    // Called on each request, including retries
    return BearerTokenCredentials(await refreshToken());
  }
}

final oauthClient = GoogleAIClient(
  config: GoogleAIConfig(
    authProvider: CustomOAuthProvider(),
  ),
);

Basic Generation #

Basic Generation Example

import 'package:googleai_dart/googleai_dart.dart';

final client = GoogleAIClient(
  config: GoogleAIConfig(
    authProvider: ApiKeyProvider('YOUR_API_KEY'),
  ),
);

final response = await client.models.generateContent(
  model: 'gemini-2.5-flash',
  request: GenerateContentRequest(
    contents: [
      Content(
        parts: [TextPart('Explain quantum computing')],
        role: 'user',
      ),
    ],
  ),
);

print(response.candidates?.first.content?.parts.first);
client.close();

Streaming #

Streaming Example

import 'package:googleai_dart/googleai_dart.dart';

// Assumes you have a configured client instance
await for (final chunk in client.models.streamGenerateContent(
  model: 'gemini-2.5-flash',
  request: request,
)) {
  // Process each chunk as it arrives
  final text = chunk.candidates?.first.content?.parts.first;
  if (text is TextPart) print(text.text);
}

Canceling Requests #

Request Cancellation Examples

You can cancel long-running requests using an abort trigger:

import 'dart:async';
import 'package:googleai_dart/googleai_dart.dart';

// Assumes you have a configured client instance
final abortController = Completer<void>();

// Start request with abort capability
final requestFuture = client.models.generateContent(
  model: 'gemini-2.5-flash',
  request: request,
  abortTrigger: abortController.future,
);

// To cancel:
abortController.complete();

// Handle cancellation
try {
  final response = await requestFuture;
} on AbortedException {
  print('Request was canceled');
}

This works for both regular and streaming requests. You can also use it with timeouts:

// Auto-cancel after 30 seconds
final response = await client.models.generateContent(
  model: 'gemini-2.5-flash',
  request: request,
  abortTrigger: Future.delayed(Duration(seconds: 30)),
);

Function Calling #

Function Calling Example

import 'package:googleai_dart/googleai_dart.dart';

// Assumes you have a configured client instance
final tools = [
  Tool(
    functionDeclarations: [
      FunctionDeclaration(
        name: 'get_weather',
        description: 'Get current weather',
        parameters: Schema(
          type: SchemaType.object,
          properties: {
            'location': Schema(type: SchemaType.string),
          },
          required: ['location'],
        ),
      ),
    ],
  ),
];

final response = await client.models.generateContent(
  model: 'gemini-2.5-flash',
  request: GenerateContentRequest(
    contents: [/* ... */],
    tools: tools,
  ),
);

Embeddings #

Embeddings Example

import 'package:googleai_dart/googleai_dart.dart';

// Assumes you have a configured client instance
final response = await client.models.embedContent(
  model: 'gemini-embedding-001',
  request: EmbedContentRequest(
    content: Content(
      parts: [TextPart('Hello world')],
    ),
    taskType: TaskType.retrievalDocument,
  ),
);

print(response.embedding.values); // List<double>

File Management #

Complete File Management Example

Upload files for use in multimodal prompts:

import 'dart:io' as io;
import 'package:googleai_dart/googleai_dart.dart';

// Assumes you have a configured client instance
// Upload a file
final file = await client.files.upload(
  filePath: '/path/to/image.jpg',
  mimeType: 'image/jpeg',
  displayName: 'My Image',
);

print('File uploaded: ${file.name}');
print('State: ${file.state}');
print('URI: ${file.uri}');

// Wait for file to be processed (if needed)
while (file.state == FileState.processing) {
  await Future.delayed(Duration(seconds: 2));
  file = await client.files.get(name: file.name);
}

// Use the file in a prompt
final response = await client.models.generateContent(
  model: 'gemini-2.0-flash-exp',
  request: GenerateContentRequest(
    contents: [
      Content(
        parts: [
          TextPart('Describe this image'),
          FileData(
            fileUri: file.uri,
            mimeType: file.mimeType,
          ),
        ],
        role: 'user',
      ),
    ],
  ),
);

// List all files
final listResponse = await client.files.list(pageSize: 10);
for (final f in listResponse.files ?? []) {
  print('${f.displayName}: ${f.state}');
}

// Download file content (if needed)
final bytes = await client.files.download(name: file.name);
// Save to disk or process bytes
await io.File('downloaded_file.jpg').writeAsBytes(bytes);

// Delete the file when done
await client.files.delete(name: file.name);

Note: Files are automatically deleted after 48 hours.

Context Caching #

Complete Context Caching Example

Context caching allows you to save frequently used content and reuse it across requests, reducing latency and costs:

import 'package:googleai_dart/googleai_dart.dart';

// Assumes you have a configured client instance
// Create cached content with system instructions
final cachedContent = await client.cachedContents.create(
  cachedContent: const CachedContent(
    model: 'models/gemini-1.5-flash-8b',
    displayName: 'Math Expert Cache',
    systemInstruction: Content(
      parts: [TextPart('You are an expert mathematician...')],
    ),
    ttl: '3600s', // Cache for 1 hour
  ),
);

// Use cached content in requests (saves tokens!)
final response = await client.models.generateContent(
  model: 'gemini-1.5-flash-8b',
  request: GenerateContentRequest(
    cachedContent: cachedContent.name,
    contents: [
      Content(parts: [TextPart('Explain the Pythagorean theorem')], role: 'user'),
    ],
  ),
);

// Update cache TTL
await client.cachedContents.update(
  name: cachedContent.name!,
  cachedContent: const CachedContent(
    model: 'models/gemini-1.5-flash-8b',
    ttl: '7200s', // Extend to 2 hours
  ),
  updateMask: 'ttl',
);

// Clean up when done
await client.cachedContents.delete(name: cachedContent.name!);

Benefits:

✅ Reduced latency for requests with large contexts
✅ Lower costs by reusing cached content
✅ Consistent system instructions across requests

Grounded Question Answering #

Complete generateAnswer Examples

The generateAnswer API provides answers grounded in specific sources, ideal for Retrieval Augmented Generation (RAG):

import 'package:googleai_dart/googleai_dart.dart';

// Assumes you have a configured client instance
// Using inline passages (for small knowledge bases)
final response = await client.models.generateAnswer(
  model: 'aqa',
  request: const GenerateAnswerRequest(
    contents: [
      Content(
        parts: [TextPart('What is the capital of France?')],
        role: 'user',
      ),
    ],
    answerStyle: AnswerStyle.abstractive, // Or: extractive, verbose
    inlinePassages: GroundingPassages(
      passages: [
        GroundingPassage(
          id: 'passage-1',
          content: Content(
            parts: [TextPart('Paris is the capital of France.')],
          ),
        ),
      ],
    ),
    temperature: 0.2, // Low temperature for factual answers
  ),
);

// Check answerability
if (response.answerableProbability != null &&
    response.answerableProbability! < 0.5) {
  print('⚠️ Answer may not be grounded in sources');
}

// Using semantic retriever (for large corpora)
final ragResponse = await client.models.generateAnswer(
  model: 'aqa',
  request: const GenerateAnswerRequest(
    contents: [
      Content(
        parts: [TextPart('What are the key features of Dart?')],
        role: 'user',
      ),
    ],
    answerStyle: AnswerStyle.verbose,
    semanticRetriever: SemanticRetrieverConfig(
      source: 'corpora/my-corpus',
      query: Content(
        parts: [TextPart('Dart programming language features')],
      ),
      maxChunksCount: 5,
      minimumRelevanceScore: 0.5,
    ),
  ),
);

Features:

✅ Multiple answer styles (abstractive, extractive, verbose)
✅ Inline passages or semantic retriever grounding
✅ Answerability probability for quality control
✅ Safety settings support
✅ Input feedback for blocked content

Batch Operations #

Batch Operations Example

import 'package:googleai_dart/googleai_dart.dart';

// Assumes you have a configured client instance
// Create a batch for processing multiple requests
final batch = await client.models.batchGenerateContent(
  model: 'gemini-2.0-flash-exp',
  batch: const GenerateContentBatch(
    displayName: 'My Batch Job',
    model: 'models/gemini-2.0-flash-exp',
    inputConfig: InputConfig(
      requests: InlinedRequests(
        requests: [
          InlinedRequest(
            request: GenerateContentRequest(
              contents: [
                Content(parts: [TextPart('What is 2+2?')], role: 'user'),
              ],
            ),
          ),
          InlinedRequest(
            request: GenerateContentRequest(
              contents: [
                Content(parts: [TextPart('What is 3+3?')], role: 'user'),
              ],
            ),
          ),
        ],
      ),
    ),
  ),
);

// Monitor batch status
final status = await client.batches.getGenerateContentBatch(name: batch.name!);
print('Batch state: ${status.state}');

Corpus & RAG #

Corpus Example

⚠️ Important: Document, chunk, and RAG features are only available in Vertex AI. Google AI only supports basic corpus management.

import 'package:googleai_dart/googleai_dart.dart';

// Assumes you have a configured Google AI client
// Google AI supports basic corpus CRUD operations
final corpus = await client.corpora.create(
  corpus: const Corpus(displayName: 'My Knowledge Base'),
);

// List corpora
final corpora = await client.corpora.list(pageSize: 10);

// Get corpus
final retrieved = await client.corpora.get(name: corpus.name!);

// Update corpus
final updated = await client.corpora.update(
  name: corpus.name!,
  corpus: const Corpus(displayName: 'Updated Name'),
  updateMask: 'displayName',
);

// Delete corpus
await client.corpora.delete(name: corpus.name!);

For full RAG capabilities (documents, chunks, semantic search):

Use Vertex AI with RAG Stores and Vector Search
The Semantic Retriever API has been succeeded by Vertex AI Vector Search

Permissions #

Permission Management Examples

import 'package:googleai_dart/googleai_dart.dart';

// Assumes you have a configured client instance
// Grant permissions to a resource
final permission = await client.tunedModels.permissions(parent: 'tunedModels/my-model').create(
  permission: const Permission(
    granteeType: GranteeType.user,
    emailAddress: 'user@example.com',
    role: PermissionRole.reader,
  ),
);

// List permissions
final permissions = await client.tunedModels.permissions(parent: 'tunedModels/my-model').list();

// Transfer ownership
await client.tunedModels.permissions(parent: 'tunedModels/my-model').transferOwnership(
  emailAddress: 'newowner@example.com',
);

Tuned Models #

List and Inspect Tuned Models

import 'package:googleai_dart/googleai_dart.dart';

// Assumes you have a configured client instance
// List all tuned models
final tunedModels = await client.tunedModels.list(
  pageSize: 10,
  filter: 'owner:me', // Filter by ownership
);

for (final model in tunedModels.tunedModels) {
  print('Model: ${model.displayName} (${model.name})');
  print('State: ${model.state}');
  print('Base model: ${model.baseModel}');
}

// Get specific tuned model details
final myModel = await client.tunedModels.get(
  name: 'tunedModels/my-model-abc123',
);

print('Model: ${myModel.displayName}');
print('State: ${myModel.state}');
print('Training examples: ${myModel.tuningTask?.trainingData?.examples?.exampleCount}');

// List operations for a tuned model (monitor training progress)
final operations = await client.tunedModels.operations(parent: 'tunedModels/my-model-abc123').list();

for (final operation in operations.operations) {
  print('Operation: ${operation.name}');
  print('Done: ${operation.done}');
  if (operation.metadata != null) {
    print('Progress: ${operation.metadata}');
  }
}

Using Tuned Models for Generation #

Generate Content with Tuned Models

Once you have a tuned model, you can use it for content generation just like base models:

import 'package:googleai_dart/googleai_dart.dart';

// Assumes you have a configured client instance
// Generate content with a tuned model
final response = await client.tunedModels.generateContent(
  tunedModel: 'my-model-abc123', // Your tuned model ID
  request: const GenerateContentRequest(
    contents: [
      Content(
        parts: [TextPart('Explain quantum computing')],
        role: 'user',
      ),
    ],
  ),
);

print(response.candidates?.first.content?.parts.first);

// Stream responses with a tuned model
await for (final chunk in client.tunedModels.streamGenerateContent(
  tunedModel: 'my-model-abc123',
  request: request,
)) {
  final text = chunk.candidates?.first.content?.parts.first;
  if (text is TextPart) print(text.text);
}

// Batch generation with a tuned model
final batch = await client.tunedModels.batchGenerateContent(
  tunedModel: 'my-model-abc123',
  batch: const GenerateContentBatch(
    model: 'models/placeholder',
    displayName: 'My Batch Job',
    inputConfig: InputConfig(
      requests: InlinedRequests(
        requests: [
          InlinedRequest(
            request: GenerateContentRequest(
              contents: [
                Content(parts: [TextPart('Question 1')], role: 'user'),
              ],
            ),
          ),
        ],
      ),
    ),
  ),
);

Benefits of tuned models:

✅ Customized behavior for your specific domain
✅ Improved accuracy for specialized tasks
✅ Consistent output style and format
✅ Reduced need for extensive prompting

Prediction (Video Generation) #

Video Generation with Veo Example

import 'package:googleai_dart/googleai_dart.dart';

// Assumes you have a configured client instance
// Synchronous prediction
final response = await client.models.predict(
  model: 'veo-3.0-generate-001',
  instances: [
    {'prompt': 'A cat playing piano in a jazz club'},
  ],
);

print('Predictions: ${response.predictions}');

// Long-running prediction for video generation
final operation = await client.models.predictLongRunning(
  model: 'veo-3.0-generate-001',
  instances: [
    {'prompt': 'A golden retriever running on a beach at sunset'},
  ],
  parameters: {
    'aspectRatio': '16:9',
  },
);

print('Operation: ${operation.name}');
print('Done: ${operation.done}');

// Check for generated videos
if (operation.done == true && operation.response != null) {
  final videoResponse = operation.response!.generateVideoResponse;
  if (videoResponse?.generatedSamples != null) {
    for (final media in videoResponse!.generatedSamples!) {
      if (media.video?.uri != null) {
        print('Video URI: ${media.video!.uri}');
      }
    }
  }

  // Check for RAI filtering
  if (videoResponse?.raiMediaFilteredCount != null) {
    print('Filtered: ${videoResponse!.raiMediaFilteredCount} videos');
    print('Reasons: ${videoResponse.raiMediaFilteredReasons}');
  }
}

Migrating from Google AI to Vertex AI #

Switching from Google AI to Vertex AI requires minimal code changes:

Step 1: Change Configuration #

Configuration Migration Examples

Before (Google AI):

import 'package:googleai_dart/googleai_dart.dart';

final client = GoogleAIClient(
  config: GoogleAIConfig.googleAI(
    authProvider: ApiKeyProvider('YOUR_API_KEY'),
  ),
);

After (Vertex AI):

import 'package:googleai_dart/googleai_dart.dart';

final client = GoogleAIClient(
  config: GoogleAIConfig.vertexAI(
    projectId: 'your-project-id',
    location: 'us-central1',
    authProvider: MyOAuthProvider(), // Implement OAuth
  ),
);

Step 2: Replace Google AI-Only Features #

If you use any of these features, you'll need to use Vertex AI alternatives:

Google AI Feature	Vertex AI Alternative
`client.files.upload()`	Upload to Cloud Storage, use `gs://` URIs in requests
`client.tunedModels.*`	Use Vertex AI Tuning API directly
`client.corpora.*`	Use Vertex AI RAG Stores
`client.models.generateAnswer()`	Use `generateContent()` with grounding

Examples #

See the example/ directory for comprehensive examples:

generate_content.dart - Basic content generation
streaming_example.dart - Real-time SSE streaming
function_calling_example.dart - Tool usage with functions
embeddings_example.dart - Text embeddings generation
error_handling_example.dart - Exception handling patterns
abort_example.dart - Request cancellation
models_example.dart - Model listing and inspection
files_example.dart - File upload and management
pagination_example.dart - Paginated API calls
corpus_example.dart - Corpus/document/chunk management
rag_example.dart - Complete RAG workflow
batch_example.dart - Batch operations
caching_example.dart - Context caching for cost/latency optimization
permissions_example.dart - Permission management
oauth_refresh_example.dart - OAuth token refresh with AuthProvider
prediction_example.dart - Video generation with Veo model
generate_answer_example.dart - Grounded question answering with inline passages or semantic retriever
tuned_model_generation_example.dart - Generate content with custom tuned models
api_versions_example.dart - Using v1 (stable) vs v1beta (beta)
vertex_ai_example.dart - Using Vertex AI with OAuth authentication
complete_api_example.dart - Demonstrating 100% API coverage

API Coverage #

This client implements 78 endpoints covering 100% of all non-deprecated Gemini API operations:

Models Resource (`client.models`) #

Generation: generateContent, streamGenerateContent, countTokens, generateAnswer
Dynamic Content: dynamicGenerateContent, dynamicStreamGenerateContent (for live content with dynamic IDs)
Embeddings: embedContent, batchEmbedContents (synchronous), asyncBatchEmbedContent (asynchronous)
Prediction: predict, predictLongRunning (for video generation with Veo)
Model Management: list, get, listOperations, create, patch, delete

Tuned Models Resource (`client.tunedModels`) #

Generation: generateContent, streamGenerateContent, batchGenerateContent, asyncBatchEmbedContent
Management: list, get
Operations: operations(parent).list
Permissions: permissions(parent).create, permissions(parent).list, permissions(parent).get, permissions(parent).update, permissions(parent).delete, permissions(parent).transferOwnership

Files Resource (`client.files`) #

Management: upload, list, get, delete, download
Upload Methods:
- filePath: Upload from file system (IO platforms, streaming)
- bytes: Upload from memory (all platforms)
- contentStream: Upload large files via streaming (IO platforms, memory efficient)
Generated Files: generatedFiles.list, generatedFiles.get, generatedFiles.getOperation (for video outputs)

Cached Contents Resource (`client.cachedContents`) #

Management: create, get, update, delete, list

Batches Resource (`client.batches`) #

Management: list, getGenerateContentBatch, getEmbedBatch, updateGenerateContentBatch, updateEmbedContentBatch, delete, cancel

Corpora Resource (`client.corpora`) #

Corpus Management: create, list, get, update, delete, query
Document Management: documents(corpus).create, documents(corpus).list, documents(corpus).get, documents(corpus).update, documents(corpus).delete, documents(corpus).query
Chunk Management: documents(corpus).chunks(document).create, documents(corpus).chunks(document).list, documents(corpus).chunks(document).get, documents(corpus).chunks(document).update, documents(corpus).chunks(document).delete
Batch Operations: documents(corpus).chunks(document).batchCreate, documents(corpus).chunks(document).batchUpdate, documents(corpus).chunks(document).batchDelete
Permissions: permissions(parent).create, permissions(parent).list, permissions(parent).get, permissions(parent).update, permissions(parent).delete

RAG Stores Resource (`client.ragStores`) #

Management: documents.list, documents.create, documents.get, documents.delete, documents.query
Operations: getOperation, getUploadOperation

Universal Operations #

getOperation: Available for all long-running operations

Development #

# Install dependencies
dart pub get

# Run tests
dart test

# Format code
dart format .

# Analyze
dart analyze

License #

googleai_dart is licensed under the MIT License.

googleai_dart 1.1.0 googleai_dart: ^1.1.0 copied to clipboard

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