mt_llmkit

KO | EN

gitlite — search

#python #hacktoberfest #llm #golang #android #rust #docker #java #windows #awesome #vue #pytorch #security #ios

(★ 14)

A Flutter toolkit for local and cloud LLM apps. Run GGUF models on Android and iOS, connect to OpenAI, Gemini, Claude and others through one unified API, and build fully local RAG pipelines. Supports streaming, multimodal vision models, and mobile-first AI workflows.

mt_llmkit 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# mt_llmkit

![Flutter](https://img.shields.io/badge/Flutter-%3E%3D3.32-02569B?logo=flutter)
![Dart](https://img.shields.io/badge/Dart-%5E3.8-0175C2?logo=dart)
![Platform](https://img.shields.io/badge/platform-iOS%20%7C%20Android-lightgrey)
![Stability](https://img.shields.io/badge/stability-beta-orange)
![License](https://img.shields.io/badge/license-MIT-green)

A Flutter plugin for running Large Language Models (LLMs) locally on Android and iOS using [llamadart](https://pub.dev/packages/llamadart) (which wraps llama.cpp). Also provides a unified interface for cloud AI chat providers (OpenAI, Gemini, Claude, Mistral) and a fully local RAG (Retrieval-Augmented Generation) pipeline.

---

## Table of Contents

1. [Installation](#installation)
2. [Local LLM Inference (GGUF)](#local-llm-inference-gguf)
   - [Quick start](#quick-start)
   - [Backends](#backends)
   - [Configuration](#configuration)
   - [Generation methods](#generation-methods)
   - [Prompt format](#prompt-format)
   - [Performance metrics](#performance-metrics)
3. [Vision (Multimodal)](#vision-multimodal)
   - [Quick start](#quick-start-vision)
   - [Supported models](#supported-models)
   - [Image input](#image-input)
   - [Generation methods](#generation-methods-1)
4. [Cloud API Providers](#cloud-api-providers)
   - [Supported providers](#supported-providers)
   - [Basic usage](#basic-usage)
   - [Multi-turn conversations](#multi-turn-conversations)
   - [Streaming](#streaming)
   - [Provider-specific config](#provider-specific-config)
   - [Error handling](#error-handling)
5. [Local RAG Pipeline](#local-rag-pipeline)
   - [How it works](#how-it-works)
   - [Quick start](#quick-start-1)
   - [Document ingestion](#document-ingestion)
   - [Querying](#querying)
   - [Index persistence](#index-persistence)
   - [Advanced: custom prompt template](#advanced-custom-prompt-template)

---

## Installation

Add to your `pubspec.yaml`:

```yaml
dependencies:
  mt_llmkit: ^0.0.1-beta.1
```

Then run:

```bash
flutter pub get
```

Import the library:

```dart
import 'package:mt_llmkit/llmcpp.dart';
```

---

## Local LLM Inference (GGUF)

Run quantized GGUF models entirely on-device — no internet connection required.

### Quick start

```dart
final model = LocalModel(
  config: LlmConfig(temp: 0.7, nCtx: 2048, nGpuLayers: 4),
);

await model.loadModel('/path/to/model.gguf');

// Stream tokens as they are generated
model.sendPrompt('What is Flutter?').listen((token) {
  stdout.write(token);
});

model.dispose();
```

### Backends

`LocalModel` supports two backends controlled by the `backend` parameter:

| Backend | Class | When to use |
|---|---|---|
| `ModelBackend.isolate` *(default)* | `LlmModelIsolated` | Production. Runs in a Dart Isolate — no UI jank. Required when loading multiple models (e.g. RAG). |
| `ModelBackend.inProcess` | `LlmModelStandard` | Lighter startup cost. Supports `clean()` to reset context without reloading the model. |

```dart
// Isolate backend (default)
final model = LocalModel(backend: ModelBackend.isolate);

// In-process backend
final model = LocalModel(backend: ModelBackend.inProcess);
model.clean(); // reset context — only available with inProcess
```

> **Note:** `clean()` throws `UnsupportedError` when called on the `isolate` backend.

### Configuration

All parameters are optional; sensible defaults are applied automatically.

```dart
final config = LlmConfig(
  nGpuLayers: 4,    // GPU layers offloaded (default: 64)
  nCtx: 2048,       // context window in tokens (default: 8192)
  nBatch: 512,      // batch size (default: 4096)
  nPredict: 1024,   // max tokens to generate (default: 8192)
  nThreads: 4,      // CPU threads (default: 6)
  temp: 0.7,        // temperature (default: 0.72)
  topK: 40,         // top-K sampling (default: 64)
  topP: 0.9,        // top-P sampling (default: 0.95)
  penaltyRepeat: 1.1, // repetition penalty (default: 1.1)
);
```

### Generation methods

Three methods are available on `LocalModel` (and any `LlmInterface` implementation):

| Method | Return type | Description |
|---|---|---|
| `sendPrompt(prompt)` | `Stream<String>` | Raw token stream. Lowest overhead. |
| `sendPromptComplete(prompt)` | `Future<String>` | Waits for the full response and returns it as a single string. |
| `sendPromptStream(prompt)` | `Stream<StreamingChunk>` | **Recommended.** Token stream with live performance metrics. |

```dart
// 1. Raw token stream
model.sendPrompt('Hello').listen(stdout.write);

// 2. Full response at once
final response = await model.sendPromptComplete('Hello');
print(response);

// 3. Streaming with live metrics (recommended)
model.sendPromptStream('Hello').listen((chunk) {
  stdout.write(chunk.text);

if (chunk.isFinal && chunk.metrics != null) {
    final m = chunk.metrics!;
    print('\n--- ${m.tokensGenerated} tokens, ${m.tokensPerSecond.toStringAsFixed(1)} t/s ---');
  }
});
```

`StreamingChunk` fields:

| Field | Type | Description |
|---|---|---|
| `text` | `String` | The generated text fragment. |
| `isFinal` | `bool` | `true` on the last chunk of the response. |
| `metrics` | `PerformanceMetrics?` | Available on every chunk; most useful on the final one. |

`PerformanceMetrics` fields: `tokensGenerated`, `durationMs`, `tokensPerSecond`, `msPerToken`.

### Prompt format

Override the model's built-in chat template by passing a raw GGUF/Jinja template string via `LlmConfig.chatTemplate`. When `chatTemplate` is `null` (the default), the template embedded in the model file is used automatically.

```dart
final config = LlmConfig(
  chatTemplate: '<|user|>\n{prompt}<|end|>\n<|assistant|>\n', // custom override
);
```

### Performance metrics

`PerformanceMetrics` is updated incrementally with every `StreamingChunk`:

```dart
model.sendPromptStream('Explain Dart isolates in detail.').listen((chunk) {
  stdout.write(chunk.text);

if (chunk.metrics != null) {
    final m = chunk.metrics!;
    // Update UI progress indicator
    print('${m.tokensGenerated} tokens | ${m.tokensPerSecond.toStringAsFixed(2)} t/s');
  }
});
```
---

## Vision (Multimodal)

`LocalModel` supports multimodal vision models (LLaVA, Gemma 3, Qwen VL, SmolVLM, etc.) that can analyse images alongside a text prompt. Vision requires two GGUF files: the main language model and a **multimodal projector** (`mmproj-*.gguf`).

### Quick start (vision) {#quick-start-vision}

```dart
final model = LocalModel(
  config: LlmConfig(
    mmprojPath: '/path/to/mmproj-model-f16-4B.gguf',
    nGpuLayers: 4,
    nCtx: 4096,
    nPredict: 512,
    temp: 0.3,
  ),
);

await model.loadModel('/path/to/gemma-3-4b-it-q4_0.gguf');

final image = LlamaImageContent(path: '/path/to/photo.jpg');

model.sendPromptStream(
  'Describe what you see in this image. <image>',
  images: [image],
).listen((chunk) {
  stdout.write(chunk.text);

if (chunk.isFinal && chunk.metrics != null) {
    print('\n--- ${chunk.metrics!.tokensPerSecond.toStringAsFixed(1)} t/s ---');
  }
});
```

> **Important:** The prompt must contain one `<image>` placeholder per image passed in the list.

### Supported models

Any vision GGUF model that uses the `libmtmd` multimodal projection layer is supported. Tested models:

| Model | Notes |
|---|---|
| Gemma 3 (4B, 12B, 27B) | Recommended. Good accuracy, available in Q4 quantisation. |
| Qwen 2.5 VL | Strong OCR and document understanding. |
| LLaVA 1.5 / 1.6 | Classic CLIP-based architecture. |
| SmolVLM | Compact, fast, good for mobile devices. |

Each model has a corresponding `mmproj-*.gguf` file available on Hugging Face alongside the main model.

### Image input

`LlamaImageContent` is created by providing the image file path:

```dart
final image = LlamaImageContent(path: '/path/to/photo.jpg');
```

Supported formats: JPEG, PNG, and any format supported by the underlying `libmtmd` library.

### Generation methods (vision) {#generation-methods-1}

All three standard generation methods accept an optional `images` parameter:

| Method | Return type | Description |
|---|---|---|
| `sendPrompt(prompt, images: images)` | `Stream<String>` | Raw token stream. |
| `sendPromptComplete(prompt, images: images)` | `Future<String>` | Full response as a single string. |
| `sendPromptStream(prompt, images: images)` | `Stream<StreamingChunk>` | **Recommended.** Streaming with live performance metrics. |

All three methods throw `UnsupportedError` if `LlmConfig.mmprojPath` was not set.

```dart
// Full response at once
final response = await model.sendPromptComplete(
  'What objects are visible in this photo? <image>',
  images: [LlamaImageContent(path: '/path/to/photo.jpg')],
);
print(response);
```

---

## Cloud API Providers

`mt_llmkit` includes a unified `AIChatProvider` interface for four cloud LLM providers. All providers share the same API surface, making it easy to swap backends.

### Supported providers

| Provider | Enum value | Default model |
|---|---|---|
| OpenAI | `AIChatProviderType.openai` | `gpt-4o-mini` |
| Google Gemini | `AIChatProviderType.gemini` | `gemini-1.5-flash` |
| Anthropic Claude | `AIChatProviderType.claude` | `claude-haiku-4-5-20251001` |
| Mistral AI | `AIChatProviderType.mistral` | `mistral-small-latest` |

### Basic usage

Use `AIChatProviderFactory` to create a provider without importing the concrete class:

```dart
// Create and initialize in one step
final provider = await AIChatProviderFactory.createAndInitialize(
  AIChatProviderType.openai,
  {'apiKey': 'sk-...'},
);

final response = await provider.sendMessage('What is Flutter?');
print(response.message.content);
print('Tokens used: ${response.inputTokens} in / ${response.outputTokens} out');

await provider.dispose();
```

Or manage the lifecycle manually:

```dart
final provider = AIChatProviderFactory.create(AIChatProviderType.gemini);
await provider.initialize({'apiKey': 'AIza...'});

final response = await provider.sendMessage('Hello!');
print(response.message.content);

await provider.dispose();
```

### Multi-turn conversations

Build conversation history with `ChatMessage`:

```dart
final history = <ChatMessage>[
  ChatMessage.system('You are a concise assistant. Reply in three sentences max.'),
  ChatMessage.user('What is a Dart isolate?'),
  ChatMessage.assistant('A Dart isolate is an independent thread of execution...'),
];

// Continue the conversation
final r = await provider.sendMessage('Give me a code example.', history: history);
print(r.message.content);

// Append the reply to keep history growing
history.add(r.message);
```

For full control, pass a complete message list directly:

```dart
final response = await provider.sendChatMessages([
  ChatMessage.system('You are a poet.'),
  ChatMessage.user('Write a haiku about Flutter.'),
]);
print(response.message.content);
```

### Streaming

All providers support token streaming via `sendMessageStream`:

```dart
await for (final token in provider.sendMessageStream('Tell me a story.')) {
  stdout.write(token);
}
```

With conversation history:

```dart
final history = [ChatMessage.system('Reply only in Spanish.')];

await for (final token in provider.sendMessageStream('Hello!', history: history)) {
  stdout.write(token);
}
```

### Provider-specific config

#### OpenAI

```dart
await provider.initialize({
  'apiKey': 'sk-...',
  'model': 'gpt-4o',               // optional, default: gpt-4o-mini
  'baseUrl': 'https://...',        // optional, for Azure OpenAI or proxies
});
```

#### Google Gemini

```dart
await provider.initialize({
  'apiKey': 'AIza...',
  'model': 'gemini-1.5-pro',       // optional
});
```

#### Anthropic Claude

```dart
await provider.initialize({
  'apiKey': 'sk-ant-...',
  'model': 'claude-opus-4-6',      // optional
});
```

#### Mistral AI

```dart
await provider.initialize({
  'apiKey': '...',
  'model': 'mistral-large-latest', // optional
});
```

### Error handling

All providers throw typed exceptions from `chat_exceptions.dart`:

| Exception | Cause |
|---|---|
| `APIKeyException` | Invalid or missing API key (HTTP 401/403) |
| `NetworkException` | Transport error (timeout, DNS, connection reset) |
| `RateLimitException` | Quota exceeded (HTTP 429); contains `retryAfter` |
| `AIChatException` | Base class for any other API error |

Network and rate-limit errors are **automatically retried** up to 3 times with exponential back-off.

```dart
try {
  final response = await provider.sendMessage('Hello');
  print(response.message.content);
} on APIKeyException catch (e) {
  print('Check your API key: $e');
} on RateLimitException catch (e) {
  print('Rate limited. Retry after ${e.retryAfter?.inSeconds}s');
} on AIChatException catch (e) {
  print('API error: $e');
}
```

---

## Local RAG Pipeline

`RagEngine` provides a fully on-device Retrieval-Augmented Generation pipeline. Documents are chunked, embedded with a local embedding model, stored in an in-memory vector store, and retrieved at query time to ground the generation model's response.

### How it works

```
Ingestion:  Document → TextChunker → chunks → EmbeddingModel → VectorStore
Query:      question → EmbeddingModel → VectorStore.search() → prompt + context → GenerationModel
```

Both the embedding model and the generation model run inside a **single worker isolate**, avoiding threading issues that arise when multiple native model instances share global state — this is handled transparently by llamadart.

### Quick start

You need two GGUF models:
- A **generation model** (e.g. Llama 3, Mistral) for producing answers.
- An **embedding model** (e.g. nomic-embed-text) for vectorising text.

```dart
final rag = RagEngine(
  genModelPath: '/path/to/llama.gguf',
  embedModelPath: '/path/to/nomic-embed.gguf',
  genConfig: LlmConfig(temp: 0.3, nCtx: 4096, nGpuLayers: 4),
);

await rag.initialize();
```

### Document ingestion

Create a `Document` from text or PDF content and ingest it into the vector store:

```dart
// From plain text
final doc = Document.fromText(
  'Flutter is Google\'s UI toolkit for building natively compiled applications...',
  source: 'flutter_intro.txt',
);

// From extracted PDF text (use a PDF parser to extract the string first)
final pdfDoc = Document.fromPdf(
  extractedText,
  source: '/path/to/manual.pdf',
  pageCount: 42,
);

// Ingest — stream progress for UI updates
await for (final progress in rag.ingestDocument(doc)) {
  print('${progress.embeddedChunks}/${progress.totalChunks} — ${progress.currentPreview}');
  // progress.fraction gives 0.0–1.0 for a progress bar
}

print('Indexed: ${rag.indexedSize} chunks across ${rag.documentIds.length} documents');
```

Manage the index:

```dart
// Remove a single document (all its chunks)
await rag.removeDocument(doc.id);

// Clear everything
await rag.clearIndex();
```

### Querying

```dart
// Stream the generated answer
await for (final chunk in rag.query('What is Flutter?')) {
  stdout.write(chunk.text);

if (chunk.isFinal && chunk.metrics != null) {
    print('\n${chunk.metrics!.tokensPerSecond.toStringAsFixed(1)} t/s');
  }
}
```

Optional query parameters:

```dart
rag.query(
  'Explain the rendering pipeline.',
  topK: 3,              // number of context chunks (default: 5)
  minSimilarity: 0.35,  // minimum cosine similarity 0.0–1.0 (default: 0.25)
)
```

Retrieve relevant chunks without generating an answer (useful for inspection):

```dart
final results = await rag.findRelevant('rendering pipeline', topK: 3);
for (final r in results) {
  print('${(r.similarity * 100).toStringAsFixed(0)}% — ${r.chunk.text.substring(0, 80)}');
}
```

### Index persistence

Pass `indexPath` to automatically save and restore the vector index between sessions:

```dart
final dir = await getApplicationDocumentsDirectory();

final rag = RagEngine(
  genModelPath: '/path/to/llama.gguf',
  embedModelPath: '/path/to/nomic-embed.gguf',
  indexPath: '${dir.path}/rag_index.json',  // auto-save on every change
);

await rag.initialize(); // loads existing index if the file exists
```

When `indexPath` is `null`, the store is in-memory only and data is lost when the app restarts.

### Advanced: custom prompt template

The default template instructs the model to answer only from the provided context. Override it if you need different behaviour:

```dart
final rag = RagEngine(
  genModelPath: '...',
  embedModelPath: '...',
  promptTemplate:
    'You are a helpful assistant. Use the context below to answer.\n\n'
    'CONTEXT:\n{context}\n\nQUESTION: {question}\n\nANSWER:',
);
```

The template must contain `{context}` and `{question}` placeholders.

### Cleanup

```dart
rag.dispose(); // releases the worker isolate and both models; safe to call twice
```

mt_llmkit

mt_llmkit 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

mt_llmkit

mt_llmkit 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README