llm_ollama 0.1.5

Ollama backend implementation for LLM interactions. Provides streaming chat, embeddings, tool calling, vision support, and model management.

example/README.md

llm_ollama Examples

Prerequisites

1. Ollama Installation: Install and run Ollama locally

   • Download from ollama.ai
   • Start the Ollama service (usually runs automatically)

2. Model: Pull a model using Ollama CLI

   ollama pull qwen2:0.5b
   # or
   ollama pull llama3:8b
   

CLI Example

A simple command-line chat interface:

cd packages/llm_ollama
dart run example/cli_example.dart

With custom model and base URL:

dart run example/cli_example.dart qwen2:0.5b
dart run example/cli_example.dart llama3:8b http://localhost:11434

Using in Your Own Code

Basic Usage

import 'package:llm_ollama/llm_ollama.dart';

Future<void> main() async {
  final repo = OllamaChatRepository(baseUrl: 'http://localhost:11434');
  
  final stream = repo.streamChat('qwen2:0.5b', messages: [
    LLMMessage(role: LLMRole.system, content: 'You are helpful.'),
    LLMMessage(role: LLMRole.user, content: 'Hello!'),
  ]);
  
  await for (final chunk in stream) {
    print(chunk.message?.content ?? '');
  }
}

With Thinking Mode

final stream = repo.streamChat(
  'deepseek-r1:1.5b', // Thinking model
  messages: messages,
  think: true, // Enable thinking tokens
);

await for (final chunk in stream) {
  // Regular content
  print(chunk.message?.content ?? '');
  
  // Thinking/reasoning content
  if (chunk.message?.thinking != null) {
    print('[Thinking: ${chunk.message!.thinking}]');
  }
}

With Builder Pattern

final repo = OllamaChatRepository.builder()
  .baseUrl('http://localhost:11434')
  .retryConfig(RetryConfig(maxAttempts: 3))
  .timeoutConfig(TimeoutConfig(readTimeout: Duration(minutes: 5)))
  .build();

Model Management

final ollamaRepo = OllamaRepository(baseUrl: 'http://localhost:11434');

// List available models
final models = await ollamaRepo.listModels();
for (final model in models) {
  print('${model.name} - ${model.size} bytes');
}

// Pull a model
await ollamaRepo.pullModel('qwen2:0.5b');

// Show model info
final info = await ollamaRepo.showModel('qwen2:0.5b');
print('Modelfile: ${info.modelfile}');

Tool Calling

class CalculatorTool extends LLMTool {
  @override
  String get name => 'calculator';
  
  @override
  String get description => 'Performs arithmetic calculations';
  
  @override
  List<LLMToolParam> get parameters => [
    LLMToolParam(
      name: 'expression',
      type: 'string',
      description: 'The math expression to evaluate',
      isRequired: true,
    ),
  ];
  
  @override
  Future<String> execute(Map<String, dynamic> args, {dynamic extra}) async {
    final expr = args['expression'] as String;
    // Evaluate expression...
    return result.toString();
  }
}

final stream = repo.streamChat(
  'qwen2:0.5b',
  messages: messages,
  tools: [CalculatorTool()],
);

Configuration

Environment Variables

You can configure the base URL via environment variable:

export OLLAMA_BASE_URL=http://localhost:11434
dart run example/cli_example.dart

Custom Base URL

For remote Ollama instances:

final repo = OllamaChatRepository(
  baseUrl: 'http://your-ollama-server:11434',
);

Troubleshooting

Connection refused

• Make sure Ollama is running: ollama serve
• Check the base URL matches your Ollama instance
• Verify network connectivity

Model not found

• Pull the model first: ollama pull <model-name>
• List available models: ollama list
• Check model name format (e.g., qwen2:0.5b)

Slow responses

• Use a smaller/faster model
• Check Ollama server resources
• Consider using a local GPU-accelerated Ollama instance