ollama-middleware
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Complete middleware infrastructure for Ollama-based backends
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# 🚀 Ollama Middleware
*A comprehensive TypeScript middleware library for building robust Ollama-based AI backends with advanced features like JSON cleaning, logging, error handling, and more.*
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[](https://github.com/loonylabs-dev/ollama-middleware)
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<!-- Table of Contents -->
<details>
<summary>📋 <strong>Table of Contents</strong></summary>
- [✨ Features](#-features)
- [🚀 Quick Start](#-quick-start)
- [📋 Prerequisites](#-prerequisites)
- [⚙️ Configuration](#️-configuration)
- [🏗️ Architecture](#️-architecture)
- [📖 Documentation](#-documentation)
- [🧪 Testing and Examples](#-testing-and-examples)
- [🔧 Advanced Features](#-advanced-features)
- [🤝 Contributing](#-contributing)
- [📄 License](#-license)
- [🙏 Acknowledgments](#-acknowledgments)
- [🔗 Links](#-links)
</details>
---
## ✨ Features
- 🏗️ **Clean Architecture**: Base classes and interfaces for scalable AI applications
- 🤖 **Ollama Integration**: Complete service layer with retry logic and authentication
- 🧹 **JSON Cleaning**: Recipe-based JSON repair system with automatic strategy selection
- 🎨 **FlatFormatter System**: Advanced data formatting for LLM consumption
- 📊 **Comprehensive Logging**: Multi-level logging with metadata support
- ⚙️ **Configuration Management**: Flexible model and application configuration
- 🛡️ **Error Handling**: Robust error handling and recovery mechanisms
- 🔧 **TypeScript First**: Full type safety throughout the entire stack
- 📦 **Modular Design**: Use only what you need
- 🧪 **Testing Ready**: Includes example implementations and test utilities
## 🚀 Quick Start
### Installation
Install from npm:
```bash
npm install ollama-middleware
```
Or install directly from GitHub:
```bash
npm install github:loonylabs-dev/ollama-middleware
```
Or using a specific version/tag:
```bash
npm install github:loonylabs-dev/ollama-middleware#v1.1.0
```
### Basic Usage
```typescript
import { BaseAIUseCase, BaseAIRequest, BaseAIResult } from 'ollama-middleware';
// Define your request/response interfaces
interface MyRequest extends BaseAIRequest<string> {
message: string;
}
interface MyResult extends BaseAIResult {
response: string;
}
// Create your use case
class MyChatUseCase extends BaseAIUseCase<string, MyRequest, MyResult> {
protected readonly systemMessage = "You are a helpful assistant.";
// Required: return user message template function
protected getUserTemplate(): (formattedPrompt: string) => string {
return (message) => message;
}
protected formatUserMessage(prompt: any): string {
return typeof prompt === 'string' ? prompt : prompt.message;
}
protected createResult(content: string, usedPrompt: string, thinking?: string): MyResult {
return {
generatedContent: content,
model: this.modelConfig.name,
usedPrompt: usedPrompt,
thinking: thinking,
response: content
};
}
}
```
<details>
<summary><strong>🎭 Advanced Example with FlatFormatter</strong></summary>
```typescript
import {
FlatFormatter,
personPreset
} from 'ollama-middleware';
class ProfileGeneratorUseCase extends BaseAIUseCase {
protected readonly systemMessage = `You are a professional profile creator.
IMPORTANT: Respond with ONLY valid JSON following this schema:
{
"name": "Person name",
"title": "Professional title",
"summary": "Brief professional overview",
"skills": "Key skills and expertise",
"achievements": "Notable accomplishments"
}`;
// Use FlatFormatter and presets for rich context building
protected formatUserMessage(prompt: any): string {
const { person, preferences, guidelines } = prompt;
const contextSections = [
// Use preset for structured data
personPreset.formatForLLM(person, "## PERSON INFO:"),
// Use FlatFormatter for custom structures
`## PREFERENCES:\n${FlatFormatter.flatten(preferences, {
format: 'bulleted',
keyValueSeparator: ': '
})}`,
// Format guidelines with FlatFormatter
`## GUIDELINES:\n${FlatFormatter.flatten(
guidelines.map(g => ({
guideline: g,
priority: "MUST FOLLOW"
})),
{
format: 'numbered',
entryTitleKey: 'guideline',
ignoredKeys: ['guideline']
}
)}`
];
return contextSections.join('\n\n');
}
protected createResult(content: string, usedPrompt: string, thinking?: string): MyResult {
return {
generatedContent: content,
model: this.modelConfig.name,
usedPrompt,
thinking,
profile: JSON.parse(content)
};
}
}
// Use it
const profileGen = new ProfileGeneratorUseCase();
const result = await profileGen.execute({
prompt: {
person: { name: "Alice", occupation: "Engineer" },
preferences: { tone: "professional", length: "concise" },
guidelines: ["Highlight technical skills", "Include leadership"]
},
authToken: "optional-token"
});
```
</details>
## 📋 Prerequisites
<details>
<summary><strong>📦 Required Dependencies</strong></summary>
- **Node.js** 18+
- **TypeScript** 4.9+
- **Ollama server** running (local or remote)
</details>
## ⚙️ Configuration
<details>
<summary><strong>🔧 Environment Setup</strong></summary>
Create a `.env` file in your project root:
```env
# Server Configuration
PORT=3000
NODE_ENV=development
# Logging
LOG_LEVEL=info
# Ollama Model Configuration (REQUIRED)
MODEL1_NAME=phi3:mini # Required: Your model name
MODEL1_URL=http://localhost:11434 # Optional: Defaults to localhost
MODEL1_TOKEN=optional-auth-token # Optional: For authenticated servers
```
</details>
## 🏗️ Architecture
The middleware follows **Clean Architecture** principles:
```
src/
├── middleware/
│ ├── controllers/base/ # Base HTTP controllers
│ ├── usecases/base/ # Base AI use cases
│ ├── services/ # External service integrations
│ │ ├── ollama/ # Ollama API service
│ │ ├── json-cleaner/ # JSON repair and validation
│ │ └── response-processor/ # AI response processing
│ └── shared/ # Common utilities and types
│ ├── config/ # Configuration management
│ ├── types/ # TypeScript interfaces
│ └── utils/ # Utility functions
└── examples/ # Example implementations
└── simple-chat/ # Basic chat example
```
## 📖 Documentation
- [Getting Started Guide](docs/GETTING_STARTED.md)
- [Architecture Overview](docs/ARCHITECTURE.md)
- [Ollama Parameters Guide](docs/OLLAMA_PARAMETERS.md) - Complete parameter reference and presets
- [Request Formatting Guide](docs/REQUEST_FORMATTING.md) - FlatFormatter vs RequestFormatterService
- [Performance Monitoring](docs/PERFORMANCE_MONITORING.md) - Metrics and logging
- [API Reference](docs/API_REFERENCE.md)
- [Examples](docs/EXAMPLES.md)
- [CHANGELOG](CHANGELOG.md) - Release notes and breaking changes
## 🧪 Testing
The middleware includes comprehensive test suites covering unit tests, integration tests, robustness tests, and end-to-end workflows.
### Quick Start
```bash
# Build the middleware first
npm run build
# Run all automated tests
npm run test:all
# Run unit tests only
npm run test:unit
```
**📖 For complete testing documentation**, see **[tests/README.md](tests/README.md)**
The test documentation includes:
- 📋 Quick reference table for all tests
- 🚀 Detailed test descriptions and prerequisites
- ⚠️ Troubleshooting guide
- 🔬 Development workflow best practices
### 🐦 Tweet Generator Example
<details>
<summary><strong>💬 Demonstrating Token Limiting with Social Media Content</strong></summary>
The **Tweet Generator** example showcases parameter configuration for controlling output length:
```typescript
import { TweetGeneratorUseCase } from 'ollama-middleware';
const tweetGenerator = new TweetGeneratorUseCase();
const result = await tweetGenerator.execute({
prompt: 'The importance of clean code in software development'
});
console.log(result.tweet); // Generated tweet
console.log(result.characterCount); // Character count
console.log(result.withinLimit); // true if ≤ 280 chars
```
**Key Features:**
- 🎯 **Token Limiting**: Uses `num_predict: 70` to limit output to ~280 characters
- 📊 **Character Validation**: Automatically checks if output is within Twitter's limit
- 🎨 **Marketing Preset**: Optimized parameters for engaging, concise content
- ✅ **Testable**: Integration test verifies parameter effectiveness
**Parameter Configuration:**
```typescript
protected getParameterOverrides(): ModelParameterOverrides {
return {
num_predict: 70, // Limit to ~280 characters
temperatureOverride: 0.7,
repeatPenalty: 1.3,
frequencyPenalty: 0.3,
presencePenalty: 0.2,
topP: 0.9,
topK: 50,
repeatLastN: 32
};
}
```
This example demonstrates:
- How to configure parameters for specific output requirements
- Token limiting as a practical use case
- Validation and testing of parameter effectiveness
- Real-world application (social media content generation)
See `src/examples/tweet-generator/` for full implementation.
</details>
### 🎯 Example Application
<details>
<summary><strong>🚀 Quick Example Setup</strong></summary>
Run the included examples:
```bash
# Clone the repository
git clone https://github.com/loonylabs-dev/ollama-middleware.git
cd ollama-middleware
# Install dependencies
npm install
# Copy environment template
cp .env.example .env
# Start Ollama (if running locally)
ollama serve
# Run the example
npm run dev
```
Test the API:
```bash
curl -X POST http://localhost:3000/api/chat \
-H "Content-Type: application/json" \
-d '{"message": "Hello, how are you?"}'
```
</details>
## 🔧 Advanced Features
<details>
<summary><strong>🧹 Recipe-Based JSON Cleaning System</strong></summary>
Advanced JSON repair with automatic strategy selection and modular operations:
```typescript
import { JsonCleanerService, JsonCleanerFactory } from 'ollama-middleware';
// Simple usage (async - uses new recipe system with fallback)
const result = await JsonCleanerService.processResponseAsync(malformedJson);
console.log(result.cleanedJson);
// Legacy sync method (still works)
const cleaned = JsonCleanerService.processResponse(malformedJson);
// Advanced: Quick clean with automatic recipe selection
const result = await JsonCleanerFactory.quickClean(malformedJson);
console.log('Success:', result.success);
console.log('Confidence:', result.confidence);
console.log('Changes:', result.totalChanges);
```
**Features:**
- 🎯 Automatic strategy selection (Conservative/Aggressive/Adaptive)
- 🔧 Modular detectors & fixers for specific problems
- ✨ Extracts JSON from Markdown/Think-Tags
- 🔄 Checkpoint/Rollback support for safe repairs
- 📊 Detailed metrics (confidence, quality, performance)
- 🛡️ Fallback to legacy system for compatibility
**Available Templates:**
```typescript
import { RecipeTemplates } from 'ollama-middleware';
const conservativeRecipe = RecipeTemplates.conservative();
const aggressiveRecipe = RecipeTemplates.aggressive();
const adaptiveRecipe = RecipeTemplates.adaptive();
```
See [Recipe System Documentation](src/middleware/services/json-cleaner/recipe-system/README.md) for details.
</details>
<details>
<summary><strong>📝 Request Formatting (FlatFormatter & RequestFormatterService)</strong></summary>
**For simple data:** Use [FlatFormatter](src/middleware/services/flat-formatter/README.md)
```typescript
const flat = FlatFormatter.flatten({ name: 'Alice', age: 30 });
```
**For complex nested prompts:** Use RequestFormatterService
```typescript
import { RequestFormatterService } from 'ollama-middleware';
const prompt = {
context: { genre: 'sci-fi', tone: 'dark' },
instruction: 'Write an opening'
};
const formatted = RequestFormatterService.formatUserMessage(
prompt, (s) => s, 'MyUseCase'
);
// Outputs: ## CONTEXT:\ngenre: sci-fi\ntone: dark\n\n## INSTRUCTION:\nWrite an opening
```
See [Request Formatting Guide](docs/REQUEST_FORMATTING.md) for details.
</details>
<details>
<summary><strong>📊 Performance Monitoring & Metrics</strong></summary>
Automatic performance tracking with `UseCaseMetricsLoggerService`:
```typescript
// Automatically logged for all use cases:
// - Execution time
// - Token usage (input/output)
// - Generation speed (tokens/sec)
// - Parameters used
```
Metrics appear in console logs:
```
✅ Completed AI use case [MyUseCase = phi3:mini] SUCCESS
Time: 2.5s | Input: 120 tokens | Output: 85 tokens | Speed: 34.0 tokens/sec
```
See [Performance Monitoring Guide](docs/PERFORMANCE_MONITORING.md) for advanced usage.
</details>
<details>
<summary><strong>📊 Comprehensive Logging</strong></summary>
Multi-level logging with contextual metadata:
```typescript
import { logger } from 'ollama-middleware';
logger.info('Operation completed', {
context: 'MyService',
metadata: { userId: 123, duration: 150 }
});
```
</details>
<details>
<summary><strong>⚙️ Model Configuration</strong></summary>
Flexible model management:
```typescript
import { getModelConfig } from 'ollama-middleware';
// MODEL1_NAME is required in .env or will throw error
const config = getModelConfig('MODEL1');
console.log(config.name); // Value from MODEL1_NAME env variable
console.log(config.baseUrl); // Value from MODEL1_URL or default localhost
```
</details>
<details>
<summary><strong>🎛️ Parameter Configuration</strong></summary>
Ollama-middleware provides fine-grained control over model parameters to optimize output for different use cases:
```typescript
import { BaseAIUseCase, ModelParameterOverrides } from 'ollama-middleware';
class MyUseCase extends BaseAIUseCase<MyRequest, MyResult> {
protected getParameterOverrides(): ModelParameterOverrides {
return {
temperatureOverride: 0.8, // Control creativity vs. determinism
repeatPenalty: 1.3, // Reduce word repetition
frequencyPenalty: 0.2, // Penalize frequent words
presencePenalty: 0.2, // Encourage topic diversity
topP: 0.92, // Nucleus sampling threshold
topK: 60, // Vocabulary selection limit
repeatLastN: 128 // Context window for repetition
};
}
}
```
**Parameter Levels:**
- **Global defaults**: Set in `ModelParameterManagerService`
- **Use-case level**: Override via `getParameterOverrides()` method
- **Request level**: Pass parameters directly in requests
**Available Presets:**
```typescript
import { ModelParameterManagerService } from 'ollama-middleware';
// Use curated presets for common use cases
const creativeParams = ModelParameterManagerService.getDefaultParametersForType('creative_writing');
const factualParams = ModelParameterManagerService.getDefaultParametersForType('factual');
const poeticParams = ModelParameterManagerService.getDefaultParametersForType('poetic');
const dialogueParams = ModelParameterManagerService.getDefaultParametersForType('dialogue');
const technicalParams = ModelParameterManagerService.getDefaultParametersForType('technical');
const marketingParams = ModelParameterManagerService.getDefaultParametersForType('marketing');
```
**Presets Include:**
- 📚 **Creative Writing**: Novels, stories, narrative fiction
- 📊 **Factual**: Reports, documentation, journalism
- 🎭 **Poetic**: Poetry, lyrics, artistic expression
- 💬 **Dialogue**: Character dialogue, conversational content
- 🔧 **Technical**: Code documentation, API references
- 📢 **Marketing**: Advertisements, promotional content
For detailed documentation about all parameters, value ranges, and preset configurations, see:
**[Ollama Parameters Guide](./docs/OLLAMA_PARAMETERS.md)**
</details>
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- [Ollama](https://ollama.ai/) for the amazing local LLM platform
- The open-source community for inspiration and contributions
## 🔗 Links
- [📚 Documentation](https://github.com/loonylabs-dev/ollama-middleware/docs)
- [🐛 Issues](https://github.com/loonylabs-dev/ollama-middleware/issues)
- [📦 NPM Package](https://www.npmjs.com/package/ollama-middleware)
---
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**Made with ❤️ for the AI community**
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[](https://github.com/loonylabs-dev)
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