@equinor/fusion-framework-module-ai

AI services module for Fusion Framework applications, providing integration with language models, embeddings, and vector stores.

Features

- Language Models: Support for various AI language models (OpenAI, Azure OpenAI, etc.)
- Embeddings: Text embedding services for semantic search and similarity
- Vector Stores: Vector database integration for storing and searching embeddings
- Flexible Configuration: Support for both eager and lazy initialization
- Type Safety: Full TypeScript support with comprehensive type definitions

Installation

``bash
pnpm add @equinor/fusion-framework-module-ai
`

Usage

$3

`typescript
import { Framework } from '@equinor/fusion-framework';
import { enableAI, type AIServiceType } from '@equinor/fusion-framework-module-ai';
import { AzureOpenAIModel } from '@equinor/fusion-framework-module-ai/lib/azure/AzureOpenAIModel';
import { AzureOpenAiEmbed } from '@equinor/fusion-framework-module-ai/lib/azure/AzureOpenAiEmbed';
import { AzureVectorStore } from '@equinor/fusion-framework-module-ai/lib/azure/AzureVectorStore';

const framework = new Framework({
name: 'AI Example App',
modules: [
enableAI(config =>
config
.setModel('gpt-4', new AzureOpenAIModel({
apiKey: process.env.AZURE_OPENAI_API_KEY!,
modelName: 'gpt-4'
}))
.setEmbedding('embeddings', new AzureOpenAiEmbed({
apiKey: process.env.AZURE_OPENAI_API_KEY!,
modelName: 'text-embedding-ada-002'
}))
.setVectorStore('vector-db', new AzureVectorStore({
endpoint: process.env.AZURE_SEARCH_ENDPOINT!,
apiKey: process.env.AZURE_SEARCH_API_KEY!,
indexName: 'documents'
}))
)
]
});
`

$3

`typescript
const framework = new Framework({
name: 'AI App with Lazy Loading',
modules: [
enableAI(config =>
config
.setModel('gpt-4-lazy', (args) => new AzureOpenAIModel({
apiKey: args.env.AZURE_OPENAI_API_KEY,
modelName: 'gpt-4'
}))
.setEmbedding('embeddings-lazy', (args) => new AzureOpenAiEmbed({
apiKey: args.env.AZURE_OPENAI_API_KEY,
modelName: 'text-embedding-ada-002'
}))
.setVectorStore('vector-db-lazy', (args) => new AzureVectorStore({
endpoint: args.env.AZURE_SEARCH_ENDPOINT,
apiKey: args.env.AZURE_SEARCH_API_KEY,
indexName: 'documents'
}))
)
]
});
`

$3

`typescript
async function useAIProvider(framework: Framework) {
const frameworkInstance = await framework.initialize();
const aiProvider = frameworkInstance.modules.ai;

// Get configured services
const chatService = aiProvider.getService('chat', 'gpt-4') as IModel;
const embeddingService = aiProvider.getService('embeddings', 'embeddings') as IEmbed;
const searchService = aiProvider.getService('search', 'vector-db') as IVectorStore;

// Use the services
const response = await chatService.execute([
{ role: 'user', content: 'Hello, how are you?' }
]);

const embeddings = await embeddingService.execute('This is a test document');

const searchResults = await searchService.execute('search query');
}
`

API Reference

$3

The AIConfigurator class provides a fluent API for configuring AI services.

#### Methods

- setModel(identifier: string, modelOrFactory: ValueOrCallback): this
- setEmbedding(identifier: string, embeddingOrFactory: ValueOrCallback): this
- setVectorStore(identifier: string, vectorStoreOrFactory: ValueOrCallback): this

$3

The AIProvider class provides access to configured AI services.

#### Methods

- getService(type: AIServiceType, identifier: string): IModel | IEmbed | IVectorStore - Get a configured AI service

Types

$3

Type for AI service types.

`typescript
type AIServiceType = 'chat' | 'embeddings' | 'search';
`

$3

Represents either a value of type T or a callback that creates a value of type T.

`typescript
type ValueOrCallback = T | ConfigBuilderCallback;
`

$3

Configuration object generated by the AIConfigurator.

`typescript
type AIModuleConfig = {
models?: Record>;
embeddings?: Record>;
vectorStores?: Record>;
};
``

Error Handling

The AI module provides comprehensive error handling:

- Service Not Found: Clear error messages when requesting non-existent services
- Configuration Errors: Validation of configuration parameters
- Service Errors: Wrapped service-specific errors with context

Contributing

Please refer to the main Fusion Framework contributing guidelines for information on how to contribute to this module.