@equinor/fusion-framework-cli-plugin-ai-index

AI indexing plugin for Fusion Framework CLI providing document embedding and chunking utilities.

Installation

pnpm add -D @equinor/fusion-framework-cli-plugin-ai-index

Configuration

After installing the plugin, create a fusion-cli.config.ts file in your project root:

import { defineFusionCli } from '@equinor/fusion-framework-cli';

export default defineFusionCli(() => ({
  plugins: [
    '@equinor/fusion-framework-cli-plugin-ai-index',
  ],
}));

The CLI will automatically discover and load plugins listed in this configuration file. The config file can be .ts, .js, or .json. The defineFusionCli helper provides type safety and IntelliSense support.

Features

This plugin extends the Fusion Framework CLI with AI indexing capabilities:

• Document embedding and chunking utilities
• Markdown/MDX document chunking with frontmatter extraction
• TypeScript/TSX TSDoc extraction and chunking
• Glob pattern support for file collection
• Git diff-based processing for workflow integration
• Dry-run mode for testing without actual processing

Usage

Once installed, the embeddings command is automatically available:

# Generate embeddings from documents
ffc ai embeddings ./src

Commands

ai embeddings

Document embedding utilities for Large Language Model processing.

Features:

• Markdown/MDX document chunking with frontmatter extraction
• TypeScript/TSX TSDoc extraction and chunking
• Glob pattern support for file collection
• Git diff-based processing for workflow integration
• Dry-run mode for testing without actual processing
• Configurable file patterns via fusion-ai.config.ts

Options:

• --dry-run - Show what would be processed without actually doing it
• --config <config> - Path to a config file (default: fusion-ai.config.ts)
• --diff - Process only changed files (workflow mode)
• --base-ref <ref> - Git reference to compare against (default: HEAD~1)
• --clean - Delete all existing documents from the vector store before processing
• --openai-api-key <key> - API key for Azure OpenAI
• --openai-api-version <version> - API version (default: 2024-02-15-preview)
• --openai-instance <name> - Azure OpenAI instance name
• --openai-embedding-deployment <name> - Azure OpenAI embedding deployment name
• --azure-search-endpoint <url> - Azure Search endpoint URL
• --azure-search-api-key <key> - Azure Search API key
• --azure-search-index-name <name> - Azure Search index name

Examples:

$ ffc ai embeddings --dry-run ./src
$ ffc ai embeddings "*.ts" "*.md" "*.mdx"
$ ffc ai embeddings --diff
$ ffc ai embeddings --diff --base-ref origin/main
$ ffc ai embeddings --clean "*.ts"

Configuration

The plugin requires Azure OpenAI and Azure Cognitive Search configuration. See the main CLI documentation for details on setting up API keys and endpoints.

You can also create a fusion-ai.config.ts file to configure file patterns and metadata processing:

import { configureFusionAI } from '@equinor/fusion-framework-cli-plugin-ai-index';
import type { FusionAIConfigWithIndex } from '@equinor/fusion-framework-cli-plugin-ai-index';

export default configureFusionAI((): FusionAIConfigWithIndex => ({
  index: {
    patterns: ['**/*.ts', '**/*.md', '**/*.mdx'],
    metadata: {
      attributeProcessor: (attributes, document) => {
        // Custom metadata processing
        return attributes;
      },
    },
    embedding: {
      chunkSize: 1000,
      chunkOverlap: 200,
    },
  },
}));

License

ISC