mongoose-ai

AI-powered Mongoose plugin for intelligent document processing

Automatically generate summaries, classify content, and search documents using AI. Works with OpenAI, Anthropic, and local LLMs via Ollama. Includes MongoDB Atlas Vector Search support for production-scale semantic search.

Features

• Auto-generate summaries when documents are saved
• AI classifies and tags content automatically
• High-performance semantic search with MongoDB Vector Search
• Privacy-first local AI processing with Ollama
• Search documents using natural language
• Works with OpenAI GPT, Anthropic Claude, and local LLMs
• Full TypeScript support
• Built for production use

Quick Start

Install

npm install @jmndao/mongoose-ai

Basic Usage

import mongoose from "mongoose";
import { aiPlugin } from "@jmndao/mongoose-ai";

const articleSchema = new mongoose.Schema({
  title: String,
  content: String,
});

// Add AI summarization
articleSchema.plugin(aiPlugin, {
  ai: {
    model: "summary",
    provider: "openai",
    field: "aiSummary",
    credentials: {
      apiKey: process.env.OPENAI_API_KEY,
    },
  },
});

const Article = mongoose.model("Article", articleSchema);

// AI summary is generated automatically
const article = new Article({
  title: "Getting Started with AI",
  content: "Artificial intelligence is changing everything...",
});

await article.save();
console.log(article.aiSummary.summary);

Local AI with Ollama

import { createOllamaConfig } from "@jmndao/mongoose-ai";

// Zero cost, privacy-first AI processing
articleSchema.plugin(aiPlugin, {
  ai: createOllamaConfig({
    model: "summary",
    field: "aiSummary",
    chatModel: "llama3.2",
  }),
});

// Setup: ollama pull llama3.2

Semantic Search

import { createAdvancedAIConfig } from "@jmndao/mongoose-ai";

articleSchema.plugin(aiPlugin, {
  ai: createAdvancedAIConfig({
    apiKey: process.env.OPENAI_API_KEY,
    provider: "openai",
    model: "embedding",
    field: "aiEmbedding",
  }),
});

// Search documents using natural language
const results = await Article.semanticSearch(
  "artificial intelligence and neural networks",
  { limit: 5, threshold: 0.7 }
);

Function Calling

import { QuickFunctions } from "@jmndao/mongoose-ai";

const reviewSchema = new mongoose.Schema({
  productName: String,
  reviewText: String,
  sentiment: String,
  rating: Number,
  tags: [String],
});

reviewSchema.plugin(aiPlugin, {
  ai: createAdvancedAIConfig({
    apiKey: process.env.OPENAI_API_KEY,
    provider: "openai",
    model: "summary",
    field: "aiSummary",
    advanced: { enableFunctions: true },
    functions: [
      QuickFunctions.updateField("sentiment", [
        "positive",
        "negative",
        "neutral",
      ]),
      QuickFunctions.scoreField("rating", 1, 5),
      QuickFunctions.manageTags("tags"),
    ],
  }),
});

// AI automatically fills sentiment, rating, and tags

Provider Comparison

| Feature | OpenAI | Anthropic | Ollama | | ----------- | --------------- | --------------- | ------------------ | | Cost | $1.50/1M tokens | $0.25/1M tokens | $0.00 | | Privacy | External API | External API | 100% Local | | Setup | API key | API key | Local install | | Offline | No | No | Yes | | Performance | Excellent | Excellent | Hardware dependent |

Performance

Processing Speed

• Basic summarization: ~1.6 seconds per document
• Function calling: ~2.1 seconds per document
• Local processing: 2-10 seconds per document (hardware dependent)

Search Performance

• MongoDB Atlas Vector Search: Sub-100ms on millions of documents
• In-memory search: Good for development and small datasets
• Automatic optimization based on deployment

Cost Analysis

• Cloud providers: $0.42-$1.39 per 1000 documents
• Local processing: $0.00 per document
• Vector search: 10-3000x faster than traditional search

Documentation

Core Guides

• Get Started - Setup and first steps
• Configuration - All providers and options
• Function Calling - Auto-classification
• Migration - Upgrade guides

Reference

• API Reference - Methods and types
• Types Reference - TypeScript definitions
• Performance - Optimization strategies

Advanced

• Usage Examples - Real-world examples
• Scaling Guide - Large deployments
• Docker Setup - Development setup

Requirements

• Node.js 16+
• Mongoose 7+
• API key (OpenAI/Anthropic) or Ollama installation

Examples

Run example demonstrations:

npm run example:basic           # Basic usage
npm run example:functions       # Function calling
npm run example:vector-search   # Semantic search
npm run example:ollama          # Local LLM processing
npm run example:benchmark       # Performance testing

Migration

From v1.3.x to v1.4.0

• Local LLM support is additive with zero breaking changes

• All existing code continues to work unchanged

• Add Ollama support optionally

• Vector search is automatic and backward compatible

• No configuration changes required

• Performance improvements on MongoDB Atlas

License

MIT © Jonathan Moussa NDAO

For detailed documentation, configuration options, and advanced usage, see the docs directory.