Unleashing the Power of Vector Search for Amazon DocumentDB

In the ever-evolving landscape of machine learning, vector search has emerged as a powerful method to discover similarities between data points by analyzing their vector representations. This technique, utilizing distance or similarity metrics, enables the extraction of semantic meaning from the data. One platform where vector search truly shines is Amazon DocumentDB, where it seamlessly combines the flexibility of a JSON-based document database with the prowess of vector search.

Understanding Vector Search

What is Vector Search?

Vector search is a technique in machine learning that identifies similar data points by comparing their vector representations. The closer two vectors are in the vector space, the more similar the underlying items are considered to be. This approach finds application in diverse fields such as recommendation systems, image recognition, and natural language processing.

Vector Search for Amazon DocumentDB

Amazon DocumentDB, known for its document-oriented structure, introduces vector search to augment its capabilities. This fusion caters to a wide array of machine learning and generative AI use cases, including semantic search experiences, product recommendations, personalization, chatbots, fraud detection, and anomaly detection.

Implementation in Amazon DocumentDB

Inserting Vectors

To kickstart your journey with vector search on Amazon DocumentDB, you need to insert vectors into your database. The process involves using existing insert methods, such as:

db.collection.insertMany([
  {"product_name": "Product A", "vectorEmbedding": [0.2, 0.5, 0.8]},
  {"product_name": "Product B", "vectorEmbedding": [0.7, 0.3, 0.9]},
  // ... other data points
]);        

Creating a Vector Index

Creating a vector index is crucial for optimizing search speed. Currently, Amazon DocumentDB supports the Inverted File with Flat Compression (IVFFlat) index. The creation involves specifying parameters such as dimensions, similarity metric (euclidean, cosine, dotProduct), and the number of lists. Here's an example using the createIndex template:

db.collection.createIndex(
  { "vectorEmbedding": "vector" },
  { "name": "myIndex",
    "vectorOptions": {
      "dimensions": 3,
      "similarity": "euclidean",
      "lists": 1
    }
  }
);
        

Exploring Different Similarity Metrics

Vector search supports three similarity metrics: euclidean, cosine, and dotProduct. Let's delve into each with examples:

1. Euclidean

Copy code
// Example Query
db.collection.aggregate([
  {
    $search: {
      "vectorSearch": {
        "vector": [0.2, 0.5, 0.8],
        "path": "vectorEmbedding",
        "similarity": "euclidean",
        "k": 2,
        "probes": 1
      }
    }
  }
]);
        

2. Cosine

// Example Query
db.collection.aggregate([
  {
    $search: {
      "vectorSearch": {
        "vector": [0.2, 0.5, 0.8],
        "path": "vectorEmbedding",
        "similarity": "cosine",
        "k": 2,
        "probes": 1
      }
    }
  }
]);        

3. Dot Product

// Example Query
db.collection.aggregate([
  {
    $search: {
      "vectorSearch": {
        "vector": [0.2, 0.5, 0.8],
        "path": "vectorEmbedding",
        "similarity": "dotProduct",
        "k": 2,
        "probes": 1
      }
    }
  }
]);        

Fine-Tuning with Probes

The probes parameter in your query is a key player in balancing recall and speed. It determines the number of clusters the vector search inspects. Setting it higher enhances recall at the expense of speed. The recommended starting point for fine-tuning is sqrt(# of lists). For example:

db.collection.aggregate([
  {
    $search: {
      "vectorSearch": {
        "vector": [0.2, 0.5, 0.8],
        "path": "vectorEmbedding",
        "similarity": "euclidean",
        "k": 2,
        "probes": 10
      }
    }
  }
]);        

By exploring different similarity metrics and understanding the role of probes, you can unlock the full potential of vector search in Amazon DocumentDB, creating a robust foundation for diverse machine learning applications. Experiment, fine-tune, and elevate your vector search experience to new heights!