Vector search for Amazon DocumentDB

Vector search is a method used in machine learning to find similar data points to a given data point by comparing their vector representations using distance or similarity metrics. The closer the two vectors are in the vector space, the more similar the underlying items are considered to be. This technique helps capture the semantic meaning of the data. This approach is useful in various applications, such as recommendation systems, natural language processing, and image recognition.

Vector search for Amazon DocumentDB combines the flexibility and rich querying capability of a JSON-based document database with the power of vector search. If you want to use your existing Amazon DocumentDB data or a flexible document data structure to build machine learning and generative AI use cases, such as semantic search experience, product recommendation, personalization, chatbots, fraud detection, and anomaly detection, then vector search for Amazon DocumentDB is an ideal choice for you. Vector search is available on Amazon DocumentDB 5.0 instance-based clusters.

Inserting vectors

To insert vectors into your Amazon DocumentDB database, you can use existing insert methods:

Example

In the following example, a collection of five documents within a test database is created. Each document includes two fields: the product name and its corresponding vector embedding.

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]},
  {"product_name": "Product C", "vectorEmbedding": [0.1, 0.2, 0.5]},
  {"product_name": "Product D", "vectorEmbedding": [0.9, 0.6, 0.4]},
  {"product_name": "Product E", "vectorEmbedding": [0.4, 0.7, 0.2]}
]);

Creating a vector index

Amazon DocumentDB supports both Hierarchical Navigable Small World (HNSW) indexing and Inverted File with Flat Compression (IVFFlat) indexing methods. An IVFFlat index segregates vectors into lists and subsequently searches a selected subset of those lists that are nearest to the query vector. On the other hand, an HNSW index organizes the vector data into a multi-layered graph. Although HNSW has slower build times compared to IVFFlat, it delivers better query performance and recall. Unlike IVFFlat, HNSW has no training step involved, allowing the index to be generated without any initial data load. For the majority of use cases, we recommend using the HNSW index type for vector search.

If you do not create a vector index, Amazon DocumentDB performs an exact nearest neighbor search, ensuring perfect recall. However, in production scenarios, speed is crucial. We recommend using vector indexes, which may trade some recall for improved speed. It's important to note that adding a vector index can lead to different query results.

Templates

You can use the following createIndex or runCommand templates to build a vector index on a vector field:

Using createIndex

In certain drivers, such as mongosh and Java, using the vectorOptions parameters in createIndex may result in an error. In such cases, we recommend using runCommand:

db.collection.createIndex(
  { "<vectorField>": "vector" },
  { "name": "<indexName>",
    "vectorOptions": {
      "type": " <hnsw> | <ivfflat> ",
      "dimensions": <number_of_dimensions>,
      "similarity": " <euclidean> | <cosine> | <dotProduct> ",
      "lists": <number_of_lists> [applicable for IVFFlat],
      "m": <max number of connections> [applicable for HNSW],
      "efConstruction": <size of the dynamic list for index build> [applicable for HNSW]
    }
  }
);

Using runCommand

In certain drivers, such as mongosh and Java, using the vectorOptions parameters in createIndex may result in an error. In such cases, we recommend using runCommand:

db.runCommand(
  { "createIndexes": "<collection>", 
  "indexes": [{
      key: { "<vectorField>": "vector" },
      vectorOptions: {
          type: " <hnsw> | <ivfflat> ",
          dimensions: <number of dimensions>,
          similarity: " <euclidean> | <cosine> | <dotProduct> ",
          lists: <number_of_lists> [applicable for IVFFlat],
          m: <max number of connections> [applicable for HNSW],
          efConstruction: <size of the dynamic list for index build> [applicable for HNSW]
          },
      name: "myIndex" 
      }] 
  }
);

It is important that you set the value of sub-parameters such as lists for IVFFlat and m and efConstruction for HNSW appropriately as it will affect the accuracy/recall, build time, and performance of your search. A higher list value increases the speed of the query as it reduces the number of vectors in each list, resulting in smaller regions. However, a smaller region size may lead to more recall errors, resulting in lower accuracy. For HNSW, increasing the value of m and efConstruction increases the accuracy, but also increases index build time and size. See the following examples:

Examples

HNSW

db.collection.createIndex(
  { "vectorEmbedding": "vector" },
  { "name": "myIndex",
    "vectorOptions": {
      "type": "hnsw",
      "dimensions": 3,
      "similarity": "euclidean",
      "m": 16,
      "efConstruction": 64
    }
  }
);

IVFFlat

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

Getting an index definition

You can view the details of your indexes, including vector indexes, using the getIndexes command:

Example

db.collection.getIndexes()

Example output

[
 {
  "v" : 4,
  "key" : {
   "_id" : 1
  },
  "name" : "_id_",
  "ns" : "test.collection"
 },
 {
  "v" : 4,
  "key" : {
   "vectorEmbedding" : "vector"
  },
  "name" : "myIndex",
  "vectorOptions" : {
   "type" : "ivfflat",
   "dimensions" : 3,
   "similarity" : "euclidean",
   "lists" : 1
  },
  "ns" : "test.collection"
 }
]

Querying vectors

Vector query template

Use the following template to query a vector:

db.collection.aggregate([
  {
    $search: {
      "vectorSearch": {
        "vector": <query vector>, 
        "path": "<vectorField>", 
        "similarity": "<distance metric>",
        "k": <number of results>,
        "probes":<number of probes> [applicable for IVFFlat],
        "efSearch":<size of the dynamic list during search> [applicable for HNSW]
      }
    }
  }
]);

It is important to fine tune the value of efSearch (HNSW) or probes (IVFlat) to achieve your desired performance and accuracy. See the following example operations:

HNSW

db.collection.aggregate([
  {
    $search: {
      "vectorSearch": {
        "vector": [0.2, 0.5, 0.8], 
        "path": "vectorEmbedding", 
        "similarity": "euclidean",
        "k": 2,
        "efSearch": 40
      }
    }
  }
]);

IVFFlat

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

Example output

Output from this operation looks something like the following:

{ "_id" : ObjectId("653d835ff96bee02cad7323c"), "product_name" : "Product A", "vectorEmbedding" : [ 0.2, 0.5, 0.8 ] }
{ "_id" : ObjectId("653d835ff96bee02cad7323e"), "product_name" : "Product C", "vectorEmbedding" : [ 0.1, 0.2, 0.5 ] }

Features and limitations

Version compatibility

Vectors

Indexes

Vector query

Best practices

Learn best practices for working with vector search in Amazon DocumentDB. This section is continually updated as new best practices are identified.

Resources