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ReferenceRAGCouchbaseVector

Couchbase Vector Store

The CouchbaseVector class provides vector search using Couchbase Vector Search . It enables efficient similarity search and metadata filtering within your Couchbase collections.

Requirements

  • Couchbase Server 7.6.4+ or a compatible Capella cluster
  • Search Service enabled on your Couchbase deployment

Installation

npm install @mastra/couchbase

Usage Example

import { CouchbaseVector } from '@mastra/couchbase';
 
const store = new CouchbaseVector({
  connectionString: process.env.COUCHBASE_CONNECTION_STRING,
  username: process.env.COUCHBASE_USERNAME,
  password: process.env.COUCHBASE_PASSWORD,
  bucketName: process.env.COUCHBASE_BUCKET,
  scopeName: process.env.COUCHBASE_SCOPE,
  collectionName: process.env.COUCHBASE_COLLECTION,
});

Constructor Options

connectionString:

string
Couchbase connection string

username:

string
Couchbase username

password:

string
Couchbase password

bucketName:

string
Name of the Couchbase bucket to use

scopeName:

string
Name of the Couchbase scope to use

collectionName:

string
Name of the Couchbase collection to use

options?:

CouchbaseClientOptions
Optional Couchbase client options

Methods

createIndex()

Creates a new vector index in Couchbase.

Note: Index creation is asynchronous. After calling createIndex, allow time (typically 1–5 seconds for small datasets, longer for large ones) before querying. For production, implement polling to check index status rather than using fixed delays.

indexName:

string
Name of the index to create

dimension:

number
Vector dimension (must match your embedding model)

metric?:

'cosine' | 'euclidean' | 'dotproduct'
= cosine
Distance metric for similarity search

upsert()

Adds or updates vectors and their metadata in the collection.

Note: You can upsert data before or after creating the index. The upsert method does not require the index to exist. Couchbase allows multiple Search indexes over the same collection.

indexName:

string
Name of the index to insert into

vectors:

number[][]
Array of embedding vectors

metadata?:

Record<string, any>[]
Metadata for each vector

ids?:

string[]
Optional vector IDs (auto-generated if not provided)

query()

Searches for similar vectors.

Warning: The filter and includeVector parameters are not currently supported. Filtering must be performed client-side after retrieving results, or by using the Couchbase SDK’s Search capabilities directly. To retrieve the vector embedding, fetch the full document by ID using the Couchbase SDK.

indexName:

string
Name of the index to search in

queryVector:

number[]
Query vector to find similar vectors for

topK?:

number
= 10
Number of results to return

filter?:

Record<string, any>
Metadata filters

includeVector?:

boolean
= false
Whether to include vector data in results

minScore?:

number
= 0
Minimum similarity score threshold

describeIndex()

Returns information about the index.

indexName:

string
Name of the index to describe

Returns:

interface IndexStats {
  dimension: number;
  count: number;
  metric: "cosine" | "euclidean" | "dotproduct";
}

deleteIndex()

Deletes an index and all its data.

indexName:

string
Name of the index to delete

listIndexes()

Lists all vector indexes in the Couchbase bucket.

Returns: Promise<string[]>

updateVector()

Updates a specific vector entry by its ID with new vector data and/or metadata.

indexName:

string
Name of the index containing the vector

id:

string
ID of the vector entry to update

update:

object
Update data containing vector and/or metadata

update.vector?:

number[]
New vector data to update

update.metadata?:

Record<string, any>
New metadata to update

deleteVector()

Deletes a specific vector entry from an index by its ID.

indexName:

string
Name of the index containing the vector

id:

string
ID of the vector entry to delete

disconnect()

Closes the Couchbase client connection. Should be called when done using the store.

Response Types

Query results are returned in this format:

interface QueryResult {
  id: string;
  score: number;
  metadata: Record<string, any>;
  vector?: number[]; // Only included if includeVector is true
}

Error Handling

The store throws typed errors that can be caught:

try {
  await store.query({
    indexName: "my_index",
    queryVector: queryVector,
  });
} catch (error) {
  // Handle specific error cases
  if (error.message.includes("Invalid index name")) {
    console.error(
      "Index name must start with a letter or underscore and contain only valid characters."
    );
  } else if (error.message.includes("Index not found")) {
    console.error("The specified index does not exist");
  } else {
    console.error("Vector store error:", error.message);
  }
}

Notes

  • Index Deletion Caveat: Deleting a Search index does NOT delete the vectors/documents in the associated Couchbase collection. Data remains unless explicitly removed.
  • Required Permissions: The Couchbase user must have permissions to connect, read/write documents in the target collection (kv role), and manage Search Indexes (search_admin role on the relevant bucket/scope).
  • Index Definition Details & Document Structure: The createIndex method constructs a Search Index definition that indexes the embedding field (as type vector) and the content field (as type text), targeting documents within the specified scopeName.collectionName. Each document stores the vector in the embedding field and metadata in the metadata field. If metadata contains a text property, its value is also copied to a top-level content field, which is indexed for text search.
  • Replication & Durability: Consider using Couchbase’s built-in replication and persistence features for data durability. Monitor index statistics regularly to ensure efficient search.

Limitations

  • Index creation delays may impact immediate querying after creation.
  • No hard enforcement of vector dimension at ingest time (dimension mismatches will error at query time).
  • Vector insertion and index updates are eventually consistent; strong consistency is not guaranteed immediately after writes.
MongoDBVectorOpenSearchVector