Qdrant Vector Store

The QdrantVector class provides vector search using Qdrant, a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage vectors with additional payload and extended filtering support.

Constructor Options

url:

string

REST URL of the Qdrant instance. Eg. https://xyz-example.eu-central.aws.cloud.qdrant.io:6333

apiKey:

string

Optional Qdrant API key

https:

boolean

Whether to use TLS when setting up the connection. Recommended.

Methods

createIndex()

indexName:

string

Name of the index to create

dimension:

number

Vector dimension (must match your embedding model). Required for single-vector collections.

metric?:

'cosine' | 'euclidean' | 'dotproduct'

= cosine

Distance metric for similarity search

namedVectors?:

Record<string, { size: number; distance: 'cosine' | 'euclidean' | 'dotproduct' }>

Configuration for named vector spaces. When provided, creates a collection with multiple named vector fields.

Creating a Named Vectors Collection

// Create a collection with multiple named vector spaces
await store.createIndex({
  indexName: "multi_modal",
  dimension: 768, // fallback
  namedVectors: {
    text: { size: 768, distance: "cosine" },
    image: { size: 512, distance: "euclidean" },
  },
});

upsert()

indexName:

string

Name of the index to upsert 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)

vectorName?:

string

Name of the vector space to upsert into when using named vectors.

Upserting into Named Vector Spaces

// Upsert into the "text" vector space
await store.upsert({
  indexName: "multi_modal",
  vectors: textEmbeddings,
  metadata: textMetadata,
  vectorName: "text",
});

// Upsert into the "image" vector space
await store.upsert({
  indexName: "multi_modal",
  vectors: imageEmbeddings,
  metadata: imageMetadata,
  vectorName: "image",
});

query()

indexName:

string

Name of the index to query

queryVector:

number[]

Query vector to find similar vectors

topK?:

number

= 10

Number of results to return

filter?:

Record<string, any>

Metadata filters for the query

includeVector?:

boolean

= false

Whether to include vectors in the results

using?:

string

Name of the vector field to query when using named vectors. Use this when your collection has multiple named vector fields.

Named Vectors

Qdrant supports named vectors, allowing multiple vector fields per collection. Use the using parameter to specify which named vector to query against:

const results = await store.query({
  indexName: "my_index",
  queryVector: embedding,
  topK: 10,
  using: "title_embedding", // Query against a specific named vector
});

listIndexes()

Returns an array of index names as strings.

describeIndex()

indexName:

string

Name of the index to describe

Returns:

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

deleteIndex()

indexName:

string

Name of the index to delete

updateVector()

Update a single vector by ID or by metadata filter. Either id or filter must be provided, but not both.

indexName:

string

Name of the index to update

id?:

string

ID of the vector to update (mutually exclusive with filter)

filter?:

Record<string, any>

Metadata filter to identify vector(s) to update (mutually exclusive with id)

update:

{ vector?: number[]; metadata?: Record<string, any>; }

Object containing the vector and/or metadata to update

Updates a vector and/or its metadata in the specified index. If both vector and metadata are provided, both will be updated. If only one is provided, only that will be updated.

deleteVector()

indexName:

string

Name of the index from which to delete the vector

id:

string

ID of the vector to delete

Deletes a vector from the specified index by its ID.

deleteVectors()

Delete multiple vectors by IDs or by metadata filter. Either ids or filter must be provided, but not both.

indexName:

string

Name of the index containing the vectors to delete

ids?:

string[]

Array of vector IDs to delete (mutually exclusive with filter)

filter?:

Record<string, any>

Metadata filter to identify vectors to delete (mutually exclusive with ids)

createPayloadIndex()

Creates a payload (metadata) index on a collection field to enable efficient filtering. This is required for Qdrant Cloud and any Qdrant instance with strict_mode_config = true.

indexName:

string

Name of the collection to create the payload index on

fieldName:

string

Name of the payload field to index

fieldSchema:

'keyword' | 'integer' | 'float' | 'geo' | 'text' | 'bool' | 'datetime' | 'uuid'

The schema type for the payload field

wait?:

boolean

= true

Whether to wait for the operation to complete

// Create a keyword index for filtering by source
await store.createPayloadIndex({
  indexName: "my_index",
  fieldName: "source",
  fieldSchema: "keyword",
});

const results = await store.query({
  indexName: "my_index",
  queryVector: queryVector,
  filter: { source: "document-a" },
});

deletePayloadIndex()

Removes a payload index from a collection field.

indexName:

string

Name of the collection to delete the payload index from

fieldName:

string

Name of the payload field index to delete

wait?:

boolean

= true

Whether to wait for the operation to complete

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: "index_name",
    queryVector: queryVector,
  });
} catch (error) {
  if (error instanceof VectorStoreError) {
    console.log(error.code); // 'connection_failed' | 'invalid_dimension' | etc
    console.log(error.details); // Additional error context
  }
}

Related

• Metadata Filters