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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)

metric?:

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

upsert()​

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()​

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

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)

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
  }
}