Qdrant

Qdrant is 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 size

metric?:

'cosine' | 'euclidean' | 'dotproduct'

Distance metric for similarity search

upsert()

vectors:

number[][]

Array of embedding vectors

metadata?:

Record<string, any>[]

Metadata for each vector

namespace?:

string

Optional namespace for organization

query()

vector:

number[]

Query vector to find similar vectors

topK?:

number

Number of results to return

filter?:

Record<string, any>

Metadata filters for the query

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

Response Types

Query results are returned in this format:

interface QueryResult {
  id: string;
  score: number;
  metadata: Record<string, any>;
}

Error Handling

The store throws typed errors that can be caught:

try {
  await store.query(queryVector);
} catch (error) {
  if (error instanceof VectorStoreError) {
    console.log(error.code); // 'connection_failed' | 'invalid_dimension' | etc
    console.log(error.details); // Additional error context
  }
}

Pinecone