MastraMastra is a Typescript framework to build AI applications and features quickly. It gives you the set of primitives you need: workflows, agents, RAG, integrations, syncs and evals. You can run Mastra on your local machine, or deploy to a serverless cloud.
Qdrant is available as a vector store in Mastra node to augment application with retrieval capabilities.
Setup Usageimport { QdrantVector } from "@mastra/rag" ;
const qdrant = new QdrantVector ({
url : "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333"
apiKey : "<YOUR_API_KEY>" ,
https : true
});
Constructor Options MethodscreateIndex()
Name Type Description Default Value indexName
string
Name of the index to create dimension
number
Vector dimension size metric
string
Distance metric for similarity search cosine
upsert()
Name Type Description Default Value vectors
number[][]
Array of embedding vectors metadata
Record<string, any>[]
Metadata for each vector (optional) namespace
string
Optional namespace for organization
query()
Name Type Description Default Value vector
number[]
Query vector to find similar vectors topK
number
Number of results to return (optional) 10
filter
Record<string, any>
Metadata filters for the query (optional)
listIndexes()
Returns an array of index names as strings.
describeIndex()
Name Type Description indexName
string
Name of the index to describe
Returnsinterface IndexStats {
dimension : number ;
count : number ;
metric : "cosine" | "euclidean" | "dotproduct" ;
}
deleteIndex()
Name Type Description indexName
string
Name of the index to delete
Response TypesQuery results are returned in this format:
interface QueryResult {
id : string ;
score : number ;
metadata : Record < string , any >;
}
Further Reading