Vector overview

Mon, 01 Jan 0001 00:00:00 +0000

Alpha

The vector API is currently in alpha.

Dapr’s vector API provides a single, portable way to store, retrieve, and run similarity queries over dense vectors (embeddings) in vector databases such as Meilisearch, Pinecone, Qdrant, Milvus, and pgvector. It is the storage and retrieval layer for AI patterns like retrieval-augmented generation (RAG), semantic search, and recommendations: your application writes embeddings with some metadata, then asks for the records closest to a query vector. You swap the underlying vector database by changing the vector component.

How-To: Store and query vectors

Mon, 01 Jan 0001 00:00:00 +0000

Alpha

The vector API is currently in alpha.

Let’s get started using the vector API. In this guide, you’ll learn how to:

Set up a Meilisearch vector component.
Create a collection.
Upsert records with vectors, metadata, and payloads.
Run similarity queries with metadata filters and score thresholds.
Run several queries in one batch.
Retrieve and delete records by ID.

The examples in this guide call the Dapr HTTP API with curl and use 4-dimensional vectors to keep them readable. In a real application, vectors come from an embedding model and typically have hundreds or thousands of dimensions. The HTTP API uses the JSON mapping of the protobuf messages: field names are camelCase, 64-bit integers are strings, and bytes are base64-encoded. Responses include fields set to their zero value and omit optional fields that aren’t set. For .NET examples using typed payloads, metadata filters, and batch queries, see the Vector .NET SDK guide, targeting SDK 1.19.0 or later.

Vector on Dapr Docs

Vector overview

Alpha

How-To: Store and query vectors

Alpha