Vector Stores on Dapr Docs

Chroma

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

Uses ChromaDB for in-memory or persistent vector search.

Installation

pip install chromadb

uv add chromadb

Usage

from dapr_agents.storage.vectorstores import ChromaVectorStore
from dapr_agents.document.embedder.openai import OpenAIEmbedder # Replace with your embedding model

store = ChromaVectorStore(
 collection_name="my_collection",
 embedding_function=OpenAIEmbedder(),
)

Postgres

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

Uses Postgres with pgvector for production-grade vector similarity search.

Installation

pip install "psycopg[binary,pool]" pgvector

uv add 'psycopg[binary,pool]' pgvector

Usage

from dapr_agents.storage.vectorstores import PostgresVectorStore
from dapr_agents.document.embedder.openai import OpenAIEmbedder # Replace with your embedding model

store = PostgresVectorStore(
 connection_string="postgresql://user:pass@localhost:5432/mydb",
 embedding_function=OpenAIEmbedder(),
 embedding_dimensions=1536,
)

Redis

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

Uses Redis Stack via the redisvl library for vector similarity search.

Installation

Note

The Redis instance started by dapr init is a vanilla Redis server and does not include the Search/vector modules required by Redis Stack. To use RedisVectorStore, you must run Redis Stack (or a Redis deployment with the RediSearch module enabled) separately.

pip install redisvl

uv add redisvl

Usage

from dapr_agents.storage.vectorstores import RedisVectorStore
from dapr_agents.document.embedder.openai import OpenAIEmbedder # Replace with your embedding model

store = RedisVectorStore(
 url="redis://localhost:6379",
 index_name="my_agent",
 embedding_function=OpenAIEmbedder(),
 embedding_dimensions=1536,
 distance_metric="cosine", # "cosine", "l2", or "ip"
 storage_type="hash", # "hash" or "json"
)