Use Redis storage

The redis backend uses Redis as the vector store. Memory text is embedded by an embedding model, written to Redis with a per-user index (the actual index name is veadk-ltm/{index}/{user_id}), and retrieved by cosine similarity.

When to use

You already run Redis with the RediSearch module enabled;
You want Redis as a low-latency vector-search backend, with persistence and multi-instance sharing.

Dependencies

pip install "veadk-python[extensions]"

Redis must support RediSearch (vector search).

Usage

from veadk import Agent
from veadk.memory.long_term_memory import LongTermMemory

ltm = LongTermMemory(backend="redis", index="ltm_demo")

agent = Agent(
    name="demo",
    instruction="Answer the user; when needed, use the `load_memory` tool to recall past conversations.",
    long_term_memory=ltm,
)

You can also pass connection config explicitly via backend_config:

from veadk.memory.long_term_memory import LongTermMemory
from veadk.configs.database_configs import RedisConfig

ltm = LongTermMemory(
    backend="redis",
    index="ltm_demo",
    backend_config={
        "index": "ltm_demo",
        "redis_config": RedisConfig(
            host="localhost",
            port=6379,
            password="your-password",
            db=0,
        ),
    },
)

Parameters

Constructor parameters

backend_config supports the following settings:

Parameter	Type	Default	Description
`index`	`str`	No default; provided by `LongTermMemory`	Memory index name. The Redis backend imposes no extra naming constraints.
`redis_config`	`RedisConfig`	Read automatically from `DATABASE_REDIS_*` env vars	Redis connection config.
`embedding_config`	`EmbeddingModelConfig`	Read automatically from `MODEL_EMBEDDING_*` env vars	Embedding model config.

Redis connection config

redis_config is a RedisConfig with env prefix DATABASE_REDIS_:

Field	Env var	Type	Default	Description
`host`	`DATABASE_REDIS_HOST`	`str`	`""`	Redis host.
`port`	`DATABASE_REDIS_PORT`	`int`	`6379`	Port.
`username`	`DATABASE_REDIS_USERNAME`	`str \| None`	`None`	Username, optional.
`password`	`DATABASE_REDIS_PASSWORD`	`str`	`""`	Password.
`db`	`DATABASE_REDIS_DB`	`int`	`0`	Database number.
`secret_token`	`DATABASE_REDIS_SECRET_TOKEN`	`str`	`""`	STS temporary-credential token; not yet enabled.

Embedding config

embedding_config is an EmbeddingModelConfig with env prefix MODEL_EMBEDDING_:

Field	Env var	Type	Default	Description
`name`	`MODEL_EMBEDDING_NAME`	`str`	`doubao-embedding-vision-250615`	Embedding model name.
`dim`	`MODEL_EMBEDDING_DIM`	`int`	`2048`	Embedding vector dimension; used to build the vector field. Distance metric is cosine.
`api_base`	`MODEL_EMBEDDING_API_BASE`	`str`	`https://ark.cn-beijing.volces.com/api/v3/`	API base of the embedding service.
`api_key`	`MODEL_EMBEDDING_API_KEY`	`str`	Falls back to `MODEL_AGENT_API_KEY`, then an auto-fetched Ark token	Key for accessing the embedding service.

Environment variables

# Redis connection
export DATABASE_REDIS_HOST="localhost"
export DATABASE_REDIS_PORT=6379
export DATABASE_REDIS_PASSWORD="your-password"
export DATABASE_REDIS_DB=0

# Embedding model
export MODEL_EMBEDDING_NAME="doubao-embedding-vision-250615"
export MODEL_EMBEDDING_DIM=2048
export MODEL_EMBEDDING_API_KEY="your-ark-api-key"

The vector dimension comes from the embedding config (MODEL_EMBEDDING_DIM). The index uses the flat algorithm with a cosine distance metric, and an index is created per user (veadk-ltm/{index}/{user_id}).

Overview

Get started

Core components

Observability

Extensions

Frontend

Security

Deployment

When to use

Dependencies

Usage

Parameters

Constructor parameters

Redis connection config

Embedding config

Environment variables

​When to use

​Dependencies

​Usage

​Parameters

​Constructor parameters

​Redis connection config

​Embedding config

​Environment variables

When to use

Dependencies

Usage

Parameters

Constructor parameters

Redis connection config

Embedding config

Environment variables