Use Redis storage

The redis backend uses Redis as the vector store. Knowledge text is embedded by an embedding model, written to Redis via LlamaIndex, and retrieved by similarity. The index collection name equals index.

When to use

You already run a Redis instance 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.knowledgebase import KnowledgeBase

kb = KnowledgeBase(backend="redis", index="company_faq")
kb.add_from_text("The standard annual leave is 15 days per year, available after one year of service.")

agent = Agent(
    name="demo",
    instruction="Answer the user; when needed, use the `load_knowledgebase` tool to search the knowledge base.",
    knowledgebase=kb,
)

You can also pass connection config explicitly via backend_config:

from veadk.knowledgebase import KnowledgeBase
from veadk.configs.database_configs import RedisConfig

kb = KnowledgeBase(
    backend="redis",
    index="company_faq",
    backend_config={
        "index": "company_faq",
        "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 `KnowledgeBase`	Knowledge base index name. The Redis backend imposes no naming restrictions.
`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 set the vector field dimension.
`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 field dimension is taken from the embedding config (MODEL_EMBEDDING_DIM). The Redis instance must have the RediSearch module enabled, otherwise the vector index cannot be created.

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