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

Before running, configure MODEL_EMBEDDING_NAME, MODEL_EMBEDDING_DIM, MODEL_EMBEDDING_API_BASE, and MODEL_EMBEDDING_API_KEY. The extensions extra includes llama-index, embedding adapters, and vector-store connectors. Text is sent to the configured embedding service. For BytePlus or another provider, explicitly set the matching endpoint, model, and credentials; the default Ark endpoint does not automatically switch Also configure the Redis variables below and grant index creation and access permissions. Durability depends on Redis persistence and backup configuration

from veadk import Agent
from veadk.knowledgebase import KnowledgeBase

kb = KnowledgeBase(backend="redis", index="company_faq")
assert kb.add_from_text("The standard annual leave is 15 days per year, available after one year of service.")
for entry in kb.search("annual leave", top_k=3):
    print(entry.content)

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:

import os

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=os.environ["DATABASE_REDIS_PASSWORD"],
            db=0,
        ),
    },
)

Parameters

KnowledgeBase parameters

Parameter	Type	Default	Description
`backend`	`str \| BaseKnowledgebaseBackend`	`"local"`	Set to `redis` or pass a backend instance
`backend_config`	`dict`	`{}`	Must include index when nonempty; does not merge the outer index
`index`	`str`	`""`	Index name; falls back to app_name when no configuration dictionary is supplied
`app_name`	`str`	`""`	Fallback for index; not a user authorization filter
`top_k`	`int`	`10`	Default result count; search(top_k=0) uses this value
`name`	`str`	`"user_knowledgebase"`	Knowledge-base name shown to the agent
`description`	`str`	`"This knowledgebase stores some user-related information."`	Explains the knowledge base to the agent
`enable_profile`	`bool`	`False`	Enables document profiles; generate profile files first, or leave disabled for ordinary retrieval
`query_with_user_profile`	`bool`	`False`	Uses the agent’s Viking long-term memory profile to guide queries; the knowledge backend itself need not be Viking

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.

The example search should return the annual-leave policy. For empty results, check successful ingestion, matching embedding dimensions, completed server processing, network access, and permissions. Managed ingestion may not be immediately searchable. Running the configured Agent also requires model credentials

Overview

Get started

Core components

Observability

Extensions

Frontend

Security

Deployment

When to use

Dependencies

Usage

Parameters

KnowledgeBase parameters

Constructor parameters

Redis connection config

Embedding config

Environment variables

​When to use

​Dependencies

​Usage

​Parameters

​KnowledgeBase parameters

​Constructor parameters

​Redis connection config

​Embedding config

​Environment variables

When to use

Dependencies

Usage

Parameters

KnowledgeBase parameters

Constructor parameters

Redis connection config

Embedding config

Environment variables