> ## Documentation Index
> Fetch the complete documentation index at: https://docs.veadk.xyz/llms.txt
> Use this file to discover all available pages before exploring further.

# 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

```bash lines theme={null}
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

```python lines theme={null}
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`:

```python lines theme={null}
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

```bash lines theme={null}
# 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"
```

<Note>
  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.
</Note>

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