Use local in-memory storage

The local backend stores knowledge in an in-memory vector index from LlamaIndex. Knowledge text is embedded by an embedding model, written to an in-process vector index, and retrieved by similarity. It requires no external service, is the default backend for KnowledgeBase, and is ideal for local debugging and quick validation.

Data lives only in process memory and is lost when the program exits. Use a persistent backend in production.

When to use

Local debugging, teaching, or quickly validating a RAG flow;
No external vector store required — only an embedding model.

Dependencies

pip install "veadk-python[extensions]"

Usage

from veadk import Agent
from veadk.knowledgebase import KnowledgeBase

kb = KnowledgeBase(backend="local", 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 embedding config explicitly via backend_config:

from veadk.knowledgebase import KnowledgeBase
from veadk.configs.model_configs import EmbeddingModelConfig

kb = KnowledgeBase(
    backend="local",
    index="company_faq",
    backend_config={
        "index": "company_faq",
        "embedding_config": EmbeddingModelConfig(
            name="doubao-embedding-vision-250615",
            dim=2048,
        ),
    },
)

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 local backend imposes no naming restrictions.
`embedding_config`	`EmbeddingModelConfig`	Read automatically from `MODEL_EMBEDDING_*` env vars	Embedding model config.

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.
`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

# 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 local backend depends only on an embedding model and requires no database connection config. Text is automatically split into chunks by file type before embedding.

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Use local in-memory storage

When to use

Dependencies

Usage

Parameters

Constructor parameters

Embedding config

Environment variables

​When to use

​Dependencies

​Usage

​Parameters

​Constructor parameters

​Embedding config

​Environment variables

When to use

Dependencies

Usage

Parameters

Constructor parameters

Embedding config

Environment variables