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

# Attributes

> Configuration options for the KnowledgeBase

## Attributes

The KnowledgeBase system is configured through the `KnowledgeBase` class, which provides the following attributes:

| Attribute            | Type                                            | Default           | Description                                                                                                                                                |
| -------------------- | ----------------------------------------------- | ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `sources`            | `Union[str, Path, List[Union[str, Path]]]`      | (required)        | File paths, directory paths, or string content to process                                                                                                  |
| `vectordb`           | `BaseVectorDBProvider`                          | (required)        | Vector database provider instance for storage                                                                                                              |
| `embedding_provider` | `EmbeddingProvider \| None`                     | `None`            | Provider for creating vector embeddings. Optional for providers that handle their own embeddings (e.g., SuperMemory)                                       |
| `splitters`          | `Union[BaseChunker, List[BaseChunker]] \| None` | `None`            | Text chunking strategies (auto-detected if None)                                                                                                           |
| `loaders`            | `Union[BaseLoader, List[BaseLoader]] \| None`   | `None`            | Document loaders for different file types (auto-detected if None)                                                                                          |
| `name`               | `str \| None`                                   | `None`            | Human-readable name for the knowledge base (auto-generated if None). Used to derive tool names when registered as a tool                                   |
| `description`        | `str \| None`                                   | `None`            | Description of the knowledge base content. Shown to agents when the KB is used as a tool, helping them decide when to search it                            |
| `topics`             | `List[str] \| None`                             | `None`            | List of topics covered by the knowledge base. Included in tool descriptions for better agent routing                                                       |
| `use_case`           | `str`                                           | `"rag_retrieval"` | Use case for chunking optimization                                                                                                                         |
| `quality_preference` | `str`                                           | `"balanced"`      | Speed vs quality preference: `"fast"`, `"balanced"`, or `"quality"`                                                                                        |
| `loader_config`      | `Dict[str, Any] \| None`                        | `None`            | Configuration options specifically for loaders                                                                                                             |
| `splitter_config`    | `Dict[str, Any] \| None`                        | `None`            | Configuration options specifically for splitters                                                                                                           |
| `isolate_search`     | `bool`                                          | `True`            | When True, search queries are scoped to only documents in this knowledge base. When False, searches across all documents in the vector database collection |
| `storage`            | `Storage \| None`                               | `None`            | Optional storage backend for persisting knowledge base state and metadata                                                                                  |

## Configuration Example

```python theme={null}
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Setup embedding provider
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())

# Setup vector database
config = ChromaConfig(
    collection_name="my_kb",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./chroma_db")
)
vectordb = ChromaProvider(config)


# Create knowledge base with configuration
kb = KnowledgeBase(
    sources=["document.pdf", "data/"],
    embedding_provider=embedding,
    vectordb=vectordb,
    name="product_docs",
    description="Product documentation including specs, guides, and FAQs",
    topics=["product specs", "user guides", "troubleshooting"],
    use_case="rag_retrieval",
    quality_preference="balanced",
    loader_config={"chunk_size": 1000},
    splitter_config={"chunk_overlap": 200}
)

# Use with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task(
    description="What are the main topics in the documents?",
    context=[kb]
)

result = agent.do(task)
print(result)
```

## SuperMemory (No Embedding Provider)

When using SuperMemory as your vector database, you don't need an embedding provider — SuperMemory handles embeddings internally:

```python theme={null}
from upsonic import Agent, Task, KnowledgeBase
from upsonic.vectordb import SuperMemoryProvider, SuperMemoryConfig

# No embedding provider needed
vectordb = SuperMemoryProvider(SuperMemoryConfig(
    collection_name="my_kb",
    api_key="sm_your_api_key_here"
))

kb = KnowledgeBase(
    sources=["document.pdf"],
    vectordb=vectordb
)

agent = Agent("anthropic/claude-sonnet-4-5")
task = Task(
    description="What are the key points?",
    context=[kb]
)
result = agent.do(task)
```