Attributes
The KnowledgeBase system is configured through theKnowledgeBase 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
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: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)