Documentation Index
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Overview
By default, when you pass a KnowledgeBase as context to a Task, the knowledge base is set up (documents are indexed) and queried automatically (query_knowledge_base=True). To disable automatic RAG retrieval, set query_knowledge_base=False on the Task.
This gives you fine-grained control over when the agent receives knowledge base context, letting you:
- Disable RAG retrieval for specific tasks that don’t need it
- Index documents once and query selectively across different tasks
- Mix knowledge-base-aware and knowledge-base-free tasks using the same agent
Usage
Default Behavior (Query Enabled)
By default, query_knowledge_base=True retrieves relevant chunks and injects them into the agent’s context:
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode
from upsonic.loaders.pdf import PdfLoader
from upsonic.loaders.config import PdfLoaderConfig
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())
vectordb = ChromaProvider(ChromaConfig(
collection_name="my_kb",
vector_size=1536,
connection=ConnectionConfig(mode=Mode.IN_MEMORY)
))
loader = PdfLoader(PdfLoaderConfig())
kb = KnowledgeBase(
sources=["company_handbook.pdf"],
embedding_provider=embedding,
vectordb=vectordb,
loaders=[loader]
)
agent = Agent("anthropic/claude-sonnet-4-5")
# RAG context IS injected — agent sees relevant document chunks
task = Task(
description="What is the vacation policy?",
context=[kb],
query_knowledge_base=True
)
result = agent.do(task)
print(result)
Disabling Knowledge Base Query
Set query_knowledge_base=False to skip querying. The knowledge base is still set up but the agent will not receive any RAG context:
# RAG context is NOT injected — KB is indexed but not queried
task = Task(
description="Write a creative story about space travel",
context=[kb],
query_knowledge_base=False
)
result = agent.do(task)
print(result)
Selective Querying Across Tasks
A common pattern is to set up one knowledge base and use it across multiple tasks, querying it only when needed:
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode
from upsonic.loaders.pdf import PdfLoader
from upsonic.loaders.config import PdfLoaderConfig
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())
vectordb = ChromaProvider(ChromaConfig(
collection_name="docs_kb",
vector_size=1536,
connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./kb_db")
))
loader = PdfLoader(PdfLoaderConfig())
kb = KnowledgeBase(
sources=["product_docs/"],
embedding_provider=embedding,
vectordb=vectordb,
loaders=[loader]
)
agent = Agent("anthropic/claude-sonnet-4-5")
# Task 1: Needs KB context
task_with_rag = Task(
description="Summarize the product specifications",
context=[kb],
query_knowledge_base=True
)
# Task 2: Does NOT need KB context
task_without_rag = Task(
description="Draft a marketing tagline for our product",
context=[kb],
query_knowledge_base=False
)
result1 = agent.do(task_with_rag) # Uses RAG context
result2 = agent.do(task_without_rag) # No RAG context
Combining with Vector Search Parameters
When query_knowledge_base=True, you can fine-tune the retrieval using vector search parameters on the same Task:
task = Task(
description="What are the compliance requirements?",
context=[kb],
query_knowledge_base=True,
vector_search_top_k=10,
vector_search_similarity_threshold=0.75
)
