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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

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Usage

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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)

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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)

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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

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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
)
See the Advanced page for more details on vector search parameters.