> ## 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.

# Query Control

> Control whether KnowledgeBase context is injected into the agent via query_knowledge_base

## 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:

```python theme={null}
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:

```python theme={null}
# 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:

```python theme={null}
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:

```python theme={null}
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 [Vector Search Tuning](/concepts/knowledgebase/advanced/vector-search-params) page for more details on vector search parameters.