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Overview

When multiple KnowledgeBase instances share the same vector database collection, isolate_search controls whether queries are scoped to only the documents belonging to that KnowledgeBase or whether they can return results from any document in the collection.
ValueBehavior
True (default)Queries only return documents indexed by this KnowledgeBase
FalseQueries return documents from all KnowledgeBases in the shared collection

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How It Works

When isolate_search=True:
  1. During indexing — every chunk is tagged with the KnowledgeBase’s unique knowledge_id in the vector database metadata
  2. During search — a knowledge_base_id filter is automatically injected into every query, so only chunks belonging to this KnowledgeBase are returned
When isolate_search=False, chunks are stored without a knowledge_base_id tag, and no filter is applied at query time.

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Example: Two Isolated KnowledgeBases

Two KnowledgeBases share the same Chroma collection but return only their own documents: 
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())

# Both KBs point to the same collection
def make_vectordb():
    return ChromaProvider(ChromaConfig(
        collection_name="company_docs",
        vector_size=1536,
        connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./chroma_db"),
    ))

# KB for HR policies
hr_kb = KnowledgeBase(
    sources=["hr_handbook.pdf"],
    vectordb=make_vectordb(),
    embedding_provider=embedding,
    name="hr_policies",
    isolate_search=True,  # default — only returns HR docs
)

# KB for engineering docs
eng_kb = KnowledgeBase(
    sources=["engineering_wiki/"],
    vectordb=make_vectordb(),
    embedding_provider=embedding,
    name="engineering_docs",
    isolate_search=True,  # default — only returns engineering docs
)

agent = Agent("anthropic/claude-sonnet-4-5")

# This query only searches HR documents
hr_task = Task(
    description="What is the PTO policy?",
    context=[hr_kb],
)
print(agent.do(hr_task))

# This query only searches engineering documents
eng_task = Task(
    description="How do we deploy to production?",
    context=[eng_kb],
)
print(agent.do(eng_task))
With isolate_search=True, querying the HR KnowledgeBase about deployment will never return engineering docs, and vice versa — even though they share the same underlying collection.

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Example: Shared Search Across KnowledgeBases

Set isolate_search=False to allow queries to match documents from any KnowledgeBase in the collection: 
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())

def make_vectordb():
    return ChromaProvider(ChromaConfig(
        collection_name="all_company_docs",
        vector_size=1536,
        connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./chroma_db"),
    ))

# Multiple KBs sharing a collection with cross-KB search
hr_kb = KnowledgeBase(
    sources=["hr_handbook.pdf"],
    vectordb=make_vectordb(),
    embedding_provider=embedding,
    name="hr_policies",
    isolate_search=False,  # can search across all docs
)

eng_kb = KnowledgeBase(
    sources=["engineering_wiki/"],
    vectordb=make_vectordb(),
    embedding_provider=embedding,
    name="engineering_docs",
    isolate_search=False,  # can search across all docs
)

agent = Agent("anthropic/claude-sonnet-4-5")

# This query searches ALL documents in the collection
task = Task(
    description="What are the company policies around on-call rotations?",
    context=[hr_kb],  # will find results from both HR and engineering docs
)
print(agent.do(task))

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When to Use Each Mode

ScenarioRecommended
Multiple distinct knowledge domains in one collectionisolate_search=True
Single KnowledgeBase per collectionEither (no difference)
Global search across all documentsisolate_search=False
Multi-tenant — each tenant’s docs must be privateisolate_search=True
Building a unified search over incrementally added sourcesisolate_search=False