Isolate Vector Search

Scope searches to a single Knowledge instance when multiple instances share the same vector database.

When multiple Knowledge instances share the same vector database, searches return results from all instances by default. Set isolate_vector_search=True to scope each instance’s searches to its own data.

from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector

vector_db = PgVector(
    table_name="shared_vectors",
    db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
)

# Only returns results from documents this instance inserted
knowledge = Knowledge(
    name="support-docs",
    vector_db=vector_db,
    isolate_vector_search=True,
)

How It Works

On insert, each document always gets linked_to metadata set to the Knowledge instance’s name (an empty string if the instance has no name), regardless of this setting. The flag controls search behavior:

When isolate_vector_search=True:

• Search: A linked_to filter is automatically injected, so only matching documents are returned.

When isolate_vector_search=False (default):

• Search: No linked_to filter is applied. Searches return results from all documents in the vector database.

When to Use

Scenario	isolate_vector_search
Single Knowledge instance	False (default)
Multiple instances, each with its own vector database	False (default)
Multiple instances sharing one vector database, need isolation	True

Example: Shared Database, Isolated Searches

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector

vector_db = PgVector(
    table_name="shared_vectors",
    db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
)

# Two knowledge instances sharing the same vector database
hr_knowledge = Knowledge(
    name="hr-docs",
    vector_db=vector_db,
    isolate_vector_search=True,
)

engineering_knowledge = Knowledge(
    name="engineering-docs",
    vector_db=vector_db,
    isolate_vector_search=True,
)

# Insert into each instance
hr_knowledge.insert(path="hr-policies/")
engineering_knowledge.insert(path="engineering-docs/")

# This agent only searches HR documents
hr_agent = Agent(knowledge=hr_knowledge, search_knowledge=True)

# This agent only searches engineering documents
eng_agent = Agent(knowledge=engineering_knowledge, search_knowledge=True)

Backwards Compatibility

isolate_vector_search defaults to False. Existing Knowledge instances behave exactly as before.

Existing data does not have linked_to metadata

Documents indexed before this flag existed do not have linked_to in their vector database metadata. When you enable isolate_vector_search=True, searches filter for linked_to=<name>. Documents without this metadata field will not match and will be invisible to the isolated search.

Enabling isolate_vector_search=True with vector databases that don’t have existing linked_to metadata will cause those documents to disappear from search results. You must re-index or manually update the metadata to restore them.

Combining with Manual Filters

When isolate_vector_search=True, the linked_to filter is automatically merged with any filters you pass, regardless of filter format:

# Dict-based filters: linked_to is merged automatically
results = hr_knowledge.search(
    query="vacation policy",
    filters={"department": "legal"},
)
# Searches for: linked_to="hr-docs" AND department="legal"

# List-based filters (FilterExpr): linked_to is also injected automatically
from agno.filters import EQ

results = hr_knowledge.search(
    query="vacation policy",
    filters=[EQ("department", "legal")],
)
# Searches for: linked_to="hr-docs" AND department="legal"

Instance Uniqueness

Each Knowledge instance must have a unique combination of name, database, and table. Registering two instances with the same name, contents database, and table in an AgentOS raises a ValueError when the AgentOS starts.

from agno.db.postgres import PostgresDb
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector

contents_db = PostgresDb(
    db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    knowledge_table="knowledge_contents",
)

vector_db = PgVector(
    table_name="shared_vectors",
    db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
)

# These two instances will conflict because they share the same
# name, contents_db, and table
knowledge_a = Knowledge(
    name="my-docs",
    contents_db=contents_db,
    vector_db=vector_db,
)

knowledge_b = Knowledge(
    name="my-docs",          # same name
    contents_db=contents_db,  # same database and table
    vector_db=vector_db,
)
# Registering both with an AgentOS raises at startup:
# ValueError: Duplicate knowledge instances detected

To fix this, give each instance a unique name, or point them to different contents databases or tables.

Requirements

• The Knowledge instance must have a name set. Without a name, documents are tagged with an empty linked_to value and no filter is applied, even when isolate_vector_search=True.
• The vector database must support metadata filtering. See Filtering for supported databases.

Next Steps

Task	Guide
Filter by other metadata	Filtering
Set up a vector database	Vector Databases
Track content metadata	Contents DB