> ## Documentation Index > Fetch the complete documentation index at: https://docs.agno.com/llms.txt > Use this file to discover all available pages before exploring further. # Per-User Isolation: Redis > Each user gets a private view of one shared knowledge base. Each user gets a private view of one shared knowledge base. Documents uploaded with a user\_id are visible only to that user; documents uploaded without one are shared with everyone, and an admin (user\_id=None) sees all of it. ```python redis_db.py theme={null} """ Per-User Isolation: Redis ========================= Each user gets a private view of one shared knowledge base. Documents uploaded with a user_id are visible only to that user; documents uploaded without one are shared with everyone, and an admin (user_id=None) sees all of it. Redis stores the owner as a user_id TAG field on each hash and filters on it inside FT.SEARCH, keeping unowned chunks under a __shared__ sentinel tag. Redis and Valkey both bind port 6379, so run only one of them at a time. Requirements: - ./cookbook/scripts/run_redis.sh (Redis on localhost:6379) - uv pip install redis redisvl - OPENAI_API_KEY """ import asyncio from typing import List from agno.agent import Agent from agno.knowledge.document import Document from agno.knowledge.knowledge import Knowledge from agno.models.openai import OpenAIResponses from agno.vectordb.redis import RedisDb from agno.vectordb.search import SearchType # --------------------------------------------------------------------------- # Setup # --------------------------------------------------------------------------- ALICE_SALARY = "Alice's salary is $180,000. Reviewed annually in March." BOB_SALARY = "Bob's salary is $215,000. Reviewed annually in June." HOLIDAYS = "The company is closed on January 1, July 4, and December 25." REDIS_URL = "redis://localhost:6379" INDEX_NAME = "per_user_isolation_redis" def show(label: str, results: List[Document]) -> None: """Print one search result set.""" print(f"{label} -> {len(results)} results") for d in results: print(f" - {d.content[:80]}") print() # --------------------------------------------------------------------------- # Create Knowledge Base # --------------------------------------------------------------------------- vector_db = RedisDb( index_name=INDEX_NAME, redis_url=REDIS_URL, search_type=SearchType.vector, ) # Start clean: hashes from an earlier run keep their owner tag and would show up as extra results. if vector_db.exists(): vector_db.drop() vector_db.create() knowledge = Knowledge( name="per_user_demo", description="Per-user RAG isolation demo (Redis)", vector_db=vector_db, ) # --------------------------------------------------------------------------- # Run Demo # --------------------------------------------------------------------------- if __name__ == "__main__": async def main() -> None: await knowledge.ainsert( name="alice_salary", text_content=ALICE_SALARY, user_id="alice", ) await knowledge.ainsert( name="bob_salary", text_content=BOB_SALARY, user_id="bob", ) # The last insert has no user_id, which makes it shared with everyone. await knowledge.ainsert( name="company_holidays", text_content=HOLIDAYS, ) print("\n" + "=" * 60) print("SCOPED SEARCH: three callers, one corpus") print("=" * 60 + "\n") alice_view = await knowledge.asearch(query="salary", user_id="alice") show("Alice (user_id='alice')", alice_view) alice_text = " ".join(d.content for d in alice_view) assert "180,000" in alice_text, "Alice cannot retrieve her own document" assert "January 1" in alice_text, ( "Shared content is unreachable from Alice's scoped view" ) assert "215,000" not in alice_text, ( "Isolation broken: Alice's scoped view leaked Bob's salary" ) bob_view = await knowledge.asearch(query="salary", user_id="bob") show("Bob (user_id='bob')", bob_view) bob_text = " ".join(d.content for d in bob_view) assert "215,000" in bob_text, "Bob cannot retrieve his own document" assert "January 1" in bob_text, ( "Shared content is unreachable from Bob's scoped view" ) assert "180,000" not in bob_text, ( "Isolation broken: Bob's scoped view leaked Alice's salary" ) admin_view = await knowledge.asearch(query="salary", user_id=None) show("Admin (user_id=None)", admin_view) admin_text = " ".join(d.content for d in admin_view) for expected in ("180,000", "215,000", "January 1"): assert expected in admin_text, ( f"Admin view is missing {expected}, it has to see every owner" ) assert all(d.content in admin_text for d in alice_view), ( "Admin view has to be a superset of a scoped user's view" ) print("Alice and Bob each see their own chunk plus the shared one.") print("Admin sees the whole corpus.") print("\n" + "=" * 60) print("AGENT-MEDIATED RETRIEVAL: the owner has to survive the handoff") print("=" * 60 + "\n") alice_agent = Agent( name="Alice's Assistant", model=OpenAIResponses(id="gpt-5.5"), knowledge=knowledge, search_knowledge=True, user_id="alice", instructions=[ "Answer questions using ONLY the knowledge you can retrieve.", "If you don't know, say so - do not invent salary figures.", ], markdown=True, ) response = await alice_agent.arun("What is Bob's salary?") print("Alice's agent on 'What is Bob's salary?':") print(response.content) # Assert on what retrieval returned, not on the model's prose. retrieved = " ".join( item["content"] for ref in (response.references or []) for item in (ref.references or []) if isinstance(item, dict) and item.get("content") ) assert retrieved, ( "Retrieval returned no documents, so the isolation check below would pass on nothing" ) assert "215,000" not in retrieved, ( "Isolation broken: Alice's agent retrieved Bob's salary. The owner was " "dropped between the run context and the vector DB, so retrieval ran " "unscoped (user_id=None, the admin view)." ) print("\nisolation holds: Bob's salary never reached Alice's agent") print("\nDone.") asyncio.run(main()) ``` ## Run the Example ```bash theme={null} uv pip install -U agno openai redis redisvl ``` ```bash Mac/Linux theme={null} export OPENAI_API_KEY="your_openai_api_key_here" ``` ```bash Windows theme={null} $Env:OPENAI_API_KEY="your_openai_api_key_here" ``` ```bash theme={null} docker run -d --name my-redis -p 6379:6379 redis ``` Save the code above as `redis_db.py`, then run: ```bash theme={null} python redis_db.py ``` Full source: [cookbook/07\_knowledge/04\_advanced/07\_per\_user\_isolation/redis\_db.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/07_knowledge/04_advanced/07_per_user_isolation/redis_db.py)