Data & RAG Integration
Consent Authorization for LlamaIndex Data Agents & RAG
Gate proprietary vector retrieval and database querying behind explicit human permission screens.
Integration Code Example (PYTHON)
from permitly import Permitly
from llamaindex.core.agent import FunctionCallingAgentWorker
permitly = Permitly(api_key="pm_live_...")
def query_restricted_documents(query_str: str, user_id: str):
permitly.assert_consent(user_id=user_id, scope="rag.query.classified")
return index.as_query_engine().query(query_str)Execution Sequence Flow
sequenceDiagram
participant Agent as 🦙 LlamaIndex Agent
participant Gate as 🛡️ Permitly Gate
participant User as 👤 User
Agent->>Gate: 1. Request Vector Query (rag.query.classified)
Gate->>User: 2. Render Consent Screen
User-->>Gate: 3. User Approves Query
Gate-->>Agent: 4. Access Granted
High-ROI Security Gate
Protecting your LlamaIndex RAG pipeline against unauthorized actions delivers an immediate compliance audit trail with zero infrastructure overhead.
Frequently Asked Questions
How does Permitly secure LlamaIndex data retrieval?
Permitly acts as a pre-query authorization gate before LlamaIndex query engines fetch sensitive vector chunks.