Pinecone & Vector Databases

Pinecone & Vector Databases

Carlo can integrate with vector databases such as Pinecone to monitor and enforce governance policies across AI retrieval systems.
This is especially useful for applications using Retrieval-Augmented Generation (RAG), where AI models retrieve knowledge from external data sources.

What is a Vector Database?

Vector databases store embeddings of text, images, or other data types.
They allow AI systems to perform semantic search and retrieve relevant information quickly.

Common vector databases include:
• Pinecone
• Weaviate
• FAISS
• Chroma

Why Use Carlo with Vector Databases?

When AI systems retrieve external knowledge, there is a risk of exposing sensitive data or generating unsafe outputs.

Carlo helps by:
• Validating user queries
• Monitoring retrieved content
• Enforcing governance policies
• Logging all interactions for auditing

Example Architecture

User Query
↓
Carlo Policy Engine
↓
Vector Database Retrieval
↓
AI Model Response

Carlo ensures every step follows governance policies.

Example Integration

import carlo
import pinecone

guardian = carlo.Guardian(
api_key=”YOUR_API_KEY”)

query = “Explain machine learning”
validated_query = guardian.validate_prompt(query)
results = pinecone.query(validated_query)
print(results)

Best Practices
• Validate all queries before vector search
• Monitor retrieved content
• Log interactions for compliance
• Apply governance policies to responses