Redis RAG with Nvidia NeMoGuardrails

Summary

This post will cover the usage of guardrails in the context of an RAG application using Redis Stack as the vector store.  

• Nvidia's guardrail package is used for the railed implementation.
• Langchain LCEL is used for the non-railed implementation.
• Content from the online Redis vector search documentation is used for the RAG content
• GUI is implemented with Chainlit

Application Architecture

This bot is operating within a Chainlit app.  It has two modes of operation:  

• 'chain' - no guardrails
• 'rails' - NeMo guardrails in place for both user inputs and LLM outputs

Screenshots

Bot without rails

This first screenshot shows the bot operating with no guardrails.  It does just fine until an off-topic question is posed - then it cheerfully deviates from its purpose.

Bot with rails

Same series of questions here with guardrails enabled.  Note that it keeps the user on topic now.

Code Snippets

Non-railed chain (LCEL)

Railed with NeMO Guardrails

Source

https://github.com/redis-developer/llm-redisrail

Copyright  ©2024 Joey E Whelan, All rights reserved.

Change Data Capture w/Redis Enterprise

Summary

Redis Enterprise has the capability for continuous data integration with 3rd party data sources.  This capability is enabled via the Redis Data Integration (RDI) product.  With RDI, change data capture (CDC) can be achieved with all the major SQL databases for ingress.  Similarly, in the other direction, updates to Redis can be continuously written to 3rd party targets via the write-behind functionality of RDI.  

This post covers a demo-grade environment of Redis Enterprise + RDI with ingress and write-behind integrations with the following SQL databases:  Oracle, MS SQL, Postgres, and MySQL.  All components are containerized and run from a Docker environment.

Architecture

Ingress

Write-behind

Code Snippets

Docker Compose - Redis Enterprise Node

Docker Compose - Oracle Enterprise

Docker Compose - Debezium

RDI Ingress w/Prometheus Integration

Source

https://github.com/Redislabs-Solution-Architects/redisdi-docker

Copyright ©1993-2024 Joey E Whelan, All rights reserved.

Document AI with Apache Airflow

Summary

In this post, I cover an approach to a document AI problem using a task flow implemented in Apache Airflow.  The particular problem is around the de-duplication of invoices.  This comes up in payment provider space.  I use Azure AI Document Intelligence for OCR, Azure OpenAI for vector embeddings, and Redis Enterprise for vector search.

Architecture

Code Snippets

File Sensor DAG

OCR DAG

OCR Client (Azure AI Doc Intelligence)

Embedding DAG

Embedding Client (Azure OpenAI)

Vector Search DAG

Vector Search Client (Redis Enterprise)

Source

https://github.com/Redislabs-Solution-Architects/docai_pipeline

Copyright ©1993-2024 Joey E Whelan, All rights reserved.