Low-Code RAG Application Building with GridGain and Langflow

Imagine an online store where prices, availability, and delivery times change constantly. You need to:

• Update product info instantly

• Help customers find similar products

• Remember customer preferences

• Give accurate, up-to-date answers

1. Quick Updates

   • New prices instantly reflected in search

   • Availability status always current

   • Delivery times automatically adjusted

2. Smart Search

   • Finds similar products even as data changes

   • Keeps recommendations fresh

3. Personal Experience

   • Remembers what each customer likes

   • Maintains conversation context

   • Gives consistent responses across sessions

The system integrates three main components:

1. Real-time data ingestion pipeline

2. Vector search infrastructure

3. Conversational AI interface

1. Data Management

   • Product embeddings generated using OpenAI’s text-embedding-ada-002 model

   • Vectorized product data stored in GridGain’s Vector Database

2. Search Infrastructure

   • Cosine similarity computations

   • Configurable number of nearest neighbors (k-NN search)

3. Conversation Handling

   • Session management through distributed key-value storage

   • Conversation context maintained using LangFlow’s Memory component

   • Automatic session cleanup through TTL mechanisms

   • Load balancing of chat requests across nodes

This implementation demonstrates how GridGain’s distributed computing capabilities can be leveraged within Langflow to create a responsive, scalable product search system that handles real-time updates efficiently while maintaining high availability and consistent performance.