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How to build a low-code RAG application with GridGain and Langflow

Guide

Build a visual RAG application on Langflow, using GridGain 8 vector search with HNSW as the vector store and chat memory, no code required.

gridgain8
Moderate|30 min|integrations
Tested on

Prerequisites​

  • A running GridGain 8 cluster with vector search enabled
  • Langflow installed and running
  • An LLM provider account with an API key (for example, OpenAI)

Overview​

Langflow is a no-code platform that makes building AI applications visual and intuitive. It is like a visual programming tool for LangChain - you can drag, drop, and connect components to create AI workflows without writing code.

GridGain extends Langflow with two key components:

  1. Vector Store
    • Implements the HNSW (Hierarchical Navigable Small World) algorithm for efficient similarity search
    • Sub-millisecond query latency for vector similarity searches
    • Real-time indexing of new vectors without blocking read operations
    • Horizontal scaling across multiple nodes
  2. Chat Memory
    • Distributed session management with consistent hashing
    • In-memory storage with disk persistence for durability
    • Automatic failover and data replication
    • Session data partitioning for optimal performance
    • Built-in TTL (Time-To-Live) management for sessions

This guide walks through a practical example that uses these capabilities, then points you to a fully functional demo project you can run yourself.

Real-time product updates: a practical example​

The dynamic product information system uses GridGain's capabilities in Langflow. Here is the technical breakdown.

The challenge​

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

How GridGain helps​

  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

System architecture​

The system integrates three main components:

  1. Real-time data ingestion pipeline
  2. Vector search infrastructure
  3. Conversational AI interface

Technical implementation​

  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.

Demonstration project​

A fully functional demo project is available on GitHub in the Langflow demo repository. It includes functional sample code, as well as step-by-step instructions on setting up and running the project.