Low-Code RAG Application Building with GridGain and Langflow
What is Langflow?
Langflow is a no-code platform that makes building AI applications visual and intuitive. It’s like a visual programming tool for LangChain \- you can drag, drop, and connect components to create AI workflows without writing code.
Why GridGain for Langflow?
GridGain extends Langflow with through two key components:
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Vector Store
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Implements HNSW (Hierarchical Navigable Small World) algorithm for efficient similarity search
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Sub-millisecond query latency for vector similarity searches
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Real-time indexing of new vectors without blocking read operations
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Horizontal scaling across multiple nodes
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Chat Memory
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Distributed session management with consistent hashing
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In-memory storage with disk persistence for durability
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Automatic failover and data replication
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Session data partitioning for optimal performance
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Built-in TTL (Time-To-Live) management for sessions
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Real-time Product Updates: A Practical Example
We’ve implemented a dynamic product information system using GridGain’s capabilities in Langflow. Here’s the technical breakdown:
The Challenge
Imagine an online store where prices, availability, and delivery times change constantly. You need to:
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Update product info instantly
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Help customers find similar products
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Remember customer preferences
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Give accurate, up-to-date answers
How GridGain Helps
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Quick Updates
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New prices instantly reflected in search
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Availability status always current
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Delivery times automatically adjusted
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Smart Search
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Finds similar products even as data changes
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Keeps recommendations fresh
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Personal Experience
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Remembers what each customer likes
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Maintains conversation context
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Gives consistent responses across sessions
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System Architecture
The system integrates three main components:
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Real-time data ingestion pipeline
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Vector search infrastructure
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Conversational AI interface
Technical Implementation
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Data Management
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Product embeddings generated using OpenAI’s text-embedding-ada-002 model
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Vectorized product data stored in GridGain’s Vector Database
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Search Infrastructure
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Cosine similarity computations
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Configurable number of nearest neighbors (k-NN search)
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Conversation Handling
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Session management through distributed key-value storage
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Conversation context maintained using LangFlow’s Memory component
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Automatic session cleanup through TTL mechanisms
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Load balancing of chat requests across nodes
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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 a step-by-step instruction on setting up and running the project.
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