Retrieval-Augmented Generation with GridGain and LangChain
What is RAG and LangChain?
Think of RAG (Retrieval-Augmented Generation) as giving an AI chatbot access to your own knowledge base. Instead of relying only on what it learned during training, the AI can look up specific information from your knowledge base to give you more answers to queries from your own data.
LangChain makes it easier to build AI applications by providing ready-to-use components.It provides a standardized interface for integrating various components essential to building LLM applications, including:
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Document Loaders: Document loading and processing
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Vector Store: Text embedding and vector storage
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Memory: Memory management and chat history
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Cache (LLM Cache): Caching mechanisms for LLM responses
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Prompt Templates: Prompt management and templating
Why GridGain for LangChain?
GridGain serves as an ideal backend storage solution for LangChain applications, offering a unified platform that addresses multiple storage requirements within a single distributed system. Its in-memory computing capabilities, combined with disk persistence, provide the perfect balance of performance and durability needed for production LLM applications.
What GridGain Brings to the Table
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Vector Store Implementation
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GridGain provides a powerful distributed in-memory Vector Search capability
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Supports efficient similarity search with low-latency lookups
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Offers horizontal scalability for growing vector databases
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Document Storage and Retrieval
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Native support for storing and managing large documents as text
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Efficient indexing and retrieval of full text content
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Key-Value Store
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High-performance in-memory key-value operations
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Support for complex data structures as values
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Transaction support for atomic operations
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Built-in eviction and expiration policies
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Flexible querying capabilities
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LLM Response Cache
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Efficient caching of LLM responses for identical queries
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Support for semantic similarity-based cache matching
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Configurable cache eviction policies
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Distributed cache coherence
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Chat History Management
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Scalable storage for conversation histories
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Real-time access to conversation context
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Support for multiple concurrent conversations
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The Laptop Recommendation Bot: A Real Example
The demonstration project implements a sophisticated laptop recommendation chatbot that leverages GridGain’s capabilities across multiple LangChain components. This system showcases how GridGain can serve as a unified backend for complex LLM applications.
Under the Hood
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Data Organization
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Laptop specifications stored in GridGain’s key-value store
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Customer reviews stored and managed through GridGain’s document loader
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Vector embeddings of both specs and reviews stored in GridGain’s vector store
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Custom retriever combines the structured data (specs) and unstructured data (reviews) that it then loads together with its embeddings into the Vector Store
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Smart Search
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Efficient vector similarity search for finding relevant products
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Queries the Custom retriever to get relevant documents which in turn queries the Vector Store for efficient vector similarity search for finding relevant products
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Sends the results from the vector search as context in the prompt along with the chat history, and the user query for the LLM to answer the user’s query based on the data provided in the context
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Performance
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LLM response caching for frequently asked questions
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Semantic cache matching for similar queries
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Keeps response times fast
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User Experience
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Understands natural language questions
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Gives personalized recommendations
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Balances technical specs with user reviews
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This implementation demonstrates how GridGain’s distributed computing capabilities can be leveraged to build a scalable, high-performance RAG system that provides accurate and contextual laptop recommendations while maintaining low latency and high availability.
Demonstration Project
A fully functional demo project is available on GitHub in the Langchain 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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