RAG vs KM (Kernel Memory)

Ever since I jumped into the world of AI, RAG (Retrieval-Augmented Generation) was something I heard about constantly, and it caught my attention. After days of learning, understanding, and questioning both people and AI, I finally grasped the concepts of RAG and how it works. Here’s my journey to understanding these concepts and how they differ.

Recently, I was bombarded with a new thing called Kernel Memory with RAG. Suddenly I was scratching my head, wondering what “kernel” had to do with RAG and how is it different from a typical RAG solution. 

After some serious time spent on it, I finally understood the difference (at least I think so) and thought I’d share it with the team as well. Let’s dive in! And yes, I promise to keep it clearer than my initial understanding of these concepts!

Traditional RAG: An Overview

Traditional RAG solutions combine the power of large language models (LLMs) with information retrieval techniques. The core components typically include:

• Document Store: Houses the raw data
• Embedding Model: Converts text into numerical representations
• Vector Database: Efficiently stores and searches embeddings
• Retrieval Mechanism: Finds relevant information based on queries
• Language Model: Generates responses using retrieved-context
   

In a standard RAG workflow, documents are embedded and stored in a vector database. When a query is received, the system retrieves relevant documents based on similarity and uses an LLM to generate a response incorporating this context.

A simple RAG solution: Credit: BentoML

Kernel Memory: Enhancing RAG Capabilities

Kernel Memory is an advanced framework that builds upon the RAG concept, offering additional features and optimisations. 

Key Enhancements:

• Advanced Document Processing:
  • Automatic splitting of documents into chunks
  • Handling of various file formats
  • Extraction of metadata
• Flexible Storage:
  • Can use different backend storage solutions
  • Supports both vector and traditional databases
• Advanced Retrieval:
  • More sophisticated search algorithms
  • Can combine semantic and keyword search
• Memory Management:
  • Information prioritisation based on relevance and recency
  • Controlled “forgetting” of outdated or less relevant data
  • Document versioning support
• Integration Capabilities:
  • Easily integrates with various AI services and models
  • Built-in support for different embedding models

Microsoft’s Kernel Memory: Specific Features and Capabilities

This is Microsoft’s open-source project for building AI applications with memory capabilities. It’s designed to work well with their Semantic Kernel project, but it can also be used independently or with other AI frameworks.

Key points about Microsoft’s Kernel Memory:

1. Open Source
2. Part of Microsoft’s broader AI development ecosystem, which includes Semantic Kernel.
3. Not limited to use with only Microsoft products.
4. Flexible and can integrate with various AI services and models.

Features:

• Ingestion Capabilities:
  • Supports a wide range of document types like PDFs, HTML, and websites, Microsoft Office files (Word, PowerPoint, Excel), and Images with OCR (Optical Character Recognition)
  • Automatic content extraction and processing
  • Website scraping for dynamic content ingestion
• Ready-to-Use API Endpoints to Upload, Download,  Search, Ask etc.
• Memory as a Service: Provides a centralised way to manage, store, and retrieve information across multiple applications.
• Citations: Tracks and returns specific sources of information used in responses, enhancing transparency and verifiability.
• Hybrid Search: Combines semantic and keyword search for more effective information retrieval.
• Scalability: Designed to efficiently handle large-scale data and complex information ecosystems.
• Integration with Semantic Kernel: Works seamlessly with Microsoft’s Semantic Kernel project, enhancing AI application development capabilities.
• Customisation Options: Allows for choosing different embedding models and storage solutions to suit specific needs.

When to Choose

Choose a regular RAG solution when:

• You have a straightforward use case with uniform data
• You need a quick setup for a proof of concept
• Your dataset is relatively small and static

Choose a RAG solution with Kernel Memory when:

• You’re dealing with diverse, complex document types
• You need advanced features like dynamic memory management
• You’re building a large-scale, production-ready system
• You require seamless integration with various AI services
• You need more control over the retrieval and processing pipeline

While traditional RAG solutions offer a solid foundation for many applications, Kernel Memory-enhanced RAG systems provide advanced capabilities suitable for more complex, large-scale, and dynamic scenarios. The choice between these approaches should be guided by specific project requirements, considering factors such as data complexity, scalability needs, and desired level of customisation.

In the end, choosing between traditional RAG and Kernel Memory comes down to your project’s needs. If you’re dealing with simple, uniform data, traditional RAG might do the trick. But for complex, large-scale projects needing advanced features, Kernel Memory could be your go-to. As AI keeps evolving, staying on top of these tools is crucial for building efficient, scalable systems.

Keen to explore how RAG or Kernel Memory could revolutionise your AI projects? We at XAM are always excited to dive into new tech challenges. Get in touch with us to discuss how we can help you navigate the world of AI.