Kernel Memory (KM) provides a comprehensive Retrieval Augmented Generation (RAG) architecture for indexing and querying diverse data sources using LLMs. It targets developers building AI applications who need to integrate natural language search, source tracking, and citations. KM offers a flexible, multi-modal service that can be deployed as a web service, Docker container, or embedded .NET library, simplifying the creation of intelligent search and Q&A systems.

How It Works

KM employs a hybrid data pipeline for efficient indexing, supporting RAG, synthetic memory, and custom semantic processing. It extracts text from various file formats, partitions it into manageable chunks, generates embeddings using configurable LLM providers (e.g., OpenAI, Azure OpenAI), and stores these embeddings in a choice of vector databases (e.g., Azure AI Search, Qdrant). This approach allows for natural language querying with precise source citations and facilitates fine-grained access control via document ownership and tags.

Quick Start & Requirements

• Docker: docker run -e OPENAI_API_KEY="..." -it --rm -p 9001:9001 kernelmemory/service
• Prerequisites: OpenAI API Key (or other configured LLM/embedding provider).
• Resources: Requires an LLM and a vector store; resource usage scales with data volume and LLM complexity.
• Documentation: Azure deployment guide, Infrastructure documentation, Docker and Serverless example.

Highlighted Details

• Supports a wide range of data formats including PDF, Word, PowerPoint, Excel, Images, and web pages.
• Offers extensive extensibility for file storage, queues, vector stores, and LLMs.
• Integrates seamlessly as a plugin for Semantic Kernel, Microsoft Copilot, and ChatGPT.
• Provides detailed token usage reports for LLM interactions.

Maintenance & Community

The project has a large number of contributors, indicating active development and community engagement. Links to community resources are not explicitly provided in the README.

Licensing & Compatibility

The project is licensed under the MIT License, permitting commercial use and integration with closed-source applications.

Limitations & Caveats

The code is presented as a demonstration and is not an officially supported Microsoft offering. While flexible, custom pipeline development requires .NET expertise.