kernel-memory

RAG architecture: index and query any data using LLM and natural language, track sources, show citations, asynchronous memory patterns.

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Kernel Memory (KM) is a multi-modal AI Service specialized in the efficient indexing of datasets through custom continuous data hybrid pipelines, with support for Retrieval Augmented Generation (RAG), synthetic memory, prompt engineering, and custom semantic memory processing. KM is available as a Web Service, as a Docker container, a Plugin for ChatGPT/Copilot/Semantic Kernel, and as a .NET library for embedded applications. Utilizing advanced embeddings and LLMs, the system enables Natural Language querying for obtaining answers from the indexed data, complete with citations and links to the original sources. Designed for seamless integration as a Plugin with Semantic Kernel, Microsoft Copilot and ChatGPT, Kernel Memory enhances data-driven features in applications built for most popular AI platforms.

README:

Kernel Memory

This repository presents best practices and a reference implementation for Memory in specific AI and LLMs application scenarios. Please note that the code provided serves as a demonstration and is not an officially supported Microsoft offering.

Kernel Memory (KM) is a multi-modal AI Service specialized in the efficient indexing of datasets through custom continuous data hybrid pipelines, with support for Retrieval Augmented Generation (RAG), synthetic memory, prompt engineering, and custom semantic memory processing.

KM is available as a Web Service, as a Docker container, a Plugin for ChatGPT/Copilot/Semantic Kernel, and as a .NET library for embedded applications.

Utilizing advanced embeddings and LLMs, the system enables Natural Language querying for obtaining answers from the indexed data, complete with citations and links to the original sources.

Kernel Memory is designed for seamless integration as a Plugin with Semantic Kernel, Microsoft Copilot and ChatGPT.

Kernel Memory Service on Azure

Kernel Memory can be deployed in various configurations, including as a Service in Azure. To learn more about deploying Kernel Memory in Azure, please refer to the Azure deployment guide. For detailed instructions on deploying to Azure, you can check the infrastructure documentation.

If you are already familiar with these resources, you can quickly deploy by clicking the following button.

Running Kernel Memory with Aspire

Kernel Memory can be easily run and imported in other projects also via .NET Aspire. For example:

var builder = DistributedApplication.CreateBuilder();

builder.AddContainer("kernel-memory", "kernelmemory/service")
    .WithEnvironment("KernelMemory__TextGeneratorType", "OpenAI")
    .WithEnvironment("KernelMemory__DataIngestion__EmbeddingGeneratorTypes__0", "OpenAI")
    .WithEnvironment("KernelMemory__Retrieval__EmbeddingGeneratorType", "OpenAI")
    .WithEnvironment("KernelMemory__Services__OpenAI__APIKey", "...your OpenAI key...");

builder.Build().Run();

Data Ingestion using Kernel Memory OpenAPI Web Service

The example show the default documents ingestion pipeline:

Extract text: automatically recognize the file format and extract the information
Partition the text in small chunks, ready for search and RAG prompts
Extract embeddings using any LLM embedding generator
Save embeddings into a vector index such as Azure AI Search, Qdrant or other DBs.

The example shows how to safeguard private information specifying who owns each document, and how to organize data for search and faceted navigation, using Tags.

C#

#r "nuget: Microsoft.KernelMemory.WebClient"

var memory = new MemoryWebClient("http://127.0.0.1:9001"); // <== URL of KM web service

// Import a file
await memory.ImportDocumentAsync("meeting-transcript.docx");

// Import a file specifying Document ID and Tags
await memory.ImportDocumentAsync("business-plan.docx",
    new Document("doc01")
        .AddTag("user", "[email protected]")
        .AddTag("collection", "business")
        .AddTag("collection", "plans")
        .AddTag("fiscalYear", "2025"));

Python

import requests

# Files to import
files = {
          "file1": ("business-plan.docx", open("business-plan.docx", "rb")),
        }

# Tags to apply, used by queries to filter memory
data = { "documentId": "doc01",
         "tags": [ "user:[email protected]",
                   "collection:business",
                   "collection:plans",
                   "fiscalYear:2025" ]
       }

response = requests.post("http://127.0.0.1:9001/upload", files=files, data=data)

Direct Data Ingestion using embedded Serverless .NET component

var memory = new KernelMemoryBuilder()
    .WithOpenAIDefaults(Environment.GetEnvironmentVariable("OPENAI_API_KEY"))
    .Build<MemoryServerless>();

// Import a file
await memory.ImportDocumentAsync("meeting-transcript.docx");

// Import a file specifying Document ID and Tags
await memory.ImportDocumentAsync("business-plan.docx",
    new Document("doc01")
        .AddTag("collection", "business")
        .AddTag("collection", "plans")
        .AddTag("fiscalYear", "2025"));

Memory retrieval and RAG

Asking questions, running RAG prompts, and filtering by user and other criteria is simple, with answers including citations and all the information needed to verify their accuracy, pointing to which documents ground the response.

C#

Asking questions:

Questions can be asked targeting the entire memory set, or a subset using filters, e.g. to implement security filters.

var answer1 = await memory.AskAsync("How many people attended the meeting?");

var answer2 = await memory.AskAsync("what's the project timeline?",
                                    filter: MemoryFilters.ByTag("user", "[email protected]"));

Token usage:

When generating answers with LLMs, the result includes a token usage report.
foreach (var report in tokenUsage)
{
    Console.WriteLine($"{report.ServiceType}: {report.ModelName} ({report.ModelType})");
    Console.WriteLine($"- Input : {report.ServiceTokensIn}");
    Console.WriteLine($"- Output: {report.ServiceTokensOut}");
}
Output:

Azure OpenAI: gpt-4o (TextGeneration)

Input : 24356 tokens

Output: 103 tokens

Data lineage, citations, referencing sources:
await memory.ImportFileAsync("NASA-news.pdf");

var answer = await memory.AskAsync("Any news from NASA about Orion?");

Console.WriteLine(answer.Result + "/n");

foreach (var x in answer.RelevantSources)
{
    Console.WriteLine($"  * {x.SourceName} -- {x.Partitions.First().LastUpdate:D}");
}
Yes, there is news from NASA about the Orion spacecraft. NASA has invited the media to see a new test version [......] For more information about the Artemis program, you can visit the NASA website.

NASA-news.pdf -- Tuesday, August 1, 2023

Python

Asking questions:

import requests
import json

data = {
    "question": "what's the project timeline?",
    "filters":  [ {"user": ["[email protected]"]} ]
}

response = requests.post(
    "http://127.0.0.1:9001/ask",
    headers={"Content-Type": "application/json"},
    data=json.dumps(data),
).json()

print(response["text"])

OpenAPI

curl http://127.0.0.1:9001/ask -d'{"query":"Any news from NASA about Orion?"}' -H 'Content-Type: application/json'

{
  "Query": "Any news from NASA about Orion?",
  "Text": "Yes, there is news from NASA about the Orion spacecraft. NASA has invited the media to see a new test version [......] For more information about the Artemis program, you can visit the NASA website.",
  "RelevantSources": [
    {
      "Link": "...",
      "SourceContentType": "application/pdf",
      "SourceName": "file5-NASA-news.pdf",
      "Partitions": [
        {
          "Text": "Skip to main content\nJul 28, 2023\nMEDIA ADVISORY M23-095\nNASA Invites Media to See Recovery Craft for\nArtemis Moon Mission\n(/sites/default/ﬁles/thumbnails/image/ksc-20230725-ph-fmx01_0003orig.jpg)\nAboard the [......] to Mars (/topics/moon-to-\nmars/),Orion Spacecraft (/exploration/systems/orion/index.html)\nNASA Invites Media to See Recovery Craft for Artemis Moon Miss... https://www.nasa.gov/press-release/nasa-invites-media-to-see-recov...\n2 of 3 7/28/23, 4:51 PM",
          "Relevance": 0.8430657,
          "SizeInTokens": 863,
          "LastUpdate": "2023-08-01T08:15:02-07:00"
        }
      ]
    }
  ]
}

The OpenAPI schema ("swagger") is available at http://127.0.0.1:9001/swagger/index.html when running the service locally with OpenAPI enabled. Here's a copy.

🔗 See also:

Kernel Memory Docker image

If you want to give the service a quick test, use the following command to start the Kernel Memory Service using OpenAI:

docker run -e OPENAI_API_KEY="..." -it --rm -p 9001:9001 kernelmemory/service

If you prefer using custom settings and services such as Azure OpenAI, Azure Document Intelligence, etc., you should create an appsettings.Development.json file overriding the default values set in appsettings.json, or using the configuration wizard included:

cd service/Service
dotnet run setup

Then run this command to start the Docker image with the configuration just created:

on Windows:

docker run --volume .\appsettings.Development.json:/app/appsettings.Production.json -it --rm -p 9001:9001 kernelmemory/service

on Linux / macOS:

docker run --volume ./appsettings.Development.json:/app/appsettings.Production.json -it --rm -p 9001:9001 kernelmemory/service

🔗 See also:

Memory as a Service: Data Ingestion Pipelines + RAG Web Service

Depending on your scenarios, you might want to run all the code remotely through an asynchronous and scalable service, or locally inside your process.

If you're importing small files, and use only .NET and can block the application process while importing documents, then local-in-process execution can be fine, using the MemoryServerless described below.

However, if you are in one of these scenarios:

My app is written in TypeScript, Java, Rust, or some other language
I'd just like a web service to import data and send questions to answer
I'm importing big documents that can require minutes to process, and I don't want to block the user interface
I need memory import to run independently, supporting failures and retry logic
I want to define custom pipelines mixing multiple languages like Python, TypeScript, etc

then you're likely looking for a Memory Service, and you can deploy Kernel Memory as a backend service, using the default ingestion logic, or your custom workflow including steps coded in Python/TypeScript/Java/etc., leveraging the asynchronous non-blocking memory encoding process, uploading documents and asking questions using the MemoryWebClient.

Here you can find a complete set of instruction about how to run the Kernel Memory service.

Embedded Memory Component (aka "serverless")

Kernel Memory works and scales at best when running as an asynchronous Web Service, allowing to ingest thousands of documents and information without blocking your app.

However, Kernel Memory can also run in serverless mode, embedding MemoryServerless class instance in .NET backend/console/desktop apps in synchronous mode. Each request is processed immediately, although calling clients are responsible for handling transient errors.

Extensions

Kernel Memory relies on external services to run stateful pipelines, store data, handle embeddings, and generate text responses. The project includes extensions that allow customization of file storage, queues, vector stores, and LLMs to fit specific requirements.

AI: Azure OpenAI, OpenAI, ONNX, Ollama, Anthropic, Azure AI Document Intelligence, Azure AI Content Safety
Vector Store: Azure AI Search, Postgres, SQL Server, Elasticsearch, Qdrant, Redis, MongoDB Atlas, In memory store
File Storage: Azure Blob storage, AWS S3, MongoDB Atlas, Local disk, In memory storage
Ingestion pipelines: Azure Queues, RabbitMQ, In memory queues

Custom memory ingestion pipelines

Document ingestion operates as a stateful pipeline, executing steps in a defined sequence. By default, Kernel Memory employs a pipeline to extract text, chunk content, vectorize, and store data.

If you need a custom data pipeline, you can modify the sequence, add new steps, or replace existing ones by providing custom “handlers” for each desired stage. This allows complete flexibility in defining how data is processed. For example:

// Memory setup, e.g. how to calculate and where to store embeddings
var memoryBuilder = new KernelMemoryBuilder()
    .WithoutDefaultHandlers()
    .WithOpenAIDefaults(Environment.GetEnvironmentVariable("OPENAI_API_KEY"));

var memory = memoryBuilder.Build();

// Plug in custom .NET handlers
memory.Orchestrator.AddHandler<MyHandler1>("step1");
memory.Orchestrator.AddHandler<MyHandler2>("step2");
memory.Orchestrator.AddHandler<MyHandler3>("step3");

// Use the custom handlers with the memory object
await memory.ImportDocumentAsync(
    new Document("mytest001")
        .AddFile("file1.docx")
        .AddFile("file2.pdf"),
    steps: new[] { "step1", "step2", "step3" });

Kernel Memory (KM) and Semantic Kernel (SK)

Semantic Kernel is an SDK for C#, Python, and Java used to develop solutions with AI. SK includes libraries that wrap direct calls to databases, supporting vector search.

Semantic Kernel is maintained in three languages, while the list of supported storage engines (known as "connectors") varies across languages.

Kernel Memory (KM) is a SERVICE built on Semantic Kernel, with additional features developed for RAG, Security, and Cloud deployment. As a service, KM can be used from any language, tool, or platform, e.g. browser extensions and ChatGPT assistants.

Kernel Memory provides several features out of the scope of Semantic Kernel, that would usually be developed manually, such as storing files, extracting text from documents, providing a framework to secure users' data, content moderation etc.

Kernel Memory is also leveraged to explore new AI patterns, which sometimes are backported to Semantic Kernel and Microsoft libraries, for instance vector stores flexible schemas, advanced filtering, authentications.

Here's comparison table:

Feature	Kernel Memory	Semantic Kernel
Runtime	Memory as a Service, Web service	SDK packages
Data formats	Web pages, PDF, Images, Word, PowerPoint, Excel, Markdown, Text, JSON	Text only
Language support	Any language	.NET, Python, Java
RAG	Yes	-
Cloud deployment	Yes	-

Examples and Tools

Examples

Tools

.NET packages

Microsoft.KernelMemory.WebClient: .NET web client to call a running instance of Kernel Memory web service.
Microsoft.KernelMemory: Kernel Memory library including all extensions and clients, it can be used to build custom pipelines and handlers. It contains also the serverless client to use memory in a synchronous way without the web service.
Microsoft.KernelMemory.Service.AspNetCore: an extension to load Kernel Memory into your ASP.NET apps.
Microsoft.KernelMemory.SemanticKernelPlugin: a Memory plugin for Semantic Kernel, replacing the original Semantic Memory available in SK.
Microsoft.KernelMemory.* packages: Kernel Memory Core and all KM extensions split into distinct packages.

Packages for Python, Java and other languages

Kernel Memory service offers a Web API out of the box, including the OpenAPI swagger documentation that you can leverage to test the API and create custom web clients. For instance, after starting the service locally, see http://127.0.0.1:9001/swagger/index.html.

A .NET Web Client and a Semantic Kernel plugin are available, see the nugets packages above.

For Python, TypeScript, Java and other languages we recommend leveraging the Web Service. We also welcome PR contributions to support more languages.

Contributors


dluc	marcominerva	anthonypuppo	crickman	TaoChenOSU	cherchyk

kbeaugrand	alexmg	alkampfergit	dependabot[bot]	slorello89	xbotter

westdavidr	luismanez	afederici75	koteus	amomra	lecramr

chaelli	pawarsum12	aaronpowell	alexibraimov	akordowski	coryisakson

DM-98	EelcoKoster	GraemeJones104	imranshams	jurepurgar	JustinRidings

Foorcee	v-msamovendyuk	qihangnet	neel015	pascalberger	pradeepr-roboticist

setuc	slapointe	spenavajr	tarekgh	teresaqhoang	tomasz-skarzynski

Valkozaur	vicperdana	walexee	aportillo83	carlodek	KSemenenko

roldengarm	snakex64

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PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

github

: 2.9k

tabby

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

github

: 30.6k

spear

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

github

: 224

Magick

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.

github

: 675