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https://github.com/kbeaugrand/SemanticKernel.Connectors.Memory.MongoDB
Atlas MongoDB connector for Semantic Kernel skills and semantic memory
https://github.com/kbeaugrand/SemanticKernel.Connectors.Memory.MongoDB
ai artificial-intelligence llm mongodb-atlas openai sdk semantic-kernel
Last synced: 3 months ago
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Atlas MongoDB connector for Semantic Kernel skills and semantic memory
- Host: GitHub
- URL: https://github.com/kbeaugrand/SemanticKernel.Connectors.Memory.MongoDB
- Owner: kbeaugrand
- License: mit
- Archived: true
- Created: 2023-09-17T16:33:06.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-18T08:30:21.000Z (about 1 year ago)
- Last Synced: 2024-08-01T13:18:16.189Z (6 months ago)
- Topics: ai, artificial-intelligence, llm, mongodb-atlas, openai, sdk, semantic-kernel
- Language: C#
- Homepage:
- Size: 67.4 KB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
- awesome-semantickernel - Semantic Kernel - MongoDB Connector
README
# Semantic Kernel - MongoDB Connector
> **This package has been deprecated as it is legacy and is no longer maintained.**
> This package is now no longer needed as the connector is now part of the Semantic Kernel Core repository.
> Suggested Alternatives: **Microsoft.SemanticKernel.Connectors.Memory.MongoDB**
[](https://github.com/kbeaugrand/SemanticKernel.Connectors.Memory.MongoDB/actions/workflows/build_test.yml)
[](https://github.com/kbeaugrand/SemanticKernel.Connectors.Memory.MongoDB/actions/workflows/publish.yml)
[](https://img.shields.io/github/v/release/kbeaugrand/SemanticKernel.Connectors.Memory.MongoDB)
[](https://img.shields.io/github/v/release/kbeaugrand/SemanticKernel.Connectors.Memory.MongoDB)
This is a connector for the [Semantic Kernel](https://aka.ms/semantic-kernel).
It provides a connection to a MongoDB Atlas database for the Semantic Kernel for the memories.
> Note: It leverage on ``MongoDB Atlas Vector Search`` to provide vector search, this cannot work while running private instance of OpenSource MongoDB clusters.
## About Semantic Kernel
**Semantic Kernel (SK)** is a lightweight SDK enabling integration of AI Large
Language Models (LLMs) with conventional programming languages. The SK
extensible programming model combines natural language **semantic functions**,
traditional code **native functions**, and **embeddings-based memory** unlocking
new potential and adding value to applications with AI.
Please take a look at [Semantic Kernel](https://aka.ms/semantic-kernel) for more information.
## Installation
To install this memory store, you need to add the required nuget package to your project:
```dotnetcli
dotnet add package SemanticKernel.Connectors.Memory.MongoDB --version 1.0.0-beta1
```
## Create the collection and the Search index
Please refer to [the documentation](https://www.mongodb.com/docs/atlas/atlas-search/field-types/knn-vector/) to get more details on how to define an ``MongoDB Atlas Vector Search`` index. You can name the index default and create the index. Finally, write the following definition in the JSON editor on MongoDB Atlas:
```json
{
"mappings": {
"dynamic": true,
"fields": {
"Embedding": {
"dimensions": 1536,
"similarity": "cosine",
"type": "knnVector"
}
}
}
}
```
## Usage
To add your MongoDB Server memory connector, add the following statements to your kernel initialization code:
```csharp
using SemanticKernel.Connectors.Memory.MongoDB;
...
var kernel = Kernel.Builder
...
.WithMemoryStorage(MongoDBMemoryStore.Connect(connectionString: , database: ))
...
.Build();
```
The memory store will populate all the needed tables during startup and let you focus on the development of your plugin.
## License
This project is licensed under the [MIT License](LICENSE).