Below is a detailed breakdown of each folder in this repository explaining a specific technology area. Each folder contains a descriptive overview and an architecture diagram explaining the interaction between various components and the flow of data.
| Folder | Notebook | Description | Details |
|---|---|---|---|
| 01 | Environment Setup | Detailed instructions for deploying Azure Services like Azure OpenAI and Cognitive Search | Includes a application.env file within the conf folder containing essential configuration details |
| 02 | Basic Chat | Examples of HTTP calls to the deployed Chat Completion LLM (gpt-3.5-turbo) | .NET code is used to perform HTTP calls, Another option could be utilizes the REST Client extension for Visual Studio Code to execute the calls |
| 02 | Other Models | Examples of HTTP calls to various LLMs like Embedding, Whisper | Also uses .NET to perform HTTP calls. |
| 02 | JSON Mode | Example of HTTP calls instructing the model to respond in valid JSON format | Also uses .NET to perform HTTP calls. |
| 02 | GPT-4 Vision | Sample using text and image data as model input | gpt-4 vision is one of the first multi-modal models being able to process text and image data as input. Image data is provided as base64-encoded string |
| 03 | Chat Completion | C# sample code to interact with the ChatCompletion LLM using the Azure.AI.OpenAI NuGet package |
|
| 03 | Chat Completion Streaming | Advanced C# sample code for streaming interactions with the ChatCompletion LLM | |
| 03 | JSON Mode | C# sample instructing the model to respond in valid JSON format | |
| 03 | GPT-4 Vision | C# sample using text and image data as model input | gpt-4 vision is one of the first multi-modal models being able to process text and image data as input. Image data is provided as URI |
| 03 | Function Calling | C# sample demonstrating the use of tools | LLMs provide functionality to accept descriptions of available functions and suggest calling on of them if expected results are beneficial in fulfilling a chat completion request. |
| 04 | Basic Embeddings | C# code to create embeddings with the Azure.AI.OpenAI NuGet package |
Embeddings are numerical text representations in a 1536-dimension vector |
| 04 | Cosine Similarity | C# examples using MathNet.Numerics to calculate the cosine distance between vectors |
The closer the distance, the more similar the semantic meanings |
| 05 | Vector Database | C# code for using Azure Cognitive Search as a vector database | Involves storing and querying embeddings with a created Search Index |
| 06 | Semantic Function Inline | Demonstrates inline definition of a Microsoft Semantic Kernel function | |
| 06 | Semantic Function File | Illustrates importing a Semantic Kernel function from an external file | |
| 06 | Native Function | Example of importing a native C# function to the Semantic Kernel | |
| 06 | Memory | Explanation of the Semantic Kernel Memory concept and usage | |
| 06 | Planner | Overview of the Semantic Kernel planner which sequences function calls for a task | |
| 06 | Logging | How to utilize the default .NET logger with the Semantic Kernel | |
| 07 | Assistants API | How to utilize the Assistants API. | Simplified sample to introduce the concepts of the Assistants API |