Create README.md

Author: HillPhelmuth

README.md

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+# LlmAsJudgeEvals
+
+This library provides a service for evaluating responses from Large Language Models (LLMs) using the LLM itself as a judge. It leverages Semantic Kernel to define and execute evaluation functions based on prompt templates. 
+
+**For a more precise evaluation score, the library utilizes `logprobs` and calculates a weighted total of probabilities for each evaluation criterion.**
+
+## Installation
+
+Install the package via NuGet:
+
+```
+Install-Package HillPhelmuth.SemanticKernel.LlmAsJudgeEvals
+```
+
+## Usage
+
+### Built-in Evaluation Functions
+
+The package includes a set of built-in evaluation functions, each focusing on a specific aspect of LLM output quality:
+
+* **Coherence:** Evaluates the logical flow and consistency of the response.
+* **Empathy:** Assesses the level of empathy and understanding conveyed in the response.
+* **Fluency:** Measures the smoothness and naturalness of the language used.
+* **GptGroundedness:** Determines how well the response is grounded in factual information.
+* **GptGroundedness2:** An alternative approach to evaluating groundedness.
+* **GptSimilarity:**  Compares the response to a reference text or objectively correct answer for similarity.
+* **Helpfulness:**  Assesses the degree to which the response is helpful and informative.
+* **PerceivedIntelligence:** Evaluates the perceived intelligence and knowledge reflected in the response.
+* **PerceivedIntelligenceNonRag:** A variant of PerceivedIntelligence tailored for non-Retrieval Augmented Generation (RAG) models.
+* **Relevance:** Measures the relevance of the response to the given prompt or question and a reference text for RAG.
+
+
+```csharp
+
+// Initialize the Semantic Kernel
+var kernel = Kernel.CreateBuilder().AddOpenAIChatCompletion("openai-model-name", "openai-apiKey").Build();
+
+// Create an instance of the EvalService
+var evalService = new EvalService(kernel);
+
+// Create an input model for the built-in evaluation function
+var coherenceInput = InputModel.CoherenceModel("This is the answer to evaluate.", "This is the question or prompt that generated the answer");
+
+// Execute the evaluation
+var result = await evalService.ExecuteEval(inputModel);
+
+
+Console.WriteLine($"Evaluation score: {result.Score}");
+
+```
+
+### Custom Evaluation Functions
+
+```csharp
+
+// Initialize the Semantic Kernel
+var kernel = Kernel.CreateBuilder().AddOpenAIChatCompletion("openai-model-name", "openai-apiKey").Build();
+
+// Create an instance of the EvalService
+var evalService = new EvalService(kernel);
+
+// Add an evaluation function (optional)
+evalService.AddEvalFunction("MyEvalFunction", "This is the prompt for my evaluation function.", new PromptExecutionSettings());
+
+// Create an input model for the evaluation function
+var inputModel = new InputModel
+{
+    FunctionName = "MyEvalFunction", // Replace with the name of your evaluation function
+    RequiredInputs = new Dictionary<string, string>
+    {
+        { "input", "This is the text to evaluate." }
+    }
+};
+
+// Execute the evaluation
+var result = await evalService.ExecuteEval(inputModel);
+
+
+Console.WriteLine($"Evaluation score: {result.Score}");
+```
+
+## Features
+
+* **Define evaluation functions using prompt templates:**  You can define evaluation functions using prompt templates written in YAML. 
+* **Execute evaluations:** The `EvalService` provides methods for executing evaluations on input data.
+* **Aggregate results:**  The `EvalService` can aggregate evaluation scores across multiple inputs.
+* **Built-in evaluation functions:** The package includes a set of pre-defined evaluation functions based on common evaluation metrics.
+* **Logprobs-based scoring:** Leverages `logprobs` for a more granular and precise evaluation score.
+