FonsecaFramework.Ai 2026.6.18.1

FonsecaFramework.Ai

AI and machine learning utilities for .NET applications.

Overview

FonsecaFramework.Ai is a .NET 9 library that provides tools for building ML models with AutoML, performing sentiment/discourse analysis, implementing RAG (Retrieval-Augmented Generation) vector stores, and integrating with LLM providers via the Microsoft.Extensions.AI abstraction. It wraps ML.NET and ONNX Runtime to offer a streamlined API for training, predicting, and exporting models.

Installation

dotnet add package FonsecaFramework.Ai

Features

Examples

Build and Use a Regression Model

using FonsecaFramework.Ai;

var data = new List<HouseData>
{
 new() { Size = 1000, Price = 200_000 },
 new() { Size = 1500, Price = 300_000 },
 new() { Size = 2000, Price = 400_000 },
 // ... more training data
};

var builder = new AiModelBuilder<HouseData>(data, nameof(HouseData.Price));

var model = builder.BuildRegressionModel(
 MaxExperimentTimeInSeconds: 30);

float prediction = model.Predict(new HouseData { Size = 1750 });
Console.WriteLine($"Predicted price: {prediction:C}");

// Export to ONNX
await model.ConvertToOnnx("house-model.onnx");

Build a Binary Classification Model

using FonsecaFramework.Ai;

var data = new List<EmailData>
{
 new() { Subject = "Free money!", Body = "Click here now", IsSpam = true },
 new() { Subject = "Meeting tomorrow", Body = "See you at 3pm", IsSpam = false },
 // ... more training data
};

var builder = new AiModelBuilder<EmailData>(data, nameof(EmailData.IsSpam));
var model = builder.BuildBinaryClassificationModel(MaxExperimentTimeInSeconds: 60);

float score = model.Predict(new EmailData { Subject = "You won!", Body = "Claim prize" });
Console.WriteLine($"Spam score: {score}");

RAG Vector Store — Build and Query

using FonsecaFramework.Ai;

// Build from a folder of .txt documents
var store = await RagVectorStore.Build(
 sourceFolder: "./docs",
 chunkSize: 512,
 chunkOverlap: 128);

// Augment a user prompt with relevant context
string augmentedPrompt = await store.Lookup(
 prompt: "How do I configure the database?",
 topK: 3);

Console.WriteLine(augmentedPrompt);
// The prompt now includes the most relevant document chunks as context

RAG with LLM-Powered Sentiment Analysis

using FonsecaFramework.Ai;
using FonsecaFramework.Ai.LLM;

// Use Ollama for richer discourse analysis
var chatClient = new VLRChatClient("http://localhost:11434");
var sentimentAnalyzer = new OllamaSentimentAnalyzer(chatClient);

var store = await RagVectorStore.Build(
 sourceFolder: "./docs",
 sentimentAnalyzer: sentimentAnalyzer);

// Filter by discourse category
string result = await store.Lookup(
 prompt: "What safety precautions should I take?",
 topK: 5,
 sentimentFilter: "cautionary");

Agent Framework Integration (TextSearchProvider)

using FonsecaFramework.Ai;
using Microsoft.Agents.AI;

var store = await RagVectorStore.Build("./docs");

// Basic wrapper for Microsoft Agent Framework RAG pattern
var provider = store.CreateTextSearchProvider(
 options: new TextSearchProviderOptions
 {
 SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke
 },
 topK: 3,
 minSimilarity: 0.05);

// Optional: direct adapter usage if you need raw search results
var results = await store.SearchTextResultsAsync("refund policy", topK: 3);

Sentiment / Discourse Analysis

using FonsecaFramework.Ai;

var analyzer = new LexiconSentimentAnalyzer();

float[] vector = await analyzer.AnalyzeAsync(
 "Warning: Do not operate the machine without safety equipment.");

// vector is a 10-element array:
// [positive, negative, instructional, technical, cautionary,
// informational, urgency, formality, specificity, actionability]

string category = analyzer.Classify(vector);
Console.WriteLine($"Dominant category: {category}"); // "cautionary"

VLRChatClient — OpenAI-Compatible LLM Integration

using FonsecaFramework.Ai.LLM;
using Microsoft.Extensions.AI;

using var client = new VLRChatClient("http://localhost:11434");

var messages = new[]
{
 new ChatMessage(ChatRole.System, "You are a helpful assistant."),
 new ChatMessage(ChatRole.User, "Explain dependency injection in one paragraph.")
};

var response = await client.GetResponseAsync(messages);
Console.WriteLine(response.Text);

Requirements

• .NET 9.0
• For OllamaSentimentAnalyzer / VLRChatClient: an OpenAI-compatible LLM endpoint (e.g. Ollama)

License

Copyright 2025 Steven Fonseca / VLR Creations

Licensed under the . You may use this library free of charge, provided you include the required attribution notices. See the file for full terms.