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 Apache License, Version 2.0. You may use this library free of charge, provided you include the required attribution notices. See the LICENSE file for full terms.