FindLiteAI.AspNetCore 1.0.0

FindLiteAI

Embedded offline AI-powered semantic, keyword, and hybrid search for .NET desktop and server applications.

FindLiteAI is a lightweight offline-first search engine for .NET applications that enables semantic, keyword, and hybrid search without requiring Python, Docker, cloud APIs, external vector databases, or AI infrastructure.

It is designed for developers who want practical AI-powered search inside ASP.NET Core, Worker Services, WPF, WinForms, desktop tools, intranet systems, enterprise applications, and offline environments.

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Why FindLiteAI?

Most AI search stacks require:

• Python environments
• Docker infrastructure
• external vector databases
• OpenAI or cloud APIs
• GPU infrastructure
• complex orchestration tools

FindLiteAI takes a different approach.

It provides:

• embedded offline AI-powered search
• lightweight local storage
• local ONNX models
• pure .NET integration
• simple NuGet-based setup
• zero external infrastructure after model download

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Key Features

• Semantic search
• Keyword search
• Hybrid search
• Offline-first architecture
• Local ONNX embedding models
• LiteDB embedded storage
• ASP.NET Core integration
• WPF desktop support
• Worker Service support
• Automatic model package installation
• No OpenAI dependency
• No Python dependency
• No Docker dependency
• No GPU required
• Cross-platform .NET support
• Lightweight deployment model

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Supported Search Modes

Search Mode	Description
Semantic	Finds meaning-based matches using AI embeddings
Keyword	Finds exact keyword matches
Hybrid	Combines semantic and keyword ranking

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Built-In Models

Model	Profile	Dimensions
all-MiniLM-L6-v2	Fast	384
all-mpnet-base-v2	Balanced	768
Snowflake Arctic Embed XS	Advanced	384

All models run locally using ONNX Runtime.

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Installation

NuGet Packages

Core engine:

dotnet add package FindLiteAI

ONNX embeddings:

dotnet add package FindLiteAI.Embeddings.Onnx

LiteDB storage:

dotnet add package FindLiteAI.Storage.LiteDb

ASP.NET Core integration:

dotnet add package FindLiteAI.AspNetCore

Dependency injection helpers:

dotnet add package FindLiteAI.Extensions.DependencyInjection

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Quick Start

ASP.NET Core Example

using FindLiteAI.Core.Abstractions;
using FindLiteAI.Core.Models;
using FindLiteAI.Embeddings.Onnx;
using FindLiteAI.Extensions.DependencyInjection;

WebApplicationBuilder builder =
    WebApplication.CreateBuilder(args);

string cacheDirectory =
    Path.Combine(
        Path.GetTempPath(),
        "FindLiteAI",
        "Models");

await ModelInstallService.InstallAsync(
    FindLiteAIModels.MiniLm,
    cacheDirectory);

builder.Services.AddFindLiteAI(options =>
{
    options.DatabasePath = "findliteai.db";

    options.ModelCacheDirectory =
        Path.Combine(
            cacheDirectory,
            FindLiteAIModels.MiniLm.Id);
});

WebApplication app =
    builder.Build();

app.MapGet(
    "/",
    () => "FindLiteAI running.");

app.Run();

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Adding Documents

await engine.AddAsync(
    "logs",
    new SemanticDocument
    {
        Text = "SMTP email relay timeout occurred."
    });

Document identifiers are automatically generated if not provided.

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Semantic Search

IReadOnlyList<SearchResult> results =
    await engine.SearchAsync(
        "logs",
        "email sending issue",
        new SearchOptions
        {
            SearchMode = SearchMode.Semantic,
            MaxResults = 5,
            MinimumScore = 0.10
        });

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Hybrid Search

IReadOnlyList<SearchResult> results =
    await engine.SearchAsync(
        "logs",
        "smtp issue",
        new SearchOptions
        {
            SearchMode = SearchMode.Hybrid,
            MaxResults = 5,
            MinimumScore = 0.10
        });

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Example Use Cases

Log Search

Stored log:

SMTP relay timeout occurred.

User searches:

email sending issue

FindLiteAI can return semantically related results without exact keyword matching.

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Helpdesk Systems

Stored ticket:

VPN authentication failed during remote access.

User searches:

cannot connect remotely

FindLiteAI can retrieve related support tickets using semantic similarity.

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Enterprise Knowledge Bases

Stored document:

Annual leave approval workflow.

User searches:

vacation request process

FindLiteAI can retrieve semantically related policies and procedures.

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Architecture

Application
    ↓
FindLiteAI Engine
    ↓
ONNX Embedding Provider
    ↓
LiteDB Embedded Storage

Documents are converted into embeddings using local ONNX models and stored alongside metadata inside LiteDB.

Search queries generate embeddings which are compared against stored vectors to retrieve similar content.

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Offline-First Design

After model packages are downloaded once:

• all searches run locally
• all embeddings run locally
• no cloud calls are required
• no external AI services are required

This makes FindLiteAI suitable for:

• intranet systems
• enterprise desktop applications
• restricted networks
• government environments
• industrial applications
• offline-capable systems

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Who Is This For?

FindLiteAI is designed for:

• .NET developers
• ASP.NET Core developers
• WPF developers
• Worker Service developers
• internal enterprise tools
• desktop applications
• intranet systems
• offline business applications
• embedded search scenarios

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Samples

The repository includes:

Sample	Description
Console Sample	Validates all official model packages
ASP.NET Core Sample	Demonstrates API integration
Worker Service Sample	Demonstrates background service integration
WPF Sample	Demonstrates desktop application integration

See:

samples/README.md

for detailed setup instructions.

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Model Packages

Official model packages are distributed as ZIP packages through GitHub Releases.

The package system supports:

• automatic download
• automatic extraction
• local caching
• offline reuse

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Storage

FindLiteAI currently uses LiteDB for embedded local storage.

LiteDB stores:

• document text
• metadata
• embeddings
• indexes

No external database server is required.

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Current Scope

FindLiteAI focuses on:

• lightweight embedded AI search
• practical .NET integration
• offline-first deployment
• simple developer experience

It is intentionally not:

• a chatbot framework
• a vector database server
• an LLM platform
• a cloud AI orchestration system

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Roadmap

Planned future improvements may include:

• metadata filtering
• additional embedding providers
• custom model registration
• batch optimization
• reranking improvements
• optional SQLite provider
• additional storage providers

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Requirements

• .NET 8
• ONNX Runtime compatible environment

No GPU required.

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License

MIT License

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Contributing

Contributions, issues, feature requests, and improvements are welcome.

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Status

Current version:

v0.1.0

FindLiteAI is under active development.

Open Source Credits

FindLiteAI is built using several excellent open source projects and publicly available embedding models.

Embedding Models

all-MiniLM-L6-v2

Source:

https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2

License:

Apache-2.0

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all-mpnet-base-v2

Source:

https://huggingface.co/sentence-transformers/all-mpnet-base-v2

License:

Apache-2.0

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Snowflake Arctic Embed XS

Source:

https://huggingface.co/Snowflake/snowflake-arctic-embed-xs

License:

Apache-2.0

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Runtime and Libraries

ONNX Runtime

Source:

https://github.com/microsoft/onnxruntime

License:

MIT

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LiteDB

Source:

https://github.com/litedb-org/LiteDB

License:

MIT

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Microsoft.ML.Tokenizers

Source:

https://github.com/dotnet/machinelearning

License:

MIT

Used during development and related tooling workflows.

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FindLiteAI itself is released under the MIT License.