FindLiteAI.Extensions.DependencyInjection 1.0.3

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 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.

Designed for lightweight embedded AI retrieval scenarios where simplicity, offline capability, and low operational overhead are )

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Architecture

Search Workflow

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NuGet Packages

• FindLiteAI
• FindLiteAI.Core
• FindLiteAI.Embeddings.Onnx
• FindLiteAI.Storage.LiteDb
• FindLiteAI.Extensions.DependencyInjection
• FindLiteAI.AspNetCore

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Package Architecture

Package	Purpose
FindLiteAI	Main search engine package (includes core dependencies)
FindLiteAI.Core	Core abstractions, models, and contracts
FindLiteAI.Embeddings.Onnx	ONNX-based embedding provider
FindLiteAI.Storage.LiteDb	LiteDB-based embedded storage provider
FindLiteAI.Extensions.DependencyInjection	Dependency injection extensions for easy setup
FindLiteAI.AspNetCore	ASP.NET Core integration for search API endpoints

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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 integration
• 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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Search Configuration

FindLiteAI search behavior can be configured using SearchOptions.

Example:

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

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SearchMode

Controls how matching is performed.

Mode	Description	Best For
Semantic	AI meaning-based similarity search	Natural language queries
Keyword	Exact keyword matching	IDs, logs, structured data
Hybrid	Combines semantic + keyword ranking	General-purpose search

Semantic

SearchMode = SearchMode.Semantic

Uses AI embeddings to find meaning-based matches.

Example:

Stored document:

SMTP authentication failed.

Search query:

email login problem

Semantic search may still retrieve the document even without exact keyword matches.

---

Keyword

SearchMode = SearchMode.Keyword

Uses direct keyword matching.

Best for:

• log analysis
• IDs
• filenames
• error codes
• exact matching scenarios

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Hybrid

SearchMode = SearchMode.Hybrid

Combines semantic similarity and keyword relevance.

Recommended as the default mode for most applications.

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MaxResults

Controls the maximum number of results returned.

Example:

MaxResults = 5

Lower Values

MaxResults = 3

Benefits:

• faster responses
• smaller payloads
• cleaner UI presentation

Higher Values

MaxResults = 25

Benefits:

• broader retrieval
• larger search coverage
• analytics scenarios

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MinimumScore

Controls the minimum similarity score required for results.

Example:

MinimumScore = 0.10

Higher values produce stricter and cleaner results.

Lower values produce broader but potentially noisier results.

Typical Score Ranges

Score Range	Meaning
0.80+	Very strong match
0.60+	Strong match
0.40+	Moderate match
0.20+	Weak semantic relation
Below 0.10	Often noisy or unrelated

Actual score behavior depends on:

• embedding model
• document quality
• query quality
• search mode
• document length

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Recommended Starting Configuration

General Applications

new SearchOptions
{
    SearchMode = SearchMode.Hybrid,
    MaxResults = 5,
    MinimumScore = 0.10
}

Enterprise Search

new SearchOptions
{
    SearchMode = SearchMode.Hybrid,
    MaxResults = 10,
    MinimumScore = 0.25
}

Broad Discovery Search

new SearchOptions
{
    SearchMode = SearchMode.Semantic,
    MaxResults = 15,
    MinimumScore = 0.05
}

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Built-In Models

Model	Profile	Dimensions	Recommended RAM
all-MiniLM-L6-v2	Fast	384	4 GB+
all-mpnet-base-v2	Balanced	768	8 GB+
Snowflake Arctic Embed XS	Advanced	384	8 GB+

All models run locally using ONNX Runtime.

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Installation

Default Installation

Desktop Applications (WPF, WinForms, Console, Worker Services)

For most desktop and general .NET applications, install the main package:

dotnet add package FindLiteAI

This package includes the core search engine and automatically installs the required ONNX embedding and LiteDB storage dependencies.

Suitable for:

• WPF applications
• WinForms applications
• Console applications
• Worker Services
• Background processing applications
• Offline desktop tools
• Local enterprise utilities

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ASP.NET Core Applications

For ASP.NET Core applications, install:

dotnet add package FindLiteAI.AspNetCore
dotnet add package FindLiteAI.Embeddings.Onnx

FindLiteAI.AspNetCore provides:

• ASP.NET Core endpoint integration
• API routing helpers
• dependency injection extensions
• search endpoint support

FindLiteAI.Embeddings.Onnx provides:

• local ONNX embedding support
• built-in model definitions
• model installer APIs
• embedding generation services

This setup is recommended for:

• ASP.NET Core Web APIs
• enterprise intranet systems
• local AI-powered search APIs
• internal knowledge systems
• offline enterprise applications

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Optional Packages

Install these packages only if you need direct access to a specific component.

ONNX Embedding Provider

dotnet add package FindLiteAI.Embeddings.Onnx

LiteDB Storage Provider

dotnet add package FindLiteAI.Storage.LiteDb

Dependency Injection Extensions

dotnet add package FindLiteAI.Extensions.DependencyInjection

Core Abstractions and Models

dotnet add package FindLiteAI.Core

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Custom Models

FindLiteAI supports custom ONNX embedding models.

Developers can use:

• custom SentenceTransformer models
• private enterprise embedding models
• internally trained ONNX models
• downloaded HuggingFace embedding models
• organization-specific embedding models

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Built-In Model Definitions

Built-in model metadata is defined in:

src/FindLiteAI.Embeddings.Onnx/FindLiteAIModels.cs

This file contains:

• model identifiers
• dimensions
• download URLs
• tokenizer paths
• runtime settings
• package metadata

Developers can use this file as a reference when adding custom models.

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Custom Model Package Structure

A custom model package should contain:

custom-model/
├── model.onnx
├── vocab.txt
├── MODEL_INFO.json
└── 1_Pooling/
    └── config.json

Optional files:

sentence_bert_config.json
modules.json
README_MODEL.txt
LICENSE.txt

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MODEL_INFO.json Example

{
  "id": "custom-model",
  "displayName": "Custom Embedding Model",
  "dimensions": 384,
  "runtime": "ONNX",
  "pooling": "Mean",
  "optimizedFor": "Semantic Search"
}

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Hosting Custom Models

Custom model packages can be distributed as ZIP files.

Example:

custom-model-v1.zip

Recommended hosting locations:

• GitHub Releases
• Azure Blob Storage
• AWS S3
• internal enterprise servers
• intranet download servers
• private package repositories

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GitHub Release Example

Repository:

https://github.com/company/custom-models/releases

ZIP asset:

custom-model-v1.zip

Direct download URL example:

https://github.com/company/custom-models/releases/download/v1/custom-model-v1.zip

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Installing a Custom Model

Extract the ZIP package locally.

Example:

D:\Models\custom-model

Then configure FindLiteAI:

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

    options.ModelCacheDirectory =
        @"D:\Models\custom-model";
});

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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();

---

Adding Documents

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

Document identifiers are automatically generated if not provided.

---

Semantic Search

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

---

Hybrid Search

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

---

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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Limitations

FindLiteAI currently focuses on lightweight embedded retrieval scenarios.

Current limitations include:

• no distributed clustering
• no GPU acceleration
• no incremental model training
• no built-in reranking models
• no distributed vector database support

The project intentionally prioritizes simplicity, portability, and local deployment.

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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.

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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.

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Links

• Repository: https://github.com/zameerthakur/FindLiteAI
• NuGet: https://www.nuget.org/packages/FindLiteAI
• ONNX Runtime: https://onnxruntime.ai
• LiteDB: https://www.litedb.org