OrtForge.AI is a comprehensive .NET library suite for building advanced Retrieval Augmented Generation (RAG) applications with fully local hosting capabilities. It leverages ONNX Runtime to provide efficient, cross-platform AI model deployment without cloud dependencies.

• Fully Local AI Processing: Run LLMs (TODO), embedding models, and rerankers entirely on your own infrastructure
• High-Performance Inference: Optimized execution through ONNX Runtime integration
• Cross-Platform Support: Works across Windows, Linux, and macOS environments (TBD on acceleration technics)
• Advanced RAG Capabilities: Build sophisticated document retrieval and generation workflows
• Modular Architecture: Mix and match components to suit your specific use case
• Production-Ready: Designed for reliability and performance in real-world applications

OrtForge.AI consists of several specialized libraries:

• OrtForge.AI.Core: Core abstractions and utilities for the entire framework
• OrtForge.AI.Models: Implementation of various AI models including embedding generators and rerankers
• OrtForge.AI.LLM: Integration with large language models for text generation
• OrtForge.AI.Rag: Building blocks for implementing RAG pipelines
• OrtForge.PgSql: PostgreSQL query generator library to enable vector search

• .NET 8.0 SDK or later
• Sufficient CPU/GPU resources for running inference (requirements vary by model)

#TBD

// Initialize an embedding model
var embeddingModel = new BgeM3Model("path/to/tokenizer.bpe.model", "path/to/model.onnx");

// Generate embeddings for text
var embeddings = await embeddingModel.CreateEmbeddingAsync("Your text here");

// Generate embeddings for multiple texts
var batchEmbeddings = await embeddingModel.CreateEmbeddingsAsync(new[] { "First text", "Second text" });

// Initialize a reranker
var reranker = new BgeRerankerM3("path/to/tokenizer.bpe.model", "path/to/reranker.onnx");

// Get reranking score between query and document
float score = await reranker.GetRerankingScoreAsync("query", "document");

// Initialize and use LLM (implementation may vary)
var llmOptions = new LlmOptions { ModelPath = "path/to/llm.onnx" };
var llm = new LlmService(llmOptions);
var response = await llm.GenerateResponseAsync("Your prompt here");

Comprehensive documentation is available in the /docs directory and includes:

• Detailed API references
• Architecture overview
• Performance optimization guides
• Example applications and use cases

The /examples directory contains complete sample applications demonstrating:

• Document indexing and retrieval
• Question-answering systems
• Custom RAG pipeline construction
• Performance benchmarking

# Clone the repository
git clone https://github.com/yourusername/OrtForge.AI.git
cd OrtForge.AI

# Build the solution
dotnet build

# Run tests
dotnet test

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.

Performance benchmarks for various models and configurations can be found in the /benchmarks directory.

• ONNX Runtime
• LangChain
• LlamaIndex