Rapid.Agentic.AI.Framework 2.0.0

Rapid.Agentic.AI.Framework

Rapid.Agentic.AI.Framework is a lightweight .NET library that simplifies integration with Retrieval-Augmented Generation (RAG) systems and Ollama LLMs. It provides intuitive gateway interfaces for building intelligent applications that can chat, generate content, and ingest domain-specific documents such as PDFs.

✨ Features

• 🤖 Ollama Integration: Send prompts and receive completions from local or remote Ollama servers.
• 📄 Document Ingestion: Upload and convert PDFs into vectorized knowledge for retrieval.
• 🧠 RAG-Powered Answers: Ask questions based on your custom data (uploaded documents).
• 🧼 Stateful & Stateless Chat: Clearable chat histories to manage conversational memory.

🔍 RAG Gateway (IRagGateway)

• Upload and process PDF documents
• Query domain-specific knowledge with context
• Generate answers and summaries
• Statefull and Stateless by default, with optional history reset

🤖 Ollama Gateway (IOllamaGateway)

• Lightweight prompt-based chat and content generation
• Statefull and Stateless by default, with optional history reset

🧪 Use Cases

• RAG-enabled customer support systems
• AI-driven PDF analysis tools
• Developer tools for querying technical documentation
• AI assistants embedded in enterprise software

📦 Installation

Install from NuGet.org:

dotnet add package Rapid.Agentic.AI.Framework

Or via Package Manager Console:

Install-Package Rapid.Agentic.AI.Framework

🚀 Getting Started

📦 Prerequisites

• .NET 6 or later
• Ollama running locally (default port http://localhost:11434)
• A downloaded model (e.g., llama3, llama3.2:1b, etc.)

🧱 Example Model Names

• llama3
• llama3.2:1b
• mistral

Any other model available through Ollama

Ensure the model is already pulled by running:

ollama run llama3.2:1b

🧪 Usage

🏭 1. Factory Class

public interface IAgenticClientFactory
{
     IRagGateway CreateRagGateway(string ollamaUrl, string model, string dbPath = "rag.db");
     IOllamaGateway CreateOllamaGateway(string ollamaUrl, string model);
}

🔐 1. RAG Gateway

public interface IRagGateway
{
    Task<string> ChatAsync(string question);
    void ClearChatHistory();
    Task<string> GenerateAsync(string question, bool enableStreaming);
    Task UploadFileAsync(string pdfPath);
}

✨ 1. Usage example - Summarizing a PDF document

var factory = new AgenticClientFactory();
var rag = factory.CreateRagGateway("http://localhost:11434", "llama3");

await rag.UploadFileAsync("documents/manual.pdf");

string summary = await rag.GenerateAsync("Summarize the manual.", enableStreaming: false);
Console.WriteLine(summary);

✨ 2. Usage example - Reviewing c# source code

var factory = new AgenticClientFactory();
var rag = factory.CreateRagGateway("http://localhost:11434", "llama3");

await rag.UploadFileAsync("documents/MyAlgorithm.cs");

string summary = await rag.GenerateAsync("Review the cs file", enableStreaming: false);
Console.WriteLine(summary);

✨ 3. Usage example - without uploading local file

var factory = new AgenticClientFactory();
var rag = factory.CreateRagGateway("http://localhost:11434", "llama3");

string summary = await rag.GenerateAsync("What are the best tourist places in Bangalore", enableStreaming: false);
Console.WriteLine(summary);

✨ 4. Chat Usage example (Multi-Turn) - Reviewing c# source code and asking for fix

var factory = new AgenticClientFactory();
var rag = factory.CreateRagGateway("http://localhost:11434", "llama3");

await rag.UploadFileAsync("documents/MyAlgorithm.cs");

string summary = await rag.ChatAsync("Review the cs file", enableStreaming: false);
Console.WriteLine(summary);

string modifiedsummary = await rag.ChatAsync("Provide me top review comments with potential fix", enableStreaming: false);
Console.WriteLine(modifiedsummary);

🔄 To Clear Chat History:

rag.ClearChatHistory();

🤖 2. IOllamaGateway

public interface IOllamaGateway
{
    string ChatAsync(string prompt, string model = "");
    void ClearChatHistory();
    string GenerateAsync(string prompt, string model = "");
}

✨ 1. Chat Usage example with Context (Multi-Turn)

var factory = new AgenticClientFactory();
var ollama = factory.CreateOllamaGateway(ollamaUrl, myModel);

string response = ollama.ChatAsync("Tell me about India?");
Console.WriteLine(response);

string responseUpdated = ollama.ChatAsync("What are the best tourist places?");
Console.WriteLine(responseUpdated);

ollama.ClearChatHistory();

✨ 1. Content generation Usage example

var factory = new AgenticClientFactory();
var ollama = factory.CreateOllamaGateway(ollamaUrl, myModel);

string response = ollama.GenerateAsync("Tell me about India?");
Console.WriteLine(response);

🔄 To Clear Chat History:

OllamaClient.ClearChatHistory();

📃 License

The Core Framework is not open-source, but is freely distributed via NuGet.

For commercial use, licensing, or integration support:

📧 support@vedicaai.com or aruna.devadiga@gmail.com

🙋 Support & Contributions

This framework is not open source, but it is freely distributed via NuGet. If you encounter issues, bugs, or have suggestions, feel free to reach out to the maintainers or submit feedback via the appropriate support channels.

📧 support@vedicaai.com or aruna.devadiga@gmail.com