🤖 AIChatBot

AIChatBot is a local and online AI-powered chatbot built using open-source language models. This project supports both locally hosted models (via Ollama) and cloud-based models via OpenRouter. It demonstrates the integration of AI with a .NET 8 API and Angular 20 frontend.

✨ Latest Features

🆕 RAG (Retrieval-Augmented Generation) Integration

Transform your chatbot experience with Knowledge-Based AI! Upload your documents and get contextually-aware responses based on your own content.

Key Highlights:

📄 Multi-format Document Support: Upload .txt, .md, and .pdf files
🔍 Intelligent Document Search: Advanced retrieval algorithms find relevant content
💬 Context-Aware Responses: AI answers based on your uploaded documents
📊 Source Attribution: See exactly which documents informed each response
🗂️ Document Management: Easy upload, view, and delete capabilities
👤 User-Specific Collections: Each user maintains their own document library

Experience AI that truly understands YOUR content!

🎬 Demo Video

Watch the AIChatBot in action on YouTube:
📺 AIChatBot Demo

🛠️ Prerequisites

To run this project locally, ensure the following:

Windows 10/11 with WSL support
Installed Ubuntu 20.04.6 LTS (via Microsoft Store)
.NET 8 SDK
Node.js (v20+)
Angular CLI (npm install -g @angular/cli)
Ollama installed in Ubuntu for running local AI models
Optional: Account on https://openrouter.ai

🧱 Environment Setup (Local Models using Ollama)

1. Install Ubuntu 20.04.6 LTS

Go to Microsoft Store → Search for Ubuntu 20.04.6 LTS → Install.
Open Ubuntu and create your UNIX user account.

2. Install Ollama inside Ubuntu

curl -fsSL https://ollama.com/install.sh | sh

3. Pull and Run Models

To pull and run the desired models:

# Pull models
ollama pull phi3:latest
ollama pull mistral:latest
ollama pull gemma:2b
ollama pull llama3:latest

# Run models
ollama run phi3
ollama run mistral
ollama run gemma:2b
ollama run llama3

4. Manage Models

# List all pulled models
ollama list

# Stop a running model
ollama stop phi3

# View running models
ps aux | grep ollama

5. Shutdown Ubuntu

From Ubuntu terminal:

shutdown now

Or simply close the terminal window if you don’t need a full shutdown.

☁️ Online Models via OpenRouter

Go to https://openrouter.ai and sign up.
Navigate to API Keys in your profile and generate an API key.
Set this key as an environment variable in your API project:
```
export OPENROUTER_API_KEY=your_key_here
```
Models used:
- google/gemma-3-27b-it:free
- deepseek/deepseek-chat-v3-0324:free

API requests are routed via OpenRouter using this key, supporting seamless AI chat.

🤖 AIChatBot Integration

🔧 Backend (API - .NET 8)

Navigate to AIChatBot.API/
Run the following commands:

dotnet restore
dotnet build
dotnet run

Ensure appsettings.json file includes:

ApiKey=YOUR_KEY_HERE

💬 Frontend (UI - Angular 20)

Navigate to AIChatBot.UI/
Run:

npm install
ng serve

Access the chatbot UI at http://localhost:4200/

📚 RAG (Retrieval-Augmented Generation) Features

The AIChatBot includes advanced RAG capabilities that allow AI models to answer questions based on your uploaded documents. This feature significantly enhances the AI's ability to provide contextually relevant and accurate responses.

🏗️ RAG Architecture

The RAG system consists of several key components:

Document Processing Pipeline:
- File upload handling for multiple formats
- Text extraction from PDF, TXT, and MD files
- Content chunking for efficient retrieval
- In-memory indexing with similarity search
Retrieval System:
- Semantic search across document chunks
- Top-K retrieval (configurable, default: 3 chunks)
- Relevance scoring and ranking
- Source attribution and metadata tracking
Generation Enhancement:
- Context-aware prompt construction
- Integration with all supported AI models
- Source citation in responses
- Fallback to general knowledge when needed

🎛️ RAG Configuration

The RAG system supports various AI models with different levels of effectiveness:

Model Category	Models	RAG Performance
Best RAG Support	GPT-3.5 Turbo Gemini Flash 2.0 (Unlimited) Gemini Flash 2.0 (Limited)	⭐⭐⭐⭐⭐
Good RAG Support	DeepSeek v3 Gemma 3 27B LLaMA 3	⭐⭐⭐⭐
Basic RAG Support	PHI-3 Mistral 7B	⭐⭐⭐

🚀 Using RAG Features

Upload Documents:

# Supported formats
- Plain text files (.txt)
- Markdown files (.md) 
- PDF documents (.pdf)

Document Management:
- View all uploaded documents
- Delete individual documents
- User-specific document collections
- Automatic indexing upon upload
RAG-Enhanced Chat:
- Select "Knowledge-Based (RAG)" mode
- Ask questions about your uploaded content
- Receive responses with source attribution
- Contextual answers based on document content

💾 Document Storage

Currently, the RAG system uses in-memory storage (InMemoryRagStore), which provides:

Fast retrieval performance
Simple deployment setup
Automatic cleanup on application restart
User-isolated document collections

Note: For production deployments, consider implementing persistent storage solutions.

🧪 Model & Environment Summary

Model	Type	Source	Access	RAG Support
PHI-3:latest	Local	Ollama	`ollama run`	⭐⭐⭐
Mistral:latest	Local	Ollama	`ollama run`	⭐⭐⭐
Gemma:2b	Local	Ollama	`ollama run`	⭐⭐⭐
Llama3:latest	Local	Ollama	`ollama run`	⭐⭐⭐⭐
google/gemma-3-27b-it:free	Online	OpenRouter.ai	API Key	⭐⭐⭐⭐
deepseek/deepseek-chat-v3-0324	Online	OpenRouter.ai	API Key	⭐⭐⭐⭐
google/gemini-2.0-flash-exp	Online	OpenRouter.ai	API Key	⭐⭐⭐⭐⭐
openai/gpt-3.5-turbo-0613	Online	OpenRouter.ai	API Key	⭐⭐⭐⭐⭐
google/gemini-2.0-flash-001	Online	OpenRouter.ai	API Key	⭐⭐⭐⭐⭐

📂 Project Structure

AIChatBot/
│
├── AIChatBot.API/           # .NET 8 API for chatbot
│   ├── Controllers/         # API endpoints
│   │   └── DocumentsController.cs    # RAG document upload/management
│   ├── Services/           # Business logic services
│   │   ├── RagChatService.cs         # RAG-enabled chat functionality
│   │   ├── InMemoryRagStore.cs       # Document indexing and search
│   │   ├── AgentService.cs           # AI tool integration
│   │   └── ChatService.cs            # Standard chat functionality
│   ├── Interfaces/         # Service contracts
│   │   └── IRagStore.cs              # RAG storage interface
│   └── Migrations/         # Database schema updates for RAG
├── AIChatBot.UI/           # Angular 20 UI frontend
│   └── src/app/components/
│       ├── document-upload/          # RAG document upload component
│       ├── chat/                     # Main chat interface
│       └── model-selector/           # AI model and mode selection
└── README.md               # Project documentation

🧠 AI Tools & Agent Integration

The AIChatBot supports three advanced operation modes beyond simple chat:

1. 🛠️ Tool-Enabled AI

In this mode, the AI can recognize specific tasks in user prompts and use internal tools (functions) to perform actions. Integrated tools include:

Tool Function	Description	Example Prompt
`CreateFile`	Creates a text file with given content	"Create a file called `report.txt` with the text `Hello world`."
`FetchWebData`	Fetches the HTML/content of a public URL	"Fetch the content of https://example.com"
`SendEmail`	Simulates sending an email (console-logged)	"Send an email to john@example.com with subject `Hello`."

These functions are executed server-side in .NET, with input parsed from natural language prompts.

2. 📚 Knowledge-Based (RAG) Mode

NEW FEATURE: The RAG (Retrieval-Augmented Generation) mode enables AI to answer questions based on your uploaded documents. This powerful feature allows you to:

🎯 Key Capabilities:

Upload Documents: Support for .txt, .md, and .pdf files
Intelligent Retrieval: Automatically finds relevant content from your documents
Source Attribution: AI responses include references to source documents
Document Management: View, organize, and delete uploaded documents
User-Specific Storage: Each user has their own document collection

📄 Supported File Formats:

Text Files: .txt - Plain text documents
Markdown Files: .md - Formatted markdown documents
PDF Documents: .pdf - Portable document format

🚀 How to Use RAG Mode:

Upload Documents: Use the drag-and-drop interface or browse to upload documents
Select RAG Mode: Choose "Knowledge-Based (RAG)" from the chat mode dropdown
Ask Questions: Query your documents using natural language
Get Contextual Answers: Receive AI responses enriched with your document content

💡 Example RAG Interaction:

User: "What are the key findings in the latest market report?"
AI: Based on the provided context from "market_analysis_2025.pdf", the key findings include:
1. Consumer spending increased by 15% in Q4
2. Digital transformation investments rose by 23%
3. Supply chain disruptions decreased significantly

*Sources: 1 document(s) referenced*

3. 🤖 AI Agent Mode (Planning + Action)

The AI agent is capable of:

Understanding high-level tasks
Selecting and invoking appropriate tools
Providing intelligent responses based on the outcome

This is powered by an AgentService that works with both local LLMs (via Ollama) and cloud models (via OpenRouter) to determine the right function to execute and handle the response.

You can toggle between AI modes via the UI:

Chat-Only Mode
AI + Tools Mode
Knowledge-Based (RAG) Mode
Agent Mode (multi-step planning, coming soon)

🚀 Get Started

Quick Start Guide

Choose your preferred model type (local or online)
Start the backend using .NET 8
Start the frontend using Angular CLI
Access AIChatBot at http://localhost:4200/

Using RAG Features (NEW!)

Upload Documents:
- Click the "Documents for RAG" section in the sidebar
- Drag & drop or browse to upload .txt, .md, or .pdf files
- Wait for successful upload confirmation
Enable Knowledge-Based Mode:
- Select "Knowledge-Based (RAG)" from the chat mode dropdown
- Choose an AI model with good RAG support (⭐⭐⭐⭐ or ⭐⭐⭐⭐⭐)

Start Asking Questions:

Examples:
"Summarize the key points from my uploaded documents"
"What does the report say about market trends?"
"Find information about [specific topic] in my files"

Review Source Attribution:
- AI responses will include "Sources: X document(s) referenced"
- Responses are enriched with content from your uploaded documents

💖 Contributing

Pull requests and suggestions are welcome! Feel free to fork the repo and enhance it.

📄 License

This project is open-source and available under the MIT License.

Name		Name	Last commit message	Last commit date
Latest commit History 82 Commits
.github		.github
AI Responses		AI Responses
AIChatBot.API		AIChatBot.API
AIChatBot.UI		AIChatBot.UI
.gitattributes		.gitattributes
.gitignore		.gitignore
AIChatBot.sln		AIChatBot.sln
LICENSE.txt		LICENSE.txt
README.md		README.md

License

hardikpatelse/AIChatBot

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