🤖 GenAI Explorer

Table of Contents

• Overview
• System Architecture
• Features
• Components
• Installation
• Usage
• Configuration
• Security

Overview

A comprehensive GenAI toolkit featuring local DeepSeek model implementation, document processing (RAG), and integration with various services including Airbnb booking and OpenAI API.

System Architecture

Complete System Flow

graph TD
    subgraph "Frontend Interfaces"
        A1[Streamlit UI - Main Chat]
        A2[Streamlit UI - RAG]
        A3[Gradio UI - Airbnb]
    end
    
    subgraph "AI Processing Layer"
        B1[DeepSeek Local]
        B2[OpenAI API]
        B3[LangChain]
    end
    
    subgraph "Document Processing"
        C1[PDF Loader]
        C2[Text Splitter]
        C3[Vector Store]
    end
    
    subgraph "External Services"
        D1[MCP Server]
        D2[Airbnb API]
        D3[OpenAI Services]
    end
    
    A1 --> B1
    A2 --> C1
    A3 --> D1
    
    C1 --> C2
    C2 --> C3
    C3 --> B3
    
    B3 --> B1
    B3 --> B2
    
    D1 --> D2
    B2 --> D3

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RAG Implementation Flow

sequenceDiagram
    participant User
    participant UI as Streamlit UI
    participant PDF as PDF Processor
    participant VS as Vector Store
    participant AI as DeepSeek AI

    User->>UI: Upload PDF
    UI->>PDF: Process Document
    PDF->>VS: Store Vectors
    User->>UI: Ask Question
    UI->>VS: Search Similar Content
    VS->>AI: Provide Context
    AI->>UI: Generate Response
    UI->>User: Display Answer

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Features

• 🤖 Local DeepSeek model integration (1.5b and 3b variants)
• 📚 RAG implementation with AI Health coach assistant Agent for PDF processing to analyze images and and documents and provide recommendation
• 🏠 Airbnb booking assistant with MCP server
• 🔄 OpenAI API integration
• 💅 Custom-styled UI interfaces
• 📊 Vector store for document retrieval

Components

1. Main Chat Application (app.py)

# Key configurations
llm_engine = ChatOllama(
    model=selected_model,
    base_url="http://localhost:11434",
    temperature=0.3
)

2. RAG Implementation (rag_deep.py)

# Core components
EMBEDDING_MODEL = OllamaEmbeddings(model="deepseek-r1:1.5b")
DOCUMENT_VECTOR_DB = InMemoryVectorStore(EMBEDDING_MODEL)
LANGUAGE_MODEL = OllamaLLM(model="deepseek-r1:1.5b")

3. AI Health Coach Agent RAG Implementation (Ai_recipe_assistant.py)

# Core components
EMBEDDING_MODEL = OllamaEmbeddings(model="deepseek-r1:1.5b")
DOCUMENT_VECTOR_DB = InMemoryVectorStore(EMBEDDING_MODEL)
LANGUAGE_MODEL = OllamaLLM(model="deepseek-r1:1.5b")

4. Airbnb Search (airbnb_search.py)

agent = Agent(
    instructions="""You help book apartments on Airbnb.""",
    llm="gpt-4o-mini",
    tools=MCP("npx -y @openbnb/mcp-server-airbnb")
)

Installation

# Clone repository
git clone <repository-url>

# Install dependencies
pip install -r requirements.txt

# Required packages
streamlit
langchain_core
langchain_community
langchain_ollama
pdfplumber
praisonaiagents 
mcp
openai
gradio
requests

Usage

1. Start Main Chat Interface

streamlit run app.py

2. Launch Document Assistant

streamlit run rag_deep.py

3. Launch AI Health Coach Agent Assistant

streamlit run ai_recipe_assistant.py

4. Start Airbnb Search Agent Assistant

python airbnb_search.py

Configuration

DeepSeek Model Settings

# Available models
models = ["deepseek-r1:1.5b", "deepseek-r1:3b"]

# RAG Configuration
CHUNK_SIZE = 1000
CHUNK_OVERLAP = 200

Security

• 🔒 Secure API key management
• 🏢 Local model execution
• 🔐 No data persistence
• 📄 Safe document handling

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📝 Note: Ensure all API keys and configurations are properly set before running the applications.

This README now includes:

• Detailed system architecture diagrams
• Component interaction flows
• Code snippets from actual implementation
• Clear installation and usage instructions
• Security considerations