🤖 C-bot: AI Chatbot

An advanced multi-functional chatbot built with Streamlit and Google Gemini API, featuring RAG (Retrieval-Augmented Generation) knowledge base enhancement, multi-modal file processing, and conversation management.

🔗 Live Demo

Try the live demo: https://chatbot-with-rag-j5kyqjupptewxu2ze9k6qc.streamlit.app/

✨ Features

• 🤖 Powerful AI: Powered by Google Gemini 2.0 Flash model
• 💬 Conversation Management: Multiple conversation tracking with history
• 📚 RAG Knowledge Base: Enhanced answers using your uploaded documents
• 🎭 Multiple AI Personas: Choose from different AI personalities
• 📝 Templates: Pre-built prompts for common tasks
• 📊 Multi-modal Support: Process documents, images, and audio files

🚀 Deployment Guide

Streamlit Cloud Deployment

1. Fork or clone this repository
2. Copy variables from .env.example to the Streamlit Cloud secrets manager
3. Deploy the app with main.py as the entry point
4. Your C-bot is now live!

Local Deployment

1. Clone the repository

   git clone https://github.com/yourusername/c-bot.git
   cd c-bot

2. Create a .env file with your credentials:

   # API Configuration
   GEMINI_API_KEY=your_api_key_here
   GEMINI_BASE_URL=https://generativelanguage.googleapis.com/v1beta/openai/
   GEMINI_MODEL=gemini-2.0-flash
   GEMINI_PICTURE_MODEL=gemini-2.0-flash
   GEMINI_EMBEDDING_MODEL=text-embedding-004
   GEMINI_EMBEDDING_DIM=768
   
   # Database Configuration
   DB_HOST=your_db_host
   DB_USER=your_db_user
   DB_PASSWORD=your_db_password
   DB_NAME=your_db_name
   DB_PORT=5432

3. Update the config.py file:

   import os
   
   GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
   GEMINI_BASE_URL = os.environ.get("GEMINI_BASE_URL", "https://generativelanguage.googleapis.com/v1beta/openai/")
   GEMINI_MODEL = os.environ.get("GEMINI_MODEL", "gemini-2.0-flash")
   GEMINI_PICTURE_MODEL = os.environ.get("GEMINI_PICTURE_MODEL", "gemini-2.0-flash")
   GEMINI_EMBEDDING_MODEL = os.environ.get("GEMINI_EMBEDDING_MODEL", "text-embedding-004")
   GEMINI_EMBEDDING_DIM = int(os.environ.get("GEMINI_EMBEDDING_DIM", "768"))
   
   DB_CONFIG = {
       "host": os.environ.get("DB_HOST"),
       "user": os.environ.get("DB_USER"),
       "password": os.environ.get("DB_PASSWORD"),
       "database": os.environ.get("DB_NAME"),
       "port": int(os.environ.get("DB_PORT", "5432"))
   }

4. Install dependencies:

   pip install -r requirements.txt

5. Run the application:

   streamlit run main.py

📋 Database Setup

This application requires a PostgreSQL database. Ensure you create the following table structure:

CREATE TABLE conversations (
    id SERIAL PRIMARY KEY,
    title VARCHAR(255) NOT NULL,
    created_at TIMESTAMP NOT NULL,
    updated_at TIMESTAMP NOT NULL
);

CREATE TABLE chat_history (
    id SERIAL PRIMARY KEY,
    conversation_id INTEGER REFERENCES conversations(id) ON DELETE CASCADE,
    role VARCHAR(50) NOT NULL,
    content TEXT NOT NULL,
    timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Exporting Chat History

You can export chat history from the database using several methods:

1. Using psql Command Line

# Export all conversations and chat history to CSV
psql -h your_db_host -U your_username -d your_database -c "COPY (SELECT c.id, c.title, c.created_at, ch.role, ch.content, ch.timestamp FROM conversations c JOIN chat_history ch ON c.id = ch.conversation_id ORDER BY c.id, ch.timestamp) TO '/path/to/export.csv' WITH CSV HEADER;"

# Export a specific conversation
psql -h your_db_host -U your_username -d your_database -c "COPY (SELECT ch.role, ch.content, ch.timestamp FROM chat_history ch WHERE ch.conversation_id = 1 ORDER BY ch.timestamp) TO '/path/to/conversation_1.csv' WITH CSV HEADER;"

2. Using pgAdmin

1. Open pgAdmin and connect to your database
2. Right-click on your database and select "Query Tool"
3. Run a query to select the data you want to export:

   SELECT c.id, c.title, ch.role, ch.content, ch.timestamp 
   FROM conversations c 
   JOIN chat_history ch ON c.id = ch.conversation_id 
   ORDER BY c.id, ch.timestamp;

4. Select the results, right-click, and choose "Save Results As" to export to CSV

3. Using Python Script

Create a script to export your chat history:

import psycopg2
import pandas as pd
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Database connection parameters
db_params = {
    "host": os.environ.get("DB_HOST"),
    "user": os.environ.get("DB_USER"),
    "password": os.environ.get("DB_PASSWORD"),
    "database": os.environ.get("DB_NAME"),
    "port": int(os.environ.get("DB_PORT", "5432"))
}

# Connect to the database
conn = psycopg2.connect(**db_params)

# Export all conversations
query = """
SELECT c.id, c.title, c.created_at, ch.role, ch.content, ch.timestamp 
FROM conversations c 
JOIN chat_history ch ON c.id = ch.conversation_id 
ORDER BY c.id, ch.timestamp
"""

# Read the data into a pandas DataFrame
df = pd.read_sql_query(query, conn)

# Export to CSV
df.to_csv("chat_history_export.csv", index=False)

# Export to Excel
df.to_excel("chat_history_export.xlsx", index=False)

print(f"Exported {len(df)} chat messages")

# Close the connection
conn.close()

📦 Dependencies

• streamlit
• openai (for Gemini API compatibility)
• psycopg2-binary (PostgreSQL connector)
• python-dotenv
• pillow
• PyMuPDF
• python-docx
• python-pptx
• pandas
• scikit-learn

👨‍💻 Usage

1. Start the application

2. Use the sidebar to:

   • Create new conversations
   • Switch between AI personas
   • Select templates for specific tasks
   • Manage your knowledge base
   • Adjust AI settings

3. Upload documents to the knowledge base to enhance AI responses

4. Upload files directly in the chat for analysis (PDF, DOCX, images, audio)

📄 License

MIT

---

Built with ❤️ using Streamlit and Google Gemini API