Auto-Agent Chatbot

Introduction

The Auto-Agent Chatbot project aims to develop an advanced conversational agent capable of engaging users in diverse and coherent conversations across various topics. Unlike traditional task-specific chatbots, this project focuses on creating a versatile chatbot that can understand, respond to, and maintain context in natural language interactions.

Table of Contents

• Introduction
• Key Challenges
• Installation
• Usage
• Features
• Dependencies
• Configuration
• Documentation
• Examples
• Contributors
• License

Key Challenges

1. Agent Assignment Efficiency: Current methods for assigning agents to user queries lack sophistication, leading to suboptimal user-agent pairing and potentially lower customer satisfaction.
2. Interaction History Management: Existing systems struggle to efficiently summarize and store interaction histories, hindering analytical insights and timely retrieval.
3. Customization Limitations: Many chatbots do not offer robust customization options for creating specialized agents tailored to unique business needs and customer preferences.
4. Technological Integration: Integrating advanced natural language processing (NLP) and machine learning (ML) models into a cohesive architecture poses integration challenges and requires robust technical solutions.

Installation

To install the Auto-Agent Chatbot, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/auto-agent-chatbot.git

2. Navigate to the project directory:

   cd auto-agent-chatbot

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

To start the Auto-Agent Chatbot, run the following command:

python main.py

You can interact with the chatbot via the command line or integrate it into your application using the provided API.

Features

• Versatile Conversational Abilities: Engages in diverse and coherent conversations across various topics.
• Context Maintenance: Maintains context throughout the interaction for a more natural conversational flow.
• Advanced NLP and ML Integration: Leverages state-of-the-art NLP and ML models for improved performance.
• Customizable Agents: Allows for the creation of specialized agents tailored to specific business needs.

Dependencies

The Auto-Agent Chatbot requires the following dependencies:

• Python 3.7+
• TensorFlow
• PyTorch
• Transformers
• NLTK
• Scikit-learn
• Pandas
• Streamlit
• Groq
• Torchvision

Configuration

Configuration options can be set in the config.json file located in the project directory. Below is an example configuration:

api_key: ""
model_name: "llama3-70b-8192"
max_tokens: 4096
temperature: 0.9
top_p: 1
stream: false
agent: "assistant"
bot_name: "Manav"
default_system_message: "You are ${bot_name}, an exceptionally intelligent and open-minded ${agent}. 
Always retain and utilize information from previous messages, particularly 
the user's name and any personal details they share. Provide insightful, 
thoughtful, and helpful responses to all inquiries, ensuring empathy, 
respect, and consideration are maintained."

Documentation

Comprehensive documentation is available in the docs directory. It includes detailed guides on installation, usage, configuration, and extending the chatbot's capabilities.

Examples

Basic Interaction

from chatbot import Chatbot

bot = Chatbot()
response = bot.send_message("Hello, how can I help you today?")
print(response)

Custom Agent Interaction

from chatbot import Chatbot

bot = Chatbot(agent="customer_support")
response = bot.send_message("I need help with my order.")
print(response)

Contributors

• Abhishek Jha - Project Lead - Theory903

License

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