https://github.com/rajesh9943/customer-support-agentic-ai
This AI customer support agent uses LangGraph and LangChain for query categorization, sentiment analysis, and smart escalation. Integrated with Groq's LLaMA 3.3 70B model, it streamlines workflows and ensures high-quality, consistent interactions.
https://github.com/rajesh9943/customer-support-agentic-ai
agentic-ai customersupport gradio-interface groq-api langchain langgraph llama3 nlp python
Last synced: 3 months ago
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This AI customer support agent uses LangGraph and LangChain for query categorization, sentiment analysis, and smart escalation. Integrated with Groq's LLaMA 3.3 70B model, it streamlines workflows and ensures high-quality, consistent interactions.
- Host: GitHub
- URL: https://github.com/rajesh9943/customer-support-agentic-ai
- Owner: rajesh9943
- Created: 2024-12-29T15:01:30.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-29T15:33:50.000Z (10 months ago)
- Last Synced: 2025-02-22T04:14:12.119Z (8 months ago)
- Topics: agentic-ai, customersupport, gradio-interface, groq-api, langchain, langgraph, llama3, nlp, python
- Language: Jupyter Notebook
- Homepage: https://huggingface.co/spaces/rajesh-a/customer_support_agentic-ai
- Size: 1.31 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Intelligent Customer Support Agentic AI An NLP - Driven Solution with LangGraph and LangChain
This intelligent customer support AI agent leverages LangGraph and LangChain to automate and enhance customer service operations. The system employs sophisticated natural language processing to categorize queries, analyze sentiment, and generate appropriate responses, while featuring smart escalation protocols for complex cases. Through its structured workflow and integration with Groq's LLaMA 3.3 70B model, it streamlines customer support processes while maintaining high-quality, consistent interactions.
## Problem Statement
Modern customer service operations face several key challenges. To address these, an intelligent customer support AI agent is implemented to enhance efficiency and maintain high-quality responses, along with appropriate escalation protocols.
## Overview
This Project outlines the implementation of an intelligent customer support agent using LangGraph and LangChain. The system automates customer query handling through a sophisticated workflow comprising query categorization, sentiment analysis, and dynamic response generation
## What is AI Agent
AI agents can encompass a wide range of functionalities beyond natural language processing including decision-making, problem-solving, interacting with external environments and executing actions.
## Context of Customer Support AI agent
An AI agent for customer support might:
• Perceive customer queries.
• Categorize the queries (e.g., billing, technical support).
• Analyze sentiment to assess customer emotions.
• Respond with relevant and accurate information.
• Escalate cases to human agents when necessary
## Challenges and Solutions
1. Manual Classification of Queries
Customer service representatives often spend significant time manually classifying incoming queries (e.g., technical, billing, or general). This process is time-consuming and inconsistent. By using natural language processing, the system automates query classification, saving time and ensuring consistency.
2. Inconsistent Feedback Quality
Different support agents may provide varying levels of service or conflicting answers to the same question. Automation ensures standardized, high-quality responses tailored to each question’s category and context.
3. Handling Negative Interactions
Without systematic analysis, negative sentiments may go unnoticed, delaying critical interventions. This system identifies and escalates negative interactions to human agents promptly, ensuring appropriate management of problematic situations.
4. Inefficient Resource Allocation
Traditional support systems often fail to prioritize queries effectively. This implementation optimizes the allocation of resources by distinguishing cases requiring human attention from those that automation can handle independently.
## Features and Benefits
The intelligent workflow offers the following capabilities:
• Automated Categorization: Classifies incoming queries.
• Sentiment Analysis: Detects priority cases based on emotional tone.
• Dynamic Response Generation: Delivers context-specific responses.
• Escalation Protocols: Routes sensitive cases to human agents.
• User-Friendly Interface: Simplifies query submission and feedback.
________________________________________
## System Architecture and Workflow
The system processes customer queries through a structured workflow:
1. Starting Point (start)
Queries enter the pipeline upon receipt.
2. Categorization Stage (categorize)
Classifies queries as:
Technical: For product/service-related issues.
Billing: For payment/account-related queries.
General: For other inquiries.
3. Sentiment Analysis (analyze_sentiment)
Assesses emotional tone as positive, negative, or neutral.
Directs queries based on sentiment.
4. Response Handling Paths
Escalation (escalate): Negative sentiment queries routed to human agents.
Technical (handle_technical): Handles technical inquiries.
Billing (handle_billing): Addresses payment/account issues.
General (handle_general): Manages miscellaneous queries.
5. Termination Point (end)
Finalizes responses and returns them to customers.
## Dependencies
• ipython
• typeddict
• langchain
• langchain_core
• langchain_groq
• langchain_community
• langgraph
• gradio (for UI)
## User Interface Implementation
Built using the Gradio framework, the interface offers:
• Text Input: For customer queries.
• Formatted Markdown Output: Displays query categorization, sentiment analysis, and generated responses.
• Custom Themes: Implements Yntec/Ha1eyCH_Theme_Orange_Green.
To host the project on Hugging Face Spaces,
## Future Enhancements
1. Conversation history implementation.
2. Multi-language support.
3. Ticketing system integration.
4. Enhanced analytics and reporting.
5. Custom response templates.
6. Knowledge base integration.
## Conclusion
The use of intelligent customer support AI agents represents a significant improvement in customer service innovation. By using state-of-the-art tools such as LangGraph and LangChain.
The system can efficiently solve critical problems such as query classification, logic, and feedback storage. and allocation of resources A combination of various functionalities sentiment analysis and dynamic analysis Helps ensure that customer complaints are resolved in an efficient and appropriate manner.
Additionally, its scalable architecture and thoughtful design allow for continuous improvement and future enhancements, such as multilingual support. Ticket system integration and insights based on statistics This AI-powered solution helps organizations Able to provide excellent customer service At the same time improving operational efficiency. Paving the way for a more responsive and customer-centric support experience.