🤖 Agentic Chatbot: Navigating Red Hat Internal resources from THE SOURCE

Overview

The Agentic Chatbot is an intelligent assistant designed to help Red Hat associates navigate internal resources efficiently.

Project Goals

• Simplify access to internal documentation
• Reduce onboarding friction
• Provide intelligent, context-aware resource discovery

Use Case

Instead of navigating the Source portal manually, associates can ask:

• "What is the process for updating a Red Hat product?"
• "Where can I find the latest documentation for a specific product?"
• "How do I resolve a common issue with a Red Hat product?"

And receive:

• Direct answers
• Links to the exact documents/pages
• Context-aware follow-up suggestions

Technical Architecture

Core Components

Layer	Technology
Backend	Python + FastAPI / Flask
LLMs	OpenAI GPT, Ollama, Hugging Face
Vector DB	ChromaDB / FAISS
Embeddings	Hugging Face Transformers
Frontend	Slack Bot (via Slack Bolt SDK)

Agentic Workflow

The chatbot operates on a multi-agent architecture, where each agent specializes in a sub-task:

• Query Understanding Agent Interprets user inputs into actionable formats.

• Content Indexing Agent Parses documents and pushes them to a vector database after generating embeddings.

• Response Generation Agent Retrieves relevant data and composes human-like, informative replies.

Slack Integration

The chatbot is tightly integrated with Slack for enterprise accessibility:

• Users interact via DM or threads
• Replies include embedded links to Source documents
• Backend uses Slack Bolt SDK with FastAPI
• Events are securely handled with workspace-level tokens

Key Features

• Semantic Search Leverages vector embeddings to match intent with content, even for vague queries

• Model Flexibility Easily swap between OpenAI, Hugging Face, or locally hosted models via Ollama

• Microservice Architecture Modular design allows for scaling and independent agent upgrades

• Multilingual Support Via Hugging Face embeddings and tokenizers

• Secure Document Handling Respects access controls and data privacy protocols

Impact

• Significantly reduces time spent navigating documentation manually
• Improves onboarding experience for new Red Hatters
• Promotes self-service culture and reduces dependency on internal channels
• Lays foundation for enterprise-grade knowledge retrieval systems

Future Scope

• EagleView API Integration Post-authentication, the chatbot can dynamically fetch the complete Source portal data and continuously update its knowledge base

• Scalable to Entire Source Ecosystem Once EagleView API access is enabled, the chatbot will be able to answer all questions across teams and departments

• Credential-Aware Access Control Role-based response customization based on user credentials

• Intelligent Logging & Feedback Loop Enable query analytics to improve answers through fine-tuning

• Horizontally Scalable Architecture Multi-agent system allows parallel processing, independent agent upgrades, and multi-tenant support

Installation

# Clone the repository
git clone https://github.com/your-org/agentic-chatbot.git

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp sample.env .env
# Edit .env with your configurations

Quick Start

Running the Slack Bot

python services/slack_service.py

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.