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Chat CLI Service - RH Team Specialist

A dockerized multi-agent chat interface that coordinates between specialized AI agents for company assistance.

Architecture

chat_cli/
├── app/
│   ├── core/           # Core utilities
│   │   ├── logger.py   # Logging configuration  
│   │   └── settings.py # Environment settings
│   ├── agents/         # Specialized AI agents
│   │   ├── hr_policies_agent.py
│   │   ├── labor_rules_agent.py
│   │   └── product_manual_agent.py
│   ├── teams/          # Multi-agent coordinators
│   │   └── rh_team_specialist.py
│   └── main.py         # Application entry point

Specialized Agents

HR Policies Agent

  • Collection: hr_policies
  • Expertise: Company policies, employee procedures, benefits, HR guidelines
  • Use cases: Policy questions, benefit inquiries, procedure clarification

Labor Rules Agent

  • Collection: labor_rules
  • Expertise: Employment law, worker rights, legal compliance, labor regulations
  • Use cases: Legal compliance, employment law questions, worker rights

Product Manual Agent

  • Collection: product_manual
  • Expertise: Product documentation, technical support, installation guides
  • Use cases: Product troubleshooting, installation help, technical specifications

Usage

Docker Compose (Recommended)

Start the chat interface:

docker compose up chat_cli

For debugging:

docker compose up chat_cli-bash

Environment Configuration

Required environment variables:

OPENAI_API_KEY=your_openai_api_key
QDRANT_URL=http://localhost:6333  # or http://qdrant:6333 in Docker

Optional configuration:

CHAT_MODEL_ID=gpt-4o-mini          # OpenAI model
NUM_DOCUMENTS=4                    # Documents per search
NUM_HISTORY_RUNS=5                 # Conversation memory
ENABLE_STREAMING=true              # Real-time responses
DEBUG_MODE=false                   # Debug logging
SHOW_MEMBERS_RESPONSES=true        # Show agent coordination

Chat Interface

The interface provides:

  • Multi-agent coordination: Automatically routes questions to appropriate specialists
  • Context awareness: Maintains conversation history
  • Streaming responses: Real-time answer generation
  • Rich formatting: Beautiful console output with emojis and colors
  • Error handling: Graceful error recovery and user guidance

Example Interactions

HR Policy Question:

💬  Your question: What's our vacation policy?
→ Routes to HR Policies Agent

Labor Law Question:

💬 Your question: What are overtime regulations in Brazil?
→ Routes to Labor Rules Agent

Product Question:

💬 Your question: How do I install the new software?
→ Routes to Product Manual Agent

Out-of-scope Question:

💬 Your question: What's the weather today?
→ Politely declines and suggests relevant topics

Technical Features

  • Agno Team Coordination: Uses coordinate mode for intelligent routing
  • OpenAI Embeddings: High-quality semantic search with text-embedding-ada-002
  • Qdrant Vector Database: Efficient vector similarity search
  • Context Sharing: Agents can reference each other's responses
  • History Management: Maintains conversation context across interactions
  • Logging: Comprehensive logging to chat_cli.log

Troubleshooting

Connection Issues:

  • Ensure Qdrant is running: docker compose up qdrant
  • Verify OPENAI_API_KEY is set correctly
  • Check network connectivity: docker compose logs chat_cli

Agent Errors:

  • Verify collections exist in Qdrant
  • Check embedding model availability
  • Review logs: docker compose logs chat_cli

Development

To extend the system:

  1. Add new agents: Create in agents/ folder with factory function
  2. Add new teams: Create coordinator in teams/ folder
  3. Modify settings: Update core/settings.py for new configurations
  4. Update main: Modify main.py for new team integration