gqc-agent 1.0.1

GQC Agent provides a multi-agent AI pipeline for intent classification, query rephrasing, and note creation using GPT and Gemini models.

GQC Agent

GQC Agent is a Python library that helps developers work with AI models using an agent-based pipeline. It includes model validation, intent classification, query rephrasing, summarization, and an orchestrator that manages all agents.

Table of Contents

• Overview
• Features
• Technologies
• Use Cases
• Installation
• Usage Examples
• Project Status
• License
• Author

Overview

GQC Agent is a lightweight, modular Python library designed to orchestrate multiple AI agents for large language model (LLM) applications. It simplifies the workflow of building intelligent conversational systems by providing robust tools for input validation, model management, intent prediction, query rephrasing, and interaction summarization.

Features

• GPT & Gemini model validator
• Intent classifier
• Query rephraser
• Summarizer agent
• Orchestrator for multi-agent flow

Technologies

Project is created with:

• Python 3.13

Use Cases

• AI chatbots with enhanced context handling
• Retrieval-Augmented Generation (RAG) for Q&A and summarization
• Workflow automation using multiple AI agents
• Note-taking and interaction summarization

Installation

Install using pip (after publishing)

pip install gqc-agent

Environment Setup

Create a .env file: OPENAI_API_KEY=your_key
GEMINI_API_KEY=your_key

Usage Examples

Example: Using OPENAI Client

from gqc_agent.core.orchestrator import AgentPipeline

OPENAI_API_KEY = "YOUR_OPENAI_API_KEY"

client = AgentPipeline(api_key=OPENAI_API_KEY, model="gpt-4o-mini")
response = client.run_gqc(
    user_input={
        "input": "Tell me more about both of them",
        "current": {"role": "user", "query": "Tell me more about both of them", "timestamp": "2025-01-01 12:30:45"},
        "history": [
            {"role": "user", "query": "What is meant by active broker", "timestamp": "2025-01-01 12:00:00"},
            {"role": "assistant", "response": "Active broker is active in treaty and claims modules.", "timestamp": "2025-01-01 12:01:10"},
            {"role": "user", "query": "Where is pending broker used?", "timestamp": "2025-01-01 12:02:00"},
            {"role": "assistant", "response": "Pending broker is used in TR Treaty module.", "timestamp": "2025-01-01 12:03:22"}
        ]
    }
)
print(response)

Example: Using GEMINI Client

from gqc_agent.core.orchestrator import AgentPipeline

GEMINI_API_KEY = "YOUR_GEMINI_API_KEY"

client = AgentPipeline(api_key=GEMINI_API_KEY, model="gemini-pro")
response = client.run_gqc({...})
print(response)

List Supported Models

from gqc_agent.core.orchestrator import AgentPipeline

print("GPT Models:", AgentPipeline.get_supported_models(api_key="YOUR_OPENAI_API_KEY"))
print("Gemini Models:", AgentPipeline.get_supported_models(api_key="YOUR_GEMINI_API_KEY"))

Load System Prompt

from gqc_agent.core.orchestrator import AgentPipeline

prompt = AgentPipeline.show_system_prompt(filename="sample.md")
print(prompt)

Project Status

• Active Development

Planned updates:

• More LLM vendor support
• Better agent routing
• Improved accuracy
• Performance optimizations

License

MIT License

Author

BIG ENTITIES
BE AI Developers