🤖 ParmaTIS AI Assistant

ParmaTIS AI Assistant is a modular, memory-aware chatbot system designed to showcase how modern AI architectures can simulate intelligent internal assistants for organizations. It combines LangChain's Retrieval-Augmented Generation (RAG) capabilities with OpenAI's function-calling Agent SDK, wrapped in a clean Gradio-based UI.

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🧠 What It Does

• ✅ Convert data from a knowledge base to vector data store
• ✅ Retrieves facts about ParmaTIS from a vector data store
• 🧭 Decides intelligently when to use retrieval or general reasoning
• 💬 Chat UI built with Gradio for a clean and responsive experience
• ⚙️ Supports multiple LLMs including GPT, LLaMA (localhost), Claude, DeepSeek

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🧠 Project Overview

This assistant operates on a fictional dataset describing Parma Tecno Intelligent Services (ParmaTIS), an innovative AI company based in Parma, Italy. The assistant answers questions about the company, including:

• Leadership and employees
• Projects and services
• Departments and history
• General knowledge questions

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🏗️ Architecture Summary

• RAG Agent (LangChain): Uses ConversationalRetrievalChain, HuggingFace embeddings, and Chroma vector store for searching ParmaTIS documents.
• Orchestrator Agent (OpenAI SDK): Determines whether to call the RAG agent or respond using general knowledge.
• Memory Layers: Provides 3 memory layers.
• Gradio UI: Interactive frontend that supports real-time communication with history context.

Memory Layers

• RAG_memory: Internal LangChain memory for RAG responses.
• chat_memory: Conversation memory for the orchestrator agent combines also RAG_memory.
• history_display: Maintains full chat history for UI display.

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🚀 Features

• Hybrid architecture (RAG + OpenAI Agents )
• Multi-agent orchestration with tool calling
• Company-specific context retrieval
• Clean, async-friendly Gradio interface
• Supports multiple models (GPT, Claude, LLaMA, DeepSeek, etc.)

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🔧 Setup Instructions

1. Clone the repository

git clone https://github.com/bahathabet/rag-openai-agent.git
cd rag-openai-agent

2. Install dependencies

pip install -r requirements.txt

3. Set up environment variables

Create a .env file with the following:

OPENAI_API_KEY=your_key_here
ANTHROPIC_API_KEY=...
DEEPSEEK_API_KEY=...
...

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💬 Example Prompts

• "Who is the CEO of ParmaTIS?"
• "What projects are currently ongoing?"
• "Tell me about the AI department."
• "What is the capital of France?" (general knowledge)

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📦 Built With

• LangChain
• OpenAI Agent SDK
• Gradio
• ChromaDB
• HuggingFace Embeddings

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📌 Notes

• This is a demo project using synthetic data for ParmaTIS.
• Extendable with additional tools: calendar, Gmail, PDF parsing, and more.
• Easily adaptable to real enterprise use cases.

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🧠 How to Install and Run Ollama Locally

from here: https://github.com/bahathabet/agentic-search/blob/master/README.md

📜 License

MIT License.