# 🧠 Django Supportal – AI-Powered Business Support Chat APIs for django projects
**Django Supportal** is an intelligent, AI-powered customer support system built with **Django**, **Django Channels**, and **OpenAI API**.
It provides APIs for businesses to upload their internal documents, and a smart assistant will handle customer inquiries via live chat – powered by a Retrieval-Augmented Generation (RAG) system.
---
## 🚀 Features
- ✅ Real-time chat via **Django Channels (WebSockets)**
- 📎 Businesses can upload **PDF, DOCX, or TXT documents**
- 🤖 Uses **OpenAI GPT models** to provide intelligent responses
- 📚 Implements **RAG (Retrieval-Augmented Generation)** to process custom business knowledge
- 🔒 Secured communication and Redis-based event layer
---
## 🧠 How it Works (RAG Architecture)
Supportal uses a **Retrieval-Augmented Generation (RAG)** approach to enable AI to answer business-specific questions:
1. **Document Upload:**
Businesses upload documents such as FAQs, product guides, manuals, or policies.
2. **Chunking & Embedding:**
Uploaded documents are:
- Split into smaller text chunks
- Converted into **vector embeddings** using OpenAI's `text-embedding` models
3. **Vector Storage:**
Embeddings are stored in a **vector database** (like FAISS) for fast similarity search.
4. **Chat Inference:**
- When a customer sends a message, it's embedded and compared against stored chunks.
- The most relevant chunks are selected as **context**.
- The context is fed into OpenAI's **chat completion API** along with the user's question.
- A tailored, relevant answer is generated based on actual business documents.
> This allows Supportal to **answer domain-specific questions accurately**, beyond what a generic AI model can do.
---
## 🛠️ Tech Stack
- **Backend:** Django + Django Channels
- **Realtime Layer:** Redis (via `channels_redis`)
- **AI Engine:** OpenAI API (GPT + Embeddings)
- **Vector DB:** FAISS (in-memory vector search)
---
## 📦 Getting Started
### 🔧 Prerequisites
- Django 4.2.2
- Channels
- Celery
- Redis
- OpenAI API key
### 🧪 Installation
```bash
Installation guide will be added after publish to pypi
```
## 📄 License
This project is licensed under the **MIT License** – see the [LICENSE](./LICENSE) file for details.
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"description": "# \ud83e\udde0 Django Supportal \u2013 AI-Powered Business Support Chat APIs for django projects\n\n**Django Supportal** is an intelligent, AI-powered customer support system built with **Django**, **Django Channels**, and **OpenAI API**. \nIt provides APIs for businesses to upload their internal documents, and a smart assistant will handle customer inquiries via live chat \u2013 powered by a Retrieval-Augmented Generation (RAG) system.\n\n---\n\n## \ud83d\ude80 Features\n\n- \u2705 Real-time chat via **Django Channels (WebSockets)**\n- \ud83d\udcce Businesses can upload **PDF, DOCX, or TXT documents**\n- \ud83e\udd16 Uses **OpenAI GPT models** to provide intelligent responses\n- \ud83d\udcda Implements **RAG (Retrieval-Augmented Generation)** to process custom business knowledge\n- \ud83d\udd12 Secured communication and Redis-based event layer\n\n---\n\n## \ud83e\udde0 How it Works (RAG Architecture)\n\nSupportal uses a **Retrieval-Augmented Generation (RAG)** approach to enable AI to answer business-specific questions:\n\n1. **Document Upload:** \n Businesses upload documents such as FAQs, product guides, manuals, or policies.\n\n2. **Chunking & Embedding:** \n Uploaded documents are:\n - Split into smaller text chunks\n - Converted into **vector embeddings** using OpenAI's `text-embedding` models\n\n3. **Vector Storage:** \n Embeddings are stored in a **vector database** (like FAISS) for fast similarity search.\n\n4. **Chat Inference:**\n - When a customer sends a message, it's embedded and compared against stored chunks.\n - The most relevant chunks are selected as **context**.\n - The context is fed into OpenAI's **chat completion API** along with the user's question.\n - A tailored, relevant answer is generated based on actual business documents.\n\n> This allows Supportal to **answer domain-specific questions accurately**, beyond what a generic AI model can do.\n\n---\n\n## \ud83d\udee0\ufe0f Tech Stack\n\n- **Backend:** Django + Django Channels\n- **Realtime Layer:** Redis (via `channels_redis`)\n- **AI Engine:** OpenAI API (GPT + Embeddings)\n- **Vector DB:** FAISS (in-memory vector search)\n\n---\n\n## \ud83d\udce6 Getting Started\n\n### \ud83d\udd27 Prerequisites\n\n- Django 4.2.2\n- Channels\n- Celery\n- Redis\n- OpenAI API key\n\n### \ud83e\uddea Installation\n\n```bash\nInstallation guide will be added after publish to pypi\n```\n\n## \ud83d\udcc4 License\nThis project is licensed under the **MIT License** \u2013 see the [LICENSE](./LICENSE) file for details.\n",
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