release-2.0.0

.github/workflows/pipeline.yml

@@ -104,3 +104,31 @@ jobs:
         with:
           app-name: 'ragai'
           slot-name: 'Production'
+
+
+  # 4. React App Hosting to Cloudflare (depends on Azure Web App deployment success)
+  ui-deployment:
+    name: UI Deployment
+    runs-on: ubuntu-latest
+    needs: fastapi-deployment
+    if: github.ref == 'refs/heads/main'
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Install dependencies for React app
+        run: npm install
+        working-directory: ./client
+
+      - name: Build React app
+        run: npm run build
+        working-directory: ./client
+
+      - name: Deploy to Cloudflare Pages
+        uses: cloudflare/pages-action@v1
+        with:
+          apiToken: ${{ secrets.CLOUDFLARE_API_TOKEN }}
+          accountId: ${{ secrets.CLOUDFLARE_ACCOUNT_ID }}
+          projectName: 'ragbot'
+          directory: 'client/dist'

README.md

@@ -1 +1,107 @@
-# GenAIChatBot
+# 🤖 GENAI-RAG-BOT
+
+A production-ready Generative AI Assistant built with Retrieval-Augmented Generation (RAG) architecture using **FastAPI**, **ChromaDB**, and **LLMs** (e.g., Mistral). This bot answers domain-specific queries using a vector store and never hallucinates — it only replies using relevant documents from its database.
+
+---
+
+## 🚀 Features
+
+* 🔍 **Context-aware Retrieval** via [ChromaDB](https://www.trychroma.com/)
+* 📚 **LangChain Integration**: Seamlessly chains LLM calls with retrieval, enabling advanced workflows like summarization, Q&A, and document parsing. 
+* 📚 **Document Embeddings**: Store and retrieve documents in vector format using cohere (embed-english-v3.0)
+* 🧠 **LLM-Powered Responses** (e.g., Mistral via `mistral.ai`)
+* 🔐 Strict: Only responds using data retrieved from vector store
+* 💾 Source-aware: Supports citation-style responses
+* 🧼 Clean **FastAPI** backend with CORS support
+* ⚙️ Ready for deployment on Azure App Service
+* 📦 Modular Router + Configured Error Handlers + Health Endpoint
+
+---
+
+## 🧠 How It Works
+
+1. **User query** → `/api/chat`
+2. **Query embedding** → Compare with ChromaDB document embeddings
+3. **Retrieve top-k chunks** → Pass to the LLM as context
+4. **Generate response** → Only if relevant chunks exist
+5. **No relevant data?** → “Sorry, I don’t have any information about this.”
+
+---
+
+## ⚙️ Environment Variables
+
+Create a `.env` file:
+
+```env
+PORT=your-port
+HOST=http://localhost:5000
+CORS_URL=http://localhost:5173
+JWT_SECRET=your-secret
+MONGO_URL=your-mongo-url
+MISTRAL_MODEL_NAME=your-model-name
+MISTRAL_API_KEY=your-mistral-api-key
+COHERE_API_KEY=your-cohere-api-key
+```
+
+---
+
+## 💻 Local Development
+
+```bash
+# 1. Clone the repo
+git clone https://github.com/amankrs21/GENAI-RAG-BOT.git
+cd GENAI-RAG-BOT
+
+# 2. Create a virtual environment
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+# 3. Install dependencies
+pip install -r requirements.txt
+
+# 4. Run the server
+uvicorn main:app --reload
+```
+
+Then visit: [http://localhost:5000/health](http://localhost:5000/health)
+
+---
+
+## 🛡️ Agent Behavior
+
+This bot **does not hallucinate**.
+
+> It only replies based on retrieved chunks from the ChromaDB vector store.
+> If nothing relevant is found, it replies:
+> *“Sorry, I don’t have any information about this.”*
+
+---
+
+## 📦 Requirements
+
+* Python 3.9+
+* FastAPI
+* Uvicorn
+* ChromaDB
+* Requests
+* Uplinks for Mistral/LLM calls
+
+(See `requirements.txt` for full list)
+
+---
+
+## 📄 License
+
+This project is licensed under the Apache 2.0 License. See the [LICENSE](LICENSE) file for details.
+
+---
+
+## 🙋‍♂️ Author
+
+Built with ❤️ by [**@amankrs21**](https://github.com/amankrs21)
+
+---
+
+## ✨ Want to Contribute?
+
+PRs are welcome! This is just the beginning of RAG-native GenAI tooling.

ZZZ.txt

@@ -1,3 +1,11 @@
+--------------------------------------------------------------------
+WHILE DEPLOYEMENT ON AZURE WEB APP:
+** NAVIGATE TO > AZURE WEB APP > SETTINGS > CONFIGURATION **
+Then set the Startup command as-
+- gunicorn -w 4 -k uvicorn.workers.UvicornWorker main:app
+--------------------------------------------------------------------
+
+--------------------------------------------------------------------------------------
 GENAIBOT/
 ├── client/         # Contains all the UI (React)
 ├── src/
@@ -15,6 +23,4 @@ GENAIBOT/
 |
 |── main.py                  # FastAPI app
 └── requirements.txt
-
-
-TODO: Need to refreactor whole chroma DB Logic.
+--------------------------------------------------------------------------------------

client/index.html

@@ -12,6 +12,9 @@
       margin: 0;
       padding: 0;
       box-sizing: border-box;
+      scrollbar-width: thin;
+      scroll-behavior: smooth;
+      scrollbar-color: #757575 transparent;
     }
   </style>