diff --git a/endee-rag-ai-project/.gitignore b/endee-rag-ai-project/.gitignore
new file mode 100644
index 000000000..7f53211b4
--- /dev/null
+++ b/endee-rag-ai-project/.gitignore
@@ -0,0 +1,20 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Environment
+.env
+.venv
+env/
+venv/
+ENV/
+
+# Project specifics
+chroma_data/
+*.log
+*.pdf
+Vector_store.json
+
+# IDE
+.vscode/
diff --git a/endee-rag-ai-project/README.md b/endee-rag-ai-project/README.md
new file mode 100644
index 000000000..1db10127a
--- /dev/null
+++ b/endee-rag-ai-project/README.md
@@ -0,0 +1,151 @@
+# 🤖 AI Workspace (RAG & Recommendations)
+
+An AI-powered document assistant and dynamic e-commerce recommendation engine.
+This project demonstrates advanced **Retrieval-Augmented Generation (RAG)**, **Semantic Search**, and **Agentic Routing Workflows** paired with a beautiful glassmorphism User Interface.
+
+*Note: This project initially targeted the Endee vector database, but was adapted to use ChromaDB to support native execution on Windows without Docker environments while maintaining identical semantic search capabilities.*
+
+---
+
+## 🚀 Features
+
+- 📄 **Upload PDF Documents**: Ingest documents on the fly into the vector knowledge base.
+- 🔍 **Semantic Search**: Powered by `sentence-transformers`.
+- 🧠 **Agentic AI Workflow**: A smart Gemini-powered agent that dynamically routes user intents to either RAG retrieval, Recommendation generation, or general conversation.
+- 🛒 **Recommendation Engine**: Provides contextual item recommendations based on vector similarity.
+- ⚡ **FastAPI Backend**: High performance API serving both the LLM logic and the static asset UI.
+- 💬 **Interactive Premium UI**: A beautiful Vanilla HTML/CSS/JS frontend featuring dark-mode, glassmorphism, and markdown support.
+
+---
+
+## 🛠 Tech Stack
+
+- **Python** (Backend Core)
+- **FastAPI** & **Uvicorn** (Web framework and application server)
+- **Google GenAI** (Gemini 2.5 Flash for LLM reasoning and routing)
+- **ChromaDB** (Persistent native vector database)
+- **Sentence Transformers** (`all-MiniLM-L6-v2` for embeddings)
+- **PyPDF2** (Document parsing)
+- **HTML5, CSS3, JS** (Frontend Interface)
+
+---
+
+## 🏗 System Architecture
+
+```text
+User Input (Chat / File Upload)
+       │
+      UI Frontend (Vanilla JS + CSS)
+       │
+   FastAPI Backend
+       ├──> PDF -> PyPDF2 -> SentenceTransformer -> ChromaDB (Knowledge Base)
+       │
+   Agentic Router (Gemini)
+       ├───> Intent: RAG       -> Query ChromaDB (Knowledge DB) -> Gemini -> User
+       ├───> Intent: RECOMMEND -> Query ChromaDB (Products DB)  -> Gemini -> User
+       └───> Intent: CHAT      -> Gemini -> User
+```
+
+---
+
+## 📸 Project Screenshots & Demo
+
+### AI Workspace Complete UI
+![UI Application](images/ai-workspace-ui.png)
+
+### RAG & Recommendations In-Action
+![Demo Recording](images/demo-final.webp)
+
+### RAG Document Upload feature
+![RAG File Upload](images/rag-success.png)
+
+---
+
+## 📂 Project Structure
+
+```text
+endee-rag-ai-project
+│
+├── api/
+│   ├── main.py         # FastAPI application and endpoint routing
+│   ├── agent.py        # Gemini Agentic routing and response generation
+│   └── db_client.py    # ChromaDB integration and embedding logic
+│
+├── ui/
+│   ├── index.html      # Main application interface
+│   ├── style.css       # Premium Glassmorphism styling
+│   └── app.js          # Client-side API interactions and chat logic
+│
+├── chroma_data/        # Persistent vector database storage
+├── requirements.txt    # Python dependencies
+└── README.md           # Project documentation
+```
+
+---
+
+## ⚙ Installation
+
+### 1. Clone the repository
+```bash
+git clone https://github.com/ajaykumarhn/endee.git
+cd endee/endee-rag-ai-project
+```
+
+### 2. Install dependencies
+Ensure you have Python 3.9+ installed.
+```bash
+pip install fastapi uvicorn sentence-transformers chromadb google-genai pypdf python-multipart
+```
+
+### 3. Configure API Keys
+You need a Google Gemini API key to run the generative AI routing. Get one from [Google AI Studio](https://aistudio.google.com/).
+
+Set the environment variable in your terminal:
+
+**Windows (PowerShell):**
+```powershell
+$env:GEMINI_API_KEY="your_api_key_here"
+```
+
+**Linux/macOS:**
+```bash
+export GEMINI_API_KEY="your_api_key_here"
+```
+
+---
+
+## ▶ Running the Project
+
+Start the unified API server and UI:
+
+```bash
+python -m uvicorn api.main:app --host 0.0.0.0 --port 8000
+```
+
+Open your browser and navigate to:
+**http://localhost:8000**
+
+---
+
+## 💡 Usage Guide
+
+Once the application is running in your browser:
+
+1. **Test the Recommendation Engine**:
+   - Click the **"Seed Catalog"** button in the sidebar to populate the database with sample products.
+   - Ask the AI: *"I need a new monitor for coding."* The agent will query the database and generate a curated response.
+   
+2. **Test RAG (Document Q&A)**:
+   - Use the **"Upload a PDF to context"** section to upload a PDF file. (You can download and use the provided [sample_doc.pdf](sample_doc.pdf) for testing).
+   - Wait for the "Success" confirmation.
+   - Ask the AI a question about the contents of the document you uploaded, for example: *"What is the secret access code to the server room?"*
+
+3. **General Chat**:
+   - Ask the AI any general question like *"What is retrieval augmented generation?"* or *"Hello there!"*
+
+---
+
+## 👨‍💻 Author
+
+Ajay Kumar  
+GitHub: [https://github.com/ajaykumarhn](https://github.com/ajaykumarhn)
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diff --git a/endee-rag-ai-project/api/__init__.py b/endee-rag-ai-project/api/__init__.py
new file mode 100644
index 000000000..d7118f11d
--- /dev/null
+++ b/endee-rag-ai-project/api/__init__.py
@@ -0,0 +1 @@
+# Exposes api as a proper python module
diff --git a/endee-rag-ai-project/api/agent.py b/endee-rag-ai-project/api/agent.py
new file mode 100644
index 000000000..2729ea6f4
--- /dev/null
+++ b/endee-rag-ai-project/api/agent.py
@@ -0,0 +1,87 @@
+import os
+from google import genai
+from .db_client import query_collection
+
+# Configure API Key (we'll set this via env var or dynamically)
+client = genai.Client()
+
+def route_and_execute(user_message: str) -> str:
+    """
+    A simple ReAct/Router agent.
+    Decides whether the user is asking for:
+    1. A search/question about knowledge base (RAG)
+    2. A recommendation
+    3. General chat
+    """
+    
+    routing_prompt = f"""
+    You are an intelligent router. Analyze the user's message and determine the intent.
+    Respond ONLY with one of the following exact words:
+    RAG - if the user is asking a factual question that might be in a document/knowledge base.
+    RECOMMEND - if the user is asking for a suggestion, recommendation, or similar items.
+    CHAT - if it's a general greeting or conversation.
+    
+    User Message: "{user_message}"
+    """
+    
+    intent_response = client.models.generate_content(
+        model='gemini-2.5-flash',
+        contents=routing_prompt
+    ).text.strip().upper()
+    
+    if "RAG" in intent_response:
+        # 1. Retrieve Context
+        results = query_collection("knowledge_base", user_message, top_k=3)
+        context = ""
+        if results and results.get("documents") and results["documents"][0]:
+            context = "\n".join(results["documents"][0])
+            
+        # 2. Generate Answer
+        rag_prompt = f"""
+        You are an AI Assistant answering questions based on the provided context.
+        If the answer is not in the context, politely say you don't know based on the documents.
+        
+        Context:
+        {context}
+        
+        Question: {user_message}
+        """
+        answer = client.models.generate_content(
+            model='gemini-2.5-flash',
+            contents=rag_prompt
+        ).text
+        return f"**(RAG Search)**\n\n{answer}"
+        
+    elif "RECOMMEND" in intent_response:
+         # 1. Retrieve recommendations based on semantic similarity
+        results = query_collection("recommendations", user_message, top_k=3)
+        context = ""
+        if results and results.get("documents") and results["documents"][0]:
+             for doc, meta in zip(results["documents"][0], results["metadatas"][0]):
+                 title = meta.get('title', 'Unknown Item') if meta else 'Unknown Item'
+                 context += f"- **{title}**: {doc}\n"
+                 
+        rec_prompt = f"""
+        You are an AI Recommendation Engine. The user asked for a recommendation.
+        Below are the top matches from our database. Present them nicely to the user.
+        
+        Matches:
+        {context}
+        
+        User Request: {user_message}
+        """
+        answer = client.models.generate_content(
+            model='gemini-2.5-flash',
+            contents=rec_prompt
+        ).text
+        return f"**(Recommendation Engine)**\n\n{answer}"
+        
+    else: # CHAT
+        chat_prompt = f"""
+        You are a helpful AI Assistant. Respond to the user's message kindly.
+        User: {user_message}
+        """
+        return client.models.generate_content(
+            model='gemini-2.5-flash',
+            contents=chat_prompt
+        ).text
diff --git a/endee-rag-ai-project/api/db_client.py b/endee-rag-ai-project/api/db_client.py
new file mode 100644
index 000000000..486e53b30
--- /dev/null
+++ b/endee-rag-ai-project/api/db_client.py
@@ -0,0 +1,42 @@
+import chromadb
+from sentence_transformers import SentenceTransformer
+
+# We use ChromaDB as our local persistent datastore since Docker/Endee isn't available
+client = chromadb.PersistentClient(path="./chroma_data")
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+def get_or_create_collection(name: str):
+    return client.get_or_create_collection(name=name)
+
+def generate_embedding(text: str) -> list[float]:
+    """Generates embedding for a single string."""
+    return model.encode(text).tolist()
+
+def upsert_document(collection_name: str, doc_id: str, text: str, metadata: dict = None):
+    """Embeds and upserts a document into the ChromaDB collection."""
+    collection = get_or_create_collection(collection_name)
+    embedding = generate_embedding(text)
+    
+    collection.add(
+        ids=[doc_id],
+        embeddings=[embedding],
+        documents=[text],
+        metadatas=[metadata] if metadata else None
+    )
+
+def query_collection(collection_name: str, query: str, top_k: int = 3):
+    """Queries the collection using semantic similarity."""
+    collection = get_or_create_collection(collection_name)
+    query_embedding = generate_embedding(query)
+    
+    results = collection.query(
+        query_embeddings=[query_embedding],
+        n_results=top_k
+    )
+    
+    return results
+
+def get_all_documents(collection_name: str):
+    """Retrieves all documents from a collection."""
+    collection = get_or_create_collection(collection_name)
+    return collection.get()
diff --git a/endee-rag-ai-project/api/main.py b/endee-rag-ai-project/api/main.py
new file mode 100644
index 000000000..e3e114eb4
--- /dev/null
+++ b/endee-rag-ai-project/api/main.py
@@ -0,0 +1,83 @@
+from fastapi import FastAPI, UploadFile, File, Form, HTTPException
+from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+import uuid
+import os
+
+from api.db_client import upsert_document
+from api.agent import route_and_execute
+import PyPDF2
+
+app = FastAPI(title="AI Document & Recommendation Assistant")
+
+# Mount UI static files
+os.makedirs("ui", exist_ok=True)
+app.mount("/ui", StaticFiles(directory="ui"), name="ui")
+
+class ChatRequest(BaseModel):
+    message: str
+
+@app.get("/", response_class=HTMLResponse)
+async def serve_ui():
+    """Serve the main UI page."""
+    with open("ui/index.html", "r", encoding="utf-8") as f:
+        return HTMLResponse(content=f.read())
+
+
+@app.post("/api/chat")
+async def chat_endpoint(req: ChatRequest):
+    """Agentic Chat endpoint."""
+    try:
+        response = route_and_execute(req.message)
+        return {"response": response}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/api/upload")
+async def upload_pdf(file: UploadFile = File(...)):
+    """Ingest a PDF document into the knowledge base."""
+    if not file.filename.endswith(".pdf"):
+         raise HTTPException(status_code=400, detail="Only PDF files are supported.")
+         
+    try:
+        pdf_reader = PyPDF2.PdfReader(file.file)
+        text = ""
+        for page in pdf_reader.pages:
+            text += page.extract_text() + "\n"
+            
+        doc_id = str(uuid.uuid4())
+        # In a real app we'd chunk this text, but for demo we just store it
+        upsert_document(
+            collection_name="knowledge_base",
+            doc_id=doc_id,
+            text=text,
+            metadata={"source": file.filename}
+        )
+        return {"message": "PDF ingested successfully! You can now ask questions about it.", "id": doc_id}
+    except Exception as e:
+         raise HTTPException(status_code=500, detail=f"Failed to process PDF: {str(e)}")
+
+
+@app.post("/api/seed_recommendations")
+async def seed_recommendations():
+    """Seeds some dummy items for the recommendation functionality."""
+    items = [
+        {"id": "rec_1", "title": "Ergonomic Office Chair", "desc": "A comfortable mesh chair with lumbar support for long working hours."},
+        {"id": "rec_2", "title": "Mechanical Keyboard", "desc": "Wireless mechanical keyboard with tactile brown switches, great for typing."},
+        {"id": "rec_3", "title": "Noise Cancelling Headphones", "desc": "Over-ear headphones with active noise cancellation and 30-hour battery life."},
+        {"id": "rec_4", "title": "4K Monitor", "desc": "27-inch 4K IPS monitor with vibrant colors, perfect for designers and coders."}
+    ]
+    
+    try:
+        for item in items:
+            upsert_document(
+                collection_name="recommendations",
+                doc_id=item["id"],
+                text=item["desc"],
+                metadata={"title": item["title"]}
+            )
+        return {"message": "Recommendation Engine seeded with sample products!"}
+    except Exception as e:
+         raise HTTPException(status_code=500, detail=str(e))
+
diff --git a/endee-rag-ai-project/app.py b/endee-rag-ai-project/app.py
new file mode 100644
index 000000000..353c14345
--- /dev/null
+++ b/endee-rag-ai-project/app.py
@@ -0,0 +1,16 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from rag_pipeline import generate_answer
+
+app = FastAPI()
+
+class Question(BaseModel):
+    question: str
+    context: str = ""
+
+@app.post("/ask")
+def ask_question(data: Question):
+
+    answer = generate_answer(data.question, data.context)
+
+    return {"answer": answer}
\ No newline at end of file
diff --git a/endee-rag-ai-project/chat_ui.py b/endee-rag-ai-project/chat_ui.py
new file mode 100644
index 000000000..2abc17b94
--- /dev/null
+++ b/endee-rag-ai-project/chat_ui.py
@@ -0,0 +1,36 @@
+import streamlit as st
+import requests
+from pdf_loader import load_pdf
+
+st.title("📄 AI Document Assistant")
+
+uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
+
+document_text = ""
+
+if uploaded_file:
+    document_text = load_pdf(uploaded_file)
+    st.success("PDF loaded successfully!")
+
+question = st.text_input("Ask a question about the document")
+
+if st.button("Ask AI"):
+
+    if question == "":
+        st.warning("Please enter a question")
+
+    else:
+        payload = {
+            "question": question,
+            "context": document_text
+        }
+
+        response = requests.post(
+            "http://127.0.0.1:8000/ask",
+            json=payload
+        )
+
+        if response.status_code == 200:
+            st.success(response.json()["answer"])
+        else:
+            st.error("Error from API")
\ No newline at end of file
diff --git a/endee-rag-ai-project/embeddings.py b/endee-rag-ai-project/embeddings.py
new file mode 100644
index 000000000..1f668643b
--- /dev/null
+++ b/endee-rag-ai-project/embeddings.py
@@ -0,0 +1,23 @@
+from sentence_transformers import SentenceTransformer
+from endee_db import store_embeddings
+
+model = SentenceTransformer("paraphrase-MiniLM-L3-v2")
+
+def load_documents():
+    with open("data/documents.txt","r") as f:
+        docs = f.readlines()
+
+    return [d.strip() for d in docs]
+
+
+def create_embeddings():
+
+    docs = load_documents()
+
+    vectors = model.encode(docs)
+
+    store_embeddings(vectors, docs)
+
+
+if __name__ == "__main__":
+    create_embeddings()
\ No newline at end of file
diff --git a/endee-rag-ai-project/endee_db.py b/endee-rag-ai-project/endee_db.py
new file mode 100644
index 000000000..4005990a1
--- /dev/null
+++ b/endee-rag-ai-project/endee_db.py
@@ -0,0 +1,26 @@
+import json
+import os
+
+DB_FILE = "vector_store.json"
+
+def store_embeddings(vectors, docs):
+
+    data = []
+
+    for i in range(len(docs)):
+        data.append({
+            "vector": vectors[i].tolist(),
+            "text": docs[i]
+        })
+
+    with open(DB_FILE, "w") as f:
+        json.dump(data, f)
+
+
+def load_vectors():
+
+    if not os.path.exists(DB_FILE):
+        return []
+
+    with open(DB_FILE, "r") as f:
+        return json.load(f)
\ No newline at end of file
diff --git a/endee-rag-ai-project/images/ai-workspace-ui.png b/endee-rag-ai-project/images/ai-workspace-ui.png
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diff --git a/endee-rag-ai-project/images/rag-demo-final.webp b/endee-rag-ai-project/images/rag-demo-final.webp
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diff --git a/endee-rag-ai-project/images/rag-success.png b/endee-rag-ai-project/images/rag-success.png
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diff --git a/endee-rag-ai-project/pdf_loader.py b/endee-rag-ai-project/pdf_loader.py
new file mode 100644
index 000000000..7ef58e2af
--- /dev/null
+++ b/endee-rag-ai-project/pdf_loader.py
@@ -0,0 +1,10 @@
+from pypdf import PdfReader
+
+def load_pdf(file):
+    reader = PdfReader(file)
+    text = ""
+
+    for page in reader.pages:
+        text += page.extract_text()
+
+    return text
\ No newline at end of file
diff --git a/endee-rag-ai-project/rag_pipeline.py b/endee-rag-ai-project/rag_pipeline.py
new file mode 100644
index 000000000..f4d521206
--- /dev/null
+++ b/endee-rag-ai-project/rag_pipeline.py
@@ -0,0 +1,11 @@
+from search import search
+
+def generate_answer(question, context=""):
+
+    docs = search(question)
+
+    combined_context = context + " " + " ".join(docs)
+
+    answer = f"Based on the document: {combined_context}"
+
+    return answer
\ No newline at end of file
diff --git a/endee-rag-ai-project/requirements.txt b/endee-rag-ai-project/requirements.txt
new file mode 100644
index 000000000..176a6c0a1
--- /dev/null
+++ b/endee-rag-ai-project/requirements.txt
@@ -0,0 +1,4 @@
+sentence-transformers
+fastapi
+uvicorn
+numpy
\ No newline at end of file
diff --git a/endee-rag-ai-project/search.py b/endee-rag-ai-project/search.py
new file mode 100644
index 000000000..0da691815
--- /dev/null
+++ b/endee-rag-ai-project/search.py
@@ -0,0 +1,22 @@
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from endee_db import load_vectors
+
+model = SentenceTransformer("paraphrase-MiniLM-L3-v2")
+
+def search(query, top_k=2):
+
+    query_vector = model.encode(query)
+
+    data = load_vectors()
+
+    vectors = [d["vector"] for d in data]
+    docs = [d["text"] for d in data]
+
+    vectors = np.array(vectors)
+
+    similarity = np.dot(vectors, query_vector)
+
+    top_indices = similarity.argsort()[-top_k:][::-1]
+
+    return [docs[i] for i in top_indices]
\ No newline at end of file
diff --git a/endee-rag-ai-project/ui/app.js b/endee-rag-ai-project/ui/app.js
new file mode 100644
index 000000000..5f5a2aba4
--- /dev/null
+++ b/endee-rag-ai-project/ui/app.js
@@ -0,0 +1,161 @@
+document.addEventListener("DOMContentLoaded", () => {
+    const chatForm = document.getElementById("chat-form");
+    const userInput = document.getElementById("user-input");
+    const chatBox = document.getElementById("chat-box");
+    
+    const uploadBtn = document.getElementById("upload-btn");
+    const pdfUpload = document.getElementById("pdf-upload");
+    const uploadStatus = document.getElementById("upload-status");
+    
+    const seedBtn = document.getElementById("seed-btn");
+    const seedStatus = document.getElementById("seed-status");
+
+    marked.setOptions({ breaks: true });
+
+    function appendMessage(text, isUser = false) {
+        const msgDiv = document.createElement("div");
+        msgDiv.className = `message ${isUser ? 'user-message' : 'ai-message'}`;
+        
+        const avatar = document.createElement("div");
+        avatar.className = "avatar";
+        avatar.textContent = isUser ? "U" : "AI";
+        
+        const content = document.createElement("div");
+        content.className = "content";
+        
+        if (isUser) {
+            content.textContent = text;
+        } else {
+            content.innerHTML = marked.parse(text);
+        }
+
+        msgDiv.appendChild(avatar);
+        msgDiv.appendChild(content);
+        chatBox.appendChild(msgDiv);
+        
+        // Scroll to bottom
+        chatBox.scrollTop = chatBox.scrollHeight;
+    }
+
+    function showTypingIndicator() {
+        const id = 'typing-' + Date.now();
+        const msgDiv = document.createElement("div");
+        msgDiv.className = "message ai-message";
+        msgDiv.id = id;
+        
+        msgDiv.innerHTML = `
+            <div class="avatar">AI</div>
+            <div class="content">
+                <div class="typing-indicator">
+                    <div class="dot"></div>
+                    <div class="dot"></div>
+                    <div class="dot"></div>
+                </div>
+            </div>
+        `;
+        chatBox.appendChild(msgDiv);
+        chatBox.scrollTop = chatBox.scrollHeight;
+        return id;
+    }
+
+    function removeTypingIndicator(id) {
+        const el = document.getElementById(id);
+        if (el) {
+            el.remove();
+        }
+    }
+
+    chatForm.addEventListener("submit", async (e) => {
+        e.preventDefault();
+        const text = userInput.value.trim();
+        if (!text) return;
+
+        appendMessage(text, true);
+        userInput.value = "";
+        
+        const typingId = showTypingIndicator();
+
+        try {
+            const response = await fetch("/api/chat", {
+                method: "POST",
+                headers: { "Content-Type": "application/json" },
+                body: JSON.stringify({ message: text })
+            });
+            
+            removeTypingIndicator(typingId);
+            
+            if (!response.ok) throw new Error("API request failed");
+            
+            const data = await response.json();
+            appendMessage(data.response);
+            
+        } catch (error) {
+            removeTypingIndicator(typingId);
+            appendMessage("**Error:** Could not connect to the Agent API.");
+            console.error(error);
+        }
+    });
+
+    uploadBtn.addEventListener("click", async () => {
+        const file = pdfUpload.files[0];
+        if (!file) {
+            uploadStatus.textContent = "Please select a file first.";
+            uploadStatus.className = "error";
+            return;
+        }
+
+        const formData = new FormData();
+        formData.append("file", file);
+
+        uploadBtn.textContent = "Uploading...";
+        uploadBtn.disabled = true;
+
+        try {
+            const response = await fetch("/api/upload", {
+                method: "POST",
+                body: formData
+            });
+            
+            const data = await response.json();
+            
+            if (response.ok) {
+                uploadStatus.textContent = "Success!";
+                uploadStatus.className = "success";
+                appendMessage(`*System:* Uploaded document **${file.name}** to Knowledge Base. You can now talk to it.`);
+            } else {
+                throw new Error(data.detail || "Upload failed");
+            }
+        } catch (error) {
+            uploadStatus.textContent = error.message;
+            uploadStatus.className = "error";
+        } finally {
+            uploadBtn.textContent = "Upload Document";
+            uploadBtn.disabled = false;
+            pdfUpload.value = ""; // clear input
+        }
+    });
+
+    seedBtn.addEventListener("click", async () => {
+        seedBtn.textContent = "Seeding...";
+        seedBtn.disabled = true;
+
+        try {
+            const response = await fetch("/api/seed_recommendations", { method: "POST" });
+            const data = await response.json();
+            
+            if (response.ok) {
+                seedStatus.textContent = "Success!";
+                seedStatus.className = "success";
+                appendMessage("*System:* Initialized recommendation engine with sample product catalog.");
+            } else {
+                throw new Error("Failed to seed");
+            }
+        } catch (error) {
+            seedStatus.textContent = "Error occurred";
+            seedStatus.className = "error";
+        } finally {
+            seedBtn.textContent = "Seed Catalog";
+            seedBtn.disabled = false;
+        }
+    });
+});
diff --git a/endee-rag-ai-project/ui/index.html b/endee-rag-ai-project/ui/index.html
new file mode 100644
index 000000000..5a179f3ad
--- /dev/null
+++ b/endee-rag-ai-project/ui/index.html
@@ -0,0 +1,69 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>AI Workspace - RAG & Recommendations</title>
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600&family=Outfit:wght@400;600;700&display=swap" rel="stylesheet">
+    <link rel="stylesheet" href="/ui/style.css">
+</head>
+<body>
+    <div class="app-container">
+        
+        <!-- Sidebar Navigation -->
+        <aside class="sidebar">
+            <div class="logo">
+                <h2>✨ AI<span>Workspace</span></h2>
+            </div>
+            
+            <div class="menu">
+                <h3>KNOWLEDGE BASE</h3>
+                
+                <div class="upload-section glass-panel">
+                    <p>Upload a PDF to context</p>
+                    <input type="file" id="pdf-upload" accept=".pdf" />
+                    <button id="upload-btn" class="btn primary">Upload Document</button>
+                    <div id="upload-status"></div>
+                </div>
+
+                <div class="recommend-section glass-panel mt-4">
+                    <p>Demo Store</p>
+                    <button id="seed-btn" class="btn secondary">Seed Catalog</button>
+                    <div id="seed-status"></div>
+                </div>
+            </div>
+        </aside>
+
+        <!-- Main Chat Area -->
+        <main class="chat-area">
+            <header class="chat-header">
+                <div>
+                    <h1>Intelligent Assistant</h1>
+                    <p>Try asking questions about your uploaded documents, or ask for product recommendations!</p>
+                </div>
+            </header>
+
+            <div class="messages-container" id="chat-box">
+                <!-- Chat messages will be dynamically injected here -->
+                <div class="message ai-message">
+                    <div class="avatar">AI</div>
+                    <div class="content">Hello! I am your AI assistant powered by Gemini. Upload a PDF and ask me questions about it (RAG), or ask me for a product recommendation!</div>
+                </div>
+            </div>
+
+            <div class="input-area">
+                <form id="chat-form" class="chat-form glass-panel">
+                    <input type="text" id="user-input" placeholder="Ask a question or request a recommendation..." autocomplete="off">
+                    <button type="submit" class="btn send-btn">
+                        <svg width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M22 2L11 13M22 2l-7 20-4-9-9-4 20-7z"/></svg>
+                    </button>
+                </form>
+            </div>
+        </main>
+    </div>
+
+    <!-- Marked JS for parsing markdown to HTML in the chat -->
+    <script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
+    <script src="/ui/app.js"></script>
+</body>
+</html>
diff --git a/endee-rag-ai-project/ui/style.css b/endee-rag-ai-project/ui/style.css
new file mode 100644
index 000000000..f8c23ba71
--- /dev/null
+++ b/endee-rag-ai-project/ui/style.css
@@ -0,0 +1,317 @@
+:root {
+    --bg-main: #0b1120;
+    --bg-panel: rgba(30, 41, 59, 0.7);
+    --border-color: rgba(255, 255, 255, 0.1);
+    --text-primary: #f8fafc;
+    --text-secondary: #94a3b8;
+    --accent-primary: #3b82f6;
+    --accent-glow: rgba(59, 130, 246, 0.5);
+    --gradient-brand: linear-gradient(135deg, #3b82f6 0%, #8b5cf6 100%);
+    --radius-sm: 8px;
+    --radius-md: 12px;
+    --radius-lg: 16px;
+}
+
+* {
+    margin: 0;
+    padding: 0;
+    box-sizing: border-box;
+}
+
+body {
+    font-family: 'Inter', sans-serif;
+    background-color: var(--bg-main);
+    color: var(--text-primary);
+    min-height: 100vh;
+    display: flex;
+    overflow: hidden;
+}
+
+/* Glassmorphism utility */
+.glass-panel {
+    background: var(--bg-panel);
+    backdrop-filter: blur(12px);
+    -webkit-backdrop-filter: blur(12px);
+    border: 1px solid var(--border-color);
+    border-radius: var(--radius-md);
+    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
+}
+
+.app-container {
+    display: flex;
+    width: 100vw;
+    height: 100vh;
+}
+
+/* Sidebar */
+.sidebar {
+    width: 300px;
+    background: rgba(15, 23, 42, 0.9);
+    border-right: 1px solid var(--border-color);
+    padding: 1.5rem;
+    display: flex;
+    flex-direction: column;
+}
+
+.logo h2 {
+    font-family: 'Outfit', sans-serif;
+    font-size: 1.5rem;
+    font-weight: 700;
+    letter-spacing: -0.5px;
+    margin-bottom: 2rem;
+    background: var(--gradient-brand);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+}
+
+.logo span {
+    color: var(--text-primary);
+    -webkit-text-fill-color: initial;
+    font-weight: 400;
+}
+
+.menu h3 {
+    font-size: 0.75rem;
+    text-transform: uppercase;
+    color: var(--text-secondary);
+    letter-spacing: 1px;
+    margin-bottom: 1rem;
+}
+
+.upload-section, .recommend-section {
+    padding: 1rem;
+    margin-bottom: 1rem;
+}
+
+.upload-section p, .recommend-section p {
+    font-size: 0.875rem;
+    color: var(--text-secondary);
+    margin-bottom: 0.5rem;
+}
+
+input[type="file"] {
+    width: 100%;
+    font-size: 0.8rem;
+    margin-bottom: 1rem;
+    color: var(--text-secondary);
+}
+
+/* Buttons */
+.btn {
+    width: 100%;
+    padding: 0.6rem 1rem;
+    border: none;
+    border-radius: var(--radius-sm);
+    font-family: 'Inter', sans-serif;
+    font-size: 0.875rem;
+    font-weight: 500;
+    cursor: pointer;
+    transition: all 0.2s ease;
+}
+
+.btn.primary {
+    background: var(--gradient-brand);
+    color: white;
+    box-shadow: 0 0 10px rgba(139, 92, 246, 0.3);
+}
+
+.btn.primary:hover {
+    box-shadow: 0 0 15px rgba(139, 92, 246, 0.6);
+    transform: translateY(-1px);
+}
+
+.btn.secondary {
+    background: rgba(255, 255, 255, 0.1);
+    color: var(--text-primary);
+    border: 1px solid var(--border-color);
+}
+
+.btn.secondary:hover {
+    background: rgba(255, 255, 255, 0.15);
+}
+
+/* Chat Area */
+.chat-area {
+    flex: 1;
+    display: flex;
+    flex-direction: column;
+    position: relative;
+    background: radial-gradient(circle at top right, rgba(59, 130, 246, 0.1), transparent 40%),
+                radial-gradient(circle at bottom left, rgba(139, 92, 246, 0.1), transparent 40%);
+}
+
+.chat-header {
+    padding: 1.5rem 2rem;
+    border-bottom: 1px solid var(--border-color);
+    backdrop-filter: blur(10px);
+    z-index: 10;
+}
+
+.chat-header h1 {
+    font-family: 'Outfit', sans-serif;
+    font-size: 1.25rem;
+    font-weight: 600;
+}
+
+.chat-header p {
+    font-size: 0.875rem;
+    color: var(--text-secondary);
+    margin-top: 0.25rem;
+}
+
+.messages-container {
+    flex: 1;
+    overflow-y: auto;
+    padding: 2rem;
+    display: flex;
+    flex-direction: column;
+    gap: 1.5rem;
+    scroll-behavior: smooth;
+}
+
+.message {
+    display: flex;
+    gap: 1rem;
+    max-width: 80%;
+    animation: fadeIn 0.3s ease forwards;
+}
+
+.message.user-message {
+    align-self: flex-end;
+    flex-direction: row-reverse;
+}
+
+.avatar {
+    width: 36px;
+    height: 36px;
+    border-radius: 50%;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    font-size: 0.75rem;
+    font-weight: 600;
+    flex-shrink: 0;
+}
+
+.ai-message .avatar {
+    background: var(--gradient-brand);
+    color: white;
+}
+
+.user-message .avatar {
+    background: var(--bg-panel);
+    border: 1px solid var(--border-color);
+}
+
+.content {
+    background: var(--bg-panel);
+    padding: 1rem 1.25rem;
+    border-radius: var(--radius-lg);
+    border: 1px solid var(--border-color);
+    font-size: 0.95rem;
+    line-height: 1.5;
+}
+
+.user-message .content {
+    background: rgba(59, 130, 246, 0.1);
+    border-color: rgba(59, 130, 246, 0.3);
+}
+
+/* Markdown styling within messages */
+.content ul, .content ol { margin-left: 1.5rem; margin-top: 0.5rem; }
+.content p { margin-bottom: 0.5rem; }
+.content p:last-child { margin-bottom: 0; }
+.content strong { color: var(--accent-primary); }
+
+.input-area {
+    padding: 1.5rem 2rem;
+    background: linear-gradient(to top, var(--bg-main) 50%, transparent);
+}
+
+.chat-form {
+    display: flex;
+    padding: 0.5rem;
+    border-radius: var(--radius-lg);
+    transition: border-color 0.2s;
+}
+
+.chat-form:focus-within {
+    border-color: var(--accent-primary);
+    box-shadow: 0 0 0 1px var(--accent-primary);
+}
+
+#user-input {
+    flex: 1;
+    background: transparent;
+    border: none;
+    padding: 0.75rem 1rem;
+    color: var(--text-primary);
+    font-size: 1rem;
+    outline: none;
+    font-family: 'Inter', sans-serif;
+}
+
+#user-input::placeholder {
+    color: var(--text-secondary);
+}
+
+.send-btn {
+    width: 44px;
+    height: 44px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    border-radius: var(--radius-md);
+    background: var(--text-primary);
+    color: var(--bg-main);
+}
+
+.send-btn:hover {
+    background: #e2e8f0;
+    transform: scale(1.05);
+}
+
+.mt-4 { margin-top: 1rem; }
+
+/* Status message styling */
+#upload-status, #seed-status {
+    font-size: 0.75rem;
+    margin-top: 0.5rem;
+    text-align: center;
+}
+.success { color: #10b981; }
+.error { color: #ef4444; }
+
+/* Typing Indicator Animation */
+.typing-indicator {
+    display: flex;
+    gap: 4px;
+    padding: 0.5rem 0;
+}
+
+.dot {
+    width: 6px;
+    height: 6px;
+    background: var(--text-secondary);
+    border-radius: 50%;
+    animation: bounce 1.4s infinite ease-in-out;
+}
+
+.dot:nth-child(1) { animation-delay: -0.32s; }
+.dot:nth-child(2) { animation-delay: -0.16s; }
+
+@keyframes bounce {
+    0%, 80%, 100% { transform: scale(0); }
+    40% { transform: scale(1); }
+}
+
+@keyframes fadeIn {
+    from { opacity: 0; transform: translateY(10px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+
+/* Custom Scrollbar */
+::-webkit-scrollbar { width: 6px; }
+::-webkit-scrollbar-track { background: transparent; }
+::-webkit-scrollbar-thumb { background: rgba(255, 255, 255, 0.1); border-radius: 10px; }
+::-webkit-scrollbar-thumb:hover { background: rgba(255, 255, 255, 0.2); }
diff --git a/endee-rag-ai-project/vector_store.json b/endee-rag-ai-project/vector_store.json
new file mode 100644
index 000000000..cececa1d7
--- /dev/null
+++ b/endee-rag-ai-project/vector_store.json
@@ -0,0 +1 @@
+[{"vector": [-0.14913879334926605, -0.5016270875930786, -0.2291143238544464, 0.01783885806798935, -0.1277567595243454, 0.04605134204030037, 0.4160287380218506, -0.12702196836471558, -0.022856665775179863, 0.29726266860961914, -0.006386343855410814, -0.22193573415279388, 0.23846274614334106, -0.13005460798740387, 0.013296976685523987, 0.050532493740320206, 0.27827516198158264, 0.2915825843811035, -0.24202173948287964, -0.2823900878429413, 0.28173020482063293, -0.31692633032798767, -0.3469747304916382, 0.12841208279132843, -0.152260884642601, -0.06019756570458412, 0.26967060565948486, -0.08699342608451843, 0.28514766693115234, -0.10807682573795319, -0.12684878706932068, -0.024091342464089394, 0.058813292533159256, 0.05372575670480728, -0.08807405829429626, 0.47467607259750366, 0.02765534073114395, 0.14146797358989716, -0.06962898373603821, -0.1019401103258133, -0.10538260638713837, -0.03523661196231842, -0.15752822160720825, 0.08872122317552567, 0.1848350167274475, -0.34282293915748596, -0.25766661763191223, -0.02813195250928402, 0.24022610485553741, -0.22547408938407898, 0.16044707596302032, 0.09874038398265839, -0.40280747413635254, -0.08506698161363602, -0.03506084159016609, 0.39985212683677673, 0.06496714055538177, -0.07630014419555664, -0.17811714112758636, -0.17395512759685516, 0.21092623472213745, -0.12583857774734497, 0.06341269612312317, 0.2556164264678955, 0.12740501761436462, 0.20392613112926483, 0.19693012535572052, -0.23101308941841125, -0.024700069800019264, -0.29101431369781494, 0.5943127870559692, -0.18559564650058746, -0.1476491093635559, 0.0343339629471302, 0.07389810681343079, 0.27577516436576843, -0.05446309596300125, -0.20817174017429352, -0.16534659266471863, 0.16622763872146606, -0.12087670713663101, 0.2005215436220169, -0.1326930820941925, 0.3578295409679413, -0.1059117391705513, 0.13117289543151855, 0.006750313565135002, 0.09700444340705872, 0.1483585089445114, -0.16160304844379425, 0.021803613752126694, -0.10879692435264587, 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