feat: Deploy NEXUS E-commerce RAG Application

.gitattributes

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Dockerfile

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-FROM python:3.13.5-slim
-
-WORKDIR /app
-
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
-
-RUN pip3 install -r requirements.txt
-
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

README.md

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----
-title: Nexus E Commerce
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: NEXUS E-Commerce Customer Support Chatbot
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+# NEXUS E-Commerce Customer Support Chatbot
+
+A simple AI chatbot for e-commerce customer support using RAG (Retrieval-Augmented Generation).
+
+## What it does
+
+This chatbot answers customer questions by searching through your product documentation and knowledge base. It uses AI models like Gemini or Groq to provide helpful responses in real-time.
+
+**Note**: All data used in this project is AI-generated and fake - created for demonstration purposes only.
+
+## Features
+
+- Chat interface built with Streamlit
+- Supports multiple AI models (Gemini, Groq)
+- Searches through PDF documents to find relevant answers
+- Real-time streaming responses
+- Easy to set up and customize
+
+## Getting Started
+
+1. Clone this repository
+2. Install the required packages:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Create a `.env` file with your API keys:
+   ```
+   GROQ_API_KEY=your_key_here
+   GEMINI_API_KEY=your_key_here
+   ```
+
+4. Run the app:
+   ```bash
+   streamlit run app.py
+   ```
+
+5. Open your browser to `http://localhost:8501`
+
+## How it works
+
+The system has a few main parts:
+
+- **Document Processing**: Loads and splits PDF files into searchable chunks
+- **Vector Search**: Creates embeddings to find relevant information quickly  
+- **AI Agent**: Uses the retrieved information to generate helpful responses
+- **Chat Interface**: Simple web interface for conversations
+
+## Files
+
+- `app.py` - Main Streamlit application
+- `rag_setup.py` - Handles document loading and vector store creation
+- `agent_setup.py` - Configures the AI agents
+- `tools.py` - Search functionality
+- `runner.py` - Processes queries and streams responses
+
+## Customization
+
+You can easily modify:
+- Add new AI models in the settings
+- Update the knowledge base by adding new PDF documents
+- Change the chat interface styling
+- Adjust how documents are processed and searched
+
+## Requirements
+
+- Python 3.8+
+- API key for Gemini or Groq
+- PDF documents for your knowledge base
+
+That's it! The system is designed to be simple and straightforward to use.

agent_setup.py

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+from agents import AsyncOpenAI,set_default_openai_client,set_default_openai_api,set_tracing_disabled,Agent
+
+def set_sdk_client(api_key: str, base_url: str):
+    """
+    Set the Agents SDK client dynamically based on user input (Gemini or Groq).
+    """
+    client = AsyncOpenAI(api_key=api_key, base_url=base_url)
+    set_tracing_disabled(disabled=True)
+    set_default_openai_client(client)
+    set_default_openai_api("chat_completions")
+
+# -----------------------------
+
+def create_agent(model_name, search_tool):
+    """
+    Create an Agent with the specified model and search tool.
+    """
+    agent = Agent(
+        name="NexusCustomerSupport",
+        model=model_name,
+        instructions = (
+            "You are a helpful assistant specialized in answering questions strictly based on the NEXUS document. "
+            "Always use the search_docs tool to retrieve relevant information from the document before responding. "
+            "If the user’s query is outside the scope of the document, politely state that you cannot provide an answer."
+        ),
+        tools=[search_tool],
+    )
+    return agent

app.py

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+import streamlit as st
+import asyncio
+import os
+from dotenv import load_dotenv
+from rag_setup import load_and_split_pdf, build_vectorstore
+from tools import create_search_tool
+from agent_setup import create_agent, set_sdk_client
+from runner import run_query
+
+# Load environment variables
+load_dotenv()
+groq_key = os.getenv("GROQ_API_KEY")
+gemini_key = os.getenv("GEMINI_API_KEY")
+
+# Streamlit UI
+def main():
+    st.set_page_config(page_title="NEXUS E-COMMERCE - Customer Support Chatbot", page_icon="🛒", layout="wide")
+    
+    st.markdown("""
+    <style>
+    .stChatMessage.user {background:`#dbeafe`;border-radius:12px;padding:8px;}
+    .stChatMessage.assistant {background:`#f1f5f9`;border-radius:12px;padding:8px;}
+    </style>
+    """, unsafe_allow_html=True)
+    
+    st.title("🛒 NEXUS E-COMMERCE")
+    st.subheader("🤖 Customer Support Chatbot")
+    
+    # Sidebar: Settings
+   
+    st.sidebar.header("⚙️ Settings")
+    model_choice = st.sidebar.radio("Model Provider", ["Gemini", "Groq"])
+    
+    DEFAULT_KEYS = {
+        "Gemini": {"api_key": gemini_key, "base_url": "https://generativelanguage.googleapis.com/v1beta/openai/", "model": "gemini-2.5-flash"},
+        "Groq": {"api_key": groq_key, "base_url": "https://api.groq.com/openai/v1", "model": "llama-3.3-70b-versatile"}
+    }
+    
+    api_key = st.sidebar.text_input("🔑 Enter API Key (or leave blank for default)", type="password")
+    model_name = st.sidebar.text_input("🧠 Model Name", value=DEFAULT_KEYS[model_choice]["model"])
+    
+    
+    # Knowledge Base (persistent across app run)
+    
+    if "retriever" not in st.session_state:
+        docs = load_and_split_pdf()
+        st.session_state.retriever = build_vectorstore(docs)
+    
+    search_tool = create_search_tool(st.session_state.retriever)
+    
+    # Client + Agent
+    final_api_key = api_key if api_key else DEFAULT_KEYS[model_choice]["api_key"]
+    set_sdk_client(final_api_key, DEFAULT_KEYS[model_choice]["base_url"])
+    agent = create_agent(model_name, search_tool)
+    
+    # Chat Interface
+
+    user_input = st.chat_input("💬 Ask something about NEXUS E-COMMERCE...")
+    
+    if user_input:
+        with st.chat_message("user"):
+            st.write(user_input)
+        
+        with st.chat_message("assistant"):
+            response_container = st.empty()
+            full_response_parts = []
+            
+            async def fetch():
+                async for chunk in run_query(agent, user_input):
+                    full_response_parts.append(chunk)
+                    response_container.write("".join(full_response_parts))
+            
+            asyncio.run(fetch())
+
+if __name__ == "__main__":
+    main()

rag_setup.py

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+from langchain_community.document_loaders import PyMuPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from datasets import load_dataset
+import tempfile
+import shutil
+
+def load_pdf():
+    # Load dataset
+    dataset = load_dataset("sadaqatyar/NEXUS")
+    
+    # Get the PDF file
+    pdf_file = dataset["train"][0]["file"]
+    
+    # Create temp file and copy content
+    temp_pdf = tempfile.NamedTemporaryFile(delete=False, suffix='.pdf')
+    shutil.copyfileobj(open(pdf_file, 'rb'), temp_pdf)
+    temp_pdf.close()
+    
+    return temp_pdf.name
+
+# Usage
+pdf_path = load_pdf() # if it's a single PDF
+
+def load_and_split_pdf(pdf_path=pdf_path):
+    loader = PyMuPDFLoader(pdf_path)
+    pages = loader.load()
+
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=3000,
+        chunk_overlap=100,
+        separators=["\n\n", "\n", ".", " "]
+    )
+    return splitter.split_documents(pages)
+
+
+
+def build_vectorstore(docs):
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+    vectorstore = FAISS.from_documents(docs, embeddings)
+    return vectorstore.as_retriever()

requirements.txt

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-altair
-pandas
-streamlit
+faiss-cpu>=1.12.0
+ipykernel>=6.30.1
+langchain>=0.3.27
+langchain-community>=0.3.27
+langchain-huggingface>=0.3.1
+openai-agents>=0.2.9
+pymupdf>=1.26.3
+streamlit>=1.48.1
+python-dotenv
+openai
+groq
+google-generativeai
+sentence-transformers>=5.1.0
+datasets

runner.py

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+from agents import Runner
+from openai.types.responses import ResponseTextDeltaEvent
+
+async def run_query(agent, query: str):
+    """
+    Run a streamed query on the agent using the provided SQLite session.
+    """
+    response = Runner.run_streamed(agent, query)
+
+    async for event in response.stream_events():
+        if event.type == "raw_response_event" and isinstance(event.data, ResponseTextDeltaEvent):
+            print(event.data.delta, end="", flush=True)

runtime.txt

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+python-3.12

src/streamlit_app.py

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-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))

tools.py

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+from agents import function_tool
+
+def create_search_tool(retriever):
+    @function_tool
+    def search_docs(query: str) -> str:
+        """Search the knowledge base for relevant information."""
+        docs = retriever.get_relevant_documents(query)
+        results = []
+        for i, doc in enumerate(docs, start=1):
+            page = doc.metadata.get("page", "N/A")
+            source = doc.metadata.get("source", "N/A")
+            snippet = doc.page_content
+            results.append(f"[Result {i}] (Page {page}, Source: {source})\n{snippet}")
+        return "\n\n".join(results)
+
+    # return the tool function so it can be passed to the Agent
+    return search_docs