📦 Sample App Templates Using DeepSeek API (2025 Full Guide)

🔍 Introduction

With the rise of open-weight LLMs like DeepSeek, developers are no longer bound to proprietary models with expensive token-based pricing. The DeepSeek ecosystem provides scalable, customizable, and cost-efficient AI alternatives, especially when paired with accessible APIs or platforms like Ollama, LangChain, or Flask.

In this guide, we’ll explore multiple app templates that integrate with the DeepSeek API, ranging from chatbots and coding assistants to search agents and productivity tools. Whether you’re a startup prototyping your next product or an enterprise building internal automation, this comprehensive blueprint will help you launch quickly and efficiently.

✅ Table of Contents

1. What Is DeepSeek and Its API?

2. How to Run DeepSeek API Locally

3. Template 1: AI Chat Assistant (React + Flask)

4. Template 2: Markdown Editor with AI Assist

5. Template 3: VS Code DeepSeek Coding Companion

6. Template 4: Terminal Chatbot Using Node.js

7. Template 5: Research Assistant with Search Tools

8. Template 6: Telegram Bot Powered by DeepSeek

9. Template 7: Streamlit Chat UI with Memory

10. Template 8: DeepSeek API + LangChain Agent

11. Deployment Tips (Localhost, Docker, Fly.io)

12. Bonus: Monetizing Your DeepSeek-Powered App

13. Common Mistakes to Avoid

14. Final Thoughts + Downloadable Templates

1. 🧠 What Is DeepSeek and Its API?

DeepSeek is an open-source LLM family developed for general-purpose and coding applications. Two widely used variants:

• deepseek-chat: For conversational AI

• deepseek-coder: For programming-related prompts

API Access Methods:

• Local: Via Ollama (http://localhost:11434)

• Custom Flask/FastAPI server: Wrap llama-cpp-python backend

• Docker containers: Deployable APIs

• LangChain abstraction: Easy integration with memory/tools

2. 🔧 How to Run DeepSeek API Locally

Using Ollama:

bash
curl -fsSL https://ollama.com/install.sh | sh
ollama pull deepseek-chat
ollama run deepseek-chat

Ollama exposes:

http=
POST http://localhost:11434/api/generate

Sample Payload:

json{
  "model": "deepseek-chat",
  "prompt": "Explain LangChain in simple terms.",
  "stream": false}

3. 💬 Template 1: AI Chat Assistant (React + Flask)

Use case: Frontend chat app that sends user input to the DeepSeek API and renders replies.

Tech Stack:

• React (Vite)

• TailwindCSS

• Flask API proxy to Ollama

Features:

• Streaming responses

• Chat history

• Theme toggle (light/dark)

Example Flask Endpoint:

python@app.route('/chat', methods=['POST'])def chat():
    prompt = request.json['prompt']
    res = requests.post("http://localhost:11434/api/generate", json={        "model": "deepseek-chat", "prompt": prompt, "stream": False
    })    return jsonify(res.json())

4. ✍️ Template 2: Markdown Editor with AI Assist

Use case: AI-enhanced writing with Markdown editing, summary, and grammar fix features.

Tech Stack:

• Next.js (or React)

• Tailwind Markdown editor

• Backend with Flask + DeepSeek

Features:

• “Summarize Paragraph” button

• “Fix Grammar” via prompt engineering

• Convert text to bullet points

5. 🧑‍💻 Template 3: VS Code DeepSeek Coding Companion

Use case: AI coding assistant that runs locally without needing OpenAI.

Tech Stack:

• VS Code extension (TypeScript)

• WebView or inline suggestion panel

• DeepSeek API integration with caching

Features:

• Inline code completions

• Chat mode for debugging

• Refactor suggestions

API Call Sample:

ts
const response = await fetch("http://localhost:11434/api/generate", {  method: "POST",  headers: { "Content-Type": "application/json" },  body: JSON.stringify({    model: "deepseek-coder",    prompt: `Explain what this function does:\n${codeSnippet}`
  })
});

6. 🖥 Template 4: Terminal Chatbot (Node.js)

Use case: Lightweight AI assistant in your terminal, perfect for server-side use.

Tech Stack:

• Node.js + Axios

• Readline interface

CLI App Example:

js
import axios from 'axios';import readline from 'readline';
const rl = readline.createInterface({ input: process.stdin, output: process.stdout });

rl.question("Ask DeepSeek: ", 
async (question) => {  const { data } = await axios.post("http://localhost:11434/api/generate", 
{    model: "deepseek-chat",    prompt: question,    stream: false
  });  console.log("AI:", data.response);
  rl.close();
});

7. 🔍 Template 5: Research Assistant with Search Tools

Use case: Combines DeepSeek with web search results for richer responses.

Tech Stack:

• LangChain

• SerpAPI / Google Custom Search

• Python backend

LangChain Tool Integration:

python=from langchain.agents import initialize_agent, Toolfrom langchain.utilities import SerpAPIWrapper

search = SerpAPIWrapper()
tools = [Tool(name="search", func=search.run, description="Current events")]

agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)

8. 🤖 Template 6: Telegram Bot Powered by DeepSeek

Use case: Build a personal AI assistant on Telegram using DeepSeek.

Tech Stack:

• python-telegram-bot

• DeepSeek backend

• Docker + systemd for uptime

Code Sample:

python
from telegram import Updatefrom telegram.ext import CommandHandler, Updaterdef respond(update: Update, context):
    prompt = update.message.text
    res = requests.post("http://localhost:11434/api/generate", 
    json={        "model": "deepseek-chat", "prompt": prompt
    }).json()
    update.message.reply_text(res['response'])

updater = Updater("TELEGRAM_BOT_TOKEN")
updater.dispatcher.add_handler(CommandHandler('ask', respond))
updater.start_polling()

9. 📈 Template 7: Streamlit Chat UI with Memory

Use case: A simple web app for interactive AI conversation with memory retention.

Tech Stack:

• Streamlit

• Session state storage

• DeepSeek backend (Ollama or llama-cpp)

Streamlit Code:

python
import streamlit as stimport requestsif 'history' not in st.session_state:
    st.session_state.history = []

prompt = st.text_input("Ask DeepSeek")if st.button("Send"):
    res = requests.post("http://localhost:11434/api/generate", json={        "model": "deepseek-chat", "prompt": prompt
    }).json()
    st.session_state.history.append((prompt, res['response']))for q, a in st.session_state.history:
    st.write(f"🧑 {q}")
    st.write(f"🤖 {a}")

10. 🧠 Template 8: DeepSeek API + LangChain Agent

Use case: A local agent that can access tools, search, and memory with DeepSeek.

Features:

• Multiple tool use

• Chat history

• Advanced prompts

• Output streaming

LangChain configuration:

python
llm = Ollama(model="deepseek-coder")
agent = initialize_agent(tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION)

11. 🚀 Deployment Tips

Method	Platform	Best For
Localhost	Dev machines	Testing
Docker Compose	Any cloud VPS	Isolation
Fly.io	Global latency	Web apps
Render.com	Hosted API	SaaS tools
Raspberry Pi	IoT, edge AI	Offline uses

12. 💸 Bonus: Monetizing Your DeepSeek App

1. Freemium chat assistant for niche markets

2. Internal developer tools with cost savings

3. Custom vertical AI (e.g., legal, medical with local LLM)

4. AI writing plugins for editors (VS Code, WordPress)

5. Subscription model with usage-based limits

13. ⚠️ Common Mistakes to Avoid

Mistake	Fix
CORS errors	Use Flask proxy server
Model not loading	Check Ollama version/model format
Memory leaks	Trim session history
Streaming broken	Verify chunked transfer handling
Slow response	Use quantized model (Q4_0 or Q5_1)

14. ✅ Final Thoughts + Template Downloads

The DeepSeek API opens up a whole new category of powerful, open, privacy-respecting apps. By combining it with popular frameworks like React, LangChain, Flask, or Telegram Bot API, you can build nearly anything that GPT-4 apps can do—but without the cost or limits.