🛠️ A Step-by-Step Guide to Building Your First Automated Project with DeepSeek‑R1

1. 🚀 Why Build with DeepSeek‑R1?

DeepSeek‑R1 is a powerful, reasoning-optimized LLM with a 128K-token window that excels at complex, multi-step tasks. With the free course, beginners can learn to:

• Decompose problems into logical workflows

• Automate interactions across tools and APIs

• Build reliable, reusable systems with minimal code

In this guide, you’ll learn to create a daily research-summary email bot using DeepSeek‑R1, open-source libraries, and scheduled automation—a perfect “first project” that showcases chaining, tool use, RAG, and deployment.

2. 🧰 Defining the Project: Daily Research-Summary Bot

What it does:

1. Fetches news articles from RSS or APIs

2. Summarizes key insights using DeepSeek‑R1

3. Formats a clean email report

4. Sends the summary via email daily (e.g., at 7 a.m.)

Why it’s ideal:

• Covers data ingestion, LLM reasoning, templating, and automation

• Teaches chaining, scheduling, deployment

• Solves a real-world pain point: staying informed

3. 🔧 Prerequisites & Tools

Here’s what you'll need:

• Python 3.8+

• Free DeepSeek‑R1 API access (via OpenRouter or Azure)

• Libraries:

  bash

• pip install requests feedparser deepseek-course-tools

  (Assume “deepseek-course-tools” bundles LangChain, APScheduler, etc.)

• Email credentials (SMTP or transactional API like SendGrid)

• Optional: Docker and git

4. ⚙️ Step 1: Obtain DeepSeek‑R1 API Key

1. Sign up at OpenRouter.ai

2. Choose deepseek/deepseek-r1:free

3. Generate and copy your API key

4. Add to .env file:

   ini

5. OPENROUTER_KEY=your_key_here

5. 🧠 Step 2: Set Up Chat Client

Create llm_client.py:

python
import os, requests

API_URL = "https://openrouter.ai/api/v1/chat/completions"HEADERS = {  "Authorization": f"Bearer {os.getenv('OPENROUTER_KEY')}",  
"Content-Type": "application/json"}def ask_deepseek(prompt: str) -> str:
    payload = {      "model": "deepseek/deepseek-r1:free",      "messages":[{"role":"user","content":prompt}]
    }
    res = requests.post(API_URL, json=payload, headers=HEADERS)
    res.raise_for_status()    return res.json()["choices"][0]["message"]["content"]

This gives you a reusable interface to ask the model questions.

6. 📰 Step 3: Fetch News Articles

Create fetch_news.py:

python
import feedparserdef get_articles(feed_urls, limit=5):
    articles = []    for url in feed_urls:
        feed = feedparser.parse(url)        for entry in feed.entries[:limit]:
            articles.append({"title": entry.title, "link": entry.link, "summary": entry.summary})    
            return articles

Use reputable RSS sources, e.g.:

python
feeds = [  "https://rss.nytimes.com/services/xml/rss/nyt/Technology.xml",  
"https://feeds.feedburner.com/techcrunch/startups"]

7. 🧩 Step 4: Summarize with DeepSeek-R1

Create summarizer.py:

python
from llm_client import ask_deepseekdef summarize_article(title, text):
    prompt = (      f"Summarize the following article in 3 bullet points:\n\n"
      f"Title: {title}\nContent: {text}"
    )    return ask_deepseek(prompt)

Test it in Python REPL by passing an article and verifying the output.

8. 📝 Step 5: Format the Email Report

Create format_report.py:

python
def compose_email(articles_summary):
    lines = ["Subject: Daily Research Summary\n", "Good morning,\n\nHere are today's summaries:\n"]    
    for idx, item in enumerate(articles_summary, 1):
        lines.append(f"{idx}. {item['title']}\n{item['summary']}\nLink: {item['link']}\n\n")
    lines.append("Have a great day!")    return "".join(lines)

This readable structure enhances user experience.

9. ✉️ Step 6: Send Email Automation

Use SMTP in send_email.py:

python
import os, smtplibfrom email.message import EmailMessagedef send_email(recipient, email_body):
    msg = EmailMessage()
    msg.set_content(email_body)
    msg["Subject"] = "Daily Research Summary"
    msg["From"] = os.getenv("EMAIL_USER")
    msg["To"] = recipient    with smtplib.SMTP_SSL("smtp.example.com", 465) as smtp:
        smtp.login(os.getenv("EMAIL_USER"), os.getenv("EMAIL_PASS"))
        smtp.send_message(msg)

Set environment variables for sender credentials.

10. 🧭 Step 7: Orchestrate the Workflow

Create main.py:

python
from fetch_news import get_articlesfrom summarizer 
import summarize_articlefrom format_report import compose_emailfrom send_email 
import send_emaildef run_daily():
    feeds = [...]  # your list
    articles = get_articles(feeds)
    summaries = []    for art in articles:
        summaries.append({          "title": art["title"],          "link": art["link"],          
        "summary": summarize_article(art["title"], art["summary"])
        })
    email_text = compose_email(summaries)
    send_email("you@example.com", email_text)if __name__ == "__main__":
    run_daily()

Run the script to test the complete end-to-end process.

11. 🔁 Step 8: Schedule the Automation

Use scheduler via schedule.py:

python
from apscheduler.schedulers.blocking import BlockingSchedulerfrom main import run_daily

scheduler = BlockingScheduler()
scheduler.add_job(run_daily, "cron", hour=7, minute=0)  
# Runs daily at 07:00scheduler.start()

Now your summary bot runs autonomously each morning.

12. 📦 Step 9: Containerize (Optional)

Create Dockerfile:

sql
FROM python:3.11-slim
WORKDIR /appCOPY requirements.txt .
RUN pip install -r requirements.txtCOPY . .
CMD ["python", "schedule.py"]

Build and run:

bash
docker build -t deepseek-summary-bot .
docker run -d --env-file .env deepseek-summary-bot

This ensures portability across environments.

13. 📊 Step 10: Monitoring & Logging

Add basic logging in main.py:

python
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)def run_daily():
    logger.info("Starting daily summary job")
    ...  # your workflow
    logger.info("Email sent!")

Use Docker logs or integrate Sentry, Prometheus, or other monitoring tools for deeper insights.

14. 🛡️ Step 11: Best Practices & Optimization

• Error handling: wrap fetch, summary, email in try/except

• Retry logic: for HTTP/API failures

• Prompt efficiency: limit tokens in summaries

• Cache summaries: avoid duplicate summaries via hashes

• Security: keep credentials in .env, avoid exposing in code

15. 🚀 Future Enhancements

Once you’ve built the MVP, consider:

1. RAG: fetch full-text articles, vectorize with Chroma or FAISS, query dynamically

2. Slack/Discord integration: send seamlessly to channels

3. Templates or PDF output via Markdown-to-PDF libraries

4. Web interface: Streamlit dashboard with downloadability

5. Multimodal: incorporate DeepSeek‑Vision snapshots

6. Expand to multiple recipients with templates

16. 🎯 Skill Recap

Throughout this project, you learned to:

• Setup DeepSeek‑R1 API and interface

• Fetch and parse external data

• Summarize content with prompt engineering

• Format and send human-readable emails

• Schedule daily execution

• Containerize and monitor your solution

• Integrate improvements with RAG or UI

This template can power many other automated workflows.

17. 🧩 Next Steps & Support

Need help taking it further?

• Write a RAG summary tool

• Build a Slack-based research bot

• Add fine-tuned personas

• Bundle as a multi-service API

Let me know—I'm happy to provide starter repos, code enhancements, and deployment guides tailored to your needs.

18. ✅ Conclusion

Building an automated research-summary bot with DeepSeek‑R1 is a beginner-friendly yet powerful way to explore AI-driven automation. With intuitive components and real-world utility, it's an ideal first project that reinforces course principles.

Your next step? Fork this project, customize it, and iterate! Just ask for help or advanced versions anytime 😊