# **Hour 3: DeepSeek API with Python and Local LLM Integration – Building the Future of Search**
Welcome to the final hour of our DeepSeek journey! If the first two hours were about understanding and using DeepSeek, this hour is about **empowering you to build something extraordinary**. We’re diving into the **DeepSeek API**, combining it with **Python**, and integrating it with a **local LLM like LLaMA 3.2**. This is where the magic happens—where you take DeepSeek’s capabilities and make them your own. Ready to code, create, and innovate? Let’s go!
---
## **1. Setting Up Your Python Environment (15 minutes)**
Before we start coding, let’s set up your environment. Think of this as laying the foundation for your masterpiece.
### **What You’ll Need:**
- **Python 3.8+**: If you don’t have it, download it from [python.org](https://www.python.org/).
- **Virtual Environment**: Keeps your dependencies organized.
- **Required Libraries**: Install these using `pip`:
```bash
pip install requests transformers
```
### **Step-by-Step Setup:**
1. Create a virtual environment:
```bash
python -m venv deepseek-env
source deepseek-env/bin/activate # On Windows: deepseek-env\Scripts\activate
```
2. Install the libraries:
```bash
pip install requests transformers
```
3. Grab your **DeepSeek API key** from your account settings.
Now you’re ready to start coding!
---
## **2. Making Your First API Call (20 minutes)**
Let’s start simple. We’ll use Python to call the DeepSeek API and fetch search results.
### **Sample Code: Basic Search**
```python
import requests
API_KEY = "your_deepseek_api_key"
endpoint = "https://api.deepseek.com/v1/search"
headers = {"Authorization": f"Bearer {API_KEY}"}
params = {"query": "latest advancements in quantum computing", "limit": 5}
response = requests.get(endpoint, headers=headers, params=params)
results = response.json()
for result in results["data"]:
print(result["title"], result["url"])
```
### **What’s Happening Here?**
- We send a **GET request** to the DeepSeek API with a search query.
- The API returns a JSON response with search results.
- We loop through the results and print the title and URL.
### **Pro Tip**:
Experiment with different queries and parameters like `limit`, `sort_by`, and `filters` to customize your search.
---
## **3. Integrating DeepSeek with a Local LLM (25 minutes)**
Now, let’s take things to the next level by integrating DeepSeek with a **local LLM like LLaMA 3.2**. This combination allows you to not only retrieve information but also generate summaries, insights, and more.
### **Step 1: Set Up LLaMA 3.2**
1. Download the LLaMA 3.2 model from the official repository.
2. Load the model using the `transformers` library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "llama-3.2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
```
### **Step 2: Combine DeepSeek Results with LLaMA**
Let’s say you want to summarize the top 5 results from a DeepSeek search. Here’s how you can do it:
```python
# Fetch search results from DeepSeek
search_results = [
"Quantum computing is advancing rapidly, with breakthroughs in qubit stability.",
"Researchers have developed new algorithms for quantum machine learning.",
"The race for quantum supremacy continues between tech giants.",
"Quantum cryptography is becoming a reality for secure communications.",
"Investments in quantum computing have doubled in the past year."
]
# Combine results into a single input for LLaMA
input_text = "Summarize the following information: " + " ".join(search_results)
# Generate a summary using LLaMA
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Generated Summary:", summary)
```
### **What’s Happening Here?**
- We fetch search results from DeepSeek (or use a sample list for demonstration).
- We combine the results into a single input and ask LLaMA to generate a summary.
- The output is a concise summary of the search results.
### **Pro Tip**:
You can extend this to generate **Q&A responses**, **reports**, or even **creative content** based on your search results.
---
## **4. Advanced Prompt Engineering (20 minutes)**
To truly unlock the power of DeepSeek and LLaMA, you need to master **prompt engineering**. Here are some tips and examples:
### **Crafting Effective Prompts:**
- **Summarization**:
- Prompt: *“Summarize the following information in 3 bullet points: [insert text]”*
- **Question Answering**:
- Prompt: *“Based on the following context, answer the question: [insert question] Context: [insert text]”*
- **Creative Writing**:
- Prompt: *“Write a short blog post about the future of AI based on this information: [insert text]”*
### **Example: Q&A with DeepSeek and LLaMA**
```python
query = "What are the benefits of quantum computing?"
search_results = deepseek_search(query) # Replace with actual API call
input_text = f"Answer the following question based on the context: {query}\nContext: {search_results}"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=150)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Answer:", answer)
```
---
## **5. Q&A and Wrap-Up (10 minutes)**
As we wrap up this session, let’s reflect on what we’ve achieved:
- You’ve learned how to use the **DeepSeek API** to fetch and process search results.
- You’ve integrated DeepSeek with a **local LLM** to generate summaries, answers, and insights.
- You’ve explored **prompt engineering** to get the most out of these tools.
### **Your Challenge**:
Take what you’ve learned and build something amazing. Whether it’s a research assistant, a content generator, or a data analysis tool, the possibilities are endless.
---
## **Inspiration for the Road**
Imagine a world where you can instantly find and understand any piece of information. With DeepSeek and LLMs, that world is within your reach. You’re not just using tools—you’re shaping the future of how we interact with knowledge. So go ahead, experiment, innovate, and create. The future is yours to build.
Happy coding, and may your searches always lead you to something extraordinary!
---
Let me know if you’d like to dive deeper into any specific topic or need help with your projects. The adventure is just beginning! 🚀
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