Introduction
A structured 3-hour tutorial plan for **DeepSeek**, covering its introduction, effective usage across web, app, and API, and code samples for integrating with a local LLM like LLaMA 3.2.
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## **3-Hour DeepSeek Tutorial Plan**
### **Hour 1: Introduction to DeepSeek**
1. **What is DeepSeek?** (15 minutes)
- Overview of DeepSeek as a search and retrieval tool.
- Key features: semantic search, context-aware retrieval, and integration with LLMs.
- Use cases: research, content discovery, and data analysis.
2. **DeepSeek Architecture** (15 minutes)
- Explanation of how DeepSeek works:
- Indexing and retrieval pipelines.
- Integration with LLMs for enhanced search.
- Differences between web, app, and API usage.
3. **Setting Up DeepSeek** (30 minutes)
- Creating an account and accessing the platform.
- Installing the DeepSeek app (if applicable).
- Generating API keys for programmatic access.
- Setting up a local environment for API usage (Python, virtual environment, etc.).
---
### **Hour 2: Effective Usage of DeepSeek**
1. **Using DeepSeek Web Interface** (20 minutes)
- Navigating the web interface.
- Performing searches: basic vs. advanced queries.
- Filtering and sorting results.
- Saving and exporting search results.
2. **Using DeepSeek Mobile/Desktop App** (20 minutes)
- Installing and configuring the app.
- Syncing searches across devices.
- Offline usage and caching.
- Customizing search preferences.
3. **DeepSeek API Overview** (20 minutes)
- Introduction to the DeepSeek API.
- API endpoints: search, retrieve, and analyze.
- Authentication and rate limits.
- Example use cases for API integration.
---
### **Hour 3: DeepSeek API with Python and Local LLM Integration**
1. **Setting Up Python Environment** (15 minutes)
- Installing required libraries: `requests`, `openai`, `transformers`, etc.
- Configuring API keys and environment variables.
2. **Basic API Usage in Python** (20 minutes)
- Making a simple search request.
- Parsing and displaying results.
- Example code:
```python
import requests
API_KEY = "your_deepseek_api_key"
endpoint = "https://api.deepseek.com/v1/search"
headers = {"Authorization": f"Bearer {API_KEY}"}
params = {"query": "semantic search", "limit": 5}
response = requests.get(endpoint, headers=headers, params=params)
results = response.json()
for result in results["data"]:
print(result["title"], result["url"])
```
3. **Integrating DeepSeek with Local LLM (LLaMA 3.2)** (25 minutes)
- Setting up LLaMA 3.2 locally using `transformers` or `llama.cpp`.
- Combining DeepSeek search results with LLaMA for enhanced responses.
- Example code:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load LLaMA 3.2 model
model_name = "llama-3.2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# DeepSeek search results
search_results = [
"DeepSeek is a powerful semantic search tool.",
"It integrates with LLMs for better context-aware retrieval."
]
# Combine results and generate a summary using LLaMA
input_text = "Summarize the following information: " + " ".join(search_results)
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)
```
4. **Advanced Prompt Engineering** (20 minutes)
- Crafting effective prompts for DeepSeek and LLaMA.
- Example prompts:
- "Find recent research papers on AI ethics and summarize key points."
- "Retrieve top 5 articles about climate change and generate a concise report."
- Combining DeepSeek results with LLaMA for Q&A:
```python
query = "What are the benefits of using DeepSeek with LLMs?"
search_results = deepseek_search(query)
input_text = f"Answer the following question based on the context: {query}\nContext: {search_results}"
```
5. **Q&A and Wrap-Up** (10 minutes)
- Recap of key concepts.
- Addressing common challenges and troubleshooting tips.
- Resources for further learning.
---
### **Additional Resources**
- **DeepSeek Documentation**: [https://docs.deepseek.com](https://docs.deepseek.com)
- **LLaMA 3.2 GitHub Repository**: [https://github.com/facebookresearch/llama](https://github.com/facebookresearch/llama)
- **Python Requests Library**: [https://docs.python-requests.org](https://docs.python-requests.org)
- **Hugging Face Transformers**: [https://huggingface.co/docs/transformers](https://huggingface.co/docs/transformers)
---
This tutorial provides a comprehensive introduction to DeepSeek, practical usage scenarios, and hands-on coding examples for integrating with a local LLM like LLaMA 3.2. Let me know if you'd like to dive deeper into any specific section!
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