Large Language Models (LLMs) like Deepseek AI are powerful tools, but they are often limited to the data they were trained on. To truly unlock their potential, especially for tasks like watching videos or accessing real-time information, connecting them to the internet is crucial. This article will guide you through the basics of enabling internet access for your local Deepseek AI setup using tools like Ollama.
Deepseek, like many LLMs, operates primarily on the data it has been pre-trained with. It doesn't have inherent access to the internet. To bridge this gap, we need to use external tools and techniques.
Ollama simplifies the process of running LLMs locally. While Ollama itself doesn't directly provide internet access to models, it provides an environment where you can integrate external tools. Here's how you can achieve this:
Here's a step-by-step approach to connecting Deepseek AI to the internet for tasks like video access:
Below is an example to get you started with the Youtube API for web scraping:
from googleapiclient.discovery import build
# Replace with your API key that you can generate in Google Cloud Console
YOUTUBE_API_KEY = "YOUR_API_KEY"
youtube = build("youtube", "v3", developerKey=YOUTUBE_API_KEY)
request = youtube.search().list(
part="snippet",
maxResults=1,
q="Ollama Tutorial"
)
response = request.execute()
print (response)
Connecting LLMs like Deepseek to the internet opens up a world of possibilities:
While LLMs like Deepseek AI don't inherently have internet access, integrating them with APIs and web scraping techniques can significantly enhance their capabilities. By following the steps outlined in this article, you can provide your local Deepseek AI setup with the ability to access real-time information and dynamic content, unlocking its full potential.
