In this tutorial, I'll guide you through the process of integrating an agent into your Large Language Model (LLM) using LangChain. We'll explore stepbystep how to seamlessly incorporate the power of a Google Search Engine into your LLM, enhancing its capabilities and providing richer responses.

What You'll Learn:

Setting up Google and Search Engine credentials
Integrating Google Search Engine API with your LLM
Enhancing LLM responses with realtime search results
Harnessing a chat model from Hugging Face

Don't forget to , the , and the for more and . Let's embark on this coding journey together!

Timestamps:
0:00 Introduction
0:45 Create credentials
2:46 Setup environment
04:05 Load Agent to use Google Search
05:55 Test Google Search Engine
06:54 Load LLM from HuggingFace
08:41 Load prompt for the LLM
10:13 Embed Agent to the LLM
12:08 Test Agent with realtime search results
15:52 Conclusion

Ready to level up your AI game? Watch now and unlock the power of LangChain integration with Google Search Engine!

Resources:
LangChain Agents: https://python.langchain.com/docs/int...
Google API Key: https://console.cloud.google.com/apis...
Google Search Engine: https://programmablesearchengine.goog...

Links:

GitHub repo for code: https://github.com/Eduardovasquezn/la...
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