# LLM Summarization Chain Example

Hello there! 👋 Welcome to this exciting example of using LangChain with Go to create a summarization chain powered by a Large Language Model (LLM)!

## What does this example do?

This nifty little program demonstrates how to use the LangChain Go library to create a summarization chain. Here's what it does in a nutshell:

1. Sets up a connection to Google's Vertex AI (a powerful LLM service)
2. Creates a summarization chain using the LLM
3. Loads a sample text about AI and large language models
4. Splits the text into manageable chunks
5. Feeds the text chunks into the summarization chain
6. Outputs a concise summary of the input text

## The cool parts

- Uses the `vertex` package to connect to Google's Vertex AI
- Demonstrates the `chains.LoadRefineSummarization` function to create a summarization chain
- Shows how to use `documentloaders` and `textsplitter` to prepare input text
- Illustrates calling the chain with `chains.Call` and extracting the result

## Running the example

To run this example, make sure you have the necessary credentials set up for Google Vertex AI. Then, simply execute the Go file:

```
go run llm_summarization_example.go
```

You'll see a neat summary of the input text about large language models printed to your console!

## Why is this useful?

This example showcases how easy it is to create powerful AI-driven applications using LangChain and Go. Summarization is just one of many tasks you can accomplish with LLMs. The techniques demonstrated here can be adapted for various other AI-powered text processing tasks.

Happy coding, and have fun exploring the world of AI with Go and LangChain! 🚀🤖
