README
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Vertex AI Embedding Example 🚀
Hello there, fellow coder! 👋 Welcome to this exciting example that demonstrates how to create embeddings using Google's Vertex AI service with the LangChain Go library. Let's dive in and see what this cool piece of code does!
What's This All About? 🤔
This example shows you how to:
- Connect to Google's Vertex AI service
- Create an embedding for a simple text input
Embeddings are super useful for converting text into numerical vectors, which can be used for all sorts of amazing things like semantic search, text classification, and more!
How It Works 🛠️
Here's a breakdown of what this nifty little program does:
- It sets up the necessary environment by reading your Google Cloud Project and location from environment variables.
- Creates a new Vertex AI client using the LangChain Go library.
- Generates an embedding for the phrase "I am a human".
- Prints out the resulting embedding vector.
Running the Example 🏃♀️
Before you run this example, make sure you've set up a few things:
- Have a Google Cloud Project with Vertex AI APIs enabled.
- Set the
VERTEX_PROJECTenvironment variable to your GCP project ID. - Set the
VERTEX_LOCATIONenvironment variable to a GCP location (e.g., "us-central1").
Once you're all set, just run the program and watch the magic happen!
What to Expect 🎉
When you run this program, you'll see a vector of floating-point numbers printed to your console. This vector represents the embedding of the phrase "I am a human". Pretty cool, right?
Why This Matters 🌟
Embeddings are a fundamental building block for many modern NLP tasks. By learning how to create them using Vertex AI, you're opening the door to a world of possibilities in natural language processing and machine learning!
Happy coding, and may your embeddings be ever meaningful! 🚀📊
Documentation
Source Files