# Redis Vector Store Example with LangChain Go

Hello there! 👋 Welcome to this exciting example that demonstrates how to use a Redis vector store with LangChain Go! Let's dive in and see what this cool code does! 🚀

## What's This All About?

This example showcases how to:

1. Set up a Redis vector store
2. Add documents to the store
3. Perform similarity searches
4. Use a retrieval-based question-answering system

It's a fantastic way to learn about vector databases and how they can be used in AI applications!

## The Magic Ingredients 🧙‍♂️

- Redis: Our trusty vector store
- Ollama: A local LLM server for embeddings and text generation
- LangChain Go: The glue that brings it all together!

## What Happens in the Code?

1. **Setting Up**: We start by connecting to a Redis server and creating a new vector store index.

2. **Adding Data**: We add a bunch of documents about cities to our vector store. Each document contains the city name and some metadata like population and area.

3. **Similarity Search**: We perform a similarity search for "Tokyo" and get the 2 most similar results. This shows how vector stores can find related information quickly!

4. **Question Answering**: Here's where it gets really cool! We set up a retrieval QA chain that:
   - Takes a question
   - Searches the vector store for relevant information
   - Passes that info to an LLM to generate an answer

5. **Embeddings**: We use the Ollama server to generate embeddings for our documents and queries. This is what makes the similarity search possible!

## Why This is Awesome 🌟

- **Fast Searches**: Vector stores allow for lightning-fast similarity searches on large datasets.
- **Flexible Data**: You can store any kind of data with associated metadata.
- **AI-Powered QA**: By combining a vector store with an LLM, you can create powerful question-answering systems.

## Ready to Try?

Make sure you have Redis running locally and an Ollama server set up with the "gemma:2b" model. Then run the code and watch the magic happen!

Happy coding, and have fun exploring the world of vector stores and AI! 🎉🤖
