There you can find a complete guide with examples written in GO.
Retrieval-Augmented Generation (RAG) is an architecture that's revolutionizing how we interact with Large Language Models. This open source project was born with the goal of explaining in detail how RAG works and providing practical implementation examples using modern, fully local technologies.
What is RAG and Why It Matters
RAG represents a popular alternative to fine-tuning large language models. Instead of retraining a model every time data changes, RAG uses a retrieval mechanism that enables more flexible content generation, leveraging pre-trained models combined with a knowledge database.
The operation is elegant in its simplicity: content is stored in a vector database, and when a user asks a question, the system transforms the query into a vector and uses vector search to find the most relevant information. This information is then provided to the language model as context, enabling more accurate and up-to-date responses.
Explaination
Retrieval-Augmented Generation (RAG) is a popular alternative to fine-tuning large language models, it allows for more flexible content generation by leveraging pre-trained models and a retrieval mechanism. Content is stored on a database, relevant information to the user query can be found by transforming the query in a vector and using a vector search on a database to find the most relevant data to what the user is talking about. By storing the data on a database you can easly obtain it without training a model each time the data changes.
A popular example of RAG is Google's new AI Overview tool.
When you search for something on Google the overview tool takes the top results from the search and tries to answer to your question using a RAG system.
This is a perfect usecase for RAG, the data that is being fetched from search results is constantly changing, it would be impossible to train the model on new data each time someone makes an update, RAG allows more flexibility at the small cost of performance.
Advantages of a Local Implementation
Implementing RAG locally presents numerous advantages over cloud solutions:
Complete Privacy - Your data never leaves your machine. This is fundamental when working with sensitive or proprietary information.
Reduced Costs - There are no costs for API calls or cloud storage. After the initial hardware investment, the system can run indefinitely without recurring costs.
Full Control - You have total control over models, data, and configurations. You can experiment freely without limitations imposed by external services.
Low Latency - Requests don't have to travel over the internet, resulting in faster response times, especially for repeated queries.
Practical Use Cases
The applications of a local RAG system are numerous:
Enterprise chatbots - Create virtual assistants that can answer questions based on internal company documentation, keeping data completely private.
Document search - Implement semantic search systems over large document archives, allowing you to find information even when exact terms don't match.
Knowledge management - Build knowledge management systems that allow natural language queries on enterprise knowledge bases.
Code analysis - Create assistants that can answer questions about large codebases, suggesting implementations or explaining complex patterns.
Conclusions
Local RAG Example demonstrates that it's possible to build sophisticated artificial intelligence systems without depending on cloud services or external APIs. With the right technologies and a good understanding of RAG architecture, you can create powerful solutions that respect privacy and offer complete control over your data.
The project represents an excellent educational resource for anyone wanting to deeply understand how RAG works, and a solid starting point for production implementations. The combination of PostgreSQL, Ollama, and Go creates a robust and scalable technology foundation, perfect for both experimentation and real applications.
