RAG GO Ollama

Building a Powerful RAG System with Golang, LangChainGo, Chroma, and Ollama

In my latest project, RAG-Go-Ollama, I combined the power of Golang, LangChainGo, Chroma, and Ollama to build an efficient Retrieval-Augmented Generation (RAG) system. This project is designed to help developers create intelligent document-based Q&A systems that deliver fast and accurate responses.

Why RAG Systems Matter

RAG systems are powerful because they combine information retrieval with generative models, enabling responses that are both accurate and context-aware. By using embeddings and efficient search mechanisms, these systems excel in processing large amounts of data while delivering precise results.

Tech Stack Overview

🔹 Golang

I chose Golang for its speed, concurrency capabilities, and performance. It’s ideal for building high-performance applications and APIs, making it a solid choice for this RAG implementation.

🔹 LangChainGo

LangChainGo provides the building blocks for creating LLM-powered applications. It simplifies the process of constructing prompt chains, memory management, and integrating various tools within the RAG system.

🔹 Chroma

Chroma is an open-source vector store that efficiently manages embeddings. It ensures fast and scalable retrieval of relevant document chunks, enhancing the system's performance.

🔹 Ollama

I integrated Ollama to leverage its flexible and powerful language models for generating high-quality text responses.

Project Highlights

✅ Fast and scalable performance using Golang’s efficient concurrency model.
✅ Document Q&A functionality that allows users to ask direct questions about stored content.
✅ Chroma integration ensures fast and accurate retrieval of document embeddings.
✅ Ollama's LLM provides detailed and contextualized answers.
✅ LangChainGo’s modular design simplifies building dynamic pipelines for improved flexibility.

How It Works

1. Document Upload: Users add documents to the system.
2. Embedding Creation: Chroma stores vector embeddings for fast retrieval.
3. Question Handling: Users submit questions through the chatbot interface.
4. Search & Response: The system retrieves relevant document parts and generates a clear response using Ollama's LLM.

Getting Started

If you're interested in building your own RAG system or exploring the tech stack I used, check out the repository here: https://github.com/erevos-13/rag-go-ollama

I’d love to hear your thoughts or discuss improvements. Feel free to contribute or reach out with ideas!

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