JoaquinRuiz

JoaquinRuiz / scalable-rag-python-gemini

Public

🚀 Production-ready RAG pipeline capable of ingesting massive datasets (2GB+) using Python Generators (Lazy Loading) and ChromaDB. Avoid OOM errors and hallucinations.

14
2
100% credibility
Found Feb 09, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This project builds a memory system that reads massive document collections and answers your questions accurately by pulling info directly from them with source citations.

How It Works

1
📺 Discover the Magic

You watch a fun video tutorial that shows how to make a smart helper remember huge stacks of your documents perfectly.

2
💾 Bring It Home

You grab the simple files and put them on your computer to get started.

3
🧠 Wake Up the AI

You connect a smart thinking service so your helper can understand and remember things like a super brain.

4
📁 Feed Your Documents

You show it the folder with all your big files, like PDFs or notes, and it quietly learns everything inside.

5
✨ It Remembers Forever

In a quick process, your helper builds a perfect memory of every detail in your documents, ready for any question.

6
❓ Start Asking Away

You type simple questions about your files, and it finds exactly what you need every time.

🎉 Smart Answers with Proof

You get clear, trustworthy replies pulled straight from your documents, complete with exact sources so you always know it's right!

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AI-Generated Review

What is scalable-rag-python-gemini?

This Python project delivers a production ready RAG pipeline that ingests massive datasets over 2GB without OOM errors, using lazy loading via generators and ChromaDB for vector storage. It pairs with Google Gemini for embeddings and response generation, pulling relevant chunks from your docs to answer queries while citing sources to avoid hallucinations. Users get a simple API to index directories of PDFs, docs, spreadsheets, or text files, then query interactively or programmatically.

Why is it gaining traction?

It stands out for handling 2GB+ workloads that crash typical RAG setups, with persistent ChromaDB storage and configurable chunking to balance recall and speed. Developers notice the interactive CLI for stats, search-only mode, and re-indexing on the fly, plus built-in similarity thresholds that filter junk results. As a production ready rag github repo, it skips toy examples for real-scale ingestion across common formats.

Who should use this?

AI engineers building production ready rag chatbots or systems querying enterprise docs like contracts or manuals. Data scientists at consultancies processing 2GB+ research PDFs without cloud costs. Teams prototyping production ready rag systems with Gemini before scaling to LangChain or Azure AI Search integrations.

Verdict

Grab it for quick production ready rag pipeline prototypes if you need 2GB+ scale on local hardware—docs and examples make setup fast. With just 12 stars and 1.0% credibility score, it's early-stage and lacks tests, so audit before deploying in anger.

(187 words)

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