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RAG Stack Builder

Answer 4 questions about your use case, scale, budget, and priorities — get an instant production-ready RAG architecture with the exact packages to install.

60s TO YOUR STACK
12+ STACK COMBINATIONS
$0 COST TO USE
2026 UPDATED VERSIONS
STEP 01
Choose your use case — chatbot, knowledge base, Arabic RAG, etc.
STEP 02
Set your document scale and monthly budget constraints.
STEP 03
Pick your priorities — speed, accuracy, privacy, or Arabic support.
STEP 04
Get your full stack with packages, cost estimate, and compatibility score.

BUILD YOUR STACK

⚡ ~60 seconds
1
USE CASE
2
SCALE
3
BUDGET
4
PRIORITY

Common Questions

What is a RAG stack?
A RAG (Retrieval-Augmented Generation) stack is the combination of tools that work together to build a system that fetches relevant documents before an LLM generates a response. It typically includes a framework, embedding model, vector database, and LLM. Read the full guide →
Which stack is best for Arabic language RAG?
For Arabic RAG, you need a multilingual embedding model (Cohere multilingual-v3 or intfloat/multilingual-e5-large), a managed vector DB like Pinecone, and an LLM with strong Arabic training data like Llama 3 70B via Groq or GPT-4o. Select “Arabic Content RAG” or “Arabic Support” in the builder above for the exact stack.
Can I build a RAG system for free?
Yes — Groq’s free tier handles ~14,400 requests/day, FAISS is open source, and HuggingFace embeddings are free locally. Select “Free / Open Source” budget in the builder for a $0 stack.
FAISS vs Pinecone — which should I choose?
FAISS for local development and under 1M vectors — unbeatable speed, zero cost. Pinecone for managed production at scale with zero DevOps overhead. The builder recommends the right one based on your scale selection.
Ready to Build?
Your stack is picked. Now build it with production-grade code, benchmarks, and deployment guides.
Full RAG Guide Production Benchmarks LangGraph Agents