Open Source RAG Stack Explained: Tools, Architecture & Workflow (2026 Guide)

Open Source RAG Stack Explained (2026 Guide)

Retrieval-Augmented Generation (RAG) is one of the most powerful techniques in modern AI systems, combining information retrieval with large language models to produce accurate and context-aware responses.

This infographic presents a complete view of the Open Source RAG Stack — from data ingestion to vector databases, embeddings, and LLM frameworks.

In this guide, we will break down each component of the RAG architecture, explain how they work together, and explore the most popular open-source tools used in real-world AI applications.

Updated for 2026: Includes latest open-source tools in RAG ecosystem.

📌 Table of Contents

• What is RAG?
• RAG Architecture Overview
• Data Ingestion & Processing
• Retrieval & Ranking
• Embedding Models
• Vector Databases
• LLM Frameworks
• LLM Models
• Frontend Frameworks
• How RAG Works (Step-by-Step)

Infographic Credit: This infographic is created by Shalini Goyal and published here with permission. 🔗 View LinkedIn Profile

📊 Open Source RAG Stack Infographic

A complete overview of the open-source tools used in building a RAG pipeline.

🔍 What is Retrieval-Augmented Generation (RAG)?

RAG (Retrieval-Augmented Generation) is an AI architecture that enhances language models by retrieving relevant information from external data sources before generating responses.

Instead of relying only on pre-trained knowledge, RAG systems fetch real-time or domain-specific data, making them more accurate, reliable, and up-to-date.

📥 Data Ingestion & Processing

This stage involves collecting and preparing data from various sources such as PDFs, databases, APIs, and documents.

• Apache Airflow – Workflow orchestration
• Apache NiFi – Data flow automation
• Kubeflow – ML pipelines
• LangChain Document Loaders – Structured ingestion

🔎 Retrieval & Ranking

This layer fetches the most relevant documents using similarity search and ranking algorithms.

• FAISS – Fast similarity search
• Weaviate – Vector search engine
• Jina AI – Neural search
• Elasticsearch KNN – Scalable retrieval

🧠 Embedding Models

Embedding models convert text into numerical vectors that can be compared mathematically.

• Sentence Transformers
• Hugging Face Transformers
• Jina AI Embeddings
• Nomic Embeddings

🗄️ Vector Databases

Vector databases store embeddings and allow efficient similarity search.

• Chroma
• Qdrant
• Weaviate
• PgVector

⚙️ LLM Frameworks

These frameworks help integrate LLMs with retrieval systems and pipelines.

• LangChain – Pipeline orchestration
• LlamaIndex – Data indexing for LLMs
• Haystack – End-to-end RAG pipelines

🤖 LLM Models

These are the core models that generate responses.

• LLaMA
• Mistral
• Gemma
• Phi-2
• DeepSeek

💻 Frontend Frameworks

• Next.js
• Streamlit
• Vue.js
• SvelteKit

🔄 How RAG Works (Step-by-Step)

1. Data is collected and processed
2. Text is converted into embeddings
3. Embeddings are stored in a vector database
4. User query is converted into a vector
5. Relevant documents are retrieved
6. LLM generates response using retrieved data

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