AI — Open Source RAG Stack
Overview
The open-source RAG (Retrieval-Augmented Generation) stack has a layered architecture. Each layer has multiple tool options — mix and match based on your requirements.
The 7-Layer Stack
Layer 1 — Ingest / Data Processing
Get data into the pipeline: chunk, clean, prepare.
| Tool | Notes |
|---|---|
| Kubeflow | ML pipeline orchestration |
| Apache Airflow | General-purpose workflow orchestration |
| Apache Nifi | Data flow automation |
| LangChain Document Loaders | 100+ document type loaders |
| Haystack Pipelines | RAG-first pipeline framework |
| OpenSearch | Search and ingest |
Layer 2 — Embedding Model
Convert text chunks to vector representations.
| Tool | Notes |
|---|---|
| HuggingFace Transformers | Wide model selection, local or hosted |
| LLMWare | Enterprise-focused embedding |
| Nomic | Long-context embeddings |
| Sentence Transformers | Lightweight, fast, widely used |
| JinaAI | Multi-modal embeddings |
| Cognita | Managed embedding service |
Layer 3 — Retrieval & Ranking
Find the most relevant chunks for a query.
| Tool | Notes |
|---|---|
| FAISS | Facebook’s fast similarity search (local) |
| Haystack Retrievers | Modular retrieval components |
| Weaviate Hybrid Search | Dense + sparse hybrid retrieval |
| Elasticsearch kNN | Vector search on familiar stack |
| Jina AI Rerankers | Cross-encoder reranking for precision |
Layer 4 — Vector Database
Store and query embeddings at scale.
| Tool | Notes |
|---|---|
| Weaviate | Hybrid search, multi-modal, self-hostable |
| Milvus | High-performance, cloud-native |
| pgVector | PostgreSQL extension — use existing DB |
| Chroma | Lightweight, local-first, easy to start |
| Pinecone | Managed cloud, production-ready |
Layer 5 — LLMs
The generative component that produces answers.
| Tool | Notes |
|---|---|
| LLaMA (Meta) | Most widely deployed open model family |
| Mistral | Fast, strong at coding and reasoning |
| Gemma (Google) | Lightweight, on-device capable |
| Phi-2 (Microsoft) | Small but capable, edge deployment |
| DeepSeek | Strong coding, low cost |
| Qwen (Alibaba) | Multi-lingual, competitive performance |
Layer 6 — LLM Frameworks
Orchestrate retrieval + generation + memory.
| Tool | Notes |
|---|---|
| LangChain | Most popular, widest ecosystem |
| Haystack | Production-grade RAG-first framework |
| LlamaIndex | Data framework, strong indexing/querying |
| HuggingFace | Model hub + inference infrastructure |
| Semantic Kernel | Microsoft’s enterprise AI SDK |
Layer 7 — Frontend Frameworks
Build the user-facing interface.
| Tool | Notes |
|---|---|
| Next.js | Full-stack React, production-ready |
| SvelteKit | Lightweight, fast |
| Streamlit | Rapid Python UI prototyping for AI apps |
| Vue.js | Progressive, flexible frontend |
Minimal Stack to Start
For a quick proof of concept:
- Ingest: LangChain Document Loaders
- Embedding: Sentence Transformers (local) or OpenAI Ada
- Vector DB: Chroma (local, zero config)
- LLM: Mistral or LLaMA via Ollama (local) or Claude API
- Framework: LlamaIndex or LangChain
- Frontend: Streamlit
For production:
- Replace Chroma with Weaviate or Pinecone
- Add Haystack Pipelines for data processing
- Add reranking (Jina AI or Cohere)
- Deploy frontend with Next.js
See Also
- AI — Agent Frameworks (N8N vs LangGraph) — orchestration layer above the RAG stack
- AI — Learning Resources & Roadmap — step 14 of GenAI roadmap covers RAG
- AI — 30-Day Mastery Mind Map — custom knowledge systems section
- LLM Wiki Pattern — uses a compiled wiki instead of RAG — an alternative architecture