---
title: "The RAG stack"
type: "stack"
slug: "rag-pipeline"
canonical_url: "https://www.graphcanon.com/stacks/rag-pipeline"
---

# The RAG stack

Retrieval-augmented generation grounds an LLM in your own data. A production RAG pipeline is four layers: ingestion, a vector store, orchestration, and evaluation.

## 1. Data & Retrieval

Ingestion & retrieval - parse, chunk, and load documents into a retrievable form.

**When not to use:** Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.

**Category:** [Data & Retrieval](/categories/data-retrieval.md)

- [generative-ai-for-beginners](/tools/microsoft-generative-ai-for-beginners.md) - 21 Lessons for Getting Started with Generative AI (★ 120,108) [Very active]
- [dify](/tools/langgenius-dify.md) - Build Agentic workflows and RAG pipelines with rich AI model and tool support on one collaborative workspace. (★ 156,243) [Very active]
- [mem0](/tools/mem0ai-mem0.md) - Drop-in memory infrastructure for AI agents and apps (★ 65,557) [Very active]
- [JeecgBoot](/tools/jeecgboot-jeecgboot.md) - 企业级AI低代码平台，一键生成前后端代码甚至整个系统 (★ 47,882) [Very active]

## 2. Vector Databases

Vector store - persist embeddings and run similarity search at query time.

**When not to use:** Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

**Compare:**
- [Qdrant vs Chroma](/compare/chroma-core-chroma-vs-qdrant-qdrant.md)
- [Qdrant vs pgvector](/compare/pgvector-pgvector-vs-qdrant-qdrant.md)
- [Qdrant vs Milvus](/compare/milvus-io-milvus-vs-qdrant-qdrant.md)

**Category:** [Vector Databases](/categories/vector-databases.md)

- [milvus](/tools/milvus-io-milvus.md) - Milvus is a high-performance, cloud-native vector database for scalable vector ANN search (★ 46,152) [Very active]
- [TencentDB-Agent-Memory](/tools/tencentcloud-tencentdb-agent-memory.md) - A team-level memory hub for AI Agents, transforming conversations, documents, and code into reusable memory assets. (★ 26,924) [Very active]
- [qdrant](/tools/qdrant-qdrant.md) - High-performance, massive-scale Vector Database and Vector Search Engine for AI (★ 34,655) [Very active]
- [cognee](/tools/topoteretes-cognee.md) - Cognee is the open-source AI memory platform for agents. (★ 30,850) [Very active]

## 3. LLM Frameworks

Orchestration - assemble retrieval, prompting, and the model into a chain.

**When not to use:** Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.

**Compare:**
- [LangChain vs LlamaIndex](/compare/langchain-ai-langchain-vs-run-llama-llama-index.md)
- [Haystack vs LangChain](/compare/deepset-ai-haystack-vs-langchain-ai-langchain.md)

**Category:** [LLM Frameworks](/categories/llm-frameworks.md)

- [ECC](/tools/affaan-m-ecc.md) - Agent harness performance optimization system for AI agents and LLMs (★ 261,432) [Very active]
- [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: state-of-the-art machine learning models for text, vision, audio, and multimodal models (★ 166,279) [Very active]
- [tensorflow](/tools/tensorflow-tensorflow.md) - An Open Source Machine Learning Framework for Everyone (★ 200,493) [Very active]
- [generative-ai-for-beginners](/tools/microsoft-generative-ai-for-beginners.md) - 21 Lessons for Getting Started with Generative AI (★ 120,108) [Very active]

## 4. Evaluation & Observability

Evaluation & tracing - measure answer quality, cost, and latency before and after changes.

**When not to use:** Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.

**Category:** [Evaluation & Observability](/categories/evaluation-observability.md)

- [generative-ai-for-beginners](/tools/microsoft-generative-ai-for-beginners.md) - 21 Lessons for Getting Started with Generative AI (★ 120,108) [Very active]
- [headroom](/tools/headroomlabs-ai-headroom.md) - Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. (★ 72,850) [Very active]
- [LibreChat](/tools/danny-avila-librechat.md) - Enhanced ChatGPT Clone with extensive features and integrations for self-hosting (★ 44,416) [Very active]
- [ray](/tools/ray-project-ray.md) - Unified framework for scaling AI and Python applications (★ 43,863) [Very active]

## Common questions

### What is the the rag stack?

Retrieval-augmented generation grounds an LLM in your own data. A production RAG pipeline is four layers: ingestion, a vector store, orchestration, and evaluation.

### When should I use the the rag stack?

Use this stack when your constraints match its layers: Data & Retrieval, Vector Databases, LLM Frameworks, Evaluation & Observability. Each step on the page includes when-not-to-use guidance so you do not over-engineer.

### How does GraphCanon pick tools for the rag stack?

Stack pages are editorial workflows over canonical categories. Representative tools are pulled live from the graph at render time (top adoption in each category), not a fixed marketing list.

### Where can I compare tools in this stack?

Follow category hubs and head-to-head compare pages linked from each step. Featured comparisons cover the highest-intent pairs (frameworks, vector DBs, agent runtimes). Start at [compare hub](/compare).

### Is there a machine-readable version of The RAG stack?

Yes. Append `.md` to [`/stacks/rag-pipeline`](/stacks/rag-pipeline) or fetch [`/md/stacks/rag-pipeline`](/md/stacks/rag-pipeline) for a markdown twin with steps, when-not notes, and tool links.

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/categories`](/api/graphcanon/categories)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_