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2.1 Overview

RAG Showcase compares seven retrieval-augmented-generation (RAG) approaches under identical conditions — same corpus, same embedding model, and the same generation model for the chunk-based approaches — so the dominant variable is the retrieval-and-reasoning approach itself. Two deliberate exceptions: graph-rag generates through LightRAG's QUERY role model, and n8n-adaptive-rag inherits the generator of whichever approach it routes to (see Evaluation Methodology §4).

1. How It Runs

Each approach is an OpenAI-compatible /rag/<name>/v1/chat/completions endpoint in a self-contained plugin package (backend_plugins/rag/) that is bind-mounted into Atlas's FastAPI backend through a generic plugin seam. atlas.consumer.yml declaratively maps seven base routes and twelve flavor aliases into Atlas's LiteLLM startup configuration, so all nineteen appear automatically as selectable models in Open WebUI without runtime registration calls.

flowchart LR
    U[Open WebUI<br/>multi-model chat] -->|/v1/chat/completions| L[LiteLLM gateway]
    L --> R[RAG plugin seam<br/>backend_plugins/rag]
    R --> V[vanilla-rag]
    R --> H[hybrid-rag]
    R --> C[contextual-rag]
    R --> G[graph-rag]
    R --> A[agentic-rag]
    R --> N[n8n-adaptive-rag]
    R --> LG[lazy-graph-rag]
    V & H & C & A & LG --> W[(Weaviate)]
    H & C -.->|rerank| TEI[(TEI reranker)]
    G & A --> LR[(LightRAG<br/>knowledge graph)]
    LG --> LC[(Persistent lazy<br/>concept-graph cache)]
    N --> WF[n8n workflow<br/>classify → route]

Open a multi-model chat, select the approaches (or flavors) you want, and one prompt fans out — every answer comes back with a uniform answer, retrieved-context, and metrics footer so they are directly comparable.

2. Flavors

Named tuning flavors such as graph-rag-wide or hybrid-rag-high-recall can also appear as model aliases. They route to the same base approach with reproducible parameter overrides (retrieval depth, rerank on/off, graph query mode, agent step budget, …). One base approach can therefore be benchmarked at several operating points without code changes. See Flavor Tuning.

3. Fair-Comparison Guarantees

  • One declared ingestion profile per dataset — Atlas writes the namespaced plain collection (RagBase_<profile>) and LightRAG graph once; the showcase derives the matching context-prefixed collection (RagContextual_<profile>) from those exact chunks.
  • Shared models — the chunk-based approaches generate through the same LiteLLM model and every approach embeds with the same embedding model (graph-rag's generator is LightRAG's QUERY role model by design); LLM roles are local-first (backend_plugins/rag/roles.yaml).
  • Uniform output contract — every approach returns the same answer/context/metrics shape, which the evaluation harness parses and scores.

4. Further Reading