n8n-nodes-ragas

A community n8n node that brings Ragas
RAG evaluation into your workflows. Pipe your RAG pipeline's outputs in, get
metric scores (faithfulness, answer relevancy, context precision/recall, and
more) out — then branch on thresholds downstream.

Self-Hosted n8n Only

This package runs Python via child_process and needs Python with ragas
installed on the host. It is designed for self-hosted n8n and cannot run on
n8n Cloud.

The node: Ragas Evaluate

A single node evaluates a batch of samples against a selected set of metrics in
one pass — the way Ragas is designed to work.

Metrics (v1)

Providers

• Judge model: OpenAI, Anthropic, Google Gemini, Ollama (local / OpenAI-compatible)
• Embeddings: OpenAI, Google, Ollama, HuggingFace-local (sentence-transformers)

Anthropic has no embeddings API. When you pick an Anthropic judge, pair it with
HuggingFace-local embeddings (no key needed) or OpenAI embeddings.

How it works

The node collects your input items, maps their fields to Ragas samples, and
spawns ragas_runner.py, passing the data as JSON on stdin. The Python
script configures the judge LLM + embeddings, runs ragas.evaluate(...), and
returns scores as JSON on stdout. Using stdin (not command-line args) keeps large
retrieved-context payloads well within OS limits.

Requirements

• n8n (self-hosted)
• Python 3.9+ on the host
• Python packages from requirements.txt:

pip install ragas langchain-openai
# plus, for the providers you use:
pip install langchain-anthropic langchain-google-genai langchain-huggingface sentence-transformers

Installation

From npm

cd ~/.n8n/custom
npm install n8n-nodes-ragas

Then restart n8n.

Via the n8n UI

1. Settings → Community Nodes → Install
2. Enter n8n-nodes-ragas
3. Restart n8n

Docker

FROM n8nio/n8n:latest
USER root
RUN apk add --no-cache python3 py3-pip
RUN pip3 install ragas langchain-openai
USER node
RUN cd /home/node/.n8n/custom && npm install n8n-nodes-ragas

Usage

1. Produce RAG samples upstream — each item should carry a question, the
   generated answer, the retrieved contexts, and (for some metrics) a
   ground-truth reference.
2. Add Ragas Evaluate and map those fields.
3. Pick your metrics, judge and embeddings providers/models.
4. Attach a Ragas API credential with your provider API key (skip it for
   fully local Ollama / HuggingFace setups).

Input

[
  {
    "question": "What is the capital of France?",
    "answer": "The capital of France is Paris.",
    "contexts": ["France is a country in Europe. Its capital is Paris."],
    "reference": "Paris"
  }
]

Output

Each input item passes through with its scores appended, followed by a summary
item:

[
  {
    "question": "What is the capital of France?",
    "answer": "The capital of France is Paris.",
    "faithfulness": 1.0,
    "answer_relevancy": 0.98,
    "context_precision": 1.0,
    "context_recall": 1.0
  },
  {
    "ragas_summary": {
      "faithfulness": 1.0,
      "answer_relevancy": 0.98,
      "context_precision": 1.0,
      "context_recall": 1.0
    },
    "metrics": ["faithfulness", "answer_relevancy", "context_precision", "context_recall"],
    "sample_count": 1,
    "judge_model": "openai:gpt-4o-mini",
    "embeddings_model": "openai:text-embedding-3-small"
  }
]

Local / private evaluation (Ollama)

Set the Judge Provider to Ollama (Local), the Judge Model to your local
model (e.g. llama3.1), the Embeddings Provider to HuggingFace (Local), and
add a Ragas API credential with an empty API Key and a Base URL of
http://localhost:11434/v1.

Troubleshooting

• ragas is not installed — install the requirements into the same Python
  the node calls (see Python Path).
• ... require a reference for every sample — map a Reference Field, or
  drop the reference-only metrics (Context Recall, Answer Correctness, Semantic
  Similarity).
• Wrong Python — set the Python Path parameter to the interpreter that has
  ragas installed.

Development

git clone https://github.com/arturovaine/n8n-nodes-ragas.git
cd n8n-nodes-ragas
npm install
npm run build      # tsc + copies icon and ragas_runner.py into dist
npm run lint

# Python helper tests (offline, no API keys needed)
pip install pytest
pytest

License

MIT

Acknowledgments

• n8n — workflow automation platform
• Ragas — RAG evaluation framework