RAG Evaluation ↗

ragas guide intermediate rag testing
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Original Documentation

The rag_eval template provides a complete RAG evaluation setup with custom metrics, dataset management, and experiment tracking.

Create the Project#

# Using uvx (no installation required)
uvx ragas quickstart rag_eval
cd rag_eval

# Or with ragas installed
ragas quickstart rag_eval
cd rag_eval

Install Dependencies#

uv sync

Or with pip:

pip install -e .

Set Your API Key#

export OPENAI_API_KEY="your-openai-key"
export ANTHROPIC_API_KEY="your-anthropic-key"

Update evals.py:

from anthropic import Anthropic
from ragas.llms import llm_factory

client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
llm = llm_factory("claude-3-5-sonnet-20241022", provider="anthropic", client=client)
export GOOGLE_API_KEY="your-google-api-key"

Update evals.py:

import google.generativeai as genai
from ragas.llms import llm_factory

genai.configure(api_key=os.environ.get("GOOGLE_API_KEY"))
client = genai.GenerativeModel("gemini-2.0-flash")
llm = llm_factory("gemini-2.0-flash", provider="google", client=client)
from openai import OpenAI
from ragas.llms import llm_factory

client = OpenAI(
    api_key="ollama",
    base_url="http://localhost:11434/v1"
)
llm = llm_factory("mistral", provider="openai", client=client)

Run the Evaluation#

uv run python evals.py

The evaluation will:

  1. Load test data from the load_dataset() function
  2. Query your RAG application with test questions
  3. Evaluate responses using custom metrics
  4. Display results in the console
  5. Save results to CSV in evals/experiments/

Project Structure#

rag_eval/
├── README.md              # Project documentation
├── pyproject.toml         # Project configuration
├── rag.py                 # RAG application implementation
├── evals.py               # Evaluation workflow
├── __init__.py            # Python package marker
└── evals/
    ├── datasets/          # Test data files
    ├── experiments/       # Evaluation results (CSV)
    └── logs/              # Execution logs and traces

Understanding the Code#

The RAG Application (rag.py)#

A simple RAG implementation with:

  • Document storage: In-memory document collection
  • Keyword retrieval: Simple keyword matching for document retrieval
  • Response generation: OpenAI API for generating answers
  • Tracing: Logs each query for debugging
from rag import default_rag_client

# Initialize with OpenAI client
rag_client = default_rag_client(llm_client=openai_client, logdir="evals/logs")

# Query the RAG system
response = rag_client.query("What is Ragas?")
print(response["answer"])

The Evaluation Script (evals.py)#

The evaluation workflow:

  1. Dataset loading: Creates test cases with questions and grading notes
  2. Metric definition: Custom DiscreteMetric for pass/fail evaluation
  3. Experiment execution: Runs queries and evaluates responses
  4. Result storage: Saves to CSV for analysis
from ragas import Dataset, experiment
from ragas.metrics import DiscreteMetric

# Define your metric
my_metric = DiscreteMetric(
    name="correctness",
    prompt="Check if the response contains points from grading notes...",
    allowed_values=["pass", "fail"],
)

# Run experiment
@experiment()
async def run_experiment(row):
    response = rag_client.query(row["question"])
    score = my_metric.score(llm=llm, response=response["answer"], ...)
    return {**row, "response": response["answer"], "score": score.value}

Customization#

Add Test Cases#

Edit the load_dataset() function in evals.py:

def load_dataset():
    dataset = Dataset(
        name="test_dataset",
        backend="local/csv",
        root_dir="evals",
    )

    data_samples = [
        {
            "question": "What is Ragas?",
            "grading_notes": "- evaluation framework - LLM applications",
        },
        {
            "question": "How do experiments work?",
            "grading_notes": "- track results - compare runs - store metrics",
        },
        # Add more test cases...
    ]

    for sample in data_samples:
        dataset.append(sample)
    dataset.save()
    return dataset

Modify the Metric#

Change evaluation criteria by updating the metric prompt:

my_metric = DiscreteMetric(
    name="quality",
    prompt="""Evaluate the response quality:

Response: {response}
Expected Points: {grading_notes}

Rate as:
- 'excellent': All points covered with clear explanation
- 'good': Most points covered
- 'poor': Missing key points

Rating:""",
    allowed_values=["excellent", "good", "poor"],
)

Add Multiple Metrics#

Create additional metrics for different evaluation aspects:

from ragas.metrics import DiscreteMetric, NumericalMetric

correctness = DiscreteMetric(
    name="correctness",
    prompt="Is the response factually correct? {response}",
    allowed_values=["correct", "incorrect"],
)

relevance = NumericalMetric(
    name="relevance",
    prompt="Rate relevance 1-5: {response} for question: {question}",
    allowed_values=(1, 5),
)

Use Your Own RAG System#

Replace the example RAG with your production system:

# In evals.py
from your_rag_module import YourRAGClient

rag_client = YourRAGClient(...)

@experiment()
async def run_experiment(row):
    # Call your RAG system
    response = await rag_client.query(row["question"])

    score = my_metric.score(
        llm=llm,
        response=response,
        grading_notes=row["grading_notes"],
    )

    return {
        **row,
        "response": response,
        "score": score.value,
    }

Viewing Results#

Results are saved to evals/experiments/ as CSV files. Each experiment run creates a new file with:

  • Input data (questions, grading notes)
  • Model responses
  • Evaluation scores
  • Timestamps
import pandas as pd

# Load results
results = pd.read_csv("evals/experiments/your_experiment.csv")

# Calculate pass rate
pass_rate = (results["score"] == "pass").mean()
print(f"Pass rate: {pass_rate:.1%}")

Next Steps#

Link last verified June 7, 2026. View original ↗
Source: RAGAS Docs
Link last verified: 2026-03-04