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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ragas demonstration\n",
"\n",
"This notebook demonstrates how to evaluate a RAG system using the [Ragas evaluation framework](https://github.com/explodinggradients/ragas?tab=readme-ov-file) and import the resulting evaluation results into InspectorRAGet for analysis.\n",
"\n",
"### Installation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting git+https://github.com/explodinggradients/ragas\n",
" Cloning https://github.com/explodinggradients/ragas to /private/var/folders/l5/fj0t2qmn44x0r042xw6cjr8w0000gn/T/pip-req-build-k70llt1h\n",
" Running command git clone --filter=blob:none --quiet https://github.com/explodinggradients/ragas /private/var/folders/l5/fj0t2qmn44x0r042xw6cjr8w0000gn/T/pip-req-build-k70llt1h\n",
" Resolved https://github.com/explodinggradients/ragas to commit d2486f117fd827dcfc3e196d4cf7798573c55b09\n",
" Installing build dependencies ... \u001b[?25ldone\n",
"\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n",
"\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
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]
}
],
"source": [
"!pip install git+https://github.com/explodinggradients/ragas"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
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]
}
],
"source": [
"!pip install ipywidgets"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from datasets import Dataset \n",
"import os\n",
"from ragas import evaluate\n",
"from ragas.metrics import faithfulness, answer_relevancy, context_precision, context_recall"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"RAGAS_DO_NOT_TRACK\"] = \"true\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import dataset\n",
"\n",
"For this example, we will be using the sample dataset provided in Ragas' quick start instructions."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"data_samples = {\n",
" 'question': ['When was the first super bowl?', 'Who won the most super bowls?'],\n",
" 'answer': ['The first superbowl was held on Jan 15, 1967', 'The most super bowls have been won by The New England Patriots'],\n",
" 'contexts' : [['The First AFL–NFL World Championship Game was an American football game played on January 15, 1967, at the Los Angeles Memorial Coliseum in Los Angeles,'], \n",
" ['The Green Bay Packers...Green Bay, Wisconsin.','The Packers compete...Football Conference']],\n",
" 'ground_truth': ['The first superbowl was held on January 15, 1967', 'The New England Patriots have won the Super Bowl a record six times']\n",
"}\n",
"\n",
"dataset = Dataset.from_dict(data_samples)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run evaluation\n",
"\n",
"In this example, we run evaluation using `gpt-4o-mini` and the following Ragas evaluation metrics: faithfulness, answer relevance, context precision, and context recall. Consult the Ragas documentation on how to use different models or metrics.\n",
"\n",
"**Note: To use one of the OpenAI model for evaluation, you have to fill in your `OPENAI_API_KEY` below.** "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"OPENAI_API_KEY\"] = \"provide-your-openai-api-key\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d221210e378a4ad0a25748d8f8f095ba",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Evaluating: 0%| | 0/8 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"<div>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>question</th>\n",
" <th>answer</th>\n",
" <th>contexts</th>\n",
" <th>ground_truth</th>\n",
" <th>faithfulness</th>\n",
" <th>answer_relevancy</th>\n",
" <th>context_precision</th>\n",
" <th>context_recall</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>When was the first super bowl?</td>\n",
" <td>The first superbowl was held on Jan 15, 1967</td>\n",
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" <td>Who won the most super bowls?</td>\n",
" <td>The most super bowls have been won by The New ...</td>\n",
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" <td>The New England Patriots have won the Super Bo...</td>\n",
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" question \\\n",
"0 When was the first super bowl? \n",
"1 Who won the most super bowls? \n",
"\n",
" answer \\\n",
"0 The first superbowl was held on Jan 15, 1967 \n",
"1 The most super bowls have been won by The New ... \n",
"\n",
" contexts \\\n",
"0 [The First AFL–NFL World Championship Game was... \n",
"1 [The Green Bay Packers...Green Bay, Wisconsin.... \n",
"\n",
" ground_truth faithfulness \\\n",
"0 The first superbowl was held on January 15, 1967 1.0 \n",
"1 The New England Patriots have won the Super Bo... 0.0 \n",
"\n",
" answer_relevancy context_precision context_recall \n",
"0 0.980714 1.0 1.0 \n",
"1 0.943043 0.0 0.0 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from langchain_openai.chat_models import ChatOpenAI\n",
"llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
"\n",
"score = evaluate(dataset,llm=llm, metrics=[faithfulness,answer_relevancy,context_precision,context_recall])\n",
"df_score = score.to_pandas()\n",
"\n",
"display(df_score)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create InspectorRAGet file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We next generate the file for InspectorRAGet. We start by specifying the experiment metadata (experiment name, model, metrics)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Specify name of experiment**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"name = \"Ragas Demo\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Specify name of model that produced the answers:** Since we do not know where the answers in the `data_samples` came from, we use a dummy name. In your experiments, replace this with the name of the model that produced the answers."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# models -> List[dict]\n",
"models = [\n",
" {\n",
" \"model_id\": \"model_a\", # e.g., \"OpenAI/gpt-3.5-turbo\"\n",
" \"name\": \"Model A\", # e.g., \"GPT-3.5-Turbo\"\n",
" \"owner\": \"Owner A\" # e.g., \"OpenAI\"\n",
" }\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Specify metrics used:** Ceate a record for each metric used in the Ragas evaluation. Note that the `name` of each metric below should match the name of the metric used in the data frame `df_score` output by Ragas."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# metrics -> List[dict]\n",
"all_metrics = [\n",
" {\n",
" \"name\": \"faithfulness\",\n",
" \"display_name\": \"Faithfulness\",\n",
" \"description\": \"Faithfulness\",\n",
" \"author\": \"algorithm\",\n",
" \"type\": \"numerical\",\n",
" \"aggregator\": \"average\",\n",
" \"range\": [0, 1.0, 0.1]\n",
" },\n",
" {\n",
" \"name\": \"answer_relevancy\",\n",
" \"display_name\": \"Answer Relevancy\",\n",
" \"description\": \"Answer Relevancy\",\n",
" \"author\": \"algorithm\",\n",
" \"type\": \"numerical\",\n",
" \"aggregator\": \"average\",\n",
" \"range\": [0, 1.0, 0.1]\n",
" },\n",
" {\n",
" \"name\": \"context_precision\",\n",
" \"display_name\": \"Context Precision\",\n",
" \"description\": \"Context Precision\",\n",
" \"author\": \"algorithm\",\n",
" \"type\": \"numerical\",\n",
" \"aggregator\": \"average\",\n",
" \"range\": [0, 1.0, 0.1]\n",
" },\n",
" {\n",
" \"name\": \"context_recall\",\n",
" \"display_name\": \"Context Recall\",\n",
" \"description\": \"Context Recall\",\n",
" \"author\": \"algorithm\",\n",
" \"type\": \"numerical\",\n",
" \"aggregator\": \"average\",\n",
" \"range\": [0, 1.0, 0.1]\n",
" }\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Compute document IDs"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"doc_id_counter = 0\n",
"\n",
"doc_text_to_id = {}\n",
"for index, row in df_score.iterrows():\n",
" for c in row[\"contexts\"]:\n",
" if c not in doc_text_to_id:\n",
" doc_text_to_id[c] = doc_id_counter\n",
" doc_id_counter += 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Populate documents, tasks, and evaluations"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"all_documents = []\n",
"all_tasks = []\n",
"all_evaluations = []\n",
"\n",
"# Populate documents\n",
"for doc_text, doc_id in doc_text_to_id.items():\n",
" document = {\n",
" \"document_id\": f\"{doc_id}\",\n",
" \"text\": f\"{doc_text}\"\n",
" }\n",
" all_documents.append(document)\n",
"\n",
"# Populate taks and evaluations\n",
"for index, row in df_score.iterrows():\n",
" instance = {\n",
" \"task_id\": f\"{index}\",\n",
" \"task_type\": \"rag\",\n",
" \"contexts\": [ {\"document_id\": f\"{doc_text_to_id[c]}\"} for c in row[\"contexts\"] ],\n",
" \"input\": [{\"speaker\": \"user\", \"text\": f\"{row['question']}\"}],\n",
" \"targets\": [{\"text\": f\"{row['ground_truth']}\"}]\n",
" }\n",
" all_tasks.append(instance)\n",
"\n",
" evaluation = {\n",
" \"task_id\": f\"{index}\",\n",
" \"model_id\": f\"{models[0]['model_id']}\",\n",
" \"model_response\": f\"{row['answer']}\",\n",
" \"annotations\": {}\n",
" }\n",
" for metric in all_metrics:\n",
" metric_name = metric[\"name\"]\n",
" evaluation[\"annotations\"][metric_name] = {\n",
" \"system\": {\n",
" \"value\": row[metric_name],\n",
" \"duration\": 0\n",
" }\n",
" }\n",
" all_evaluations.append(evaluation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Write final json file to `ragas-inspectorraget-demo.json` in working directory"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"output = {\n",
" \"name\": name,\n",
" \"models\": models,\n",
" \"metrics\": all_metrics,\n",
" \"documents\": all_documents,\n",
" \"tasks\": all_tasks,\n",
" \"evaluations\": all_evaluations,\n",
"}\n",
"\n",
"with open(\n",
" file=\"ragas-inspectorraget-demo.json\", mode=\"w\", encoding=\"utf-8\"\n",
") as fp:\n",
" json.dump(output, fp, indent=4)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "bin",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|