diff --git "a/evaluation.ipynb" "b/evaluation.ipynb" --- "a/evaluation.ipynb" +++ "b/evaluation.ipynb" @@ -1430,7 +1430,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ @@ -1443,7 +1443,6 @@ "from ragas.metrics.critique import harmfulness\n", "from ragas import evaluate\n", "\n", - "from langchain_openai import ChatOpenAI\n", "from libs.embeddings import get_embeddings" ] }, @@ -1474,72 +1473,3169 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "from ragas.llms import LangchainLLMWrapper\n", "\n", + "# from langchain.chat_models import ChatOpenAI\n", + "from langchain_openai import ChatOpenAI\n", + "\n", "llm = ChatOpenAI(model=\"gpt-4o\", temperature=0.0)\n", "langchain_llm = LangchainLLMWrapper(llm)" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 99, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - ", log_tenacity=False))>" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "langchain_llm.set_run_config" + "# list of metrics we're going to use\n", + "metrics = [\n", + " faithfulness,\n", + " answer_relevancy,\n", + " context_recall,\n", + " context_precision,\n", + " harmfulness,\n", + "]\n", + "\n", + "for m in metrics:\n", + " # change LLM for metric\n", + " m.__setattr__(\"llm\", langchain_llm)\n", + "\n", + " # check if this metric needs embeddings\n", + " if hasattr(m, \"embeddings\"):\n", + " m.__setattr__(\"embeddings\", embeddings)" ] }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 100, "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'ChatOpenAI' object has no attribute 'set_run_config'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[79], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetrics\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mfaithfulness\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43manswer_relevancy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mcontext_recall\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mcontext_precision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mharmfulness\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mllm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlangchain_llm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43membeddings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43membeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mraise_exceptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 15\u001b[0m result\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/law-bot-C3zMZhS7-py3.11/lib/python3.11/site-packages/ragas/evaluation.py:198\u001b[0m, in \u001b[0;36mevaluate\u001b[0;34m(dataset, metrics, llm, embeddings, callbacks, in_ci, is_async, run_config, raise_exceptions, column_map)\u001b[0m\n\u001b[1;32m 195\u001b[0m reproducable_metrics\u001b[38;5;241m.\u001b[39mappend(i)\n\u001b[1;32m 197\u001b[0m \u001b[38;5;66;03m# init all the models\u001b[39;00m\n\u001b[0;32m--> 198\u001b[0m \u001b[43mmetric\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_config\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 200\u001b[0m executor \u001b[38;5;241m=\u001b[39m Executor(\n\u001b[1;32m 201\u001b[0m desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEvaluating\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 202\u001b[0m keep_progress_bar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 203\u001b[0m raise_exceptions\u001b[38;5;241m=\u001b[39mraise_exceptions,\n\u001b[1;32m 204\u001b[0m run_config\u001b[38;5;241m=\u001b[39mrun_config,\n\u001b[1;32m 205\u001b[0m )\n\u001b[1;32m 206\u001b[0m \u001b[38;5;66;03m# new evaluation chain\u001b[39;00m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/law-bot-C3zMZhS7-py3.11/lib/python3.11/site-packages/ragas/metrics/base.py:159\u001b[0m, in \u001b[0;36mMetricWithLLM.init\u001b[0;34m(self, run_config)\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 157\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMetric \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m has no valid LLM provided (self.llm is None). Please initantiate a the metric with an LLM to run.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 158\u001b[0m )\n\u001b[0;32m--> 159\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mllm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_run_config\u001b[49m(run_config)\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/law-bot-C3zMZhS7-py3.11/lib/python3.11/site-packages/langchain_core/runnables/configurable.py:244\u001b[0m, in \u001b[0;36mDynamicRunnable.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getattr__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m--> 244\u001b[0m attr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdefault\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(attr):\n\u001b[1;32m 247\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(attr)\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n", - "\u001b[0;31mAttributeError\u001b[0m: 'ChatOpenAI' object has no attribute 'set_run_config'" - ] - } - ], - "source": [ - "result = evaluate(\n", - " dataset,\n", - " metrics=[\n", - " faithfulness,\n", - " answer_relevancy,\n", - " context_recall,\n", - " context_precision,\n", - " harmfulness,\n", - " ],\n", - " llm=langchain_llm,\n", - " embeddings=embeddings,\n", - " raise_exceptions=False,\n", - ")\n", - "\n", - "result" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "85689792d97247c28e745042069227b7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Evaluating: 0%| | 0/100 [00:00[07/12/24 00:59:46] INFO [_client.py:1773] >> HTTP Request: POST _client.py:1773\n", + " https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\" \n", + "\n" + ], + "text/plain": [ + "\u001b[2;36m[07/12/24 00:59:46]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0m_client.py:\u001b[1;36m1773\u001b[0m\u001b[1m]\u001b[0m >> HTTP Request: \u001b[1;33mPOST\u001b[0m \u001b]8;id=523397;file:///Users/anpigon/Library/Caches/pypoetry/virtualenvs/law-bot-C3zMZhS7-py3.11/lib/python3.11/site-packages/httpx/_client.py\u001b\\\u001b[2m_client.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=260756;file:///Users/anpigon/Library/Caches/pypoetry/virtualenvs/law-bot-C3zMZhS7-py3.11/lib/python3.11/site-packages/httpx/_client.py#1773\u001b\\\u001b[2m1773\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[2;36m \u001b[0m \u001b[4;94mhttps://api.openai.com/v1/chat/completions\u001b[0m \u001b[32m\"HTTP/1.1 200 OK\"\u001b[0m \u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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MetricScore
0faithfulness0.532313
1answer_relevancy0.252284
2context_recall0.443333
3context_precision0.578889
4harmfulness0.050000
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" + ], + "text/plain": [ + " Metric Score\n", + "0 faithfulness 0.532313\n", + "1 answer_relevancy 0.252284\n", + "2 context_recall 0.443333\n", + "3 context_precision 0.578889\n", + "4 harmfulness 0.050000" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_result = pd.DataFrame(list(result.items()), columns=[\"Metric\", \"Score\"])\n", + "df_result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> 각 지표에 대한 설명은 [여기](https://docs.ragas.io/en/stable/concepts/metrics/index.html)에서 확인 가능하다.\n" ] }, {