{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "tpRjj9c9iqU_" }, "source": [ "# 📊 Evaluasi RAG Politeknik Negeri Padang dengan LangSmith\n", "\n", "Evaluasi menggunakan metrik:\n", "- **CP**: Context Precision\n", "- **CR**: Context Recall\n", "- **F1**: Harmonik CP dan CR\n", "- **AR**: Answer Relevance (semantic similarity)\n", "\n", "Menggunakan LangChain + Replicate + Vectorstore (FAISS), dan logging ke LangSmith.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting openai\n", " Downloading openai-1.88.0-py3-none-any.whl.metadata (25 kB)\n", "Requirement already satisfied: anyio<5,>=3.5.0 in c:\\users\\user\\onedrive\\tugas akhir\\pnp-chatbot-v1\\venv\\lib\\site-packages (from openai) (4.9.0)\n", "Collecting distro<2,>=1.7.0 (from openai)\n", " Using cached distro-1.9.0-py3-none-any.whl.metadata (6.8 kB)\n", "Requirement already satisfied: httpx<1,>=0.23.0 in 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'langchain_core.evaluation'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[15]\u001b[39m\u001b[32m, line 13\u001b[39m\n\u001b[32m 11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlangchain\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mllms\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Replicate\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mdataset_rag_pnp\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m dataset \u001b[38;5;66;03m# pastikan file ini ada\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlangchain_core\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mevaluation\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m load_evaluator\n\u001b[32m 15\u001b[39m \u001b[38;5;66;03m# ✅ Load ENV dari file .env\u001b[39;00m\n\u001b[32m 16\u001b[39m dotenv.load_dotenv()\n", "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'langchain_core.evaluation'" ] } ], "source": [ "import os\n", "import dotenv\n", "import numpy as np\n", "import pandas as pd\n", "from langsmith import traceable\n", "from langchain.embeddings.openai import OpenAIEmbeddings\n", "from langchain.evaluation import load_evaluator\n", "from langchain.vectorstores import FAISS\n", "from langchain.embeddings import HuggingFaceEmbeddings\n", "from langchain.chains import ConversationalRetrievalChain\n", "from langchain.llms import Replicate\n", "from dataset_rag_pnp import dataset # pastikan file ini ada\n", "\n", "# ✅ Load ENV dari file .env\n", "dotenv.load_dotenv()\n", "\n", "# ✅ Set ENV untuk LangSmith & OpenAI\n", "os.environ['LANGCHAIN_PROJECT'] = os.getenv('LANGSMITH_PROJECT', 'PNP RAG Eval')\n", "os.environ['LANGCHAIN_ENDPOINT'] = os.getenv('LANGSMITH_ENDPOINT', 'https://api.smith.langchain.com')\n", "os.environ['LANGCHAIN_API_KEY'] = os.getenv('LANGSMITH_API_KEY')\n", "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')\n", "os.environ['REPLICATE_API_TOKEN'] = os.getenv('REPLICATE_API_TOKEN')\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "FA5PZj-cTRWA" }, "outputs": [], "source": [ "embeddings = HuggingFaceEmbeddings(\n", " model_name=\"LazarusNLP/all-indo-e5-small-v4\",\n", " model_kwargs={\"device\": \"cpu\"},\n", " encode_kwargs={\"normalize_embeddings\": True}\n", ")\n", "\n", "vectorstore = FAISS.load_local(\"vector_store_data\", embeddings, allow_dangerous_deserialization=True)\n", "retriever = vectorstore.as_retriever(search_kwargs={\"k\": 3})\n", "\n", "llm = Replicate(\n", " model=\"fauziisyrinapridal/sahabat-ai-v1:afb9fa89fe786362f619fd4fef34bd1f7a4a4da23073d8a6fbf54dcbe458f216\",\n", " model_kwargs={\"temperature\": 0.1, \"top_p\": 0.9, \"max_new_tokens\": 6000}\n", " )\n", "\n", "qa_chain = ConversationalRetrievalChain.from_llm(\n", " llm=llm,\n", " retriever=retriever,\n", " return_source_documents=True\n", ")\n", "\n", "chat_history = []\n", "\n", "def run_rag(question: str):\n", " global chat_history\n", " result = qa_chain({\"question\": question, \"chat_history\": chat_history})\n", " chat_history.append((question, result['answer']))\n", " contexts = [doc.page_content for doc in result['source_documents']]\n", " return {\"answer\": result['answer'], \"contexts\": contexts}\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Y0o9b_-kVu-p" }, "outputs": [], "source": [ "from langchain_core.evaluation import load_evaluator\n", "from langchain.embeddings.openai import OpenAIEmbeddings\n", "import numpy as np\n", "\n", "@traceable(name=\"Evaluate RAG Question\")\n", "def evaluate_question(q, ground_truth):\n", " emb = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n", " ar_eval = load_evaluator(\"embedding_distance\")\n", "\n", " def embed(text):\n", " return emb.embed_query(text)\n", "\n", " def cosine_sim(a, b):\n", " return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))) if np.linalg.norm(a) and np.linalg.norm(b) else 0.0\n", "\n", " rag_out = run_rag(q)\n", " answer = rag_out['answer']\n", " contexts = rag_out['contexts']\n", " gt_emb = embed(ground_truth)\n", " flags = [cosine_sim(gt_emb, embed(ctx)) >= 0.78 for ctx in contexts]\n", " cp = sum(flags) / len(contexts) if contexts else 0.0\n", " cr = 1.0 if any(flags) else 0.0\n", " f1 = 2 * cp * cr / (cp + cr) if cp + cr > 0 else 0.0\n", "\n", " ar_score = 1.0 - ar_eval.evaluate_strings(\n", " prediction=answer,\n", " reference=ground_truth,\n", " input=q\n", " )[\"score\"]\n", "\n", " return {\n", " \"question\": q,\n", " \"answer\": answer,\n", " \"CP\": round(cp, 3),\n", " \"CR\": round(cr, 3),\n", " \"F1\": round(f1, 3),\n", " \"AR\": round(ar_score, 3),\n", " \"contexts\": contexts\n", " }\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "LWKj0zhoS5td" }, "outputs": [ { "ename": "ImportError", "evalue": "The tiktoken library is required to use the default OpenAI embeddings with embedding distance evaluators. Please either manually select a different Embeddings object or install tiktoken using `pip install tiktoken`.", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\USER\\OneDrive\\Tugas Akhir\\pnp-chatbot-v1\\venv\\Lib\\site-packages\\langchain\\evaluation\\embedding_distance\\base.py:140\u001b[39m, in \u001b[36m_EmbeddingDistanceChainMixin._validate_tiktoken_installed\u001b[39m\u001b[34m(cls, values)\u001b[39m\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m140\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtiktoken\u001b[39;00m \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[32m 141\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n", "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'tiktoken'", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m results = []\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m dataset:\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m result = \u001b[43mevaluate_question\u001b[49m\u001b[43m(\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mquestion\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mitem\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mground_truth\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m results.append(result)\n\u001b[32m 6\u001b[39m pd.DataFrame(results)[[\u001b[33m\"\u001b[39m\u001b[33mquestion\u001b[39m\u001b[33m\"\u001b[39m,\u001b[33m\"\u001b[39m\u001b[33mground_truth\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mCP\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mCR\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mF1\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mAR\u001b[39m\u001b[33m\"\u001b[39m]]\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\USER\\OneDrive\\Tugas Akhir\\pnp-chatbot-v1\\venv\\Lib\\site-packages\\langsmith\\run_helpers.py:635\u001b[39m, in \u001b[36mtraceable..decorator..wrapper\u001b[39m\u001b[34m(langsmith_extra, *args, **kwargs)\u001b[39m\n\u001b[32m 633\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m func_accepts_parent_run:\n\u001b[32m 634\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33mrun_tree\u001b[39m\u001b[33m\"\u001b[39m] = run_container[\u001b[33m\"\u001b[39m\u001b[33mnew_run\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m--> \u001b[39m\u001b[32m635\u001b[39m function_result = \u001b[43mrun_container\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcontext\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 636\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 637\u001b[39m _cleanup_traceback(e)\n", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 6\u001b[39m, in \u001b[36mevaluate_question\u001b[39m\u001b[34m(q, ground_truth)\u001b[39m\n\u001b[32m 3\u001b[39m \u001b[38;5;129m@traceable\u001b[39m(name=\u001b[33m\"\u001b[39m\u001b[33mEvaluate RAG Question\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mevaluate_question\u001b[39m(q, ground_truth):\n\u001b[32m 5\u001b[39m emb = OpenAIEmbeddings(model=\u001b[33m\"\u001b[39m\u001b[33mtext-embedding-3-small\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m ar_eval = \u001b[43mload_evaluator\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43membedding_distance\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34membed\u001b[39m(text):\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m emb.embed_query(text)\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\USER\\OneDrive\\Tugas Akhir\\pnp-chatbot-v1\\venv\\Lib\\site-packages\\langchain\\evaluation\\loading.py:163\u001b[39m, in \u001b[36mload_evaluator\u001b[39m\u001b[34m(evaluator, llm, **kwargs)\u001b[39m\n\u001b[32m 161\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m evaluator_cls.from_llm(llm=llm, **kwargs)\n\u001b[32m 162\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m163\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mevaluator_cls\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\USER\\OneDrive\\Tugas Akhir\\pnp-chatbot-v1\\venv\\Lib\\site-packages\\langchain_core\\load\\serializable.py:130\u001b[39m, in \u001b[36mSerializable.__init__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 128\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args: Any, **kwargs: Any) -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 129\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\"\"\"\u001b[39;00m \u001b[38;5;66;03m# noqa: D419\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m130\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", " \u001b[31m[... skipping hidden 1 frame]\u001b[39m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\USER\\OneDrive\\Tugas Akhir\\pnp-chatbot-v1\\venv\\Lib\\site-packages\\pydantic\\_internal\\_decorators_v1.py:148\u001b[39m, in \u001b[36mmake_v1_generic_root_validator.._wrapper1\u001b[39m\u001b[34m(values, _)\u001b[39m\n\u001b[32m 147\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_wrapper1\u001b[39m(values: RootValidatorValues, _: core_schema.ValidationInfo) -> RootValidatorValues:\n\u001b[32m--> \u001b[39m\u001b[32m148\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvalidator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\USER\\OneDrive\\Tugas Akhir\\pnp-chatbot-v1\\venv\\Lib\\site-packages\\langchain_core\\utils\\pydantic.py:226\u001b[39m, in \u001b[36mpre_init..wrapper\u001b[39m\u001b[34m(cls, values)\u001b[39m\n\u001b[32m 223\u001b[39m values[name] = field_info.default\n\u001b[32m 225\u001b[39m \u001b[38;5;66;03m# Call the decorated function\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m226\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\USER\\OneDrive\\Tugas Akhir\\pnp-chatbot-v1\\venv\\Lib\\site-packages\\langchain\\evaluation\\embedding_distance\\base.py:142\u001b[39m, in \u001b[36m_EmbeddingDistanceChainMixin._validate_tiktoken_installed\u001b[39m\u001b[34m(cls, values)\u001b[39m\n\u001b[32m 140\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtiktoken\u001b[39;00m \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[32m 141\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m142\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[32m 143\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mThe tiktoken library is required to use the default \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 144\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mOpenAI embeddings with embedding distance evaluators.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 145\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m Please either manually select a different Embeddings object\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 146\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m or install tiktoken using `pip install tiktoken`.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 147\u001b[39m )\n\u001b[32m 148\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m values\n", "\u001b[31mImportError\u001b[39m: The tiktoken library is required to use the default OpenAI embeddings with embedding distance evaluators. Please either manually select a different Embeddings object or install tiktoken using `pip install tiktoken`." ] } ], "source": [ "results = []\n", "for item in dataset:\n", " result = evaluate_question(item['question'], item['ground_truth'])\n", " results.append(result)\n", "\n", "pd.DataFrame(results)[[\"question\",\"ground_truth\", \"CP\", \"CR\", \"F1\", \"AR\"]]\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "venv", "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": "3.12.5" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0036f0205e8045ea8b19ea0da01abc03": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", 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