{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "uriHEO8pkgSo" }, "source": [ "# Evaluating AI with Haystack\n", "\n", "by Bilge Yucel ([X](https://x.com/bilgeycl), [Linkedin](https://www.linkedin.com/in/bilge-yucel/))\n", "\n", "In this cookbook, we walk through the [Evaluators](https://docs.haystack.deepset.ai/docs/evaluators) in Haystack, create an evaluation pipeline and try different Evaluation Frameworks like [FlowJudge](https://haystack.deepset.ai/integrations/flow-judge). \n", "\n", "πŸ“š **Useful Resources:**\n", "* [Article: Benchmarking Haystack Pipelines for Optimal Performance](https://haystack.deepset.ai/blog/benchmarking-haystack-pipelines)\n", "* [Evaluation Walkthrough](https://haystack.deepset.ai/tutorials/guide_evaluation)\n", "* [Evaluation tutorial](https://haystack.deepset.ai/tutorials/35_evaluating_rag_pipelines)\n", "* [Evaluation Docs](https://docs.haystack.deepset.ai/docs/evaluation)\n", "* [haystack-evaluation repo](https://github.com/deepset-ai/haystack-evaluation/tree/main)\n", "\n", "## πŸ“Ί Watch Along\n", "\n", "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jOtY28-vJs35", "scrolled": true }, "outputs": [], "source": [ "!pip install haystack-ai \"sentence-transformers>=3.0.0\" pypdf \"flow-judge[hf]\"" ] }, { "cell_type": "markdown", "metadata": { "id": "C_WUXQzEQWv8" }, "source": [ "## 1. Building your pipeline" ] }, { "cell_type": "markdown", "metadata": { "id": "Dms5Ict6NGXq" }, "source": [ "### ARAGOG\n", "\n", "This dataset is based on the paper [Advanced Retrieval Augmented Generation Output Grading (ARAGOG)](https://arxiv.org/pdf/2404.01037). It's a\n", "collection of papers from ArXiv covering topics around Transformers and Large Language Models, all in PDF format.\n", "\n", "The dataset contains:\n", "- 13 PDF papers.\n", "- 107 questions and answers generated with the assistance of GPT-4, and validated/corrected by humans.\n", "\n", "We have:\n", "- ground-truth answers\n", "- questions\n", "\n", "Get the dataset [here](https://github.com/deepset-ai/haystack-evaluation/blob/main/datasets/README.md)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Run this to download the dataset\n", "!mkdir -p ARAGOG/papers_for_questions\n", "\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/DetectGPT.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/MMLU_measure.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/PAL.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/bert.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/codenet.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/distilbert.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/glm_130b.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/hellaswag.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/llama.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/llm_long_tail.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/meaning_of_prompt.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/megatron.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/red_teaming.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/roberta.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/superglue.pdf -P ARAGOG/papers_for_questions\n", "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/papers_for_questions/task2vec.pdf -P ARAGOG/papers_for_questions\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Indexing Pipeline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 193, "referenced_widgets": [ "63a2250eeac3468cabbeea4d4aa737da", "61ccdd2e814d440c8dfc126007d6942f", "3f932254e79f4dc894c2a09c9d6f3aa9", "370eda14b4db451e8e0d567b336f1357", "ccf595bc10094c468aa1d7e3db5e2001", "d498664616c744fe914e8ccca0fcc5d9", "b23e940bb57d45f5a660d56f1a5de12c", "505907ef6eba4f558a4b2a17791c1057", "f2e56c86e07c46eaa2b192977021565a", "2573bf08f6274e709759b664c5c35b0a", "15377ea70c0548049f90993b7edca597", "e532542b46164c59a585eed928f954b7", "5851d89610744d0388ba40fc24ca2029", "ddf9042eeff94c81b0153db06238b8ac", "fbdfb8b539684373a1ec4d0b37242aa6", "82434a705be54bc6a296a4ebecb6cdbb", "c091207cae2940d29fe445132fe36f6a", "d96185207ec449c0ab128c2fbd427f27", "256cddebfac24ca1bf5ac2618edb6432", "c2f6cd4d91bf4368bc1018cbda44c915", "c55a9a25325c4a82b977abd962d809e7", "f4bca807b90d462983a29393d9533c7a", "a44f7688d3014e3291397457dac7588b", "da33fab43a554e1599b69d32833f6866", "aa6786192b6f47dca6e74b7a637025a2", "631a54146c824ad2beccfdcd7415adaf", "3d7894a9bd364127b6f4bf2fdbc9ccda", "97baa482ea8941b7b4690355cb0dbfab", "c7885fabac1845a68f87a9851f990a61", "30bfabda14d942b7823c37c00ca09a44", "de823f9a25174ffb8a96593837f1dc4b", "231f709581a64f7986f4d03609d14e9b", "d0bddde6fa49451fb3dca4ce2d0be43e", "01866330e5b344a08ec8f93164d30b97", "2489569c1c064626b9643ba405fecc17", "cf9656d7589649a2b9bbcaf804bdf74e", "cf99f5f162d346ed9ea9b5649a7f0bce", "4cf8257e422840bba63046487e7ba8d9", "4ca58cf2ce034b568b737701a1e22893", "8e887c552ece49de8b0b267447701ccf", "f1a68ff6ddb14c0ebe3d23e541993a39", "441aea8c41484163b0fea778105177cf", "6e5aa6c450c149bca8feb8ab54574342", "c0d174f917ed408ebb43ea89b6123b00", "44e4dafde124442fb8ec03cca27fbfec", "ec415fb1fee74460835ec20ba868ce28", "1c945fce1a834897976cdf65203d9359", "f5a46e3237404d4d8f2f5bf28ddb5b15", "a3cb6353fb834ff5915b4dbaadf4b4c3", "ccb687db5a8e4502979d8b80f17d864e", "4f3c64d7a0554970bbd768650c1f8087", "645abc85f3024837adc8d8b0c1380f44", "aaebedbdfe3e4592ab8bb5aef600386e", "1ce2816e564d4afd9531639914d4d002", "26a5ae86d1604d41bc0bf97257d241ef", "8a296418e81549d68bd6e7540fe58411", "81bdb2ec8f584de58c58bdd1f710507d", "368a19bb275d47ba91e19198b0f00e52", "54f986fe1fb64fd3992871245674f717", "50863e3929e54ac092116a98ad126096", "a8ce80d082584584886b891fcf753b70", "7fac1705485d45d28e915962d22b5704", "2b3aaef9c8ae434ca49454991a565e3b", "3480b56144164c93992f962a5ae5ec68", "021f75fbbb4c4cd39850724e5658ceb5", "198bd89d6d0d4fca8eab21f52c101e64", "c17b2dd231e34437af28e271d5fef893", "82874942fd564d4c9ee822a76620f408", "3685f8efff6d413f94a0277231d1e07a", "fc2847d3341947a09fc5ba6c3e0f8dee", "d6c42191eb4147beafefce541234df5e", "902bc8a6be114106a3ff0cc5e8c279e2", "0a5a23affad44ca9924233997001773c", "5292150749354885842137c42b859410", "6cc16aa262c94fa2b6d50ec05cac03e2", "1a9f640ea2f3404bb4cf833d8ee4a504", "f15c8b65d5cb46cb8d54504638ed8354", "082721263d2849f3ae103627c4ed5726", "e60a261e229449f5a8dd52b65164b76a", "7c54b173b0774419b493a266d62296f8", "adfb074b0802452ebe33cdf334e42f82", "86cb846f6e53472c959ee0fb3f10414d", "ac9c6339a14547c183141cde27a4e636", "fbca2085e8424045940184c102fff1eb", "be8fa174e9ed4df4908c48c6d91f6b61", "d3d9aff5076c4401a629e55984961ad5", "634b2e381f9144018796cd7b36a33348", "4f9d0a50e98348d9a5ada7dc3f6d0bbf", "8ad5409906b5415cbfd2122e905f4b22", "287481e1430a4142b1025f5b42aac459", "d955dd6963a24daf8fc9fdea5bce73d0", "9d01e7dac83246e197b435eb18b8e24e", "b10688f66a60455cba45660c00330d05", "1ce6bedd727e4b5985a0c549d1e2f434", "c1eb7fa89c3c4528a7d23152b2b63630", "ffcf6c83efd94681a70e20333712d883", "320a6af43c0c4997a41a4a7e3bbb55da", "db3d178744d541519df4d63183fc0507", "e09ba7aa34bf4756ac85ec2749c8b6ab", "838ae6fde98149a5b506dd4f4b1f96f0", "cdf97d9d96a5462ebc25888f4d63aa6c", "ca62e5b3498b41a5bf9f5143aa6bd6a5", "8f5e5ed8a4db43018f1f9faae4e88505", "09c0a4e916b241d3b41190d72ef52e30", "868f0b6a0404492c8d210ac301a5ddaa", "34622bf1f4ce4ab696c2db62af51f6aa", "05d5ef00e89d4d2e8591e2e8202211fc", "7d7cec045ca64b4bbb0cfd00eedc9872", "d79cb73f2d4a4421bb7101e838a8c696", "a28eb87083c64b37b5906749f280061f", "b3e900b3b32144998dee7dce0e41fb95", "8759d1dea3094361a3f15cdd5f918b07", "af11c39be6a74322ba3d3a29e665310b", "8279362c4e594717b75ac8e62b5ce292", "30c2ac05df8d4380bb15330e22b222b3", "f23906ff1ec048fc96a8bb84344c349d", "5e2efb0b46ab49a29f36393902936fd8", "c548cd0a428643cc8ec53c8dab844257", "1209eb96bec9449088f1b92d52532d9f", "aac78527b4f2478fbaf4476a4fbef263", "cbeb17c359094ffdbe9f90810b329ba6", "8cac6309504c4e9fbbb8a0c19df99278", "68385a2690aa450395aa6968e608bbfb", "340d238889c3472187adfb787bf547d1", "aa8f7b2e52b6437d8b38f1c4af74bcbc", "1e64cc4d56684457931f7d383502012e", "e2b228cc746c4639a92707b1ece82d24", "3bd88ff892684f9a864eeeadb7bb3138", "b6b86e3e25854a7c953b988818773d37", "1c063c12f0d6483a9ba1d3177a5a4b15", "90c6345aeb9b4698a7bc3e0270b5ba5c", "2f23727f57dc4fd1a2274b3e957a0bc6" ] }, "id": "1FOM5mr7K7UU", "outputId": "afe37c76-bb4b-410f-b06f-3cd9e72f6c04" }, "outputs": [], "source": [ "import os\n", "\n", "from haystack import Pipeline\n", "from haystack.document_stores.in_memory import InMemoryDocumentStore\n", "from haystack.components.converters import PyPDFToDocument\n", "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n", "from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter\n", "from haystack.components.writers import DocumentWriter\n", "from haystack.document_stores.types import DuplicatePolicy\n", "\n", "embedding_model=\"sentence-transformers/all-MiniLM-L6-v2\"\n", "document_store = InMemoryDocumentStore()\n", "\n", "files_path = \"./ARAGOG/papers_for_questions\"\n", "pipeline = Pipeline()\n", "pipeline.add_component(\"converter\", PyPDFToDocument())\n", "pipeline.add_component(\"cleaner\", DocumentCleaner())\n", "pipeline.add_component(\"splitter\", DocumentSplitter(split_length=250, split_by=\"word\")) # default splitting by word\n", "pipeline.add_component(\"writer\", DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP))\n", "pipeline.add_component(\"embedder\", SentenceTransformersDocumentEmbedder(embedding_model))\n", "pipeline.connect(\"converter\", \"cleaner\")\n", "pipeline.connect(\"cleaner\", \"splitter\")\n", "pipeline.connect(\"splitter\", \"embedder\")\n", "pipeline.connect(\"embedder\", \"writer\")\n", "pdf_files = [files_path + \"/\" + f_name for f_name in os.listdir(files_path)]\n", "\n", "pipeline.run({\"converter\": {\"sources\": pdf_files}})" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eBV3-XqaM-QT", "outputId": "f0618a8b-3424-4943-8c8b-5a5397155d01" }, "outputs": [ { "data": { "text/plain": [ "691" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "document_store.count_documents()" ] }, { "cell_type": "markdown", "metadata": { "id": "Jqhpts_R_RQK" }, "source": [ "### RAG" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "ltjnGARjVng3" }, "outputs": [], "source": [ "import os\n", "from getpass import getpass\n", "\n", "if not os.getenv(\"OPENAI_API_KEY\"):\n", " os.environ[\"OPENAI_API_KEY\"] = getpass('OPENAI_API_KEY: ')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0HA4suneP5Ww", "outputId": "e9b23191-d971-4631-b158-00aaab063dad" }, "outputs": [ { "data": { "text/plain": [ "\n", "πŸš… Components\n", " - query_embedder: SentenceTransformersTextEmbedder\n", " - retriever: InMemoryEmbeddingRetriever\n", " - chat_prompt_builder: ChatPromptBuilder\n", " - chat_generator: OpenAIChatGenerator\n", "πŸ›€οΈ Connections\n", " - query_embedder.embedding -> retriever.query_embedding (List[float])\n", " - retriever.documents -> chat_prompt_builder.documents (List[Document])\n", " - chat_prompt_builder.prompt -> chat_generator.messages (List[ChatMessage])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from haystack import Pipeline\n", "from haystack.components.builders import ChatPromptBuilder\n", "from haystack.components.embedders import SentenceTransformersTextEmbedder\n", "from haystack.components.generators.chat import OpenAIChatGenerator\n", "from haystack.components.retrievers import InMemoryEmbeddingRetriever\n", "from haystack.dataclasses import ChatMessage\n", "\n", "chat_message = ChatMessage.from_user(\n", " text=\"\"\"You have to answer the following question based on the given context information only.\n", "If the context is empty or just a '\\\\n' answer with None, example: \"None\".\n", "\n", "Context:\n", "{% for document in documents %}\n", " {{ document.content }}\n", "{% endfor %}\n", "\n", "Question: {{question}}\n", "Answer:\n", "\"\"\"\n", ")\n", "\n", "basic_rag = Pipeline()\n", "basic_rag.add_component(\"query_embedder\", SentenceTransformersTextEmbedder(\n", " model=embedding_model, progress_bar=False\n", "))\n", "basic_rag.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store))\n", "basic_rag.add_component(\"chat_prompt_builder\", ChatPromptBuilder(template=[chat_message], required_variables=\"*\"))\n", "basic_rag.add_component(\"chat_generator\", OpenAIChatGenerator(model=\"gpt-4o-mini\"))\n", "\n", "basic_rag.connect(\"query_embedder\", \"retriever.query_embedding\")\n", "basic_rag.connect(\"retriever\", \"chat_prompt_builder.documents\")\n", "basic_rag.connect(\"chat_prompt_builder\", \"chat_generator\")" ] }, { "cell_type": "markdown", "metadata": { "id": "zTbmQzeXQY1F" }, "source": [ "## 2. Human Evaluation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!wget https://raw.githubusercontent.com/deepset-ai/haystack-evaluation/main/datasets/ARAGOG/eval_questions.json -P ARAGOG\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "DCAJIn3gQbv1" }, "outputs": [], "source": [ "from typing import List, Tuple\n", "import json\n", "\n", "def read_question_answers() -> Tuple[List[str], List[str]]:\n", " with open(\"./ARAGOG/eval_questions.json\", \"r\") as f:\n", " data = json.load(f)\n", " questions = data[\"questions\"]\n", " answers = data[\"ground_truths\"]\n", " return questions, answers\n", "\n", "all_questions, all_answers = read_question_answers()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XSeU0xFPSbAi", "outputId": "b8e5ab0e-11e5-4610-cdf3-3dd9095b6ff4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "107\n", "107\n" ] } ], "source": [ "print(len(all_questions))\n", "print(len(all_answers))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "esFxSGF0nfTJ" }, "outputs": [], "source": [ "questions = all_questions[:15]\n", "answers = all_answers[:15]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BSj3dUnASfS_", "outputId": "8cbf15d6-6d02-46ce-9022-18952d02e8da" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "How were the questions for the multitask test sourced, and what was the criteria for their inclusion?\n", "Questions were manually collected by graduate and undergraduate students from freely available online sources, including practice questions for standardized tests and undergraduate courses, ensuring a wide representation of difficulty levels and subjects.\n" ] } ], "source": [ "index = 5\n", "print(questions[index])\n", "print(answers[index])\n", "question = questions[index]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jIIXQu0LV8dU", "outputId": "b51c5b29-da7b-4620-adff-2b424c4a016e" }, "outputs": [ { "data": { "text/plain": [ "{'chat_generator': {'replies': [ChatMessage(_role=, _content=[TextContent(text='The questions for the multitask test were manually collected by graduate and undergraduate students from freely available sources online. These sources included practice questions for tests such as the Graduate Record Examination and the United States Medical Licensing Examination, as well as questions designed for undergraduate courses and readers of Oxford University Press books. The criteria for inclusion involved ensuring that each subject contained a sufficient number of test examples, with each subject having a minimum of 100 test examples. Tasks that were either too challenging for humans without extensive training or too easy for the machine baselines were filtered out.')], _name=None, _meta={'model': 'gpt-4o-mini-2024-07-18', 'index': 0, 'finish_reason': 'stop', 'usage': {'completion_tokens': 110, 'prompt_tokens': 4550, 'total_tokens': 4660, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}})]}}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "basic_rag.run({\"query_embedder\":{\"text\":question}, \"chat_prompt_builder\":{\"question\": question}})" ] }, { "cell_type": "markdown", "metadata": { "id": "-U-QnCBqQcd6" }, "source": [ "## 3. Deciding on Metrics\n", "\n", "* **Semantic Answer Similarity**: SASEvaluator compares the embedding of a generated answer against a ground-truth answer based on a common embedding model.\n", "* **ContextRelevanceEvaluator** will assess the relevancy of the retrieved context to answer the query question\n", "* **FaithfulnessEvaluator** evaluates whether the generated answer can be derived from the context\n" ] }, { "cell_type": "markdown", "metadata": { "id": "yLkAcM_5Qfat" }, "source": [ "## 4. Building an Evaluation Pipeline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NN_M_UZYQkJ-" }, "outputs": [], "source": [ "from haystack import Pipeline\n", "from haystack.components.evaluators import ContextRelevanceEvaluator, FaithfulnessEvaluator, SASEvaluator\n", "\n", "eval_pipeline = Pipeline()\n", "eval_pipeline.add_component(\"context_relevance\", ContextRelevanceEvaluator(raise_on_failure=False))\n", "eval_pipeline.add_component(\"faithfulness\", FaithfulnessEvaluator(raise_on_failure=False))\n", "eval_pipeline.add_component(\"sas\", SASEvaluator(model=embedding_model))" ] }, { "cell_type": "markdown", "metadata": { "id": "p76stWMQQmPD" }, "source": [ "## 5. Running Evaluation" ] }, { "cell_type": "markdown", "metadata": { "id": "rUfQQzusXhgk" }, "source": [ "### Run the RAG Pipeline" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "Wmhn-WYnQmjv" }, "outputs": [], "source": [ "predicted_answers = []\n", "retrieved_context = []\n", "\n", "for question in questions: # loops over 15 questions\n", " result = basic_rag.run(\n", " {\"query_embedder\":{\"text\":question}, \"chat_prompt_builder\":{\"question\": question}}, include_outputs_from={\"retriever\"}\n", " )\n", " predicted_answers.append(result[\"chat_generator\"][\"replies\"][0].text)\n", " retrieved_context.append(result[\"retriever\"][\"documents\"])" ] }, { "cell_type": "markdown", "metadata": { "id": "mfepD9HwXk4Q" }, "source": [ "### Run the Evaluation" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6QuMPaJBYGbo", "outputId": "ab0ef478-8743-40ee-d1a3-c50b1f965e64" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 15/15 [00:10<00:00, 1.43it/s]\n", "100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 15/15 [00:33<00:00, 2.23s/it]\n" ] } ], "source": [ "eval_pipeline_results = eval_pipeline.run(\n", " {\n", " \"context_relevance\": {\"questions\": questions, \"contexts\": retrieved_context},\n", " \"faithfulness\": {\"questions\": questions, \"contexts\": retrieved_context, \"predicted_answers\": predicted_answers},\n", " \"sas\": {\"predicted_answers\": predicted_answers, \"ground_truth_answers\": answers},\n", " }\n", ")\n", "\n", "results = {\n", " \"context_relevance\": eval_pipeline_results['context_relevance'],\n", " \"faithfulness\": eval_pipeline_results['faithfulness'],\n", " \"sas\": eval_pipeline_results['sas']\n", "}" ] }, { "cell_type": "markdown", "metadata": { "id": "mC_mIqdMQqZG" }, "source": [ "## 6. Analyzing Results" ] }, { "cell_type": "markdown", "metadata": { "id": "bmamofudbWDw" }, "source": [ "[EvaluationRunResult](https://docs.haystack.deepset.ai/reference/evaluation-api#evaluationrunresult)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "hkfPmQf6Qq6L", "outputId": "4d6012bc-d0de-4714-c177-10d6e23965dd" }, "outputs": [ { "data": { "text/plain": [ "{'metrics': ['context_relevance', 'faithfulness', 'sas'],\n", " 'score': [0.26666666666666666, 0.7, 0.5344941093275944]}" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from haystack.evaluation import EvaluationRunResult\n", "\n", "inputs = {\n", " 'questions': questions,\n", " 'contexts': retrieved_context,\n", " 'true_answers': answers,\n", " 'predicted_answers': predicted_answers\n", "}\n", "run_name=\"rag_eval\"\n", "eval_results = EvaluationRunResult(run_name=run_name, inputs=inputs, results=results)\n", "eval_results.aggregated_report()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dFrIfDXiaP41", "outputId": "4f187782-7933-4bc0-89b9-51c6672c20ec", "scrolled": true }, "outputs": [], "source": [ "index = 2\n", "print(eval_pipeline_results['context_relevance'][\"individual_scores\"][index], \"\\nQuestion:\", questions[index],\"\\nTrue Answer:\", answers[index], \"\\nAnswer:\", predicted_answers[index])\n", "print(\"\".join([doc.content for doc in retrieved_context[index]]))" ] }, { "cell_type": "markdown", "metadata": { "id": "gKfrFf1CebJJ" }, "source": [ "## Evaluation Frameworks" ] }, { "cell_type": "markdown", "metadata": { "id": "sSkCqiIgem8X" }, "source": [ "* For [RagasEvaluator](https://docs.haystack.deepset.ai/docs/ragasevaluator) check out our cookbook [RAG Pipeline Evaluation Using RAGAS](https://haystack.deepset.ai/cookbook/rag_eval_ragas)\n", "* Here we will show how to use [FlowJudge](https://haystack.deepset.ai/integrations/flow-judge)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "c9e01aa81130454a9ff78424e64e7636", "eea7dd11d5744b0cbaa64b2d1f1e9bb2", "e167a31fb4a64cf585659c6146d53cd9", "da651e97b04e4c20a514380acaa6e1a3", "f27b118b20454f659bedd44f6b8279df", "653bec3b02a94e358109480df1edc51c", "f212f062c39440f48a22a64f20e1ea38", "075cf1cf976d4e7dbe1a90bfb7de3d38", "f4083eb8314f4f17a6d4d728062854f0", "d588388158d0420fba6e078fd4e8d9f6", "ed6d745f8ecc482eb1b14a93ea7dbcb0", "2e4919f889414ff9b4dc4724dfedb37c", "2fff778006f94de3bf1f70ea8f6ea6ea", "6842a54b398b4e0d9faf505a82780e1d", "5094cd53c72a420681e0246fa2160740", "f5c00158d342473b8b639d5577968f28", "93ea390f70cc479992b8bb1a94c4c71c", "a13eacf3a2574ca4ac34d20d8c239b71", "1b3290fee5d247ad91bd42f72e7fe7db", "5e675a39b19d445c928594f098af41eb", "5491629c609f4cd5a42d2a75d5954eb7", "ff21aefa2e454d31bdb9e55c7546f14c", "283317af490040bcb7362374422f18a0", "52d9654c65034dc6b766de5fc185bc33", "60c184484456464390e5c5044a5a64c5", "debba7bd202c48e3976eb5441e635f8c", "35efa24612bf4b129250e3aa3866855e", "a0f3a8c7df314fa7ac7811124332e491", "b10cea675b0f4f68a6fc2a33c2dddfde", "a949aac0b2a2492da659a058ab6288f6", "a7c15c86e5fc42aea2f6db2ab5ee3485", "e8c9bda938294246a6a386c0ca0fce20", "cdfe62178a1f481eae5b3d8b808ab421", "98f0797e51574af1b3877c5978c7dd01", "6906427fd1f647bb99c6a85f46be8910", "78cac3ef81eb4726bea3dc14be25367f", "d30b7c0a62bb4ce0a182a6efc6214cd0", "48d3b77a242848d1a348aa1c4e438bde", "bc07a01489554fbda675557a32ba5e0c", "a644bddb21d2440fb25d16ea3d6b487d", "746cc7c657244ed384e15a8635126b00", "ab00467118fa4eff8aaf9e0a3a9c8268", "891acb2e0ed24333b1eaeeca2c605c2c", "3242e682319740d8ac12b639ce8076f7", "355cdcb0f55a4e61b02fbdcf261ddcff", "66c2131a86444ed4900d349e45e0da16", "712f9ab37cba41ddad32a1bdf89e97d1", "02cc9dc20c1d42abaca00d0429dacd78", "522f8c15d5ed4a27b37a24c6483f43d5", "a42c691f0a7b4abf98c412af86160886", "24a7829c53ef4689a7a3dd7ea4b60de1", "100f11799e54486c93087157ca627499", "6d79148c38d1428f8c1df5a6d8905f43", "f0bacaaec7c4487798e49877e8117fa6", "0ec90aaf7961466f8585365d0eca784f", "cd1a14abf19b4a75adcaa048790693b1", "9e77f8f7858b4ce7b4a5ce9b26a46321", "9cdcfb6fb2084fd0a1095e270374ef88", "46e6fdcee1b64b49aba3c4bf6e8f5cde", "abef8a13ae594e7ba5227cf92f474f4c", "7f048f90cb034d7888468a9a36049bfb", "99f0c94a03954217b4051f398b6123a7", "434cb11be9fd4df298d21741f28bf804", "20a902872bdc47cb9012fa7927dc971d", "bebea714233148288022d06bb0d799a2", "e1f348ac8ce14a19933d7072028a317f", "dcb4948d346a4be4891f9adb19600e5a", "72849571bbc04e3fa04c1b42a2f733cb", "1ec79510add34f8a8fe4b6684467d16e", "cb24c59d1fd24494b8da0a1401353345", "481b5a4d86c647229bd65fdece4c678e", "7ecf510e5b464640a5a891f79ce267f1", "a6dd52565f6e441584b420efd4dc5f45", "2230e9a479e54eccaee61ab62d311cf4", "531c6c2e46224b17ac1bf496958f95d2", "0d73a404c2a54a36825f2f575500ec6a", "a9d95fb50c404ef598b4f9c030b1c04b", "2a61033ed31d484698d443c1c9dc3a5f", "08baac6a4c604047817d2e86b5621f4e", "a712c786e0b349be88e7dc0e44746dce", "c6fa3b6eb8604694b73c915f235101d3", "0a0ca849bc2b4898822fd70142cff888", "cab2c6189e3544a2b027a796c32acb14", "27c3332383954d1095a077b3f52932e5", "8ab71d975093439690fafcb717c2b5ba", "798d4eb219c84e6c8d448aa3a4f8eeb9", "be764ee2f34d4aea872ad5117e70d9f2", "48360e5402a14788950612b0f18b46a5", "a976ee060fb04ae8971828e08d5b2ddb", "fdb294e653974f278f232a92a323a21f", "a0b8b2865fdb442983957ae358d4a09f", "ad7fdd68c08f44f7a77b511680b39378", "e98a5467c74c4a16b93671ae244adc75", "8a24fecc97aa4ce1bf7f8e7134c2113e", "b5418e89b2b4404799101f04b28fdea1", "8838c44b5cf2466f90cb107996c72098", "3fd0682779b14b209a2cf429de2be224", "3012252aa33141ff992146d7b6b6e32e", "efa33506f7a44f2caf5b1617810ee800", "f70521a3072c4e0386744f90d27ad4f8", "53d3b4ebe8b645949565830626a0f37a", "452a6eca65274cb4b1fa9fcb1eb4a626", "1153bd91d1e24008a615df3a723856d9", "b8a4d7e645ab450bbdcca6f2045e8baa", "99badb6d2f3b4a65b623aa1fd4ae0c2c", "4eadc6efb5384c3088ef10f75c0321f7", "1012146e9b3c443685cce6d3cb47915c", "a3b11be65fc4424daf43023625de82ef", "0cb9da97027a4cbe866c30503c1a05e4", "596c19632d0148e38240c7319f6fc6e8", "2eca53c4646643688a5720dc91e2fdf8", "42c23a38cf9849479501023a1e09efa0", "875f0a109b3340868f68e8f1b1b163fc", "d44591b1141043c2b62a120f96d6040e", "d97ae561482d42d0af876eefc41ac87c", "fcca40c3558843319a2cb626473d7b30", "efb9e73513914694912f650d74b79028", "e8e3035eb0474b339674119feb7a4f30", "198bc777716249168e5123ff03dcb186", "7d5989e0fc4f4cdebfd8ec0d5a08c66f", "67094be0906b497fb196a1a66f425367", "76ed9a265ea8458c87e1e7b1923632c9", "9d3d58ee56484950b8771689cb0c2748", "e14a56494d6a42bc8a74cc5a4fc8e6c9", "78a8df437dc848b6a2042c87d6e0d07d", "c690d36170ca4b3f8490ec7f50db580a", "7fe1225e285b44678bbc1207250e3360", "1b8986650a9d4776aeac30f7858a42ad", "e9fcdbef2b0f4dafa32606eedbec2c9e", "b021e4f565524cef8c50ffb5c7bd69d9", "da928d7dd89646ef999d93252934cc1f", "eb0b1f8179af4b319b7751f26f577440", "d81e7c8d919142debb6160fca76b1ec1", "705956bf75a04b41a54a2eb6361eda40", "cda7e4c99a96406bb706e260e78d1b9a", "a09c30b008f04b73bab0e9d6ef052a10", "d00e93e92b45477db7f3fce9f8228e35", "0585796e760047b48f1593bad5fafe9b", "178103ae87f849309a1fa0d91fc01d19", "0d7f52784362470b9f2868ac4dc4960c", "7a2e9f9c890c42e79910e9d26e5b1853", "ec805f8e515e4162a286ece91efd29c5", "dc9c0b2f590c4659bb0c63d324e81ada", "5d9f76fdbe034fc5b2e6c6a1bed959fd", "3fb3003ff6a14ab1a3c9fc75aa1a63e4", "392dbaa82a9e479da371d5467a0b1de9", "af3f3f946884485ebe748362a7ea4fef", "a05de003bc614774890a70ac382c2b6f", "258ca52527254d26a54f289ae2df56c3", "731d797675b74d0f9ae0f21004c56910", "cfd2787003154b75ab3902309d5605a2", "5c64d75e179f4e859a13e7acab51500f", "9c1f443d70904f9d86f32c6f45f78245", "78333a4099d8475db3ec7db59968c195" ] }, "id": "n6sHf2fUempZ", "outputId": "4cefb8ea-d0e2-4661-9887-456f23b7d4ca" }, "outputs": [], "source": [ "from flow_judge.integrations.haystack import HaystackFlowJudge\n", "from flow_judge.metrics.presets import RESPONSE_FAITHFULNESS_5POINT\n", "from flow_judge import Hf\n", "\n", "model = Hf(flash_attn=False)\n", "\n", "flow_judge_evaluator = HaystackFlowJudge(\n", " metric=RESPONSE_FAITHFULNESS_5POINT,\n", " model=model,\n", " progress_bar=True,\n", " raise_on_failure=True,\n", " save_results=True,\n", " fail_on_parse_error=False\n", ")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "import os\n", "from getpass import getpass\n", "\n", "if not os.getenv(\"OPENAI_API_KEY\"):\n", " os.environ[\"OPENAI_API_KEY\"] = getpass('OPENAI_API_KEY: ')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "id": "oolLnIzG0z6N" }, "outputs": [], "source": [ "str_fj_retrieved_context = []\n", "for context in retrieved_context:\n", " str_context = [doc.content for doc in context]\n", " str_fj_retrieved_context.append(\" \".join(str_context)) # [\"\", \"\", ...]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0, "referenced_widgets": [ "1268c19e80b14924864219ec60f19770", "4773d22c8eeb483cb05d408c9a122240", "7de92f3ec0e84e6c8d536b8c4a8635da", "d5cb42a52ac143ee8bf54a06a01ca16e", "152c377acd4f474196d623f76b76eca2", "1baf44df004346f59add2579feb6ccb5", "fc2da2ca084740bab692dd9bfc276908", "430220d701ee4aaf94e72b155d4fe4e2", "2bc2dc3cc3a740caa207ef52f32711da", "595f1aa5b30c4c96b7735c8a3c2bb23c", "45ff7e6994ee42e1a53c08d1e99963a3" ] }, "id": "LNgcH2Klj-5g", "outputId": "609cc808-1f73-4f62-de82-47d1d825dd41" }, "outputs": [], "source": [ "from haystack import Pipeline\n", "\n", "integration_eval_pipeline = Pipeline()\n", "integration_eval_pipeline.add_component(\"flow_judge_evaluator\", flow_judge_evaluator)\n", "\n", "eval_framework_pipeline_results = integration_eval_pipeline.run(\n", " {\n", " \"flow_judge_evaluator\": 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