{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "2lxE_Peqxt23" }, "source": [ "# SET UP" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip install langchain langchain-core==0.2.38 langchain-community==0.2.16 langchain-text-splitters langchain_huggingface==0.0.3 langchain-openai langchain-qdrant qdrant-client tiktoken pymupdf ragas==0.1.14 sentence_transformers datasets pyarrow" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zs3Chu9tx1c0" }, "outputs": [], "source": [ "import os\n", "import getpass\n", "import pandas as pd\n", "from uuid import uuid4\n", "import re\n", "from tqdm import tqdm\n", "from operator import itemgetter\n", "import json\n", "\n", "from langchain_core.runnables import RunnablePassthrough, RunnableParallel\n", "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n", "\n", "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain.prompts import ChatPromptTemplate\n", "from langchain.schema import StrOutputParser\n", "\n", "from langchain_community.document_loaders import PyMuPDFLoader\n", "from langchain_community.vectorstores import Qdrant\n", "\n", "from langchain_huggingface import HuggingFaceEmbeddings\n", "\n", "from ragas.testset.generator import TestsetGenerator\n", "from ragas.testset.evolutions import simple, reasoning, multi_context\n", "\n", "import datasets\n", "\n", "from ragas import evaluate\n", "from ragas.metrics import (\n", " faithfulness,\n", " answer_relevancy,\n", " answer_correctness,\n", " context_recall,\n", " context_precision,\n", ")\n", "\n", "from sentence_transformers import SentenceTransformer\n", "from torch.utils.data import DataLoader\n", "from torch.utils.data import Dataset\n", "from sentence_transformers import InputExample\n", "\n", "from sentence_transformers.losses import MatryoshkaLoss, MultipleNegativesRankingLoss\n", "from sentence_transformers.evaluation import InformationRetrievalEvaluator" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-5vbTkh05pjL", "outputId": "67e43538-233a-4e43-eca0-68a0f0e77e90" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OpenAI API Key:··········\n", "LangChain API Key:··········\n" ] } ], "source": [ "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n", "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", "os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"LangChain API Key:\")\n", "os.environ[\"LANGCHAIN_PROJECT\"] = f\"AIM - SDG Midterm - {uuid4().hex[0:8]}\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "AonUyne8lFk3" }, "outputs": [], "source": [ "# DATA LOADER\n", "DATA_LINK1 = \"https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf\"\n", "DATA_LINK2 = \"https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf\"\n", "\n", "# CHUNKING CONFIGS\n", "CHUNK_SIZE = 500\n", "CHUNK_OVERLAP = 50\n", "SGD_CHUNK_SIZE = 800\n", "SGD_CHUNK_OVERLAP = 100\n", "FINETUNE_CHUNK_SIZE = 1000\n", "FINETUNE_CHUNK_OVERLAP = 75\n", "\n", "# RETRIEVER CONFIGS\n", "COLLECTION_NAME = \"AI Bill of Rights\"\n", "\n", "EMBEDDING_MODEL = \"text-embedding-3-small\"\n", "\n", "# SGD CONFIGS\n", "GENERATOR_MODEL = \"gpt-3.5-turbo\"\n", "CRITIC_MODEL = \"gpt-4o-mini\"\n", "\n", "# FINAL RAG CONFIGS\n", "QA_MODEL = \"gpt-4o\"\n", "\n", "RAG_PROMPT = \"\"\"\\\n", "Given a provided context and question, you must answer the question based only on context.\n", "\n", "If you cannot answer the question based on the context - you must say \"I don't know\".\n", "\n", "Context: {context}\n", "Question: {question}\n", "\"\"\"\n", "\n", "# EVAL CONFIGS\n", "metrics = [\n", " faithfulness,\n", " answer_relevancy,\n", " context_recall,\n", " context_precision,\n", " answer_correctness,\n", "]\n", "\n", "# FINETUNING CONFIGS\n", "BATCH_SIZE = 20\n", "EPOCHS = 5" ] }, { "cell_type": "markdown", "metadata": { "id": "4tpKCVO13TeL" }, "source": [] }, { "cell_type": "markdown", "metadata": { "id": "wxyfosVD3jOl" }, "source": [ "# HELPER FUNCTIONS" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LcCfhdV53lAE" }, "outputs": [], "source": [ "def chunk_documents(unchunked_documents, chunk_size, chunk_overlap):\n", " text_splitter = RecursiveCharacterTextSplitter(\n", " chunk_size=chunk_size,\n", " chunk_overlap=chunk_overlap,\n", " )\n", "\n", " return text_splitter.split_documents(unchunked_documents)\n", "\n", "\n", "def build_retriever(chunked_documents, embeddings, collection_name):\n", " vectorstore = Qdrant.from_documents(\n", " documents=chunked_documents,\n", " embedding=embeddings,\n", " location=\":memory:\",\n", " collection_name=collection_name,\n", " )\n", "\n", " retriever = vectorstore.as_retriever()\n", "\n", " return retriever\n", "\n", "\n", "def generate_sgd(generator_llm, critic_llm, embeddings, chunked_documents, sgd_ct=20, with_debugging_logs=False):\n", " generator = TestsetGenerator.from_langchain(\n", " generator_llm,\n", " critic_llm,\n", " embeddings,\n", " )\n", "\n", " distributions = {\n", " simple: 0.5,\n", " multi_context: 0.4,\n", " reasoning: 0.1\n", " }\n", "\n", " testset = generator.generate_with_langchain_docs(chunked_documents, sgd_ct, distributions, with_debugging_logs=with_debugging_logs)\n", "\n", " return testset.to_pandas()\n", "\n", "\n", "def generate_response_dataset(sgd_df, chain):\n", " answers = []\n", " contexts = []\n", " questions = sgd_df[\"question\"].values.tolist()\n", " ground_truths = sgd_df[\"ground_truth\"].values.tolist()\n", "\n", " for question in tqdm(questions):\n", " answer = chain.invoke({\"question\" : question})\n", " answers.append(answer[\"response\"].content)\n", " contexts.append([context.page_content for context in answer[\"context\"]])\n", "\n", " return datasets.Dataset.from_dict({\n", " \"question\" : questions,\n", " \"answer\" : answers,\n", " \"contexts\" : contexts,\n", " \"ground_truth\" : ground_truths\n", " })\n" ] }, { "cell_type": "markdown", "metadata": { "id": "5FAXGBSD7X62" }, "source": [ "# LOAD DOCUMENTS" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SlpP-TAs7asD" }, "outputs": [], "source": [ "rag_documents_1 = PyMuPDFLoader(file_path=DATA_LINK1).load()\n", "rag_documents_2 = PyMuPDFLoader(file_path=DATA_LINK2).load()" ] }, { "cell_type": "markdown", "metadata": { "id": "CSGSUWqq6XIm" }, "source": [ "# BUILD RAG" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "chunked_rag_documents = chunk_documents(rag_documents_1, CHUNK_SIZE, CHUNK_OVERLAP) + chunk_documents(rag_documents_2, CHUNK_SIZE, CHUNK_OVERLAP)\n", "embeddings = OpenAIEmbeddings(model=EMBEDDING_MODEL)\n", "retriever = build_retriever(chunked_rag_documents, embeddings, COLLECTION_NAME)\n", "\n", "rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)\n", "qa_llm = ChatOpenAI(model=QA_MODEL)\n", "\n", "rag_chain = (\n", " {\"context\": itemgetter(\"question\") | retriever, \"question\": itemgetter(\"question\")}\n", " | RunnablePassthrough.assign(context=itemgetter(\"context\"))\n", " | {\"response\": rag_prompt | qa_llm, \"context\": itemgetter(\"context\")}\n", ")\n", "rag_chain.invoke({\"question\" : \"What are the risks of AI and how can we manage it?\"})\n", "\n", "# rag_chain = (\n", "# {\"context\": itemgetter(\"question\") | retriever, \"question\": itemgetter(\"question\")}\n", "# | rag_prompt | llm | StrOutputParser()\n", "# )" ] }, { "cell_type": "markdown", "metadata": { "id": "udqAsEt-y_hd" }, "source": [ "# SGD" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "m_gMREgdk26r" }, "outputs": [], "source": [ "generator_llm = ChatOpenAI(model=GENERATOR_MODEL)\n", "critic_llm = ChatOpenAI(model=CRITIC_MODEL)\n", "sgd_chunked_documents = chunk_documents(rag_documents_1, SGD_CHUNK_SIZE, SGD_CHUNK_OVERLAP) + chunk_documents(rag_documents_2, SGD_CHUNK_SIZE, SGD_CHUNK_OVERLAP)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sgd_df = generate_sgd(generator_llm, critic_llm, embeddings, sgd_chunked_documents, 20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vkrTOWrr8Sn_", "outputId": "0ef71349-a618-4269-a992-4e4000bbedbe" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 20/20 [00:30<00:00, 1.51s/it]\n" ] } ], "source": [ "response_dataset = generate_response_dataset(sgd_df, rag_chain)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 294, "referenced_widgets": [ "33ac718b67a34911854014b9793ce82a", "a37c345534e24099a93d20b3915a310c", "6b59f554a7434fe1a3b82b5d0602a65e", "e61672856a974ce7b248be19ac32532c", "61449d4600c8419bbf81dd4c7d50e333", "2c77046a9f8f44e09cf13b6c1b8eb3c2", "f8ee029ee7a6402ba09d64d39d78e8e0", "f526a4a98e49423cb68f67da89f049d8", "a2c982eab70c4bb5bedee6de5716f96e", "fb9da6a9781349319e980bbfe18fcda3", "9e0f35c2b0704cf280167e3562ad54e0" ] }, "id": "WozLbwC7qO9F", "outputId": "3f47efa4-1641-42a8-b073-d917cf872e46" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "33ac718b67a34911854014b9793ce82a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Evaluating: 0%| | 0/100 [00:00\n", "
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MetricValue
0faithfulness0.735909
1answer_relevancy0.774979
2context_recall0.783333
3context_precision0.780556
4answer_correctness0.660136
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\n", " \n" ], "text/plain": [ " Metric Value\n", "0 faithfulness 0.735909\n", "1 answer_relevancy 0.774979\n", "2 context_recall 0.783333\n", "3 context_precision 0.780556\n", "4 answer_correctness 0.660136" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results = evaluate(response_dataset, metrics)\n", "pd.DataFrame(list(results.items()), columns=['Metric', 'Value'])" ] }, { "cell_type": "markdown", "metadata": { "id": "oWCN4qpz9JiL" }, "source": [ "# FINETUNE" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DLObXBj9AKS-" }, "outputs": [], "source": [ "training_documents = chunk_documents(rag_documents_1, FINETUNE_CHUNK_SIZE, FINETUNE_CHUNK_OVERLAP) + chunk_documents(rag_documents_2, FINETUNE_CHUNK_SIZE, FINETUNE_CHUNK_OVERLAP)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TLBL9fDW-oxY" }, "outputs": [], "source": [ "id_set = set()\n", "for document in training_documents:\n", " id = str(uuid4())\n", " while id in id_set:\n", " id = uuid4()\n", " id_set.add(id)\n", " document.metadata[\"id\"] = id" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hi-WzXUvAjgl", "outputId": "fd90f67f-a0f3-4823-b3a2-c151c711a91f" }, "outputs": [ { "data": { "text/plain": [ "466" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(training_documents)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iyhQS1mnvadd" }, "outputs": [], "source": [ "training_split_documents = training_documents[:300]\n", "val_split_documents = training_documents[300:400]\n", "test_split_documents = training_documents[400:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZBRRiDOjvkkk" }, "outputs": [], "source": [ "qa_chat_model = ChatOpenAI(\n", " model=\"gpt-4o-mini\",\n", " temperature=0\n", ")\n", "\n", "qa_prompt = \"\"\"\\\n", "Given the following context, you must generate questions based on only the provided context.\n", "\n", "You are to generate {n_questions} questions which should be provided in the following format:\n", "\n", "1. QUESTION #1\n", "2. QUESTION #2\n", "...\n", "\n", "Context:\n", "{context}\n", "\"\"\"\n", "\n", "qa_prompt_template = ChatPromptTemplate.from_template(qa_prompt)\n", "\n", "question_generation_chain = qa_prompt_template | qa_chat_model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QbIGU5N9AzDn" }, "outputs": [], "source": [ "def create_questions(documents, n_questions):\n", " questions = {}\n", " relevant_docs = {}\n", " id_set = set()\n", " for document in tqdm(documents, desc=\"Processing documents\"):\n", " response = question_generation_chain.invoke({\"context\": document, \"n_questions\": n_questions}).content\n", " questions_list = re.findall(r'\\d+\\.\\s+(.*)', response.strip())\n", " for question in questions_list:\n", " id = str(uuid4())\n", " while id in id_set:\n", " id = uuid4()\n", " id_set.add(id)\n", " questions[id] = question\n", " relevant_docs[id] = [document.metadata[\"id\"]]\n", "\n", " return questions, relevant_docs\n", "\n", "\n", "def create_dataset(documents, questions, contexts):\n", " corpus = {item.metadata[\"id\"] : item.page_content for item in documents}\n", "\n", " dataset = {\n", " \"questions\" : questions,\n", " \"relevant_contexts\" : contexts,\n", " \"corpus\" : corpus\n", " }\n", "\n", " return dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "training_questions, training_relevant_contexts = create_questions(training_split_documents, 2)\n", "val_questions, val_relevant_contexts = create_questions(val_split_documents, 2)\n", "test_questions, test_relevant_contexts = create_questions(test_split_documents, 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NukUT6lwB0k-" }, "outputs": [], "source": [ "train_dataset = create_dataset(training_split_documents, training_questions, training_relevant_contexts)\n", "val_dataset = create_dataset(val_split_documents, val_questions, val_relevant_contexts)\n", "test_dataset = create_dataset(test_split_documents, test_questions, test_relevant_contexts)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pwUnjPfWwFv9" }, "outputs": [], "source": [ "corpus = train_dataset['corpus']\n", "queries = train_dataset['questions']\n", "relevant_docs = train_dataset['relevant_contexts']\n", "\n", "examples = []\n", "for query_id, query in queries.items():\n", " doc_id = relevant_docs[query_id][0]\n", " text = corpus[doc_id]\n", " example = InputExample(texts=[query, text])\n", " examples.append(example)\n", "\n", "loader = DataLoader(\n", " examples, batch_size=BATCH_SIZE\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 369, "referenced_widgets": [ "c2003fe7ddf04c3ba844edbede3a00f6", "df45c3dcb19d4939bb35c612910b330a", "ea61e04615b248089a44dddaa6bb628e", "6f562b9b638f4351886462c6f03cddfe", "7f20409d005148fda11ddb3595fe8b2d", "5be36f73eef843699c8e4334fa4430d4", "abe50565a69349bc9152cd27f0a6bc90", "f35e960ec3b448e58adbbda12c139246", "3e1c5deb62234af98d04ca575bf5a481", "d43ccc7ec844456a9ba3601224c0bdd7", "1ff331c98c4844c4b103270f0a149a42", "5413ca38f11d46a49d7a924bbc4adc0d", "e5d38475c2a848d49bd2742401262bbf", "f435cadfe836498698380330c65306b3", "1549981e58eb42ad8b5f0d4147e4991a", "03cb2d713dc44a74b524ab3d8eff4ff6", "961ea4cde9cd448997f060f70665c551", "508f0f0ab1e24582b693af7e95763fdc", "4f2d34c69156424f9ebdcf0ea71f9331", "f38696f05cd841d7928d62b5f6009862", "e5b0d5fbb74e4d35978b01e188d21c04", "c00c2ce8197f4df5a7de1adc0a41414e", "2a0deb8d5afc49158a37202c119955d1", "60597aa75ad44810909211f8b11e7a72", "7b48c3e8008d460182badcf29d0bcffe", "0ee70954e8574fd5a8877bb1348b8b97", "b5940727913743fa86507046185a3698", "fd5f8aa248374e81a716d866fb85980d", "f37d89e08a594738ba836846cd431e69", "136dbe52a5c340b58a19e2bd7c3821f6", "ae5e0d93bab44e94ad7462485a35d91e", "2625c6afeb6342c08171442519eb5a6a", "9804c00586dc401f962e1288e42fbf38", 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StepTraining LossValidation LossCosine Accuracy@1Cosine Accuracy@3Cosine Accuracy@5Cosine Accuracy@10Cosine Precision@1Cosine Precision@3Cosine Precision@5Cosine Precision@10Cosine Recall@1Cosine Recall@3Cosine Recall@5Cosine Recall@10Cosine Ndcg@10Cosine Mrr@10Cosine Map@100Dot Accuracy@1Dot Accuracy@3Dot Accuracy@5Dot Accuracy@10Dot Precision@1Dot Precision@3Dot Precision@5Dot Precision@10Dot Recall@1Dot Recall@3Dot Recall@5Dot Recall@10Dot Ndcg@10Dot Mrr@10Dot Map@100
30No logNo log0.7100000.8600000.9150000.9400000.7100000.2866670.1830000.0940000.7100000.8600000.9150000.9400000.8285880.7923250.7952730.7100000.8600000.9150000.9400000.7100000.2866670.1830000.0940000.7100000.8600000.9150000.9400000.8285880.7923250.795273
50No logNo log0.7600000.8900000.9300000.9700000.7600000.2966670.1860000.0970000.7600000.8900000.9300000.9700000.8652890.8318310.8325820.7600000.8900000.9300000.9700000.7600000.2966670.1860000.0970000.7600000.8900000.9300000.9700000.8652890.8318310.832582
60No logNo log0.7450000.8950000.9300000.9650000.7450000.2983330.1860000.0965000.7450000.8950000.9300000.9650000.8604560.8264780.8276520.7450000.8950000.9300000.9650000.7450000.2983330.1860000.0965000.7450000.8950000.9300000.9650000.8604560.8264780.827652
90No logNo log0.7400000.9050000.9400000.9650000.7400000.3016670.1880000.0965000.7400000.9050000.9400000.9650000.8585750.8237920.8250100.7400000.9050000.9400000.9650000.7400000.3016670.1880000.0965000.7400000.9050000.9400000.9650000.8585750.8237920.825010
100No logNo log0.7500000.9000000.9350000.9650000.7500000.3000000.1870000.0965000.7500000.9000000.9350000.9650000.8609520.8271810.8284350.7500000.9000000.9350000.9650000.7500000.3000000.1870000.0965000.7500000.9000000.9350000.9650000.8609520.8271810.828435
120No logNo log0.7350000.9000000.9350000.9600000.7350000.3000000.1870000.0960000.7350000.9000000.9350000.9600000.8533060.8183590.8200110.7350000.9000000.9350000.9600000.7350000.3000000.1870000.0960000.7350000.9000000.9350000.9600000.8533060.8183590.820011
150No logNo log0.7300000.9000000.9350000.9600000.7300000.3000000.1870000.0960000.7300000.9000000.9350000.9600000.8511690.8155400.8172230.7300000.9000000.9350000.9600000.7300000.3000000.1870000.0960000.7300000.9000000.9350000.9600000.8511690.8155400.817223

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Metrictext-embedding-3-smallfinetuned_arctic
0faithfulness0.7359090.707843
1answer_relevancy0.7749790.752002
2context_recall0.7833330.777778
3context_precision0.7805560.736111
4answer_correctness0.6601360.505122
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