diff --git "a/report/report.ipynb" "b/report/report.ipynb" new file mode 100644--- /dev/null +++ "b/report/report.ipynb" @@ -0,0 +1,9537 @@ +{ + "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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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": [ + 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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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