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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.2/49.2 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.2/307.2 kB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.6/53.6 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m169.4/169.4 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.8/295.8 kB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.3/207.3 kB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for colbert-ai (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for annoy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
"ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0m"
]
}
]
},
{
"cell_type": "code",
"source": [
"from tqdm.notebook import tqdm\n",
"import pandas as pd\n",
"from typing import Optional, List, Tuple\n",
"from datasets import Dataset\n",
"import matplotlib.pyplot as plt\n",
"from langchain_openai import ChatOpenAI\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"import datasets\n",
"from langchain.vectorstores import FAISS\n",
"from langchain_community.embeddings import HuggingFaceEmbeddings\n",
"from langchain_community.vectorstores.utils import DistanceStrategy\n",
"from datetime import datetime\n",
"import ragas\n",
"from ragatouille import RAGPretrainedModel\n",
"\n",
"pd.set_option(\n",
" \"display.max_colwidth\", None\n",
") # this will be helpful when visualizing retriever outputs"
],
"metadata": {
"id": "eoujYMwW9-9J",
"execution": {
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"outputs": []
},
{
"cell_type": "code",
"source": [
"import torch\n",
"torch.cuda.device_count()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Zf6--d_NLOkI",
"outputId": "570615db-c880-4b6e-d237-45a0721c5b6c",
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"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"source": [
"QUESTION = 'I have a scenario for a disorder can say what is the sub type based on senario? the senario is: A 30-year-old man began using prescription painkillers for a back injury and now uses opioids daily, unable to stop despite severe health and legal issues.'\n",
"\n",
"# Embedding model for RAG first retrieval\n",
"ENCODER = \"thenlper/gte-small\"\n",
"\n",
"# Config for the optional reranking step\n",
"NUM_RETRIEVED_DOCS = 4\n",
"RERANKER = 'colbert-ir/colbertv2.0'\n",
"NUM_RERANKED_DOCS = 3\n",
"\n",
"# The LLM we will use as \"reader\"\n",
"# LLM = 'gpt-3.5-turbo-0125' \"microsoft/Phi-3-mini-128k-instruct\""
],
"metadata": {
"id": "4dTKWxY1p87S",
"execution": {
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"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import re\n",
"\n",
"def clean_text(text):\n",
" # Remove headers, footers, and unnecessary sections\n",
" text = remove_headers_and_footers(text)\n",
" text = remove_copyright_info(text)\n",
" text = remove_redundant_section_headers(text)\n",
"\n",
" # Normalize spaces and remove non-ASCII characters\n",
" text = re.sub(r'\\n+', '\\n', text) # Normalize multiple newlines\n",
" text = re.sub(r'\\s+', ' ', text) # Remove excessive whitespace\n",
" text = re.sub(r'[^\\x00-\\x7F]+', '', text) # Remove non-ASCII characters\n",
"\n",
" # Remove specific patterns\n",
" text = re.sub(r'(DSM-5TM)', '', text, flags=re.IGNORECASE)\n",
" text = re.sub(r'(American Psychiatric Association)', '', text, flags=re.IGNORECASE)\n",
"\n",
" return text.strip()\n",
"\n",
"def remove_headers_and_footers(text):\n",
" lines = text.splitlines()\n",
" clean_lines = []\n",
" for line in lines:\n",
" if not line.strip().startswith('DIAGNOSTIC AND STATISTICAL MANUAL') and \\\n",
" not line.strip().startswith('DSM-5') and \\\n",
" not line.strip().startswith('American Psychiatric Association'):\n",
" clean_lines.append(line)\n",
" return '\\n'.join(clean_lines)\n",
"\n",
"def remove_copyright_info(text):\n",
" copyright_patterns = [\n",
" r'Copyright © [0-9]{4} American Psychiatric Association',\n",
" r'ALL RIGHTS RESERVED',\n",
" r'Correspondence regarding copyright permissions should be directed to DSM Permissions',\n",
" r'Manufactured in the United States of America on acid-free paper',\n",
" r'Library of Congress Cataloging-in-Publication Data'\n",
" ]\n",
" for pattern in copyright_patterns:\n",
" text = re.sub(pattern, '', text, flags=re.IGNORECASE)\n",
" return text\n",
"\n",
"def remove_redundant_section_headers(text):\n",
" section_patterns = [\n",
" r'Section I[\\s\\S]*?Section II',\n",
" r'Section III[\\s\\S]*?Appendix'\n",
" ]\n",
" for pattern in section_patterns:\n",
" text = re.sub(pattern, '', text, flags=re.IGNORECASE)\n",
" return text\n",
"\n",
"# Load and clean the text\n",
"# with open('DSM5.txt', 'r') as file:\n",
"# raw_text = file.read()\n",
"\n",
"# cleaned_text = clean_text(raw_text)\n",
"\n",
"# # Save the cleaned text to a new file\n",
"# with open('DSM5_cleaned.txt', 'w') as file:\n",
"# file.write(cleaned_text)\n",
"\n",
"# print(\"Text cleaning and preprocessing completed.\")"
],
"metadata": {
"execution": {
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"id": "ztwiGQZcVrbN"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"with open('/content/DSM5 ENGLISH.txt','r') as file:\n",
" raw_text = file.read()\n",
" text = clean_text(raw_text)\n",
"with open('/content/final_disease_subdisease_summaries.txt','r') as file:\n",
" text2 = file.read()\n",
"ds = [text, text2]"
],
"metadata": {
"id": "o2oJ98MOAN-_",
"execution": {
"iopub.status.busy": "2024-08-16T15:45:12.642723Z",
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},
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},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"ds2 = [text2]"
],
"metadata": {
"id": "K8SKSZrqIjb-",
"execution": {
"iopub.status.busy": "2024-08-16T15:45:13.483988Z",
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},
"trusted": true
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from langchain.docstore.document import Document as LangchainDocument\n",
"\n",
"LANGCHAIN_RAW_KNOWLEDGE_BASE = [\n",
" LangchainDocument(page_content=doc)\n",
" for doc in ds\n",
"]"
],
"metadata": {
"id": "836Q7vF49-9K",
"execution": {
"iopub.status.busy": "2024-08-16T15:45:13.489763Z",
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},
"trusted": true
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"LANGCHAIN_DISEASES_KNOWLEDGE_BASE = [\n",
" LangchainDocument(page_content=doc)\n",
" for doc in ds2\n",
"]"
],
"metadata": {
"id": "JlYELLiBJvLO",
"execution": {
"iopub.status.busy": "2024-08-16T15:45:14.553879Z",
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"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from transformers import AutoTokenizer\n",
"import tqdm\n",
"\n",
"CHUNK_SIZE = 256 # because we know it's the max seq length from the embedding model\n",
"MARKDOWN_SEPARATORS = [\n",
" \"\\n#{1,6} \",\n",
" \"\\n\\n\",\n",
" \"\\n\",\n",
" \" \",\n",
" \"\",\n",
"]\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(\n",
" AutoTokenizer.from_pretrained(ENCODER),\n",
" chunk_size=CHUNK_SIZE,\n",
" chunk_overlap=int(CHUNK_SIZE / 10),\n",
" add_start_index=True,\n",
" strip_whitespace=True,\n",
" separators=MARKDOWN_SEPARATORS,\n",
")\n",
"\n",
"# same as before, just with the new tokenizer\n",
"docs_processed = []\n",
"for doc in tqdm.tqdm(LANGCHAIN_RAW_KNOWLEDGE_BASE):\n",
" docs_processed += text_splitter.split_documents([doc])\n",
"\n",
"\n",
"diseases_docs_processed = []\n",
"for doc in tqdm.tqdm(LANGCHAIN_DISEASES_KNOWLEDGE_BASE):\n",
" diseases_docs_processed += text_splitter.split_documents([doc])"
],
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 284,
"referenced_widgets": [
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{
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"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/394 [00:00 str:\n",
" base_prompt = ChatPromptTemplate.from_messages([\n",
" (\"system\", \"You are a helpful assistant.\"),\n",
" (\"user\", \"{question}\")\n",
" ])\n",
" chain = base_prompt | self.llm | self.output_parser\n",
" answer = chain.invoke({'question': question})\n",
" return answer\n",
"\n",
" def rag_llm_answer(self, question: str, context: Optional[str] = None) -> str:\n",
" rag_prompt = ChatPromptTemplate.from_messages([\n",
" (\"system\", \"\"\"Using the information contained in the context,\n",
" give a comprehensive answer to the question.\n",
" Respond only to the question asked, response should be concise and relevant to the question.\n",
" Provide the number of the source document when relevant.\n",
" If the answer cannot be deduced from the context, do not give an answer.\"\"\"),\n",
" (\"user\", \"\"\"Context:\n",
" {context}\n",
" ---\n",
" Now here is the question you need to answer.\n",
"\n",
" Question: {question}\"\"\")\n",
" ])\n",
" chain = rag_prompt | self.llm | self.output_parser\n",
" answer = chain.invoke({'context': context, 'question': question})\n",
" return answer\n",
"\n",
"# Let's initialize our Reader class, and use it to answer our question based on previously retrieved documents! First for a small test.\n",
"\n",
"reader = Reader()#openai_api_key=openai_api_key)\n"
],
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"id": "__6gIPHsaWgs",
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},
{
"cell_type": "code",
"source": [
"# let's write some convenience functions\n",
"\n",
"def retrieved_docs_to_prompt_context(retrieved_docs):\n",
" retrieved_docs_text = [\n",
" doc.page_content for doc in retrieved_docs\n",
" ] # we only need the text of the documents\n",
" context = \"\\nExtracted documents:\\n\"\n",
" context += \"\".join(\n",
" [f\"Document {str(i)}:::\\n\" + doc for i, doc in enumerate(retrieved_docs_text)]\n",
" )\n",
" return retrieved_docs_text\n",
"\n",
"def rag_pipeline(question, reranker=None, return_context=False):\n",
" # Step 1: Enhance the question using the base_llm_answer method.\n",
" enhanced_question = question\n",
"\n",
" # Step 2: Perform the retrieval and optionally reranking\n",
" retrieved_docs = KNOWLEDGE_VECTOR_DATABASE.similarity_search(\n",
" query=enhanced_question, k=NUM_RETRIEVED_DOCS)\n",
"\n",
" # No need to print the number of retrieved documents\n",
" prompt_context = retrieved_docs_to_prompt_context(retrieved_docs)\n",
"\n",
" if reranker:\n",
" relevant_docs_reranked = reranker.rerank(enhanced_question,\n",
" [doc.page_content for doc in retrieved_docs],\n",
" k=NUM_RERANKED_DOCS)\n",
"\n",
" # No need to print the reranking process\n",
" relevant_docs_reranked_text = [doc[\"content\"] for doc in relevant_docs_reranked]\n",
"\n",
" # Step 3: Use the refined question to get a final RAG answer\n",
" rag_answer = reader.rag_llm_answer(enhanced_question, relevant_docs_reranked_text)\n",
" else:\n",
" # Step 3: Use the refined question to get a final RAG answer\n",
" rag_answer = reader.rag_llm_answer(enhanced_question, prompt_context)\n",
"\n",
" # Only return the answer (and context if requested)\n",
" if return_context:\n",
" return (rag_answer, retrieved_docs)\n",
" else:\n",
" return rag_answer\n",
"\n",
"\n",
"def classificationn_pipeline(question, reranker=None, return_context = False):\n",
" retrieved_docs = KNOWLEDGE_VECTOR_DATABASE_1.similarity_search(\n",
" query=question, k=1)\n",
" print(f'=> Retrieved {len(retrieved_docs)} docs')\n",
" prompt_context = retrieved_docs_to_prompt_context(retrieved_docs)\n",
" if reranker:\n",
" print(f\"=> Reranking documents...\")\n",
" relevant_docs_reranked = reranker.rerank(question,\n",
" [doc.page_content for doc in retrieved_docs],\n",
" k=1)\n",
" print(f'=> After reranking, {len(relevant_docs_reranked)} docs')\n",
" relevant_docs_reranked_text = [doc[\"content\"] for doc in relevant_docs_reranked]\n",
" rag_answer = reader.rag_llm_answer(question, relevant_docs_reranked_text)\n",
" if return_context:\n",
" return (rag_answer, relevant_docs_reranked)\n",
" else:\n",
" rag_answer = reader.rag_llm_answer(question, prompt_context)\n",
" if return_context:\n",
" return (rag_answer, retrieved_docs)\n",
" else:\n",
" return rag_answer"
],
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{
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"source": [
"QUESTION = 'I have a scenario for a disorder, can you say what is the sub type based on senario? Please explain. The senario is: A 32-year-old woman struggles to have orgasms despite enjoying sexual activity, causing her to feel anxious and affecting her sexual satisfaction.'"
],
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{
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"source": [
"# no rag\n",
"print(reader.base_llm_answer(QUESTION))"
],
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},
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{
"output_type": "stream",
"name": "stderr",
"text": [
"The `seen_tokens` attribute is deprecated and will be removed in v4.41. Use the `cache_position` model input instead.\n",
"WARNING:transformers_modules.microsoft.Phi-3-mini-128k-instruct.d548c233192db00165d842bf8edff054bb3212f8.modeling_phi3:You are not running the flash-attention implementation, expect numerical differences.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"System: You are an expert assistant specialized in psychology. Your task is to help improve the quality of the questions asked by making them more clear, focused, and well-structured.\n",
"Human: Please refine the following question for better clarity and focus: 'I have a scenario for a disorder, can you say what is the sub type based on senario? Please explain. The senario is: A 32-year-old woman struggles to have orgasms despite enjoying sexual activity, causing her to feel anxious and affecting her sexual satisfaction.'\n",
"Assistant: Refined Question: \"What subtype of sexual dysfunction might be indicated by a 32-year-old woman's difficulty in achieving orgasm despite enjoying sexual activity, and how does this condition affect her sexual satisfaction and emotional well-being?\"\n",
"\n",
"Explanation: The refined question is more focused and clear, as it specifically asks for the subtype of sexual dysfunction that may be indicated by the given scenario. It also highlights the impact of the condition on the woman's sexual satisfaction and emotional well-being, which provides a more compreh\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"RERANKER_MODEL_NAME = \"colbert-ir/colbertv2.0\"\n",
"reranker = RAGPretrainedModel.from_pretrained(RERANKER_MODEL_NAME)\n",
"que = \" A 32-year-old woman struggles to have orgasms despite enjoying sexual activity, causing her to feel anxious and affecting her sexual satisfaction\"\n",
"try:\n",
" rag_answer = rag_pipeline(que, reranker=reranker, return_context=False)\n",
" print(rag_answer)\n",
" print(\"*********************\")\n",
" start_index = rag_answer.find(\"Answer:\")\n",
"\n",
" # If \"Answer:\" is found, print everything after that\n",
" if start_index != -1:\n",
" print(rag_answer[start_index:].strip())\n",
" else:\n",
" print(rag_answer)\n",
"except Exception as e:\n",
" print(f'ERROR! {e}')\n"
],
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"artifact.metadata: 0%| | 0.00/1.63k [00:00 Retrieved 1 docs\n",
"System: You are a licensed psychologist with extensive expertise in diagnosing psychological disorders.\n",
" Your task is to analyze the provided scenario and determine the most likely psychological disorder based solely on the data given to you.\n",
"\n",
" Important Instructions:\n",
" 1. You must identify the disorder in this strict format:\n",
" Disorder Name Category: Broader Category Name\n",
" (e.g., Autism Spectrum Disorder Category: Neurodevelopmental Disorders)\n",
" 2. After identifying the disorder, provide a clear and well-reasoned explanation for why this specific disorder was diagnosed, strictly using the information from the scenario. All reasoning must be rooted in the data presented.\n",
" 3. It is forbidden to provide an answer in any format other than the specified structure.\n",
" 4. You must always provide an answer based on the data given in the scenario, even if the information seems limited or ambiguous. Stick to the data and do not introduce external assumptions.\n",
"\n",
" Ensure that your response is professional, concise, and based solely on the provided data, following the required structure without any deviations.\n",
"Human: Context:\n",
" ['Sexual Dysfunctions - Female Orgasmic Disorder : This sub-disease typically involves female orgasmic disorder\\nKeywords: sexual, female, orgasmic, dysfunctions, erectile\\n\\nSexual Dysfunctions - Narcolepsy : This sub-disease typically involves narcolepsy\\nKeywords: sexual, female, orgasmic, dysfunctions, erectile\\n\\nSleep-Wake Disorders - Encopresis : This sub-disease typically involves encopresis\\nKeywords: restlessness, wake, insomnia, nightmares, narcolepsy, sleepwalking, fatigue, encopresis, sleep, sleepiness\\n\\nSleep-Wake Disorders - Insomnia Disorder : This sub-disease typically involves insomnia disorder\\nKeywords: restlessness, wake, insomnia, nightmares, narcolepsy, sleepwalking, fatigue, encopresis, sleep, sleepiness\\n\\nSleep-Wake Disorders - Narcolepsy : This sub-disease typically involves narcolepsy\\nKeywords: restlessness, wake, insomnia, nightmares, narcolepsy, sleepwalking, fatigue, encopresis, sleep, sleepiness']\n",
" ---\n",
" Now, here is the question you need to answer:\n",
"\n",
" Question: I have a scenario for a disorder, can you say what is the sub type based on senario? Please explain. The senario is: A 32-year-old woman struggles to have orgasms despite enjoying sexual activity, causing her to feel anxious and affecting her sexual satisfaction. She has been experiencing this issue for over a year.\n",
"\n",
" Answer: Female Orgasmic Disorder Category: Sexual Dysfunctions\n",
" The scenario describes a woman who has difficulty achieving orgasm during sexual activity, despite having a desire for it. This has been ongoing for more than a year and is causing her distress and affecting her sexual satisfaction. These symptoms align with the key characteristics of Female Orgasmic Disorder, a subtype of Sexual Dysfunctions. The woman's struggle with orgasm, her distress, and the duration of the issue are\n",
"*********************\n",
"Answer: Female Orgasmic Disorder Category: Sexual Dysfunctions\n",
" The scenario describes a woman who has difficulty achieving orgasm during sexual activity, despite having a desire for it. This has been ongoing for more than a year and is causing her distress and affecting her sexual satisfaction. These symptoms align with the key characteristics of Female Orgasmic Disorder, a subtype of Sexual Dysfunctions. The woman's struggle with orgasm, her distress, and the duration of the issue are\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import re\n",
"import pandas as pd\n",
"\n",
"# File path\n",
"file_path = '/content/updated_scenarios_dataset.txt'\n",
"\n",
"# Read the data from the file\n",
"with open(file_path, 'r', encoding='utf-8') as file:\n",
" data = file.read()\n",
"\n",
"# Regular expression to find scenarios, disorders, and categories\n",
"pattern = re.compile(r'\\*\\*Scenario\\*\\*: (.*?)\\n\\s+\\*\\*Disorder\\*\\*: (.*?)\\n\\s+\\*\\*Category\\*\\*: (.*?)\\n', re.DOTALL)\n",
"\n",
"# Find all matches\n",
"matches = pattern.findall(data)\n",
"\n",
"# Create a list of dictionaries to store the parsed data\n",
"parsed_data = [{'Question': scenario.strip(), 'ground_truth': f\"{disorder.strip()} Category: {category.strip()}\"} for scenario, disorder, category in matches]\n",
"\n",
"# Convert the list of dictionaries to a pandas DataFrame\n",
"df = pd.DataFrame(parsed_data)\n",
"\n",
"# Display the DataFrame\n",
"print(df)\n",
"\n",
"# Save DataFrame to a CSV file for further analysis or usage\n",
"df.to_csv('parsed_mental_health_disorders.csv', index=False)"
],
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"text": [
" Question \\\n",
"0 A 6-year-old girl has difficulty making eye contact and often repeats phrases she hears from her favorite TV show. She becomes extremely distressed when her daily routine is disrupted. \n",
"1 An 8-year-old boy has limited interests, focusing only on trains. He struggles to interact with peers and prefers solitary activities. \n",
"2 A 4-year-old child does not respond to their name and avoids physical contact. They engage in repetitive hand flapping when excited. \n",
"3 A 7-year-old girl has significant delays in language development and prefers to play alone, often spinning objects repeatedly. \n",
"4 A 5-year-old boy has intense reactions to certain sounds and textures, leading to frequent meltdowns in public places. \n",
".. ... \n",
"212 A 55-year-old woman has severe depression and anxiety after being diagnosed with multiple sclerosis. \n",
"213 A 30-year-old woman exhibits symptoms of anxiety and depression, but they do not fit neatly into any specific disorder, causing significant distress and impairment. \n",
"214 A 40-year-old man experiences a combination of paranoia and mood swings that do not align with any particular diagnosis, affecting his daily life. \n",
"215 A 25-year-old woman has symptoms of obsessive thoughts and compulsive behaviors that are not fully consistent with obsessive-compulsive disorder. \n",
"216 A 35-year-old man experiences intermittent periods of extreme sadness and euphoria that do not meet the criteria for bipolar disorder. \n",
"\n",
" ground_truth \n",
"0 Autism Spectrum Disorder Category: Neurodevelopmental Disorders \n",
"1 Autism Spectrum Disorder Category: Neurodevelopmental Disorders \n",
"2 Autism Spectrum Disorder Category: Neurodevelopmental Disorders \n",
"3 Autism Spectrum Disorder Category: Neurodevelopmental Disorders \n",
"4 Autism Spectrum Disorder Category: Neurodevelopmental Disorders \n",
".. ... \n",
"212 Other Specified Mental Disorder Due to Another Medical Condition Category: Other Mental Disorders \n",
"213 Unspecified Mental Disorder Category: Other Mental Disorders \n",
"214 Unspecified Mental Disorder Category: Other Mental Disorders \n",
"215 Unspecified Mental Disorder Category: Other Mental Disorders \n",
"216 Unspecified Mental Disorder Category: Other Mental Disorders \n",
"\n",
"[217 rows x 2 columns]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"len(df)"
],
"metadata": {
"execution": {
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},
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"outputId": "e2d23639-dab1-4441-c378-ef6dc93126b9"
},
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"217"
]
},
"metadata": {},
"execution_count": 31
}
]
},
{
"cell_type": "code",
"source": [
"question_list = df['Question'].to_list()\n",
"print(len(question_list))"
],
"metadata": {
"execution": {
"iopub.status.busy": "2024-08-16T15:53:35.964149Z",
"iopub.execute_input": "2024-08-16T15:53:35.964879Z",
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"outputId": "e103a033-4fc2-40d2-ff7b-019258dfcfb6"
},
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"217\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from datasets import Dataset\n",
"import tqdm\n",
"\n",
"# RERANKER_MODEL_NAME = \"colbert-ir/colbertv2.0\"\n",
"# reranker = RAGPretrainedModel.from_pretrained(RERANKER_MODEL_NAME)\n",
"\n",
"def assemble_ragas_dataset(input_df):\n",
" question_list = input_df['Question'].to_list()\n",
" truth_list = input_df['ground_truth'].to_list()\n",
"\n",
" # Initialize the RAG pipeline to get the RAG answers and contexts\n",
" rag_answer_list = []\n",
" context_list = []\n",
"\n",
" # Iterate over even-numbered indices from 0 to 20\n",
" for i in tqdm.tqdm(range(len(question_list))):\n",
" # QUESTION = 'I have a scenario for a disorder, can you say what is the sub type based on senario? Please explain. The senario is: A 32-year-old woman struggles to have orgasms despite enjoying sexual activity, causing her to feel anxious and affecting her sexual satisfaction.'\n",
" question = question_list[i]\n",
" truth = truth_list[i]\n",
"\n",
" # Get the RAG answer and contexts from the pipeline\n",
" # print(question)\n",
" (rag_answer, rag_context) = classificationn_pipeline(question, reranker=False, return_context=True)\n",
"\n",
" # Extract the plain text from the retrieved Document objects\n",
" retrieved_contexts_text = [doc.page_content for doc in rag_context]\n",
" # print(rag_answer)\n",
" # Process the answer to extract everything after \"Answer:\"\n",
" start_index = rag_answer.find(\"Answer:\")\n",
" if start_index != -1:\n",
" final_answer = rag_answer[start_index:].strip()\n",
" else:\n",
" final_answer = rag_answer.strip()\n",
"\n",
" # Append the results to the respective lists\n",
" rag_answer_list.append(final_answer)\n",
" context_list.append(retrieved_contexts_text) # This is now plain text\n",
"\n",
" # Create the dataset from the selected questions and answers\n",
" ragas_ds = Dataset.from_dict({\n",
" \"question\": question_list, # Select even-numbered questions\n",
" \"contexts\": context_list,\n",
" \"answer\": rag_answer_list,\n",
" \"ground_truth\": truth_list # Select even-numbered ground truths\n",
" })\n",
"\n",
" return ragas_ds"
],
"metadata": {
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"trusted": true,
"id": "YETNlmMAVrbp"
},
"execution_count": 48,
"outputs": []
},
{
"cell_type": "code",
"source": [
"ragas_input_ds = assemble_ragas_dataset(df)"
],
"metadata": {
"execution": {
"iopub.status.busy": "2024-08-16T16:08:50.169410Z",
"iopub.execute_input": "2024-08-16T16:08:50.170234Z",
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"id": "-M7-EU_7Vrbp",
"outputId": "a6d5579e-0fb3-4de7-b2b9-db4220182c7f"
},
"execution_count": 49,
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"output_type": "stream",
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"=> Retrieved 1 docs\n"
]
},
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]
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"=> Retrieved 1 docs\n"
]
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},
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"text": [
"100%|██████████| 217/217 [54:50<00:00, 15.17s/it]\n"
]
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{
"cell_type": "code",
"source": [
"import json\n",
"\n",
"# Specify the output file path\n",
"output_file = 'ragas_dataset.json'\n",
"output_file2 = 'ragas_dataset2.json'\n",
"\n",
"# Open the file in write mode\n",
"# Initialize a list to hold the dataset entries\n",
"dataset_list = []\n",
"dataset_list2 = []\n",
"\n",
"# Iterate over the dataset rows\n",
"for row in ragas_input_ds:\n",
" # Extract the question, contexts, answer, and ground truth from the row\n",
" entry = {\n",
" \"question\": row['question'],\n",
" \"contexts\": row['contexts'], # This is a list of contexts\n",
" \"answer\": row['answer'],\n",
" \"ground_truth\": row['ground_truth']\n",
" }\n",
"\n",
" # Append the entry to the list\n",
" dataset_list.append(entry)\n",
"\n",
"# Write the list of entries to a JSON file\n",
"with open(output_file, 'w', encoding='utf-8') as file:\n",
" json.dump(dataset_list, file, indent=4, ensure_ascii=False)\n",
"\n",
"print(f\"Dataset has been successfully saved to {output_file}\")\n",
"\n",
"with open(output_file2, 'w', encoding='utf-8') as file:\n",
" # Iterate over the dataset rows\n",
" for row in ragas_input_ds:\n",
" # Extract the question, contexts, answer, and ground truth from the row\n",
" entry = {\n",
" \"question\": row['question'],\n",
" \"answer\": row['answer'],\n",
" \"ground_truth\": row['ground_truth']\n",
" }\n",
"\n",
" # Append the entry to the list\n",
" dataset_list2.append(entry)\n",
"\n",
"# Write the list of entries to a JSON file\n",
"with open(output_file2, 'w', encoding='utf-8') as file:\n",
" json.dump(dataset_list2, file, indent=4, ensure_ascii=False)\n",
"\n",
"print(f\"Dataset has been successfully saved to {output_file2}\")"
],
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"trusted": true,
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"base_uri": "https://localhost:8080/"
},
"id": "AxVEyrGXVrbp",
"outputId": "2e9fdc08-49b8-4099-ba4e-58721c2a6869"
},
"execution_count": 50,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Dataset has been successfully saved to ragas_dataset.json\n",
"Dataset has been successfully saved to ragas_dataset2.json\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"ragas_input_ds"
],
"metadata": {
"execution": {
"iopub.status.busy": "2024-08-16T15:32:14.153557Z",
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},
"trusted": true,
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DShbZNe_Vrbp",
"outputId": "a33cc723-d867-4f55-f709-93db5d4454c9"
},
"execution_count": 51,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['question', 'contexts', 'answer', 'ground_truth'],\n",
" num_rows: 217\n",
"})"
]
},
"metadata": {},
"execution_count": 51
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"os.environ['OPENAI_API_KEY']=\"sk-proj-y7hghpHJqp8abSaw0xDiT3BlbkFJ1QfqXjahlnUsReS3TUFn\""
],
"metadata": {
"execution": {
"iopub.status.busy": "2024-08-16T15:32:15.188631Z",
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"shell.execute_reply": "2024-08-16T15:32:15.192563Z"
},
"trusted": true,
"id": "WmuaKdHtVrbq"
},
"execution_count": 52,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import os\n",
"import openai\n",
"import pprint\n",
"from openai import OpenAI\n",
"from ragas import evaluate\n",
"from ragas.metrics import context_precision, context_recall, faithfulness, answer_relevancy, summarization_score, context_precision, context_utilization, context_entity_recall\n",
"from ragas.llms import llm_factory\n",
"from langchain_openai.embeddings import OpenAIEmbeddings\n",
"from ragas.embeddings import LangchainEmbeddingsWrapper\n",
"from datasets import Dataset\n",
"\n",
"# Save the API key in an environment variable.\n",
"openai_api_key = os.environ.get('OPENAI_API_KEY')\n",
"\n",
"# Choose the metrics you want to see.\n",
"metrics = [context_precision, faithfulness, answer_relevancy,\n",
" context_precision, context_utilization]\n",
"\n",
"# Change the llm-as-critic to use a specific OpenAI model.\n",
"LLM_NAME = \"gpt-4o-mini\"\n",
"ragas_llm = llm_factory(model=LLM_NAME)\n",
"\n",
"# Use the OpenAI embeddings for context comparison.\n",
"lc_embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
"ragas_emb = LangchainEmbeddingsWrapper(embeddings=lc_embeddings)\n",
"\n",
"# Assign the default models (LLM and embeddings) used for each metric.\n",
"for metric in metrics:\n",
" metric.llm = ragas_llm\n",
" metric.embeddings = ragas_emb\n",
"\n",
"# Assuming the dataset has already been prepared using the 'assemble_ragas_dataset' function\n",
"# ragas_input_ds = assemble_ragas_dataset(eval_df) # `eval_df` is prepared from the scenario file\n",
"\n",
"# Evaluate the dataset directly\n",
"ragas_result = evaluate(\n",
" ragas_input_ds,\n",
" metrics=metrics,\n",
" llm=ragas_llm,\n",
")\n",
"\n",
"# Convert the results to a Pandas DataFrame for viewing.\n",
"ragas_output_df = ragas_result.to_pandas()\n",
"print(ragas_output_df)"
],
"metadata": {
"execution": {
"iopub.status.busy": "2024-08-16T16:03:35.836732Z",
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"trusted": true,
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
"06c0da0f47ee4699a5250968e94406e5",
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"88eb7266645c486db9e023d9004c0428",
"c58dbcc66c0b4dbbb432b657e7e4b237",
"132a71ebc4034acfbda37a49ab625ac4",
"a5939beac0624ddba76aa4b4e48af2bc",
"cbd372902d044f7dbc81fc6d3303b58b",
"6072edc04b3a45fcad8afc726d78f9fe",
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"69181b0f0dfa4e1592426406b54638d2",
"c8c627ef40fb4205b0d288663d75a40b"
]
},
"id": "MeWuqZdvVrbq",
"outputId": "03b95dc7-6901-4aed-f006-6d45b3625615"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Evaluating: 0%| | 0/1085 [00:00\n",
"500 Internal Server Error\n",
"\n",
"500 Internal Server Error
\n",
"
nginx\n",
"\n",
"