llama_medical_QA.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "cfpP9B7PkeJ8",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!pip install -qqq datasets\n",
    "!pip install -qqq accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7\n",
    "!pip install -qqq sentencepiece\n",
    "!pip install -qqq torch\n",
    "!pip install -qqq sentence-transformers\n",
    "!pip install -qqq chromadb\n",
    "!pip install -qqq einops\n",
    "!pip install -qqq ctranslate2\n",
    "!pip install -qqq langchain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
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     ]
    },
    "id": "8rHRFUGdkiWc",
    "outputId": "01dc4bee-9094-4310-9034-ca57493cb376"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "323dd41008cb478dbbb78f4a8a764da4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!ct2-transformers-converter --model meta-llama/Llama-2-13b-chat-hf --output_dir llama-13b-ct2 --force"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e19d6af95dd947c4bc0e557fcf072ef8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "(…)at-hf/resolve/main/tokenizer_config.json:   0%|          | 0.00/776 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "db08380d836643b28da52fef645fdba6",
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       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
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     },
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     "output_type": "display_data"
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    {
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       "version_minor": 0
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      "text/plain": [
       "(…)-13b-chat-hf/resolve/main/tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54e5358199ea4354a82cee4380a8456e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "(…)-hf/resolve/main/special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import transformers\n",
    "import ctranslate2\n",
    "tokenizer = transformers.AutoTokenizer.from_pretrained(\"meta-llama/Llama-2-13b-chat-hf\")\n",
    "generator = ctranslate2.Generator(\"llama-13b-ct2\", device=\"cuda\", compute_type=\"float16\", device_index=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "import os\n",
    "\n",
    "zip_file_path = 'data.zip'\n",
    "extracted_dir = os.getcwd()\n",
    "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
    "    zip_ref.extractall(extracted_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "pUyXvWnwnSrA"
   },
   "outputs": [],
   "source": [
    "from langchain.document_loaders import DirectoryLoader, TextLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain.embeddings import HuggingFaceEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = DirectoryLoader(os.getcwd() + \"/data/\", glob=\"*/*.txt\", loader_cls=TextLoader)\n",
    "documents = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "id": "9DWSIkW8-Mak"
   },
   "outputs": [],
   "source": [
    "splitter = RecursiveCharacterTextSplitter(chunk_size=1024,\n",
    "                                          chunk_overlap=0)\n",
    "texts = splitter.split_documents(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7BJMynFS-PtB",
    "outputId": "38e05468-9f40-417b-c00b-561fc1103dac"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='About the adrenal glands\\nThe adrenals are small glands that sit above each of the kidneys. The kidneys are located deep inside the upper part of the abdomen.\\nEach adrenal gland has 2 parts. The outer part, the cortex, is where most tumors develop. The cortex makes certain hormones for the body. These hormones all have a similar chemical structure and are called steroids:\\n* Cortisol causes changes in metabolism to help the body to handle stress.\\n* Aldosterone helps the kidneys regulate the amount of salt in the blood and helps regulate blood pressure.\\n* Adrenal androgens can be converted to more common forms of the sex hormones estrogen and testosterone in other parts of the body. The amount of these hormones that result from conversion of adrenal androgens is small compared to what is made in other parts of the body. The testicles produce most of the androgens (male hormones) in men. The ovaries produce most of the estrogens (female hormones) in women.', metadata={'source': '/workspace/data/Adrenal-cancer/adrenal-about.txt'}),\n",
       " Document(page_content='The inner part of the adrenal gland, the medulla, is really an extension of the nervous system. Nervous system hormones such as norepinephrine and epinephrine (also called adrenaline) are made in the medulla. Tumors and cancers that start in the adrenal medulla include pheochromocytomas (which are most often benign) and neuroblastomas.\\nTumors and cancers of the adrenal cortex are covered here, but tumors of the adrenal medulla are not. Neuroblastomas are covered separately elsewhere.\\nAdrenal cortex tumors\\nThe 2 main types of adrenal cortex tumors are:\\n* Adenomas (benign or non-cancerous tumors)\\n* Carcinomas (malignant or cancerous tumors)\\nThese types of tumors can sometimes be hard to tell apart when the cells are looked at under the microscope. Sometimes the only way to know for sure that an adrenal tumor is a cancer is when it spreads to lymph nodes or other organs and tissues. Adenomas do not spread outside the adrenal gland.\\nAdrenal cortex adenomas', metadata={'source': '/workspace/data/Adrenal-cancer/adrenal-about.txt'}),\n",
       " Document(page_content=\"Most tumors of the adrenal cortex are benign tumors known as adenomas. These tumors are usually less than 2 inches (5 centimeters) across. They usually occur in only one adrenal gland, but sometimes both.\\nMost people with adrenal adenomas have no symptoms and don't know that they have an adrenal tumor. Some of these adenomas are discovered by accident (incidentally) when CT or MRI scans of the abdomen are done because of an unrelated health problem. About 5% of people who have a CT scan of the abdomen are found to have an adrenal tumor that was not suspected. Many of these are nonfunctional, meaning that they don't make adrenal hormones. Sometimes these tumors are known by the nickname incidentalomas because they aren't causing problems and were only found by accident.\", metadata={'source': '/workspace/data/Adrenal-cancer/adrenal-about.txt'}),\n",
       " Document(page_content='Some adenomas make too many adrenal steroid hormones. Sometimes the excess hormones can cause the same symptoms as those from adrenal carcinomas (cancers). To learn more, see Signs and Symptoms of Adrenal Cancers. Adenomas are much more likely than carcinomas to make high levels of aldosterone, which can cause high blood pressure.\\nTreatment: Adenomas can be cured by removing the adrenal gland that contains the adenoma. Some adrenal adenomas that cause hormone-related symptoms can be treated effectively with drugs that block the production or actions of these hormones. This may be the best treatment choice for patients with other serious medical problems who might not be able to have a major operation.', metadata={'source': '/workspace/data/Adrenal-cancer/adrenal-about.txt'}),\n",
       " Document(page_content=\"The treatment of an adenoma depends on the chance that it may be a cancer and whether or not it is raising hormone levels. When an adrenal tumor is found accidentally, tests are often done to see if it is making hormones. If it is, surgery is often recommended. Otherwise, surgery may only be recommended if it is likely to be a cancer. Small tumors are less likely to be cancer, and are often watched but not treated right away. The CT (or MRI) scan can be repeated in 6 to 24 months to see if the tumor has grown. If it has, it may need to be removed. If it hasn't grown, hormone levels will be watched over the next few years. If the tumor stays small and doesn't make any hormones, it might not need to be treated at all.\\nAdrenal cortex cancer\\nThe type of cancer that develops in the cortex of the adrenal gland is called adrenal cortical carcinoma or just adrenal cancer. This rare type of cancer is also known as adrenocortical cancer (or carcinoma).\\nAdrenal cancer most often is discovered when:\", metadata={'source': '/workspace/data/Adrenal-cancer/adrenal-about.txt'})]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import chromadb\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "text/plain": [
       "(…)5d33dd6f612ecd4625eb87a7d/.gitattributes:   0%|          | 0.00/1.52k [00:00<?, ?B/s]"
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    {
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       "(…)f612ecd4625eb87a7d/1_Pooling/config.json:   0%|          | 0.00/191 [00:00<?, ?B/s]"
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      "text/plain": [
       "(…)013e65d33dd6f612ecd4625eb87a7d/README.md:   0%|          | 0.00/89.1k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
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       "(…)3e65d33dd6f612ecd4625eb87a7d/config.json:   0%|          | 0.00/779 [00:00<?, ?B/s]"
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "(…)b87a7d/config_sentence_transformers.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "58ef17cddfa8450296469cbfea311fbd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pytorch_model.bin:   0%|          | 0.00/1.34G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
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       "version_minor": 0
      },
      "text/plain": [
       "(…)ecd4625eb87a7d/sentence_bert_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "bb0e6d26f4ac433daf27032d3a481c3f",
       "version_major": 2,
       "version_minor": 0
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       "(…)12ecd4625eb87a7d/special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
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       "(…)5d33dd6f612ecd4625eb87a7d/tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
      ]
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     "output_type": "display_data"
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       "version_minor": 0
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      "text/plain": [
       "(…)f612ecd4625eb87a7d/tokenizer_config.json:   0%|          | 0.00/366 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "(…)013e65d33dd6f612ecd4625eb87a7d/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "(…)e65d33dd6f612ecd4625eb87a7d/modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "persist_directory = 'doccuments_db'\n",
    "model_name = \"BAAI/bge-large-en-v1.5\"\n",
    "model_kwargs = {'device': 'cpu'}\n",
    "encode_kwargs = {'normalize_embeddings': False}\n",
    "embeddings = HuggingFaceEmbeddings(\n",
    "    model_name=model_name,\n",
    "    model_kwargs=model_kwargs,\n",
    "    encode_kwargs=encode_kwargs\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = chromadb.PersistentClient(path=os.path.join(os.getcwd(), persist_directory))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "collection = client.get_or_create_collection(persist_directory, metadata={\"hnsw:space\": \"cosine\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for idx, text in enumerate(texts):\n",
    "    \n",
    "    curent_text = text.page_content\n",
    "    # print(curent_text)\n",
    "    embedding = embeddings.embed_query(curent_text)\n",
    "    collection.add(\n",
    "        embeddings=[embedding],\n",
    "        documents=[curent_text], \n",
    "        metadatas=[{'metadata': text.metadata['source']}],\n",
    "        ids=[str(idx)],\n",
    "    )\n",
    "    print(f'{idx+1} / {len(texts)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_documents(user_question):\n",
    "    query_embedding = embeddings.embed_query(user_question)\n",
    "\n",
    "    query_result = collection.query(\n",
    "            query_embeddings=[query_embedding],\n",
    "            n_results=5,\n",
    "        )\n",
    "    \n",
    "    docs = []\n",
    "\n",
    "    for distance, document, metadata in zip(query_result['distances'][0], query_result['documents'][0], query_result['metadatas'][0]):\n",
    "        if distance > 0.30:\n",
    "            continue\n",
    "    \n",
    "        docs.append(document)\n",
    "\n",
    "    return docs\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_as_json(string):\n",
    "    string = string.replace(\"'answer'\", '\"answer\"').replace(\"'}\", '\"}')\n",
    "    string = re.sub(\"\\\":.*\\'\", '\\\": \\\"', string)\n",
    "    return string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "async def get_initial_answer(docs, user_question):\n",
    "    context = \"\"\n",
    "    for doc in docs[:1]:\n",
    "        context+=f'{doc}\\n'\n",
    "    \n",
    "    prompt = f\"\"\"\n",
    "[INST] <<SYS>>\n",
    "You are an expert AI assistant that answers the questions, based on the provided context. You don't know anything about medicine, so it's important that you give an answer only using the context. If the question is not related to context or there is no answer to that question - say that you can't answer it, because it's out of context.\n",
    "Answer must be strictly in the below format:\n",
    "\n",
    "```\n",
    "{{\"answer\": \"your answer here\"}}\n",
    "```\n",
    "<</SYS>>\n",
    "context={context}\n",
    "question={user_question}\n",
    "[/INST]\n",
    "\"\"\"\n",
    "    \n",
    "    initial = \"\"\n",
    "    \n",
    "    tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt))\n",
    "    \n",
    "    result_genrator = generator.async_generate_tokens(\n",
    "        [tokens], \n",
    "        sampling_topk=10,\n",
    "        sampling_temperature=0.001,\n",
    "        max_length=2048, \n",
    "    )\n",
    "    \n",
    "    async for item in result_genrator:\n",
    "        initial += item.token + \"\"\n",
    "    \n",
    "    initial = initial.strip().replace('▁', ' ').replace('<0x0A>', '\\\\n').replace('</s>', '')\n",
    "    initial = re.findall(r'{.*}', initial)\n",
    "    if len(initial) > 0:\n",
    "        initial = initial[0]\n",
    "\n",
    "    initial = format_as_json(initial)\n",
    "    initial = json.loads(initial, strict=False)\n",
    "    initial = initial[\"answer\"]\n",
    "\n",
    "    return initial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "async def refine(docs, intermediate):\n",
    "    for doc in docs[1:]:\n",
    "        context = \"\"\n",
    "        context+=f'{doc}\\n'\n",
    "        \n",
    "        prompt = f\"\"\"\n",
    "[INST] <<SYS>>\n",
    "Look at the intermediate answer below. Given the new context add new details to the intermediate answer. You must add details, that are present in the context, do not use your own knowledge.\n",
    "Answer must be strictly in the below format:\n",
    "\n",
    "```\n",
    "{{\"answer\": \"your answer here\"}}\n",
    "```\n",
    "<</SYS>>\n",
    "context: {context}\n",
    "intermediate answer: {intermediate}\n",
    "[/INST]\n",
    "\"\"\"\n",
    "        res = \"\"\n",
    "        tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt))\n",
    "        result_genrator = generator.async_generate_tokens(\n",
    "            [tokens], \n",
    "            sampling_topk=10,\n",
    "            sampling_temperature=0.001,\n",
    "            max_length=4096, \n",
    "        )\n",
    "        async for item in result_genrator:\n",
    "            res += item.token + \"\"\n",
    "        intermediate = res.strip().replace('▁', ' ').replace('<0x0A>', '\\\\n').replace('</s>', '')        \n",
    "        intermediate = re.findall(r'{.*}', intermediate)\n",
    "        \n",
    "        if len(intermediate) > 0:\n",
    "            intermediate = intermediate[0]\n",
    "        try:\n",
    "            intermediate = json.loads(intermediate, strict=False)\n",
    "        except:\n",
    "            format_as_json(intermediate)\n",
    "\n",
    "        # print(intermediate)\n",
    "        \n",
    "        intermediate = intermediate[\"answer\"]\n",
    "\n",
    "    return intermediate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "async def answer_question(user_question):\n",
    "\n",
    "    docs = filter_documents(user_question)\n",
    "    \n",
    "    if len(docs) == 0:\n",
    "        return f\"Sorry, I don't know the answer to \\\"{user_question}\\\"\"\n",
    "        \n",
    "    initial = await get_initial_answer(docs, user_question)\n",
    "    final = await refine(docs, initial)\n",
    "    \n",
    "    \n",
    "        \n",
    "    return final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question, present in the knowledge base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The symptoms of bladder cancer can include being unable to urinate, lower back pain on one side, loss of appetite and weight loss, feeling tired or weak, swelling in the feet, and bone pain, as well as blood in the urine or other urinary symptoms that cause a person to see a health care provider. Additionally, a digital rectal exam (DRE) or pelvic exam may be performed during a physical exam to feel for any tumors or abnormalities in the bladder or pelvic area.\n",
      "------------------------------\n",
      "\n",
      "Took 0.3438388228416443 minutes\n"
     ]
    }
   ],
   "source": [
    "user_question = \"What are the symptoms of bladder cancer?\"\n",
    "\n",
    "import time\n",
    "import re\n",
    "\n",
    "start = time.time()\n",
    "answer = await answer_question(user_question)\n",
    "print(answer)\n",
    "end = time.time()\n",
    "print('-'*30 + '\\n')\n",
    "print(f'Took {(end-start)/60} minutes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question, present in the knowledge base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The main treatment for adrenal cancer is an adrenalectomy, which involves removing the adrenal gland and any affected tissues or organs. The surgery can be performed through an incision in the back or the front of the abdomen, depending on the size and location of the tumor. Chemotherapy (chemo) may also be used in combination with an adrenalectomy, particularly if the cancer has spread to other parts of the body. Mitotane, a chemotherapy drug, is often used to treat adrenal cancer that has spread beyond the adrenal gland.\n",
      "------------------------------\n",
      "\n",
      "Took 0.7370374997456869 minutes\n"
     ]
    }
   ],
   "source": [
    "user_question = \"My friend got diagnosed with adrenal cancer. What is the treatment?\"\n",
    "\n",
    "import time\n",
    "import re\n",
    "\n",
    "start = time.time()\n",
    "answer = await answer_question(user_question)\n",
    "print(answer)\n",
    "end = time.time()\n",
    "print('-'*30 + '\\n')\n",
    "print(f'Took {(end-start)/60} minutes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question not present in the knowledge base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorry, I don't know the answer to \"How to treat skin cancer?\"\n",
      "------------------------------\n",
      "\n",
      "Took 0.008040090401967367 minutes\n"
     ]
    }
   ],
   "source": [
    "user_question = \"How to treat skin cancer?\"\n",
    "\n",
    "import time\n",
    "import re\n",
    "\n",
    "start = time.time()\n",
    "answer = await answer_question(user_question)\n",
    "print(answer)\n",
    "end = time.time()\n",
    "print('-'*30 + '\\n')\n",
    "print(f'Took {(end-start)/60} minutes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question, present in the knowledge base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anal cancer screening may be recommended for individuals with high-risk factors such as a history of HPV infection, a weakened immune system, or a family history of anal cancer. Screening may include a digital rectal exam (DRE) and/or an anal Pap test to check for abnormal cell changes in the anus. Early detection through screening can improve the chances of successful treatment and survival for individuals with anal cancer. Additionally, if anal cancer is suspected, exams and tests will be needed to confirm the diagnosis. If cancer is found, more tests will be done to help determine the extent (stage) of the cancer. A thorough medical history and physical exam can help identify risk factors and symptoms that may indicate the presence of anal cancer. In some cases, anal cancer may extend from one area into the other, making it difficult to determine the exact location of the cancer. Squamous cell carcinoma is the most common type of anal cancer, and it starts in the squamous cells that line the anal canal and the anal margin. Treatment for anal cancer may vary depending on the location and stage of the cancer, but it often involves a combination of chemotherapy, radiation therapy, and surgery.\n",
      "------------------------------\n",
      "\n",
      "Took 0.5399686296780905 minutes\n"
     ]
    }
   ],
   "source": [
    "user_question = \"What is anal cancer?\"\n",
    "\n",
    "import time\n",
    "import re\n",
    "\n",
    "start = time.time()\n",
    "answer = await answer_question(user_question)\n",
    "print(answer)\n",
    "end = time.time()\n",
    "print('-'*30 + '\\n')\n",
    "print(f'Took {(end-start)/60} minutes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unrelated medical question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorry, I don't know the answer to \"How do I treat the cold?\"\n",
      "------------------------------\n",
      "\n",
      "Took 0.010103058815002442 minutes\n"
     ]
    }
   ],
   "source": [
    "user_question = \"How do I treat the cold?\"\n",
    "\n",
    "import time\n",
    "import re\n",
    "\n",
    "start = time.time()\n",
    "answer = await answer_question(user_question)\n",
    "print(answer)\n",
    "end = time.time()\n",
    "print('-'*30 + '\\n')\n",
    "print(f'Took {(end-start)/60} minutes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Totally unrelated question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorry, I don't know the answer to \"Tell me a funny story\"\n",
      "------------------------------\n",
      "\n",
      "Took 0.013049805164337158 minutes\n"
     ]
    }
   ],
   "source": [
    "user_question = \"Tell me a funny story\"\n",
    "\n",
    "import time\n",
    "import re\n",
    "\n",
    "start = time.time()\n",
    "answer = await answer_question(user_question)\n",
    "print(answer)\n",
    "end = time.time()\n",
    "print('-'*30 + '\\n')\n",
    "print(f'Took {(end-start)/60} minutes')"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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