unstructured-data-medical/end-to-end-lupus.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# Unstructured Text Data: GraphRAG Python package End-to-End Example\n",
    "\n",
    "This notebook contains an end-to-end worked example using the [GraphRAG Python package](https://neo4j.com/docs/neo4j-graphrag-python/current/index.html) for Neo4j. It starts with high;y unstructured text data (in this case pdfs), and progresses through knowledge graph construction, knowledge graph retriever design, and complete GraphRAG pipelines. \n",
    "\n",
    "Research papers on Lupus are used as the data source. We design a couple of different retrievers based on different knowledge graph retrieval patterns. \n",
    "\n",
    "For more details and explanations around each of the below steps, see the [corresponding blog post](https://neo4j.com/blog/graphrag-python-package/) which contains a full write-up, in-depth comparison of the retrieval patterns, and additional learning resources. \n",
    "\n",
    "## Pre-Requisites\n",
    "\n",
    "1. __Configure your Environment to Access the Bedrock API__ following this [guide]().\n",
    "2. __Create a Neo4j Database__: To work through this RAG example, you need a database for storing and retrieving data. There are many options for this. You can quickly start a free Neo4j Graph Database using [Neo4j AuraDB](https://neo4j.com/product/auradb/?ref=neo4j-home-hero). You can use __AuraDB Free__ or start an __AuraDB Professional (Pro) free trial__ for higher ingestion and retrieval performance. The Pro instances have a bit more RAM; we recommend them for the best user experience.\n",
    "3. __Fill in Credentials__: Either by copying the [`.env.template`](cred.env.template) file, naming it `.env`, and filling in the appropriate credentials, or by manually putting the credentials into the second code cell below. You will need the Neo4j URI, username, and password variables from when you created the database. If you created your database on AuraDB, they are in the file you downloaded.\n",
    "   \n",
    "\n"
   ],
   "id": "cf0be1ddc2bd2434"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Setup",
   "id": "799a7b09475ed2a4"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T18:56:14.499234Z",
     "start_time": "2024-12-01T18:56:13.752033Z"
    }
   },
   "cell_type": "code",
   "source": [
    "%%capture\n",
    "%pip install fsspec langchain-text-splitters tiktoken boto3 python-dotenv numpy torch neo4j-graphrag pypdf"
   ],
   "id": "9820f541adf30bfd",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T18:56:14.508267Z",
     "start_time": "2024-12-01T18:56:14.500568Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from dotenv import load_dotenv\n",
    "import os\n",
    "\n",
    "# load neo4j credentials\n",
    "load_dotenv('cred.env', override=True)\n",
    "NEO4J_URI = os.getenv('NEO4J_URI')\n",
    "NEO4J_USERNAME = os.getenv('NEO4J_USERNAME')\n",
    "NEO4J_PASSWORD = os.getenv('NEO4J_PASSWORD')"
   ],
   "id": "a023c71324bf6e7f",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Knowledge Graph Building\n",
    "\n",
    "The `SimpleKGPipeline` class allows you to automatically build a knowledge graph with a few key inputs, including\n",
    "- a driver to connect to Neo4j,\n",
    "- an LLM for entity extraction, and\n",
    "- an embedding model to create vectors on text chunks for similarity search.\n",
    "\n",
    "There are also some optional inputs, such as node labels, relationship types, and a custom prompt template, which we will use to improve the quality of the knowledge graph. For full details on this, see [the blog](https://neo4j.com/blog/graphrag-python-package/).\n"
   ],
   "id": "418bd212bae7f492"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T18:56:14.753686Z",
     "start_time": "2024-12-01T18:56:14.509070Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from typing import Optional, Any\n",
    "from neo4j_graphrag.embeddings import Embedder\n",
    "from neo4j_graphrag.llm import LLMInterface, LLMResponse\n",
    "import boto3\n",
    "\n",
    "client = boto3.client(\"bedrock-runtime\", region_name=\"us-east-1\")\n",
    "\n",
    "\n",
    "class BedrockLLM(LLMInterface):\n",
    " \n",
    "    def invoke(self, input: str) -> LLMResponse:\n",
    " \n",
    "        response = client.converse(\n",
    "            modelId=self.model_name,\n",
    "            messages=[{\n",
    "                'role': 'user',\n",
    "                'content': [{\"text\": input}],\n",
    "            }],\n",
    "            inferenceConfig=self.model_params\n",
    "        )      \n",
    "        return LLMResponse(\n",
    "            content=response[\"output\"][\"message\"][\"content\"][0][\"text\"]\n",
    "        )\n",
    "\n",
    "    async def ainvoke(self, input: str) -> LLMResponse:\n",
    "        return self.invoke(input)\n",
    "\n",
    "\n",
    "class BedrockEmbedder(Embedder):\n",
    "    def __init__(self, model_id):\n",
    "        self.model_id = model_id\n",
    "\n",
    "    def embed_query(self, text: str) -> list[float]:\n",
    "        # Create the request for the model.\n",
    "        native_request = {\"inputText\": text}\n",
    "        # Convert the native request to JSON.\n",
    "        request = json.dumps(native_request)\n",
    "        # Invoke the model with the request.\n",
    "        response = client.invoke_model(modelId=self.model_id, body=request)\n",
    "        # Decode the model's native response body.\n",
    "        model_response = json.loads(response[\"body\"].read())\n",
    "        # Extract and print the generated embedding and the input text token count.\n",
    "        return model_response[\"embedding\"]"
   ],
   "id": "1ca8bd72026102b2",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T18:56:15.397307Z",
     "start_time": "2024-12-01T18:56:14.754324Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import neo4j\n",
    "\n",
    "driver = neo4j.GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))\n",
    "\n",
    "ex_llm = BedrockLLM(\n",
    "    model_name=\"us.anthropic.claude-3-5-haiku-20241022-v1:0\",\n",
    "    model_params={\n",
    "        \"temperature\": 0 # turning temperature down for more deterministic results\n",
    "    }\n",
    ")\n",
    "\n",
    "#create text embedder\n",
    "embedder = BedrockEmbedder(model_id=\"amazon.titan-embed-text-v1\")"
   ],
   "id": "fd60a2f512b9701d",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T18:56:15.401431Z",
     "start_time": "2024-12-01T18:56:15.399029Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#define node labels\n",
    "basic_node_labels = [\"Object\", \"Entity\", \"Group\", \"Person\", \"Organization\", \"Place\"]\n",
    "\n",
    "academic_node_labels = [\"ArticleOrPaper\", \"PublicationOrJournal\"]\n",
    "\n",
    "medical_node_labels = [\"Anatomy\", \"BiologicalProcess\", \"Cell\", \"CellularComponent\", \n",
    "                       \"CellType\", \"Condition\", \"Disease\", \"Drug\",\n",
    "                       \"EffectOrPhenotype\", \"Exposure\", \"GeneOrProtein\", \"Molecule\",\n",
    "                       \"MolecularFunction\", \"Pathway\"]\n",
    "\n",
    "node_labels = basic_node_labels + academic_node_labels + medical_node_labels\n",
    "\n",
    "# define relationship types\n",
    "rel_types = [\"ACTIVATES\", \"AFFECTS\", \"ASSESSES\", \"ASSOCIATED_WITH\", \"AUTHORED\",\n",
    "    \"BIOMARKER_FOR\", \"CAUSES\", \"CITES\", \"CONTRIBUTES_TO\", \"DESCRIBES\", \"EXPRESSES\",\n",
    "    \"HAS_REACTION\", \"HAS_SYMPTOM\", \"INCLUDES\", \"INTERACTS_WITH\", \"PRESCRIBED\",\n",
    "    \"PRODUCES\", \"RECEIVED\", \"RESULTS_IN\", \"TREATS\", \"USED_FOR\"]\n"
   ],
   "id": "6cba83fa4638e21d",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T18:56:15.404090Z",
     "start_time": "2024-12-01T18:56:15.402176Z"
    }
   },
   "cell_type": "code",
   "source": [
    "prompt_template = '''\n",
    "You are a medical researcher tasks with extracting information from papers \n",
    "and structuring it in a property graph to inform further medical and research Q&A.\n",
    "\n",
    "Extract the entities (nodes) and specify their type from the following Input text.\n",
    "Also extract the relationships between these nodes. the relationship direction goes from the start node to the end node. \n",
    "\n",
    "\n",
    "Return result as JSON using the following format:\n",
    "{{\"nodes\": [ {{\"id\": \"0\", \"label\": \"the type of entity\", \"properties\": {{\"name\": \"name of entity\" }} }}],\n",
    "  \"relationships\": [{{\"type\": \"TYPE_OF_RELATIONSHIP\", \"start_node_id\": \"0\", \"end_node_id\": \"1\", \"properties\": {{\"details\": \"Description of the relationship\"}} }}] }}\n",
    "\n",
    "- Use only the information from the Input text.  Do not add any additional information.  \n",
    "- If the input text is empty, return empty Json. \n",
    "- Make sure to create as many nodes and relationships as needed to offer rich medical context for further research.\n",
    "- An AI knowledge assistant must be able to read this graph and immediately understand the context to inform detailed research questions. \n",
    "- Multiple documents will be ingested from different sources and we are using this property graph to connect information, so make sure entity types are fairly general. \n",
    "\n",
    "Use only fhe following nodes and relationships (if provided):\n",
    "{schema}\n",
    "\n",
    "Assign a unique ID (string) to each node, and reuse it to define relationships.\n",
    "Do respect the source and target node types for relationship and\n",
    "the relationship direction.\n",
    "\n",
    "Do not return any additional information other than the JSON in it.\n",
    "\n",
    "Examples:\n",
    "{examples}\n",
    "\n",
    "Input text:\n",
    "\n",
    "{text}\n",
    "'''"
   ],
   "id": "8cbcdedcd1757b1d",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T18:56:16.013481Z",
     "start_time": "2024-12-01T18:56:15.404851Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from neo4j_graphrag.experimental.components.text_splitters.fixed_size_splitter import FixedSizeSplitter\n",
    "from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline\n",
    "\n",
    "kg_builder_pdf = SimpleKGPipeline(\n",
    "    llm=ex_llm,\n",
    "    driver=driver,\n",
    "    text_splitter=FixedSizeSplitter(chunk_size=2000, chunk_overlap=200),\n",
    "    embedder=embedder,\n",
    "    entities=node_labels,\n",
    "    relations=rel_types,\n",
    "    prompt_template=prompt_template,\n",
    "    from_pdf=True\n",
    ")"
   ],
   "id": "94e1dfac980e6527",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Below, we run the `SimpleKGPipeline` to construct our knowledge graph from 3 pdf documents and store in Neo4j.",
   "id": "4d8ef81d0b5ac70c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:06:54.711511Z",
     "start_time": "2024-12-01T18:56:16.018665Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import json\n",
    "\n",
    "pdf_file_paths = ['truncated-pdfs/biomolecules-11-00928-v2-trunc.pdf', \n",
    "             'truncated-pdfs/GAP-between-patients-and-clinicians_2023_Best-Practice-trunc.pdf', \n",
    "             'truncated-pdfs/pgpm-13-39-trunc.pdf']\n",
    "\n",
    "for path in pdf_file_paths:\n",
    "    print(f\"Processing : {path}\")\n",
    "    pdf_result = await kg_builder_pdf.run_async(file_path=path)\n",
    "    print(f\"Result: {pdf_result}\")"
   ],
   "id": "edeee98826a970a8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing : truncated-pdfs/biomolecules-11-00928-v2-trunc.pdf\n",
      "Result: run_id='80f616a6-eb45-49bb-b0d5-905b56a5e988' result={'resolver': {'number_of_nodes_to_resolve': 213, 'number_of_created_nodes': 181}}\n",
      "Processing : truncated-pdfs/GAP-between-patients-and-clinicians_2023_Best-Practice-trunc.pdf\n",
      "Result: run_id='d45dd36d-6da3-437f-8e08-d5cefe9da53a' result={'resolver': {'number_of_nodes_to_resolve': 323, 'number_of_created_nodes': 269}}\n",
      "Processing : truncated-pdfs/pgpm-13-39-trunc.pdf\n",
      "Result: run_id='395b532b-1569-43e6-8015-c1a3cf24f10a' result={'resolver': {'number_of_nodes_to_resolve': 492, 'number_of_created_nodes': 429}}\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Knowledge Graph Retrieval",
   "id": "a9c0fd965b15b143"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "We will leverage Neo4j's vector search capabilities here. To do this, we need to begin by creating a vector index on the text chunks from the PDFs, which are stored on `Chunk` nodes in our knowledge graph.",
   "id": "4d7b98e310104246"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:06:54.897783Z",
     "start_time": "2024-12-01T19:06:54.713852Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from neo4j_graphrag.indexes import create_vector_index\n",
    "\n",
    "create_vector_index(driver, name=\"text_embeddings\", label=\"Chunk\",\n",
    "                    embedding_property=\"embedding\", dimensions=1536, similarity_fn=\"cosine\")"
   ],
   "id": "940b051107b89204",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Now that the index is set up, we will start simple with a __VectorRetriever__.  The __VectorRetriever__ just queries `Chunk` nodes via vector search, bringing back the text and some metadata.",
   "id": "ec95391c989ee694"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:06:55.213199Z",
     "start_time": "2024-12-01T19:06:54.898676Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from neo4j_graphrag.retrievers import VectorRetriever\n",
    "\n",
    "vector_retriever = VectorRetriever(\n",
    "    driver,\n",
    "    index_name=\"text_embeddings\",\n",
    "    embedder=embedder,\n",
    "    return_properties=[\"text\"],\n",
    ")"
   ],
   "id": "eeda7e519c60a02b",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Below we visualize the context we get back when submitting a search prompt. ",
   "id": "982112cc273a472e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:06:55.561646Z",
     "start_time": "2024-12-01T19:06:55.221617Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import json\n",
    "\n",
    "vector_res = vector_retriever.get_search_results(query_text = \"How is precision medicine applied to Lupus?\", \n",
    "                                                 top_k=3)\n",
    "for i in vector_res.records: print(\"====\\n\" + json.dumps(i.data(), indent=4))"
   ],
   "id": "ca27731a1f99d71d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====\n",
      "{\n",
      "    \"node\": {\n",
      "        \"text\": \"REVIEW\\nT owards Precision Medicine in Systemic Lupus\\nErythematosus\\nThis article was published in the following Dove Press journal:\\nPharmacogenomics and Personalized Medicine\\nElliott Lever 1\\nMarta R Alves2\\nDavid A Isenberg 1\\n1Centre for Rheumatology, Division of\\nMedicine, University College Hospital\\nLondon, London, UK;2Internal Medicine,\\nDepartment of Medicine, Centro\\nHospitalar do Porto, Porto, Portugal\\nAbstract: Systemic lupus erythematosus (SLE) is a remarkable condition characterised by\\ndiversity amongst its clinical features and immunological abnormalities. In this review, we\\nattempt to capture the major immunological changes linked to the pathophysiology of lupus\\nand discuss the challenge it presents in moving towards the concept of precision medicine.\\nCurrently broadly similar types of drugs, e.g., steroids, immunosuppressives, hydroxychlor-\\noquine are used to treat many of the diverse clinical features of SLE. We suspect that, as the\\nprecise immunopathological abnormalities differ between the various organs/systems in\\nlupus patients, it will be some time before precision medicine can be fully applied to SLE.\\nKeywords: systemic lupus erythematosus, anti-DNA antibodies, lupus nephritis, lupus\\ngenetics, immunosuppression\\nIntroduction\\nSystemic lupus erythematosus (SLE) is a chronic, complex, auto-immune disease of\\nunknown origin with multiorgan involvement. Knowledge of its epidemiology,\\ngenetic susceptibility and pathophysiologic mechanisms has evolved, but it remains\\na disease with a highly variable course and considerable inter-individual variability.\\nIt has an unpredictable prognosis, depending mainly on the severity of the disease\\nactivity, organ damage and response to treatment. SLE is characterized by loss of\\nself-immune tolerance, production of self-reacting antibodies and formation of\\nimmune complexes (IC) that precipitate in tissues, causing chronic systemic in\\ufb02am-\\nmation and organ damage.1 It affects primarily women (90%) during childbearing\\nyears and \"\n",
      "    },\n",
      "    \"nodeLabels\": [\n",
      "        \"Chunk\",\n",
      "        \"__KGBuilder__\"\n",
      "    ],\n",
      "    \"elementId\": \"4:e60f4adb-0b5e-487b-8e20-7da647831f76:34\",\n",
      "    \"id\": \"4:e60f4adb-0b5e-487b-8e20-7da647831f76:34\",\n",
      "    \"score\": 0.8901805877685547\n",
      "}\n",
      "====\n",
      "{\n",
      "    \"node\": {\n",
      "        \"text\": \"ide; LMWH, low\\nmolecular weight heparin; PE, plasma exchange.\\nDovepress Lever et al\\nPharmacogenomics and Personalized Medicine 2020:13 submit your manuscript| www.dovepress.com\\nDovePress\\n45\\ndrugs steroids, immunosuppressives and Hydroxychloroquine\\n[see Tables 1& 2] are used with some success to treat these\\n\\u201cdiversely induced\\u201d clinical features. Given that the mortality\\n\\ufb01gures for lupus have improved from 50% 4-year survival in\\n195078 to approximately 90% 10-year survival now79 these\\nstandard drugs have been bene\\ufb01cial. Some modest clues to\\nprecision targeting do exist with conventional drugs. Thus it\\nhas been shown that black lupus patients do not respond as\\nwell to Cyclophosphamide as Caucasians.80 There is a debate\\nabout whether a seemingly well patient with a rising dsDNA\\nantibody level and falling C3 should be treated prophylacti-\\ncally as these markers anticipate a\\ufb02are.81 However, as we\\nhave argued elsewhere the improvement in survival has lar-\\ngely stalled82 and it is likely that biologic drugs capable of\\ntargeting individual molecules will be needed to provide the\\nkind of precise approach that precision medicine warrants.\\nSadly SLE lags far behind the precision approaches available\\nfor the treatment of rheumatoid arthritis where currently\\ufb01ve-\\nbiologic/small molecule drugs [anti-TNF, anti-CD20, blocking\\nthe link between the antigen-presenting cell and the T-cell,\\nanti-IL6/IL6 receptor and JAK-STAT inhibition] are approved\\nfor treatment.81 In SLE the approval by the Federal Drug\\nAdministration of Benlysta [anti-BAFF] in 2012 remains the\\nonly biologic to be allowed in the disease in the USA.82 In the\\nUK NHS England permits the use of Rituximab but both\\nthe American College of Rheumatology83 and the European\\nLeague Against Rheumatism84 recommend its use in renal\\nlupus. Our [unpublished] observations are that it often works\\nquickly to correct haematological problems [thrombocytope-\\nnia or haemolytic anaemia] but is much slower to bene\\ufb01tr e n a l\\ndisease.\\nThe prospe\"\n",
      "    },\n",
      "    \"nodeLabels\": [\n",
      "        \"Chunk\",\n",
      "        \"__KGBuilder__\"\n",
      "    ],\n",
      "    \"elementId\": \"4:e60f4adb-0b5e-487b-8e20-7da647831f76:187\",\n",
      "    \"id\": \"4:e60f4adb-0b5e-487b-8e20-7da647831f76:187\",\n",
      "    \"score\": 0.8588149547576904\n",
      "}\n",
      "====\n",
      "{\n",
      "    \"node\": {\n",
      "        \"text\": \"scular risk.\\nOur approach as detailed in Table 1 does not help in\\ndistinguishing subgroups of patients for methotrexate or\\nazathioprine. The molecular tools for this are on their way\\nto the clinic. In rheumatoid arthritis gene signature has\\nbeen shown to assist response prediction.77\\nIn the current treatment of lupus longterm clinical and\\nimmunological remission is only achieved in a proportion\\nof patients. The inherent pathophysiological complexity of\\nlupus described at the cellular and molecular level cannot\\nbe overstated. Using targeted drug therapy and improving\\nclinical assessment remains vital to improving disease\\ncontrol and patient outcomes.\\nThe Challenge of Moving T owards\\nPrecision Medicine in SLE\\nAs the foregoing text has con\\ufb01rmed the pathophysiological\\naspects of SLE are highly diverse and the combination of\\nfactors leading to say skin, kidney and renal disease are very\\ndifferent. In spite of these differences, a rather restricted set of\\nTable 2T reatment Based on the Organ/Systemic Involved\\nExample 1 2 3 4 5 6 7 8\\nConstitutional\\nSymptoms\\nWidespread\\nDiscoid Lupus\\nErythematosus\\nPolyarthritis Thrombo-\\ncytopenia\\nLupus APS Arterial\\nThrombolysis\\nMononeuritis\\nMultiplex\\nNephritis\\nIII/IV\\nNephritis V\\n1st Line HCQ/CS/ HCQ + CS HCQ + CS CS +\\nHCQ\\nWarfarin/LMWH CS + IV Cyclo CS + MMF CS + MMF\\nImmunomodulation\\n2nd Line MMF ADD AZA ADD MTX AZA/MMF ADD aspirin/\\nDipyridamole/Platelet\\naggregation inhibitor\\nADD Rituximab\\nor IVIG/PE\\nIV Cyclo \\u2192 AZA/IV\\nCyclo or\\nRituximab\\n3rd Line Rituximab/\\nBelimumab\\nAZA \\u2192 MTX/\\nBenlysta\\nBenlysta/\\nRituximab\\nRituximab/\\nIV Cyclo/\\nIVIG\\n/ / ADD\\nRituximab\\n/\\nNote: This table is based on the authors\\u2019 own experience.\\nAbbreviations: CS, Corticosteroids; HCQ, Hydroxychloroquine, MTX, methotrexate; AZA, azathioprine; MMF , mycophenolate; Cyclo, Cyclophosphamide; LMWH, low\\nmolecular weight heparin; PE, plasma exchange.\\nDovepress Lever et al\\nPharmacogenomics and Personalized Medicine 2020:13 submit your manuscript| www.dovepress.com\\nDovePress\\n45\\ndrugs ster\"\n",
      "    },\n",
      "    \"nodeLabels\": [\n",
      "        \"Chunk\",\n",
      "        \"__KGBuilder__\"\n",
      "    ],\n",
      "    \"elementId\": \"4:e60f4adb-0b5e-487b-8e20-7da647831f76:185\",\n",
      "    \"id\": \"4:e60f4adb-0b5e-487b-8e20-7da647831f76:185\",\n",
      "    \"score\": 0.8465092182159424\n",
      "}\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "The GraphRAG Python Package offers [a wide range of useful retrievers](https://neo4j.com/docs/neo4j-graphrag-python/current/user_guide_rag.html#retriever-configuration), each covering different knowledge graph retrieval patterns.\n",
    "\n",
    "Below we will use the __`VectorCypherRetriever`__, which allows you to run a graph traversal after finding nodes with vector search.  This uses Cypher, Neo4j's graph query language, to define the logic for traversing the graph. \n",
    "\n",
    "As a simple starting point, we'll traverse up to 3 hops out from each Chunk, capture the relationships encountered, and include them in the response alongside our text chunks.\n"
   ],
   "id": "2b2ee3da2339365f"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:06:55.827355Z",
     "start_time": "2024-12-01T19:06:55.562810Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from neo4j_graphrag.retrievers import VectorCypherRetriever\n",
    "\n",
    "vc_retriever = VectorCypherRetriever(\n",
    "    driver,\n",
    "    index_name=\"text_embeddings\",\n",
    "    embedder=embedder,\n",
    "    retrieval_query=\"\"\"\n",
    "//1) Go out 2-3 hops in the entity graph and get relationships\n",
    "WITH node AS chunk\n",
    "MATCH (chunk)<-[:FROM_CHUNK]-()-[relList:!FROM_CHUNK]-{1,2}()\n",
    "UNWIND relList AS rel\n",
    "\n",
    "//2) collect relationships and text chunks\n",
    "WITH collect(DISTINCT chunk) AS chunks, \n",
    "  collect(DISTINCT rel) AS rels\n",
    "\n",
    "//3) format and return context\n",
    "RETURN '=== text ===\\n' + apoc.text.join([c in chunks | c.text], '\\n---\\n') + '\\n\\n=== kg_rels ===\\n' +\n",
    "  apoc.text.join([r in rels | startNode(r).name + ' - ' + type(r) + '(' + coalesce(r.details, '') + ')' +  ' -> ' + endNode(r).name ], '\\n---\\n') AS info\n",
    "\"\"\"\n",
    ")"
   ],
   "id": "2c81ad815a7b1bf9",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Below we visualize the context we get back when submitting a search prompt. ",
   "id": "ef5183123679ca2d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:06:56.207497Z",
     "start_time": "2024-12-01T19:06:55.828533Z"
    }
   },
   "cell_type": "code",
   "source": [
    "vc_res = vc_retriever.get_search_results(query_text = \"How is precision medicine applied to Lupus?\", top_k=3)\n",
    "\n",
    "# print output\n",
    "kg_rel_pos = vc_res.records[0]['info'].find('\\n\\n=== kg_rels ===\\n')\n",
    "print(\"# Text Chunk Context:\")\n",
    "print(vc_res.records[0]['info'][:kg_rel_pos])\n",
    "print(\"# KG Context From Relationships:\")\n",
    "print(vc_res.records[0]['info'][kg_rel_pos:])"
   ],
   "id": "4a1df583f2450f35",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Text Chunk Context:\n",
      "=== text ===\n",
      "REVIEW\n",
      "T owards Precision Medicine in Systemic Lupus\n",
      "Erythematosus\n",
      "This article was published in the following Dove Press journal:\n",
      "Pharmacogenomics and Personalized Medicine\n",
      "Elliott Lever 1\n",
      "Marta R Alves2\n",
      "David A Isenberg 1\n",
      "1Centre for Rheumatology, Division of\n",
      "Medicine, University College Hospital\n",
      "London, London, UK;2Internal Medicine,\n",
      "Department of Medicine, Centro\n",
      "Hospitalar do Porto, Porto, Portugal\n",
      "Abstract: Systemic lupus erythematosus (SLE) is a remarkable condition characterised by\n",
      "diversity amongst its clinical features and immunological abnormalities. In this review, we\n",
      "attempt to capture the major immunological changes linked to the pathophysiology of lupus\n",
      "and discuss the challenge it presents in moving towards the concept of precision medicine.\n",
      "Currently broadly similar types of drugs, e.g., steroids, immunosuppressives, hydroxychlor-\n",
      "oquine are used to treat many of the diverse clinical features of SLE. We suspect that, as the\n",
      "precise immunopathological abnormalities differ between the various organs/systems in\n",
      "lupus patients, it will be some time before precision medicine can be fully applied to SLE.\n",
      "Keywords: systemic lupus erythematosus, anti-DNA antibodies, lupus nephritis, lupus\n",
      "genetics, immunosuppression\n",
      "Introduction\n",
      "Systemic lupus erythematosus (SLE) is a chronic, complex, auto-immune disease of\n",
      "unknown origin with multiorgan involvement. Knowledge of its epidemiology,\n",
      "genetic susceptibility and pathophysiologic mechanisms has evolved, but it remains\n",
      "a disease with a highly variable course and considerable inter-individual variability.\n",
      "It has an unpredictable prognosis, depending mainly on the severity of the disease\n",
      "activity, organ damage and response to treatment. SLE is characterized by loss of\n",
      "self-immune tolerance, production of self-reacting antibodies and formation of\n",
      "immune complexes (IC) that precipitate in tissues, causing chronic systemic inﬂam-\n",
      "mation and organ damage.1 It affects primarily women (90%) during childbearing\n",
      "years and \n",
      "---\n",
      "ide; LMWH, low\n",
      "molecular weight heparin; PE, plasma exchange.\n",
      "Dovepress Lever et al\n",
      "Pharmacogenomics and Personalized Medicine 2020:13 submit your manuscript| www.dovepress.com\n",
      "DovePress\n",
      "45\n",
      "drugs steroids, immunosuppressives and Hydroxychloroquine\n",
      "[see Tables 1& 2] are used with some success to treat these\n",
      "“diversely induced” clinical features. Given that the mortality\n",
      "ﬁgures for lupus have improved from 50% 4-year survival in\n",
      "195078 to approximately 90% 10-year survival now79 these\n",
      "standard drugs have been beneﬁcial. Some modest clues to\n",
      "precision targeting do exist with conventional drugs. Thus it\n",
      "has been shown that black lupus patients do not respond as\n",
      "well to Cyclophosphamide as Caucasians.80 There is a debate\n",
      "about whether a seemingly well patient with a rising dsDNA\n",
      "antibody level and falling C3 should be treated prophylacti-\n",
      "cally as these markers anticipate aﬂare.81 However, as we\n",
      "have argued elsewhere the improvement in survival has lar-\n",
      "gely stalled82 and it is likely that biologic drugs capable of\n",
      "targeting individual molecules will be needed to provide the\n",
      "kind of precise approach that precision medicine warrants.\n",
      "Sadly SLE lags far behind the precision approaches available\n",
      "for the treatment of rheumatoid arthritis where currentlyﬁve-\n",
      "biologic/small molecule drugs [anti-TNF, anti-CD20, blocking\n",
      "the link between the antigen-presenting cell and the T-cell,\n",
      "anti-IL6/IL6 receptor and JAK-STAT inhibition] are approved\n",
      "for treatment.81 In SLE the approval by the Federal Drug\n",
      "Administration of Benlysta [anti-BAFF] in 2012 remains the\n",
      "only biologic to be allowed in the disease in the USA.82 In the\n",
      "UK NHS England permits the use of Rituximab but both\n",
      "the American College of Rheumatology83 and the European\n",
      "League Against Rheumatism84 recommend its use in renal\n",
      "lupus. Our [unpublished] observations are that it often works\n",
      "quickly to correct haematological problems [thrombocytope-\n",
      "nia or haemolytic anaemia] but is much slower to beneﬁtr e n a l\n",
      "disease.\n",
      "The prospe\n",
      "---\n",
      "scular risk.\n",
      "Our approach as detailed in Table 1 does not help in\n",
      "distinguishing subgroups of patients for methotrexate or\n",
      "azathioprine. The molecular tools for this are on their way\n",
      "to the clinic. In rheumatoid arthritis gene signature has\n",
      "been shown to assist response prediction.77\n",
      "In the current treatment of lupus longterm clinical and\n",
      "immunological remission is only achieved in a proportion\n",
      "of patients. The inherent pathophysiological complexity of\n",
      "lupus described at the cellular and molecular level cannot\n",
      "be overstated. Using targeted drug therapy and improving\n",
      "clinical assessment remains vital to improving disease\n",
      "control and patient outcomes.\n",
      "The Challenge of Moving T owards\n",
      "Precision Medicine in SLE\n",
      "As the foregoing text has conﬁrmed the pathophysiological\n",
      "aspects of SLE are highly diverse and the combination of\n",
      "factors leading to say skin, kidney and renal disease are very\n",
      "different. In spite of these differences, a rather restricted set of\n",
      "Table 2T reatment Based on the Organ/Systemic Involved\n",
      "Example 1 2 3 4 5 6 7 8\n",
      "Constitutional\n",
      "Symptoms\n",
      "Widespread\n",
      "Discoid Lupus\n",
      "Erythematosus\n",
      "Polyarthritis Thrombo-\n",
      "cytopenia\n",
      "Lupus APS Arterial\n",
      "Thrombolysis\n",
      "Mononeuritis\n",
      "Multiplex\n",
      "Nephritis\n",
      "III/IV\n",
      "Nephritis V\n",
      "1st Line HCQ/CS/ HCQ + CS HCQ + CS CS +\n",
      "HCQ\n",
      "Warfarin/LMWH CS + IV Cyclo CS + MMF CS + MMF\n",
      "Immunomodulation\n",
      "2nd Line MMF ADD AZA ADD MTX AZA/MMF ADD aspirin/\n",
      "Dipyridamole/Platelet\n",
      "aggregation inhibitor\n",
      "ADD Rituximab\n",
      "or IVIG/PE\n",
      "IV Cyclo → AZA/IV\n",
      "Cyclo or\n",
      "Rituximab\n",
      "3rd Line Rituximab/\n",
      "Belimumab\n",
      "AZA → MTX/\n",
      "Benlysta\n",
      "Benlysta/\n",
      "Rituximab\n",
      "Rituximab/\n",
      "IV Cyclo/\n",
      "IVIG\n",
      "/ / ADD\n",
      "Rituximab\n",
      "/\n",
      "Note: This table is based on the authors’ own experience.\n",
      "Abbreviations: CS, Corticosteroids; HCQ, Hydroxychloroquine, MTX, methotrexate; AZA, azathioprine; MMF , mycophenolate; Cyclo, Cyclophosphamide; LMWH, low\n",
      "molecular weight heparin; PE, plasma exchange.\n",
      "Dovepress Lever et al\n",
      "Pharmacogenomics and Personalized Medicine 2020:13 submit your manuscript| www.dovepress.com\n",
      "DovePress\n",
      "45\n",
      "drugs ster\n",
      "# KG Context From Relationships:\n",
      "\n",
      "\n",
      "=== kg_rels ===\n",
      "American College of Rheumatology - DESCRIBES(Developed classification criteria for disease) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Systemic Lupus Erythematosus - ASSOCIATED_WITH(Classification criteria established) -> American College of Rheumatology\n",
      "---\n",
      "American College of Rheumatology - RECOMMENDS(Recommends for renal lupus) -> Rituximab\n",
      "---\n",
      "Systemic Lupus International Collaborating Clinics - DESCRIBES(Developed classification criteria for disease) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Clinical and Immunological Biomarkers for Systemic Lupus Erythematosus - DESCRIBES() -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Haitao Yu - AUTHORED() -> Clinical and Immunological Biomarkers for Systemic Lupus Erythematosus\n",
      "---\n",
      "Yasuo Nagafuchi - AUTHORED() -> Clinical and Immunological Biomarkers for Systemic Lupus Erythematosus\n",
      "---\n",
      "Keishi Fujio - AUTHORED() -> Clinical and Immunological Biomarkers for Systemic Lupus Erythematosus\n",
      "---\n",
      "Clinical and Immunological Biomarkers for Systemic Lupus Erythematosus - PUBLISHED_IN() -> Biomolecules\n",
      "---\n",
      "Rheumatology 2017 - DESCRIBES(Publication about disease) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "nyon, P. - AUTHORED(Author of publication) -> Rheumatology 2017\n",
      "---\n",
      "Zhang, W. - AUTHORED(Author of publication) -> Rheumatology 2017\n",
      "---\n",
      "Lupus 2016 - DESCRIBES(Publication about disease) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "J. Rheumatol. 2021 - DESCRIBES(Publication about disease) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Towards Precision Medicine in Systemic Lupus Erythematosus - DESCRIBES() -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Elliott Lever - AUTHORED() -> Towards Precision Medicine in Systemic Lupus Erythematosus\n",
      "---\n",
      "Marta R Alves - AUTHORED() -> Towards Precision Medicine in Systemic Lupus Erythematosus\n",
      "---\n",
      "David A Isenberg - AUTHORED() -> Towards Precision Medicine in Systemic Lupus Erythematosus\n",
      "---\n",
      "Towards Precision Medicine in Systemic Lupus Erythematosus - PUBLISHED_IN() -> Pharmacogenomics and Personalized Medicine\n",
      "---\n",
      "Chronic Inflammatory Systemic Disease - DESCRIBES(Characterization of SLE) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "European League against Rheumatism - DESCRIBES(Developed classification criteria for disease) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Aringer M - DESCRIBES(Classification criteria research) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Aringer M - AUTHORED(Publication about SLE) -> Ann Rheum Dis\n",
      "---\n",
      "Costenbader KH - DESCRIBES(Classification criteria research) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Systemic Lupus Erythematosus - ASSOCIATED_WITH(Lupus Nephritis develops in up to 50% of patients) -> Lupus Nephritis\n",
      "---\n",
      "Anti-dsDNA antibodies - ASSOCIATED_WITH() -> Lupus Nephritis\n",
      "---\n",
      "Anti-Sm antibodies - ASSOCIATED_WITH() -> Lupus Nephritis\n",
      "---\n",
      "Renal Biopsy - ASSESSES(Used to diagnose condition) -> Lupus Nephritis\n",
      "---\n",
      "Anti-dsDNA Antibodies - BIOMARKER_FOR(Can predict development of condition) -> Lupus Nephritis\n",
      "---\n",
      "Coronin-1A - BIOMARKER_FOR(Distinguishes Lupus Nephritis) -> Lupus Nephritis\n",
      "---\n",
      "Lupus Nephritis - AFFECTS(Glomerular lesions in nephritis) -> Glomerulus\n",
      "---\n",
      "Lupus Nephritis - AFFECTS(Impacts mesangial cells) -> Mesangial Cells\n",
      "---\n",
      "Neutrophils - AFFECTS(Neutrophils as risk factor for lupus nephritis) -> Lupus Nephritis\n",
      "---\n",
      "Lupus Nephritis - RESULTS_IN(Causes podocyte damage) -> Podocytes\n",
      "---\n",
      "Anti-C1q antibodies - PREDICTS() -> Lupus Nephritis\n",
      "---\n",
      "Systemic Lupus Erythematosus - ASSOCIATED_WITH(Characterized by aberrant immune system activity) -> Autoimmune Disease\n",
      "---\n",
      "Systemic Lupus Erythematosus - ASSOCIATED_WITH(Differential gut microbiota) -> Gut Microbiota\n",
      "---\n",
      "Gut Microbiota - PRODUCES(Anti-dsDNA antibodies) -> Anti-dsDNA Antibodies\n",
      "---\n",
      "Gut Microbiota - PRODUCES(Interferon expression) -> Interferon\n",
      "---\n",
      "Gut Microbiota - CAUSES(Bacterial translocation) -> Liver\n",
      "---\n",
      "Systemic Lupus Erythematosus - ASSOCIATED_WITH(Disease manifestation) -> Skin Disease\n",
      "---\n",
      "Systemic Lupus Erythematosus - ASSOCIATED_WITH(Disease manifestation) -> Kidney Disease\n",
      "---\n",
      "Systemic Lupus Erythematosus - ASSOCIATED_WITH(Disease manifestation) -> Renal Disease\n",
      "---\n",
      "Hemolytic Anemia - ASSOCIATED_WITH(Clinical manifestation) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "B cells - ASSOCIATED_WITH(Cell subpopulation in SLE) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Omics approaches - INCLUDES(Omics approaches include B cells) -> B cells\n",
      "---\n",
      "Monocytes - ASSOCIATED_WITH(Cell subpopulation in SLE) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "CD4+ T cells - ASSOCIATED_WITH(Cell subpopulation in SLE) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "CD8+ T cells - ASSOCIATED_WITH(Cell subpopulation in SLE) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Natural killer cells - ASSOCIATED_WITH(Cell subpopulation in SLE) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Dendritic cells - ASSOCIATED_WITH(Cell subpopulation in SLE) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Plasma cells - ASSOCIATED_WITH(Cell subpopulation in SLE) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Patients - ASSOCIATED_WITH(Patient experience with disease) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Patients - ASSOCIATED_WITH(Patients concerned with quality of life) -> Quality of Life\n",
      "---\n",
      "Lupus Flare - AFFECTS(Lupus flare impacts patient's quality of life) -> Patients\n",
      "---\n",
      "Patients - INTERACTS_WITH(Patient-physician communication) -> Physicians\n",
      "---\n",
      "Patients - INTERACTS_WITH(Patient-clinician communication) -> Clinicians\n",
      "---\n",
      "Cigarette Smoking - ASSOCIATED_WITH(Smoking risk factor for disease) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Viruses - ASSOCIATED_WITH(Viral interaction with disease) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Stress - ASSOCIATED_WITH(Stress impact on disease) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Environmental Factors - ASSOCIATED_WITH(Environmental factors influence) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Microbes and Diet - ASSOCIATED_WITH(Microbes and diet influence) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Patient Reported Outcomes - ASSESSES(PROs used to evaluate disease) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Patient Reported Outcomes - ASSOCIATED_WITH(PROs used for SLE management) -> SLE\n",
      "---\n",
      "Patients - CONTRIBUTES_TO(Patients contribute to outcomes) -> Patient Reported Outcomes\n",
      "---\n",
      "Patient Reported Outcomes - USED_FOR(Outcomes measurement for disease) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Patient Reported Outcomes - USED_FOR(Patient Reported Outcomes used in SLE management) -> SLE\n",
      "---\n",
      "Anti-SSA Antibodies - BIOMARKER_FOR(Diagnostic marker) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Double-Stranded DNA - BIOMARKER_FOR(Diagnostic marker) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Immunoglobulin G - BIOMARKER_FOR(Diagnostic marker) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Antinuclear Antibody - BIOMARKER_FOR(Antinuclear antibody used for SLE screening and diagnosis) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Double-stranded DNA Antibody - BIOMARKER_FOR(Diagnostic marker) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Anticardiolipin Antibody - BIOMARKER_FOR(Diagnostic marker) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Smith Antibody - BIOMARKER_FOR(Diagnostic marker) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Proteinuria - BIOMARKER_FOR(Diagnostic criteria) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Urinary Casts - BIOMARKER_FOR(Diagnostic criteria) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "White Blood Cell Count - BIOMARKER_FOR(Diagnostic criteria) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Platelet Count - BIOMARKER_FOR(Diagnostic criteria) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Anticardiolipin Antibodies - BIOMARKER_FOR(Anticardiolipin antibodies as biomarker for SLE) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Anti-β2-glycoprotein I Antibodies - BIOMARKER_FOR(Anti-β2-glycoprotein I antibodies as biomarker for SLE) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "IFI44L - BIOMARKER_FOR(IFI44L promoter methylation as diagnostic marker) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Anti-NMDAR Antibodies - BIOMARKER_FOR(Diagnostic marker) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Anti-RibP Antibodies - BIOMARKER_FOR(Diagnostic marker) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Systemic Lupus Erythematosus - AFFECTS(Disease impact on quality of life) -> Health-Related Quality of Life\n",
      "---\n",
      "Remission - ASSOCIATED_WITH(Potential improvement in patient quality of life) -> Health-Related Quality of Life\n",
      "---\n",
      "Lupus Low Disease Activity - ASSOCIATED_WITH(Potential improvement in patient quality of life) -> Health-Related Quality of Life\n",
      "---\n",
      "Systemic Lupus Erythematosus - AFFECTS(Neuropsychiatric manifestation) -> Neuropsychiatric Involvement\n",
      "---\n",
      "Cyclophosphamide - TREATS(Treatment for Neuropsychiatric Involvement) -> Neuropsychiatric Involvement\n",
      "---\n",
      "Systemic Lupus Erythematosus - AFFECTS(Renal manifestation) -> Renal Involvement\n",
      "---\n",
      "Missed Diagnosis - AFFECTS(Diagnosis challenges) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "British Society for Rheumatology - PRESCRIBED(Treatment guidelines) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "British Society for Rheumatology - PRESCRIBED(Recommended medication) -> Hydroxychloroquine\n",
      "---\n",
      "British Society for Rheumatology - PRESCRIBED(Recommended medication) -> Prednisolone\n",
      "---\n",
      "Systemic Lupus Erythematosus - INCLUDES(Clinical manifestation of SLE) -> Multi-organ Involvement\n",
      "---\n",
      "Biomarkers - USED_FOR(Diagnose and monitor disease activity) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Belimumab - TREATS(Third line treatment) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "National Institute of Clinical Excellence - ASSESSES(Evaluates Belimumab usage) -> Belimumab\n",
      "---\n",
      "Belimumab - PRESCRIBED(Used for skin and joint disease in lupus) -> Lupus\n",
      "---\n",
      "Belimumab - TREATS(Potential treatment for SLE) -> SLE\n",
      "---\n",
      "Belimumab - TREATS(treatment for lupus) -> lupus\n",
      "---\n",
      "Cyclophosphamide - TREATS(Third line treatment) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Severe Disease - PRESCRIBED(Treatment for severe disease) -> Cyclophosphamide\n",
      "---\n",
      "Cyclophosphamide - TREATS(Treatment for Lupus) -> Lupus\n",
      "---\n",
      "Cyclophosphamide - TREATS(Treatment for Severe Renal Lupus) -> Renal Lupus\n",
      "---\n",
      "Hydroxychloroquine - TREATS(First line treatment) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Mild Disease - PRESCRIBED(Treatment for mild disease) -> Hydroxychloroquine\n",
      "---\n",
      "Hydroxychloroquine - TREATS(Treatment for Lupus) -> Lupus\n",
      "---\n",
      "Rituximab - TREATS(Third line treatment) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Rituximab - PRESCRIBED(Used for treating lupus) -> Lupus\n",
      "---\n",
      "Severe Disease - PRESCRIBED(Treatment for severe disease) -> Rituximab\n",
      "---\n",
      "Rituximab - TREATS(Potential treatment for SLE) -> SLE\n",
      "---\n",
      "Rituximab - TREATS(Recommended for renal lupus) -> Lupus\n",
      "---\n",
      "European League Against Rheumatism - RECOMMENDS(Recommends for renal lupus) -> Rituximab\n",
      "---\n",
      "Methotrexate - TREATS(Second line treatment) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Azathioprine - TREATS(Second line treatment) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Mycophenolate - TREATS(Second line treatment) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Mycophenolate - TREATS(Potential treatment for SLE) -> SLE\n",
      "---\n",
      "Corticosteroids - TREATS(First line treatment) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Systemic Lupus Erythematosus - PRODUCES(Anti-dsDNA antibodies relate to disease activity) -> Anti-dsDNA Antibodies\n",
      "---\n",
      "Anti-dsDNA Antibodies - ASSOCIATED_WITH(Biomarker for disease activity) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "Systemic Lupus Erythematosus - PRODUCES(Lupus antibodies develop prior to clinical manifestations) -> Lupus Antibodies\n",
      "---\n",
      "Systemic Lupus Erythematosus - PRODUCES(Anti-phospholipid antibodies produced) -> Anti-phospholipid Antibodies\n",
      "---\n",
      "Ann Rheum Dis - CITES(Research publication) -> Systemic Lupus Erythematosus\n",
      "---\n",
      "David A Isenberg - AUTHORED(Works at institution) -> University College Hospital London\n",
      "---\n",
      "www.lupus100.org - DESCRIBES(Platform providing lupus information) -> Lupus\n",
      "---\n",
      "Patient - ASSOCIATED_WITH(Patient managing disease) -> Lupus\n",
      "---\n",
      "Patient - ASSOCIATED_WITH(Patient-doctor communication) -> Physician\n",
      "---\n",
      "Patient - ASSOCIATED_WITH(Patient-doctor communication relationship) -> Doctor\n",
      "---\n",
      "Patient - ASSOCIATED_WITH(Patient experiences communication challenges) -> Communication Gap\n",
      "---\n",
      "Patient - HAS_SYMPTOM(Patient experiences fatigue) -> Fatigue\n",
      "---\n",
      "Systemic Lupus Erythematosus (SLE) - AFFECTS(SLE impacts patient's daily life) -> Patient\n",
      "---\n",
      "Lupus - AFFECTS(Disease impacts patient experience) -> Patient\n",
      "---\n",
      "Treatment - AFFECTS(Treatment impacts patient's life) -> Patient\n",
      "---\n",
      "Patient - CONTRIBUTES_TO(Patient self-management of disease) -> Lupus\n",
      "---\n",
      "Dr Google - USED_FOR(Patients consult for information) -> Patient\n",
      "---\n",
      "Doctor - COMMUNICATES_WITH(Medical consultation communication) -> Patient\n",
      "---\n",
      "Lupus Nurse - SUPPORTS(Provides patient support before and after consultation) -> Patient\n",
      "---\n",
      "Lupus Nurse - ASSOCIATED_WITH(Specialized nurse for lupus patients) -> Lupus\n",
      "---\n",
      "BILAG scoring system - ASSESSES(Scoring system for lupus disease) -> Lupus\n",
      "---\n",
      "Lupus Assessment Group - DESCRIBES(Developed BILAG scoring system) -> BILAG scoring system\n",
      "---\n",
      "Lupus - HAS_SYMPTOM(Lupus patients experience fatigue) -> Fatigue\n",
      "---\n",
      "FACIT-F scale - ASSESSES(Measures fatigue) -> Fatigue\n",
      "---\n",
      "Systemic Lupus Erythematosus (SLE) - HAS_SYMPTOM(SLE causes fatigue) -> Fatigue\n",
      "---\n",
      "SLE - HAS_SYMPTOM(SLE patients experience fatigue) -> Fatigue\n",
      "---\n",
      "Anifrolumab - AFFECTS(Improves fatigue) -> Fatigue\n",
      "---\n",
      "Lupus - HAS_SYMPTOM(Lupus has disease activity) -> Disease Activity\n",
      "---\n",
      "Physician - ASSESSES(Physician evaluates disease activity) -> Disease Activity\n",
      "---\n",
      "Disease Activity - AFFECTS(Disease activity contributes to organ damage) -> Organ Damage\n",
      "---\n",
      "Joint Pain - AFFECTS(Joint pain impacts disease activity perception) -> Disease Activity\n",
      "---\n",
      "Lupus - HAS_SYMPTOM(Lupus presents multiple symptoms) -> Symptoms\n",
      "---\n",
      "Lupus - HAS_SYMPTOM(Patients experience lupus flares differently from doctors) -> Lupus Flare\n",
      "---\n",
      "Doctors - ASSESSES(Doctors evaluate lupus flares through biomedical tests) -> Lupus Flare\n",
      "---\n",
      "Lupus Flare - AFFECTS(Flare impacts quality of life) -> Quality of Life\n",
      "---\n",
      "Lupus Flare - PRODUCES(Flare causes anxiety) -> Anxiety\n",
      "---\n",
      "Lupus - INCLUDES(Mild disease classification) -> Mild Disease\n",
      "---\n",
      "Mild Disease - PRESCRIBED(Treatment for mild disease) -> Topical Steroid\n",
      "---\n",
      "Mild Disease - PRESCRIBED(Treatment for mild disease) -> Oral Steroid\n",
      "---\n",
      "Lupus - INCLUDES(Moderate disease classification) -> Moderate Disease\n",
      "---\n",
      "Lupus - INCLUDES(Severe disease classification) -> Severe Disease\n",
      "---\n",
      "Lupus - INCLUDES(Renal manifestation of lupus) -> Renal lupus\n",
      "---\n",
      "Lupus - INCLUDES(Cerebral manifestation of lupus) -> Cerebral lupus\n",
      "---\n",
      "Treatment - USED_FOR(Treatment for lupus) -> Lupus\n",
      "---\n",
      "Prednisolone - TREATS(Treatment for Lupus) -> Lupus\n",
      "---\n",
      "Methylprednisolone - TREATS(Treatment for Lupus) -> Lupus\n",
      "---\n",
      "Benlysta - TREATS(FDA approved treatment) -> Lupus\n",
      "---\n",
      "Federal Drug Administration - ASSESSES(Approved Benlysta) -> Benlysta\n",
      "---\n",
      "SLE - PUBLISHED_IN(Research about SLE biomarkers) -> Biomolecules\n",
      "---\n",
      "Pisetsky - DESCRIBES(Proposed categorization of SLE manifestations) -> SLE\n",
      "---\n",
      "Best Practice & Research Clinical Rheumatology - DESCRIBES() -> SLE\n",
      "---\n",
      "SLE - ASSOCIATED_WITH(Cardiovascular events in SLE patients) -> Cardiovascular events\n",
      "---\n",
      "SLE - ASSOCIATED_WITH(Patient experience with SLE) -> Patients\n",
      "---\n",
      "SLE - ASSOCIATED_WITH(Physician management of SLE) -> Physicians\n",
      "---\n",
      "SLE - ASSOCIATED_WITH(Potential comorbid condition) -> Fibromyalgia\n",
      "---\n",
      "SLE - ASSOCIATED_WITH(Disease relationship to quality of life) -> Quality of Life\n",
      "---\n",
      "SLE - ASSOCIATED_WITH(Involves innate immune system) -> Innate Immune System\n",
      "---\n",
      "Cardiac troponin T - ASSOCIATED_WITH(In SLE patients) -> SLE\n",
      "---\n",
      "IgG-anticardiolipin antibodies - ASSOCIATED_WITH(Correlated with disease activity) -> SLE\n",
      "---\n",
      "anti-dsDNA antibodies - ASSOCIATED_WITH(Potential disease marker) -> SLE\n",
      "---\n",
      "Genome-wide association studies - ASSOCIATED_WITH(Used for studying SLE genetics) -> SLE\n",
      "---\n",
      "Microparticles - ASSOCIATED_WITH(potential autoantigen source) -> SLE\n",
      "---\n",
      "complement system - ASSOCIATED_WITH(Complement system in SLE) -> SLE\n",
      "---\n",
      "T lymphocytes - ASSOCIATED_WITH(T lymphocytes in SLE) -> SLE\n",
      "---\n",
      "HLA - ASSOCIATED_WITH(HLA associated with SLE) -> SLE\n",
      "---\n",
      "DRB1 - ASSOCIATED_WITH(DRB1 associated with SLE) -> SLE\n",
      "---\n",
      "BQA1 - ASSOCIATED_WITH(BQA1 associated with SLE) -> SLE\n",
      "---\n",
      "Patient Reported Outcomes (PROs) - ASSESSES(Used to evaluate disease status) -> SLE\n",
      "---\n",
      "anti-Sm antibodies - BIOMARKER_FOR(Specific diagnostic marker) -> SLE\n",
      "---\n",
      "anti-RibP antibodies - BIOMARKER_FOR(Specific biomarker for diagnosing SLE) -> SLE\n",
      "---\n",
      "Polygenic risk scores - BIOMARKER_FOR(Predicts SLE risk) -> SLE\n",
      "---\n",
      "L-valine - BIOMARKER_FOR(Potential diagnostic marker) -> SLE\n",
      "---\n",
      "Pyrimidine - BIOMARKER_FOR(Potential diagnostic marker) -> SLE\n",
      "---\n",
      "Erucamide - BIOMARKER_FOR(Potential diagnostic marker) -> SLE\n",
      "---\n",
      "L-leucine - BIOMARKER_FOR(Potential diagnostic marker) -> SLE\n",
      "---\n",
      "Biomarker - BIOMARKER_FOR(Used for diagnosing and monitoring SLE) -> SLE\n",
      "---\n",
      "SLE - HAS_SYMPTOM(Joint pain in SLE) -> Joint Pain\n",
      "---\n",
      "SLE - HAS_SYMPTOM(SLE patients experience chronic pain) -> Chronic Pain\n",
      "---\n",
      "SLE - AFFECTS(Disease impact on damage accrual) -> Damage Accrual\n",
      "---\n",
      "SLE - AFFECTS(Disease impact on cardiovascular risk) -> Cardiovascular Risk\n",
      "---\n",
      "SLE - AFFECTS(Disease impact on mortality) -> Mortality\n",
      "---\n",
      "SLE - AFFECTS(Alters T cell signaling) -> T cell\n",
      "---\n",
      "SLE - AFFECTS(Treg cells are deficient) -> Treg cell\n",
      "---\n",
      "SLE - AFFECTS(Impairs B cell regulation) -> B cell\n",
      "---\n",
      "SLE - AFFECTS(Impairs IL-2 production) -> IL-2\n",
      "---\n",
      "TLR - AFFECTS(TLR involvement in SLE pathogenesis) -> SLE\n",
      "---\n",
      "IL-2 - AFFECTS(IL-2 affects SLE) -> SLE\n",
      "---\n",
      "FOXP3 - AFFECTS(FOXP3 affects SLE) -> SLE\n",
      "---\n",
      "ITGAM - CONTRIBUTES_TO(Susceptibility gene) -> SLE\n",
      "---\n",
      "reactive oxygen species - CONTRIBUTES_TO(disease severity) -> SLE\n",
      "---\n",
      "SLE - PRESCRIBED(Glucocorticoids used in SLE treatment) -> Glucocorticoids\n",
      "---\n",
      "Biomarker - USED_FOR(Diagnosing and monitoring disease) -> SLE\n",
      "---\n",
      "PROs - USED_FOR(Monitoring and treatment) -> SLE\n",
      "---\n",
      "Electronic Diary - USED_FOR(Recording clinical data) -> SLE\n",
      "---\n",
      "Anifrolumab - TREATS(Potential treatment for SLE) -> SLE\n",
      "---\n",
      "belimumab - TREATS(Targets B cell pathway) -> SLE\n",
      "---\n",
      "rituximab - TREATS(B cell depletion therapy) -> SLE\n",
      "---\n",
      "Atacicept - TREATS(Potential treatment for SLE) -> SLE\n",
      "---\n",
      "miR-146a - ASSOCIATED_WITH(linked to lupus) -> lupus\n",
      "---\n",
      "Epstein-Barr Virus - ASSOCIATED_WITH(occurs in lupus patients) -> lupus\n",
      "---\n",
      "histone modification - AFFECTS(associated with disease state) -> lupus\n",
      "---\n",
      "Ultraviolet exposure - AFFECTS(associated with reduced DNTM1 expression) -> lupus\n",
      "---\n",
      "mTOR pathway - CONTRIBUTES_TO(potential role in cellular proliferation) -> lupus\n",
      "---\n",
      "rituximab - TREATS(treatment for lupus) -> lupus\n",
      "---\n",
      "de novo methylation - CAUSES(cause organ selectivity) -> lupus\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## GraphRAG\n",
    " \n",
    " You can construct GraphRAG pipelines with the `GraphRAG` class.  At a minimum, you will need to pass the constructor an LLM and a retriever. You can optionally pass a custom prompt template. We will do so here just to provide a bit more guidance for the LLM to stick to information from our data source.\n",
    " \n",
    "Below we create `GraphRAG` objects for both the vector and vector-cypher retrievers. "
   ],
   "id": "f2e55b8b3511cf1"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:06:56.210725Z",
     "start_time": "2024-12-01T19:06:56.208322Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from neo4j_graphrag.generation import RagTemplate\n",
    "from neo4j_graphrag.generation.graphrag import GraphRAG\n",
    "\n",
    "llm = BedrockLLM(model_name=\"anthropic.claude-3-5-sonnet-20240620-v1:0\",  model_params={\"temperature\": 0.0})\n",
    "\n",
    "rag_template = RagTemplate(template='''Answer the Question using the following Context. Only respond with information mentioned in the Context. Do not inject any speculative information not mentioned. \n",
    "\n",
    "# Question:\n",
    "{query_text}\n",
    " \n",
    "# Context:\n",
    "{context}\n",
    "\n",
    "# Answer:\n",
    "''', expected_inputs=['query_text', 'context'])\n",
    "\n",
    "v_rag  = GraphRAG(llm=llm, retriever=vector_retriever, prompt_template=rag_template)\n",
    "vc_rag = GraphRAG(llm=llm, retriever=vc_retriever, prompt_template=rag_template)"
   ],
   "id": "8e2cf317a83d59ae",
   "outputs": [],
   "execution_count": 14
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Now we can run GraphRAG and examine the outputs. ",
   "id": "fc8e13302fdeadd3"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:07:03.473986Z",
     "start_time": "2024-12-01T19:06:56.211436Z"
    }
   },
   "cell_type": "code",
   "source": [
    "q = \"How is precision medicine applied to Lupus? provide in list format.\"\n",
    "print(f\"Vector Response: \\n{v_rag.search(q, retriever_config={'top_k':5}).answer}\")"
   ],
   "id": "4d0aff643e689611",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vector Response: \n",
      "Based on the provided context, precision medicine is applied to Lupus in the following ways:\n",
      "\n",
      "1. Using molecular tools to predict response to treatments like methotrexate or azathioprine.\n",
      "\n",
      "2. Utilizing gene signatures to assist in response prediction, as has been shown in rheumatoid arthritis.\n",
      "\n",
      "3. Targeting specific molecules with biologic drugs to provide a more precise approach.\n",
      "\n",
      "4. Considering racial differences in treatment response, such as black lupus patients not responding as well to Cyclophosphamide compared to Caucasians.\n",
      "\n",
      "5. Potentially treating patients prophylactically based on rising dsDNA antibody levels and falling C3, which can anticipate a flare.\n",
      "\n",
      "6. Using biomarkers for SLE diagnosis and monitoring, as defined in various criteria (ACR-1997, SLICC-2012, and EULAR/ACR-2019).\n",
      "\n",
      "7. Considering the role of the gut microbiome in SLE and potential interventions based on microbiota findings.\n",
      "\n",
      "8. Tailoring treatment based on the specific organ or system involved in individual patients, as outlined in the treatment table provided.\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:07:13.845291Z",
     "start_time": "2024-12-01T19:07:03.474794Z"
    }
   },
   "cell_type": "code",
   "source": "print(f\"Vector + Cypher Response: \\n{vc_rag.search(q, retriever_config={'top_k':5}).answer}\")",
   "id": "36252f209cb938cf",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vector + Cypher Response: \n",
      "Based on the context provided, precision medicine is applied to Lupus in the following ways:\n",
      "\n",
      "1. Use of biomarkers for diagnosis and monitoring disease activity, including:\n",
      "   - Anti-dsDNA antibodies\n",
      "   - Antiphospholipid antibodies\n",
      "   - Complement proteins (C3, C4)\n",
      "   - SLE-specific antibodies\n",
      "   - Anti-C1q antibodies\n",
      "\n",
      "2. Genetic studies:\n",
      "   - Genome-wide association studies used for studying SLE genetics\n",
      "   - Polygenic risk scores to predict SLE risk\n",
      "\n",
      "3. Targeted treatments based on specific pathways:\n",
      "   - Belimumab targeting B cell pathway\n",
      "   - Rituximab for B cell depletion therapy\n",
      "   - Anifrolumab as a potential treatment\n",
      "\n",
      "4. Use of scoring systems to assess disease activity:\n",
      "   - BILAG scoring system\n",
      "   - SLE Disease Activity Index (SLEDAI)\n",
      "\n",
      "5. Patient-reported outcomes (PROs) used for monitoring and treatment evaluation\n",
      "\n",
      "6. Consideration of individual patient factors:\n",
      "   - Ethnicity (e.g., black lupus patients responding differently to cyclophosphamide)\n",
      "   - Organ-specific manifestations requiring different treatment approaches\n",
      "\n",
      "7. Potential use of metabolomics:\n",
      "   - L-valine, pyrimidine, erucamide, and L-leucine as potential diagnostic markers\n",
      "\n",
      "8. Consideration of environmental factors and gut microbiota in disease management\n",
      "\n",
      "9. Use of electronic diaries for recording clinical data\n",
      "\n",
      "10. Tailoring treatment based on disease severity (mild, moderate, severe) and specific organ involvement\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:07:25.540191Z",
     "start_time": "2024-12-01T19:07:13.846337Z"
    }
   },
   "cell_type": "code",
   "source": [
    "q = \"Can you summarize systemic lupus erythematosus (SLE)? including common effects, biomarkers, and treatments? Provide in detailed list format.\"\n",
    "\n",
    "v_rag_result = v_rag.search(q, retriever_config={'top_k': 5}, return_context=True)\n",
    "print(f\"Vector Response: \\n{v_rag_result.answer}\")"
   ],
   "id": "a2c44f834bdb13ad",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vector Response: \n",
      "Based on the provided context, here is a summary of systemic lupus erythematosus (SLE) in a detailed list format:\n",
      "\n",
      "1. General characteristics:\n",
      "   - Chronic inflammatory systemic disease\n",
      "   - Multi-organ involvement\n",
      "   - Complex clinical picture\n",
      "   - Wide range of manifestations with varying severity\n",
      "   - Unpredictable relapsing and remitting course\n",
      "\n",
      "2. Common effects:\n",
      "   - Constitutional symptoms (e.g., fatigue, fever)\n",
      "   - Skin involvement (e.g., malar rash, discoid lupus erythematosus)\n",
      "   - Polyarthritis\n",
      "   - Thrombocytopenia\n",
      "   - Nephritis\n",
      "   - Pleurisy\n",
      "   - Pericarditis\n",
      "   - Neurological involvement (e.g., psychosis, mononeuritis multiplex)\n",
      "\n",
      "3. Biomarkers:\n",
      "   - Anti-dsDNA antibodies\n",
      "   - Ro60 antigen\n",
      "\n",
      "4. Treatments:\n",
      "   - First-line treatments:\n",
      "     - Hydroxychloroquine (HCQ)\n",
      "     - Corticosteroids (CS)\n",
      "     - Mycophenolate (MMF)\n",
      "     - Cyclophosphamide (IV Cyclo)\n",
      "     - Warfarin/Low molecular weight heparin (for antiphospholipid syndrome)\n",
      "\n",
      "   - Second-line treatments:\n",
      "     - Azathioprine (AZA)\n",
      "     - Methotrexate (MTX)\n",
      "     - Rituximab\n",
      "     - Intravenous immunoglobulin (IVIG)\n",
      "     - Plasma exchange (PE)\n",
      "\n",
      "   - Third-line treatments:\n",
      "     - Belimumab\n",
      "\n",
      "5. Treatment approach:\n",
      "   - Based on disease severity (mild, moderate, severe)\n",
      "   - Utilizes British Isles Lupus Assessment Group (BILAG) scoring system\n",
      "   - Aims to control disease activity, prevent damage accrual, minimize treatment-related toxicities, and improve quality of life\n",
      "\n",
      "6. Treatment targets:\n",
      "   - DORIS definition of remission\n",
      "   - Lupus Low Disease Activity State (LLDAS)\n",
      "\n",
      "7. Emerging areas of research:\n",
      "   - Intestinal microbiota's role in SLE\n",
      "   - Precision medicine approaches (still in development)\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:07:25.545192Z",
     "start_time": "2024-12-01T19:07:25.541535Z"
    }
   },
   "cell_type": "code",
   "source": "for i in v_rag_result.retriever_result.items: print(json.dumps(eval(i.content), indent=1))",
   "id": "1088766f7109b6b3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      " \"text\": \"scular risk.\\nOur approach as detailed in Table 1 does not help in\\ndistinguishing subgroups of patients for methotrexate or\\nazathioprine. The molecular tools for this are on their way\\nto the clinic. In rheumatoid arthritis gene signature has\\nbeen shown to assist response prediction.77\\nIn the current treatment of lupus longterm clinical and\\nimmunological remission is only achieved in a proportion\\nof patients. The inherent pathophysiological complexity of\\nlupus described at the cellular and molecular level cannot\\nbe overstated. Using targeted drug therapy and improving\\nclinical assessment remains vital to improving disease\\ncontrol and patient outcomes.\\nThe Challenge of Moving T owards\\nPrecision Medicine in SLE\\nAs the foregoing text has con\\ufb01rmed the pathophysiological\\naspects of SLE are highly diverse and the combination of\\nfactors leading to say skin, kidney and renal disease are very\\ndifferent. In spite of these differences, a rather restricted set of\\nTable 2T reatment Based on the Organ/Systemic Involved\\nExample 1 2 3 4 5 6 7 8\\nConstitutional\\nSymptoms\\nWidespread\\nDiscoid Lupus\\nErythematosus\\nPolyarthritis Thrombo-\\ncytopenia\\nLupus APS Arterial\\nThrombolysis\\nMononeuritis\\nMultiplex\\nNephritis\\nIII/IV\\nNephritis V\\n1st Line HCQ/CS/ HCQ + CS HCQ + CS CS +\\nHCQ\\nWarfarin/LMWH CS + IV Cyclo CS + MMF CS + MMF\\nImmunomodulation\\n2nd Line MMF ADD AZA ADD MTX AZA/MMF ADD aspirin/\\nDipyridamole/Platelet\\naggregation inhibitor\\nADD Rituximab\\nor IVIG/PE\\nIV Cyclo \\u2192 AZA/IV\\nCyclo or\\nRituximab\\n3rd Line Rituximab/\\nBelimumab\\nAZA \\u2192 MTX/\\nBenlysta\\nBenlysta/\\nRituximab\\nRituximab/\\nIV Cyclo/\\nIVIG\\n/ / ADD\\nRituximab\\n/\\nNote: This table is based on the authors\\u2019 own experience.\\nAbbreviations: CS, Corticosteroids; HCQ, Hydroxychloroquine, MTX, methotrexate; AZA, azathioprine; MMF , mycophenolate; Cyclo, Cyclophosphamide; LMWH, low\\nmolecular weight heparin; PE, plasma exchange.\\nDovepress Lever et al\\nPharmacogenomics and Personalized Medicine 2020:13 submit your manuscript| www.dovepress.com\\nDovePress\\n45\\ndrugs ster\"\n",
      "}\n",
      "{\n",
      " \"text\": \" by \\nthe doctor, a dedicated nurse.   \\nSystemic Lupus Erythematosus (SLE) is a chronic inflammatory systemic disease characterized by multi-organ involvement and a \\ncomplex clinical picture, with a wide range of manifestations of varying severity and an unpredictable relapsing and remitting course \\n[ 1 , 2 ]. Due to its complexity and heterogeneity, SLE is not a single-target disease, and many aspects must be taken into consideration in \\nthe management of the disease: controlling disease activity, preventing damage accrual, minimizing treatment-related toxicities, and \\nimproving patients \\u2019 quality of life (QoL). \\nSince when the principle of \\u201c treat to target \\u201d (T2T) has been applied in SLE [ 3 ], many efforts have been made to define targets in \\nSLE. Over the last few years, the DORIS definition of remission [ 4 , 5 ] and the LLDAS definition of Lupus Low Disease Activity [ 6 ] have \\nemerged as the most meaningful treatment targets, after multiple validation studies in large independent cohorts. \\nThe physicians \\u2019 view on lupus dominated the development of remission criteria and it was postulated that a control of disease \\nactivity would improve QoL in SLE, although the definitions of remission and LLDAS do not address Health-Related Quality of Life \\n(HRQoL) or disease burden. \\nWhile some studies have demonstrated an association between a stable condition of remission or LLDAS and better HRQoL in SLE \\npatients, this may sometimes prove to be a weak association [ 7 ]. \\nThe disease imposes a great burden on patient \\u2019 s lives and 35 \\u2013 50% of patients with SLE perceive their health as \\u201c fair/not so good \\u201d or \\n* Corresponding author. Rheumatology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Via Roma 67, Pisa, Italy. \\nE-mail address: marta.mosca@unipi.it (M. Mosca).  \\nContents lists available at ScienceDirect \\nBest Practice & Research Clinical Rheumatology \\njournal homepag e: www.else vierhealth.c om/berh \\nhttps://doi.org/10.1016/j.berh.2024.\"\n",
      "}\n",
      "{\n",
      " \"text\": \"les Lupus Assessment Group\\n(BILAG) scoring system provides a comprehensive\\napproach to managing patients with lupus72 The assessor\\nneeds to de\\ufb01ne activity accurately distinguishing clinical\\nfeatures due to activity, distinct from previous damage or\\ncomplications of therapy to capture active disease cor-\\nrectly. The multimodal facets of the BILAG allow classi-\\n\\ufb01cation of mild, moderate and severe disease. Mild disease\\nincludes non-life threatening organ involvement, equiva-\\nlent to SLEDAI <6 with one or more grade C scores or\\na single grade B. Treatment may include topical steroid,\\nlow dose oral steroid and/or hydroxychloroquine. In con-\\ntrast, patients with moderate disease require stronger sup-\\npression of the immune system and would consistently\\nhave BILAG scores of 2 Bs or more. The most severe\\ndisease BILAG A implies high dose steroid (\\u226520mg/day;\\nintravenous or oral, and other immunosuppressive agents.\\nOther therapies outlined in Table 2 (which designates\\ntreatments according to disease severity) include anticoa-\\ngulation or antiplatelet therapy in antiphospholipid patients\\nand the use of rituximab or cyclophosphamide as second-\\nline treatments to induce remission.\\nPatients with skin-limited disease usually respond to non-\\naggressive treatment including topical and oral steroids and\\nhydroxychloroquine. We recently reported on the use of ritux-\\nimab in severe cutaneous lupus73 and showed that some types\\nof subcutaneous lupus [e.g., acute cutaneous LE] respond\\nT able 1SLE T reatment Based on Severity of the Disease\\nMild Disease Moderate Disease Severe Disease\\n[BILAG C Scores/Single B/SLEDAI\\u22646] 2 or more BILAG Bs/SLEDAI 6-12 1 or more BILAG A/SLEDAI \\u2265 12\\nFeatures Fatigue, malar rash, diffuse alopecia, myalgia,\\nplatelets 50\\u2013 149 x 109/L\\nFever, rash (< 2/9 body surface area),\\ncutaneous vasculitis, renal, pleurisy,\\npericarditis, platelets 25\\u2013 49 x 109/L\\nRash > 2/9 body surface area, severe pleurisy/\\npericarditis, psychosis, renal/myositis/platelets\\n<2 5x1 09/L\\nT ypica\"\n",
      "}\n",
      "{\n",
      " \"text\": \"t your manuscript| www.dovepress.com\\nDovePress\\n43\\nIntestinal Microbiota and SLE\\nThere have been recent advances in understanding the\\nmicrobiome in lupus. SLE and Sjogrens patients have\\nbeen found to have differential gut microbiota.66,67 Work\\non mice have suggested the gut is leaky in lupus like\\ndisease models.68 An enterococcus was found translocated\\nto the liver and triggering interferon expression and anti-\\ndsDNA antibodies.68 Hyper-responsiveness to gut \\ufb02ora\\noccurs in a mouse model that has overexpression of\\nTLR4 and develops lupus. Oestrogen receptor de\\ufb01ciency\\nimpairs TLR4 signaling and reduces activity in lupus\\nprone mice.68 In MRL/lpr mice lactobacillus species are\\nreduced and supplementation with lactobacillus caused\\nincreased gut mucosal barrier, reduced gut in\\ufb02ammation\\nand nephritis.69 Bacterial gut commensals have been found\\nto produce a variant of Ro60 antigen which stimulates\\nimmune cells.70 Diet modi\\ufb01es the gut with\\ufb01sh oils redu-\\ncing proin \\ufb02ammatory cytokines. Polyunsaturated fatty\\nacids and polyphenols ingestion are associated with bene-\\n\\ufb01cial changes to microorganisms present in the gastroin-\\ntestinal tract.71 Septrin, tetracylines and pencillins can\\ntrigger lupus. The suspicion remains that the gut \\ufb02ora\\nchanges can interplay with immune regulatory cells such\\nas Tregs.\\nCurrent Treatment of Lupus\\nThe clinical assessment of patients dictates the manage-\\nment. Diagnostic tools help to de\\ufb01ne the affected organ\\nsystem. The condition is heterogeneous and can present in\\nunusual ways. Most patients are given hydroxychloroquine\\nand prednisolone accompanied by immunosuppressives. In\\nTable 1 we indicate the general outline for the manage-\\nment of SLE in the treatment of different organ and\\nsystems. This is adapted from BSR guideline on SLE.72\\nUse of the British Isles Lupus Assessment Group\\n(BILAG) scoring system provides a comprehensive\\napproach to managing patients with lupus72 The assessor\\nneeds to de\\ufb01ne activity accurately distinguishing clinical\\nfeatures d\"\n",
      "}\n",
      "{\n",
      " \"text\": \"REVIEW\\nT owards Precision Medicine in Systemic Lupus\\nErythematosus\\nThis article was published in the following Dove Press journal:\\nPharmacogenomics and Personalized Medicine\\nElliott Lever 1\\nMarta R Alves2\\nDavid A Isenberg 1\\n1Centre for Rheumatology, Division of\\nMedicine, University College Hospital\\nLondon, London, UK;2Internal Medicine,\\nDepartment of Medicine, Centro\\nHospitalar do Porto, Porto, Portugal\\nAbstract: Systemic lupus erythematosus (SLE) is a remarkable condition characterised by\\ndiversity amongst its clinical features and immunological abnormalities. In this review, we\\nattempt to capture the major immunological changes linked to the pathophysiology of lupus\\nand discuss the challenge it presents in moving towards the concept of precision medicine.\\nCurrently broadly similar types of drugs, e.g., steroids, immunosuppressives, hydroxychlor-\\noquine are used to treat many of the diverse clinical features of SLE. We suspect that, as the\\nprecise immunopathological abnormalities differ between the various organs/systems in\\nlupus patients, it will be some time before precision medicine can be fully applied to SLE.\\nKeywords: systemic lupus erythematosus, anti-DNA antibodies, lupus nephritis, lupus\\ngenetics, immunosuppression\\nIntroduction\\nSystemic lupus erythematosus (SLE) is a chronic, complex, auto-immune disease of\\nunknown origin with multiorgan involvement. Knowledge of its epidemiology,\\ngenetic susceptibility and pathophysiologic mechanisms has evolved, but it remains\\na disease with a highly variable course and considerable inter-individual variability.\\nIt has an unpredictable prognosis, depending mainly on the severity of the disease\\nactivity, organ damage and response to treatment. SLE is characterized by loss of\\nself-immune tolerance, production of self-reacting antibodies and formation of\\nimmune complexes (IC) that precipitate in tissues, causing chronic systemic in\\ufb02am-\\nmation and organ damage.1 It affects primarily women (90%) during childbearing\\nyears and \"\n",
      "}\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:08:41.114243Z",
     "start_time": "2024-12-01T19:08:28.547907Z"
    }
   },
   "cell_type": "code",
   "source": [
    "vc_rag_result = vc_rag.search(q, retriever_config={'top_k': 5}, return_context=True)\n",
    "print(f\"Vector + Cypher Response: \\n{vc_rag_result.answer}\")"
   ],
   "id": "2f1a56ab4a763c8e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vector + Cypher Response: \n",
      "Here is a detailed summary of systemic lupus erythematosus (SLE) based on the provided context:\n",
      "\n",
      "Common Effects:\n",
      "1. Multi-organ involvement\n",
      "2. Skin disease\n",
      "3. Kidney disease/renal involvement\n",
      "4. Neuropsychiatric involvement\n",
      "5. Hemolytic anemia\n",
      "6. Joint pain\n",
      "7. Chronic pain\n",
      "8. Fatigue\n",
      "9. Cardiovascular events/increased cardiovascular risk\n",
      "10. Damage accrual\n",
      "11. Increased mortality\n",
      "\n",
      "Biomarkers:\n",
      "1. Anti-dsDNA antibodies\n",
      "2. Anti-Sm antibodies  \n",
      "3. Antinuclear antibody (ANA)\n",
      "4. Anti-SSA antibodies\n",
      "5. Anticardiolipin antibodies\n",
      "6. Anti-β2-glycoprotein I antibodies\n",
      "7. Anti-NMDAR antibodies\n",
      "8. Anti-RibP antibodies\n",
      "9. IFI44L promoter methylation\n",
      "10. Proteinuria\n",
      "11. Urinary casts\n",
      "12. White blood cell count\n",
      "13. Platelet count\n",
      "14. Cardiac troponin T\n",
      "15. IgG-anticardiolipin antibodies\n",
      "16. L-valine\n",
      "17. Pyrimidine  \n",
      "18. Erucamide\n",
      "19. L-leucine\n",
      "\n",
      "Treatments:\n",
      "1. First-line:\n",
      "   - Hydroxychloroquine\n",
      "   - Corticosteroids (e.g. prednisolone)\n",
      "\n",
      "2. Second-line:\n",
      "   - Methotrexate\n",
      "   - Azathioprine\n",
      "   - Mycophenolate\n",
      "\n",
      "3. Third-line:\n",
      "   - Belimumab\n",
      "   - Rituximab\n",
      "   - Cyclophosphamide\n",
      "\n",
      "4. Other:\n",
      "   - Topical steroids (for mild disease)\n",
      "   - Low-dose oral steroids (for mild disease)\n",
      "   - Anifrolumab (potential treatment)\n",
      "   - Atacicept (potential treatment)\n",
      "\n",
      "5. Non-pharmacological:\n",
      "   - Patient education and self-management\n",
      "   - Monitoring with patient-reported outcomes (PROs)\n",
      "\n",
      "The treatment approach is based on disease severity and organ involvement. Management aims to control disease activity, prevent damage, minimize treatment toxicity, and improve quality of life.\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:08:51.973157Z",
     "start_time": "2024-12-01T19:08:51.970260Z"
    }
   },
   "cell_type": "code",
   "source": [
    "vc_ls = vc_rag_result.retriever_result.items[0].content.split('\\\\n---\\\\n')\n",
    "for i in vc_ls:\n",
    "    if \"biomarker\" in i: print(i)"
   ],
   "id": "8b10fdab707a0d4a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anticardiolipin Antibodies - BIOMARKER_FOR(Anticardiolipin antibodies as biomarker for SLE) -> Systemic Lupus Erythematosus\n",
      "Anti-β2-glycoprotein I Antibodies - BIOMARKER_FOR(Anti-β2-glycoprotein I antibodies as biomarker for SLE) -> Systemic Lupus Erythematosus\n",
      "SLE - PUBLISHED_IN(Research about SLE biomarkers) -> Biomolecules\n",
      "anti-RibP antibodies - BIOMARKER_FOR(Specific biomarker for diagnosing SLE) -> SLE\n"
     ]
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:09:09.883654Z",
     "start_time": "2024-12-01T19:09:09.880719Z"
    }
   },
   "cell_type": "code",
   "source": [
    "vc_ls = vc_rag_result.retriever_result.items[0].content.split('\\\\n---\\\\n')\n",
    "for i in vc_ls:\n",
    "    if \"treat\" in i: print(i)"
   ],
   "id": "7f1510ef53a9d253",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Record info=\"=== text ===\\nscular risk.\\nOur approach as detailed in Table 1 does not help in\\ndistinguishing subgroups of patients for methotrexate or\\nazathioprine. The molecular tools for this are on their way\\nto the clinic. In rheumatoid arthritis gene signature has\\nbeen shown to assist response prediction.77\\nIn the current treatment of lupus longterm clinical and\\nimmunological remission is only achieved in a proportion\\nof patients. The inherent pathophysiological complexity of\\nlupus described at the cellular and molecular level cannot\\nbe overstated. Using targeted drug therapy and improving\\nclinical assessment remains vital to improving disease\\ncontrol and patient outcomes.\\nThe Challenge of Moving T owards\\nPrecision Medicine in SLE\\nAs the foregoing text has conﬁrmed the pathophysiological\\naspects of SLE are highly diverse and the combination of\\nfactors leading to say skin, kidney and renal disease are very\\ndifferent. In spite of these differences, a rather restricted set of\\nTable 2T reatment Based on the Organ/Systemic Involved\\nExample 1 2 3 4 5 6 7 8\\nConstitutional\\nSymptoms\\nWidespread\\nDiscoid Lupus\\nErythematosus\\nPolyarthritis Thrombo-\\ncytopenia\\nLupus APS Arterial\\nThrombolysis\\nMononeuritis\\nMultiplex\\nNephritis\\nIII/IV\\nNephritis V\\n1st Line HCQ/CS/ HCQ + CS HCQ + CS CS +\\nHCQ\\nWarfarin/LMWH CS + IV Cyclo CS + MMF CS + MMF\\nImmunomodulation\\n2nd Line MMF ADD AZA ADD MTX AZA/MMF ADD aspirin/\\nDipyridamole/Platelet\\naggregation inhibitor\\nADD Rituximab\\nor IVIG/PE\\nIV Cyclo → AZA/IV\\nCyclo or\\nRituximab\\n3rd Line Rituximab/\\nBelimumab\\nAZA → MTX/\\nBenlysta\\nBenlysta/\\nRituximab\\nRituximab/\\nIV Cyclo/\\nIVIG\\n/ / ADD\\nRituximab\\n/\\nNote: This table is based on the authors’ own experience.\\nAbbreviations: CS, Corticosteroids; HCQ, Hydroxychloroquine, MTX, methotrexate; AZA, azathioprine; MMF , mycophenolate; Cyclo, Cyclophosphamide; LMWH, low\\nmolecular weight heparin; PE, plasma exchange.\\nDovepress Lever et al\\nPharmacogenomics and Personalized Medicine 2020:13 submit your manuscript| www.dovepress.com\\nDovePress\\n45\\ndrugs ster\n",
      " by \\nthe doctor, a dedicated nurse.   \\nSystemic Lupus Erythematosus (SLE) is a chronic inflammatory systemic disease characterized by multi-organ involvement and a \\ncomplex clinical picture, with a wide range of manifestations of varying severity and an unpredictable relapsing and remitting course \\n[ 1 , 2 ]. Due to its complexity and heterogeneity, SLE is not a single-target disease, and many aspects must be taken into consideration in \\nthe management of the disease: controlling disease activity, preventing damage accrual, minimizing treatment-related toxicities, and \\nimproving patients ’ quality of life (QoL). \\nSince when the principle of “ treat to target ” (T2T) has been applied in SLE [ 3 ], many efforts have been made to define targets in \\nSLE. Over the last few years, the DORIS definition of remission [ 4 , 5 ] and the LLDAS definition of Lupus Low Disease Activity [ 6 ] have \\nemerged as the most meaningful treatment targets, after multiple validation studies in large independent cohorts. \\nThe physicians ’ view on lupus dominated the development of remission criteria and it was postulated that a control of disease \\nactivity would improve QoL in SLE, although the definitions of remission and LLDAS do not address Health-Related Quality of Life \\n(HRQoL) or disease burden. \\nWhile some studies have demonstrated an association between a stable condition of remission or LLDAS and better HRQoL in SLE \\npatients, this may sometimes prove to be a weak association [ 7 ]. \\nThe disease imposes a great burden on patient ’ s lives and 35 – 50% of patients with SLE perceive their health as “ fair/not so good ” or \\n* Corresponding author. Rheumatology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Via Roma 67, Pisa, Italy. \\nE-mail address: marta.mosca@unipi.it (M. Mosca).  \\nContents lists available at ScienceDirect \\nBest Practice & Research Clinical Rheumatology \\njournal homepag e: www.else vierhealth.c om/berh \\nhttps://doi.org/10.1016/j.berh.2024.\n",
      "les Lupus Assessment Group\\n(BILAG) scoring system provides a comprehensive\\napproach to managing patients with lupus72 The assessor\\nneeds to deﬁne activity accurately distinguishing clinical\\nfeatures due to activity, distinct from previous damage or\\ncomplications of therapy to capture active disease cor-\\nrectly. The multimodal facets of the BILAG allow classi-\\nﬁcation of mild, moderate and severe disease. Mild disease\\nincludes non-life threatening organ involvement, equiva-\\nlent to SLEDAI <6 with one or more grade C scores or\\na single grade B. Treatment may include topical steroid,\\nlow dose oral steroid and/or hydroxychloroquine. In con-\\ntrast, patients with moderate disease require stronger sup-\\npression of the immune system and would consistently\\nhave BILAG scores of 2 Bs or more. The most severe\\ndisease BILAG A implies high dose steroid (≥20mg/day;\\nintravenous or oral, and other immunosuppressive agents.\\nOther therapies outlined in Table 2 (which designates\\ntreatments according to disease severity) include anticoa-\\ngulation or antiplatelet therapy in antiphospholipid patients\\nand the use of rituximab or cyclophosphamide as second-\\nline treatments to induce remission.\\nPatients with skin-limited disease usually respond to non-\\naggressive treatment including topical and oral steroids and\\nhydroxychloroquine. We recently reported on the use of ritux-\\nimab in severe cutaneous lupus73 and showed that some types\\nof subcutaneous lupus [e.g., acute cutaneous LE] respond\\nT able 1SLE T reatment Based on Severity of the Disease\\nMild Disease Moderate Disease Severe Disease\\n[BILAG C Scores/Single B/SLEDAI≤6] 2 or more BILAG Bs/SLEDAI 6-12 1 or more BILAG A/SLEDAI ≥ 12\\nFeatures Fatigue, malar rash, diffuse alopecia, myalgia,\\nplatelets 50– 149 x 109/L\\nFever, rash (< 2/9 body surface area),\\ncutaneous vasculitis, renal, pleurisy,\\npericarditis, platelets 25– 49 x 109/L\\nRash > 2/9 body surface area, severe pleurisy/\\npericarditis, psychosis, renal/myositis/platelets\\n<2 5x1 09/L\\nT ypica\n",
      "t your manuscript| www.dovepress.com\\nDovePress\\n43\\nIntestinal Microbiota and SLE\\nThere have been recent advances in understanding the\\nmicrobiome in lupus. SLE and Sjogrens patients have\\nbeen found to have differential gut microbiota.66,67 Work\\non mice have suggested the gut is leaky in lupus like\\ndisease models.68 An enterococcus was found translocated\\nto the liver and triggering interferon expression and anti-\\ndsDNA antibodies.68 Hyper-responsiveness to gut ﬂora\\noccurs in a mouse model that has overexpression of\\nTLR4 and develops lupus. Oestrogen receptor deﬁciency\\nimpairs TLR4 signaling and reduces activity in lupus\\nprone mice.68 In MRL/lpr mice lactobacillus species are\\nreduced and supplementation with lactobacillus caused\\nincreased gut mucosal barrier, reduced gut inﬂammation\\nand nephritis.69 Bacterial gut commensals have been found\\nto produce a variant of Ro60 antigen which stimulates\\nimmune cells.70 Diet modiﬁes the gut withﬁsh oils redu-\\ncing proin ﬂammatory cytokines. Polyunsaturated fatty\\nacids and polyphenols ingestion are associated with bene-\\nﬁcial changes to microorganisms present in the gastroin-\\ntestinal tract.71 Septrin, tetracylines and pencillins can\\ntrigger lupus. The suspicion remains that the gut ﬂora\\nchanges can interplay with immune regulatory cells such\\nas Tregs.\\nCurrent Treatment of Lupus\\nThe clinical assessment of patients dictates the manage-\\nment. Diagnostic tools help to deﬁne the affected organ\\nsystem. The condition is heterogeneous and can present in\\nunusual ways. Most patients are given hydroxychloroquine\\nand prednisolone accompanied by immunosuppressives. In\\nTable 1 we indicate the general outline for the manage-\\nment of SLE in the treatment of different organ and\\nsystems. This is adapted from BSR guideline on SLE.72\\nUse of the British Isles Lupus Assessment Group\\n(BILAG) scoring system provides a comprehensive\\napproach to managing patients with lupus72 The assessor\\nneeds to deﬁne activity accurately distinguishing clinical\\nfeatures d\n",
      "REVIEW\\nT owards Precision Medicine in Systemic Lupus\\nErythematosus\\nThis article was published in the following Dove Press journal:\\nPharmacogenomics and Personalized Medicine\\nElliott Lever 1\\nMarta R Alves2\\nDavid A Isenberg 1\\n1Centre for Rheumatology, Division of\\nMedicine, University College Hospital\\nLondon, London, UK;2Internal Medicine,\\nDepartment of Medicine, Centro\\nHospitalar do Porto, Porto, Portugal\\nAbstract: Systemic lupus erythematosus (SLE) is a remarkable condition characterised by\\ndiversity amongst its clinical features and immunological abnormalities. In this review, we\\nattempt to capture the major immunological changes linked to the pathophysiology of lupus\\nand discuss the challenge it presents in moving towards the concept of precision medicine.\\nCurrently broadly similar types of drugs, e.g., steroids, immunosuppressives, hydroxychlor-\\noquine are used to treat many of the diverse clinical features of SLE. We suspect that, as the\\nprecise immunopathological abnormalities differ between the various organs/systems in\\nlupus patients, it will be some time before precision medicine can be fully applied to SLE.\\nKeywords: systemic lupus erythematosus, anti-DNA antibodies, lupus nephritis, lupus\\ngenetics, immunosuppression\\nIntroduction\\nSystemic lupus erythematosus (SLE) is a chronic, complex, auto-immune disease of\\nunknown origin with multiorgan involvement. Knowledge of its epidemiology,\\ngenetic susceptibility and pathophysiologic mechanisms has evolved, but it remains\\na disease with a highly variable course and considerable inter-individual variability.\\nIt has an unpredictable prognosis, depending mainly on the severity of the disease\\nactivity, organ damage and response to treatment. SLE is characterized by loss of\\nself-immune tolerance, production of self-reacting antibodies and formation of\\nimmune complexes (IC) that precipitate in tissues, causing chronic systemic inﬂam-\\nmation and organ damage.1 It affects primarily women (90%) during childbearing\\nyears and \\n\\n=== kg_rels ===\\nAmerican College of Rheumatology - DESCRIBES(Developed classification criteria for disease) -> Systemic Lupus Erythematosus\n",
      "Belimumab - TREATS(Third line treatment) -> Systemic Lupus Erythematosus\n",
      "Belimumab - TREATS(Potential treatment for SLE) -> SLE\n",
      "Belimumab - TREATS(treatment for lupus) -> lupus\n",
      "Cyclophosphamide - TREATS(Third line treatment) -> Systemic Lupus Erythematosus\n",
      "Hydroxychloroquine - TREATS(First line treatment) -> Systemic Lupus Erythematosus\n",
      "Rituximab - TREATS(Third line treatment) -> Systemic Lupus Erythematosus\n",
      "Rituximab - PRESCRIBED(Used for treating lupus) -> Lupus\n",
      "Rituximab - TREATS(Potential treatment for SLE) -> SLE\n",
      "Methotrexate - TREATS(Second line treatment) -> Systemic Lupus Erythematosus\n",
      "Azathioprine - TREATS(Second line treatment) -> Systemic Lupus Erythematosus\n",
      "Mycophenolate - TREATS(Second line treatment) -> Systemic Lupus Erythematosus\n",
      "Mycophenolate - TREATS(Potential treatment for SLE) -> SLE\n",
      "Corticosteroids - TREATS(First line treatment) -> Systemic Lupus Erythematosus\n",
      "Benlysta - TREATS(FDA approved treatment) -> Lupus\n",
      "SLE - PRESCRIBED(Glucocorticoids used in SLE treatment) -> Glucocorticoids\n",
      "PROs - USED_FOR(Monitoring and treatment) -> SLE\n",
      "Anifrolumab - TREATS(Potential treatment for SLE) -> SLE\n",
      "Atacicept - TREATS(Potential treatment for SLE) -> SLE\n",
      "rituximab - TREATS(treatment for lupus) -> lupus\n"
     ]
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:09:23.848922Z",
     "start_time": "2024-12-01T19:09:15.435242Z"
    }
   },
   "cell_type": "code",
   "source": [
    "q = \"Can you summarize systemic lupus erythematosus (SLE)? including common effects, biomarkers, treatments, and current challenges faced by Physicians and patients? provide in list format with details for each item.\"\n",
    "print(f\"Vector Response: \\n{v_rag.search(q, retriever_config={'top_k': 5}).answer}\")"
   ],
   "id": "d03ec43f6f02f608",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vector Response: \n",
      "Based on the provided context, here is a summary of systemic lupus erythematosus (SLE) in list format:\n",
      "\n",
      "1. Common effects:\n",
      "   - Constitutional symptoms\n",
      "   - Discoid lupus erythematosus (skin involvement)\n",
      "   - Polyarthritis\n",
      "   - Thrombocytopenia\n",
      "   - Nephritis\n",
      "   - Mononeuritis multiplex\n",
      "\n",
      "2. Biomarkers:\n",
      "   - Proteinuria\n",
      "   - Urinary casts\n",
      "   - Hemolytic anemia\n",
      "   - Low white blood cell count\n",
      "   - Low platelet count\n",
      "   - Direct Coombs' test\n",
      "\n",
      "3. Treatments:\n",
      "   - Hydroxychloroquine\n",
      "   - Corticosteroids\n",
      "   - Immunosuppressives (e.g., methotrexate, azathioprine, mycophenolate)\n",
      "   - Cyclophosphamide\n",
      "   - Rituximab\n",
      "   - Belimumab\n",
      "   - Anticoagulation or antiplatelet therapy for antiphospholipid syndrome\n",
      "\n",
      "4. Current challenges faced by physicians and patients:\n",
      "   - Difficulty in diagnosis due to heterogeneous presentation\n",
      "   - Suboptimal sensitivity and specificity of current biomarkers\n",
      "   - High level of physician skill and experience required for diagnosis\n",
      "   - Limited symptoms in early stages of disease\n",
      "   - Diverging concerns between patients and doctors during consultations\n",
      "   - Different perspectives on the concept of \"treatment\"\n",
      "   - Difficulty in addressing sensitive topics during consultations\n",
      "   - Need for a more holistic approach to health solutions\n",
      "\n",
      "Note: This summary is based solely on the information provided in the given context and does not include any speculative or additional information.\n"
     ]
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T19:09:33.933930Z",
     "start_time": "2024-12-01T19:09:23.851167Z"
    }
   },
   "cell_type": "code",
   "source": "print(f\"Vector + Cypher Response: \\n{vc_rag.search(q, retriever_config={'top_k': 5}).answer}\")",
   "id": "c7d66300af9eac12",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vector + Cypher Response: \n",
      "Here is a summary of systemic lupus erythematosus (SLE) based on the provided context:\n",
      "\n",
      "1. Common effects:\n",
      "   - Multi-organ involvement \n",
      "   - Skin disease\n",
      "   - Kidney disease\n",
      "   - Renal disease\n",
      "   - Neuropsychiatric involvement\n",
      "   - Fatigue\n",
      "   - Joint pain\n",
      "   - Chronic pain\n",
      "\n",
      "2. Biomarkers:\n",
      "   - Anti-dsDNA antibodies\n",
      "   - Antinuclear antibody (ANA)\n",
      "   - Anti-SSA antibodies  \n",
      "   - Anticardiolipin antibodies\n",
      "   - Smith antibody\n",
      "   - Complement proteins (C3, C4)\n",
      "   - Proteinuria\n",
      "   - Urinary casts\n",
      "   - White blood cell count\n",
      "   - Platelet count\n",
      "\n",
      "3. Treatments:\n",
      "   - First-line: Hydroxychloroquine, Corticosteroids\n",
      "   - Second-line: Methotrexate, Azathioprine, Mycophenolate\n",
      "   - Third-line: Belimumab, Rituximab, Cyclophosphamide\n",
      "   - Other: Antimalarials, Immunosuppressants\n",
      "\n",
      "4. Current challenges:\n",
      "   - Diagnosis can be challenging due to varied symptoms\n",
      "   - Sensitivity and specificity of current biomarkers are not ideal\n",
      "   - High levels of physician skill and experience required for diagnosis\n",
      "   - Few patients show clinical symptoms in early stages\n",
      "   - Communication gap between patients and clinicians\n",
      "   - Patients and doctors may have different expectations and concerns during consultations\n",
      "   - Managing quality of life impacts alongside disease activity\n",
      "   - Balancing treatment efficacy with side effects\n"
     ]
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "bfe5e9575b3f50a1"
  }
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