Group_30_Brightside_Health.ipynb

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        "2ffa532600c84663a1f8347184a76c1b": {
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/camilasalinasc/Group-30-Brightside-Health/blob/main/Group_30_Brightside_Health.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Initial setup"
      ],
      "metadata": {
        "id": "rCdubJ4q1cGe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install openai llama_index pyvis"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ubIpXsdUXbCc",
        "outputId": "de96f665-d7b5-47e6-a42f-2f8f6604c3da",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: striprtf, filetype, dirtyjson, tenacity, pypdf, mypy-extensions, marshmallow, jedi, typing-inspect, tiktoken, pyvis, llama-cloud, dataclasses-json, llama-index-legacy, llama-index-core, llama-parse, llama-index-readers-file, llama-index-llms-openai, llama-index-indices-managed-llama-cloud, llama-index-embeddings-openai, llama-index-readers-llama-parse, llama-index-multi-modal-llms-openai, llama-index-cli, llama-index-agent-openai, llama-index-program-openai, llama-index-question-gen-openai, llama_index\n",
            "  Attempting uninstall: tenacity\n",
            "    Found existing installation: tenacity 9.0.0\n",
            "    Uninstalling tenacity-9.0.0:\n",
            "      Successfully uninstalled tenacity-9.0.0\n",
            "Successfully installed dataclasses-json-0.6.7 dirtyjson-1.0.8 filetype-1.2.0 jedi-0.19.2 llama-cloud-0.1.5 llama-index-agent-openai-0.4.0 llama-index-cli-0.4.0 llama-index-core-0.12.2 llama-index-embeddings-openai-0.3.1 llama-index-indices-managed-llama-cloud-0.6.3 llama-index-legacy-0.9.48.post4 llama-index-llms-openai-0.3.2 llama-index-multi-modal-llms-openai-0.3.0 llama-index-program-openai-0.3.1 llama-index-question-gen-openai-0.3.0 llama-index-readers-file-0.4.0 llama-index-readers-llama-parse-0.4.0 llama-parse-0.5.15 llama_index-0.12.2 marshmallow-3.23.1 mypy-extensions-1.0.0 pypdf-5.1.0 pyvis-0.3.2 striprtf-0.0.26 tenacity-8.5.0 tiktoken-0.8.0 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItuOQbIIjMTx",
        "outputId": "17bd43cc-d326-424e-8c3f-085356433d30",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import the directory with all the PDF files"
      ],
      "metadata": {
        "id": "lKb-xS9ZoQpa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core import SimpleDirectoryReader\n",
        "import nest_asyncio\n",
        "\n",
        "nest_asyncio.apply()\n",
        "\n",
        "documents = SimpleDirectoryReader(\"/content/drive/Shareddrives/BrightSide/papers/\").load_data()"
      ],
      "metadata": {
        "id": "BJB_mvsGouwu",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set up schema-based extraction model"
      ],
      "metadata": {
        "id": "-UpiAIh5-cva"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary modules\n",
        "import openai\n",
        "from typing import Literal\n",
        "from llama_index.core.indices.property_graph import SchemaLLMPathExtractor, PropertyGraphIndex\n",
        "from llama_index.llms.openai import OpenAI\n",
        "from llama_index.embeddings.openai import OpenAIEmbedding\n",
        "import os\n",
        "\n",
        "# Set your OpenAI API key\n",
        "openai.api_key = \"API_KEY_HERE\"  # Replace with your OpenAI API key\n",
        "\n",
        "# Define models and temperature settings\n",
        "LLM_MODEL = \"gpt-4o-mini\"\n",
        "TEMPERATURE = 0.3\n",
        "EMBEDDING_MODEL = \"text-embedding-ada-002\"\n",
        "\n",
        "# Define entities and relations for schema-based knowledge extraction\n",
        "entities = Literal[\"CONDITION\", \"SYMPTOM\", \"TREATMENT\", \"SIDE EFFECT\"]\n",
        "relations = Literal[\"CAUSES\", \"TREATS\", \"TARGETS\", \"INTERACTS WITH\", \"RECOMMENDED FOR\", \"IS COMORBID WITH\"]\n",
        "\n",
        "schema = {\n",
        "    \"CONDITION\": [\"CAUSES\", \"IS COMORBID WITH\"],\n",
        "    \"SYMPTOM\": [],\n",
        "    \"TREATMENT\": [\"CAUSES\", \"TREATS\", \"TARGETS\", \"INTERACTS WITH\", \"RECOMMENDED FOR\"],\n",
        "    \"SIDE EFFECT\": [\"CAUSES\"],\n",
        "}\n",
        "\n",
        "# Initialize Schema-based extractor\n",
        "schema_kg_extractor = SchemaLLMPathExtractor(\n",
        "    llm=OpenAI(model=LLM_MODEL, temperature=TEMPERATURE),\n",
        "    possible_entities=entities,\n",
        "    possible_relations=relations,\n",
        "    kg_validation_schema=schema,\n",
        "    strict=True,\n",
        ")\n",
        "\n",
        "# Assuming `documents` is already a list of extracted text or document objects\n",
        "# Initialize the PropertyGraphIndex with the schema-based extractor\n",
        "schema_index = PropertyGraphIndex.from_documents(\n",
        "    documents,  # replace `documents` with your actual document list or extracted text\n",
        "    embed_model=OpenAIEmbedding(model_name=EMBEDDING_MODEL),\n",
        "    show_progress=True,\n",
        "    kg_extractors=[schema_kg_extractor],\n",
        ")"
      ],
      "metadata": {
        "id": "tDmbiiVbn9FU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123,
          "referenced_widgets": [
            "d6ddb960164d46928045e982be178617",
            "41f7ca78cfe84a17820437ebaf0d0953",
            "c47eff148d3a423ea8815bfdcb64ee60",
            "7fbe9ee61ece48c0a6bf9854d6f6e468",
            "c4e43a15de8344b89fb668671e1bece8",
            "eb62dc67f24642b59eb1716383ee65da",
            "b9c38b1adc3845f48f6e8f6f1c71fe2d",
            "0b199e7c6c17491495ffafc90cef974e",
            "c21c1e6ce19a47abac16470c837ca8ec",
            "1020d2d0eeae4ca7ba6364d85ab8b608",
            "6e800234033c457c98af404927ce1a67"
          ]
        },
        "outputId": "03a569bf-a3b2-4018-dc61-670248ecb6fd",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing nodes:   0%|          | 0/42 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d6ddb960164d46928045e982be178617"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting paths from text with schema: 100%|██████████| 103/103 [03:21<00:00,  1.96s/it]\n",
            "Generating embeddings: 100%|██████████| 2/2 [00:01<00:00,  1.07it/s]\n",
            "Generating embeddings: 100%|██████████| 14/14 [00:02<00:00,  5.10it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set up free-form extraction model"
      ],
      "metadata": {
        "id": "fd2icjF-Nq9g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core.indices.property_graph import SimpleLLMPathExtractor\n",
        "\n",
        "# Create free form extractor\n",
        "free_form_kg_extractor = SimpleLLMPathExtractor(\n",
        "    llm=OpenAI(model=LLM_MODEL, temperature=TEMPERATURE)\n",
        ")\n",
        "\n",
        "# Initialize the PropertyGraphIndex with documents and extractor\n",
        "free_form_index = PropertyGraphIndex.from_documents(\n",
        "    documents,\n",
        "    embed_model=OpenAIEmbedding(model_name=EMBEDDING_MODEL),\n",
        "    show_progress=True,\n",
        "    kg_extractors=[free_form_kg_extractor],\n",
        ")"
      ],
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123,
          "referenced_widgets": [
            "b1f2609dac6642768bbd5711dec30720",
            "3892fa2ce9a6483e985774d054c86cf3",
            "8ff16587c10f4754aec096a1066cc535",
            "f57dd1c5d1774603a6328280ea99d3c5",
            "aa32d54772f4454688fd90b4a995ef4e",
            "d7712d88cc5d45a4829c49e8b29b1ab5",
            "76a6691b61cf4bf9b7351b902ebd4137",
            "be28f147895b42ee8a1b520c6021bcd9",
            "f32e12f2615b48239a0746d25ada2dc2",
            "5468d1ab55404422bf923ec4e6f002ce",
            "2ffa532600c84663a1f8347184a76c1b"
          ]
        },
        "id": "bntL-pTpBnTC",
        "outputId": "1bc4aafb-5e23-4c93-fd7c-18ea4d99dc12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Parsing nodes:   0%|          | 0/42 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b1f2609dac6642768bbd5711dec30720"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting paths from text: 100%|██████████| 103/103 [01:12<00:00,  1.42it/s]\n",
            "Generating embeddings: 100%|██████████| 2/2 [00:01<00:00,  1.18it/s]\n",
            "Generating embeddings: 100%|██████████| 20/20 [00:04<00:00,  4.59it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Save both model's knowledge graphs in html files"
      ],
      "metadata": {
        "id": "Nt7oO6q5NwKd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path_output_storage = \"/content/drive/Shareddrives/BrightSide//knowledge_graphs\"\n",
        "kw_extractor_names = [\"schema_kg_extractor\", \"free_form_kg_extractor\"]\n",
        "\n",
        "# Set up the output storage path for all models\n",
        "path_output_storage_kg_extractor_schema = f\"{path_output_storage}/{kw_extractor_names[0]}/\"\n",
        "if not os.path.exists(path_output_storage_kg_extractor_schema):\n",
        "  os.makedirs(path_output_storage_kg_extractor_schema)\n",
        "path_output_storage_kg_extractor_free_form = f\"{path_output_storage}/{kw_extractor_names[1]}/\"\n",
        "if not os.path.exists(path_output_storage_kg_extractor_free_form):\n",
        "  os.makedirs(path_output_storage_kg_extractor_free_form)\n",
        "\n",
        "# Persist the indexes\n",
        "schema_index.storage_context.persist(persist_dir=path_output_storage_kg_extractor_schema)\n",
        "free_form_index.storage_context.persist(persist_dir=path_output_storage_kg_extractor_free_form)\n",
        "\n",
        "# Save the knowledge graphs as a NetworkX graph to a file\n",
        "schema_index.property_graph_store.save_networkx_graph(name=f\"{path_output_storage}/schema_knowledge_graph.html\")\n",
        "free_form_index.property_graph_store.save_networkx_graph(name=f\"{path_output_storage}/free_form_knowledge_graph.html\")"
      ],
      "metadata": {
        "id": "-GRuLedPLajC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Testing the query on the API without uploading the documents"
      ],
      "metadata": {
        "id": "6CZqmugFmVy8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "\n",
        "client = openai.OpenAI(\n",
        "    api_key=\"API_KEY_HERE\",\n",
        ")\n",
        "modeltype = 'gpt-4o-mini'\n",
        "\n",
        "# Define the assistant and thread content without any files\n",
        "assistant_name = \"Research Assistant\"\n",
        "instructions = \"\"\"\n",
        "You are an expert clinician that is using the provided data to give treatment advice related to depression and anxiety.\n",
        "\"\"\"\n",
        "content = \"\"\"\n",
        "Treating patients with anxious depression poses challenges due to the potential for poorer treatment outcomes with antidepressant monotherapy. Patients with anxious depression may have a higher physical illness burden, lower socioeconomic status, greater severity of depression, and later onset of depression, which can contribute to a more difficult treatment process. In terms of medication performance, for nonanxious depression, citalopram, venlafaxine, sertraline, and bupropion have been identified as effective treatments. On the other hand, for anxious depression, the remission rates with bupropion, sertraline, and venlafaxine were lower compared to nonanxious depression, indicating a difference in treatment response between the two groups.\n",
        "\"\"\"\n",
        "\n",
        "# Create the assistant\n",
        "assistant = client.beta.assistants.create(\n",
        "    name=assistant_name,\n",
        "    instructions=instructions,\n",
        "    model=modeltype,\n",
        "    tools=[]\n",
        ")\n",
        "\n",
        "# Create a thread without attaching any files\n",
        "thread = client.beta.threads.create(\n",
        "    messages=[\n",
        "        {\n",
        "            \"role\": \"user\",\n",
        "            \"content\": content,\n",
        "        }\n",
        "    ]\n",
        ")\n",
        "\n",
        "# Run and get the assistant's answer to your message\n",
        "run = client.beta.threads.runs.create_and_poll(\n",
        "    thread_id=thread.id, assistant_id=assistant.id\n",
        ")\n",
        "\n",
        "messages = list(client.beta.threads.messages.list(thread_id=thread.id, run_id=run.id))\n",
        "message_content = messages[0].content[0].text\n",
        "\n",
        "# Process and print the response content\n",
        "annotations = message_content.annotations\n",
        "citations = []\n",
        "for index, annotation in enumerate(annotations):\n",
        "    message_content.value = message_content.value.replace(annotation.text, \"\").replace(\".\", \"\")\n",
        "    if file_citation := getattr(annotation, \"file_citation\", None):\n",
        "        cited_file = client.files.retrieve(file_citation.file_id)\n",
        "        citations.append(f\"[{index}] {cited_file.filename}\")\n",
        "\n",
        "# Final output\n",
        "cand_name = message_content.value.replace(\".\", \"\").strip()\n",
        "print(cand_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SG0tahk0mZSG",
        "outputId": "c468eff7-8c50-456a-84e8-9312199dcbe1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "When treating patients with anxious depression, it is crucial to adopt a tailored approach due to the complex interplay of anxiety and depressive symptoms, which can affect treatment efficacy Here are some considerations and strategies based on the information provided:\n",
            "\n",
            "1 **Combination Therapy**: Given that antidepressant monotherapy may not be as effective for anxious depression, consider a combination of antidepressants and anxiolytics (eg, SSRIs like sertraline or venlafaxine with benzodiazepines or buspirone, if appropriate) to address both depressive and anxiety symptoms\n",
            "\n",
            "2 **Prioritize Response Over Remission**: Since remission rates for certain antidepressants like bupropion, sertraline, and venlafaxine are lower in anxious depression, focus on managing symptoms and improving overall functioning rather than solely aiming for complete remission \n",
            "\n",
            "3 **Medication Selection**: \n",
            "   - **SSRIs like Sertraline and Escitalopram**: These are often first-line treatments and have been shown to be effective in generalized anxiety disorder as well as depression\n",
            "   - **SNRIs like Venlafaxine**: They may be beneficial for patients with significant anxiety symptoms alongside depression \n",
            "   - **Bupropion**: While effective for nonanxious depression, its lower remission rates in anxious depression suggest careful consideration or avoided use in certain cases, particularly in patients with significant anxiety symptoms\n",
            "\n",
            "4 **Monitor Side Effects**: Be vigilant about potential side effects of the chosen medications, especially those that may exacerbate anxiety (eg, increased jitteriness with some SSRIs) Adjust dosages or consider switching medications if side effects negatively impact the treatment progress\n",
            "\n",
            "5 **Psychotherapy Options**: Encourage psychotherapy, such as cognitive-behavioral therapy (CBT), which can be particularly beneficial in treating both anxiety and depression, providing patients with skills to manage symptoms effectively\n",
            "\n",
            "6 **Lifestyle Interventions**: Integrating lifestyle modifications, such as regular physical exercise, healthy nutrition, and adequate sleep, can enhance treatment outcomes and reduce symptom severity\n",
            "\n",
            "7 **Regular Monitoring**: Implement regular follow-up appointments to monitor treatment response, side effects, and make adjustments as needed Evaluate both depressive and anxiety symptoms systematically\n",
            "\n",
            "8 **Consider Patient Factors**: Take into account the patient’s physical health, socioeconomic status, and support systems, as these can impact treatment adherence and outcomes\n",
            "\n",
            "Overall, treating anxious depression requires a comprehensive approach that addresses both the psychological and potential physiological aspects of the condition Collaborative care involving medication, psychotherapy, and lifestyle adjustments is likely the best strategy for achieving improved patient outcomes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Testing the query on the API with uploading the documents"
      ],
      "metadata": {
        "id": "qv31qv8-jV6P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "\n",
        "client = openai.OpenAI(\n",
        "    api_key=\"API_KEY_HERE\",\n",
        ")\n",
        "modeltype = 'gpt-4o-mini'\n",
        "\n",
        "\n",
        "assistant_name = \"Research Assistant\"\n",
        "\n",
        "\n",
        "instructions = \"\"\n",
        "\"\"\"\n",
        "You are an expert clinician that is using the uploaded documents to give treatment advice related to depression and anxiety.\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "content = \"\"\"\n",
        "What are the challenges in treating patients with anxious depression compared to nonanxious depression, and how do different medications perform across these groups?\n",
        "\"\"\"\n",
        "\n",
        "# Define a list of file paths you want to upload\n",
        "file_paths = [\n",
        "    \"/content/drive/Shareddrives/BrightSide/papers/WJCC-9-9350.pdf\",\n",
        "    \"/content/drive/Shareddrives/BrightSide/papers/100-Papers-in-Clinical-Psychiatry-Depressive-Disorders-Comparative-efficacy-and-acceptability-of-12-new-generation-antidepressants-a-multiple-treatments-meta-analysis.pdf\",\n",
        "    \"/content/drive/Shareddrives/BrightSide/papers/fava-et-al-2008-difference-in-treatment-outcome-in-outpatients-with-anxious-versus-nonanxious-depression-a-star_d-report.pdf\"\n",
        "]\n",
        "\n",
        "# Create the assistant\n",
        "assistant = client.beta.assistants.create(\n",
        "    name=assistant_name,\n",
        "    instructions=instructions,\n",
        "    model=modeltype,\n",
        "    tools=[{\"type\": \"file_search\"}]\n",
        ")\n",
        "\n",
        "# Upload each file and store the file IDs\n",
        "attachments = []\n",
        "for path in file_paths:\n",
        "    message_file = client.files.create(\n",
        "        file=open(path, \"rb\"),\n",
        "        purpose=\"assistants\"\n",
        "    )\n",
        "    attachments.append({\n",
        "        \"file_id\": message_file.id,\n",
        "        \"tools\": [{\"type\": \"file_search\"}]\n",
        "    })\n",
        "\n",
        "# Create a thread and attach all files to the message\n",
        "thread = client.beta.threads.create(\n",
        "    messages=[\n",
        "        {\n",
        "            \"role\": \"user\",\n",
        "            \"content\": content,\n",
        "            # Attach the files to the message.\n",
        "            \"attachments\": attachments\n",
        "        }\n",
        "    ]\n",
        ")\n",
        "\n",
        "# Run and get the assistant's answer\n",
        "run = client.beta.threads.runs.create_and_poll(\n",
        "    thread_id=thread.id, assistant_id=assistant.id\n",
        ")\n",
        "\n",
        "messages = list(client.beta.threads.messages.list(thread_id=thread.id, run_id=run.id))\n",
        "message_content = messages[0].content[0].text\n",
        "\n",
        "# Process and print the response content\n",
        "annotations = message_content.annotations\n",
        "citations = []\n",
        "for index, annotation in enumerate(annotations):\n",
        "    message_content.value = message_content.value.replace(annotation.text, \"\").replace(\".\", \"\")\n",
        "    if file_citation := getattr(annotation, \"file_citation\", None):\n",
        "        cited_file = client.files.retrieve(file_citation.file_id)\n",
        "        citations.append(f\"[{index}] {cited_file.filename}\")\n",
        "\n",
        "# Final output\n",
        "cand_name = message_content.value.replace(\".\", \"\").strip()\n",
        "print(cand_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAt5nyLHjTMo",
        "outputId": "a390b119-c9f5-4a68-fb7d-32f8f5c2c535"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Treating patients with anxious depression presents specific challenges when compared to those with nonanxious depression, as evidenced by findings from the STAR*D study\n",
            "\n",
            "### Challenges in Treatment\n",
            "\n",
            "1 **Poorer Treatment Outcomes**:\n",
            "   Patients with anxious depression tend to have a significantly lower likelihood of achieving remission from depression compared to those without anxiety symptoms For instance, in the STAR*D study, patients with anxious depression had a remission rate of 222%, compared to 334% for nonanxious depression Additionally, these patients often take longer to achieve remission and experience more severe depressive symptoms\n",
            "\n",
            "2 **Higher Rates of Side Effects**:\n",
            "   Anxious depression is associated with a greater intensity and burden of side effects from antidepressant treatment Patients often report more severe side effects and a higher incidence of serious adverse events, leading to difficulties in adhering to treatment\n",
            "\n",
            "3 **Comorbid Conditions**:\n",
            "   Individuals with anxious depression frequently have comorbid anxiety disorders, which complicates treatment approaches These comorbidities can exacerbate depressive symptoms and further hinder effective treatment\n",
            "\n",
            "4 **Sensitivity to Side Effects**:\n",
            "   Patients with anxious depression might be more sensitive to the physical side effects of medications This sensitivity can lead to increased treatment discontinuation rates due to intolerable side effects, further complicating the management of their condition\n",
            "\n",
            "5 **Functional Impairment**:\n",
            "   Patients with anxious depression often exhibit greater functional impairment and chronicity of illness compared to those with nonanxious depression, which can affect their overall response to treatment\n",
            "\n",
            "### Performance of Different Medications\n",
            "\n",
            "When examining the effectiveness of various medications, results indicate that anxious depression patients typically fare worse:\n",
            "\n",
            "1 **First-Line Treatment**:\n",
            "   The STAR*D study showed that patients with anxious depression had lower response rates to citalopram, the initial treatment, compared to nonanxious patients \n",
            "\n",
            "2 **Switching and Augmentation Strategies**:\n",
            "   In the second phase of the study (Level 2), patients with anxious depression who switched to medications like bupropion, sertraline, or venlafaxine also demonstrated lower remission rates compared to nonanxious patients For example, only 102% of anxious depression patients achieved remission with sustained-release bupropion, versus 339% in nonanxious patients \n",
            "\n",
            "3 **Overall Efficacy**:\n",
            "   Overall, it was found that regardless of the treatment approach (switch or augmentation), those with anxious depression had lower rates of remission and response For instance, when augmented with bupropion, only 179% of anxious patients achieved remission compared to 367% of nonanxious patients\n",
            "\n",
            "### Conclusion\n",
            "\n",
            "In summary, the presence of anxiety in depressive disorders complicates both the treatment outcomes and tolerability of antidepressant medications Anxious depression is associated with a higher burden of side effects, lower remission rates, and a more complicated treatment landscape, necessitating careful management strategies tailored for these patients\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Retrieve responses using a vector context retriever on both models"
      ],
      "metadata": {
        "id": "xvE9CNQ1OB25"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.embeddings.openai import OpenAIEmbedding\n",
        "from llama_index.llms.openai import OpenAI\n",
        "\n",
        "from llama_index.core.indices.property_graph import VectorContextRetriever\n",
        "\n",
        "# Create a sub-retriever using VectorContextRetriever\n",
        "# This will use the property graph store and vector store from the loaded index\n",
        "# The embed_model parameter specifies the model to be used for embedding queries\n",
        "sub_retriever = VectorContextRetriever(\n",
        "schema_index.property_graph_store,\n",
        "  vector_store=schema_index.vector_store,\n",
        "  embed_model=OpenAIEmbedding(model_name=EMBEDDING_MODEL),\n",
        ")\n",
        "\n",
        "# Create a retriever from the index using the previously defined sub-retriever\n",
        "retriever = schema_index.as_retriever(sub_retrievers=[sub_retriever])\n",
        "# Initialize the query engine using the retriever\n",
        "# The query engine will use the retriever(s) to process and return responses to\n",
        "query_engine = schema_index.as_query_engine(\n",
        "    sub_retrievers=[retriever]\n",
        ")\n",
        "\n",
        "print(\n",
        "    query_engine.query(\"What are the long-term considerations for using a treatment like ketamine, and how often should I assess the patient for potential dependency or side effects?\").response\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e4qCpsXh82iy",
        "outputId": "b6d6f99d-f8e7-4b5b-f5f8-f1f861ea2015"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Long-term considerations for using a treatment like ketamine include defining the most effective dose, determining the optimal administration route, and establishing guidelines for therapeutic monitoring. It is important to carefully monitor patients for potential dependency and side effects regularly due to the associated risks of drug abuse and addiction.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.embeddings.openai import OpenAIEmbedding\n",
        "from llama_index.llms.openai import OpenAI\n",
        "\n",
        "from llama_index.core.indices.property_graph import VectorContextRetriever\n",
        "\n",
        "# Create a sub-retriever using VectorContextRetriever\n",
        "# This will use the property graph store and vector store from the loaded index\n",
        "# The embed_model parameter specifies the model to be used for embedding queries\n",
        "sub_retriever_free_form = VectorContextRetriever(\n",
        "free_form_index.property_graph_store,\n",
        "  vector_store=free_form_index.vector_store,\n",
        "  embed_model=OpenAIEmbedding(model_name=EMBEDDING_MODEL),\n",
        ")\n",
        "\n",
        "# Create a retriever from the index using the previously defined sub-retriever\n",
        "sub_retriever_free_form = free_form_index.as_retriever(sub_retrievers=[sub_retriever_free_form])\n",
        "# Initialize the query engine using the retriever\n",
        "# The query engine will use the retriever(s) to process and return responses to\n",
        "query_engine_free_form = free_form_index.as_query_engine(\n",
        "    sub_retrievers=[retriever]\n",
        ")\n",
        "\n",
        "print(\n",
        "    query_engine_free_form.query(\"Which antidepressants are associated with higher risks of severe side effects, particularly in patients with anxious depression?\").response\n",
        ")"
      ],
      "metadata": {
        "outputId": "9a967ba6-4370-4f50-b4aa-e7e0f7ca9892",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20vxc27tBwb-"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Monoamine oxidase inhibitors (MAOIs) are associated with higher risks of severe side effects, particularly in patients with anxious depression.\n"
          ]
        }
      ]
    }
  ]
}