RAG_with_Graph_Database.ipynb

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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m670.2/670.2 MB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: flash-attn in /usr/local/lib/python3.10/dist-packages (2.5.5)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from flash-attn) (2.1.0)\n",
            "Requirement already satisfied: einops in /usr/local/lib/python3.10/dist-packages (from flash-attn) (0.7.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from flash-attn) (23.2)\n",
            "Requirement already satisfied: ninja in /usr/local/lib/python3.10/dist-packages (from flash-attn) (1.11.1.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (4.9.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (2023.6.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.18.1 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (2.18.1)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (12.1.105)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn) (2.1.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->flash-attn) (12.3.101)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->flash-attn) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->flash-attn) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install -qU transformers\n",
        "!pip install -qU langchain\n",
        "!pip install -qU huggingface_hub\n",
        "!pip install -qU tiktoken\n",
        "!pip install -qU neo4j\n",
        "!pip install -qU python-dotenv\n",
        "!pip install -qU accelerate\n",
        "!pip install -qU sentence_transformers\n",
        "!pip install -qU  bitsandbytes\n",
        "!pip install -qU  optimum\n",
        "!pip install -qU unstructured unstructured[pdf]\n",
        "!pip install flash-attn --no-build-isolation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import re\n",
        "from langchain.vectorstores.neo4j_vector import Neo4jVector\n",
        "from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter\n",
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "from dotenv import load_dotenv"
      ],
      "metadata": {
        "id": "wu99tQMp8cYs"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Setting API in Environment Variable:**"
      ],
      "metadata": {
        "id": "ec1_j3db9-On"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()\n",
        "load_dotenv()\n",
        "os.environ[\"NEO4J_URI\"] = 'neo4j+s://d5dffe81.databases.neo4j.io'\n",
        "os.environ[\"NEO4J_USERNAME\"] = 'neo4j'\n",
        "os.environ[\"NEO4J_PASSWORD\"] = 'C8A_mt9s8yar3i44Xi1bVbkrFVK3aCXE1w5cQvHv6LM'\n",
        "os.environ['NEO4J_URL'] = \"bolt://server_ip:7687\"\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "TV8qynpS8ndS",
        "outputId": "ce43227b-a99f-4863-98a7-c4ac47624dd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348,
          "referenced_widgets": [
            "a5f9427673584f24b42922ebcf8714f4",
            "78051657194346bc99fb58409ed67870",
            "f716d2a99aae4bf4b09349c5d7fc695a",
            "2f2d4b6731c042cf8b1d64db40f6234a",
            "1d686d4c60474afd907b722e20af1452",
            "3ada03b7a08244a797a4178bbe935318",
            "5b26c881dd42490eb144e255ae685fcd",
            "8b78231bf8a94374b6cf468e9fa85929",
            "aecb1c13c7b4493ba613d690bdd2707a",
            "50b3cb1532554487b840a0a8539be7dc",
            "0516db4733d84ca1a04be0d90f3cbfa9",
            "ef0ef461e11a4c9f9c7106709388c9ea",
            "bc0abd1fc56e4b418d551936ccbf724d",
            "febb82295cce487abe735f9ecb72796e",
            "2dd16270e324409aa8a5e5f8d9464665",
            "202446995dd6467fab421b26fdd4189d",
            "e771c4cb03be41b2bf7c0ee82e255d5a"
          ]
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a5f9427673584f24b42922ebcf8714f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Loading Model in Notebook:**"
      ],
      "metadata": {
        "id": "k1HiUGtY-hVg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import cuda, bfloat16\n",
        "import transformers\n",
        "model_id = 'google/gemma-7b'\n",
        "device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'"
      ],
      "metadata": {
        "id": "Wb_P1tFB86E4"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# begin initializing HF items, you need an access token\n",
        "model_config = transformers.AutoConfig.from_pretrained(\n",
        "    model_id,\n",
        ")"
      ],
      "metadata": {
        "id": "8-5HISb4-qgq"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# BnB Configuration\n",
        "bnb_config = transformers.BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type='nf4',\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_compute_dtype=bfloat16\n",
        ")"
      ],
      "metadata": {
        "id": "C45XOIQEAVDz"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = transformers.AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    config=model_config,\n",
        "    device_map='auto',\n",
        "    quantization_config=bnb_config,\n",
        "    low_cpu_mem_usage=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "f73c306cce8d4be1aea3ad912e9ef5dc",
            "6f7ec6e81fbc4babab80f5b352caedc8",
            "6d830be2c25a4e499ed8f57758638e23",
            "50deede7561347e8af1e0b213d33c3de",
            "55b60bfe36b14fc6837959b6b9f56fac",
            "24db6f406a024e6f8c78ef83752fe336",
            "42e137910a8b45859ae3517d5bd0cb17",
            "fc1fe6d6b77145828e33cc228c694082",
            "93c80c4fcf01411e8f70d4c7daf23423",
            "52d69bc57f3c4299b9aad7c536735f99",
            "78de396d6b2d4ce384faf393b84b73a2",
            "88f08961299f438ea00dd88d61ec99d2",
            "4ab68fde0d5845e498b23536ee61e828",
            "476045c378f942a6b3066e6ffd8e93b6",
            "31faf6bbc65340f6ae0bae2b20f825f0",
            "d7a88f0f41e64fbb9eb04c208276d5e9",
            "26d4da72a16b4381aef9244f59c536b7",
            "d97e2daa67f8430fbe62fd64cf753f73",
            "9ba5c9837ffa48a1a98375cf8f7ea58c",
            "7b0b028a1df640d49ec7b6a3ea8ed5cd",
            "694328c260294586bf00c5f8904b26a2",
            "e444efde30f146efba56fa1d0bcee1fd"
          ]
        },
        "id": "JSb__D2C-uMx",
        "outputId": "2f34fe24-fc5d-46f4-d7b6-622ac0af285f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f73c306cce8d4be1aea3ad912e9ef5dc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "88f08961299f438ea00dd88d61ec99d2"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# How model looks like:\n",
        "model.eval()"
      ],
      "metadata": {
        "id": "nILHQLiu-zIs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = transformers.AutoTokenizer.from_pretrained(\n",
        "    model_id,\n",
        ")"
      ],
      "metadata": {
        "id": "HoHZiP3SFJHE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def bytes_to_giga_bytes(bytes):\n",
        "  return bytes / 1024 / 1024 / 1024"
      ],
      "metadata": {
        "id": "_725TDgc6oit"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bytes_to_giga_bytes(torch.cuda.max_memory_allocated())"
      ],
      "metadata": {
        "id": "-6YYkQq_6sgT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gc\n",
        "import torch\n",
        "\n",
        "def flush():\n",
        "  gc.collect()\n",
        "  torch.cuda.empty_cache()\n",
        "  torch.cuda.reset_peak_memory_stats()"
      ],
      "metadata": {
        "id": "G6J7mgxF7Jit"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "flush()"
      ],
      "metadata": {
        "id": "nnbBYqzc7ZBk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# List of strings representing stop signals or markers\n",
        "stop_list = ['\\nHuman:', '\\n```\\n']\n",
        "stop_token_ids = [tokenizer(x)['input_ids'] for x in stop_list]\n",
        "stop_token_ids"
      ],
      "metadata": {
        "id": "YTICB6xsGxw-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert token IDs to LongTensor objects\n",
        "import torch\n",
        "stop_token_ids = [torch.LongTensor(x).to(device) for x in stop_token_ids]\n",
        "stop_token_ids"
      ],
      "metadata": {
        "id": "7p6mgjI9HOcD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Stopping Criteria for Transformer Training:**"
      ],
      "metadata": {
        "id": "XNiM5KxWH9qV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import StoppingCriteria, StoppingCriteriaList\n",
        "\n",
        "# Define a custom stopping criteria class\n",
        "class StopOnTokens(StoppingCriteria):\n",
        "    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:\n",
        "        for stop_ids in stop_token_ids:\n",
        "            if torch.equal(input_ids[0][-len(stop_ids):], stop_ids):\n",
        "                return True\n",
        "        return False\n",
        "\n",
        "stopping_criteria = StoppingCriteriaList([StopOnTokens()])"
      ],
      "metadata": {
        "id": "UeHmT-GzHerh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Testing Huggingface Pipeline:**"
      ],
      "metadata": {
        "id": "4DRI1tSuOPpd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up text generation pipeline\n",
        "generate_text = transformers.pipeline(\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    return_full_text=True,\n",
        "    task='text-generation',\n",
        "    stopping_criteria=stopping_criteria,\n",
        "    temperature=0.3,\n",
        "    max_new_tokens=512,\n",
        "    repetition_penalty=1.1\n",
        ")"
      ],
      "metadata": {
        "id": "g3A-YXDjHrHu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result = generate_text(\"What are the primary mechanisms underlying antibiotic resistance, and how can we develop strategies to combat it?\")\n",
        "print(result)"
      ],
      "metadata": {
        "id": "uTRpa0_mInOA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.llms import HuggingFacePipeline\n",
        "\n",
        "llm = HuggingFacePipeline(pipeline=generate_text)\n",
        "llm(prompt=\"How can we enhance the specificity and efficiency of CRISPR/Cas9 gene-editing technology to minimize off-target effects and increase its potential for therapeutic applications?\")"
      ],
      "metadata": {
        "id": "Uh3unyirTtTa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Loading Document Data:**"
      ],
      "metadata": {
        "id": "kBNYzCfyOJRz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.document_loaders import DirectoryLoader\n",
        "loader = DirectoryLoader('/content/drive/MyDrive/BioMedical-Dataset', glob=\"**/*.pdf\")\n",
        "documents = loader.load()"
      ],
      "metadata": {
        "id": "knhECj2bOUkh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(documents))"
      ],
      "metadata": {
        "id": "T0Yp2a8Goeqh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "\n",
        "MARKDOWN_SEPARATORS = [\n",
        "    \"\\n#{1,6} \",\n",
        "    \"```\\n\",\n",
        "    \"\\n\\\\*\\\\*\\\\*+\\n\",\n",
        "    \"\\n---+\\n\",\n",
        "    \"\\n___+\\n\",\n",
        "    \"\\n\\n\",\n",
        "    \"\\n\",\n",
        "    \" \",\n",
        "    \"\",\n",
        "]\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500,\n",
        "                                               chunk_overlap=30,\n",
        "                                               add_start_index=True,\n",
        "                                               separators=MARKDOWN_SEPARATORS)\n",
        "\n",
        "processed_text_splits = text_splitter.split_documents(documents)"
      ],
      "metadata": {
        "id": "6Tznkr3PIawL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processed_text_splits[120].page_content"
      ],
      "metadata": {
        "id": "91wesl_N4yYm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(processed_text_splits))"
      ],
      "metadata": {
        "id": "El9WlHtHCiTv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating Embdeddings of the sentences and storing it into Graph DB\n",
        "from langchain_community.embeddings import HuggingFaceBgeEmbeddings\n",
        "\n",
        "model_name = \"BAAI/bge-base-en-v1.5\"\n",
        "model_kwargs = {\"device\": \"cuda\"}\n",
        "encode_kwargs = {\"normalize_embeddings\": True}\n",
        "embeddings = HuggingFaceBgeEmbeddings(\n",
        "    model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs\n",
        ")"
      ],
      "metadata": {
        "id": "KqOudIQtIayw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Load Neo4j Graph:**"
      ],
      "metadata": {
        "id": "WaGxQILaCwQv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.graphs import Neo4jGraph\n",
        "\n",
        "graph = Neo4jGraph(\n",
        "    url=os.environ[\"NEO4J_URI\"],\n",
        "    username=os.environ[\"NEO4J_USERNAME\"],\n",
        "    password=os.environ[\"NEO4J_PASSWORD\"]\n",
        ")"
      ],
      "metadata": {
        "id": "v__99eeVDVdB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Creating a new custom Index using Cypher:**"
      ],
      "metadata": {
        "id": "KasRmCh0bWJP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create New index with custom embedding model and dimensions\n",
        "# I have already created\n",
        "'''\n",
        "graph.query(\"\"\"\n",
        "CALL db.index.vector.createNodeIndex(\n",
        "  'KG-Enhanced-QnA-Biomedical',\n",
        "  'text_splits',\n",
        "  'embeddings',\n",
        "   768,\n",
        "   'cosine'\n",
        ")\n",
        "\"\"\")\n",
        "'''"
      ],
      "metadata": {
        "id": "Lhzqh0uKbVJu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Show Created Vector Index:**"
      ],
      "metadata": {
        "id": "z3ayEjLyHTAm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from neo4j import GraphDatabase\n",
        "uri = os.environ[\"NEO4J_URI\"]\n",
        "username = os.environ[\"NEO4J_USERNAME\"]\n",
        "password = os.environ[\"NEO4J_PASSWORD\"]\n",
        "\n",
        "driver = GraphDatabase.driver(uri, auth=(username, password))\n",
        "session = driver.session()\n",
        "\n",
        "result = session.run(\"SHOW VECTOR INDEXES\")\n",
        "\n",
        "for record in result:\n",
        "   print(record)"
      ],
      "metadata": {
        "id": "SFLExy9OxptZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "''' chunks = [{'text': document.page_content, 'embedding': embeddings.embed_query(document.page_content)}\n",
        "          for document in documents if len(document.page_content) >  50]  '''"
      ],
      "metadata": {
        "id": "ii7wVWOmJYBb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "graph.query(\"\"\"\n",
        "UNWIND $data AS row\n",
        "CREATE (c:Chunk {text: row.text})\n",
        "WITH c, row\n",
        "CALL db.create.setVectorProperty(c, 'embedding', row.embedding)\n",
        "YIELD node\n",
        "RETURN distinct 'done'\n",
        "\"\"\", {'data': chunks})\n",
        "'''"
      ],
      "metadata": {
        "id": "B0KQnPT8Lxqi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "vector_search = \"\"\"\n",
        "WITH $embedding AS e\n",
        "CALL db.index.vector.queryNodes('KG-Enhanced-QnA-Biomedical',$k, e) yield node, score\n",
        "RETURN node.text AS result\n",
        "ORDER BY score DESC\n",
        "LIMIT 3\n",
        "\"\"\"\n",
        "'''"
      ],
      "metadata": {
        "id": "0Tbwl91LjKd4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate Neo4j vector from documents\n",
        "neo4j_vector = Neo4jVector.from_documents(\n",
        "    processed_text_splits,\n",
        "    embeddings,\n",
        "    index_name='KG-Enhanced-QnA-Biomedical',\n",
        "     url=os.environ[\"NEO4J_URI\"],\n",
        "    username=os.environ[\"NEO4J_USERNAME\"],\n",
        "    password=os.environ[\"NEO4J_PASSWORD\"]\n",
        ")"
      ],
      "metadata": {
        "id": "HHjtYv6JDVfg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Performing Similarity Search\n",
        "query = \"How can we enhance the specificity and efficiency of CRISPR/Cas9 gene-editing technology to minimize off-target effects and increase its potential for therapeutic applications?\"\n",
        "vector_results = neo4j_vector.similarity_search(query, k=2)\n",
        "\n",
        "for i, res in enumerate(vector_results):\n",
        "    print(res.page_content)\n",
        "    if i != len(vector_results) - 1:\n",
        "        print()\n",
        "vector_result = vector_results[0].page_content"
      ],
      "metadata": {
        "id": "UAV-XTaHJIjT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains import GraphCypherQAChain\n",
        "from langchain.graphs import Neo4jGraph"
      ],
      "metadata": {
        "id": "Az7n09kuW4k4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains.base import Chain\n",
        "from langchain.chains.llm import LLMChain\n",
        "from langchain.chat_models import ChatOpenAI\n",
        "from langchain.chains.question_answering.stuff_prompt import CHAT_PROMPT\n",
        "from langchain.callbacks.manager import CallbackManagerForChainRun\n",
        "from typing import Any, Dict, List\n",
        "from pydantic import Field"
      ],
      "metadata": {
        "id": "uE_4FkZtMyaL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vector_search = \"\"\"\n",
        "WITH $embedding AS e\n",
        "CALL db.index.vector.queryNodes('KG-Enhanced-QnA-Biomedical',$k, e) yield node, score\n",
        "RETURN node.text AS result\n",
        "ORDER BY score DESC\n",
        "LIMIT 3\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "jqnt60GeNB3O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(graph.schema)"
      ],
      "metadata": {
        "id": "yf3jH9ZGXPUN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Neo4jVectorChain(Chain):\n",
        "    graph: Neo4jGraph = Field(exclude=True)\n",
        "    input_key: str = \"query\"\n",
        "    output_key: str = \"result\"\n",
        "    embeddings: HuggingFaceBgeEmbeddings = HuggingFaceBgeEmbeddings()\n",
        "    qa_chain: LLMChain = LLMChain(llm=llm, prompt=CHAT_PROMPT)\n",
        "\n",
        "    @property\n",
        "    def input_keys(self) -> List[str]:\n",
        "        return [self.input_key]\n",
        "\n",
        "    @property\n",
        "    def output_keys(self) -> List[str]:\n",
        "        _output_keys = [self.output_key]\n",
        "        return _output_keys\n",
        "\n",
        "    def _call(self, inputs: Dict[str, str], run_manager, k=3) -> Dict[str, Any]:\n",
        "        question = inputs[self.input_key]\n",
        "        embedding = self.embeddings.embed_query(question)\n",
        "\n",
        "        context = self.graph.query(vector_search, {'embedding': embedding, 'k': 3})\n",
        "        context = [el['result'] for el in context]\n",
        "\n",
        "        result = self.qa_chain({\"question\": question, \"context\": context})\n",
        "        final_result = result[self.qa_chain.output_key]\n",
        "        return {self.output_key: final_result}"
      ],
      "metadata": {
        "id": "wYZQ44hsNT4y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain = Neo4jVectorChain(graph=graph, embeddings=embeddings, verbose=True)"
      ],
      "metadata": {
        "id": "tksLTpsqOGGq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graph_result = chain.run(\"How can we enhance the specificity and efficiency of CRISPR/Cas9 gene-editing technology to minimize off-target effects and increase its potential for therapeutic applications?\")"
      ],
      "metadata": {
        "id": "TS4Nwf6pONk1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chain = GraphCypherQAChain.from_llm(\n",
        "    cypher_llm=llm,\n",
        "    qa_llm=llm,\n",
        "    graph=graph,\n",
        "    verbose=True,\n",
        "    return_intermediate_steps=True,\n",
        "    validate_cypher=True\n",
        ")"
      ],
      "metadata": {
        "id": "2Q-g7wGhXR0V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graph_result = chain.run(\"How can we enhance the specificity and efficiency of CRISPR/Cas9 gene-editing technology to minimize off-target effects and increase its potential for therapeutic applications?\")"
      ],
      "metadata": {
        "id": "JUxlLbCEXczj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}