enhancing_rag_with_graph.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "5x3LkpUztHNU"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "%pip install --upgrade --quiet  langchain langchain-community langchain-ollama langchain-experimental neo4j tiktoken yfiles_jupyter_graphs python-dotenv json-repair langchain-openai langchain_core"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "jPIRSGz4tHNV"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from langchain_core.runnables import  RunnablePassthrough\n",
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "from pydantic import BaseModel, Field\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain_community.graphs import Neo4jGraph\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain_community.chat_models import ChatOllama\n",
        "from langchain_experimental.graph_transformers import LLMGraphTransformer\n",
        "from neo4j import GraphDatabase\n",
        "from yfiles_jupyter_graphs import GraphWidget\n",
        "from langchain_community.vectorstores import Neo4jVector\n",
        "from langchain_community.document_loaders import TextLoader\n",
        "from langchain_community.vectorstores.neo4j_vector import remove_lucene_chars\n",
        "from langchain_ollama import OllamaEmbeddings\n",
        "import os\n",
        "from langchain_experimental.llms.ollama_functions import OllamaFunctions\n",
        "from neo4j import  Driver\n",
        "\n",
        "from dotenv import load_dotenv\n",
        "\n",
        "load_dotenv()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "L0nXP1aYtHNW"
      },
      "outputs": [],
      "source": [
        "graph = Neo4jGraph()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGhtLTAStHNW",
        "outputId": "39f65561-6c6b-4370-a51f-1d8118591158"
      },
      "outputs": [],
      "source": [
        "loader = TextLoader(file_path=\"dummytext.txt\")\n",
        "docs = loader.load()\n",
        "\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=250, chunk_overlap=24)\n",
        "documents = text_splitter.split_documents(documents=docs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "pXf7OTGHtHNW"
      },
      "outputs": [],
      "source": [
        "\n",
        "llm = OllamaFunctions(model=\"llama3.1\", temperature=0, format=\"json\")\n",
        "\n",
        "llm_transformer = LLMGraphTransformer(llm=llm)\n",
        "\n",
        "graph_documents = llm_transformer.convert_to_graph_documents(documents)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "GraphDocument(nodes=[Node(id=\"Amico'S Family\", type='Family', properties={})], relationships=[], source=Document(metadata={'source': 'dummytext.txt', 'id': 'ed648f5744c61a703b28736f1a7cf0c0'}, page_content='1. The Story of Amico’s Family: A Legacy of Love and Tradition'))"
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "graph_documents[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {},
      "outputs": [],
      "source": [
        "graph.add_graph_documents(\n",
        "    graph_documents,\n",
        "    baseEntityLabel=True,\n",
        "    include_source=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "GHbJPMfDtHNW"
      },
      "outputs": [],
      "source": [
        "embeddings = OllamaEmbeddings(\n",
        "    model=\"mxbai-embed-large\",\n",
        ")\n",
        "\n",
        "vector_index = Neo4jVector.from_existing_graph(\n",
        "    embeddings,\n",
        "    search_type=\"hybrid\",\n",
        "    node_label=\"Document\",\n",
        "    text_node_properties=[\"text\"],\n",
        "    embedding_node_property=\"embedding\"\n",
        ")\n",
        "vector_retriever = vector_index.as_retriever()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
            "8e37edd9789a4d57a7be401628e7ff7f",
            "9bac7003afd84cecb4e67a81a396ec8d"
          ]
        },
        "id": "RMZlhtDmtHNW",
        "outputId": "86efa842-3297-45d6-dab2-681bbc836b4d"
      },
      "outputs": [],
      "source": [
        "driver = GraphDatabase.driver(\n",
        "        uri = os.environ[\"NEO4J_URI\"],\n",
        "        auth = (os.environ[\"NEO4J_USERNAME\"],\n",
        "                os.environ[\"NEO4J_PASSWORD\"]))\n",
        "\n",
        "def create_fulltext_index(tx):\n",
        "    query = '''\n",
        "    CREATE FULLTEXT INDEX `fulltext_entity_id` \n",
        "    FOR (n:__Entity__) \n",
        "    ON EACH [n.id];\n",
        "    '''\n",
        "    tx.run(query)\n",
        "\n",
        "# Function to execute the query\n",
        "def create_index():\n",
        "    with driver.session() as session:\n",
        "        session.execute_write(create_fulltext_index)\n",
        "        print(\"Fulltext index created successfully.\")\n",
        "\n",
        "# Call the function to create the index\n",
        "try:\n",
        "    create_index()\n",
        "except:\n",
        "    pass\n",
        "\n",
        "# Close the driver connection\n",
        "driver.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6yCMz_sRtHNW",
        "outputId": "f533f279-9a2b-48d6-830b-28d04c43550b"
      },
      "outputs": [],
      "source": [
        "\n",
        "class Entities(BaseModel):\n",
        "    \"\"\"Identifying information about entities.\"\"\"\n",
        "\n",
        "    names: list[str] = Field(\n",
        "        ...,\n",
        "        description=\"All the person, organization, or business entities that \"\n",
        "        \"appear in the text\",\n",
        "    )\n",
        "\n",
        "prompt = ChatPromptTemplate.from_messages(\n",
        "    [\n",
        "        (\n",
        "            \"system\",\n",
        "            \"You are extracting organization and person entities from the text.\",\n",
        "        ),\n",
        "        (\n",
        "            \"human\",\n",
        "            \"Use the given format to extract information from the following \"\n",
        "            \"input: {question}\",\n",
        "        ),\n",
        "    ]\n",
        ")\n",
        "\n",
        "\n",
        "entity_chain = llm.with_structured_output(Entities)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54H15KNAtHNX",
        "outputId": "236df5a6-9f9e-49fd-a360-e4bb51dc7288"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Entities(names=['Nonna Lucia', 'Giovanni Caruso'])"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "entity_chain.invoke(\"Who are Nonna Lucia and Giovanni Caruso?\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "dY8huoM8tHNX"
      },
      "outputs": [],
      "source": [
        "def generate_full_text_query(input: str) -> str:\n",
        "    words = [el for el in remove_lucene_chars(input).split() if el]\n",
        "    if not words:\n",
        "        return \"\"\n",
        "    full_text_query = \" AND \".join([f\"{word}~2\" for word in words])\n",
        "    print(f\"Generated Query: {full_text_query}\")\n",
        "    return full_text_query.strip()\n",
        "\n",
        "\n",
        "# Fulltext index query\n",
        "def graph_retriever(question: str) -> str:\n",
        "    \"\"\"\n",
        "    Collects the neighborhood of entities mentioned\n",
        "    in the question\n",
        "    \"\"\"\n",
        "    result = \"\"\n",
        "    entities = entity_chain.invoke(question)\n",
        "    for entity in entities.names:\n",
        "        response = graph.query(\n",
        "            \"\"\"CALL db.index.fulltext.queryNodes('fulltext_entity_id', $query, {limit:2})\n",
        "            YIELD node,score\n",
        "            CALL {\n",
        "              WITH node\n",
        "              MATCH (node)-[r:!MENTIONS]->(neighbor)\n",
        "              RETURN node.id + ' - ' + type(r) + ' -> ' + neighbor.id AS output\n",
        "              UNION ALL\n",
        "              WITH node\n",
        "              MATCH (node)<-[r:!MENTIONS]-(neighbor)\n",
        "              RETURN neighbor.id + ' - ' + type(r) + ' -> ' +  node.id AS output\n",
        "            }\n",
        "            RETURN output LIMIT 50\n",
        "            \"\"\",\n",
        "            {\"query\": entity},\n",
        "        )\n",
        "        result += \"\\n\".join([el['output'] for el in response])\n",
        "    return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6fOJRPntHNX",
        "outputId": "a99ffca0-2d4d-4374-8519-c6e37c395f1f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Nonna Lucia - INFLUENCED -> Caponata\n",
            "Nonna Lucia - INFLUENCED -> Fresh Pasta\n",
            "Nonna Lucia - TAUGHT -> Amico\n",
            "Lucia - USES -> Organic And Local Ingredients\n",
            "Lucia - PRACTICES -> Sustainable Cooking Practices\n",
            "Bella Vita - OWNER -> Lucia\n"
          ]
        }
      ],
      "source": [
        "print(graph_retriever(\"Who is Nonna Lucia?\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "iCTMp3prtHNX"
      },
      "outputs": [],
      "source": [
        "def full_retriever(question: str):\n",
        "    graph_data = graph_retriever(question)\n",
        "    vector_data = [el.page_content for el in vector_retriever.invoke(question)]\n",
        "    final_data = f\"\"\"Graph data:\n",
        "{graph_data}\n",
        "vector data:\n",
        "{\"#Document \". join(vector_data)}\n",
        "    \"\"\"\n",
        "    return final_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "Dzb2jcittHNY"
      },
      "outputs": [],
      "source": [
        "template = \"\"\"Answer the question based only on the following context:\n",
        "{context}\n",
        "\n",
        "Question: {question}\n",
        "Use natural language and be concise.\n",
        "Answer:\"\"\"\n",
        "prompt = ChatPromptTemplate.from_template(template)\n",
        "\n",
        "chain = (\n",
        "        {\n",
        "            \"context\": full_retriever,\n",
        "            \"question\": RunnablePassthrough(),\n",
        "        }\n",
        "    | prompt\n",
        "    | llm\n",
        "    | StrOutputParser()\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "dtU0iMNgtHNY",
        "outputId": "bf3cf94c-f030-41b4-fb3b-356c5bff98f5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\"Nonna Lucia is the matriarch of the Caruso family, a culinary sage who taught her grandchildren, including Amico, the art of Sicilian cooking. She's also the owner of Bella Vita.\""
            ]
          },
          "execution_count": 101,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "chain.invoke(input=\"Who is Nonna Lucia? Did she teach anyone about restaurants or cooking?\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.10"
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    "widgets": {
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          "model_module_version": "^1.6.1",
          "model_name": "GraphModel",
          "state": {
            "_context_pane_mapping": [
              {
                "id": "Neighborhood",
                "title": "Neighborhood"
              },
              {
                "id": "Data",
                "title": "Data"
              },
              {
                "id": "Search",
                "title": "Search"
              },
              {
                "id": "About",
                "title": "About"
              }
            ],
            "_data_importer": "neo4j",
            "_directed": true,
            "_dom_classes": [],
            "_edges": [
              {
                "color": "#9C27B0",
                "directed": true,
                "end": 2,
                "id": 14,
                "label": "RULED",
                "properties": {
                  "label": "RULED"
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                "directed": true,
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                "label": "BELONGED_TO",
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                  "label": "BELONGED_TO"
                },
                "start": 1,
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                "color": "#F44336",
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                "label": "SPOUSE",
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                  "label": "SPOUSE"
                },
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                "label": "IGNORED_CLAIMS_OF",
                "properties": {
                  "label": "IGNORED_CLAIMS_OF"
                },
                "start": 7,
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                "color": "#673AB7",
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                "properties": {
                  "label": "IGNORED_CLAIMS_OF"
                },
                "start": 7,
                "thickness_factor": 1
              },
              {
                "color": "#CDDC39",
                "directed": true,
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                "id": 24,
                "label": "IMPRISONED",
                "properties": {
                  "label": "IMPRISONED"
                },
                "start": 9,
                "thickness_factor": 1
              },
              {
                "color": "#9E9E9E",
                "directed": true,
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                "properties": {
                  "label": "DEPENDED_ON"
                },
                "start": 1,
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              },
              {
                "color": "#9C27B0",
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                "label": "CREATED_TITLE",
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                },
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              },
              {
                "color": "#2196F3",
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                  "label": "SUCCEEDED_BY"
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                "color": "#673AB7",
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                "thickness_factor": 1
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                "end": 30,
                "id": 68,
                "label": "SUCCESSOR",
                "properties": {
                  "label": "SUCCESSOR"
                },
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                "thickness_factor": 1
              },
              {
                "color": "#673AB7",
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