Chat_with_PDFs_Custom_Knowledge_ChatGPT_with_LangChain.ipynb

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      "name": "python3",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/asotjrs/Langchain/blob/main/Chat_with_PDFs_Custom_Knowledge_ChatGPT_with_LangChain.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Custom Knowledge ChatGPT with LangChain - Chat with PDFs**\n",
        "\n",
        "**By Liam Ottley:** on youtube (cloned for learning purposes)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "0.   Installs, Imports and API Keys\n",
        "1.   Loading PDFs and chunking with LangChain\n",
        "2.   Embedding text and storing embeddings\n",
        "3.   Creating retrieval function\n",
        "4.   Creating chatbot with chat memory (OPTIONAL) \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "_x1GI7Fo8Y7x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 0. Installs, Imports and API Keys"
      ],
      "metadata": {
        "id": "Q24Y-g6h-Bg0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# RUN THIS CELL FIRST!\n",
        "!pip install -q langchain==0.0.150 pypdf pandas matplotlib tiktoken textract transformers openai faiss-cpu"
      ],
      "metadata": {
        "id": "gk2J2sYYjTkM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e8e08f7-de10-4b74-e325-a1dfb07b39b2"
      },
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      "outputs": [
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            "\u001b[?25h  Building wheel for docx2txt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for python-pptx (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for compressed-rtf (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for olefile (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "yfinance 0.2.18 requires beautifulsoup4>=4.11.1, but you have beautifulsoup4 4.8.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from transformers import GPT2TokenizerFast\n",
        "from langchain.document_loaders import PyPDFLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.embeddings import OpenAIEmbeddings\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.chains.question_answering import load_qa_chain\n",
        "from langchain.llms import OpenAI\n",
        "from langchain.chains import ConversationalRetrievalChain"
      ],
      "metadata": {
        "id": "l-uszlwN641q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ[\"OPENAI_API_KEY\"] = \"YOUR-OWN-KEY\""
      ],
      "metadata": {
        "id": "E2Buv5Y0uFr8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Loading PDFs and chunking with LangChain"
      ],
      "metadata": {
        "id": "RLULMPXa-Hu8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# You MUST add your PDF to local files in this notebook (folder icon on left hand side of screen)\n",
        "\n",
        "# Simple method - Split by pages \n",
        "loader = PyPDFLoader(\"./bayan4.pdf\")\n",
        "pages = loader.load_and_split()\n",
        "print(pages[0])\n",
        "\n",
        "# SKIP TO STEP 2 IF YOU'RE USING THIS METHOD\n",
        "chunks = pages"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KH546j3nkFwX",
        "outputId": "45839c73-c9a2-4bcb-eba2-014163f412dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "page_content='1 /3 \\n ﭑ ﭒ ﭓ ﭔ \\n \\nبيان واستنكار \\nحول دفن إمام في فناء المسجد \\nببلدية عين مران  ـ الشلف ـ \\n \\nالحمد لله ربِّ العالمين، والصَّلاة والسَّلام على من أرسله الله رحمةً للعالمين، وعلى آله  وصحبه وإخوانه \\nإلى يوم الدِّين، أمَّا بعد:  \\nفإنَّ  أغلى ما أنعم الله به علينا أن هدانا إلى دين التَّ وحيد، وجنَّ بنا أديان الشِّّ ك والوثنية؛ فالواجب ش   كر \\nهذه النِّ عمة وحفظها وعدم التَّ فريط فيها؛ إلَّّ  أنَّ  أقوامً ا منَّ ا ينت  سب  ون إلى الإسلام وه  م ي  مارس  ون أعمالًّ  ت  ناقض   \\nالتَّ وحيد، ويعت  قدون اعتقادات   ت  ضاد  ه؛ وهؤلّء يُ دِث ون  شرخًا في كيان الأمَّة، وجرحًا في جس دِها يصع ب   \\nان  دِ م  ال  ه، في  ؤخِّرون نصْ  ها  وعزَّها، ويُولون بينها وبين تأييد الله وحمايته لها، ذلك لأنَّ الله  عزَّ وجلَّ  لّ ينصْ \\nولّ ي عز  ولّ ي  ؤيِّ د ولّ يُمي مشّكًا  قال الله تعالى: (ﭱ ﭲ ﭳ ﭴ ) [لقمان13]، وقال(ﭺ ﭻ \\nﭼ ﭽ ﭾ ﭿ ﮀ ﮁ ﮂ ﮃ ﮄ ﮅ ﮆ ﮇ ﮈ ﮉ)[المائدة72]، وقال(ﭗ ﭘ ﭙ  \\nﭚ ﭛ ﭜ ﭝ ﭞ ﭟ ﭠ ﭡ ﭢ ﭣ ﭤ ﭥ ﭦ)[الحج31]؛ فيجب   على الأمَّة أن تأخ ذ على \\nيد كلِّ م ن ي ريد أن يمسَّ جناب  التَّوحيد، سدًّا لأبواب  الشِّّ ك ووسائله، ودرءًا لأسبابه ومظاهره. \\nوإنَّ من الع مل الق بيح الَّذي يناقض التَّوحيد الصَّحيح ما حدث ببلدية عين مران بولّية الشَّ لف يوم \\nالثَّامن والعشّين من رمض  ان (1438) الموافق23 جوان2017)، حيث   قام جمع   من   النَّ اس بدفن إمام \\nالمسجد بفن اء المسجد، وهي سابقة خطيرة وفعلة  شنيعة ، وإنَّ من ورائها شرًّا م  ست  طيرً ا.  \\nوإنَّنا ما كنَّا نظن  أنَّه بقي في أ مَّتنا الجزائريَّة ـ بعد كلِّ  هذه السِّنين من انتشار العلم وظهور الس نَّة ـ م ن  \\nيريد   أن يعود   بنا إلى زم  ن الضَّ لالة والبدعة، زمن القبوريَّ ة وتعظيم الأضرح  ة وتمريغ الوجوهِ  عند عت  بات \\nالمشاهِ د، زمن الجهل وأيَّام الّستعمار الغاشم.' metadata={'source': './bayan4.pdf', 'page': 0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Advanced method - Split by chunk\n",
        "\n",
        "# Step 1: Convert PDF to text\n",
        "import textract\n",
        "doc = textract.process(\"./bayan4.pdf\")\n",
        "\n",
        "# Step 2: Save to .txt and reopen (helps prevent issues)\n",
        "with open('bayan4.txt', 'w') as f:\n",
        "    f.write(doc.decode('utf-8'))\n",
        "\n",
        "with open('bayan4.txt', 'r') as f:\n",
        "    text = f.read()\n",
        "\n",
        "# Step 3: Create function to count tokens\n",
        "tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")\n",
        "\n",
        "def count_tokens(text: str) -> int:\n",
        "    return len(tokenizer.encode(text))\n",
        "\n",
        "# Step 4: Split text into chunks\n",
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    # Set a really small chunk size, just to show.\n",
        "    chunk_size = 512,\n",
        "    chunk_overlap  = 24,\n",
        "    length_function = count_tokens,\n",
        ")\n",
        "\n",
        "chunks = text_splitter.create_documents([text])"
      ],
      "metadata": {
        "id": "iADY2CXNlNq9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Result is many LangChain 'Documents' around 500 tokens or less (Recursive splitter sometimes allows more tokens to retain context)\n",
        "type(chunks[0]) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQ_gDkwep4q7",
        "outputId": "a050d157-fc7b-41fd-8844-00e0e5fba1ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "langchain.schema.Document"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Quick data visualization to ensure chunking was successful\n",
        "\n",
        "# Create a list of token counts\n",
        "token_counts = [count_tokens(chunk.page_content) for chunk in chunks]\n",
        "\n",
        "# Create a DataFrame from the token counts\n",
        "df = pd.DataFrame({'Token Count': token_counts})\n",
        "\n",
        "# Create a histogram of the token count distribution\n",
        "df.hist(bins=40, )\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "fK31bxDOpz1l",
        "outputId": "8719e80f-fd00-487a-b913-4b0a9cb159a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Embed text and store embeddings"
      ],
      "metadata": {
        "id": "_IlznUDK-i2m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get embedding model\n",
        "embeddings = OpenAIEmbeddings()\n",
        "\n",
        "# Create vector database\n",
        "db = FAISS.from_documents(chunks, embeddings)"
      ],
      "metadata": {
        "id": "92ObhTAKnZzQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Setup retrieval function"
      ],
      "metadata": {
        "id": "2LPwdGDP-nPO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check similarity search is working\n",
        "query = \"لماذا لا يجوز الدفن في المساجد ؟\"\n",
        "docs = db.similarity_search(query)\n",
        "docs[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RWP92zGg5Nb_",
        "outputId": "05cc0732-41cd-4c36-f0d6-83347d30051a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Document(page_content='23 \\n\\n( قفاولما\\n\\n) \\n\\n1438\\n\\n(\\n نا\\n\\n   ضمر نم نيشّعلاو\\n\\n \\n\\nنماَّثلا\\n\\n ًيرطا.  \\n\\n   م  تس\\n\\nا ًّشر اهئار\\n\\n نمو\\n\\nو  َّنإ\\n\\n ، ةعينش   ةلعف\\n\\nو ةيرطخ ةقباس يهو\\n\\n ،دجس\\n\\nلما\\n\\n ءا نفب دجسلما\\n\\n ن م ـ ةَّن  سلا روهظو ملعلا راشتنا نم \\n\\nينن ِّسلا هذه\\n\\n \\n\\n  ِّلك دعب ـ ةَّيرئازلجا انتَّم أ في يقب هَّنأ   نظن اَّنك ام\\n\\n انَّنإو\\n\\n تاب\\n\\n  تع دنع\\n \\n\\n ِهوجولا غيرتمو ة\\n\\n  حضرلأا ميظعتو\\n\\nة \\n\\n َّيروبقلا\\n\\n نمز\\n\\n \\n\\nن \\n\\n  َّضلا\\n\\n،ةعدبلاو ةللا\\n\\n  ديري\\n \\n ِهاشلما\\n.مشاغلا رماعتسلّا ماَّيأو لهلجا نمز ،\\nد\\n\\n  مز لىإ انب\\n\\n  دوعي نأ', metadata={})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create QA chain to integrate similarity search with user queries (answer query from knowledge base)\n",
        "\n",
        "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")\n",
        "\n",
        "query = \"لماذا لا يجوز الدفن في المساجد ؟\"\n",
        "docs = db.similarity_search(query)\n",
        "\n",
        "chain.run(input_documents=docs, question=query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "1Kv_sM8G5qAo",
        "outputId": "014e38ec-4a27-4ee3-e68a-14fa5a757285"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' لأن المساجد يتم استخدامها للصلاة والذكر والتذكير بالله، ولذلك لا يجوز الدفن فيها.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Create chatbot with chat memory "
      ],
      "metadata": {
        "id": "U_nH1qoL-w--"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display\n",
        "import ipywidgets as widgets\n",
        "\n",
        "# Create conversation chain that uses our vectordb as retriver, this also allows for chat history management\n",
        "qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0.1), db.as_retriever())"
      ],
      "metadata": {
        "id": "evF7_Dyhtcaf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chat_history = []\n",
        "\n",
        "def on_submit(_):\n",
        "    query = input_box.value\n",
        "    input_box.value = \"\"\n",
        "    \n",
        "    if query.lower() == 'exit':\n",
        "        print(\"Thank you for using the State of the Union chatbot!\")\n",
        "        return\n",
        "    \n",
        "    result = qa({\"question\": query, \"chat_history\": chat_history})\n",
        "    chat_history.append((query, result['answer']))\n",
        "    \n",
        "    display(widgets.HTML(f'<b>User:</b> {query}'))\n",
        "    display(widgets.HTML(f'<b><font color=\"blue\">Chatbot:</font></b> {result[\"answer\"]}'))\n",
        "\n",
        "print(\"Welcome to the Transformers chatbot! Type 'exit' to stop.\")\n",
        "\n",
        "input_box = widgets.Text(placeholder='Please enter your question:')\n",
        "input_box.on_submit(on_submit)\n",
        "\n",
        "display(input_box)"
      ],
      "metadata": {
        "id": "-pHw5siewPNt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}