Retrieval_Augmented_Generation_with_Hugging_Face_Models_in_LangChain.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Detailed Article Explaination\n",
        "\n",
        "The detailed code explanation for this article is available at the following link:\n",
        "\n",
        "https://www.daniweb.com/programming/computer-science/tutorials/541841/retrieval-augmented-generation-with-hugging-face-models-in-langchain\n",
        "\n",
        "For my other articles for Daniweb.com, please see this link:\n",
        "\n",
        "https://www.daniweb.com/members/1235222/usmanmalik57"
      ],
      "metadata": {
        "id": "tIn-dOg2IsBl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing and Installing Required Libraries"
      ],
      "metadata": {
        "id": "26tFg9TzVrut"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -U transformers==4.38.0\n",
        "!pip install -q -U sentence-transformers\n",
        "!pip install -q -U faiss-cpu\n",
        "!pip install -q -U bitsandbytes==0.42.0\n",
        "!pip install -q -U accelerate==0.27.1\n",
        "!pip install -q -U huggingface_hub\n",
        "!pip install -q -U langchain\n",
        "!pip install -q -U pypdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pq28mwqkVlC-",
        "outputId": "1c0c34a1-450b-4294-d96a-e0a96164ca13"
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m286.1/286.1 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, logging, pipeline\n",
        "from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "from langchain_community.document_loaders import PyPDFLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain_core.prompts import PromptTemplate\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
        "from langchain.chains import create_retrieval_chain\n",
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from transformers import BitsAndBytesConfig\n",
        "import torch"
      ],
      "metadata": {
        "id": "PjfGid7YAtCv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing a Hugging Face LLM in LangChain"
      ],
      "metadata": {
        "id": "a7Ehe77TTB9i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Ignore warnings\n",
        "logging.set_verbosity(logging.CRITICAL)\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")"
      ],
      "metadata": {
        "id": "9ImJ8IE5GaW5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_id = \"mistralai/Mistral-7B-Instruct-v0.2\"\n",
        "device = \"cuda\" # the device to load the model onto\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id,\n",
        "                                             quantization_config=bnb_config,\n",
        "                                             device_map={\"\":0})\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525,
          "referenced_widgets": [
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        },
        "id": "RF-VkcHJA1w_",
        "outputId": "71ff72e6-c20f-4bc3-a1a8-c22b9e11e662"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
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        {
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            ],
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        {
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          "metadata": {}
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        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe = pipeline(\"text-generation\",\n",
        "                model=model,\n",
        "                tokenizer=tokenizer,\n",
        "                max_new_tokens=1000)\n",
        "\n",
        "hf = HuggingFacePipeline(pipeline=pipe)"
      ],
      "metadata": {
        "id": "3SUtwmgjHvqB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "template = \"\"\"You are a an expert baking chef.\n",
        "{Question}\"\"\"\n",
        "\n",
        "prompt = PromptTemplate.from_template(template)\n",
        "\n",
        "chain = prompt | hf\n",
        "\n",
        "question = \"How to bake a pizza?\"\n",
        "print(chain.invoke({\"Question\": question}))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "10mbb8UrAk5_",
        "outputId": "0d70aaa5-7c82-4449-b8c6-2d2293e4b4f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You are a an expert baking chef.\n",
            "How to bake a pizza?\n",
            "\n",
            "To bake a pizza, follow these steps:\n",
            "\n",
            "1. Preheat your oven to the highest temperature possible, usually around 500°F (260°C) or as high as it will go.\n",
            "2. Roll out your pizza dough on a lightly floured surface to your desired thickness.\n",
            "3. Transfer the dough to a lightly greased or cornmeal-dusted pizza peel or baking sheet.\n",
            "4. Spread a thin layer of tomato sauce over the dough, leaving a border for the crust.\n",
            "5. Add your desired toppings, such as cheese, vegetables, meats, and herbs.\n",
            "6. Drizzle the pizza with olive oil and sprinkle with salt and pepper, if desired.\n",
            "7. Carefully transfer the pizza to the preheated oven, either on the pizza peel or by sliding it off onto a preheated baking stone or sheet.\n",
            "8. Bake for 8-12 minutes, or until the crust is golden brown and the cheese is melted and bubbly.\n",
            "9. Remove the pizza from the oven and let it cool for a few minutes before slicing and serving.\n",
            "\n",
            "Remember, the key to a great pizza is using high-quality ingredients and allowing the dough to rest and rise for at least an hour before rolling it out. Enjoy your homemade pizza!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generating Hugging Face Model Embeddings in LangChain"
      ],
      "metadata": {
        "id": "_5djWJAHH4-F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loader = PyPDFLoader(\"https://ecfr.eu/wp-content/uploads/2023/05/Brace-yourself-How-the-2024-US-presidential-election-could-affect-Europe.pdf\")\n",
        "pages = loader.load_and_split()"
      ],
      "metadata": {
        "id": "tgHJ2f5dVIgD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "splitter = RecursiveCharacterTextSplitter(\n",
        "    chunk_size=1000,\n",
        "    chunk_overlap=200,\n",
        "    length_function=len\n",
        ")\n",
        "\n",
        "all_text_chunks = []  # To store chunks from all documents\n",
        "for doc in pages:\n",
        "    text_content = doc.page_content\n",
        "    text_chunks = splitter.split_text(text_content)\n",
        "    all_text_chunks.extend(text_chunks)\n",
        "\n",
        "print(\"Total chunks:\", len(all_text_chunks))\n",
        "print(\"============================\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pu3atGmDXe22",
        "outputId": "c04e9395-aaac-4489-d41c-08e88ee4bc26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total chunks: 77\n",
            "============================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_path = \"thenlper/gte-large\"\n",
        "embeddings = HuggingFaceEmbeddings(\n",
        "    model_name = model_path\n",
        ")\n",
        "\n",
        "embedding_vectors = FAISS.from_texts(all_text_chunks, embeddings)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337,
          "referenced_widgets": [
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        "id": "8bvwQTVfYqtW",
        "outputId": "569082e2-833f-40cd-b663-25d491f42920"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/385 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/67.9k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/57.0 [00:00<?, ?B/s]"
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            "text/plain": [
              "config.json:   0%|          | 0.00/619 [00:00<?, ?B/s]"
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        {
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              "model.safetensors:   0%|          | 0.00/670M [00:00<?, ?B/s]"
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        {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/191 [00:00<?, ?B/s]"
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RAG Application Using Open Source LLM and Embeddings from Hugging Face"
      ],
      "metadata": {
        "id": "qEzhImv_kMMZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = ChatPromptTemplate.from_template(\"\"\"Answer the following question based only on the provided context:\n",
        "\n",
        "Question: {input}\n",
        "\n",
        "Context: {context}\n",
        "<end>\n",
        "\"\"\"\n",
        ")\n",
        "\n",
        "document_chain = create_stuff_documents_chain(hf, prompt)"
      ],
      "metadata": {
        "id": "SJNmopscZyo_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "retriever = embedding_vectors.as_retriever()\n",
        "retrieval_chain = create_retrieval_chain(retriever, document_chain)"
      ],
      "metadata": {
        "id": "lYS4DGYNZ-bK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_response(query):\n",
        "    response = retrieval_chain.invoke({\"input\": query})\n",
        "    return response[\"answer\"]"
      ],
      "metadata": {
        "id": "sjJzpnXQZ-fl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"What are the three points where Republicans and Democrats agree?\"\n",
        "result = generate_response(query)"
      ],
      "metadata": {
        "id": "L8YqO3skfmze"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(result.split(\"<end>\")[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQBs1VKQgTNt",
        "outputId": "107a6a65-81e9-4bf5-e75e-71b9aa61e14a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Answer: Republicans and Democrats agree on the following three points:\n",
            "\n",
            "1. Promoting US manufacturing jobs and building greater domestic production capacity.\n",
            "2. Rejecting overseas military interventions, especially in the Middle East and North Africa.\n",
            "3. Converging on a less neoliberal vision of the American economy, particularly regarding free trade pacts.\n",
            "\n",
            "These points reflect a growing bipartisan consensus on certain foreign policy issues that resonate with white working-class constituencies in swing states. However, it is important to note that the parties remain deeply divided on other issues such as energy and climate, the utility of allies, and the approach to international institutions.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NcIpzguxZMj-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}