IRI_LAW_RAG.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Build the Vector DB"
      ],
      "metadata": {
        "id": "rs1gEJcnXVFm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ],
      "metadata": {
        "id": "ARamAVK1J0L5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate langchain langchain_community unstructured sentence-transformers chromadb gradio"
      ],
      "metadata": {
        "id": "rD6UKIWPYjYW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.document_loaders import DirectoryLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.schema import Document\n",
        "from langchain_community.embeddings import HuggingFaceEmbeddings\n",
        "from langchain.vectorstores.chroma import Chroma\n",
        "import os\n",
        "import shutil\n",
        "import torch"
      ],
      "metadata": {
        "id": "D4JK6mSDY74E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ],
      "metadata": {
        "id": "NZ4kWNHuY0me"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CHROMA_PATH = \"./chroma\"\n",
        "DATA_PATH = \"./datasets\"\n",
        "\n",
        "embeding_model_name = \"sentence-transformers/LaBSE\"\n",
        "embeding_model_kwargs = {'device': device}\n",
        "embeding_encode_kwargs = {'normalize_embeddings': False}\n",
        "\n",
        "embedding_function = HuggingFaceEmbeddings(model_name=embeding_model_name,model_kwargs=embeding_model_kwargs,encode_kwargs=embeding_encode_kwargs)"
      ],
      "metadata": {
        "id": "Y2rc8KMUZGPE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_data_store():\n",
        "    documents = load_documents()\n",
        "    chunks = split_text(documents)\n",
        "    save_to_chroma(chunks)\n",
        "\n",
        "\n",
        "def load_documents():\n",
        "    loader = DirectoryLoader(DATA_PATH, glob=\"*.txt\")\n",
        "    documents = loader.load()\n",
        "    return documents\n",
        "\n",
        "\n",
        "def split_text(documents: list[Document]):\n",
        "    text_splitter = RecursiveCharacterTextSplitter(\n",
        "        chunk_size=300,\n",
        "        chunk_overlap=100,\n",
        "        length_function=len,\n",
        "        add_start_index=True,\n",
        "    )\n",
        "    chunks = text_splitter.split_documents(documents)\n",
        "    print(f\"Split {len(documents)} documents into {len(chunks)} chunks.\")\n",
        "\n",
        "    return chunks\n",
        "\n",
        "\n",
        "def save_to_chroma(chunks: list[Document]):\n",
        "    if os.path.exists(CHROMA_PATH):\n",
        "        shutil.rmtree(CHROMA_PATH)\n",
        "\n",
        "    db = Chroma.from_documents(\n",
        "        chunks,\n",
        "        embedding_function,\n",
        "        persist_directory=CHROMA_PATH\n",
        "    )\n",
        "\n",
        "    db.persist()\n",
        "    print(f\"Saved {len(chunks)} chunks to {CHROMA_PATH}.\")"
      ],
      "metadata": {
        "id": "M3hITnhIZGwU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generate_data_store()"
      ],
      "metadata": {
        "id": "xm8nco-PZVhC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b4zPKunnen9f",
        "outputId": "6db53c64-0ea5-4229-c328-da664a272109"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# backup the sqlite database"
      ],
      "metadata": {
        "id": "L0U_1NBagUQq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load the Vector DB"
      ],
      "metadata": {
        "id": "po6_FyCKZbyy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iEPgUGEjgU2e",
        "outputId": "9aaae41f-5757-4e4b-a9df-63aa34fbc4b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# vector database for business law\n",
        "!cp -r /content/drive/MyDrive/IRI_LAW/chroma ./chroma"
      ],
      "metadata": {
        "id": "DI2BzS6LgWXw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CHROMA_PATH = \"./chroma\"\n",
        "\n",
        "embeding_model_name = \"sentence-transformers/LaBSE\"\n",
        "embeding_model_kwargs = {'device': device}\n",
        "embeding_encode_kwargs = {'normalize_embeddings': False}\n",
        "\n",
        "embedding_function = HuggingFaceEmbeddings(model_name=embeding_model_name,model_kwargs=embeding_model_kwargs,encode_kwargs=embeding_encode_kwargs)\n",
        "db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding_function)"
      ],
      "metadata": {
        "id": "kKj8NsY9ZhJ_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def find_relevant_results(query, k=3):\n",
        "  results = db.similarity_search_with_relevance_scores(query, k=k)\n",
        "\n",
        "  return results"
      ],
      "metadata": {
        "id": "mdq2HmK1hVfV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Run the LLM Model"
      ],
      "metadata": {
        "id": "c_psFXw3kAad"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig\n",
        "\n",
        "model_name_or_id = \"MaralGPT/Maral-7B-alpha-1\""
      ],
      "metadata": {
        "id": "CzwdojH-kJui"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name_or_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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        },
        "id": "eDG-P34hlFNc",
        "outputId": "d3f26c64-b104-418d-ae05-2a763eaca003"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/967 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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        {
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          "metadata": {}
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    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(model_name_or_id, torch_dtype=torch.bfloat16, device_map=\"auto\")"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
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          "name": "stderr",
          "text": [
            "WARNING:root:Some parameters are on the meta device device because they were offloaded to the cpu.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generation_config = GenerationConfig(\n",
        "    do_sample=True,\n",
        "    top_k=1,\n",
        "    temperature=0.5,\n",
        "    max_new_tokens=300,\n",
        "    pad_token_id=tokenizer.eos_token_id\n",
        ")"
      ],
      "metadata": {
        "id": "Sr0vSOOflHME"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "PROMPT_TEMPLATE = \"\"\"\n",
        "فقط بر اساس متن زیر به سوال پاسخ دهید:\n",
        "\n",
        "{context}\n",
        "\n",
        "---\n",
        "\n",
        "با توجه به متن بالا به سوال پاسخ دهید:\n",
        "{question}\n",
        "\n",
        "Answer:\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "ihA0PdmTlbC1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.prompts import ChatPromptTemplate\n",
        "prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)"
      ],
      "metadata": {
        "id": "lGwW-Rvylb5S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process_query(query):\n",
        "    results = find_relevant_results(query)\n",
        "    if len(results) == 0 or results[0][1] < 0.25:\n",
        "        return (\"اطلاعاتی که مرتبط با سوال شما باشد را در پایگاه دانش خود پیدا نکردم!\")\n",
        "    context = \"\\n\\n---\\n\\n\".join([doc.page_content for doc, _score in results])\n",
        "    prompt = prompt_template.format(context=context, question=query)\n",
        "    inputs = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "    outputs = model.generate(**inputs, generation_config=generation_config)\n",
        "    return tokenizer.decode(outputs[0], skip_special_tokens=True)"
      ],
      "metadata": {
        "id": "Pepd35Xtpixb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Serve in Gradio"
      ],
      "metadata": {
        "id": "NxTdDblKJsvk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "\n",
        "def run_query(query):\n",
        "    return process_query(query)\n",
        "\n",
        "with gr.Blocks() as demo:\n",
        "    query = gr.Textbox(label=\"Question\", rtl=True, lines=5)\n",
        "    output = gr.Textbox(label=\"Answer\", rtl=True, lines=10)\n",
        "    greet_btn = gr.Button(\"Query\")\n",
        "    greet_btn.click(fn=run_query, inputs=query, outputs=output, api_name=\"run_query\")\n",
        "\n",
        "demo.launch();"
      ],
      "metadata": {
        "id": "MKG25KjoVONc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation"
      ],
      "metadata": {
        "id": "Rqegq27Yweq0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "6ZaYYZyjwgHe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r /content/drive/MyDrive/IRI_LAW/evaluation ./evaluation"
      ],
      "metadata": {
        "id": "v2Osn1gLwq9n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO evaluation on bussiness law"
      ],
      "metadata": {
        "id": "1Ifz6cbmw3Ic"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}