gradio_docling_RAG_langchain.ipynb

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      "cell_type": "markdown",
      "source": [
        "Gradio interface for Docking + Langchain + Milvus + Mistral + all-MiniLM-L6-v2\n",
        "\n",
        "**RAG**\n",
        "\n",
        "**Supports multiple file uploads** (PDF, Images, HTML, PPTx)\n",
        "\n",
        "\n",
        "Developed by: Partha Prati Ray, https://github.com/ParthaPRay"
      ],
      "metadata": {
        "id": "amSj0iWN9UWH"
      }
    },
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          "text": [
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            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "# requirements for this example:\n",
        "%pip install -qq docling docling-core python-dotenv langchain-text-splitters langchain-huggingface langchain-milvus gradio"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# For local machine only\n",
        "# import os\n",
        "\n",
        "# from dotenv import load_dotenv\n",
        "\n",
        "# load_dotenv()\n"
      ],
      "metadata": {
        "id": "yMoQmS_Vj_Bn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# For colab only\n",
        "\n",
        "# from google.colab import userdata\n",
        "# HF_API_KEY = userdata.get('HF_TOKEN')\n",
        "\n",
        "# HF_API_KEY"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "JjkZz08hkAtJ",
        "outputId": "82bd8400-a093-4b8e-de40-eaf4084b4391"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'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'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Supports PDF, Image, PPTx, HTML,\n",
        "\n",
        "# .TXT, .MD, .asciidoc to be tested\n",
        "\n",
        "## Word .docx, Excel .xlsx are not supported\n",
        "\n",
        "\n",
        "import gradio as gr\n",
        "import os\n",
        "from tempfile import NamedTemporaryFile, TemporaryDirectory\n",
        "\n",
        "# Docling & LangChain imports\n",
        "from docling.document_converter import (\n",
        "    DocumentConverter,\n",
        "    PdfFormatOption,\n",
        "    WordFormatOption,\n",
        ")\n",
        "from docling.datamodel.base_models import InputFormat\n",
        "from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend\n",
        "#from docling.backend.msword_backend import MsWordDocumentBackend\n",
        "\n",
        "from docling.pipeline.simple_pipeline import SimplePipeline\n",
        "from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline\n",
        "\n",
        "from langchain_core.documents import Document as LCDocument\n",
        "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
        "from langchain_huggingface.embeddings import HuggingFaceEmbeddings\n",
        "from langchain_milvus import Milvus\n",
        "from langchain_huggingface import HuggingFaceEndpoint\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain_core.prompts import PromptTemplate\n",
        "from langchain_core.runnables import RunnablePassthrough\n",
        "\n",
        "# ------------------------------------------------------------------\n",
        "# 1) Configure Docling's DocumentConverter for multi-format\n",
        "# ------------------------------------------------------------------\n",
        "doc_converter = (DocumentConverter(\n",
        "    allowed_formats=[\n",
        "        InputFormat.PDF,\n",
        "        InputFormat.IMAGE,    # for images (requires OCR libs if actual text extraction is desired)\n",
        "        #InputFormat.DOCX,     # Word\n",
        "        InputFormat.HTML,     # HTML\n",
        "        InputFormat.PPTX,     # PowerPoint\n",
        "      #  InputFormat.ASCIIDOC, # AsciiDoc\n",
        "      #  InputFormat.MD,       # Markdown\n",
        "    ],\n",
        "    format_options={\n",
        "        InputFormat.PDF: PdfFormatOption(\n",
        "            pipeline_cls=StandardPdfPipeline,\n",
        "            backend=PyPdfiumDocumentBackend\n",
        "        ),\n",
        "        InputFormat.DOCX: WordFormatOption(  # word not supported yet\n",
        "            pipeline_cls=SimplePipeline #, backend=MsWordDocumentBackend\n",
        "        ),\n",
        "        # You can add further format_options here if needed,\n",
        "        # e.g., PPTX -> PptxFormatOption, etc.\n",
        "    },\n",
        "  )\n",
        ")\n",
        "\n",
        "# ------------------------------------------------------------------\n",
        "# 2) A simple \"loader\" that uses this doc_converter\n",
        "# ------------------------------------------------------------------\n",
        "class DoclingLoader:\n",
        "    def __init__(self, file_paths: list[str]):\n",
        "        # Accept either single path or list\n",
        "        if isinstance(file_paths, str):\n",
        "            file_paths = [file_paths]\n",
        "        self.file_paths = file_paths\n",
        "\n",
        "    def load_docs(self):\n",
        "        docs = []\n",
        "        for fp in self.file_paths:\n",
        "            # Let Docling auto-detect & convert\n",
        "            conv_result = doc_converter.convert(fp)  # returns a ConversionResult\n",
        "            dl_doc = conv_result.document            # the actual Docling Document\n",
        "            text = dl_doc.export_to_markdown(strict_text=True)\n",
        "            docs.append(LCDocument(page_content=text))\n",
        "        return docs\n",
        "\n",
        "# ------------------------------------------------------------------\n",
        "# 3) Global state for splitted docs and RAG chain\n",
        "# ------------------------------------------------------------------\n",
        "splitted_docs = None\n",
        "rag_chain = None\n",
        "\n",
        "# ------------------------------------------------------------------\n",
        "# 4) Build RAG chain\n",
        "# ------------------------------------------------------------------\n",
        "def build_rag_chain(docs):\n",
        "    # Create embeddings\n",
        "    HF_EMBED_MODEL_ID = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
        "    embeddings = HuggingFaceEmbeddings(model_name=HF_EMBED_MODEL_ID)\n",
        "\n",
        "    # Temporary local Milvus DB\n",
        "    tmp_dir = TemporaryDirectory()\n",
        "    MILVUS_URI = f\"{tmp_dir.name}/milvus_demo.db\"\n",
        "\n",
        "    # Build vector store\n",
        "    vectorstore = Milvus.from_documents(\n",
        "        docs,\n",
        "        embeddings,\n",
        "        connection_args={\"uri\": MILVUS_URI},\n",
        "        drop_old=True,\n",
        "    )\n",
        "\n",
        "    # Create retriever\n",
        "    retriever = vectorstore.as_retriever()\n",
        "\n",
        "    # Prompt\n",
        "    prompt = PromptTemplate.from_template(\n",
        "        \"Context information is below.\\n---------------------\\n{context}\\n\"\n",
        "        \"---------------------\\nGiven the context information and not prior knowledge, \"\n",
        "        \"answer the query.\\nQuery: {question}\\nAnswer:\\n\"\n",
        "    )\n",
        "\n",
        "    # Setup HuggingFace LLM\n",
        "    #HF_API_KEY = os.environ.get(\"HF_TOKEN\", \"YOUR_HF_TOKEN_HERE\")\n",
        "\n",
        "    HF_API_KEY = \"YOUR_HF_API_TOKEN\"   # Use your HuggingFace Token Key Here\n",
        "\n",
        "    HF_LLM_MODEL_ID = \"mistralai/Mistral-7B-Instruct-v0.3\"\n",
        "    llm = HuggingFaceEndpoint(\n",
        "        repo_id=HF_LLM_MODEL_ID,\n",
        "        huggingfacehub_api_token=HF_API_KEY,\n",
        "    )\n",
        "\n",
        "    def format_docs(selected_docs):\n",
        "        return \"\\n\\n\".join(doc.page_content for doc in selected_docs)\n",
        "\n",
        "    # Build pipeline (RAG chain)\n",
        "    rag_chain_temp = (\n",
        "        {\n",
        "            \"context\": retriever | format_docs,\n",
        "            \"question\": RunnablePassthrough()\n",
        "        }\n",
        "        | prompt\n",
        "        | llm\n",
        "        | StrOutputParser()\n",
        "    )\n",
        "    return rag_chain_temp\n",
        "\n",
        "# ------------------------------------------------------------------\n",
        "# 5) Gradio callback: Text Splitting\n",
        "# ------------------------------------------------------------------\n",
        "def text_splitting(files):\n",
        "    \"\"\"\n",
        "    1. Save the uploaded bytes to temporary disk paths.\n",
        "    2. Convert them via DoclingLoader (doc_converter).\n",
        "    3. Split them with LangChain.\n",
        "    4. Build the RAG chain.\n",
        "    5. Yield intermediate status to Gradio.\n",
        "    \"\"\"\n",
        "    global splitted_docs, rag_chain\n",
        "\n",
        "    yield \"Splitting in progress...\"\n",
        "\n",
        "    temp_file_paths = []\n",
        "    for f in files:\n",
        "        # If type=\"binary\" in gr.File, 'f' is raw bytes\n",
        "        with NamedTemporaryFile(delete=False) as tmp:\n",
        "            tmp.write(f)\n",
        "            temp_file_paths.append(tmp.name)\n",
        "\n",
        "    # Load docs using Docling\n",
        "    loader = DoclingLoader(temp_file_paths)\n",
        "    docs = loader.load_docs()\n",
        "\n",
        "    # Split\n",
        "    text_splitter = RecursiveCharacterTextSplitter(\n",
        "        chunk_size=1000,\n",
        "        chunk_overlap=200\n",
        "    )\n",
        "    splitted_docs = text_splitter.split_documents(docs)\n",
        "\n",
        "    # Build RAG chain\n",
        "    rag_chain = build_rag_chain(splitted_docs)\n",
        "\n",
        "    yield \"Split complete!\"\n",
        "\n",
        "# ------------------------------------------------------------------\n",
        "# 6) Gradio callback: RAG Q&A\n",
        "# ------------------------------------------------------------------\n",
        "def ask_question(question):\n",
        "    global splitted_docs, rag_chain\n",
        "    if not splitted_docs or not rag_chain:\n",
        "        return \"No splitted docs available. Please upload and split first!\"\n",
        "    return rag_chain.invoke(question)\n",
        "\n",
        "# ------------------------------------------------------------------\n",
        "# 7) Build Gradio UI\n",
        "# ------------------------------------------------------------------\n",
        "def build_app():\n",
        "    with gr.Blocks() as demo:\n",
        "        with gr.Tabs():\n",
        "            with gr.Tab(\"Upload & Split\"):\n",
        "                gr.Markdown(\n",
        "                    \"### Upload your files (PDF, HTML, PPTX, Images)\"\n",
        "                )\n",
        "                file_upload = gr.File(\n",
        "                    label=\"Upload Files\",\n",
        "                    file_count=\"multiple\",\n",
        "                    type=\"binary\"\n",
        "                )\n",
        "                status_box = gr.Textbox(label=\"Status\", interactive=False)\n",
        "                split_button = gr.Button(\"Split Documents\")\n",
        "\n",
        "                # This streams status messages (\"Splitting in progress...\", \"Split complete!\")\n",
        "                split_button.click(\n",
        "                    fn=text_splitting,\n",
        "                    inputs=[file_upload],\n",
        "                    outputs=status_box\n",
        "                )\n",
        "\n",
        "            with gr.Tab(\"RAG Q&A\"):\n",
        "                gr.Markdown(\"### Ask a question about your splitted documents\")\n",
        "                question_box = gr.Textbox(\n",
        "                    label=\"Question\",\n",
        "                    value=\"Does Docling implement a linear pipeline of operations?\"\n",
        "                )\n",
        "                answer_box = gr.Textbox(label=\"Answer\", interactive=False)\n",
        "                ask_button = gr.Button(\"Ask\")\n",
        "\n",
        "                ask_button.click(\n",
        "                    fn=ask_question,\n",
        "                    inputs=question_box,\n",
        "                    outputs=answer_box\n",
        "                )\n",
        "    return demo\n",
        "\n",
        "# ------------------------------------------------------------------\n",
        "# 8) Launch the Gradio app\n",
        "# ------------------------------------------------------------------\n",
        "if __name__ == \"__main__\":\n",
        "    app = build_app()\n",
        "    app.launch(debug=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "id": "o2pKIrkV_HNm",
        "outputId": "d8ebe0ab-36f7-4edc-f976-db6fa966270f"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running Gradio in a Colab notebook requires sharing enabled. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "* Running on public URL: https://d387d6d20da41e5235.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"https://d387d6d20da41e5235.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:docling.backend.html_backend:detected nested tables: skipping for now\n",
            "WARNING:docling.backend.html_backend:detected nested tables: skipping for now\n"
          ]
        }
      ]
    }
  ]
}