examples/RAG-On-PDF/main.ipynb

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 "metadata": {
  "colab": {
   "provenance": [],
   "gpuType": "T4"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  },
  "accelerator": "GPU"
 },
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# FarmerGPT - Build Chat with PDF using lancedb"
   ],
   "metadata": {
    "id": "10yLPb9Fneh0"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We utilized this PDF: Dataset/FARM5CROPS_farmergpt.pdf,\n",
    " which contains crop variety information for sugarcane, turmeric, bamboo, cashew nuts, and more.\n",
    " This is a sample project designed to demonstrate how to build an application using LanceDB and LangChain\n",
    "The use case and prompts can be customized as needed to suit specific requirements"
   ],
   "metadata": {
    "id": "xFHTAtayosS-"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Import pacages"
   ],
   "metadata": {
    "id": "eXsCZEUvb39u"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EaKlPN9u6bnc",
    "outputId": "89f72bd3-ddf9-4ccc-a249-50d50e4012be"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
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     ]
    }
   ],
   "source": [
    "! pip install langchain lancedb\n",
    "! pip install tantivy\n",
    "! pip install -U langchain-openai langchain-community pypdf\n",
    "! pip install gradio"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# pass opeani key or use any LLM\n",
    "import os\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-\""
   ],
   "metadata": {
    "id": "ylj23ZpC-SG1"
   },
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# Download the sample pdf\n",
    "!wget https://github.com/vectordb-recipes/examples/raw/main/RAG-On-PDF/Dataset/FARM5CROPS_farmergpt.pdf -O FARM5CROPS_farmergpt.pdf"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xtRI4pTPvahR",
    "outputId": "02f3b2dc-4595-403e-e3d2-068fa4b73c4a"
   },
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--2024-12-20 11:04:42--  https://github.com/vectordb-recipes/examples/raw/main/RAG-On-PDF/Dataset/FARM5CROPS_farmergpt.pdf\n",
      "Resolving github.com (github.com)... 140.82.112.3\n",
      "Connecting to github.com (github.com)|140.82.112.3|:443... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://raw.githubusercontent.com/vectordb-recipes/examples/main/RAG-On-PDF/Dataset/FARM5CROPS_farmergp.pdf [following]\n",
      "--2024-12-20 11:04:43--  https://raw.githubusercontent.com/vectordb-recipes/examples/main/RAG-On-PDF/Dataset/FARM5CROPS_farmergpt.pdf\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1398674 (1.3M) [application/octet-stream]\n",
      "Saving to: ‘FARM5CROPS_farmergpt.pdf’\n",
      "\n",
      "FARM5CROPS_farmergp 100%[===================>]   1.33M  --.-KB/s    in 0.04s   \n",
      "\n",
      "2024-12-20 11:04:43 (34.5 MB/s) - ‘FARM5CROPS_farmergpt.pdf’ saved [1398674/1398674]\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from lancedb.rerankers import LinearCombinationReranker\n",
    "from langchain_community.document_loaders import PyPDFLoader\n",
    "from langchain_community.vectorstores import LanceDB\n",
    "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
    "from langchain_text_splitters import CharacterTextSplitter\n",
    "from langchain_core.prompts import PromptTemplate\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "\n",
    "\n",
    "class QueryProcessor:\n",
    "    def __init__(self, file_path, db_url=\"lancedb_temp\", table_name=\"lancedb_indic\"):\n",
    "        \"\"\"\n",
    "        Initialize the QueryProcessor with the PDF file and set up the vector store.\n",
    "\n",
    "        Parameters:\n",
    "            file_path (str): Path to the PDF file.\n",
    "            db_url (str): URI for the LanceDB vector store.\n",
    "            table_name (str): Name of the table in LanceDB.\n",
    "        \"\"\"\n",
    "        # Load and process the PDF document\n",
    "        loader = PyPDFLoader(file_path)\n",
    "        documents = loader.load()\n",
    "        text_splitter = CharacterTextSplitter()\n",
    "        self.documents = text_splitter.split_documents(documents)\n",
    "\n",
    "        # Initialize embeddings and vector store\n",
    "        embeddings = OpenAIEmbeddings()\n",
    "        self.vector_store = LanceDB(\n",
    "            uri=db_url, embedding=embeddings, table_name=table_name\n",
    "        )\n",
    "\n",
    "        # Add reranker\n",
    "        self.reranker = LinearCombinationReranker(weight=0.3)\n",
    "        self.docsearch = LanceDB.from_documents(\n",
    "            self.documents, embeddings, reranker=self.reranker\n",
    "        )\n",
    "\n",
    "        print(\"Embedding stored in lancedb\")\n",
    "        # Initialize LLM and memory\n",
    "        self.llm = ChatOpenAI(\n",
    "            model_name=\"gpt-4o\",\n",
    "            temperature=0.01,\n",
    "        )\n",
    "        self.memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
    "\n",
    "    def generate_prompt_template(\n",
    "        self, main_instructions, prompt_instructions, context_name, query\n",
    "    ):\n",
    "        \"\"\"\n",
    "        Generate a prompt template for LangChain LLM.\n",
    "\n",
    "        Parameters:\n",
    "            main_instructions (str): Main instructions for the LLM.\n",
    "            prompt_instructions (str): Additional instructions for how to use the data.\n",
    "            context_name (str): The name of the context (e.g., search results).\n",
    "            query (str): The query from the user.\n",
    "\n",
    "        Returns:\n",
    "            PromptTemplate: The generated prompt template.\n",
    "        \"\"\"\n",
    "        template = f\"\"\"{main_instructions}\n",
    "\n",
    "        {prompt_instructions}\n",
    "\n",
    "        {context_name}:\n",
    "        {{context}}\n",
    "\n",
    "        Previous Conversations:\n",
    "        {{chat_history}}\n",
    "        Human: {query}\n",
    "        Chatbot:\"\"\"\n",
    "        return PromptTemplate(\n",
    "            template=template, input_variables=[\"context\", \"chat_history\"]\n",
    "        )\n",
    "\n",
    "    def get_answer(self, query):\n",
    "        \"\"\"\n",
    "        Process a query and return the answer based on the preloaded PDF.\n",
    "\n",
    "        Parameters:\n",
    "            query (str): The user's query.\n",
    "\n",
    "        Returns:\n",
    "            str: The answer to the query.\n",
    "        \"\"\"\n",
    "        # Perform similarity search\n",
    "        docs = self.docsearch.similarity_search_with_relevance_scores(query)\n",
    "\n",
    "        # Generate a prompt\n",
    "        prompt = self.generate_prompt_template(\n",
    "            main_instructions=\"Act as a knowledgeable assistant. Answer the query comprehensively and concisely based on the provided content.\",\n",
    "            prompt_instructions=(\n",
    "                \"Focus on extracting the most relevant and accurate information from the context. \"\n",
    "                \"Prioritize clarity, conciseness, and detail in your response. \"\n",
    "                \"When summarizing, ensure key points are highlighted without losing important nuances. \"\n",
    "                \"If the context is insufficient to fully address the query, acknowledge the limitation clearly.\"\n",
    "            ),\n",
    "            context_name=\"PDF Content\",\n",
    "            query=query,\n",
    "        )\n",
    "\n",
    "        # Create the LangChain pipeline\n",
    "        chain = prompt | self.llm | StrOutputParser()\n",
    "\n",
    "        # Invoke the chain and get the answer\n",
    "        answer = chain.invoke({\"context\": docs, \"chat_history\": self.memory})\n",
    "        return answer\n",
    "\n",
    "\n",
    "# Initialize the QueryProcessor with the PDF file (done once)\n",
    "file_path = \"/content/FARM5CROPS_farmergpt.pdf\"\n",
    "query_processor = QueryProcessor(file_path)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pzs-a0Kcdokq",
    "outputId": "a68fa8d5-edb0-4e7d-964c-4d151ad10a28"
   },
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Embedding stored in lancedb\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "query = \"give me some sugarcane variety names?\"\n",
    "answer = query_processor.get_answer(query)\n",
    "print(\"Answer:\", answer)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dt7rIDwDdonc",
    "outputId": "489273aa-b269-4fdf-9cab-cae516f0c766"
   },
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Answer: The sugarcane varieties are categorized by states in India as follows:\n",
      "\n",
      "- **Andhra Pradesh:**\n",
      "  - Early varieties: Co.6907, 84A125, 81A99, 83A30, 85A261, 87A298, Co.8014, 86V96, 91V83.\n",
      "  - Mid-late varieties: COA7607, CO8021, COT.8201, Co7805, COV92102 (83V15), 83V288.\n",
      "  - Late varieties: Co.7219, CoR8001, 87A380, Co7706.\n",
      "\n",
      "- **Bihar:**\n",
      "  - Varieties: Bo 99, CoP 9301, CoSe 98231, CoS 8436, Cos 95255, Bo 102, Bo 91, Bo 110, CoP 9206, CoSe 95422, CoSe 92423, UP 9530.\n",
      "\n",
      "- **Gujarat:**\n",
      "  - Varieties: Co 86002, Co 86032, CoSi 95071, Co 86249, CoN 05072.\n",
      "\n",
      "- **Haryana:**\n",
      "  - Varieties: CoJ 64, CoS 8436, CoS 88230, CoS 767.\n",
      "\n",
      "- **Karnataka:**\n",
      "  - Varieties: Co 94012, CoC 671, Co 92020, Co 8014, Co 86032, Co 62175, Co 8371, Co 740, Co 8011.\n",
      "\n",
      "- **Maharashtra:**\n",
      "  - Varieties: CoC 671, Co 86032, Co 8011, Co 94012, CoM 265, Co 92005.\n",
      "\n",
      "- **Odisha:**\n",
      "  - Varieties: Co 62175, CoA 89085, Co 87A298, Co86V96.\n",
      "\n",
      "- **Punjab:**\n",
      "  - Varieties: CoJ 85, CoJ 88, CoS8436, CoH 119, Co89003.\n",
      "\n",
      "- **Tamil Nadu:**\n",
      "  - Varieties: Co 94012, Co 94010, CoC 24.\n",
      "\n",
      "- **Uttar Pradesh:**\n",
      "  - Varieties: CoS 8436, Coj 64, CoS88230, CoS 98231, CoS 767, CoS 8432, CoPt 90223, CoS 92423, CoS97264, CoLk 8102.\n",
      "\n",
      "- **Uttrakhand:**\n",
      "  - Varieties: CoS 8436, CoS 88230, Cos 767, CoS 97264, CoSe 92423.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# Reuse with another query\n",
    "query2 = \"What crops grow in dry regions?\"\n",
    "answer2 = query_processor.get_answer(query2)\n",
    "print(\"Answer:\", answer2)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lGUJMdVodorC",
    "outputId": "4909b2ff-e97a-45be-83f3-71894e2a9162"
   },
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Answer: In dry regions, crops that are typically grown include those that are drought-resistant and can thrive with minimal water. Some common crops suitable for dry regions are:\n",
      "\n",
      "1. **Millets**: These are hardy grains that require less water and can grow in poor soil conditions. Examples include pearl millet (bajra) and finger millet (ragi).\n",
      "\n",
      "2. **Sorghum**: Known for its drought tolerance, sorghum is a staple in many dry areas.\n",
      "\n",
      "3. **Pulses**: Legumes such as chickpeas, lentils, and pigeon peas are often grown in dry regions due to their ability to fix nitrogen and improve soil fertility.\n",
      "\n",
      "4. **Oilseeds**: Crops like sesame and mustard are suitable for dry climates.\n",
      "\n",
      "5. **Cotton**: This crop can be grown in semi-arid regions with proper irrigation management.\n",
      "\n",
      "6. **Cactus and Succulents**: While not traditional crops, these plants are increasingly being explored for their potential in arid agriculture.\n",
      "\n",
      "These crops are chosen for their ability to withstand water scarcity and their adaptability to the challenging conditions of dry regions.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# General template for your chat with pdf\n",
    "##### change the promts as per requirements"
   ],
   "metadata": {
    "id": "1dpAeJtLnL4D"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# gradio app\n",
    "\n",
    "import gradio as gr\n",
    "from lancedb.rerankers import LinearCombinationReranker\n",
    "from langchain_community.document_loaders import PyPDFLoader\n",
    "from langchain_community.vectorstores import LanceDB\n",
    "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
    "from langchain_text_splitters import CharacterTextSplitter\n",
    "from langchain_core.prompts import PromptTemplate\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "\n",
    "\n",
    "class QueryProcessor:\n",
    "    def __init__(self, file_path, db_url=\"lancedb_temp\", table_name=\"lancedb_indic\"):\n",
    "        loader = PyPDFLoader(file_path)\n",
    "        documents = loader.load()\n",
    "        text_splitter = CharacterTextSplitter()\n",
    "        self.documents = text_splitter.split_documents(documents)\n",
    "\n",
    "        embeddings = OpenAIEmbeddings()\n",
    "        self.vector_store = LanceDB(\n",
    "            uri=db_url, embedding=embeddings, table_name=table_name\n",
    "        )\n",
    "\n",
    "        self.reranker = LinearCombinationReranker(weight=0.3)\n",
    "        self.docsearch = LanceDB.from_documents(\n",
    "            self.documents, embeddings, reranker=self.reranker\n",
    "        )\n",
    "\n",
    "        self.llm = ChatOpenAI(\n",
    "            model_name=\"gpt-4o\",\n",
    "            temperature=0.01,\n",
    "        )\n",
    "        self.memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
    "\n",
    "    def generate_prompt_template(\n",
    "        self, main_instructions, prompt_instructions, context_name, query\n",
    "    ):\n",
    "        template = f\"\"\"{main_instructions}\n",
    "\n",
    "        {prompt_instructions}\n",
    "\n",
    "        {context_name}:\n",
    "        {{context}}\n",
    "\n",
    "        Previous Conversations:\n",
    "        {{chat_history}}\n",
    "        Human: {query}\n",
    "        Chatbot:\"\"\"\n",
    "        return PromptTemplate(\n",
    "            template=template, input_variables=[\"context\", \"chat_history\"]\n",
    "        )\n",
    "\n",
    "    def get_answer(self, query):\n",
    "        docs = self.docsearch.similarity_search_with_relevance_scores(query)\n",
    "\n",
    "        prompt = self.generate_prompt_template(\n",
    "            main_instructions=\"Act as a knowledgeable assistant. Answer the query comprehensively and concisely based on the provided content.\",\n",
    "            prompt_instructions=(\n",
    "                \"Focus on extracting the most relevant and accurate information from the context. \"\n",
    "                \"Prioritize clarity, conciseness, and detail in your response. \"\n",
    "                \"When summarizing, ensure key points are highlighted without losing important nuances. \"\n",
    "                \"If the context is insufficient to fully address the query, acknowledge the limitation clearly.\"\n",
    "            ),\n",
    "            context_name=\"Search Results\",\n",
    "            query=query,\n",
    "        )\n",
    "\n",
    "        chain = prompt | self.llm | StrOutputParser()\n",
    "\n",
    "        answer = chain.invoke({\"context\": docs, \"chat_history\": self.memory})\n",
    "        return answer\n",
    "\n",
    "\n",
    "def initialize_processor(pdf_file):\n",
    "    global query_processor\n",
    "    query_processor = QueryProcessor(pdf_file.name)\n",
    "    return \"PDF successfully loaded and processed. You can now ask questions.\"\n",
    "\n",
    "\n",
    "def query_processor_fn(question):\n",
    "    global query_processor\n",
    "    if query_processor is None:\n",
    "        return \"Please upload a PDF first.\"\n",
    "    return query_processor.get_answer(question)\n",
    "\n",
    "\n",
    "query_processor = None\n",
    "\n",
    "# Define Gradio interface\n",
    "with gr.Blocks() as app:\n",
    "    gr.Markdown(\"# RAG On PDF - FarmersGPT\")\n",
    "\n",
    "    with gr.Row():\n",
    "        pdf_upload = gr.File(label=\"Upload PDF\", file_types=[\".pdf\"])\n",
    "        pdf_status = gr.Textbox(label=\"Status\", interactive=False)\n",
    "\n",
    "    load_pdf_btn = gr.Button(\"Load PDF\")\n",
    "\n",
    "    with gr.Row():\n",
    "        user_query = gr.Textbox(\n",
    "            label=\"Ask a question\", placeholder=\"Enter your question here...\"\n",
    "        )\n",
    "        answer_box = gr.Textbox(label=\"Answer\", interactive=False)\n",
    "\n",
    "    ask_question_btn = gr.Button(\"Get Answer\")\n",
    "\n",
    "    load_pdf_btn.click(initialize_processor, inputs=[pdf_upload], outputs=[pdf_status])\n",
    "    ask_question_btn.click(\n",
    "        query_processor_fn, inputs=[user_query], outputs=[answer_box]\n",
    "    )\n",
    "\n",
    "app.launch(debug=True, share=True)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "id": "RVc-wh3ld_Ga",
    "outputId": "05b0d7de-e095-4d19-a836-63edfa715af6"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "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://6e972183d1c4f70073.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"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "<div><iframe src=\"https://6e972183d1c4f70073.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ]
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "lWPuyELzxiRq"
   },
   "execution_count": null,
   "outputs": []
  }
 ]
}