From 466ad2d428794210837a4be053207e6e39e434bc Mon Sep 17 00:00:00 2001 From: Akash A Desai <62583018+akashAD98@users.noreply.github.com> Date: Fri, 20 Dec 2024 16:27:45 +0530 Subject: [PATCH 01/11] Create README.md for RAG on pdf --- examples/RAG-On-PDF/README.md | 44 +++++++++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) create mode 100644 examples/RAG-On-PDF/README.md diff --git a/examples/RAG-On-PDF/README.md b/examples/RAG-On-PDF/README.md new file mode 100644 index 0000000..fdcea6c --- /dev/null +++ b/examples/RAG-On-PDF/README.md @@ -0,0 +1,44 @@ +# FarmerGPT: RAG with PDF for Farmer Data + +This project demonstrates how to build a Retrieval-Augmented Generation (RAG) application using farmer-specific data. The data is extracted from a PDF containing crop variety information for sugarcane, turmeric, bamboo, cashew nuts, and more. The application leverages **LangChain** and **LanceDB** to create a customizable and extensible FarmerGPT solution. + +--- + +## Features + +- **Customizable Prompts**: Easily adapt prompts to suit specific queries and use cases. +- **Memory Support**: Incorporates memory capabilities to retain context during interactions. +- **PDF Integration**: Processes and retrieves data from the provided PDF file. + +--- + +## How to Use + +. **Use the Colab Notebook**: + - To try FarmerGPT directly without setup, use the provided Google Colab notebook: Open In Colab + - Open the notebook, follow the instructions, and run the cells to interact with the application. + +--- + +## Key Technologies + +- **LangChain**: Framework for building applications powered by large language models. +- **LanceDB**: Vector database used for efficient document retrieval. +--- + +## Sample Use Cases + +- Assisting farmers with queries about crop varieties. +- Providing tailored advice based on specific crop information. +- Serving as a reference for agricultural experts and enthusiasts. +--- + +## Customization + +You can build upon this template to include: +- Additional crop varieties. +- New functionalities such as integration with IoT devices. +- Multi-language support for wider accessibility. + +--- From 971d78cca6f78a6502be70077e0731429e239199 Mon Sep 17 00:00:00 2001 From: Akash A Desai <62583018+akashAD98@users.noreply.github.com> Date: Fri, 20 Dec 2024 17:06:49 +0530 Subject: [PATCH 02/11] Add files via upload --- assets/RAG_PDF_FarmerQA.png | Bin 0 -> 88573 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 assets/RAG_PDF_FarmerQA.png diff --git a/assets/RAG_PDF_FarmerQA.png b/assets/RAG_PDF_FarmerQA.png new file mode 100644 index 0000000000000000000000000000000000000000..75f59ab5430b6afa592c3c826f8d1af7c23e7fc4 GIT binary patch literal 88573 zcmeFYRaBf!^Dl~%5F|JR_uvF~3GNo$J-E9CcXtmS+#Lq@!QGt+?rvw2_j|wp-goD$ 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z8i#K3nzo;$74M<4dGgA7lF`s}tIs*&zR-#$u-~D~l7hI2jmBun1K-%*oW6+aS(PS$ zpK9OiWlau6r@At#54T*Pc4$VN9C_l)cp)Y+_cjFF-s%pawP`E;orSEl z3H3^B8q8JVZJYGY4}KQ^L)!eTXAzXJ$W^EpV4}er*Nd_ohmFU2#AuI~q}FQUg6ftl zGTLy_zyVk9c~U$WlIPT$UE6eKOrKJ5n&>+zQEEQiyZ(f3KEX)1 z$iXMNUEc=xO5fsEW_`G6_sryYc@u2rfPCcyfcB$lXp?w3rBRl3{$n<;(HIG?JyLD9 z^)X+1sLD}AwpG}#4R~Q&u^>a%axj25MOH~j6L4VdJQ8ez35R(&5Ok;vWX{4#)`%_V z@gke0Ikfi|DW%H_CMt`87W66dg;s#?ikjDx6I9dU?5kU1E8BxViN#zkRA{(mx@_<* zlXX~ss4GPI<*haeb5B{9Udrnp4A*EGD8vN0G-DjY;ok?3KF-Dqy$S29bfl4a9Nexg zFP{X7$JxwunfUMMr%C)UWWcUwQ6Fhe0Q^ zq#m!I1Jd!VFCC%KHs{Iz3D~HkBOwYA3HJ3pe(@fs7kWKN@EtlH9{&fuT+@;rbyKqb z4|Ky{4lcLEO@=Sv@tn@{|7ie1=f!X`d{MbI@*5+EB_Bd>;Jj?e=q3xf2L#(Rm>>j^jUPnGLI{lmYUup J^us5w{u=;K#Ww%| literal 0 HcmV?d00001 From 8d192dfecc78bb68351499a2c26b31bb443423cf Mon Sep 17 00:00:00 2001 From: Akash A Desai <62583018+akashAD98@users.noreply.github.com> Date: Fri, 20 Dec 2024 17:08:45 +0530 Subject: [PATCH 03/11] Update README.md --- examples/RAG-On-PDF/README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/RAG-On-PDF/README.md b/examples/RAG-On-PDF/README.md index fdcea6c..ecc7ee3 100644 --- a/examples/RAG-On-PDF/README.md +++ b/examples/RAG-On-PDF/README.md @@ -1,5 +1,7 @@ # FarmerGPT: RAG with PDF for Farmer Data +![image](../../assets/RAG_PDF_FarmerQA.png) + This project demonstrates how to build a Retrieval-Augmented Generation (RAG) application using farmer-specific data. The data is extracted from a PDF containing crop variety information for sugarcane, turmeric, bamboo, cashew nuts, and more. The application leverages **LangChain** and **LanceDB** to create a customizable and extensible FarmerGPT solution. --- @@ -18,7 +20,6 @@ This project demonstrates how to build a Retrieval-Augmented Generation (RAG) ap - To try FarmerGPT directly without setup, use the provided Google Colab notebook: Open In Colab - Open the notebook, follow the instructions, and run the cells to interact with the application. - --- ## Key Technologies From a79f3f55e7e39b0b92f00c134f7d5818a7cfe4f8 Mon Sep 17 00:00:00 2001 From: Akash A Desai <62583018+akashAD98@users.noreply.github.com> Date: Fri, 20 Dec 2024 17:15:39 +0530 Subject: [PATCH 04/11] Add files via upload --- examples/RAG-On-PDF/main.ipynb | 779 +++++++++++++++++++++++++++++++++ 1 file changed, 779 insertions(+) create mode 100644 examples/RAG-On-PDF/main.ipynb diff --git a/examples/RAG-On-PDF/main.ipynb b/examples/RAG-On-PDF/main.ipynb new file mode 100644 index 0000000..8db0d22 --- /dev/null +++ b/examples/RAG-On-PDF/main.ipynb @@ -0,0 +1,779 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "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: https://github.com/akashAD98/dummy_data/blob/main/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", + "text": [ + "Requirement already satisfied: langchain in /usr/local/lib/python3.10/dist-packages (0.3.12)\n", + "Collecting lancedb\n", + " Downloading lancedb-0.17.0-cp39-abi3-manylinux_2_28_x86_64.whl.metadata (4.7 kB)\n", + "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (6.0.2)\n", + "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.0.36)\n", + "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (3.11.10)\n", + "Requirement already satisfied: async-timeout<5.0.0,>=4.0.0 in 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"\u001b[?25hDownloading ffmpy-0.5.0-py3-none-any.whl (6.0 kB)\n", + "Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n", + "Installing collected packages: pydub, uvicorn, tomlkit, semantic-version, ruff, python-multipart, markupsafe, ffmpy, aiofiles, starlette, safehttpx, gradio-client, fastapi, gradio\n", + " Attempting uninstall: markupsafe\n", + " Found existing installation: MarkupSafe 3.0.2\n", + " Uninstalling MarkupSafe-3.0.2:\n", + " Successfully uninstalled MarkupSafe-3.0.2\n", + "Successfully installed aiofiles-23.2.1 fastapi-0.115.6 ffmpy-0.5.0 gradio-5.9.1 gradio-client-1.5.2 markupsafe-2.1.5 pydub-0.25.1 python-multipart-0.0.20 ruff-0.8.4 safehttpx-0.1.6 semantic-version-2.10.0 starlette-0.41.3 tomlkit-0.13.2 uvicorn-0.34.0\n" + ] + } + ], + "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", + "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/akashAD98/dummy_data/raw/main/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/akashAD98/dummy_data/raw/main/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/akashAD98/dummy_data/main/FARM5CROPS_farmergpt.pdf [following]\n", + "--2024-12-20 11:04:43-- https://raw.githubusercontent.com/akashAD98/dummy_data/main/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", + "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,\n", + " embedding=embeddings,\n", + " 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(self, main_instructions, prompt_instructions, context_name, query):\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(template=template, input_variables=[\"context\", \"chat_history\"])\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", + "# 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", + "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,\n", + " embedding=embeddings,\n", + " 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(self, main_instructions, prompt_instructions, context_name, query):\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(template=template, input_variables=[\"context\", \"chat_history\"])\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", + "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", + "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", + "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(label=\"Ask a question\", placeholder=\"Enter your question here...\")\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(query_processor_fn, inputs=[user_query], outputs=[answer_box])\n", + "\n", + "app.launch(debug=True,share=True)\n" + ], + "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": [ + "" + ], + "text/html": [ + "

" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "lWPuyELzxiRq" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 3c52564d1ebc2646dc26175e1b852902f06cd802 Mon Sep 17 00:00:00 2001 From: Akash A Desai <62583018+akashAD98@users.noreply.github.com> Date: Fri, 20 Dec 2024 17:24:55 +0530 Subject: [PATCH 05/11] Update README.md for RAG On pdf --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 5aea821..be5e72d 100644 --- a/README.md +++ b/README.md @@ -73,6 +73,7 @@ Develop a Retrieval-Augmented Generation (RAG) application using LanceDB for eff | RAG    | Interactive Notebook & Scripts | Blog | | --------- | -------------------------- | ----------- | |||| +| [RAG On PDF](/examples/RAG-On-PDF/) | Open In Colab [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | [RAG with Contextual Retrieval and Hybrid search](./examples/Contextual-RAG/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/guide-to-use-contextual-retrieval-and-prompt-caching-with-lancedb/) | | [RAG with Matryoshka Embeddings and LlamaIndex](./tutorials/RAG-with_MatryoshkaEmbed-Llamaindex/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|| | [RAG with IBM Watsonx](./examples/RAG-with-watsonx/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![watsonx LLM](https://img.shields.io/badge/watsonx-api-lightblue)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)|| From 351a6dc367d567069fb9a41b46773217f08e002b Mon Sep 17 00:00:00 2001 From: Akash A Desai <62583018+akashAD98@users.noreply.github.com> Date: Sat, 21 Dec 2024 11:09:32 +0530 Subject: [PATCH 06/11] Create info.txt --- examples/RAG-On-PDF/Dataset/info.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 examples/RAG-On-PDF/Dataset/info.txt diff --git a/examples/RAG-On-PDF/Dataset/info.txt b/examples/RAG-On-PDF/Dataset/info.txt new file mode 100644 index 0000000..4316359 --- /dev/null +++ b/examples/RAG-On-PDF/Dataset/info.txt @@ -0,0 +1 @@ +Data is collected from open search. 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z49ayNX)whRz{pHG1wA6nCnsSTYzz#} zu>iy1It?(U`UV_>1al;SVQ}8S5EO%boPoi*!T>v9J`140OlJlvg<=&$03l);2S@|N z836f1;C>Y&l2ACN0fxb~5hId8#)tF=Iv1vWiDW8VX8_qCGwv|{J5~XPQJ~y}fv%QG z_e7jbfn_7&7y(|7L?AHv0f|6_YY#>O%@tJ30EWXg1$53#oEl&xCS8$0Lj&7`LCs+Q zqXJS0kHg7iIPYQ%abekTG79G$Fb;!x7{CBgg!GI99tPzcDsVrTQ&UkYi2~IvfHB2B zz;HO{fKH96b~Cy+_&$P)VI){r07K!}r-I%9mY0FSx(66jKSKq=!8MOc1@sEm0qBT{ zke)%i0IJ?<{efsQs#p640|QEcL?S`8`xgfBaRx?#{GNfqcfJe^ zq>k15f^G)B^95H>AfH0AfvYN*Q!}ow5a^zZfstW*Fs`|vn!*^zq!&g+Lp=##SHQmT znE?Z4S`Uyes9yzhLAwgs0|8tQg=IrQa)FCU z4LBs=7Er8$o0eanN&HvcXIzE>HV^GVB~qcD1dJmQpgRwM0WJl_G{E4#2aLl&?tu3I z>wvR>8wq$ExMqNK00IH-i2)G>p#{wa)C}C1tX>Zg zYCtlXFwjjhVK8Svz+AX)0rtGQ&e+*pFuQiehMi4PlATTLw3U^eAXr)OG273 zMEUhX)6C@x Date: Sat, 21 Dec 2024 11:10:53 +0530 Subject: [PATCH 08/11] Update main.ipynb --- examples/RAG-On-PDF/main.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/RAG-On-PDF/main.ipynb b/examples/RAG-On-PDF/main.ipynb index 8db0d22..99a82be 100644 --- a/examples/RAG-On-PDF/main.ipynb +++ b/examples/RAG-On-PDF/main.ipynb @@ -28,7 +28,7 @@ { "cell_type": "markdown", "source": [ - "We utilized this PDF: https://github.com/akashAD98/dummy_data/blob/main/FARM5CROPS_farmergpt.pdf,\n", + "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" @@ -776,4 +776,4 @@ "outputs": [] } ] -} \ No newline at end of file +} From c7e7946bd85716ee1157ecab6bf472acd6147e13 Mon Sep 17 00:00:00 2001 From: Akash A Desai <62583018+akashAD98@users.noreply.github.com> Date: Sat, 21 Dec 2024 11:33:30 +0530 Subject: [PATCH 09/11] Update main.ipynb --- examples/RAG-On-PDF/main.ipynb | 39 ---------------------------------- 1 file changed, 39 deletions(-) diff --git a/examples/RAG-On-PDF/main.ipynb b/examples/RAG-On-PDF/main.ipynb index 99a82be..9382b4c 100644 --- a/examples/RAG-On-PDF/main.ipynb +++ b/examples/RAG-On-PDF/main.ipynb @@ -344,45 +344,6 @@ "execution_count": 7, "outputs": [] }, - { - "cell_type": "code", - "source": [ - "# Download the sample pdf\n", - "!wget https://github.com/akashAD98/dummy_data/raw/main/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/akashAD98/dummy_data/raw/main/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/akashAD98/dummy_data/main/FARM5CROPS_farmergpt.pdf [following]\n", - "--2024-12-20 11:04:43-- https://raw.githubusercontent.com/akashAD98/dummy_data/main/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 340aefcf6364a29e75370fa56ad3d1f028980dd8 Mon Sep 17 00:00:00 2001 From: Akash A Desai <62583018+akashAD98@users.noreply.github.com> Date: Sat, 21 Dec 2024 11:59:03 +0530 Subject: [PATCH 10/11] Update main.ipynb with dataset download link --- examples/RAG-On-PDF/main.ipynb | 40 ++++++++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) diff --git a/examples/RAG-On-PDF/main.ipynb b/examples/RAG-On-PDF/main.ipynb index 9382b4c..29a8aa7 100644 --- a/examples/RAG-On-PDF/main.ipynb +++ b/examples/RAG-On-PDF/main.ipynb @@ -344,6 +344,46 @@ "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/akashAD98/dummy_data/main/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 d45bfc81f82db80659bbe7f8eb9ec5e31ec6fc9e Mon Sep 17 00:00:00 2001 From: Akash A Desai <62583018+akashAD98@users.noreply.github.com> Date: Sat, 21 Dec 2024 12:00:21 +0530 Subject: [PATCH 11/11] Update main.ipynb --- examples/RAG-On-PDF/main.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/RAG-On-PDF/main.ipynb b/examples/RAG-On-PDF/main.ipynb index 29a8aa7..e4b1898 100644 --- a/examples/RAG-On-PDF/main.ipynb +++ b/examples/RAG-On-PDF/main.ipynb @@ -369,7 +369,7 @@ "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/akashAD98/dummy_data/main/FARM5CROPS_farmergpt.pdf\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",

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