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This behaviour is the source of the following dependency conflicts.\n", "torchaudio 2.2.1+cu121 requires torch==2.2.1, but you have torch 2.2.2 which is incompatible.\n", "torchtext 0.17.1 requires torch==2.2.1, but you have torch 2.2.2 which is incompatible.\n", "torchvision 0.17.1+cu121 requires torch==2.2.1, but you have torch 2.2.2 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m 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deprecated-1.2.14 fastapi-0.110.1 h11-0.14.0 httptools-0.6.1 humanfriendly-10.0 importlib-metadata-7.0.0 kubernetes-29.0.0 mmh3-4.1.0 monotonic-1.6 onnxruntime-1.17.1 opentelemetry-api-1.24.0 opentelemetry-exporter-otlp-proto-common-1.24.0 opentelemetry-exporter-otlp-proto-grpc-1.24.0 opentelemetry-instrumentation-0.45b0 opentelemetry-instrumentation-asgi-0.45b0 opentelemetry-instrumentation-fastapi-0.45b0 opentelemetry-proto-1.24.0 opentelemetry-sdk-1.24.0 opentelemetry-semantic-conventions-0.45b0 opentelemetry-util-http-0.45b0 overrides-7.7.0 posthog-3.5.0 pulsar-client-3.4.0 pypika-0.48.9 python-dotenv-1.0.1 starlette-0.37.2 uvicorn-0.29.0 uvloop-0.19.0 watchfiles-0.21.0 websockets-12.0\n" ] } ], "source": [ "!pip install chromadb" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uXrd-lqZkkfJ", "outputId": "06176488-9f31-4ec3-f88d-d0d1211b1cfc" }, "outputs": [ { "name": "stdout", "output_type": "stream", 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TextLoader\n", "from langchain.document_loaders import Docx2txtLoader\n", "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain.embeddings import HuggingFaceEmbeddings\n", "from langchain.vectorstores import Chroma\n", "from huggingface_hub import notebook_login\n", "import torch\n", "import transformers\n", "from transformers import AutoTokenizer, AutoModelForCausalLM\n", "from transformers import pipeline\n", "from langchain import HuggingFacePipeline\n", "from langchain.chains import ConversationalRetrievalChain\n", "from langchain.memory import ConversationBufferMemory\n", "from langchain.embeddings.openai import OpenAIEmbeddings\n", "from langchain.chat_models import ChatOpenAI\n", "import os\n", "import sys" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DrM1RFkKxs8R", "outputId": "8727008d-3d20-4b10-f2db-5a5b2ef6ec41" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mkdir: cannot create directory ‘docs’: File exists\n" ] } ], "source": [ "!mkdir docs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "b8jUj3dbmFey" }, "outputs": [], "source": [ "document=[]\n", "for file in os.listdir(\"docs\"):\n", " if file.endswith(\".pdf\"):\n", " pdf_path=\"./docs/\"+file\n", " loader=PyPDFLoader(pdf_path)\n", " document.extend(loader.load())\n", " elif file.endswith('.docx') or file.endswith('.doc'):\n", " doc_path=\"./docs/\"+file\n", " loader=Docx2txtLoader(doc_path)\n", " document.extend(loader.load())\n", " elif file.endswith('.txt'):\n", " text_path=\"./docs/\"+file\n", " loader=TextLoader(text_path)\n", " document.extend(loader.load())\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SEGpKykPmK0g", "outputId": "5b436b41-021f-4764-f13b-500b4d73a8f0" }, "outputs": [ { "data": { "text/plain": [ "[Document(page_content='AI Chatbots are applications that businesses and other entities employ to facilitate \\nautomated conversations between AI and humans. These conversations can occur \\nthrough text or speech. Chatbots must comprehend and imitate human conversation \\nwhen engaging with people worldwide. Chatbots have made significant progress from \\nELIZA, the first -ever chatbot, to today’s Amazon ALEXA. This tutorial will cover all \\nthe fundamental concepts necessary for creating a basic chatbot that can \\ncomprehend and respond to hum an interactions. We will utilize speech recognition \\nAPIs and pre -trained Transformer models. NLP, or Natural Language Processing, \\nstands for teaching machines to understand human speech and spoken words. NLP \\ncombines computational linguistics, which involves rule -based modeling of human \\nlanguage, with intelligent algorithms like statistical, machine, and deep learning \\nalgorithms. Together, these technologies create the smart voice assistants and \\nchatbots we use daily. Issn human speech, there are vari ous errors, differences, and \\nunique intonations. NLP technology, including AI chatbots, empowers machines to \\nrapidly understand, process, and respond to large volumes of text in real -time. You’ve \\nlikely encountered NLP in voice -guided GPS apps, virtual ass istants, speech -to-text \\nnote creation apps, and other chatbots that offer app support in your everyday life. In \\nthe business world, NLP, particularly in the context of AI chatbots, is instrumental in \\nstreamlining processes, monitoring employee productivity , and enhancing sales and \\nafter -sales efficiency. \\n ', metadata={'source': './docs/pic.pdf', 'page': 0})]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "document" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "p457y8YQmNzW", "outputId": "8cbdeeb7-81d8-4f00-ab78-2517610fb12b" }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(document)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yEyG1VUbmWbg" }, "outputs": [], "source": [ "document_splitter=CharacterTextSplitter(separator='\\n', chunk_size=500, chunk_overlap=100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "siTPQ-rhmbXJ" }, "outputs": [], "source": [ "document_chunks=document_splitter.split_documents(document)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "C9HulDt7mfUk", "outputId": "39162353-9f59-4e91-8559-8aa02f66be03" }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(document_chunks)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PLyaqBICmjHX", "outputId": "8e76dabd-bc23-4294-c0fc-623702fc4b46" }, "outputs": [ { "data": { "text/plain": [ "Document(page_content='AI Chatbots are applications that businesses and other entities employ to facilitate \\nautomated conversations between AI and humans. These conversations can occur \\nthrough text or speech. Chatbots must comprehend and imitate human conversation \\nwhen engaging with people worldwide. Chatbots have made significant progress from \\nELIZA, the first -ever chatbot, to today’s Amazon ALEXA. This tutorial will cover all \\nthe fundamental concepts necessary for creating a basic chatbot that can', metadata={'source': './docs/pic.pdf', 'page': 0})" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "document_chunks[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "l2dKAZfAmmrq", "outputId": "2bb7ae31-9fda-4527-a5f9-4d72605c432f" }, "outputs": [ { "data": { "text/plain": [ "Document(page_content='the fundamental concepts necessary for creating a basic chatbot that can \\ncomprehend and respond to hum an interactions. We will utilize speech recognition \\nAPIs and pre -trained Transformer models. NLP, or Natural Language Processing, \\nstands for teaching machines to understand human speech and spoken words. NLP \\ncombines computational linguistics, which involves rule -based modeling of human \\nlanguage, with intelligent algorithms like statistical, machine, and deep learning', metadata={'source': './docs/pic.pdf', 'page': 0})" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "document_chunks[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GDZ9qzuCmn-a", "outputId": "6219c015-0bea-4120-e3f5-f9fb2810c3d6" }, "outputs": [ { "data": { "text/plain": [ "Document(page_content='language, with intelligent algorithms like statistical, machine, and deep learning \\nalgorithms. Together, these technologies create the smart voice assistants and \\nchatbots we use daily. Issn human speech, there are vari ous errors, differences, and \\nunique intonations. NLP technology, including AI chatbots, empowers machines to \\nrapidly understand, process, and respond to large volumes of text in real -time. You’ve', metadata={'source': './docs/pic.pdf', 'page': 0})" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "document_chunks[2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kfpThbX7mqLS", "outputId": "bdbde549-4521-48b7-b383-753412bb96ba" }, "outputs": [ { "data": { "text/plain": [ "Document(page_content='rapidly understand, process, and respond to large volumes of text in real -time. You’ve \\nlikely encountered NLP in voice -guided GPS apps, virtual ass istants, speech -to-text \\nnote creation apps, and other chatbots that offer app support in your everyday life. In \\nthe business world, NLP, particularly in the context of AI chatbots, is instrumental in \\nstreamlining processes, monitoring employee productivity , and enhancing sales and \\nafter -sales efficiency.', metadata={'source': './docs/pic.pdf', 'page': 0})" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "document_chunks[3]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 497, "referenced_widgets": [ "3d4d4f64201e40d0aa1d3567c232e865", "96705300b35645f0a753b1a6bd54c599", "5c4a3c547e8d4410943e35875805bd8d", "ecb4ab4d8ded47b0bfa32907a7f218d0", "4dd0e9d23928493090b9ba4ab380a6c8", "cef53b19b4684f9799173c82837598d6", "9120c4770e3e48a1b98dda32c071d074", "a9eb5bb4c17949a78ba2f312898cf1f5", "1b98b1fe794e47a2b549b65ba1726b2f", "b3afa5446e4d483b84ff0f6fb0a39bc4", "ab05f0789f884cf5af45ce7a0317487f", "c5b9df50c948471a8184f0d57d3f77bc", "bb97e37f92ca49b89aa5207b07b1d863", 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"outputId": "0b1a3a99-0d41-4302-b9e1-605d3605e282" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3d4d4f64201e40d0aa1d3567c232e865", "version_major": 2, "version_minor": 0 }, "text/plain": [ "modules.json: 0%| | 0.00/349 [00:00, async_client=, model='text-embedding-ada-002', deployment='text-embedding-ada-002', openai_api_version='', openai_api_base=None, openai_api_type='', openai_proxy='', embedding_ctx_length=8191, openai_api_key='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', openai_organization=None, allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=2, request_timeout=None, headers=None, tiktoken_enabled=True, tiktoken_model_name=None, show_progress_bar=False, model_kwargs={}, skip_empty=False, default_headers=None, default_query=None, retry_min_seconds=4, retry_max_seconds=20, http_client=None)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "embeddings" ] }, { "cell_type": "markdown", "metadata": { "id": "Q6C4FggwtoIz" }, "source": [ "Setting Up Chroma as our Vector Database" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zqeoCS_wn1VZ" }, "outputs": [], "source": [ "vectordb=Chroma.from_documents(document_chunks,embedding=embeddings, persist_directory='./data')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Yvz77Wvdodl1" }, "outputs": [], "source": [ "vectordb.persist()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "5ad00f7a7dec4e9f800b548ca3626b30", "5b68c13aa1874489b5a2739d80baef04", "c588f3a7d81d4cc5a6971d0903984102", "f83e7b5387714299b0dcfbf74dea8477", "b44975a066774a2cb510ea03f84f29ba", "e72558b15b7d404198a315a5854e80df", "adf5c7eaea61475c87497fb70d60d2d7", "17955c7a972f4e5ebe849edd84cfee28", "52074dcedfc440c1937741acde5dc478", "0766c3f98eb548ab93942be0ac899609", "06135a87b239482c86ea8fd357d1b5d2", "ea0e8c854a184c75ac1ed0a88228f3f5", "6d820898faf34c1796e62560b8cdd0cc", "786e294d404d496889b47b9af9ddc56e" ] }, "id": "BPceCzJdo3ls", "outputId": "f9ca5eb9-36c7-4930-c89e-773d6c4586b7" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5ad00f7a7dec4e9f800b548ca3626b30", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
, async_client=, openai_api_key='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', openai_proxy='')" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "llm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_VoYBHYKNVtW" }, "outputs": [], "source": [ "memory=ConversationBufferMemory(memory_key='chat_history', return_messages=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ndgyGEi8NaKY" }, "outputs": [], "source": [ "#Create our Q/A Chain\n", "pdf_qa=ConversationalRetrievalChain.from_llm(llm=llm,\n", " retriever=vectordb.as_retriever(search_kwargs={'k':6}),\n", " verbose=False, memory=memory)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6xoN7cRoNc22", "outputId": "6dc10230-8a47-418e-cba5-bb6201726fa0" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n", " warn_deprecated(\n", "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 6 is greater than number of elements in index 4, updating n_results = 4\n" ] } ], "source": [ "result=pdf_qa({\"question\":\"YOLOv7 is trained on which dataset\"})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "id": "hcR7m-DGNgln", "outputId": "fbdc939a-777f-480a-ffab-c92bf71582dd" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "\"I'm not sure about the specific dataset that YOLOv7 is trained on.\"" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result['answer']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true, "base_uri": "https://localhost:8080/" }, "id": "V_ZAaP5aNkhw", "outputId": "4bfd7a4f-4c2d-4783-9868-968c2c3c7df4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---------------------------------------------------------------------------------\n", "Welcome to the DocBot. You are now ready to start interacting with your documents\n", "---------------------------------------------------------------------------------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 6 is greater than number of elements in index 4, updating n_results = 4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Answer: I'm sorry, but based on the context provided, there is no specific mention of the name of a PDF document.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 6 is greater than number of elements in index 4, updating n_results = 4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Answer: The PDF is about Natural Language Processing (NLP) and AI chatbots. It covers the fundamental concepts necessary for creating a basic chatbot that can comprehend and respond to human interactions using speech recognition APIs and pre-trained Transformer models. It explains how NLP teaches machines to understand human speech and spoken words, combining computational linguistics with intelligent algorithms like statistical, machine, and deep learning. The document also discusses how NLP technology, including AI chatbots, enables machines to rapidly understand, process, and respond to large volumes of text in real-time.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 6 is greater than number of elements in index 4, updating n_results = 4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Answer: NLP stands for Natural Language Processing.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 6 is greater than number of elements in index 4, updating n_results = 4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Answer: NLP stands for Natural Language Processing, which involves teaching machines to understand human speech and spoken words. NLP combines computational linguistics with intelligent algorithms like statistical, machine, and deep learning to enable machines to comprehend and respond to human language in real-time.\n" ] } ], "source": [ "print('---------------------------------------------------------------------------------')\n", "print('Welcome to the DocBot. You are now ready to start interacting with your documents')\n", "print('---------------------------------------------------------------------------------')\n", "\n", "while True:\n", " query=input(f\"Prompt:\")\n", " if query == \"exit\" or query == \"quit\" or query == \"q\" or query == \"f\":\n", " print('Exiting')\n", " sys.exit()\n", " if query == '':\n", " continue\n", " result = pdf_qa({\"question\": query})\n", " print(f\"Answer: \" + result[\"answer\"])" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "010525ebf4f4436aba13fc7b5b436804": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, 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