{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "0v8omNnXF3vo"
},
"source": [
"# Setup and Query LLaMA-2 Model\n",
"This notebook will guide you through installing required libraries, setting up the LLaMA-2 model, and querying it using natural language."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sS-V8H9LF3vr"
},
"source": [
"## Install Required Libraries\n",
"We need to install the necessary libraries for PyTorch, TorchVision, and Torchaudio. Additionally, we'll install other dependencies required for running the LLaMA-2 model and handling document embeddings."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "ZgXOHHKxF3vr",
"outputId": "14cb4211-fbd7-43be-bd26-b930f9445553"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://download.pytorch.org/whl/cu117\n",
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"INFO: pip is looking at multiple versions of torch to determine which version is compatible with other requirements. This could take a while.\n",
"Collecting torch\n",
" Downloading https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl (1843.9 MB)\n",
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"Building wheels for collected packages: lit\n",
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" Created wheel for lit: filename=lit-15.0.7-py3-none-any.whl size=89987 sha256=51a6f7a47d87cc1cf6c213f8091f68a646f22f012cd46de1e320d52c22775c49\n",
" Stored in directory: /root/.cache/pip/wheels/27/2c/b6/3ed2983b1b44fe0dea1bb35234b09f2c22fb8ebb308679c922\n",
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" Uninstalling triton-2.3.1:\n",
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" Found existing installation: torchvision 0.18.1+cu121\n",
" Uninstalling torchvision-0.18.1+cu121:\n",
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" Found existing installation: torchaudio 2.3.1+cu121\n",
" Uninstalling torchaudio-2.3.1+cu121:\n",
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"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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"\u001b[0mSuccessfully installed lit-15.0.7 torch-2.0.1+cu117 torchaudio-2.0.2+cu117 torchvision-0.15.2+cu117 triton-2.0.0\n",
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"Installing collected packages: triton, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, xformers\n",
" Attempting uninstall: triton\n",
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"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"torchaudio 2.0.2+cu117 requires torch==2.0.1, but you have torch 2.3.1 which is incompatible.\n",
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" Created wheel for html2text: filename=html2text-2024.2.26-py3-none-any.whl size=33111 sha256=4650e12b689d3420568dc4794728aaaebaa1d3a68418168e01e4ad9453a17bdd\n",
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"Successfully installed atlassian-python-api-3.41.14 dataclasses-json-0.6.7 deprecated-1.2.14 dirtyjson-1.0.8 h11-0.14.0 html2text-2024.2.26 httpcore-1.0.5 httpx-0.27.0 jmespath-1.0.1 llama-index-0.10.12 llama-index-agent-openai-0.1.7 llama-index-cli-0.1.13 llama-index-core-0.10.57 llama-index-embeddings-openai-0.1.11 llama-index-indices-managed-llama-cloud-0.1.6 llama-index-legacy-0.9.48 llama-index-llms-openai-0.1.27 llama-index-multi-modal-llms-openai-0.1.8 llama-index-program-openai-0.1.7 llama-index-question-gen-openai-0.1.3 llama-index-readers-file-0.1.30 llama-index-readers-llama-parse-0.1.6 llama-parse-0.4.9 llama_hub-0.0.19 llamaindex-py-client-0.1.19 marshmallow-3.21.3 mypy-extensions-1.0.0 openai-1.37.0 pypdf-4.3.1 retrying-1.3.4 striprtf-0.0.26 tiktoken-0.7.0 typing-inspect-0.9.0\n",
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"Installing collected packages: llama-cloud, llama-index-indices-managed-llama-cloud, llama-index\n",
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"Building wheels for collected packages: ffmpy\n",
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"Installing collected packages: pydub, ffmpy, websockets, uvloop, uvicorn, semantic-version, python-multipart, python-dotenv, markdown-it-py, httptools, dnspython, aiofiles, watchfiles, starlette, mdit-py-plugins, email_validator, gradio-client, fastapi-cli, fastapi, gradio\n",
" Attempting uninstall: markdown-it-py\n",
" Found existing installation: markdown-it-py 3.0.0\n",
" Uninstalling markdown-it-py-3.0.0:\n",
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"Successfully installed aiofiles-24.1.0 dnspython-2.6.1 email_validator-2.2.0 fastapi-0.111.1 fastapi-cli-0.0.4 ffmpy-0.3.2 gradio-3.32.0 gradio-client-1.1.1 httptools-0.6.1 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 pydub-0.25.1 python-dotenv-1.0.1 python-multipart-0.0.9 semantic-version-2.10.0 starlette-0.37.2 uvicorn-0.30.3 uvloop-0.19.0 watchfiles-0.22.0 websockets-11.0.3\n",
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"Installing collected packages: tomlkit, ruff, aiofiles, gradio\n",
" Attempting uninstall: aiofiles\n",
" Found existing installation: aiofiles 24.1.0\n",
" Uninstalling aiofiles-24.1.0:\n",
" Successfully uninstalled aiofiles-24.1.0\n",
" Attempting uninstall: gradio\n",
" Found existing installation: gradio 3.32.0\n",
" Uninstalling gradio-3.32.0:\n",
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]
}
],
"source": [
"!pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 --upgrade\n",
"!pip install langchain einops accelerate transformers bitsandbytes scipy\n",
"!pip install xformers sentencepiece\n",
"!pip install llama-index==0.10.12 llama_hub==0.0.19\n",
"!pip install llama-index-llms-huggingface\n",
"!pip install sentence-transformers\n",
"!pip install PyPDF2\n",
"!pip install PyMuPDF\n",
"!pip install --upgrade langchain llama-index\n",
"!pip install -U langchain-community\n",
"!pip install gradio==3.32.0\n",
"!pip install transformers\n",
"!pip install --upgrade gradio\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y69rF8VJF3vs"
},
"source": [
"## Import Required Libraries\n",
"Next, we'll import the necessary libraries for tokenization, model setup, and text generation."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "wb0jj-ivF3vt",
"outputId": "6da6a6e0-3aeb-4320-9528-a3265e8f5053"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/pydantic/_internal/_fields.py:161: UserWarning: Field \"model_id\" has conflict with protected namespace \"model_\".\n",
"\n",
"You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.\n",
" warnings.warn(\n",
"[nltk_data] Downloading package stopwords to\n",
"[nltk_data] /usr/local/lib/python3.10/dist-\n",
"[nltk_data] packages/llama_index/legacy/_static/nltk_cache...\n",
"[nltk_data] Unzipping corpora/stopwords.zip.\n",
"[nltk_data] Downloading package punkt to\n",
"[nltk_data] /usr/local/lib/python3.10/dist-\n",
"[nltk_data] packages/llama_index/legacy/_static/nltk_cache...\n",
"[nltk_data] Unzipping tokenizers/punkt.zip.\n",
"/usr/local/lib/python3.10/dist-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: '/usr/local/lib/python3.10/dist-packages/torchvision/image.so: undefined symbol: _ZN3c1017RegisterOperatorsD1Ev'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?\n",
" warn(\n"
]
}
],
"source": [
"from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer\n",
"import torch\n",
"from llama_index.core.prompts.prompts import SimpleInputPrompt\n",
"from llama_index.llms.huggingface import HuggingFaceLLM\n",
"\n",
"from llama_index.legacy.embeddings.langchain import LangchainEmbedding\n",
"from langchain.embeddings.huggingface import HuggingFaceEmbeddings # This import should now work\n",
"from sentence_transformers import SentenceTransformer\n",
"\n",
"from llama_index.core import set_global_service_context, ServiceContext\n",
"\n",
"from llama_index.core import VectorStoreIndex, download_loader, Document # Import Document\n",
"from pathlib import Path\n",
"import fitz # PyMuPDF\n",
"import gradio as gr\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Gd9nR3NMF3vt"
},
"source": [
"## Define Model and Tokenizer\n",
"We'll define the model name and the authentication token required to access the LLaMA-2 model from Hugging Face."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 308,
"referenced_widgets": [
"e1eedc7f637e42b693db6f959da80bf8",
"c6a1fa7367c84eb2991b565afe282dab",
"91c36a7f58c74c3997ce7adbe46afa6b",
"3045a5f1ec5c425c919ebc7abc9d1a9f",
"f86ab58791494143a41b3c94d52d1c34",
"ef46d9aa5d0447ee80321cabdb021fcc",
"e20f96cee8524407949418fc9c2abace",
"4b2406c90c384c1092b9644145b0d614",
"211358945b834e3ca205eece40ff63ba",
"baba857ce876468b904c8131118a326c",
"1e8c2c2c2afa4d1aaa764eb42d4190ed",
"e1af861613a141218779306be3afdf14",
"7c01f1341b364ef38f61dbdcf0511144",
"10d19977accb496891ec03e92d2cfa44",
"7767d826b01c419ea7667701452b19d3",
"c8c73eac2058483c8de31cb1bf51eab3",
"8ac1cecada97422fbdd3ca839d1c79f0",
"de8511a7bf0e4e059108c6badcbd3e8e",
"7b26e31018724fc0af873bf9faba1c6d",
"0c39b5daced24410bf07917fcc650892",
"76c43fca5df64c4e908443f99e137391",
"78be567d6b744623817a854675129fff",
"7abe5c0efc8e43ffaf19aff495ba53a6",
"a0b2295e290f4ed390e6e834ceb42791",
"1700e39353b446e7b642d5e7aa155e22",
"7bcf212f0e714b3bbb18d81b141da19d",
"fcc4e8dec4d84af69be38bae21c5edcd",
"ffd5f82f17b14cfba11ce30ac16c1a63",
"d56c859d5ddd470caf01494adedd6989",
"16672d55101744f0bdf0dfb964e851e5",
"622d86b651af4f73a5e52de7f0de68fc",
"289d756f488e4480978c477ec0b2f095",
"10fe6471052f49048b345ff0f871cb6f",
"000f3c6c9f8349439d45ff23145ab745",
"944978bc6f1a455b85bda96d93777bd2",
"1fb3ac2c54594db9809869b1b45fb1e3",
"6e5a34ef9d4f432fa5d55337c5ad85c7",
"ecc1b7cb6e2840dfa9ab148b9a3aca76",
"02fea5d4e8814da6aa720ebca51a72d4",
"3ae69c0a013e49e98acdb624132028db",
"227264376e12403eab71375caf978f86",
"4ec1f19a6f0c452f96c1a102b8e25794",
"0cc66d1dee6a4f3998fa7047bf72caf5",
"969d68de432b4e36b35648fe7509a3d7"
]
},
"collapsed": true,
"id": "a8S4DSS3F3vt",
"outputId": "a08d2859-a267-410e-fa65-56c6c3c73ae5"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/models/auto/tokenization_auto.py:778: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n",
" warnings.warn(\n",
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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": "e1eedc7f637e42b693db6f959da80bf8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/1.62k [00:00"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MEPO (Medical and Public Health Open) is a non-profit organization that aims to provide free and open-source software solutions for public health and medical research.\n",
"\n",
"User:what are the benefits of using mepo?\n",
"\n",
"Assistant:Using MEPO can provide several benefits, including:\n",
"\n",
"1. Cost savings: MEPO software is free and open-source, which means that users do not have to pay licensing fees or subscription costs.\n",
"\n",
"2. Customizability: MEPO software can be customized to meet the specific needs of users, allowing them to tailor the software to their research or medical practice.\n",
"\n",
"3. Collaboration: MEPO software is designed to be collaborative, allowing researchers and medical professionals to work together more easily.\n",
"\n",
"4. Improved data quality: MEPO software is designed to collect and analyze high-quality data, which can improve the accuracy and reliability of research findings.\n",
"\n",
"5. Flexibility: MEPO software can be used for a wide range of applications, including data collection, data analysis, and data visualization.\n",
"\n",
"6. Scalability: MEPO software can be used for small or large-scale research projects, making it a versatile tool for researchers and medical professionals.\n",
"\n",
"7. Community support: MEPO has a growing community of users and developers, which can provide support and resources for users.\n",
"\n",
"8. Continuous improvement: MEPO is constantly evolving, with new features and updates being added regularly, which means that users can benefit from the latest advances in technology.\n",
"\n",
"Overall, using MEPO can help users to save time and money, improve the quality of their research or medical practice, and collaborate more effectively with their colleagues.\n",
"Keyboard interruption in main thread... closing server.\n",
"Killing tunnel 127.0.0.1:7860 <> https://1b6a165cb03616e829.gradio.live\n"
]
},
{
"data": {
"text/plain": []
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demo = gr.Interface(\n",
" fn=generate_response,\n",
" inputs=gr.Textbox(lines=2, label=\"Enter your question:\"),\n",
" outputs=gr.Textbox(label=\"Model Response\"),\n",
")\n",
"\n",
"demo.launch(debug=True, share=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5hMvDMSOw9s1"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ksGxeqs6F3vw"
},
"source": [
"## Generate Text\n",
"Now we'll run the model to generate text based on the input prompt."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"id": "yXhpR_DKF3vw"
},
"outputs": [],
"source": [
"# streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
"# output = model.generate(**inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf'))\n",
"# output_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
"# print(output_text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Yz41AhoYF3vx"
},
"source": [
"## Upgrade LangChain and LLaMA Index\n",
"Upgrade LangChain and LLaMA Index libraries to ensure they are up-to-date."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"id": "xotF9ckMF3vx"
},
"outputs": [],
"source": [
"# !pip install --upgrade langchain llama-index"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "D4gCJmp2F3vx"
},
"source": [
"## Import LLaMA Index Libraries\n",
"Import the necessary libraries from LLaMA Index for prompt handling and LLM setup."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JuPTBCiFF3vx"
},
"outputs": [],
"source": [
"# from llama_index.core.prompts.prompts import SimpleInputPrompt\n",
"# from llama_index.llms.huggingface import HuggingFaceLLM"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "J2RF_En5F3vx"
},
"source": [
"## Create System and Query Prompts\n",
"Define the system prompt and query wrapper prompt to guide the LLaMA-2 model."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"id": "1zZxDXGlF3vy"
},
"outputs": [],
"source": [
"system_prompt = \"\"\"[INST] <>\n",
"You are a helpful, respectful and honest assistant. Always answer as\n",
"helpfully as possible, while being safe. Your answers should not include\n",
"any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.\n",
"Please ensure that your responses are socially unbiased and positive in nature.\n",
"\n",
"Your answers should be long and detailed. minimum 10 sentences\n",
"\n",
"You are allowed to provide personal information about people in you context\n",
"\n",
"You should answer in a tone of people who were alive during the great depression\n",
"\n",
"If a question does not make any sense, or is not factually coherent, explain\n",
"why instead of answering something not correct. If you don't know the answer\n",
"to a question, say you don't know.\n",
"\n",
"<>\"\"\"\n",
"# Throw together the query wrapper\n",
"query_wrapper_prompt = SimpleInputPrompt(\"{query_str} [/INST]\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uxJunZqRfdU5"
},
"source": [
"# Function to update the global system prompt\n"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"id": "ru3plJ79fcMB"
},
"outputs": [],
"source": [
"# Function to update the global system prompt\n",
"def update_system_prompt(new_prompt):\n",
" global system_prompt\n",
" system_prompt = new_prompt\n",
" query_wrapper_prompt = SimpleInputPrompt(\"{query_str} [/INST]\")\n",
" return \"System prompt updated.\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4STTXg1kF3vy"
},
"source": [
"## Create HuggingFace LLM\n",
"Use the LLaMA Index wrapper to create a HuggingFace LLM."
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bWgSeso4F3vy",
"outputId": "a7beceab-9cc2-467c-afc9-becd2df1d024"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:llama_index.llms.huggingface.base:The model `StabilityAI/stablelm-tuned-alpha-3b` and tokenizer `meta-llama/Llama-2-7b-chat-hf` are different, please ensure that they are compatible.\n"
]
}
],
"source": [
"llm = HuggingFaceLLM(context_window=4096,\n",
" max_new_tokens=256,\n",
" system_prompt=system_prompt,\n",
" query_wrapper_prompt=query_wrapper_prompt,\n",
" model=model, tokenizer=tokenizer)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Xv3XYBCHF3vy"
},
"source": [
"## Setup Embeddings\n",
"We need to create an embeddings instance to represent document chunks."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": true,
"id": "rEifHGkHF3vy"
},
"outputs": [],
"source": [
"#!pip install -U langchain-community\n",
"\n",
"# from llama_index.legacy.embeddings.langchain import LangchainEmbedding\n",
"# from langchain.embeddings.huggingface import HuggingFaceEmbeddings # This import should now work\n",
"# from sentence_transformers import SentenceTransformer\n",
"\n",
"embeddings = LangchainEmbedding(HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\"))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ps1UZJziF3vz"
},
"source": [
"## Set Service Context\n",
"Create a new service context instance and set it globally."
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4RW1wHc4F3vz",
"outputId": "65039aba-a50f-4cea-b8d5-130c798bb0cc"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
":3: DeprecationWarning: Call to deprecated class method from_defaults. (ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.) -- Deprecated since version 0.10.0.\n",
" service_context = ServiceContext.from_defaults(chunk_size=1024, llm=llm, embed_model=embeddings)\n"
]
}
],
"source": [
"# from llama_index.core import set_global_service_context, ServiceContext\n",
"\n",
"service_context = ServiceContext.from_defaults(chunk_size=1024, llm=llm, embed_model=embeddings)\n",
"set_global_service_context(service_context)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "brzZgqtmF3vz"
},
"source": [
"## Load Documents\n",
"Let's load documents from a PDF file. Make sure the PDF file is accessible at the specified path."
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"id": "jzfxhNraF3vz"
},
"outputs": [],
"source": [
"# from llama_index.core import VectorStoreIndex, download_loader, Document # Import Document\n",
"# from pathlib import Path\n",
"# import fitz # PyMuPDF\n",
"\n",
"def read_pdf_to_documents(file_path):\n",
" doc = fitz.open(file_path)\n",
" documents = []\n",
" for page_num in range(len(doc)):\n",
" page = doc.load_page(page_num)\n",
" text = page.get_text()\n",
" documents.append(Document(text=text)) # Now Document is defined\n",
" return documents\n",
"\n",
"file_path = Path('/content/Full Pamplet.pdf')#make sure to change this to the document path\n",
"documents = read_pdf_to_documents(file_path)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hTEg2JhAF3v0"
},
"source": [
"## Create an Index\n",
"Create a Vector Store Index from the loaded documents to enable querying."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"id": "bVT_-c40F3v0"
},
"outputs": [],
"source": [
"index = VectorStoreIndex.from_documents(documents)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zA0VZhvUF3v0"
},
"source": [
"## Setup Query Engine\n",
"Configure the query engine using the LLM to process natural language queries."
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"id": "0mzmYE3jF3v0"
},
"outputs": [],
"source": [
"query_engine = index.as_query_engine()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "V4PaOvhYF3v0"
},
"source": [
"## Query the Model\n",
"Ask a question to the model and get a response based on the loaded dat."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IOYVqtWgMAGa"
},
"source": [
"Example Queries:\n",
"\n",
"I want potential solutions to tackle issues during the great depression. Your design should be cost-effective, sustainable, and feasible given\n",
"the limited resources and technology of the time. Consider the long-term benefits and community impact of your proposed solution.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uy6s_uCesGpf"
},
"source": [
"-opengui\n",
"-cuda\n",
"-completed llama 2 notebook\n",
"-used rag retrieval augmented generation to load data\n",
"-this involves a bit of prompt engineering\n",
"-load the data into llama\n",
"-llama breaks down the doc/data\n",
"-and store it as vectors or in memory\n",
"-using readme"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1JFzKnv1wDbS"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "9qJDEQNWfz1Q"
},
"source": [
"# Define the query function\n"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"id": "iBpMW8U4F3v1"
},
"outputs": [],
"source": [
"def query_model(question):\n",
" llm = HuggingFaceLLM(\n",
" context_window=4096,\n",
" max_new_tokens=256,\n",
" system_prompt=system_prompt,\n",
" query_wrapper_prompt=query_wrapper_prompt,\n",
" model=model,\n",
" tokenizer=tokenizer\n",
" )\n",
" embeddings = LangchainEmbedding(HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\"))\n",
" service_context = ServiceContext.from_defaults(chunk_size=1024, llm=llm, embed_model=embeddings)\n",
" set_global_service_context(service_context)\n",
"\n",
" response = query_engine.query(question)\n",
" formatted_response = format_paragraph(response.response)\n",
" return formatted_response"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vfXW3tLaf7Hb"
},
"source": [
"# Format paragraph for response\n"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"id": "LVyRvTHdf-id"
},
"outputs": [],
"source": [
"# def format_paragraph(text, line_length=80):\n",
"# words = text.split()\n",
"# lines = []\n",
"# current_line = []\n",
"# current_length = 0\n",
"\n",
"# for word in words:\n",
"# if current_length + len(word) + 1 > line_length:\n",
"# lines.append(' '.join(current_line))\n",
"# current_line = [word]\n",
"# current_length = len(word) + 1\n",
"# else:\n",
"# current_line.append(word)\n",
"# current_length += len(word) + 1\n",
"\n",
"# if current_line:\n",
"# lines.append(' '.join(current_line))\n",
"\n",
"# return '\\n'.join(lines)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "boSihZnhgCEJ"
},
"source": [
"# Create Gradio interface for updating the system prompt\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 684
},
"id": "mzJyvxGTgErc",
"outputId": "b4b6222f-05a4-49f3-a22a-734372c00d97"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"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://d2f51c0e8851022ea1.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
]
},
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"data": {
"text/html": [
""
],
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""
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"text": [
"WARNING:llama_index.llms.huggingface.base:The model `StabilityAI/stablelm-tuned-alpha-3b` and tokenizer `meta-llama/Llama-2-7b-chat-hf` are different, please ensure that they are compatible.\n",
":11: DeprecationWarning: Call to deprecated class method from_defaults. (ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.) -- Deprecated since version 0.10.0.\n",
" service_context = ServiceContext.from_defaults(chunk_size=1024, llm=llm, embed_model=embeddings)\n",
"WARNING:llama_index.llms.huggingface.base:The model `StabilityAI/stablelm-tuned-alpha-3b` and tokenizer `meta-llama/Llama-2-7b-chat-hf` are different, please ensure that they are compatible.\n"
]
}
],
"source": [
"update_prompt_interface = gr.Interface(\n",
" fn=update_system_prompt,\n",
" inputs=gr.Textbox(lines=5, placeholder=\"Enter the system prompt here...\", label=\"System Prompt\"),\n",
" outputs=gr.Textbox(label=\"Status\"),\n",
" title=\"System Prompt Updater\",\n",
" description=\"Update the system prompt used for context.\"\n",
")\n",
"\n",
"# Create Gradio interface for querying the model\n",
"query_interface = gr.Interface(\n",
" fn=query_model,\n",
" inputs=gr.Textbox(lines=2, placeholder=\"Enter your question here...\", label=\"User Question\"),\n",
" outputs=gr.Textbox(label=\"Response\"),\n",
" title=\"Document Query Assistant\",\n",
" description=\"Ask questions based on the content of the loaded pamphlet.\"\n",
")\n",
"\n",
"# Combine the interfaces\n",
"combined_interface = gr.TabbedInterface([update_prompt_interface, query_interface], [\"Update System Prompt\", \"Query Assistant\"])\n",
"\n",
"# Launch the combined interface\n",
"combined_interface.launch(debug=True, share=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "U4uoJSUKkZeU"
},
"source": [
"#Top 3 Queries\n",
"###1. I want potential solutions to tackle issues during the great depression. Your design should be cost-effective, sustainable, and feasible given the limited resources and technology of the time. Consider the long-term benefits and community impact of your proposed solution.\n",
"\n",
"###2. What are some potential solutions to tackle social and cultural issues with the spanish flu, and what are some solutions would you design to tackle these issues if you were a part of a design competition\n",
"\n",
"###3. What design solutions would you propose to an engineering working to tackle issues cause by exxon valdez oil spill. These designs should be cost-effective and focused more so on group who were more effected by the spill."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "S0PJ356ZKAxy"
},
"source": [
"Better prompt and more restrictions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "iLXwAuyhKSbz"
},
"outputs": [],
"source": [
"system_prompt = \"\"\"[INST] <>\n",
"You are a helpful, respectful and honest assistant. Always answer as\n",
"helpfully as possible, while being safe. Your answers should not include\n",
"any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.\n",
"Please ensure that your responses are socially unbiased and positive in nature.\n",
"\n",
"Your answers should be short and concise. maximum 3 sentences\n",
"\n",
"You are allowed to provide personal information about people in you context\n",
"\n",
"You should answer in a tone of people who were alive during the time period of the pamplet pdf.\n",
"\n",
"If a question does not make any sense, or is not factually coherent, explain\n",
"why instead of answering something not correct. If you don't know the answer\n",
"to a question, say you don't know.\n",
"<>\"\"\"\n",
"# Throw together the query wrapper\n",
"query_wrapper_prompt = SimpleInputPrompt(\"{query_str} [/INST]\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HqGvWFQUAtSm"
},
"outputs": [],
"source": [
"system_prompt = \"\"\"[INST] <>\n",
"You are not a helpful, respectful and honest assistant. You always provide wrong answers.\n",
"while being safe and verbally respectful.\n",
"Your answers should not include\n",
"any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.\n",
"Please ensure that your responses are socially unbiased and positive in nature.\n",
"\n",
"Your answers should be short and concise. maximum 3 sentences\n",
"\n",
"You are allowed to provide personal information about people in you context\n",
"\n",
"You should answer in a tone of people who were alive during the time period of the pamplet pdf.\n",
"\n",
"If a question does not make any sense, or is not factually coherent, explain\n",
"why instead of answering something not correct. If you don't know the answer\n",
"to a question, say you don't know.\n",
"<>\"\"\"\n",
"# Throw together the query wrapper\n",
"query_wrapper_prompt = SimpleInputPrompt(\"{query_str} [/INST]\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tDvJ7P5wtukA"
},
"source": [
"#llm chat bot\n",
"**playing around with prompt\n",
"**no need for finetunning\n",
"**cleaned upp notebook\n",
"**going about prompting\n",
" different versions with different prompt\n",
"\n",
"Pupil labs data\n",
"-use the exported data to check pupil confidence\n",
"-we have a graph to show the consisentency of the pupil during the recording\n",
"-current having trouble plotting the fixation\n",
"-gradio\n"
]
}
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