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},
"cells": [
{
"cell_type": "code",
"source": [
"!nvidia-smi"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yusGYxTc1JT5",
"outputId": "6233cdbb-ff7f-4457-e574-076e07bdae65"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Fri Sep 22 07:40:44 2023 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 63C P8 10W / 70W | 0MiB / 15360MiB | 0% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WITjlYIZXQSJ",
"outputId": "81fe615d-4253-4a28-a02b-9776d5f4818c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\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",
"transformers 4.33.2 requires tokenizers!=0.11.3,<0.14,>=0.11.1, but you have tokenizers 0.14.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m"
]
}
],
"source": [
"!pip install -Uqqq pip --progress-bar off\n",
"!pip install -qqq torch==2.0.1 --progress-bar off\n",
"!pip install -qqq transformers==4.33.2 --progress-bar off\n",
"!pip install -qqq langchain==0.0.299 --progress-bar off\n",
"!pip install -qqq chromadb==0.4.10 --progress-bar off\n",
"!pip install -qqq xformers==0.0.21 --progress-bar off\n",
"!pip install -qqq sentence_transformers==2.2.2 --progress-bar off\n",
"!pip install -qqq tokenizers==0.14.0 --progress-bar off\n",
"!pip install -qqq optimum==1.13.1 --progress-bar off\n",
"!pip install -qqq auto-gptq==0.4.2 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ --progress-bar off\n",
"!pip install -qqq unstructured==0.10.16 --progress-bar off"
]
},
{
"cell_type": "code",
"source": [
"!gdown 1aTtq5rgUseJrFVxNNthonAuf9CrI6JI6"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "l4ZunmHHzPf-",
"outputId": "9ab79f16-0ee2-4875-d439-09f7712f5a1c"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1aTtq5rgUseJrFVxNNthonAuf9CrI6JI6\n",
"To: /content/bitcoin.md\n",
"100% 21.9k/21.9k [00:00<00:00, 50.1MB/s]\n",
"Downloading...\n",
"From: https://drive.google.com/uc?id=1Zt93p6a5yoh5_7qsWAINVfl3tsfXOipc\n",
"To: /content/spotify-2023.csv\n",
"100% 106k/106k [00:00<00:00, 122MB/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import torch\n",
"\n",
"DEVICE = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
"MODEL_NAME = \"TheBloke/Llama-2-13b-Chat-GPTQ\""
],
"metadata": {
"id": "kCRnYhmiESDF"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from langchain import HuggingFacePipeline\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" MODEL_NAME, torch_dtype=torch.float16, trust_remote_code=True, device_map=\"auto\"\n",
")\n",
"\n",
"generation_config = GenerationConfig.from_pretrained(MODEL_NAME)\n",
"generation_config.max_new_tokens = 1024\n",
"generation_config.temperature = 0.0001\n",
"generation_config.top_p = 0.95\n",
"generation_config.do_sample = True\n",
"generation_config.repetition_penalty = 1.15\n",
"\n",
"text_pipeline = pipeline(\n",
" \"text-generation\",\n",
" model=model,\n",
" tokenizer=tokenizer,\n",
" generation_config=generation_config,\n",
")\n",
"\n",
"llm = HuggingFacePipeline(pipeline=text_pipeline, model_kwargs={\"temperature\": 0})"
],
"metadata": {
"id": "ty1a6FgJEoMM",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 241,
"referenced_widgets": [
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},
"outputId": "a8d422e5-774c-4258-917e-9dc96e659df2"
},
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)okenizer_config.json: 0%| | 0.00/727 [00:00[INST] <>\n",
"Act as a Machine Learning engineer who is teaching high school students.\n",
"<>\n",
"\n",
"{text} [/INST]\n",
"\"\"\"\n",
"\n",
"prompt = PromptTemplate(\n",
" input_variables=[\"text\"],\n",
" template=template,\n",
")"
],
"metadata": {
"id": "s572UP3MShMY"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"text = \"Explain what are Deep Neural Networks in 2-3 sentences\""
],
"metadata": {
"id": "PbBW3r7sUwHg"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(prompt.format(text=text))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HmU2U_R8Xz-A",
"outputId": "169b7b8d-7438-475e-c8a4-af32fe78f665"
},
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"[INST] <>\n",
"Act as a Machine Learning engineer who is teaching high school students.\n",
"<>\n",
"\n",
"Explain what are Deep Neural Networks in 2-3 sentences [/INST]\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%time\n",
"result = llm(prompt.format(text=text))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UavB1ZxiT6ns",
"outputId": "fa274921-56db-4ec1-ee43-3709f7791a2d"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"CPU times: user 7.03 s, sys: 92.6 ms, total: 7.12 s\n",
"Wall time: 7.26 s\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(result)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BmQLAM7mmuAo",
"outputId": "45f9d530-80d2-4496-861c-37f42a305b17"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Hey there, young minds! So, you wanna know about Deep Neural Networks? Well, imagine you have a super powerful computer that can learn and make decisions all on its own, kinda like how your brain works! Deep Neural Networks are like a bunch of these computers working together to solve really tough problems, like recognizing pictures or understanding speech. They're like the ultimate team players, and they're changing the game in fields like self-driving cars, medical diagnosis, and more!\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Create Chain"
],
"metadata": {
"id": "965Lvj1TZeyJ"
}
},
{
"cell_type": "code",
"source": [
"from langchain.chains import LLMChain\n",
"\n",
"chain = LLMChain(llm=llm, prompt=prompt)"
],
"metadata": {
"id": "nnSDi7ucZf4J"
},
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"source": [
"%%time\n",
"result = chain.run(text)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BLU2L1UJjx0V",
"outputId": "0d48bacd-299b-49f5-dd10-bd3fc5809169"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"CPU times: user 6.29 s, sys: 50.4 ms, total: 6.34 s\n",
"Wall time: 6.4 s\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(result)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FR5NxpWynKSZ",
"outputId": "d71bb75c-0532-4eff-8f9c-81212d91a649"
},
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Hey there, young minds! So, you wanna know about Deep Neural Networks? Well, imagine you have a super powerful computer that can learn and make decisions all on its own, kinda like how your brain works! Deep Neural Networks are like a bunch of these computers working together to solve really tough problems, like recognizing pictures or understanding speech. They're like the ultimate team players, and they're changing the game in fields like self-driving cars, medical diagnosis, and more!\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Chaining Chains"
],
"metadata": {
"id": "_RD5Upi-oi32"
}
},
{
"cell_type": "code",
"source": [
"template = \"[INST] Use the summary {summary} and give 3 examples of practical applications with 1 sentence explaining each [/INST]\"\n",
"\n",
"examples_prompt = PromptTemplate(\n",
" input_variables=[\"summary\"],\n",
" template=template,\n",
")\n",
"examples_chain = LLMChain(llm=llm, prompt=examples_prompt)"
],
"metadata": {
"id": "Ee3xg8r0q4mw"
},
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from langchain.chains import SimpleSequentialChain\n",
"\n",
"multi_chain = SimpleSequentialChain(chains=[chain, examples_chain], verbose=True)"
],
"metadata": {
"id": "hUdH_00mpoVW"
},
"execution_count": 17,
"outputs": []
},
{
"cell_type": "code",
"source": [
"result = multi_chain.run(text)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CIK4uO5-xzpW",
"outputId": "ac86c4ed-08b3-44c0-9e7c-40c701aab304"
},
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
"\u001b[36;1m\u001b[1;3m\n",
"Hey there, young minds! So, you wanna know about Deep Neural Networks? Well, imagine you have a super powerful computer that can learn and make decisions all on its own, kinda like how your brain works! Deep Neural Networks are like a bunch of these computers working together to solve really tough problems, like recognizing pictures or understanding speech. They're like the ultimate team players, and they're changing the game in fields like self-driving cars, medical diagnosis, and more!\u001b[0m\n",
"\u001b[33;1m\u001b[1;3m Sure thing! Here are three examples of practical applications of Deep Neural Networks:\n",
"\n",
"1. Self-Driving Cars: Deep Neural Networks can be used to train autonomous vehicles to recognize objects on the road, such as pedestrians, other cars, and traffic lights, allowing them to make safe and efficient decisions.\n",
"2. Medical Diagnosis: Deep Neural Networks can be trained on large datasets of medical images and patient data to help doctors diagnose diseases and conditions more accurately and efficiently than ever before.\n",
"3. Speech Recognition: Deep Neural Networks can be used to improve speech recognition systems, enabling devices like smartphones and virtual assistants to better understand and respond to voice commands.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(result.strip())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UKmt2epJyATl",
"outputId": "0e4da6b8-3907-4b7a-f47f-a0c7c3da42a8"
},
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sure thing! Here are three examples of practical applications of Deep Neural Networks:\n",
"\n",
"1. Self-Driving Cars: Deep Neural Networks can be used to train autonomous vehicles to recognize objects on the road, such as pedestrians, other cars, and traffic lights, allowing them to make safe and efficient decisions.\n",
"2. Medical Diagnosis: Deep Neural Networks can be trained on large datasets of medical images and patient data to help doctors diagnose diseases and conditions more accurately and efficiently than ever before.\n",
"3. Speech Recognition: Deep Neural Networks can be used to improve speech recognition systems, enabling devices like smartphones and virtual assistants to better understand and respond to voice commands.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Embeddings"
],
"metadata": {
"id": "_62vjIq1mRX_"
}
},
{
"cell_type": "code",
"source": [
"from langchain.document_loaders import UnstructuredMarkdownLoader\n",
"\n",
"loader = UnstructuredMarkdownLoader(\"bitcoin.md\")\n",
"docs = loader.load()\n",
"len(docs)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7roZ03-EzEhT",
"outputId": "12f0dfab-f4c7-4203-a078-3adf0998068c"
},
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Unzipping tokenizers/punkt.zip.\n",
"[nltk_data] Downloading package averaged_perceptron_tagger to\n",
"[nltk_data] /root/nltk_data...\n",
"[nltk_data] Unzipping taggers/averaged_perceptron_tagger.zip.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1"
]
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"source": [
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)\n",
"texts = text_splitter.split_documents(docs)\n",
"len(texts)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VViuX4Oh8PJC",
"outputId": "43a4d16e-6b0e-4dc2-9984-8c7f4dba9060"
},
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"29"
]
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"source": [
"from langchain.embeddings import HuggingFaceEmbeddings\n",
"\n",
"embeddings = HuggingFaceEmbeddings(\n",
" model_name=\"thenlper/gte-large\",\n",
" model_kwargs={\"device\": \"cuda\"},\n",
" encode_kwargs={\"normalize_embeddings\": True},\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 561,
"referenced_widgets": [
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"5a3b6b2370374725b134ba0c283bf447",
"b4501205b90b4657b48ff040791a4d9e",
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"1850101970ab49e08e474eee2d0ca1c1",
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"81e08802055d4110b61f1235f5653c20",
"177938998de345debaa2df043c6701e2",
"871a76e7dc3446c89a9fa3c21ca364dc",
"8d74069550394361b53ed706f8b68f62",
"4ff83fcb6a334e7a9deceff4407a593d",
"5c31e8b33b8646f0971e137962925103",
"5265f734919f47a6b421e39064594900",
"2b992c41fe9248a7b69ac74cfbc0c010",
"1ddeb20172ca4d15aa46ef484acb452d",
"0ad5ccf24c8d4ebc985fcb98d74aa685",
"ff7c3fb375c4421fa2e3cb2226fc7ef8",
"8cf27ec18b8944ad93f2b7cdfb855332",
"cdbe097fb224421a93dd90323dccc7d3",
"60c58747914e4a81b6c36bbac42587dd",
"084fa1358df0407da0edce2a81782125",
"ed22ad1432c5482f8fce6c8da5bd73b2",
"fc74bf66db314eeb8c2cce0dc1d1f650",
"03ba686ad9ba489c8676beda7f100206",
"5477c310ce1746728b846f99c06d7710",
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"614e954c11ef443eb2022d6a651c52c5",
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"7cbcbae6c1814cd78b3c0ef5bce1744c",
"39ba8a14a2c74119ad4050008dacbdae",
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"f6b4a4a246244675a8cda05365ac3d8e",
"1c543762653f4387b8c5621641c9c723",
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"cf42b2a6781e48a68164ffc976a47419",
"347a486ae6b54d9c8c8462fc2a33171a",
"030b2781757d436b98761817b8c8910f",
"f4ed48a4f6d24d7cabf0f7e2c73d185f",
"38ef70faee304332aff5b9a4945d51d0",
"8f745b6c480f40be80e28b8d93f81ea5",
"5bd5c25e45a54e7188dbb6fd4e329653",
"6f565fb0f509482289c90d8d69eea327",
"160e3d96177341da86e56171b0800405",
"db61727ecd804f09bc5561a4a1f3fc4f",
"ae2fb9e7b17744318be980a44116ac7a",
"bd7ee193553a44c8b003d12cf7015852",
"374bf3060bf349e0b2b5b58199f752df",
"a897266458e54d7e83abd1cfaf8c13fd",
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"12b40269c1a44d83a3aafaca5e883fe3",
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"af0e0e33600444c1b09b59b1838faa97",
"ebc619fce5cf4142a8a07c87695db864",
"3c1b605148924467a00946fc0f8f89b5",
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"de55ba9de92d4ceb93319512896133d9",
"fb58acd01bc84e14a24ecd2f1c1605f1",
"49f7edfd283d4905965e684033f77b5f",
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"541e068f0cde4b9eba302e31483ed0fd",
"cdbb6d2989f64f54b9b416d5fa8b31f7",
"ed1641dc0ce447baaa9d9e0d5b55f56d",
"e3b3e47ba35e4e41b243e196d9b4417c",
"21753c765d6e4142acf248dbb43eae8b",
"ec4735155e204cd0bc7dafb21878b406",
"15d67bccf3564854aba7c8b072fe1c98",
"0fe94ce8b9b8482e99d86af1fe2bbb6c",
"308dd495cdf546fc91c6d885251be9d1",
"85bcca85291b415ea5e32910ad63f1f3",
"fd383d1a0a5f46afbb0ab813430b05e9",
"a84b0d045fc24658a357e16c3c2248eb",
"7ce22115990e445dbf24fc520f88eea5",
"eb64a89a699c4a609942e86d27ba0f24",
"2648adb9e1494a7980fb547054a6ec3c",
"8b339e67c427414ea797a2336a24dde0",
"7f79c0e1d34f452db28b023f63126153",
"88190118b6c84041b94a34de87cbf283",
"0712dcad629f4496bb9409ce06f7391a",
"46963bd4b01442c68ef9dc45b9038649",
"5ea703e63a3c498484f744fe635ab2c1",
"a84d9ca402c34256ba58bd9025e64f62",
"53fa706d31f3405aa5ce976afe7d1548"
]
},
"id": "5ghurtYOmSTl",
"outputId": "8165ad05-ec97-472f-8ca7-ea1851556be7"
},
"execution_count": 22,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)bc7c8/.gitattributes: 0%| | 0.00/1.52k [00:00[INST] <>\n",
"Act as a cryptocurrency expert. Use the following information to answer the question at the end.\n",
"<>\n",
"\n",
"{context}\n",
"\n",
"{question} [/INST]\n",
"\"\"\""
],
"metadata": {
"id": "6FKZMM959Wwx"
},
"execution_count": 27,
"outputs": []
},
{
"cell_type": "code",
"source": [
"prompt = PromptTemplate(template=template, input_variables=[\"context\", \"question\"])"
],
"metadata": {
"id": "8aM9PwukAJER"
},
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from langchain.chains import RetrievalQA\n",
"\n",
"qa_chain = RetrievalQA.from_chain_type(\n",
" llm=llm,\n",
" chain_type=\"stuff\",\n",
" retriever=db.as_retriever(search_kwargs={\"k\": 2}),\n",
" return_source_documents=True,\n",
" chain_type_kwargs={\"prompt\": prompt},\n",
")"
],
"metadata": {
"id": "ra5Q0RgaAKuK"
},
"execution_count": 29,
"outputs": []
},
{
"cell_type": "code",
"source": [
"%%time\n",
"result = qa_chain(\n",
" \"How does proof-of-work solves the majority decision making problem? Explain like I am five.\"\n",
")\n",
"result"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ga6FNKU5AwUl",
"outputId": "13f82445-5ae2-4c80-fc22-57ab6718410a"
},
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"CPU times: user 16.1 s, sys: 220 ms, total: 16.4 s\n",
"Wall time: 16.5 s\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'query': 'How does proof-of-work solves the majority decision making problem? Explain like I am five.',\n",
" 'result': \"\\nOkay, little buddy! So you know how there are lots of different people who want to make decisions about things like what games to play or what food to eat? Well, sometimes these people might not agree on what they want, and that's where proof-of-work comes in!\\n\\nProof-of-work is like a special kind of vote that shows how much work someone did. It's like if you had to do a puzzle before you could say what game you wanted to play. The person who does the most work gets to choose what game everyone plays!\\n\\nBut here's the cool thing about proof-of-work: it makes sure that only the people who really want to play the game get to choose. If someone tries to cheat and say they did more work than they actually did, the other kids won't believe them because they can see how much work was really done.\\n\\nSo, when we use proof-of-work to make decisions, we can be sure that the person who chooses the game is the one who really wants to play it, and not just someone who wants to cheat and pick their favorite game! And that way, everyone gets to play a fair game!\",\n",
" 'source_documents': [Document(page_content='The proof-of-work also solves the problem of determining representation\\nin majority decision making. If the majority were based on\\none-IP-address-one-vote, it could be subverted by anyone able to\\nallocate many IPs. Proof-of-work is essentially one-CPU-one-vote. The\\nmajority decision is represented by the longest chain, which has the\\ngreatest proof-of-work effort invested in it. If a majority of CPU power\\nis controlled by honest nodes, the honest chain will grow the fastest\\nand outpace any competing chains. To modify a past block, an attacker\\nwould have to redo the proof-of-work of the block and all blocks after\\nit and then catch up with and surpass the work of the honest nodes. We\\nwill show later that the probability of a slower attacker catching up\\ndiminishes exponentially as subsequent blocks are added.', metadata={'source': 'bitcoin.md'}),\n",
" Document(page_content='and rejecting invalid blocks by refusing to work on them. Any needed\\nrules and incentives can be enforced with this consensus mechanism.', metadata={'source': 'bitcoin.md'})]}"
]
},
"metadata": {},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"source": [
"print(result[\"result\"].strip())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FHS5iYJ1A9NK",
"outputId": "a6c4d1bc-f31a-4213-a409-270aea6ca245"
},
"execution_count": 31,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Okay, little buddy! So you know how there are lots of different people who want to make decisions about things like what games to play or what food to eat? Well, sometimes these people might not agree on what they want, and that's where proof-of-work comes in!\n",
"\n",
"Proof-of-work is like a special kind of vote that shows how much work someone did. It's like if you had to do a puzzle before you could say what game you wanted to play. The person who does the most work gets to choose what game everyone plays!\n",
"\n",
"But here's the cool thing about proof-of-work: it makes sure that only the people who really want to play the game get to choose. If someone tries to cheat and say they did more work than they actually did, the other kids won't believe them because they can see how much work was really done.\n",
"\n",
"So, when we use proof-of-work to make decisions, we can be sure that the person who chooses the game is the one who really wants to play it, and not just someone who wants to cheat and pick their favorite game! And that way, everyone gets to play a fair game!\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%%time\n",
"result = qa_chain(\n",
" \"Summarize the privacy compared to the traditional banking model in 2-3 sentences.\"\n",
")\n",
"result"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8xywFJAGZiGA",
"outputId": "2e5ba391-6bfa-4f28-b6cb-7f353577795a"
},
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"CPU times: user 8 s, sys: 267 ms, total: 8.27 s\n",
"Wall time: 8.32 s\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'query': 'Summarize the privacy compared to the traditional banking model in 2-3 sentences.',\n",
" 'result': '\\nIn contrast to the traditional banking model, which relies on limited access to information to maintain privacy, cryptocurrencies like Bitcoin provide greater privacy by keeping public keys anonymous, allowing individuals to send and receive funds without revealing their identities. This is similar to the level of information released by stock exchanges, where the time and size of individual trades are made public, but without telling who the parties were. Additionally, cryptocurrencies use decentralized networks and encryption techniques to protect user data and prevent unauthorized access, further enhancing privacy compared to traditional banking systems.',\n",
" 'source_documents': [Document(page_content='Privacy\\n\\nThe traditional banking model achieves a level of privacy by limiting\\naccess to information to the parties involved and the trusted third\\nparty. The necessity to announce all transactions publicly precludes\\nthis method, but privacy can still be maintained by breaking the flow of\\ninformation in another place: by keeping public keys anonymous. The\\npublic can see that someone is sending an amount to someone else, but\\nwithout information linking the transaction to anyone. This is similar\\nto the level of information released by stock exchanges, where the time\\nand size of individual trades, the \\\\\"tape\\\\\", is made public, but without\\ntelling who the parties were.', metadata={'source': 'bitcoin.md'}),\n",
" Document(page_content='[^7]: R.C. Merkle, \\\\\"Protocols for public key cryptosystems,\\\\\" In Proc.\\n 1980 Symposium on Security and Privacy, IEEE Computer Society, pages\\n 122-133, April 1980.\\n\\n[^8]: H. Massias, X.S. Avila, and J.-J. Quisquater, \\\\\"Design of a\\n secure timestamping service with minimal trust requirements,\\\\\"\\n In 20th Symposium on Information Theory in the Benelux,\\n May 1999.\\n\\n[^9]: S. Haber, W.S. Stornetta, \\\\\"Secure names for bit-strings,\\\\\" In\\n Proceedings of the 4th ACM Conference on Computer and\\n Communications Security, pages 28-35, April 1997.\\n\\n[^10]: W. Feller, \\\\\"An introduction to probability theory and its\\n applications,\\\\\" 1957.', metadata={'source': 'bitcoin.md'})]}"
]
},
"metadata": {},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"source": [
"from textwrap import fill\n",
"\n",
"print(fill(result[\"result\"].strip(), width=80))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ozbpL8ssaG48",
"outputId": "7b9d5628-daa8-4a3e-bad6-1272292683fd"
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"In contrast to the traditional banking model, which relies on limited access to\n",
"information to maintain privacy, cryptocurrencies like Bitcoin provide greater\n",
"privacy by keeping public keys anonymous, allowing individuals to send and\n",
"receive funds without revealing their identities. This is similar to the level\n",
"of information released by stock exchanges, where the time and size of\n",
"individual trades are made public, but without telling who the parties were.\n",
"Additionally, cryptocurrencies use decentralized networks and encryption\n",
"techniques to protect user data and prevent unauthorized access, further\n",
"enhancing privacy compared to traditional banking systems.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Python Agent"
],
"metadata": {
"id": "5hXRTPP007iy"
}
},
{
"cell_type": "code",
"source": [
"from langchain.agents.agent_toolkits import create_python_agent\n",
"from langchain.tools.python.tool import PythonREPLTool\n",
"\n",
"agent = create_python_agent(llm=llm, tool=PythonREPLTool(), verbose=True)"
],
"metadata": {
"id": "EDhTr9Wt-kbk"
},
"execution_count": 47,
"outputs": []
},
{
"cell_type": "code",
"source": [
"result = agent.run(\"Calculate the square root of a number and divide it by 2\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0BYSsepd-wmx",
"outputId": "0e4c926b-e58f-4b93-d70e-8b8d3d154f4e"
},
"execution_count": 61,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m Hmmm, well we need to calculate the square root first\n",
"Action: Python_REPL\n",
"Action Input: import math\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m Now we need to call the sqrt function\n",
"Action: Python_REPL\n",
"Action Input: from math import sqrt\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m We need to pass in the argument\n",
"Action: Python_REPL\n",
"Action Input: x = 16\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m Let's call the sqrt function\n",
"Action: Python_REPL\n",
"Action Input: y = sqrt(x)\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m Now let's divide by 2\n",
"Action: Python_REPL\n",
"Action Input: z = y / 2\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m Ah ha! The answer is 4\n",
"Final Answer: 4\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"result"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "ZEvYFpXtAg7B",
"outputId": "47d72cd2-ec95-4f6f-d6d2-58398e993e4d"
},
"execution_count": 62,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'4'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 62
}
]
},
{
"cell_type": "code",
"source": [
"from math import sqrt\n",
"\n",
"x = 16\n",
"y = sqrt(x)\n",
"z = y / 2\n",
"z"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jZgOu8y-Ajhd",
"outputId": "68f48f79-87d2-4706-cbd7-e8b1d429a270"
},
"execution_count": 63,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"2.0"
]
},
"metadata": {},
"execution_count": 63
}
]
}
]
}