{ "cells": [ { "cell_type": "markdown", "id": "dfe37963-1af6-44fc-a841-8e462443f5e6", "metadata": {}, "source": [ "## Chat with Ed - the Expert on LLM engineering\n", "This project will: \n", "- use subtitle files from Ed Donners excellent LLM engineering course on Udemy.\n", "- use Document loading using Langchain\n", "- use Vectorization, embeddings and store vectors in a Chroma DB\n", "- use RAG (Retrieval Augmented Generation) to ensure our question/answering assistant has high accuracy.\n", "\n", "These subtitles can be downloaded using the following process:\n", "- Using an android phone, download Udemy app and open the LLM engineering course. \n", "- There is option to download the videos as single files or section wise. \n", "- Download them and along with those videos subs or cc are also downloaded as .srt’s.\n", "- Plug in your laptop to the android phone using USB and select file transfer in the notification.\n", "- Open a file explorer and copy the subtitle files (srt format)\n", "- Here’s the location of subs in android \"internal storage/android/data/com.udemy.android/files/udemy-subtitle-downloads\"\n", "\n", "the raw srt files are stored in the folder \"subtitles/srts\". The code below will use the langchain textloader but will preprocess the srt files to remove the timestamps.\n", "\n", "### Note: this is only for educational and testing purposes and you should contact Ed Donnner to seek his permission if you want to use the subtitles.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "ba2779af-84ef-4227-9e9e-6eaf0df87e77", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import glob\n", "from dotenv import load_dotenv\n", "import gradio as gr\n", "import re" ] }, { "cell_type": "code", "execution_count": 2, "id": "802137aa-8a74-45e0-a487-d1974927d7ca", "metadata": {}, "outputs": [], "source": [ "# imports for langchain\n", "\n", "from langchain.document_loaders import DirectoryLoader, TextLoader\n", "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain.schema import Document\n", "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n", "from langchain_chroma import Chroma\n", "import numpy as np\n", "from sklearn.manifold import TSNE\n", "import plotly.graph_objects as go\n", "from langchain.memory import ConversationBufferMemory\n", "from langchain.chains import ConversationalRetrievalChain" ] }, { "cell_type": "code", "execution_count": 3, "id": "58c85082-e417-4708-9efe-81a5d55d1424", "metadata": {}, "outputs": [], "source": [ "# price is a factor for our company, so we're going to use a low cost model\n", "\n", "MODEL = \"gpt-4o-mini\"\n", "db_name = \"vector_db\"" ] }, { "cell_type": "code", "execution_count": 4, "id": "ee78efcb-60fe-449e-a944-40bab26261af", "metadata": {}, "outputs": [], "source": [ "# Load environment variables in a file called .env\n", "\n", "load_dotenv()\n", "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')" ] }, { "cell_type": "code", "execution_count": 5, "id": "730711a9-6ffe-4eee-8f48-d6cfb7314905", "metadata": {}, "outputs": [], "source": [ "# Read in documents using LangChain's loaders\n", "# Take everything in all the sub-folders of our knowledgebase\n", "\n", "folders = glob.glob(\"subtitles/srts/*\")\n", "\n", "# With thanks to CG and Jon R, students on the course, for this fix needed for some users \n", "text_loader_kwargs = {'encoding': 'utf-8'}\n", "# If that doesn't work, some Windows users might need to uncomment the next line instead\n", "# text_loader_kwargs={'autodetect_encoding': True}\n", "\n", "def preprocess_srt_content(content):\n", " \"\"\"\n", " Preprocess the content of an SRT file to remove timing information and the WEBVTT header.\n", " \"\"\"\n", " # Remove the WEBVTT header\n", " content = re.sub(r'^WEBVTT\\s*', '', content, flags=re.IGNORECASE)\n", " # Remove timing lines (e.g., 00:00.680 --> 00:08.540)\n", " content = re.sub(r'\\d{2}:\\d{2}\\.\\d{3} --> \\d{2}:\\d{2}\\.\\d{3}', '', content)\n", " # Remove extra newlines and strip leading/trailing whitespace\n", " return \"\\n\".join(line.strip() for line in content.splitlines() if line.strip())\n", "\n", "documents = []\n", "for folder in folders:\n", " video_number = os.path.basename(folder)\n", " loader = DirectoryLoader(folder, glob=\"**/en_US.srt\", loader_cls=TextLoader)\n", " folder_docs = loader.load()\n", "\n", " for doc in folder_docs:\n", " # Preprocess the document content\n", " cleaned_content = preprocess_srt_content(doc.page_content)\n", " # Replace the original content with the cleaned content\n", " doc.page_content = cleaned_content\n", " # Add metadata\n", " doc.metadata[\"video_number\"] = video_number\n", " documents.append(doc)" ] }, { "cell_type": "code", "execution_count": 6, "id": "7310c9c8-03c1-4efc-a104-5e89aec6db1a", "metadata": {}, "outputs": [], "source": [ "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", "chunks = text_splitter.split_documents(documents)" ] }, { "cell_type": "code", "execution_count": 7, "id": "cd06e02f-6d9b-44cc-a43d-e1faa8acc7bb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "217" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(chunks)" ] }, { "cell_type": "code", "execution_count": 8, "id": "2c54b4b6-06da-463d-bee7-4dd456c2b887", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Video numbers found: 59507785, 59472503, 59170107, 60616493, 59504887, 59297735, 59472429, 59170291, 60595637, 59473019, 59472441, 59295423, 59170043, 59472067, 59295363, 59472425, 59297723, 59473137, 59473159, 59669375, 59472011, 59295431, 59673721, 59473101, 59167015, 59670087, 60619429, 59667365, 59673639, 59169985, 59507489, 60620143, 59505329, 59670369, 59295549, 60395261, 59668181, 59671231, 60619281, 59506713, 59472491, 59295579, 59167007, 59167009, 59666211, 59673431, 59671567, 59170055, 59472017, 59473021, 59297599, 59472027, 59166947, 59473201, 60619123, 59472873, 59295601, 60614591, 60614541, 59472007, 59507313, 60619721, 59297595, 59472693, 59295527, 60619501, 59166981, 59166421, 59507423, 59170165, 59166951, 59170227, 59673663, 59670121, 59166453, 60616845, 59471979, 59670171, 59503705, 59668923, 60617163, 60616629, 59297693, 59166915, 60617259, 59166847, 59295459, 60619439, 59297593, 59295619, 59472883, 59295439, 59670933, 60619651, 59670073, 59166465, 59295429, 59669631, 59170233, 59472333, 59507635, 60619227, 59667829, 59166353, 60614589, 59295599, 59507687, 59671441, 59170057, 59670259, 59170235, 59472307, 59472421, 59667841, 59667357, 59166949, 59170297, 59504785, 59170093, 59166443, 59673595, 59669211, 60620025, 59297773, 60619883, 60616423, 59295493, 59166461, 60616855, 59297601, 59295435, 59673449, 59503703, 59472505, 59295377, 59166281, 59507435, 59297575, 59504769, 59170037, 60622463, 59508289, 60616663, 60616895, 60620375, 60619247, 59665129, 59170135, 59297743, 59169991, 59506929, 60616407, 59508297, 59297603, 60616927, 60617255, 59295441, 59668027, 59297609, 60620169, 59472383, 59297585, 60616623, 60617251, 59666831, 59295553, 59473191, 59473089, 59669217, 59508175, 60616833, 59297749, 59295609, 59295545, 59669389, 59170025, 60619619, 60620397, 59166481, 59295541, 59297561, 59166919, 59507329, 59506611, 59170223, 60619447, 59166317, 59473071, 60619299, 59507017, 59509185, 59170255, 60619577, 59671221, 60619289, 59508121, 59295583, 60619149, 59665127, 59473147, 59295451, 59271655, 59472137, 59295607, 59669049, 59295587, 59472463, 59506507, 59472413, 59297721, 59508057, 59508055, 59671315, 59297733, 60619275, 60620395, 59505337\n" ] } ], "source": [ "video_numbers = set(chunk.metadata['video_number'] for chunk in chunks)\n", "print(f\"Video numbers found: {', '.join(video_numbers)}\")" ] }, { "cell_type": "markdown", "id": "77f7d2a6-ccfa-425b-a1c3-5e55b23bd013", "metadata": {}, "source": [ "## A sidenote on Embeddings, and \"Auto-Encoding LLMs\"\n", "\n", "We will be mapping each chunk of text into a Vector that represents the meaning of the text, known as an embedding.\n", "\n", "OpenAI offers a model to do this, which we will use by calling their API with some LangChain code.\n", "\n", "This model is an example of an \"Auto-Encoding LLM\" which generates an output given a complete input.\n", "It's different to all the other LLMs we've discussed today, which are known as \"Auto-Regressive LLMs\", and generate future tokens based only on past context.\n", "\n", "Another example of an Auto-Encoding LLMs is BERT from Google. In addition to embedding, Auto-encoding LLMs are often used for classification.\n", "\n", "### Sidenote\n", "\n", "In week 8 we will return to RAG and vector embeddings, and we will use an open-source vector encoder so that the data never leaves our computer - that's an important consideration when building enterprise systems and the data needs to remain internal." ] }, { "cell_type": "code", "execution_count": 9, "id": "78998399-ac17-4e28-b15f-0b5f51e6ee23", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vectorstore created with 217 documents\n" ] } ], "source": [ "# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n", "# Chroma is a popular open source Vector Database based on SQLLite\n", "\n", "embeddings = OpenAIEmbeddings()\n", "\n", "# If you would rather use the free Vector Embeddings from HuggingFace sentence-transformers\n", "# Then replace embeddings = OpenAIEmbeddings()\n", "# with:\n", "# from langchain.embeddings import HuggingFaceEmbeddings\n", "# embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n", "\n", "# Delete if already exists\n", "\n", "if os.path.exists(db_name):\n", " Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n", "\n", "# Create vectorstore\n", "\n", "vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n", "print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "057868f6-51a6-4087-94d1-380145821550", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The vectors have 1,536 dimensions\n" ] } ], "source": [ "# Get one vector and find how many dimensions it has\n", "\n", "collection = vectorstore._collection\n", "sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n", "dimensions = len(sample_embedding)\n", "print(f\"The vectors have {dimensions:,} dimensions\")" ] }, { "cell_type": "markdown", "id": "b0d45462-a818-441c-b010-b85b32bcf618", "metadata": {}, "source": [ "## Visualizing the Vector Store\n", "\n", "Let's take a minute to look at the documents and their embedding vectors to see what's going on." ] }, { "cell_type": "code", "execution_count": 11, "id": "bf021654-a60b-4905-bdb5-d4517bd0c297", "metadata": {}, "outputs": [], "source": [ "# Convert the video numbers into unique colors that we can visualize\n", "import hashlib\n", "\n", "def video_numbers_to_hex_colors(video_numbers):\n", " return [f\"#{hashlib.sha256(v.encode()).hexdigest()[:6]}\" for v in video_numbers]" ] }, { "cell_type": "code", "execution_count": 13, "id": "b98adf5e-d464-4bd2-9bdf-bc5b6770263b", "metadata": {}, "outputs": [], "source": [ "# Prework\n", "\n", "result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n", "vectors = np.array(result['embeddings'])\n", "documents = result['documents']\n", "video_numbers = [metadata['video_number'] for metadata in result['metadatas']]\n", "colors = video_numbers_to_hex_colors(video_numbers)" ] }, { "cell_type": "code", "execution_count": 14, "id": "427149d5-e5d8-4abd-bb6f-7ef0333cca21", "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hoverinfo": "text", "marker": { "color": [ "#f8d349", "#d6d07a", "#a958c9", "#7341ee", "#268bba", "#4862ce", "#dd8cd7", "#6a6c06", "#8a29da", "#0d2037", "#805527", "#e69670", "#75b5e3", "#796278", "#6d4052", "#1f6ab0", "#99fe53", "#3f0a72", "#fe8e92", "#c3e1f2", "#f645e0", "#b43417", "#e0a8df", "#7740be", "#43c2e8", "#64f999", "#2cde7f", "#29fa15", "#580c96", "#10384a", "#845aa9", "#7f03bd", "#2b3af3", "#335dcf", "#22398f", "#c932c1", "#d43c00", "#e6f378", "#08808d", "#6a0fce", "#e1b5db", "#75195e", "#6ff3c5", "#4099c1", "#b25d7b", "#d65c3a", "#9b9d6e", "#fc2b74", "#571122", "#422abb", "#efed10", "#dfc6c7", "#02cada", "#3ec815", "#8e8cab", "#df5d2e", "#c457d7", "#ec0a37", "#da28db", "#2d7f7d", "#b27d2e", "#d01b19", "#fb9dce", "#35303c", "#4f86b8", "#fbfef2", "#ca3592", "#c1e3c5", "#c97596", "#091a90", "#b280bb", "#7b4427", "#b2140a", "#dbde1c", "#7ea8e9", "#539908", "#8069bc", "#d01f72", "#4ce72d", "#73e76a", "#20f2c3", "#996ff1", "#91f4db", "#d70d97", "#3678a7", "#5af098", "#ae5204", "#badd6d", "#a9541c", "#d4b1ce", "#51d0da", "#ff2d6a", "#1c2c7e", "#ae7afe", "#d156c8", "#480c89", "#e2a239", "#39821f", "#7bee34", "#92b4fa", "#b9fd23", "#591ab9", "#0bdacc", "#2a2d25", "#dc152c", "#ac9648", "#6ad041", "#fe62a5", "#52b6df", "#4aaf9f", "#d34482", "#2fef1a", "#7dd58b", "#987252", "#94a85d", "#2b9f18", "#ee26df", "#c6016b", "#9df332", "#9b5e28", "#2ebca4", "#1b312a", "#2e1afc", "#574e28", "#ac55ba", "#f090af", "#5cb9ca", "#2dcfac", 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"size": 5 }, "mode": "markers", "text": [ "Video: 59506507
Text: Well, I realized that was a whole lot of theory, but I hope it gave you a good intuition that will\nb...", "Video: 59671315
Text: Okay, so here we are, back in Jupyter Lab, ready for the next use of a frontier model, and see this\n...", "Video: 60616895
Text: It feels like 100 videos ago that I told you that we were going to have instant gratification with o...", "Video: 60619275
Text: And we will conclude our expedition into the world of frontier models through their chat interface b...", "Video: 59472693
Text: Friends.\nI am absolutely exhausted.\nI am exhausted and a little tiny bit traumatized.\nAnd you are so...", "Video: 59670121
Text: So it's business time right now.\nWe are going to build a Rag pipeline to estimate the price of produ...", "Video: 59295619
Text: Welcome back to the the moment when we bring it all together into a beautiful user interface.\nBut fi...", "Video: 60617163
Text: And already that wraps up day two.\nNow that you have built that solution.\nAnd congratulations on tha...", "Video: 60616423
Text: So I hope you've just enjoyed yourself experimenting with different LMS locally on your box using th...", "Video: 59170227
Text: Welcome back to Google Colab.\nHere we are ready to explore the wonderful world of Tokenizers.\nSo, uh...", "Video: 59169985
Text: So I hope you enjoyed that whirlwind tour of Google Colab.\nHere's just a little screenshot example o...", "Video: 60616927
Text: It's time for our first LM experiment at this point.\nSo some of this you may know well, you may know...", "Video: 59673721
Text: And here we are in JupyterLab for the last time, and we are looking here at day five, the last day\no...", "Video: 59508055
Text: I'm so very happy that you've reached this epic moment in the course and that you're hanging in ther...", "Video: 59670259
Text: It's remarkable.\nBut you are now at the 95% point.\nThere's 5% remaining of this course.\nUh, maybe it...", "Video: 60616623
Text: So we're now going to start week one of the course when we are going to be looking at exploring fron...", "Video: 59472383
Text: And welcome back to the week six folder.\nWe're now at day two, which is the second and final stage o...", "Video: 59670171
Text: So as the very final step on this part four of day two of week eight, we are now going to build an\ne...", "Video: 59297721
Text: And so now the time has come to talk about the most crucial aspect of Rag, which is the idea of vect...", "Video: 59297599
Text: Well, that was a sneaky detour I took you on in the last one.\nI hope you enjoyed it though, and I ho...", "Video: 59507635
Text: Look, I hope you're excited.\nYou really should be.\nYou've been through 80% of the course and it's al...", "Video: 59669375
Text: Here we are for the day.\n2.1 notebook.\nAnd don't let it be said that I don't ever do anything for yo...", "Video: 59297733
Text: Welcome back to JupyterLab and welcome to your first experiment with the world of Long Chain.\nLet me...", "Video: 59670369
Text: It is terrific that you're hanging on in there and making such great progress with this course.\nAs w...", "Video: 59166281
Text: And with that, amazingly, you completed day one of week two already and that gets you to the 15% poi...", "Video: 59671567
Text: Well, the first thing you're going to notice is that I don't have a notebook open for you.\nAnd that'...", "Video: 59297593
Text: And welcome to continuing our journey with Hrag.\nAnd today it's time to unveil Liang Chen.\nSo first,...", "Video: 59166461
Text: And welcome back to the lab.\nHere we are in Jupyter Lab and we are going to go into week two.\nAnd we...", "Video: 59167007
Text: Well, how fabulous is that?\nI hope that you are as wowed as I am by our new airline, I assistant and...", "Video: 59508121
Text: The moment has arrived.\nHere we go.\nWe're in fine tuning.\nWe do fine tuning.\nTrain.\nThere is also a ...", "Video: 59295579
Text: All right.\nAre you excited to see how this goes?\nLet's give it a try.\nSo in this next section, I cre...", "Video: 60620375
Text: And with that, we've reached an important milestone.\nThe first week of our eight week journey is com...", "Video: 59472491
Text: Welcome back.\nIf you are following along with me in JupyterLab, as I hope you are, then you will nee...", "Video: 59472425
Text: Welcome to week six, day three.\nToday is going to be a day that you will either love or you will hat...", "Video: 59508057
Text: Actually slight change in plan.\nI'm going to wrap up the day.\nDay three at this point, and say that ...", "Video: 60619577
Text: And for the final piece of background information, I wanted to take another moment to talk about API...", "Video: 59170291
Text: Welcome back to Colab and welcome back to our business project.\nSo again our assignment, we are due ...", "Video: 60619651
Text: I mentioned before an AI company called vellum.\nWhen we were talking about the different questions, ...", "Video: 59473191
Text: And you thought we'd never get here.\nHere we are in Jupyter Lab, running our fine tuning for a front...", "Video: 59170297
Text: And here we are in Google Colab, ready for fun with models.\nSo first we do the usual Pip installs an...", "Video: 59167015
Text: Welcome back to Jupyter Lab and welcome to Day Five's Lab.\nAnd this is going to be lots of creativit...", "Video: 59170043
Text: Let me enthusiastically welcome you all back to week three of our LLM engineering journey.\nIf you en...", "Video: 59473147
Text: Well, I'm very relieved.\nI've got that behind me.\nNo more human testing for me.\nWe'll have one final...", "Video: 59166453
Text: Welcome back and welcome to our continuing JupyterLab experience.\nUh, I'm hopefully going to keep yo...", "Video: 59166915
Text: Welcome back to the wonderful world of JupyterLab.\nAnd here we are in week two.\nDay three.\nUh, bring...", "Video: 59667365
Text: Here we are back in Colab, looking at the week seven, day five of the Colab notebooks and I'm on a\nT...", "Video: 60616845
Text: We're on the home stretch.\nThis is the final step in the environment setup, and it's an easy one.\nIt...", "Video: 59295459
Text: And welcome back to More Leaderboard Fest as we go through some more leaderboards.\nBut this time we'...", "Video: 59471979
Text: So we now turn to the parts of the problem, which is perhaps, let's say, not as glamorous as some\nof...", "Video: 59503705
Text: And so now we talk about quantization the q and q Laura.\nQ stands for quantized quantized.\nLaura.\nAn...", "Video: 59472505
Text: So the good news is that this is the very final video about data set curation.\nYou were probably fed...", "Video: 59669217
Text: And welcome to the next part of visualizing the data.\nAnd just very quickly to show it to you in 3D....", "Video: 59671221
Text: I gotta tell you, I don't like to toot my horn a whole lot, but I do think that I've done a great\njo...", "Video: 59503703
Text: Well.\nHello there everybody.\nI am so grateful that you've made it through to the start of week seven...", "Video: 59473201
Text: Well, before we do a postmortem on what happened, let's just quickly look at the standing the rankin...", "Video: 60622463
Text: In this video, we're going to set up a full data science environment for Mac users.\nIn the next vide...", "Video: 60619299
Text: Well, I hope you found that both educational and enjoyable.\nAs we went through and learned so much a...", "Video: 59295607
Text: So to revisit then the solution that we built in the previous day and talk about the metrics.\nAs I s...", "Video: 59297575
Text: Well, welcome to the final part on rag.\nAnd this is the session where you go from being a rag expert...", "Video: 59507687
Text: It's time for action, everybody.\nWe've set up our colab.\nHere we are, week seven, day three.\nWe've g...", "Video: 59671441
Text: And welcome once more to our favorite place to be Jupyter Lab, the Paradise for a data scientist exp...", "Video: 59673431
Text: And here we have it.\nThe user interface is completed.\nThe extra notification came through on my phon...", "Video: 59473137
Text: Let's get straight to it.\nSo the place where you can see everything that's going on and get knee dee...", "Video: 59166421
Text: Welcome back to the radio day in the lab.\nMore to do.\nLet's keep going.\nWhere we left off is we had ...", "Video: 59295599
Text: Welcome to the Jupyter Lab for day four.\nIt's going to look very familiar because it's actually I've...", "Video: 59669631
Text: Here we are in our favorite place to be in JupyterLab, ready for some coding and a lot of coding tha...", "Video: 59673663
Text: But wait, there's more.\nWe need to add some more to the user interface just to make it look more coo...", "Video: 59506929
Text: And we return to the hugging face open LLM leaderboard.\nThe first place you go when selecting your b...", "Video: 59504785
Text: So at this point we're going to talk about hyperparameters.\nAnd we're going to introduce three of th...", "Video: 59505337
Text: So we're now going to look at four bit quantization, the rather remarkable effect of reducing the pr...", "Video: 59271655
Text: So here we are on Hugging Face's main landing page at Hugging Face Core.\nA URL you know.\nWell, since...", "Video: 59472883
Text: Okay, time to reveal the results.\nIt has run to completion.\nAnd here it is.\nSo a moment to pause.\nIt...", "Video: 59673639
Text: And welcome now to the code for our user interface, which we will find in this Python module.\nPrice ...", "Video: 59472463
Text: So last time we looked at a humble linear regression model with feature engineering, and now we say\n...", "Video: 59297595
Text: So by the time you're watching this, hopefully you have played yourself with vectors.\nYou've created...", "Video: 60619149
Text: So we're going to start our exploration into the world of frontier models by playing with the famous...", "Video: 59297735
Text: And at last the time has come to see rag in action.\nAfter all of this talk, and here we are.\nWe're i...", "Video: 60616407
Text: And now over to my Mac people.\nAnd I have news for you.\nIt's exactly the same thing.\nYou go to a fav...", "Video: 59170235
Text: So here we are in Google Colab for our first collaborative session on the cloud using a GPU box.\nOn ...", "Video: 59472067
Text: So we've covered steps 1 to 4 of the five step strategy.\nAnd that brings us to step five, which is p...", "Video: 59472011
Text: Welcome everybody.\nSo in the past I've said quite a few times, I am excited to start this this week ...", "Video: 59295553
Text: Welcome back.\nIn the last part, we gave our GPT four and clawed the challenge of converting a simple...", "Video: 59297773
Text: Well, I hope you're eager with anticipation for this session in JupyterLab as we finally get to see\n...", "Video: 59295583
Text: And here we are back in JupyterLab.\nIt's been a minute.\nWe've been working in Colab for last week, a...", "Video: 59507329
Text: Okay.\nIt's moment of truth time.\nI have just taken our class tester.\nYou remember this class?\nUh, it...", "Video: 59295429
Text: Continuing our investigation of benchmarks, and this will become more real when we actually see some...", "Video: 60595637
Text: Here we are back in the Colab, which has been running overnight for me and probably for you too, I\nh...", "Video: 59668027
Text: And so here we are at the home page for modal.\nAt modal.com spelt model not not model which is confu...", "Video: 59295527
Text: I'm so happy to welcome you to week four, day four on our journey to LLM Engineering and Mastery.\nHe...", "Video: 59295377
Text: Just before we go on to some of the more advanced metrics, I want to mention for a second something\n...", "Video: 59666211
Text: So before we try our new model and one more recap on the models so far and keep notes of this so we\n...", "Video: 59170107
Text: And once again, it's that moment when you take a pause and congratulate yourself on another day of\ns...", "Video: 60616833
Text: So I realized that day one of week one has been a pretty long day, and I assure you that the other,\n...", "Video: 59472413
Text: Wonderful.\nWhere we left off is we had just created the Get Features function, which builds our feat...", "Video: 59297561
Text: And would you believe at this point you're 55% of the way along the journey?\nUh, it's been a while s...", "Video: 59669211
Text: Well, we took on a lot today and we seem to have been successful.\nThese red icons that you see on th...", "Video: 59166981
Text: Welcome to week two, day five.\nThe last day of week two where a lot is coming together.\nI am so grat...", "Video: 60619227
Text: And now let's move to Claude from anthropic, my favorite model and typically the favorite model of\nm...", "Video: 60620395
Text: Welcome back to Jupyter Lab, where I want to show you the assignment, the homework exercise for you\n...", "Video: 59665127
Text: Well hi there everybody.\nI'm not going to give you my usual song and dance about how excited you are...", "Video: 59668923
Text: Well, welcome back to Jupyter Lab for what will be an epic finale to our time in this platform.\nAnd ...", "Video: 59504887
Text: Well, here we are again in Google Colab.\nIt's been a minute since we were here, and welcome back to ...", "Video: 59170165
Text: Welcome, everybody to the last day of week three.\nWeek three.\nDay five.\nWe're here already wrapping ...", "Video: 60617251
Text: Congratulations are definitely in order.\nYesterday was a mammoth first day on this course and you go...", "Video: 59166951
Text: All right, back to the lab.\nBack to our project.\nTime to work with tools.\nI am in the week two folde...", "Video: 60619619
Text: Well, day four was an information dense day.\nI do hope that you learned some something useful here, ...", "Video: 60616663
Text: Well.\nHi there, this is time for PC people to get set up.\nSo all you Mac people out there, you don't...", "Video: 59508175
Text: So I'm taking a moment now to explain that the training costs of optimizing a model for this course\n...", "Video: 59670087
Text: And welcome to part four of day two of week eight.\nUh, there's a lot happening this week, and I have...", "Video: 59506713
Text: Hi everyone.\nSo the reason I'm so fired up about week seven is that this is the time when we actuall...", "Video: 60620169
Text: Hopefully you found this super satisfying to be able to have this nice business result and have it c...", "Video: 59295435
Text: Well, just before we wrap up, let me introduce this week's challenge and talk about what we're going...", "Video: 59297609
Text: Last week, we worked with models that were able to speed up code by a factor of 60,000 times, which\n...", "Video: 59507489
Text: Continuing our adventure through hyperparameters for training.\nThe next one is pretty crucial and it...", "Video: 59295549
Text: And welcome back to our challenge again.\nAnd this time we are working with our beautiful prototype.\n...", "Video: 59665129
Text: And now let me make this real for you by showing you some, some diagrams, particularly now looking\na...", "Video: 59169991
Text: Okay, so that was your introduction to Hugging Face.\nAnd now I'm going to turn to a different resour...", "Video: 59472027
Text: And now the time has come to curate our data set.\nAnd the way we're going to do this is we're going ...", "Video: 59472307
Text: Welcome to week six.\nDay two a day.\nWhen we get back into the data, we look back in anger at our dat...", "Video: 59508289
Text: So here we are now, back in the Colab, in the same one that we kicked off in the previous day.\nIt's ...", "Video: 59472333
Text: Thank you for putting up with me during my foray into traditional machine learning.\nI think it was u...", "Video: 59295431
Text: Now I want to take a quick moment to give you a flyby of five different ways that llms are used comm...", "Video: 59673449
Text: Well, I have to tell you that I'm a little bit sad.\nThis is the beginning of the beginning of the en...", "Video: 59669389
Text: Well.\nHi there.\nSo you've made it to day two of week eight, and I am super grateful that you've been...", "Video: 59170057
Text: And so at the beginning of this week, we started by talking about hugging face pipelines.\nAnd you us...", "Video: 59166949
Text: Welcome back to making chatbots.\nLet's keep going.\nSo for the next part we're going to beef up the s...", "Video: 59473019
Text: Welcome back to an action packed time of of training.\nSo now, after waiting about five minutes when ...", "Video: 59297585
Text: Before we move on, let me show you one more time this fabulous slide that describes the simple three...", "Video: 59170255
Text: And welcome back to us continuing our journey through the model class in Hugging Face Transformers l...", "Video: 60614589
Text: So we're now going to run a large language model directly on your box using a platform called llama,...", "Video: 59297601
Text: I'm not going to lie, at this point you have every reason to be impatient with me.\nWe've been yammer...", "Video: 60616629
Text: And welcome back to team PC and Team Mac as we come back together again for a quick video.\nIn this o...", "Video: 59297749
Text: It's always welcome back to JupyterLab, my favorite place to be.\nAnd now we are, of course in the we...", "Video: 59170135
Text: Welcome.\nIt's week three.\nIt's day four.\nWe are back on the adventure in open source land, back inve...", "Video: 59472017
Text: And this is the first time that we'll be coding against our big project of the course.\nWelcome to Ju...", "Video: 59507017
Text: Welcome to Colab.\nWelcome to the week seven day two Colab.\nAnd just before we try our base model, we...", "Video: 60619883
Text: And now we've arrived at an exciting moment in our first week.\nThe conclusion of the first week is w...", "Video: 59508297
Text: What more is there to say, really?\nTomorrow is the day for results.\nA day that very excited indeed a...", "Video: 60619247
Text: We're going to spend a little bit more time with GPT just to try out a few more interesting things.\n...", "Video: 59504769
Text: Without further ado, we're going to get stuck into it.\nTalking about Laura.\nLow rank adaptation.\nAnd...", "Video: 59170233
Text: Welcome back to our continued exploits with Tokenizers.\nWhat we're now going to look at is what's ca...", "Video: 59671231
Text: And here we are back in the Jupyter Lab for the fast approaching conclusion with a really great proj...", "Video: 60620397
Text: Well, that's a fantastic result to have now arrived towards the end of week one and having completed...", "Video: 59170093
Text: I'm delighted to see you again.\nAs we get started with day three of week three of our adventure and ...", "Video: 59473089
Text: Welcome back.\nSo hopefully you are still impressed by the GPT four mini results.\nThe frontier model ...", "Video: 60395261
Text: Let's keep going with our project to equip our LM with a tool.\nWe just created this piece of code to...", "Video: 60617259
Text: I'm excited to introduce you to your first exercise, and I'm looking forward to seeing what you make...", "Video: 59507313
Text: And it's this time again, when we look at the podium of how our models are performing across the boa...", "Video: 60619721
Text: Now it's time to talk for a minute about tokens.\nTokens are the individual units which get passed in...", "Video: 59295451
Text: I know that everybody.\nIt seems like just the other day that we were embarking on our quest together...", "Video: 59166919
Text: And with that, it concludes our session on tools.\nAnd at this point, you are probably an expert on t...", "Video: 59295441
Text: Okay, so welcome to our leaderboard fast as we go through a ton of essential leaderboards for your\nc...", "Video: 59295541
Text: And welcome back.\nYou've just seen GPT four zero spectacularly failed to work on our hard Python con...", "Video: 59473101
Text: Welcome back.\nSo about ten minutes later, maybe 15 minutes later, the run has completed.\nAnd how do ...", "Video: 59507423
Text: So you may remember eons ago when we were building our data set.\nAt the end of that, we uploaded our...", "Video: 59295545
Text: I really hope you've enjoyed this week.\nWe've got tons done.\nWe've experimented with all sorts of ne...", "Video: 59472503
Text: Welcome back to Jupyter Lab.\nLast time, we looked at some silly models for predicting the price of p...", "Video: 60614591
Text: The mantra of this course is that the best way to learn is by doing, and we will be doing stuff toge...", "Video: 59473021
Text: Welcome to our favorite place to be to JupyterLab.\nHere we are again now in day three.\nIn week six.\n...", "Video: 60617255
Text: I'm now going to talk for a bit about models.\nA term you often hear is the term frontier models, whi...", "Video: 59667829
Text: Well.\nHello there.\nLook, I know what you're thinking.\nYou're thinking I peaked too early.\nLast week ...", "Video: 59505329
Text: Welcome back.\nYou may, like me, have just gone off and got a coffee while things loaded back up agai...", "Video: 59669049
Text: So what you just saw was an ephemeral app, as it's called, which means just a temporary app that you...", "Video: 60619439
Text: This now brings us to an extremely important property of LMS called the context window that I want t...", "Video: 59668181
Text: And so it gives me great pleasure to introduce to you the project that I've lined up for you this we...", "Video: 59472441
Text: Welcome back.\nSo we've been doing the thoroughly distasteful, unsavory work of feature engineering.\n...", "Video: 59507785
Text: Well, I'm sure you're fed up of me saying that the moment of truth has arrived, but it really has.\nT...", "Video: 59295587
Text: When I left you, we had just created this simple user interface for converting from Python to C plus...", "Video: 59166465
Text: Welcome back to the JupyterLab on Gradio day, so you'll remember where we left off.\nWe'd written two...", "Video: 59473071
Text: Hey, gang.\nLook, I know what you're thinking.\nThis week was supposed to be training week.\nI set it a...", "Video: 59295423
Text: Welcome to day two of week four, when we get more into leaderboards so that by the end of this, you'...", "Video: 59297723
Text: So I know what you're thinking.\nYou're thinking, what's going on here?\nWe're on day five.\nWe're on d...", "Video: 59166947
Text: Well, thank you for coming along for week two, day four.\nWe have lots of good stuff in store today.\n...", "Video: 59666831
Text: Take one more moment to look at this very nice diagram that lays it all out, and we will move on.\nNo...", "Video: 59295493
Text: And welcome to week four, day three.\nAs we are about to embark upon another business project which w...", "Video: 60616855
Text: Now I know what you're thinking.\nWe've been building environments for so long.\nAre we not done yet?\n...", "Video: 59506611
Text: So in a future day, I'm going to be training, fine tuning a model and creating a fine tuned model.\nA...", "Video: 60616493
Text: I'll just take a quick moment to introduce myself to convince you that I am actually qualified to be...", "Video: 59166317
Text: And welcome to week two, day two, as we continue our adventure into the realm of LMS.\nUh, so today, ...", "Video: 59295439
Text: So I'm aware that there's a big risk that you are getting fed up of leaderboards, because we've done...", "Video: 59472421
Text: And welcome back to our final time in Jupyter Lab with traditional machine learning.\nIt's almost ove...", "Video: 59472137
Text: Well, well, well, it's been a long day, but congratulations, you've made it.\nWe've gone through and ...", "Video: 59297693
Text: So at the end of each week, it's customary for me to give you a challenge, an assignment to do on\nyo...", "Video: 60620143
Text: So we're going to make a call to GPT four.\nOh, that's going to ask it to look through a set of links...", "Video: 60619501
Text: I welcome to day four of our time together.\nThis is a very important day.\nToday we're going to be lo...", "Video: 59297743
Text: And welcome to day five.\nFor reals.\nWe're actually in the proper Jupyter notebook.\nThis time we're i...", "Video: 59166847
Text: Well, they say that time flies when you're having fun, and it certainly feels like time is flying.\nU...", "Video: 59170223
Text: Well.\nFantastic.\nIt's coming up to the end of the week, and that means it's coming up to a challenge...", "Video: 59170037
Text: So how does it feel to be 30% of the way down the journey to being a proficient LLM engineer?\nTake a...", "Video: 59295609
Text: You must be feeling absolutely exhausted at this point.\nAnd if you are, that is okay.\nYou have done ...", "Video: 60619281
Text: Well, I'm delighted to welcome you to day three of our eight week journey together.\nAnd today we're ...", "Video: 59472429
Text: And continuing on our strategy to solve commercial problems with LMS, we get to step four, which is\n...", "Video: 59167009
Text: Welcome back.\nIt's time to make our full agent framework.\nI'm super excited about this.\nIt's pulling...", "Video: 59166481
Text: And here, once more we find ourselves in our favorite place, the Jupyter Lab.\nReady to go with weeks...", "Video: 59670933
Text: I realized my enthusiasm for this project is a bit insane, but I have to tell you that I am very sat...", "Video: 59670073
Text: Okay, it's time to complete the Rag workflow in our Jupyter Lab on day 2.3.\nWe've got this function ...", "Video: 59673595
Text: That concludes a mammoth project.\nThree weeks in the making.\nIn the course of those three weeks, sta...", "Video: 59297603
Text: And I'm delighted to welcome you back to LM engineering on the day that we turn to vectors.\nFinally,...", "Video: 60614541
Text: I am delighted to welcome you to the first day of our eight weeks together as you join me on this ad...", "Video: 59667357
Text: Let's now see our results side by side.\nWe started our journey with a constant model that was at $1....", "Video: 59667841
Text: Now, look, I know that I went through that very fast, but maybe, uh, you're still, uh, blinking\nat t...", "Video: 59472007
Text: So I hope you enjoyed our first bash at Scrubbing Data, and that you are now looking through the cod...", "Video: 59507435
Text: So I'm now going to talk about five important hyperparameters for the training process.\nAnd some of ...", "Video: 59509185
Text: So this is where I left you looking at this satisfying chart on training loss and seeing the trainin...", "Video: 59473159
Text: Welcome to Jupyter Lab and welcome to our experiments at the frontier.\nSo we are going to put our fr...", "Video: 60619447
Text: I want to take a moment to talk about something that's very fundamental to an LLM, which is the numb...", "Video: 59166353
Text: Well, congratulations on leveling up yet again.\nYou've got some real hard skills that you've added t...", "Video: 60619123
Text: So what we're now going to do is we're going to look at some models in practice and start to compare...", "Video: 59295363
Text: Well, another congratulations moment.\nYou have 40% on the way to being an LM engineer at a high leve...", "Video: 60619289
Text: And now we'll go a bit faster through the other models.\nWe'll start with Google's Gemini.\nI have the...", "Video: 59472873
Text: So it's quite an adventure that we had at the frontier of what's capable with Llms today, solving a\n...", "Video: 60619429
Text: Let me talk about some other phenomena that have happened over the last few years.\nOne of them has b...", "Video: 59295601
Text: So it's time to continue our journey into the world of open source and understand which models we sh...", "Video: 59170025
Text: And a massive welcome back one more time to LM engineering.\nWe are in week three, day two and we are...", "Video: 59166443
Text: And welcome back everybody.\nWelcome to week two day three.\nIt's a continuation of our enjoyment of r...", "Video: 60620025
Text: And welcome back to Jupyter Lab, one of my very favorite places to be.\nWhen Jupyter Lab sprung up on...", "Video: 59170055
Text: Welcome to the world of Google Colab.\nYou may already be very familiar with Google Colab, even if so..." ], "type": "scatter", "x": [ -12.833365, -6.9742827, 12.4054785, 0.7444725, -3.2209346, -1.8923138, 12.045013, 3.3449032, 3.1842198, -4.9479027, -5.4305677, 8.906914, 7.4986606, -8.522678, -0.6965641, 6.603374, -11.045361, -4.2061296, -0.6122766, 4.145742, -15.19937, -3.4401643, 1.4189938, -1.4075196, 2.407112, -2.5531256, 3.4384673, 8.128717, 2.1237493, -12.902143, 15.229833, 2.7304206, -10.246402, -3.2447436, -8.882521, 8.555937, 5.628159, 7.8938856, -5.265052, -8.822166, 7.8464785, -3.648399, -8.064129, 9.394255, 8.501753, -9.501365, 15.182271, 4.720065, -1.1797574, -13.243277, -9.353854, -2.998534, -1.1271738, 0.3913053, -8.308189, 14.194027, 1.6540549, 1.3559673, 4.259716, -9.247647, -5.802019, -3.195949, -10.075436, 9.626325, 11.068077, -3.1101823, 6.4528036, 5.0787916, -15.360518, -12.8956175, -5.790258, -9.99366, 6.8768044, -4.6994433, 0.35191682, -0.29200283, 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We humans find it easier to visalize things in 2D!\n", "# Reduce the dimensionality of the vectors to 2D using t-SNE\n", "# (t-distributed stochastic neighbor embedding)\n", "\n", "tsne = TSNE(n_components=2, random_state=42)\n", "reduced_vectors = tsne.fit_transform(vectors)\n", "\n", "# Create the 2D scatter plot\n", "fig = go.Figure(data=[go.Scatter(\n", " x=reduced_vectors[:, 0],\n", " y=reduced_vectors[:, 1],\n", " mode='markers',\n", " marker=dict(size=5, color=colors, opacity=0.8),\n", " text=[f\"Video: {t}
Text: {d[:100]}...\" for t, d in zip(video_numbers , documents)],\n", " hoverinfo='text'\n", ")])\n", "\n", "fig.update_layout(\n", " title='2D Chroma Vector Store Visualization',\n", " scene=dict(xaxis_title='x',yaxis_title='y'),\n", " width=800,\n", " height=600,\n", " margin=dict(r=20, b=10, l=10, t=40)\n", ")\n", "\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 15, "id": "e1418e88-acd5-460a-bf2b-4e6efc88e3dd", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hoverinfo": "text", "marker": { "color": [ "#f8d349", "#d6d07a", "#a958c9", "#7341ee", "#268bba", "#4862ce", "#dd8cd7", "#6a6c06", "#8a29da", "#0d2037", "#805527", "#e69670", "#75b5e3", "#796278", "#6d4052", "#1f6ab0", "#99fe53", "#3f0a72", "#fe8e92", "#c3e1f2", "#f645e0", "#b43417", "#e0a8df", "#7740be", "#43c2e8", "#64f999", "#2cde7f", "#29fa15", "#580c96", "#10384a", "#845aa9", "#7f03bd", "#2b3af3", "#335dcf", "#22398f", "#c932c1", "#d43c00", "#e6f378", "#08808d", "#6a0fce", "#e1b5db", "#75195e", "#6ff3c5", "#4099c1", "#b25d7b", "#d65c3a", "#9b9d6e", "#fc2b74", "#571122", "#422abb", "#efed10", "#dfc6c7", "#02cada", "#3ec815", "#8e8cab", "#df5d2e", "#c457d7", "#ec0a37", "#da28db", "#2d7f7d", "#b27d2e", "#d01b19", "#fb9dce", "#35303c", "#4f86b8", "#fbfef2", "#ca3592", "#c1e3c5", "#c97596", "#091a90", "#b280bb", "#7b4427", "#b2140a", "#dbde1c", "#7ea8e9", "#539908", "#8069bc", "#d01f72", "#4ce72d", "#73e76a", "#20f2c3", "#996ff1", "#91f4db", "#d70d97", "#3678a7", "#5af098", "#ae5204", "#badd6d", "#a9541c", "#d4b1ce", "#51d0da", "#ff2d6a", "#1c2c7e", "#ae7afe", "#d156c8", "#480c89", "#e2a239", "#39821f", "#7bee34", "#92b4fa", "#b9fd23", "#591ab9", "#0bdacc", "#2a2d25", "#dc152c", "#ac9648", "#6ad041", "#fe62a5", "#52b6df", "#4aaf9f", "#d34482", "#2fef1a", "#7dd58b", "#987252", "#94a85d", "#2b9f18", "#ee26df", "#c6016b", "#9df332", "#9b5e28", "#2ebca4", "#1b312a", "#2e1afc", "#574e28", "#ac55ba", "#f090af", "#5cb9ca", "#2dcfac", "#804ce2", "#ce865d", "#3e5237", "#482281", "#2ae342", "#6df6ca", "#85fa26", "#793548", "#bbfe83", "#15ae86", "#70d1d9", "#bb0ee6", "#a95826", "#8afd40", "#505bd9", "#0c777d", "#ed694d", "#4e797a", "#dc95ec", "#612b32", "#ad8b14", "#474ff9", "#71c500", "#bd53b1", "#11a70e", "#144ada", "#72e048", "#188ca3", "#b52bf6", "#b64eac", "#f59c06", "#b1c27d", "#ac5faf", "#5b3f83", "#108c41", "#b61e76", "#22463b", "#c959de", "#a64739", "#659222", "#0f8781", "#2c168d", "#0faf59", "#68bece", "#696eaa", "#af0f59", "#a9e927", "#601568", "#9780cf", "#e01073", "#dd889c", "#046e5c", "#c6eff5", "#b3dba5", "#426575", "#913568", "#de30e4", "#50f10d", "#9a5ba2", "#cc8ec0", "#79c82a", "#9baca0", "#1a5613", "#246fa5", "#cb725f", "#682d42", "#a03134", "#d54222", "#01f59b", "#12897b", "#74a788", "#fcdcad", "#048452", "#3626a5", "#4dfb77", "#4212f1", "#116019", "#ad6bd0", "#a63fa4", "#d24e5d", "#1a6fdf", "#6f745a", "#cf7e83", "#4b9a93", "#799a24", "#e6e164", "#011995", "#4c4355", "#d937bd" ], "opacity": 0.8, "size": 5 }, "mode": "markers", "text": [ "Video: 59506507
Text: Well, I realized that was a whole lot of theory, but I hope it gave you a good intuition that will\nb...", "Video: 59671315
Text: Okay, so here we are, back in Jupyter Lab, ready for the next use of a frontier model, and see this\n...", "Video: 60616895
Text: It feels like 100 videos ago that I told you that we were going to have instant gratification with o...", "Video: 60619275
Text: And we will conclude our expedition into the world of frontier models through their chat interface b...", "Video: 59472693
Text: Friends.\nI am absolutely exhausted.\nI am exhausted and a little tiny bit traumatized.\nAnd you are so...", "Video: 59670121
Text: So it's business time right now.\nWe are going to build a Rag pipeline to estimate the price of produ...", "Video: 59295619
Text: Welcome back to the the moment when we bring it all together into a beautiful user interface.\nBut fi...", "Video: 60617163
Text: And already that wraps up day two.\nNow that you have built that solution.\nAnd congratulations on tha...", "Video: 60616423
Text: So I hope you've just enjoyed yourself experimenting with different LMS locally on your box using th...", "Video: 59170227
Text: Welcome back to Google Colab.\nHere we are ready to explore the wonderful world of Tokenizers.\nSo, uh...", "Video: 59169985
Text: So I hope you enjoyed that whirlwind tour of Google Colab.\nHere's just a little screenshot example o...", "Video: 60616927
Text: It's time for our first LM experiment at this point.\nSo some of this you may know well, you may know...", "Video: 59673721
Text: And here we are in JupyterLab for the last time, and we are looking here at day five, the last day\no...", "Video: 59508055
Text: I'm so very happy that you've reached this epic moment in the course and that you're hanging in ther...", "Video: 59670259
Text: It's remarkable.\nBut you are now at the 95% point.\nThere's 5% remaining of this course.\nUh, maybe it...", "Video: 60616623
Text: So we're now going to start week one of the course when we are going to be looking at exploring fron...", "Video: 59472383
Text: And welcome back to the week six folder.\nWe're now at day two, which is the second and final stage o...", "Video: 59670171
Text: So as the very final step on this part four of day two of week eight, we are now going to build an\ne...", "Video: 59297721
Text: And so now the time has come to talk about the most crucial aspect of Rag, which is the idea of vect...", "Video: 59297599
Text: Well, that was a sneaky detour I took you on in the last one.\nI hope you enjoyed it though, and I ho...", "Video: 59507635
Text: Look, I hope you're excited.\nYou really should be.\nYou've been through 80% of the course and it's al...", "Video: 59669375
Text: Here we are for the day.\n2.1 notebook.\nAnd don't let it be said that I don't ever do anything for yo...", "Video: 59297733
Text: Welcome back to JupyterLab and welcome to your first experiment with the world of Long Chain.\nLet me...", "Video: 59670369
Text: It is terrific that you're hanging on in there and making such great progress with this course.\nAs w...", "Video: 59166281
Text: And with that, amazingly, you completed day one of week two already and that gets you to the 15% poi...", "Video: 59671567
Text: Well, the first thing you're going to notice is that I don't have a notebook open for you.\nAnd that'...", "Video: 59297593
Text: And welcome to continuing our journey with Hrag.\nAnd today it's time to unveil Liang Chen.\nSo first,...", "Video: 59166461
Text: And welcome back to the lab.\nHere we are in Jupyter Lab and we are going to go into week two.\nAnd we...", "Video: 59167007
Text: Well, how fabulous is that?\nI hope that you are as wowed as I am by our new airline, I assistant and...", "Video: 59508121
Text: The moment has arrived.\nHere we go.\nWe're in fine tuning.\nWe do fine tuning.\nTrain.\nThere is also a ...", "Video: 59295579
Text: All right.\nAre you excited to see how this goes?\nLet's give it a try.\nSo in this next section, I cre...", "Video: 60620375
Text: And with that, we've reached an important milestone.\nThe first week of our eight week journey is com...", "Video: 59472491
Text: Welcome back.\nIf you are following along with me in JupyterLab, as I hope you are, then you will nee...", "Video: 59472425
Text: Welcome to week six, day three.\nToday is going to be a day that you will either love or you will hat...", "Video: 59508057
Text: Actually slight change in plan.\nI'm going to wrap up the day.\nDay three at this point, and say that ...", "Video: 60619577
Text: And for the final piece of background information, I wanted to take another moment to talk about API...", "Video: 59170291
Text: Welcome back to Colab and welcome back to our business project.\nSo again our assignment, we are due ...", "Video: 60619651
Text: I mentioned before an AI company called vellum.\nWhen we were talking about the different questions, ...", "Video: 59473191
Text: And you thought we'd never get here.\nHere we are in Jupyter Lab, running our fine tuning for a front...", "Video: 59170297
Text: And here we are in Google Colab, ready for fun with models.\nSo first we do the usual Pip installs an...", "Video: 59167015
Text: Welcome back to Jupyter Lab and welcome to Day Five's Lab.\nAnd this is going to be lots of creativit...", "Video: 59170043
Text: Let me enthusiastically welcome you all back to week three of our LLM engineering journey.\nIf you en...", "Video: 59473147
Text: Well, I'm very relieved.\nI've got that behind me.\nNo more human testing for me.\nWe'll have one final...", "Video: 59166453
Text: Welcome back and welcome to our continuing JupyterLab experience.\nUh, I'm hopefully going to keep yo...", "Video: 59166915
Text: Welcome back to the wonderful world of JupyterLab.\nAnd here we are in week two.\nDay three.\nUh, bring...", "Video: 59667365
Text: Here we are back in Colab, looking at the week seven, day five of the Colab notebooks and I'm on a\nT...", "Video: 60616845
Text: We're on the home stretch.\nThis is the final step in the environment setup, and it's an easy one.\nIt...", "Video: 59295459
Text: And welcome back to More Leaderboard Fest as we go through some more leaderboards.\nBut this time we'...", "Video: 59471979
Text: So we now turn to the parts of the problem, which is perhaps, let's say, not as glamorous as some\nof...", "Video: 59503705
Text: And so now we talk about quantization the q and q Laura.\nQ stands for quantized quantized.\nLaura.\nAn...", "Video: 59472505
Text: So the good news is that this is the very final video about data set curation.\nYou were probably fed...", "Video: 59669217
Text: And welcome to the next part of visualizing the data.\nAnd just very quickly to show it to you in 3D....", "Video: 59671221
Text: I gotta tell you, I don't like to toot my horn a whole lot, but I do think that I've done a great\njo...", "Video: 59503703
Text: Well.\nHello there everybody.\nI am so grateful that you've made it through to the start of week seven...", "Video: 59473201
Text: Well, before we do a postmortem on what happened, let's just quickly look at the standing the rankin...", "Video: 60622463
Text: In this video, we're going to set up a full data science environment for Mac users.\nIn the next vide...", "Video: 60619299
Text: Well, I hope you found that both educational and enjoyable.\nAs we went through and learned so much a...", "Video: 59295607
Text: So to revisit then the solution that we built in the previous day and talk about the metrics.\nAs I s...", "Video: 59297575
Text: Well, welcome to the final part on rag.\nAnd this is the session where you go from being a rag expert...", "Video: 59507687
Text: It's time for action, everybody.\nWe've set up our colab.\nHere we are, week seven, day three.\nWe've g...", "Video: 59671441
Text: And welcome once more to our favorite place to be Jupyter Lab, the Paradise for a data scientist exp...", "Video: 59673431
Text: And here we have it.\nThe user interface is completed.\nThe extra notification came through on my phon...", "Video: 59473137
Text: Let's get straight to it.\nSo the place where you can see everything that's going on and get knee dee...", "Video: 59166421
Text: Welcome back to the radio day in the lab.\nMore to do.\nLet's keep going.\nWhere we left off is we had ...", "Video: 59295599
Text: Welcome to the Jupyter Lab for day four.\nIt's going to look very familiar because it's actually I've...", "Video: 59669631
Text: Here we are in our favorite place to be in JupyterLab, ready for some coding and a lot of coding tha...", "Video: 59673663
Text: But wait, there's more.\nWe need to add some more to the user interface just to make it look more coo...", "Video: 59506929
Text: And we return to the hugging face open LLM leaderboard.\nThe first place you go when selecting your b...", "Video: 59504785
Text: So at this point we're going to talk about hyperparameters.\nAnd we're going to introduce three of th...", "Video: 59505337
Text: So we're now going to look at four bit quantization, the rather remarkable effect of reducing the pr...", "Video: 59271655
Text: So here we are on Hugging Face's main landing page at Hugging Face Core.\nA URL you know.\nWell, since...", "Video: 59472883
Text: Okay, time to reveal the results.\nIt has run to completion.\nAnd here it is.\nSo a moment to pause.\nIt...", "Video: 59673639
Text: And welcome now to the code for our user interface, which we will find in this Python module.\nPrice ...", "Video: 59472463
Text: So last time we looked at a humble linear regression model with feature engineering, and now we say\n...", "Video: 59297595
Text: So by the time you're watching this, hopefully you have played yourself with vectors.\nYou've created...", "Video: 60619149
Text: So we're going to start our exploration into the world of frontier models by playing with the famous...", "Video: 59297735
Text: And at last the time has come to see rag in action.\nAfter all of this talk, and here we are.\nWe're i...", "Video: 60616407
Text: And now over to my Mac people.\nAnd I have news for you.\nIt's exactly the same thing.\nYou go to a fav...", "Video: 59170235
Text: So here we are in Google Colab for our first collaborative session on the cloud using a GPU box.\nOn ...", "Video: 59472067
Text: So we've covered steps 1 to 4 of the five step strategy.\nAnd that brings us to step five, which is p...", "Video: 59472011
Text: Welcome everybody.\nSo in the past I've said quite a few times, I am excited to start this this week ...", "Video: 59295553
Text: Welcome back.\nIn the last part, we gave our GPT four and clawed the challenge of converting a simple...", "Video: 59297773
Text: Well, I hope you're eager with anticipation for this session in JupyterLab as we finally get to see\n...", "Video: 59295583
Text: And here we are back in JupyterLab.\nIt's been a minute.\nWe've been working in Colab for last week, a...", "Video: 59507329
Text: Okay.\nIt's moment of truth time.\nI have just taken our class tester.\nYou remember this class?\nUh, it...", "Video: 59295429
Text: Continuing our investigation of benchmarks, and this will become more real when we actually see some...", "Video: 60595637
Text: Here we are back in the Colab, which has been running overnight for me and probably for you too, I\nh...", "Video: 59668027
Text: And so here we are at the home page for modal.\nAt modal.com spelt model not not model which is confu...", "Video: 59295527
Text: I'm so happy to welcome you to week four, day four on our journey to LLM Engineering and Mastery.\nHe...", "Video: 59295377
Text: Just before we go on to some of the more advanced metrics, I want to mention for a second something\n...", "Video: 59666211
Text: So before we try our new model and one more recap on the models so far and keep notes of this so we\n...", "Video: 59170107
Text: And once again, it's that moment when you take a pause and congratulate yourself on another day of\ns...", "Video: 60616833
Text: So I realized that day one of week one has been a pretty long day, and I assure you that the other,\n...", "Video: 59472413
Text: Wonderful.\nWhere we left off is we had just created the Get Features function, which builds our feat...", "Video: 59297561
Text: And would you believe at this point you're 55% of the way along the journey?\nUh, it's been a while s...", "Video: 59669211
Text: Well, we took on a lot today and we seem to have been successful.\nThese red icons that you see on th...", "Video: 59166981
Text: Welcome to week two, day five.\nThe last day of week two where a lot is coming together.\nI am so grat...", "Video: 60619227
Text: And now let's move to Claude from anthropic, my favorite model and typically the favorite model of\nm...", "Video: 60620395
Text: Welcome back to Jupyter Lab, where I want to show you the assignment, the homework exercise for you\n...", "Video: 59665127
Text: Well hi there everybody.\nI'm not going to give you my usual song and dance about how excited you are...", "Video: 59668923
Text: Well, welcome back to Jupyter Lab for what will be an epic finale to our time in this platform.\nAnd ...", "Video: 59504887
Text: Well, here we are again in Google Colab.\nIt's been a minute since we were here, and welcome back to ...", "Video: 59170165
Text: Welcome, everybody to the last day of week three.\nWeek three.\nDay five.\nWe're here already wrapping ...", "Video: 60617251
Text: Congratulations are definitely in order.\nYesterday was a mammoth first day on this course and you go...", "Video: 59166951
Text: All right, back to the lab.\nBack to our project.\nTime to work with tools.\nI am in the week two folde...", "Video: 60619619
Text: Well, day four was an information dense day.\nI do hope that you learned some something useful here, ...", "Video: 60616663
Text: Well.\nHi there, this is time for PC people to get set up.\nSo all you Mac people out there, you don't...", "Video: 59508175
Text: So I'm taking a moment now to explain that the training costs of optimizing a model for this course\n...", "Video: 59670087
Text: And welcome to part four of day two of week eight.\nUh, there's a lot happening this week, and I have...", "Video: 59506713
Text: Hi everyone.\nSo the reason I'm so fired up about week seven is that this is the time when we actuall...", "Video: 60620169
Text: Hopefully you found this super satisfying to be able to have this nice business result and have it c...", "Video: 59295435
Text: Well, just before we wrap up, let me introduce this week's challenge and talk about what we're going...", "Video: 59297609
Text: Last week, we worked with models that were able to speed up code by a factor of 60,000 times, which\n...", "Video: 59507489
Text: Continuing our adventure through hyperparameters for training.\nThe next one is pretty crucial and it...", "Video: 59295549
Text: And welcome back to our challenge again.\nAnd this time we are working with our beautiful prototype.\n...", "Video: 59665129
Text: And now let me make this real for you by showing you some, some diagrams, particularly now looking\na...", "Video: 59169991
Text: Okay, so that was your introduction to Hugging Face.\nAnd now I'm going to turn to a different resour...", "Video: 59472027
Text: And now the time has come to curate our data set.\nAnd the way we're going to do this is we're going ...", "Video: 59472307
Text: Welcome to week six.\nDay two a day.\nWhen we get back into the data, we look back in anger at our dat...", "Video: 59508289
Text: So here we are now, back in the Colab, in the same one that we kicked off in the previous day.\nIt's ...", "Video: 59472333
Text: Thank you for putting up with me during my foray into traditional machine learning.\nI think it was u...", "Video: 59295431
Text: Now I want to take a quick moment to give you a flyby of five different ways that llms are used comm...", "Video: 59673449
Text: Well, I have to tell you that I'm a little bit sad.\nThis is the beginning of the beginning of the en...", "Video: 59669389
Text: Well.\nHi there.\nSo you've made it to day two of week eight, and I am super grateful that you've been...", "Video: 59170057
Text: And so at the beginning of this week, we started by talking about hugging face pipelines.\nAnd you us...", "Video: 59166949
Text: Welcome back to making chatbots.\nLet's keep going.\nSo for the next part we're going to beef up the s...", "Video: 59473019
Text: Welcome back to an action packed time of of training.\nSo now, after waiting about five minutes when ...", "Video: 59297585
Text: Before we move on, let me show you one more time this fabulous slide that describes the simple three...", "Video: 59170255
Text: And welcome back to us continuing our journey through the model class in Hugging Face Transformers l...", "Video: 60614589
Text: So we're now going to run a large language model directly on your box using a platform called llama,...", "Video: 59297601
Text: I'm not going to lie, at this point you have every reason to be impatient with me.\nWe've been yammer...", "Video: 60616629
Text: And welcome back to team PC and Team Mac as we come back together again for a quick video.\nIn this o...", "Video: 59297749
Text: It's always welcome back to JupyterLab, my favorite place to be.\nAnd now we are, of course in the we...", "Video: 59170135
Text: Welcome.\nIt's week three.\nIt's day four.\nWe are back on the adventure in open source land, back inve...", "Video: 59472017
Text: And this is the first time that we'll be coding against our big project of the course.\nWelcome to Ju...", "Video: 59507017
Text: Welcome to Colab.\nWelcome to the week seven day two Colab.\nAnd just before we try our base model, we...", "Video: 60619883
Text: And now we've arrived at an exciting moment in our first week.\nThe conclusion of the first week is w...", "Video: 59508297
Text: What more is there to say, really?\nTomorrow is the day for results.\nA day that very excited indeed a...", "Video: 60619247
Text: We're going to spend a little bit more time with GPT just to try out a few more interesting things.\n...", "Video: 59504769
Text: Without further ado, we're going to get stuck into it.\nTalking about Laura.\nLow rank adaptation.\nAnd...", "Video: 59170233
Text: Welcome back to our continued exploits with Tokenizers.\nWhat we're now going to look at is what's ca...", "Video: 59671231
Text: And here we are back in the Jupyter Lab for the fast approaching conclusion with a really great proj...", "Video: 60620397
Text: Well, that's a fantastic result to have now arrived towards the end of week one and having completed...", "Video: 59170093
Text: I'm delighted to see you again.\nAs we get started with day three of week three of our adventure and ...", "Video: 59473089
Text: Welcome back.\nSo hopefully you are still impressed by the GPT four mini results.\nThe frontier model ...", "Video: 60395261
Text: Let's keep going with our project to equip our LM with a tool.\nWe just created this piece of code to...", "Video: 60617259
Text: I'm excited to introduce you to your first exercise, and I'm looking forward to seeing what you make...", "Video: 59507313
Text: And it's this time again, when we look at the podium of how our models are performing across the boa...", "Video: 60619721
Text: Now it's time to talk for a minute about tokens.\nTokens are the individual units which get passed in...", "Video: 59295451
Text: I know that everybody.\nIt seems like just the other day that we were embarking on our quest together...", "Video: 59166919
Text: And with that, it concludes our session on tools.\nAnd at this point, you are probably an expert on t...", "Video: 59295441
Text: Okay, so welcome to our leaderboard fast as we go through a ton of essential leaderboards for your\nc...", "Video: 59295541
Text: And welcome back.\nYou've just seen GPT four zero spectacularly failed to work on our hard Python con...", "Video: 59473101
Text: Welcome back.\nSo about ten minutes later, maybe 15 minutes later, the run has completed.\nAnd how do ...", "Video: 59507423
Text: So you may remember eons ago when we were building our data set.\nAt the end of that, we uploaded our...", "Video: 59295545
Text: I really hope you've enjoyed this week.\nWe've got tons done.\nWe've experimented with all sorts of ne...", "Video: 59472503
Text: Welcome back to Jupyter Lab.\nLast time, we looked at some silly models for predicting the price of p...", "Video: 60614591
Text: The mantra of this course is that the best way to learn is by doing, and we will be doing stuff toge...", "Video: 59473021
Text: Welcome to our favorite place to be to JupyterLab.\nHere we are again now in day three.\nIn week six.\n...", "Video: 60617255
Text: I'm now going to talk for a bit about models.\nA term you often hear is the term frontier models, whi...", "Video: 59667829
Text: Well.\nHello there.\nLook, I know what you're thinking.\nYou're thinking I peaked too early.\nLast week ...", "Video: 59505329
Text: Welcome back.\nYou may, like me, have just gone off and got a coffee while things loaded back up agai...", "Video: 59669049
Text: So what you just saw was an ephemeral app, as it's called, which means just a temporary app that you...", "Video: 60619439
Text: This now brings us to an extremely important property of LMS called the context window that I want t...", "Video: 59668181
Text: And so it gives me great pleasure to introduce to you the project that I've lined up for you this we...", "Video: 59472441
Text: Welcome back.\nSo we've been doing the thoroughly distasteful, unsavory work of feature engineering.\n...", "Video: 59507785
Text: Well, I'm sure you're fed up of me saying that the moment of truth has arrived, but it really has.\nT...", "Video: 59295587
Text: When I left you, we had just created this simple user interface for converting from Python to C plus...", "Video: 59166465
Text: Welcome back to the JupyterLab on Gradio day, so you'll remember where we left off.\nWe'd written two...", "Video: 59473071
Text: Hey, gang.\nLook, I know what you're thinking.\nThis week was supposed to be training week.\nI set it a...", "Video: 59295423
Text: Welcome to day two of week four, when we get more into leaderboards so that by the end of this, you'...", "Video: 59297723
Text: So I know what you're thinking.\nYou're thinking, what's going on here?\nWe're on day five.\nWe're on d...", "Video: 59166947
Text: Well, thank you for coming along for week two, day four.\nWe have lots of good stuff in store today.\n...", "Video: 59666831
Text: Take one more moment to look at this very nice diagram that lays it all out, and we will move on.\nNo...", "Video: 59295493
Text: And welcome to week four, day three.\nAs we are about to embark upon another business project which w...", "Video: 60616855
Text: Now I know what you're thinking.\nWe've been building environments for so long.\nAre we not done yet?\n...", "Video: 59506611
Text: So in a future day, I'm going to be training, fine tuning a model and creating a fine tuned model.\nA...", "Video: 60616493
Text: I'll just take a quick moment to introduce myself to convince you that I am actually qualified to be...", "Video: 59166317
Text: And welcome to week two, day two, as we continue our adventure into the realm of LMS.\nUh, so today, ...", "Video: 59295439
Text: So I'm aware that there's a big risk that you are getting fed up of leaderboards, because we've done...", "Video: 59472421
Text: And welcome back to our final time in Jupyter Lab with traditional machine learning.\nIt's almost ove...", "Video: 59472137
Text: Well, well, well, it's been a long day, but congratulations, you've made it.\nWe've gone through and ...", "Video: 59297693
Text: So at the end of each week, it's customary for me to give you a challenge, an assignment to do on\nyo...", "Video: 60620143
Text: So we're going to make a call to GPT four.\nOh, that's going to ask it to look through a set of links...", "Video: 60619501
Text: I welcome to day four of our time together.\nThis is a very important day.\nToday we're going to be lo...", "Video: 59297743
Text: And welcome to day five.\nFor reals.\nWe're actually in the proper Jupyter notebook.\nThis time we're i...", "Video: 59166847
Text: Well, they say that time flies when you're having fun, and it certainly feels like time is flying.\nU...", "Video: 59170223
Text: Well.\nFantastic.\nIt's coming up to the end of the week, and that means it's coming up to a challenge...", "Video: 59170037
Text: So how does it feel to be 30% of the way down the journey to being a proficient LLM engineer?\nTake a...", "Video: 59295609
Text: You must be feeling absolutely exhausted at this point.\nAnd if you are, that is okay.\nYou have done ...", "Video: 60619281
Text: Well, I'm delighted to welcome you to day three of our eight week journey together.\nAnd today we're ...", "Video: 59472429
Text: And continuing on our strategy to solve commercial problems with LMS, we get to step four, which is\n...", "Video: 59167009
Text: Welcome back.\nIt's time to make our full agent framework.\nI'm super excited about this.\nIt's pulling...", "Video: 59166481
Text: And here, once more we find ourselves in our favorite place, the Jupyter Lab.\nReady to go with weeks...", "Video: 59670933
Text: I realized my enthusiasm for this project is a bit insane, but I have to tell you that I am very sat...", "Video: 59670073
Text: Okay, it's time to complete the Rag workflow in our Jupyter Lab on day 2.3.\nWe've got this function ...", "Video: 59673595
Text: That concludes a mammoth project.\nThree weeks in the making.\nIn the course of those three weeks, sta...", "Video: 59297603
Text: And I'm delighted to welcome you back to LM engineering on the day that we turn to vectors.\nFinally,...", "Video: 60614541
Text: I am delighted to welcome you to the first day of our eight weeks together as you join me on this ad...", "Video: 59667357
Text: Let's now see our results side by side.\nWe started our journey with a constant model that was at $1....", "Video: 59667841
Text: Now, look, I know that I went through that very fast, but maybe, uh, you're still, uh, blinking\nat t...", "Video: 59472007
Text: So I hope you enjoyed our first bash at Scrubbing Data, and that you are now looking through the cod...", "Video: 59507435
Text: So I'm now going to talk about five important hyperparameters for the training process.\nAnd some of ...", "Video: 59509185
Text: So this is where I left you looking at this satisfying chart on training loss and seeing the trainin...", "Video: 59473159
Text: Welcome to Jupyter Lab and welcome to our experiments at the frontier.\nSo we are going to put our fr...", "Video: 60619447
Text: I want to take a moment to talk about something that's very fundamental to an LLM, which is the numb...", "Video: 59166353
Text: Well, congratulations on leveling up yet again.\nYou've got some real hard skills that you've added t...", "Video: 60619123
Text: So what we're now going to do is we're going to look at some models in practice and start to compare...", "Video: 59295363
Text: Well, another congratulations moment.\nYou have 40% on the way to being an LM engineer at a high leve...", "Video: 60619289
Text: And now we'll go a bit faster through the other models.\nWe'll start with Google's Gemini.\nI have the...", "Video: 59472873
Text: So it's quite an adventure that we had at the frontier of what's capable with Llms today, solving a\n...", "Video: 60619429
Text: Let me talk about some other phenomena that have happened over the last few years.\nOne of them has b...", "Video: 59295601
Text: So it's time to continue our journey into the world of open source and understand which models we sh...", "Video: 59170025
Text: And a massive welcome back one more time to LM engineering.\nWe are in week three, day two and we are...", "Video: 59166443
Text: And welcome back everybody.\nWelcome to week two day three.\nIt's a continuation of our enjoyment of r...", "Video: 60620025
Text: And welcome back to Jupyter Lab, one of my very favorite places to be.\nWhen Jupyter Lab sprung up on...", "Video: 59170055
Text: Welcome to the world of Google Colab.\nYou may already be very familiar with Google Colab, even if so..." ], "type": "scatter3d", "x": [ -54.867924, -10.016936, -31.258097, 31.70372, 30.986057, -6.185337, -33.489285, 28.219635, 17.128328, 62.72473, 47.31179, -24.2394, -1.2053607, -51.674755, 21.652782, -16.459143, -20.696423, 10.337022, 19.14492, -3.3044112, -35.514133, -5.4719872, -5.1226387, 39.50191, 4.9317994, 2.9549847, 4.1326537, -11.681201, 13.737143, -38.953957, 38.51183, 12.244814, -13.795913, 25.426931, -41.59921, -4.846219, 26.703289, 4.1719337, -16.057596, -45.16751, -42.312576, 41.194798, -5.312441, -25.082775, -19.01139, -25.420565, -33.847595, 44.942863, 42.343197, -52.003693, -2.027634, -8.932509, 7.358109, 21.236534, 8.489856, -32.668716, 19.729362, 38.779385, 11.676811, -34.77253, -19.88353, -5.9252167, -27.363443, -23.733849, -38.90037, 0.8739669, -8.800101, 52.81528, -42.25234, -60.99824, 40.039967, 8.218847, 1.5493853, 18.742544, 12.445063, 15.729233, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Let's try 3D!\n", "\n", "tsne = TSNE(n_components=3, random_state=42)\n", "reduced_vectors = tsne.fit_transform(vectors)\n", "\n", "# Create the 3D scatter plot\n", "fig = go.Figure(data=[go.Scatter3d(\n", " x=reduced_vectors[:, 0],\n", " y=reduced_vectors[:, 1],\n", " z=reduced_vectors[:, 2],\n", " mode='markers',\n", " marker=dict(size=5, color=colors, opacity=0.8),\n", " text=[f\"Video: {t}
Text: {d[:100]}...\" for t, d in zip(video_numbers, documents)],\n", " hoverinfo='text'\n", ")])\n", "\n", "fig.update_layout(\n", " title='3D Chroma Vector Store Visualization',\n", " scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n", " width=900,\n", " height=700,\n", " margin=dict(r=20, b=10, l=10, t=40)\n", ")\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "9468860b-86a2-41df-af01-b2400cc985be", "metadata": {}, "source": [ "# Time to use LangChain to bring it all together" ] }, { "cell_type": "code", "execution_count": 23, "id": "129c7d1e-0094-4479-9459-f9360b95f244", "metadata": {}, "outputs": [], "source": [ "# create a new Chat with OpenAI\n", "llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n", "\n", "# set up the conversation memory for the chat\n", "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", "\n", "# the retriever is an abstraction over the VectorStore that will be used during RAG\n", "retriever = vectorstore.as_retriever()\n", "\n", "# putting it together: set up the conversation chain with the GPT 4o-mini LLM, the vector store and memory\n", "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" ] }, { "cell_type": "markdown", "id": "e85ddd60-6d97-44c4-a47a-1c7a6d4ce4df", "metadata": {}, "source": [ "### When you run the next cell, you will get a LangChainDeprecationWarning about the simple way we use LangChain memory. They ask us to migrate to their new approach for memory. Just ignore this. Ed feels quite conflicted about this. \n" ] }, { "cell_type": "code", "execution_count": 24, "id": "968e7bf2-e862-4679-a11f-6c1efb6ec8ca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ed Donner currently lives in New York City.\n" ] } ], "source": [ "query = \"Can tell me where Ed Donner currently lives.\"\n", "result = conversation_chain.invoke({\"question\":query})\n", "print(result[\"answer\"])" ] }, { "cell_type": "code", "execution_count": 20, "id": "e6eb99fb-33ec-4025-ab92-b634ede03647", "metadata": {}, "outputs": [], "source": [ "# set up a new conversation memory for the chat\n", "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", "\n", "# putting it together: set up the conversation chain with the GPT 4o-mini LLM, the vector store and memory\n", "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" ] }, { "cell_type": "markdown", "id": "bbbcb659-13ce-47ab-8a5e-01b930494964", "metadata": {}, "source": [ "## Now we will bring this up in Gradio using the Chat interface -\n", "\n", "A quick and easy way to prototype a chat with an LLM" ] }, { "cell_type": "code", "execution_count": 21, "id": "c3536590-85c7-4155-bd87-ae78a1467670", "metadata": {}, "outputs": [], "source": [ "# Wrapping in a function - note that history isn't used, as the memory is in the conversation_chain\n", "\n", "def chat(message, history):\n", " result = conversation_chain.invoke({\"question\": message})\n", " return result[\"answer\"]" ] }, { "cell_type": "code", "execution_count": 22, "id": "b252d8c1-61a8-406d-b57a-8f708a62b014", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7861\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# And in Gradio:\n", "\n", "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "5435b2b9-935c-48cd-aaf3-73a837ecde49", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 5 }