title
4. LangChain Chat with Your Data | Andrew Ng | DeepLearning.ai - Full Course

description
The course comes from [https://learn.deeplearning.ai/langchain-chat-with-your-data](https://learn.deeplearning.ai/langchain-chat-with-your-data) created by Andrew Ng In this YouTube video, a new lesson on how to use LangChain to talk to data is introduced. LangChain is an open source developer framework for building Large Language model (LLMs) applications. The course covers how to use the LangChain document loader to load data from various sources, including websites, databases, YouTube, Notion, and more. It then teaches how to split the loaded document into semantically meaningful fragments to support semantic search. Next, you showed how to use LangChain's text splitter to split these fragments into smaller pieces for search enhancement generation. Finally, we discuss how to use embedding and vector storage to store these data blocks in order to facilitate subsequent retrieval and answer questions. Get free course notes: https://t.me/NoteForYoutubeCourse

detail
{'title': '4. LangChain Chat with Your Data | Andrew Ng | DeepLearning.ai - Full Course', 'heatmap': [], 'summary': "Covers langchain's applications in data chat, document loaders, text splitting, embeddings, semantic search, retrieval techniques in nlp, building qa chatbot, and conversational buffer memory, emphasizing the potential benefits for individuals and organizations, and demonstrating various methods and real-world examples.", 'chapters': [{'end': 41.921, 'segs': [{'end': 32.017, 'src': 'embed', 'start': 0.33, 'weight': 0, 'content': [{'end': 5.859, 'text': "Hi, I'm excited to share with you this new course on using LanChain to chat with your data.", 'start': 0.33, 'duration': 5.529}, {'end': 11.85, 'text': 'This is built in collaboration with Harrison Chase, co-founder and CEO of LanChain.', 'start': 6.56, 'duration': 5.29}, {'end': 24.274, 'text': 'Large language models or LLMs, such as ChatGPT, can answer questions about a lot of topics, but an LLM in isolation knows only what it was trained on,', 'start': 13.129, 'duration': 11.145}, {'end': 32.017, 'text': "which doesn't include your personal data, such as if you're in a company and have proprietary documents not on the internet,", 'start': 24.274, 'duration': 7.743}], 'summary': 'New course on using lanchain for data chat, in collaboration with harrison chase, co-founder and ceo of lanchain, focuses on utilizing large language models like chatgpt for answering various topics while addressing privacy concerns in company data.', 'duration': 31.687, 'max_score': 0.33, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM330.jpg'}], 'start': 0.33, 'title': 'Lanchain for data chat', 'summary': 'Discusses the use of lanchain for data chat, emphasizing the limitations of large language models and the need for access to personal and proprietary data, and the potential benefits for individuals and organizations.', 'chapters': [{'end': 41.921, 'start': 0.33, 'title': 'Lanchain for data chat', 'summary': 'Discusses the use of lanchain for data chat, highlighting the limitations of large language models and the need for access to personal and proprietary data, and the potential benefits for individuals and organizations.', 'duration': 41.591, 'highlights': ['LanChain enables chatting with personal and proprietary data, not available to large language models like ChatGPT.', 'Collaboration with Harrison Chase, co-founder and CEO of LanChain, emphasizes the importance of the course.', 'Large language models like ChatGPT lack access to personal and proprietary data, limiting their effectiveness.', "Access to data or articles written after the LLM's training is highlighted as a key benefit of using LanChain."]}], 'duration': 41.591, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM330.jpg', 'highlights': ['LanChain enables chatting with personal and proprietary data, not available to large language models like ChatGPT.', "Access to data or articles written after the LLM's training is highlighted as a key benefit of using LanChain.", 'Large language models like ChatGPT lack access to personal and proprietary data, limiting their effectiveness.', 'Collaboration with Harrison Chase, co-founder and CEO of LanChain, emphasizes the importance of the course.']}, {'end': 575.648, 'segs': [{'end': 107.216, 'src': 'embed', 'start': 65.529, 'weight': 0, 'content': [{'end': 72.154, 'text': 'The modular components in Langchain include prompts, models, indexes, chains, and agents.', 'start': 65.529, 'duration': 6.625}, {'end': 76.837, 'text': 'For a more detailed look at these components, you can see our first course that I taught with Andrew.', 'start': 73.075, 'duration': 3.762}, {'end': 84.543, 'text': 'In this course, we will zoom in and focus on one of the more popular use cases of Langchain, how to use Langchain to chat with your data.', 'start': 78.018, 'duration': 6.525}, {'end': 90.768, 'text': 'We will first cover how to use Langchain document loaders to load data from a variety of exciting sources.', 'start': 85.224, 'duration': 5.544}, {'end': 95.725, 'text': 'We will then touch on how to split these documents into semantically meaningful chunks.', 'start': 91.701, 'duration': 4.024}, {'end': 99.789, 'text': 'This pre-processing step may seem simple, but has a lot of nuance.', 'start': 96.346, 'duration': 3.443}, {'end': 107.216, 'text': "Next, we'll give an overview of semantic search, a basic method for fetching relevant information given a user question.", 'start': 100.67, 'duration': 6.546}], 'summary': 'Langchain components: prompts, models, indexes, chains, and agents. course covers data loading, document splitting, and semantic search.', 'duration': 41.687, 'max_score': 65.529, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM65529.jpg'}, {'end': 263.606, 'src': 'embed', 'start': 237.901, 'weight': 2, 'content': [{'end': 242.364, 'text': "There are a lot of different type of document loaders in link chain and we won't have time to cover them all,", 'start': 237.901, 'duration': 4.463}, {'end': 246.106, 'text': 'but here is a rough categorization of the 80 plus that we have.', 'start': 242.364, 'duration': 3.742}, {'end': 255.135, 'text': 'There are a lot that deal with loading unstructured data like text files from public data sources like YouTube, Twitter, Hacker News.', 'start': 247.005, 'duration': 8.13}, {'end': 262.544, 'text': 'And there are also even more that deal with loading unstructured data from the proprietary data sources that you or your company might have,', 'start': 255.596, 'duration': 6.948}, {'end': 263.606, 'text': 'like Figma Notion.', 'start': 262.544, 'duration': 1.062}], 'summary': 'Over 80 document loaders categorized for structured and unstructured data from public and proprietary sources.', 'duration': 25.705, 'max_score': 237.901, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM237901.jpg'}, {'end': 342.249, 'src': 'embed', 'start': 303.235, 'weight': 3, 'content': [{'end': 305.055, 'text': "We're going to use the PI PDF loader.", 'start': 303.235, 'duration': 1.82}, {'end': 311.896, 'text': "We've loaded a bunch of PDFs into the documents folder in the workspace.", 'start': 307.916, 'duration': 3.98}, {'end': 314.877, 'text': "And so let's choose one and put that in the loader.", 'start': 312.516, 'duration': 2.361}, {'end': 320.298, 'text': "Now let's load the documents by just calling the load method.", 'start': 317.717, 'duration': 2.581}, {'end': 330.881, 'text': "Let's take a look at what exactly we've loaded.", 'start': 328.539, 'duration': 2.342}, {'end': 334.383, 'text': 'So this by default will load a list of documents.', 'start': 331.781, 'duration': 2.602}, {'end': 342.249, 'text': 'In this case, there are 22 different pages in this PDF.', 'start': 339.547, 'duration': 2.702}], 'summary': 'Using pi pdf loader to load 22 pages of pdf documents.', 'duration': 39.014, 'max_score': 303.235, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM303235.jpg'}, {'end': 432.653, 'src': 'embed', 'start': 401.774, 'weight': 4, 'content': [{'end': 403.714, 'text': "There's a lot of fun content on YouTube,", 'start': 401.774, 'duration': 1.94}, {'end': 411.376, 'text': 'and so a lot of people use this document loader to be able to ask questions of their favorite videos or lectures or anything like that.', 'start': 403.714, 'duration': 7.662}, {'end': 416.257, 'text': "We're going to import a few different things here.", 'start': 414.857, 'duration': 1.4}, {'end': 421.098, 'text': 'The key parts are the YouTube Audio Loader, which loads an audio file from a YouTube video.', 'start': 416.857, 'duration': 4.241}, {'end': 424.539, 'text': 'The other key part is the OpenAI Whisper Parser.', 'start': 421.959, 'duration': 2.58}, {'end': 432.653, 'text': "This will use OpenAI's Whisper model a speech-to-text model to convert the YouTube audio into a text format that we can work with.", 'start': 425.199, 'duration': 7.454}], 'summary': 'Youtube audio loader and openai whisper parser are used to convert youtube audio to text for questions.', 'duration': 30.879, 'max_score': 401.774, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM401774.jpg'}], 'start': 41.921, 'title': "Langchain's document loaders and chat application", 'summary': "Covers using langchain for chat application development, including modular components, document loading, semantic search, and fixing cases of failure. it also introduces lanchain's document loaders with over 80 different types, covering loading data from various sources like websites, databases, youtube, and formats like pdf, html, and json.", 'chapters': [{'end': 166.157, 'start': 41.921, 'title': 'Chat with langchain: llm application development', 'summary': 'Covers using langchain to chat with data, including details on modular components, document loading, semantic search, cases of failure, fixing them, and building a fully functioning chatbot, with gratitude to the team and a suggestion for a refresher course.', 'duration': 124.236, 'highlights': ['The chapter covers using Langchain to chat with data, including details on modular components, document loading, semantic search, cases of failure, fixing them, and building a fully functioning chatbot, with gratitude to the team and a suggestion for a refresher course.', 'The modular components in Langchain include prompts, models, indexes, chains, and agents, and the course will focus on the popular use case of using Langchain to chat with data.', 'The course will cover how to use Langchain document loaders to load data from various sources and how to split these documents into semantically meaningful chunks.', 'The chapter will give an overview of semantic search, a basic method for fetching relevant information given a user question, and discuss cases where it fails and how to fix them.', 'Finally, it will cover the missing piece, memory, and show how to build a fully functioning chatbot through which you can chat with your data.']}, {'end': 575.648, 'start': 167.397, 'title': "Using lanchain's document loaders", 'summary': "Introduces lanchain's document loaders with over 80 different types, covering loading data from various sources like websites, databases, youtube, and formats like pdf, html, and json, and demonstrates the process of loading pdfs, youtube content, web-based content, and notion data using specific document loaders.", 'duration': 408.251, 'highlights': ['LanChain offers over 80 different types of document loaders, covering various sources and formats LanChain provides a wide range of document loaders, including loading data from websites, databases, YouTube, and formats like PDF, HTML, and JSON, offering convenience and versatility for data processing.', 'Demonstrates loading PDFs using the PI PDF loader and accessing content and metadata The tutorial demonstrates the process of loading PDFs into the document folder, extracting content from pages, and accessing associated metadata, showcasing the functionality of the PI PDF loader.', 'Shows loading content from YouTube and converting audio to text using specific loaders The chapter illustrates the process of loading content from YouTube, converting audio into text using the YouTube Audio Loader and OpenAI Whisper Parser, enabling text-based analysis of YouTube content.', 'Introduces loading web-based content and the need for post-processing The tutorial introduces loading web-based content using the web-based loader, highlighting the necessity for post-processing to make the information workable for further analysis and interaction.', 'Explains the process of loading data from Notion using the Notion directory loader The tutorial explains the process of loading data from Notion using the Notion directory loader, providing a means to work with Notion databases and enabling interaction with personal and company data stored in Notion.']}], 'duration': 533.727, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM41921.jpg', 'highlights': ['The chapter covers using Langchain to chat with data, including details on modular components, document loading, semantic search, cases of failure, fixing them, and building a fully functioning chatbot.', 'The course will cover how to use Langchain document loaders to load data from various sources and how to split these documents into semantically meaningful chunks.', 'LanChain offers over 80 different types of document loaders, covering various sources and formats LanChain provides a wide range of document loaders, including loading data from websites, databases, YouTube, and formats like PDF, HTML, and JSON, offering convenience and versatility for data processing.', 'The tutorial demonstrates the process of loading PDFs into the document folder, extracting content from pages, and accessing associated metadata, showcasing the functionality of the PI PDF loader.', 'The chapter illustrates the process of loading content from YouTube, converting audio into text using the YouTube Audio Loader and OpenAI Whisper Parser, enabling text-based analysis of YouTube content.']}, {'end': 1185.152, 'segs': [{'end': 612.432, 'src': 'embed', 'start': 576.983, 'weight': 3, 'content': [{'end': 578.084, 'text': "That's it for document loading.", 'start': 576.983, 'duration': 1.101}, {'end': 584.651, 'text': "Here, we've covered how to load data from a variety of sources and get it into a standardized document interface.", 'start': 578.565, 'duration': 6.086}, {'end': 588.214, 'text': 'However, these documents are still rather large.', 'start': 585.391, 'duration': 2.823}, {'end': 592.999, 'text': "And so in the next section, we're going to go over how to split them up into smaller chunks.", 'start': 588.955, 'duration': 4.044}, {'end': 602.69, 'text': "This is relevant and important because when you're doing this retrieval augmented generation you need to retrieve only the pieces of content that are most relevant.", 'start': 594.008, 'duration': 8.682}, {'end': 606.751, 'text': "And so you don't want to select the whole documents that we've loaded here,", 'start': 603.19, 'duration': 3.561}, {'end': 612.432, 'text': "but rather only the paragraph or few sentences that are most topical to what you're talking about.", 'start': 606.751, 'duration': 5.681}], 'summary': 'Covered loading data from various sources, planning to split large documents into smaller chunks for retrieval augmented generation.', 'duration': 35.449, 'max_score': 576.983, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM576983.jpg'}, {'end': 711.31, 'src': 'embed', 'start': 681.727, 'weight': 5, 'content': [{'end': 686.697, 'text': "And then, when we're trying to answer a question down the line about what are the specifications on the Camry,", 'start': 681.727, 'duration': 4.97}, {'end': 690.565, 'text': "We actually don't have the right information in either chunk.", 'start': 687.364, 'duration': 3.201}, {'end': 691.525, 'text': "And so it's split apart.", 'start': 690.605, 'duration': 0.92}, {'end': 694.026, 'text': "And so we wouldn't be able to answer this question correctly.", 'start': 691.665, 'duration': 2.361}, {'end': 701.608, 'text': "So there's a lot of nuance and importance in how you split the chunks so that you get semantically relevant chunks together.", 'start': 694.066, 'duration': 7.542}, {'end': 711.31, 'text': 'The basis of all the text splitters in lane chain involves splitting on chunks in some chunk size with some chunk overlap.', 'start': 702.368, 'duration': 8.942}], 'summary': 'Text splitting in lane chain involves chunking for semantic relevance.', 'duration': 29.583, 'max_score': 681.727, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM681727.jpg'}, {'end': 778.593, 'src': 'embed', 'start': 752.123, 'weight': 0, 'content': [{'end': 756.424, 'text': 'The text splitters in link chain all have a create documents and a split documents method.', 'start': 752.123, 'duration': 4.301}, {'end': 759.142, 'text': 'This involves the same logic under the hood.', 'start': 757.441, 'duration': 1.701}, {'end': 761.503, 'text': 'It just exposes a slightly different interface.', 'start': 759.662, 'duration': 1.841}, {'end': 765.486, 'text': 'One that takes in a list of texts and another that takes in a list of documents.', 'start': 762.024, 'duration': 3.462}, {'end': 771.689, 'text': "There are a lot of different types of splitters in link chain and we'll cover a few of them in this lesson,", 'start': 766.286, 'duration': 5.403}, {'end': 774.691, 'text': 'but I would encourage you to check out the rest of them in your spare time.', 'start': 771.689, 'duration': 3.002}, {'end': 778.593, 'text': 'These text splitters vary across a bunch of dimensions.', 'start': 775.992, 'duration': 2.601}], 'summary': 'Link chain has various text splitters with different interfaces and methods.', 'duration': 26.47, 'max_score': 752.123, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM752123.jpg'}, {'end': 817.368, 'src': 'embed', 'start': 790.698, 'weight': 2, 'content': [{'end': 797.679, 'text': 'There are even some that use other, smaller models to determine when the end of a sentence might be and use that as a way of splitting chunks.', 'start': 790.698, 'duration': 6.981}, {'end': 801.98, 'text': 'Another important part of splitting into chunks is also the metadata.', 'start': 798.58, 'duration': 3.4}, {'end': 808.622, 'text': 'Maintaining the same metadata across all chunks, but also adding in new pieces of metadata when relevant.', 'start': 802.421, 'duration': 6.201}, {'end': 811.563, 'text': 'And so there are some text splitters that are really focused on that.', 'start': 808.802, 'duration': 2.761}, {'end': 817.368, 'text': "The splitting of chunks can often be specific on the type of document that we're working with.", 'start': 812.867, 'duration': 4.501}], 'summary': 'Text splitters use smaller models to determine sentence ends, focus on maintaining and adding metadata, and tailor chunk splitting to document type.', 'duration': 26.67, 'max_score': 790.698, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM790698.jpg'}, {'end': 913.326, 'src': 'embed', 'start': 887.401, 'weight': 1, 'content': [{'end': 891.583, 'text': "Let's load in the first one, a, b, c, d, all the way down to z.", 'start': 887.401, 'duration': 4.182}, {'end': 894.225, 'text': "And let's look at what happens when we use the various splitters.", 'start': 891.583, 'duration': 2.642}, {'end': 899.34, 'text': 'When we split it with the recursive character text splitter, it still ends up as one string.', 'start': 895.899, 'duration': 3.441}, {'end': 904.042, 'text': "This is because this is 26 characters long, and we've specified a chunk size of 26.", 'start': 899.64, 'duration': 4.402}, {'end': 906.303, 'text': "So there's actually no need to even do any splitting here.", 'start': 904.042, 'duration': 2.261}, {'end': 913.326, 'text': "Now let's do it on a slightly longer string, where it's longer than the 26 characters that we've specified as the chunk size.", 'start': 907.584, 'duration': 5.742}], 'summary': 'Testing various splitters on strings, with 26 characters long and longer.', 'duration': 25.925, 'max_score': 887.401, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM887401.jpg'}], 'start': 576.983, 'title': 'Text splitting in langchain', 'summary': 'Covers loading data into a standardized document interface, splitting documents into smaller chunks, and the use of different text splitters in langchain. it emphasizes the impact on chunk splitting, metadata maintenance, and showcases real-world examples.', 'chapters': [{'end': 750.642, 'start': 576.983, 'title': 'Document loading and splitting', 'summary': 'Covers how to load data from various sources into a standardized document interface, the importance of splitting documents into smaller chunks for retrieval augmented generation, and the nuances involved in ensuring semantically relevant chunks are created, with a focus on chunk size and overlap.', 'duration': 173.659, 'highlights': ['The chapter covers how to load data from various sources into a standardized document interface Loading data from various sources into a standardized document interface is covered.', 'the importance of splitting documents into smaller chunks for retrieval augmented generation The importance of splitting documents into smaller chunks for retrieval augmented generation is emphasized.', 'the nuances involved in ensuring semantically relevant chunks are created, with a focus on chunk size and overlap The nuances involved in ensuring semantically relevant chunks are created, with a focus on chunk size and overlap, are highlighted.']}, {'end': 1185.152, 'start': 752.123, 'title': 'Text splitters in langchain', 'summary': 'Covers different types of text splitters in langchain, including their methods, dimensions of variation, and use cases, emphasizing the impact on chunk splitting and metadata maintenance. it also demonstrates the application of recursive character text splitters and character text splitters on various strings and real-world examples, showcasing their effectiveness and limitations.', 'duration': 433.029, 'highlights': ['The chapter covers different types of text splitters in Langchain, including their methods, dimensions of variation, and use cases It explains the create documents and split documents methods of text splitters in Langchain, highlighting their variations in chunk splitting techniques, measurement of chunk length, and use of smaller models for sentence end detection.', 'The chapter demonstrates the application of recursive character text splitters and character text splitters on various strings and real-world examples It showcases the effectiveness of recursive character text splitters in splitting paragraphs based on specified separators, and the limitations of character text splitters when splitting based on a single character, while emphasizing the importance of experimenting with different separators, chunk sizes, and chunk overlaps.', 'The chapter emphasizes the impact of chunk splitting and metadata maintenance It highlights the importance of maintaining consistent metadata across chunks and adding relevant metadata, and demonstrates how different text splitters handle chunk splitting based on document types, such as code documents with language-specific separators.']}], 'duration': 608.169, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM576983.jpg', 'highlights': ['The chapter covers different types of text splitters in Langchain, including their methods, dimensions of variation, and use cases', 'The chapter demonstrates the application of recursive character text splitters and character text splitters on various strings and real-world examples', 'The chapter emphasizes the impact of chunk splitting and metadata maintenance', 'The chapter covers how to load data from various sources into a standardized document interface', 'The importance of splitting documents into smaller chunks for retrieval augmented generation is emphasized', 'The nuances involved in ensuring semantically relevant chunks are created, with a focus on chunk size and overlap']}, {'end': 1849.606, 'segs': [{'end': 1210.798, 'src': 'embed', 'start': 1186.363, 'weight': 0, 'content': [{'end': 1191.966, 'text': "Because we now want to use documents, we're using the split documents method and we're passing in a list of documents.", 'start': 1186.363, 'duration': 5.603}, {'end': 1200.612, 'text': 'If we compare the length of those documents to the length of the original pages,', 'start': 1192.587, 'duration': 8.025}, {'end': 1204.614, 'text': "we can see that there's been a bunch more documents that have been created as a result of this splitting.", 'start': 1200.612, 'duration': 4.002}, {'end': 1210.798, 'text': 'We can do a similar thing with the notion db that we used in the first lecture as well.', 'start': 1206.215, 'duration': 4.583}], 'summary': 'Using split documents method, more documents created.', 'duration': 24.435, 'max_score': 1186.363, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM1186363.jpg'}, {'end': 1297.072, 'src': 'embed', 'start': 1240.69, 'weight': 1, 'content': [{'end': 1242.612, 'text': "And for this, let's import the token text splitter.", 'start': 1240.69, 'duration': 1.922}, {'end': 1249.237, 'text': 'The reason that this is useful is because often LLMs have context windows that are designated by token count.', 'start': 1243.633, 'duration': 5.604}, {'end': 1254.833, 'text': "And so it's important to know what the tokens are and where they appear,", 'start': 1250.97, 'duration': 3.863}, {'end': 1260.277, 'text': 'and then we can split on them to have a slightly more representative idea of how the LLM would view them.', 'start': 1254.833, 'duration': 5.444}, {'end': 1265.401, 'text': 'To really get a sense for what the difference is between tokens and characters,', 'start': 1261.278, 'duration': 4.123}, {'end': 1270.324, 'text': "let's initialize the token text splitter with a chunk size of one and a chunk overlap of zero.", 'start': 1265.401, 'duration': 4.923}, {'end': 1273.827, 'text': 'So this will split any text into a list of the relevant tokens.', 'start': 1270.664, 'duration': 3.163}, {'end': 1281.195, 'text': "Let's create a fun made up text.", 'start': 1279.073, 'duration': 2.122}, {'end': 1297.072, 'text': "And when we split it, we can see that it's split into a bunch of different tokens.", 'start': 1293.688, 'duration': 3.384}], 'summary': "Imported token text splitter to analyze llm's context windows and split text into relevant tokens.", 'duration': 56.382, 'max_score': 1240.69, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM1240690.jpg'}, {'end': 1399.064, 'src': 'embed', 'start': 1368.262, 'weight': 3, 'content': [{'end': 1373.104, 'text': 'But there can also be cases where you actually want to add more metadata to the chunks as you split them.', 'start': 1368.262, 'duration': 4.842}, {'end': 1381.549, 'text': 'This can contain information like where in the document the chunk came from, where it is relative to other things or concepts in the document.', 'start': 1373.965, 'duration': 7.584}, {'end': 1389.273, 'text': 'And generally, this information can be used when answering questions to provide more context about what this chunk is exactly.', 'start': 1382.049, 'duration': 7.224}, {'end': 1399.064, 'text': "To see a concrete example of this, let's look at another type of text splitter that actually adds information into the metadata of each chunk.", 'start': 1390.219, 'duration': 8.845}], 'summary': 'Adding metadata to text chunks provides context for better understanding and answering questions.', 'duration': 30.802, 'max_score': 1368.262, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM1368262.jpg'}, {'end': 1623.515, 'src': 'embed', 'start': 1596.656, 'weight': 4, 'content': [{'end': 1600.117, 'text': "But for now, let's talk about vector stores and embeddings.", 'start': 1596.656, 'duration': 3.461}, {'end': 1605.618, 'text': "And this comes after text splitting when we're ready to store the documents in an easily accessible format.", 'start': 1600.497, 'duration': 5.121}, {'end': 1612.08, 'text': 'What embeddings are they take a piece of text and they create a numerical representation of that text.', 'start': 1606.778, 'duration': 5.302}, {'end': 1616.964, 'text': 'Text with similar content will have similar vectors in this numeric space.', 'start': 1613.018, 'duration': 3.946}, {'end': 1623.515, 'text': 'What that means is we can then compare those vectors and find pieces of text that are similar.', 'start': 1617.966, 'duration': 5.549}], 'summary': 'Vector stores and embeddings create numerical representations of text for easy comparison.', 'duration': 26.859, 'max_score': 1596.656, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM1596656.jpg'}, {'end': 1730.571, 'src': 'embed', 'start': 1701.606, 'weight': 5, 'content': [{'end': 1706.449, 'text': 'After the documents are loaded, we can then use the recursive character text splitter to create chunks.', 'start': 1701.606, 'duration': 4.843}, {'end': 1710.618, 'text': "We can see that we've now created over 200 different chunks.", 'start': 1708.396, 'duration': 2.222}, {'end': 1713.84, 'text': 'Time to move on to the next section and create embeddings for all of them.', 'start': 1711.058, 'duration': 2.782}, {'end': 1716.041, 'text': "We'll use OpenAI to create these embeddings.", 'start': 1714.2, 'duration': 1.841}, {'end': 1724.187, 'text': "Before jumping into a real world example, let's try it out with a few toy test cases just to get a sense for what's going on underneath the hood.", 'start': 1716.882, 'duration': 7.305}, {'end': 1730.571, 'text': "We've got a few example sentences where the first two are very similar and the third one is unrelated.", 'start': 1725.988, 'duration': 4.583}], 'summary': 'Over 200 chunks created, moving to embedding creation using openai.', 'duration': 28.965, 'max_score': 1701.606, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM1701606.jpg'}], 'start': 1186.363, 'title': 'Document splitting and embeddings', 'summary': 'Discusses document splitting, emphasizing the increase in document count, and the relevance of tokenization for llm understanding. it also covers text splitting, metadata handling, and introduces embeddings and vector stores for efficient data retrieval, including the creation of over 200 document chunks and their embeddings using openai.', 'chapters': [{'end': 1316.945, 'start': 1186.363, 'title': 'Document splitting and tokenization', 'summary': 'Discusses the process of splitting documents and comparing the resulting document lengths, highlighting the creation of more documents as a result of splitting, and the importance of tokenization for understanding llms, demonstrated through a comparison of splitting based on characters and tokens.', 'duration': 130.582, 'highlights': ['The process of splitting documents and comparing the resulting document lengths highlights the creation of more documents as a result of splitting, indicating the impact of the method used (e.g., characters or tokens) on the number of resulting documents.', 'The importance of tokenization for understanding LLMs is emphasized, as it allows for a more representative idea of how the LLM would view the tokens, and it is demonstrated through comparing the difference between splitting based on characters and tokens, highlighting the significance of token count in LLM context windows.', 'The utilization of token text splitter with a chunk size of one and a chunk overlap of zero is demonstrated, showcasing the process of splitting any text into a list of relevant tokens, providing a practical example of tokenization and its impact on creating distinct tokens based on the given parameters.']}, {'end': 1849.606, 'start': 1319.187, 'title': 'Text splitting and embeddings', 'summary': 'Discusses text splitting, including metadata handling, and introduces the concept of embeddings and vector stores for efficient data retrieval, covering the creation of over 200 document chunks and their embeddings using openai.', 'duration': 530.419, 'highlights': ['The chapter discusses text splitting and metadata handling, ensuring appropriate metadata is carried through to each chunk, and also exploring the addition of more metadata to the split chunks. It explains the process of text splitting and the importance of carrying metadata to each chunk, showcasing the addition of more metadata to the split chunks for additional context.', 'The concept of embeddings and vector stores is introduced, outlining their role in creating a numerical representation of text and enabling efficient retrieval of similar content. It introduces the concept of embeddings and vector stores, highlighting their role in creating a numerical representation of text and enabling efficient retrieval of similar content for answering questions.', 'Over 200 document chunks are created and utilized for creating embeddings using OpenAI, demonstrating the practical application of embeddings for efficient data processing. It showcases the creation of over 200 document chunks and their utilization for creating embeddings using OpenAI, demonstrating the practical application of embeddings for efficient data processing.']}], 'duration': 663.243, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM1186363.jpg', 'highlights': ['The creation of more documents as a result of splitting, indicating the impact of the method used on the number of resulting documents.', 'The importance of tokenization for understanding LLMs, highlighting the significance of token count in LLM context windows.', 'The process of splitting any text into a list of relevant tokens, providing a practical example of tokenization and its impact on creating distinct tokens based on the given parameters.', 'The chapter discusses text splitting and metadata handling, ensuring appropriate metadata is carried through to each chunk.', 'The concept of embeddings and vector stores, outlining their role in creating a numerical representation of text and enabling efficient retrieval of similar content.', 'The creation of over 200 document chunks and their utilization for creating embeddings using OpenAI, demonstrating the practical application of embeddings for efficient data processing.']}, {'end': 2289.911, 'segs': [{'end': 1921.072, 'src': 'embed', 'start': 1891.177, 'weight': 1, 'content': [{'end': 1898.14, 'text': "We're going to use the similarity search method, and we're going to pass in the question, and then we'll also pass in k equals 3.", 'start': 1891.177, 'duration': 6.963}, {'end': 1900.861, 'text': 'This specifies the number of documents that we want to return.', 'start': 1898.14, 'duration': 2.721}, {'end': 1907.788, 'text': "So if we run that and we look at the length of the documents, we can see that it's three as we specified.", 'start': 1902.847, 'duration': 4.941}, {'end': 1921.072, 'text': 'If we take a look at the content of the first document, we can see that it is in fact about an email address, cs229-qa.cs.stanford.edu.', 'start': 1909.909, 'duration': 11.163}], 'summary': 'Using similarity search with k=3 returns 3 documents, including one about email address cs229-qa.cs.stanford.edu.', 'duration': 29.895, 'max_score': 1891.177, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM1891177.jpg'}, {'end': 1964.968, 'src': 'embed', 'start': 1938.937, 'weight': 0, 'content': [{'end': 1946.739, 'text': "This has covered the basics of semantic search and shown us that we can get pretty good results based on just embeddings alone, but it isn't perfect.", 'start': 1938.937, 'duration': 7.802}, {'end': 1950.58, 'text': "And here we'll go over a few edge cases and show where this can fail.", 'start': 1947.479, 'duration': 3.101}, {'end': 1952.96, 'text': "Let's try a new question.", 'start': 1951.98, 'duration': 0.98}, {'end': 1960.383, 'text': "What did they say about MATLAB? Let's run this specifying k equals 5 and get back some results.", 'start': 1953.521, 'duration': 6.862}, {'end': 1964.968, 'text': "If we take a look at the first two results, we can see that they're actually identical.", 'start': 1961.324, 'duration': 3.644}], 'summary': 'Semantic search using embeddings can yield good results, but not perfect. demonstrated edge cases and specified k=5 for better understanding.', 'duration': 26.031, 'max_score': 1938.937, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM1938937.jpg'}, {'end': 2007.707, 'src': 'embed', 'start': 1981.971, 'weight': 2, 'content': [{'end': 1988.075, 'text': "There's no real value in the second piece of information, and it would be much better if there was a different,", 'start': 1981.971, 'duration': 6.104}, {'end': 1990.476, 'text': 'distinct chunk that the language model could learn from.', 'start': 1988.075, 'duration': 2.401}, {'end': 1999.001, 'text': "One of the things that we'll cover in the next lesson is how to retrieve both relevant and distinct chunks at the same time.", 'start': 1993.038, 'duration': 5.963}, {'end': 2003.844, 'text': "There's another type of failure mode that can also happen.", 'start': 2001.623, 'duration': 2.221}, {'end': 2007.707, 'text': "Let's take a look at the question what did they say about regression in the third lecture?", 'start': 2004.085, 'duration': 3.622}], 'summary': 'Next lesson covers retrieving relevant and distinct chunks.', 'duration': 25.736, 'max_score': 1981.971, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM1981971.jpg'}, {'end': 2146.434, 'src': 'embed', 'start': 2121.303, 'weight': 3, 'content': [{'end': 2127.827, 'text': 'In the last lesson, we covered the basics of semantic search and saw that it worked pretty well for a good amount of use cases.', 'start': 2121.303, 'duration': 6.524}, {'end': 2132.37, 'text': 'But we also saw some edge cases and saw how things could go a little bit wrong.', 'start': 2128.348, 'duration': 4.022}, {'end': 2141.196, 'text': "In this lesson, we're going to deep dive on retrieval and cover a few more advanced methods for overcoming those edge cases.", 'start': 2133.711, 'duration': 7.485}, {'end': 2146.434, 'text': "I'm really excited by this because I think retrieval is something that is new,", 'start': 2142.35, 'duration': 4.084}], 'summary': 'Covered basics of semantic search, good for use cases, some edge cases identified, upcoming lesson on advanced retrieval methods.', 'duration': 25.131, 'max_score': 2121.303, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM2121303.jpg'}, {'end': 2268.2, 'src': 'embed', 'start': 2220.959, 'weight': 4, 'content': [{'end': 2226.783, 'text': 'The idea behind MMR is that we send a query in and then we initially get back a set of responses,', 'start': 2220.959, 'duration': 5.824}, {'end': 2232.566, 'text': 'with fetch underscore K being a parameter that we can control in order to determine how many responses we get.', 'start': 2226.783, 'duration': 5.783}, {'end': 2235.688, 'text': 'This is based solely on semantic similarity.', 'start': 2233.527, 'duration': 2.161}, {'end': 2245.574, 'text': 'From there we then work with that smaller set of documents and optimize for not only the most relevant ones, based on semantic similarity,', 'start': 2236.609, 'duration': 8.965}, {'end': 2246.935, 'text': 'but also ones that are diverse.', 'start': 2245.574, 'duration': 1.361}, {'end': 2251.949, 'text': 'And from that set of documents, we choose a final K to return to the user.', 'start': 2248.126, 'duration': 3.823}, {'end': 2256.032, 'text': 'Another type of retrieval we can do is what we call self query.', 'start': 2252.709, 'duration': 3.323}, {'end': 2263.497, 'text': "So this is useful when you get questions that aren't solely about the content that you want to look up semantically,", 'start': 2257.473, 'duration': 6.024}, {'end': 2268.2, 'text': 'but also include some mention of some metadata that you want to do a filter on.', 'start': 2263.497, 'duration': 4.703}], 'summary': 'Using mmr for semantic similarity retrieval and self query with metadata filtering.', 'duration': 47.241, 'max_score': 2220.959, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM2220959.jpg'}], 'start': 1849.606, 'title': 'Semantic search methods', 'summary': 'Covers the basics of semantic search, including using openai embedding model and performing similarity search, addressing failure modes, and previewing advanced methods for overcoming edge cases. it also discusses methods for improving query retrieval in semantic search, focusing on diversity and metadata inclusion, with practical examples and comparisons.', 'chapters': [{'end': 2160.786, 'start': 1849.606, 'title': 'Semantic search basics & edge cases', 'summary': 'Covers the basics of semantic search, including using openai embedding model, performing similarity search, and addressing failure modes, with examples of failure in returning relevant and distinct chunks of information and structured information, and offers a preview of advanced methods for overcoming edge cases.', 'duration': 311.18, 'highlights': ['The OpenAI embedding model is used for semantic search. The chapter mentions passing in the OpenAI embedding model for semantic search.', 'Similarity search is performed with k equals 3 to return 3 documents. The chapter demonstrates performing a similarity search with k equals 3 to return 3 documents.', 'Failure mode of returning duplicate entries is addressed. The chapter discusses the issue of returning duplicate entries and the negative impact on the language model.', 'Structured information is not perfectly captured in the semantic embedding. The chapter explains how structured information, such as retrieving documents from a specific lecture, is not perfectly captured in the semantic embedding.', 'Preview of advanced methods for overcoming edge cases is provided. The chapter offers a preview of advanced methods for addressing the identified edge cases in semantic search.']}, {'end': 2289.911, 'start': 2162.187, 'title': 'Query time methods for semantic search', 'summary': "Discusses methods for improving query retrieval in semantic search, including maximum marginal relevance (mmr) and self query, focusing on diversity and metadata inclusion, with an example of a chef's query and a comparison of semantic and metadata components.", 'duration': 127.724, 'highlights': ["MMR is used to select a diverse set of documents based on semantic similarity, ensuring retrieval of relevant and diverse information, as demonstrated in the example of a chef's query about white mushrooms and poisonous characteristics.", 'The fetch_K parameter in MMR can be controlled to determine the number of responses obtained, allowing optimization for relevance and diversity in the final set of documents returned to the user.', 'Self query retrieval is useful for questions that involve both semantic content and metadata filtering, such as finding movies about aliens made in a specific year, addressing both the semantic and metadata components of the query.']}], 'duration': 440.305, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM1849606.jpg', 'highlights': ['The OpenAI embedding model is used for semantic search.', 'Similarity search is performed with k equals 3 to return 3 documents.', 'Failure mode of returning duplicate entries is addressed.', 'Preview of advanced methods for overcoming edge cases is provided.', 'MMR is used to select a diverse set of documents based on semantic similarity.', 'Self query retrieval is useful for questions that involve both semantic content and metadata filtering.']}, {'end': 2816.397, 'segs': [{'end': 2335.446, 'src': 'embed', 'start': 2289.911, 'weight': 0, 'content': [{'end': 2297.653, 'text': 'What we can do is we can use a language model itself to split that original question into two separate things, a filter and a search term.', 'start': 2289.911, 'duration': 7.742}, {'end': 2300.534, 'text': 'Most vector stores support a metadata filter.', 'start': 2298.573, 'duration': 1.961}, {'end': 2308.188, 'text': 'So you can easily filter records based on metadata, like the year being 1980.', 'start': 2301.303, 'duration': 6.885}, {'end': 2309.689, 'text': "Finally, we'll talk about compression.", 'start': 2308.188, 'duration': 1.501}, {'end': 2315.312, 'text': 'This can be useful to really pull out only the most relevant bits of the retrieved passages.', 'start': 2310.669, 'duration': 4.643}, {'end': 2320.896, 'text': 'For example, when asking a question, you get back the whole document that was stored,', 'start': 2316.093, 'duration': 4.803}, {'end': 2323.938, 'text': 'even if only the first one or two sentences are the relevant parts.', 'start': 2320.896, 'duration': 3.042}, {'end': 2331.703, 'text': 'With compression, you can then run all those documents through a language model and extract the most relevant segments,', 'start': 2325.097, 'duration': 6.606}, {'end': 2335.446, 'text': 'and then pass only the most relevant segments into a final language model call.', 'start': 2331.703, 'duration': 3.743}], 'summary': 'Using language model for filtering, searching, and compression of data.', 'duration': 45.535, 'max_score': 2289.911, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM2289911.jpg'}, {'end': 2394.527, 'src': 'embed', 'start': 2364.795, 'weight': 1, 'content': [{'end': 2369.637, 'text': "And we can see by looking at the collection count that it's got the 209 documents that we loaded them before.", 'start': 2364.795, 'duration': 4.842}, {'end': 2375.659, 'text': "Let's now go over the example of max marginal relevance.", 'start': 2372.598, 'duration': 3.061}, {'end': 2381.301, 'text': "And so we'll load in the text from the example where we have the information about mushrooms.", 'start': 2376.579, 'duration': 4.722}, {'end': 2388.263, 'text': "For this example, we'll create a small database that we can just use as a toy example.", 'start': 2384.54, 'duration': 3.723}, {'end': 2394.527, 'text': "We've got our question and now we can run a similarity search.", 'start': 2391.985, 'duration': 2.542}], 'summary': 'Database has 209 documents, running similarity search on mushrooms example.', 'duration': 29.732, 'max_score': 2364.795, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM2364795.jpg'}, {'end': 2629.008, 'src': 'embed', 'start': 2603.399, 'weight': 2, 'content': [{'end': 2609.023, 'text': "And so if we loop over the documents and print out the metadata, we should see that they're all from this third lecture.", 'start': 2603.399, 'duration': 5.624}, {'end': 2613.146, 'text': 'And indeed they are.', 'start': 2612.506, 'duration': 0.64}, {'end': 2618.19, 'text': 'So this is an example where the self query retriever can be used to filter exactly on metadata.', 'start': 2613.626, 'duration': 4.564}, {'end': 2622.553, 'text': 'A final retrieval technique that we can talk about is contextual compression.', 'start': 2619.17, 'duration': 3.383}, {'end': 2629.008, 'text': "And so let's load in some relevant modules here, the contextual compression retriever, and then LLM chain extractor.", 'start': 2623.345, 'duration': 5.663}], 'summary': 'The self query retriever filters documents by metadata; contextual compression retrieval uses relevant modules.', 'duration': 25.609, 'max_score': 2603.399, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM2603399.jpg'}, {'end': 2735.228, 'src': 'embed', 'start': 2711.327, 'weight': 4, 'content': [{'end': 2717.592, 'text': "It's worth mentioning that there's other types of retrieval that don't use a vector database at all and instead uses other,", 'start': 2711.327, 'duration': 6.265}, {'end': 2719.314, 'text': 'more traditional NLP techniques.', 'start': 2717.592, 'duration': 1.722}, {'end': 2728.384, 'text': "Here, we're going to recreate a retrieval pipeline with two different types of retrievers, an SVM retriever and a TF-IDF retriever.", 'start': 2721.621, 'duration': 6.763}, {'end': 2733.287, 'text': "If you recognize these terms from traditional NLP or traditional machine learning, that's great.", 'start': 2729.045, 'duration': 4.242}, {'end': 2735.228, 'text': "If you don't, it's also fine.", 'start': 2734.067, 'duration': 1.161}], 'summary': 'Comparison of vector database retrieval with svm and tf-idf retrievers.', 'duration': 23.901, 'max_score': 2711.327, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM2711327.jpg'}], 'start': 2289.911, 'title': 'Retrieval techniques in nlp', 'summary': 'Discusses retrieval techniques in nlp, including self-query retriever, contextual compression retriever, and traditional nlp techniques, demonstrating their applications and effectiveness in filtering and extracting relevant information from documents.', 'chapters': [{'end': 2470.335, 'start': 2289.911, 'title': 'Techniques in information retrieval', 'summary': 'Discusses using language models for filtering and searching, metadata filtering, and compression to extract the most relevant segments, with examples of max marginal relevance and self query.', 'duration': 180.424, 'highlights': ['The chapter discusses techniques in information retrieval such as using language models for filtering and searching, metadata filtering, and compression to extract the most relevant segments. The chapter covers using language models for filtering and searching, metadata filtering, and compression to extract the most relevant segments.', 'Example of max marginal relevance is provided, demonstrating its use in returning relevant documents and introducing diversity in responses. An example of max marginal relevance is provided, showcasing its role in returning relevant documents and introducing diversity in responses.', 'The chapter also presents the self query example, illustrating its application in information retrieval. The self query example is presented to illustrate its application in information retrieval.']}, {'end': 2816.397, 'start': 2471.84, 'title': 'Retrieval techniques in nlp', 'summary': 'Discusses retrieval techniques in nlp, including self-query retriever, contextual compression retriever, and traditional nlp techniques, demonstrating their applications and effectiveness in filtering and extracting relevant information from documents.', 'duration': 344.557, 'highlights': ['The self-query retriever can be used to filter exactly on metadata, allowing for precise retrieval of documents from a specific source, as demonstrated with the example of retrieving documents from the third lecture. Demonstrates the use of the self-query retriever to filter documents based on metadata, ensuring retrieval from a specific source, such as the example of retrieving documents from the third lecture.', 'The chapter also introduces the contextual compression retriever, which extracts relevant information from documents, and demonstrates its application in compressing document content to improve search results. Introduces the contextual compression retriever for extracting relevant information from documents and improving search results through compressed document content.', 'Additionally, the chapter explores traditional NLP techniques such as SVM retriever and TF-IDF retriever, illustrating their use in retrieving information based on text and embedding modules. Explores traditional NLP techniques like SVM retriever and TF-IDF retriever for retrieving information based on text and embedding modules.']}], 'duration': 526.486, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM2289911.jpg', 'highlights': ['The chapter discusses techniques in information retrieval such as using language models for filtering and searching, metadata filtering, and compression to extract the most relevant segments.', 'Example of max marginal relevance is provided, demonstrating its use in returning relevant documents and introducing diversity in responses.', 'The self-query retriever can be used to filter exactly on metadata, allowing for precise retrieval of documents from a specific source, as demonstrated with the example of retrieving documents from the third lecture.', 'The chapter also introduces the contextual compression retriever, which extracts relevant information from documents, and demonstrates its application in compressing document content to improve search results.', 'Additionally, the chapter explores traditional NLP techniques such as SVM retriever and TF-IDF retriever, illustrating their use in retrieving information based on text and embedding modules.']}, {'end': 3517.948, 'segs': [{'end': 2903.891, 'src': 'embed', 'start': 2861.213, 'weight': 4, 'content': [{'end': 2864.156, 'text': 'Now we need to pass that into a language model to get to an answer.', 'start': 2861.213, 'duration': 2.943}, {'end': 2866.057, 'text': 'The general flow for this goes.', 'start': 2864.596, 'duration': 1.461}, {'end': 2869.2, 'text': 'the question comes in, we look up the relevant documents,', 'start': 2866.057, 'duration': 3.143}, {'end': 2874.624, 'text': 'we then pass those splits along with a system prompt and the human question to the language model and get the answer.', 'start': 2869.2, 'duration': 5.424}, {'end': 2880.989, 'text': 'By default, we just pass all the chunks into the same context window, into the same call of the language model.', 'start': 2875.845, 'duration': 5.144}, {'end': 2886.186, 'text': 'However, there are a few different methods we can use that have pros and cons to that.', 'start': 2882.025, 'duration': 4.161}, {'end': 2893.708, 'text': "Most of the pros come from the fact that sometimes there can be a lot of documents and you just simply can't pass them all into the same context window.", 'start': 2887.086, 'duration': 6.622}, {'end': 2901.29, 'text': 'MapReduce, Refine, and MapRerank are three methods to get around this issue of short context windows.', 'start': 2894.748, 'duration': 6.542}, {'end': 2903.891, 'text': "And we'll cover a few of them in the lesson today.", 'start': 2901.87, 'duration': 2.021}], 'summary': 'Using language model for answering questions, with methods like mapreduce, refine, and maprerank to address short context windows.', 'duration': 42.678, 'max_score': 2861.213, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM2861213.jpg'}, {'end': 2958.217, 'src': 'embed', 'start': 2934.084, 'weight': 0, 'content': [{'end': 2940.727, 'text': "We do a quick little check of similarity search just to make sure it's working for this first question of what are major topics for this class.", 'start': 2934.084, 'duration': 6.643}, {'end': 2948.73, 'text': "Now we initialize the language model that we're going to use to answer the question.", 'start': 2944.887, 'duration': 3.843}, {'end': 2954.715, 'text': "We're going to use the chat open AI model, GPT 3.5, and we're going to set temperature equal to zero.", 'start': 2949.431, 'duration': 5.284}, {'end': 2958.217, 'text': 'This is really good when we want factual answers to come out,', 'start': 2955.295, 'duration': 2.922}], 'summary': 'Testing similarity search and initializing gpt 3.5 language model for factual answers.', 'duration': 24.133, 'max_score': 2934.084, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM2934084.jpg'}, {'end': 3010.683, 'src': 'embed', 'start': 2982.446, 'weight': 3, 'content': [{'end': 2986.149, 'text': 'We can then call it with the query being equal to the question that we want to ask.', 'start': 2982.446, 'duration': 3.703}, {'end': 2989.531, 'text': 'And then when we look at the result, we get an answer.', 'start': 2987.449, 'duration': 2.082}, {'end': 2991.572, 'text': 'The major topic for this class is machine learning.', 'start': 2989.751, 'duration': 1.821}, {'end': 2996.656, 'text': 'Additionally, the class may cover statistics and algebra as refreshers in the discussion sections.', 'start': 2992.233, 'duration': 4.423}, {'end': 3001.74, 'text': 'Later in the quarter, the discussion sections will also cover extensions for the material taught in the main lectures.', 'start': 2997.137, 'duration': 4.603}, {'end': 3010.683, 'text': "Let's try to understand a little bit better what's going on underneath the hood and expose a few of the different knobs that you can turn.", 'start': 3004.141, 'duration': 6.542}], 'summary': 'Class covers machine learning, statistics, algebra; includes discussion sections for extensions.', 'duration': 28.237, 'max_score': 2982.446, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM2982446.jpg'}, {'end': 3150.582, 'src': 'embed', 'start': 3126.148, 'weight': 2, 'content': [{'end': 3132.273, 'text': 'In this technique, each of the individual documents is first sent to the language model by itself to get an original answer,', 'start': 3126.148, 'duration': 6.125}, {'end': 3136.617, 'text': 'and then those answers are composed into a final answer with a final call to the language model.', 'start': 3132.273, 'duration': 4.344}, {'end': 3143.423, 'text': 'This involves many more calls to the language model, but it does have the advantage in that it can operate over arbitrarily many documents.', 'start': 3137.338, 'duration': 6.085}, {'end': 3150.582, 'text': 'When we run the previous question through this chain, we can see another limitation of this method, or actually we can see two.', 'start': 3144.659, 'duration': 5.923}], 'summary': 'This technique involves multiple calls to the language model for composing final answers.', 'duration': 24.434, 'max_score': 3126.148, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM3126148.jpg'}, {'end': 3486.588, 'src': 'embed', 'start': 3458.543, 'weight': 1, 'content': [{'end': 3463.585, 'text': "And this is any previous conversations or messages that you've exchanged with the chain.", 'start': 3458.543, 'duration': 5.042}, {'end': 3470.688, 'text': "What that's going to allow it to do is it's going to allow it to take that chat history into context when it's trying to answer the question.", 'start': 3464.625, 'duration': 6.063}, {'end': 3473.97, 'text': "So if you're asking a follow-up question, it'll know what you're talking about.", 'start': 3471.169, 'duration': 2.801}, {'end': 3481.127, 'text': 'An important thing to note here is that all the cool types of retrieval that we talked about up to this point,', 'start': 3475.546, 'duration': 5.581}, {'end': 3486.588, 'text': 'like self query or compression or anything like that, you can absolutely use them here.', 'start': 3481.127, 'duration': 5.461}], 'summary': 'New feature allows context-based responses in chat using chat history.', 'duration': 28.045, 'max_score': 3458.543, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM3458543.jpg'}], 'start': 2818.097, 'title': 'Building qa chatbot', 'summary': 'Covers the process of using retrieved documents to answer user questions, including the steps involved and different methods such as mapreduce, refine, and maprerank to handle short context windows. it also demonstrates the process of building a question-answering chatbot using retrieval qa chains and language models, covering topics such as major class topics, refining answers, and incorporating chat history for handling follow-up questions.', 'chapters': [{'end': 2903.891, 'start': 2818.097, 'title': 'Question answering with retrieved documents', 'summary': 'Covers the process of using retrieved documents to answer user questions, including the steps involved and different methods such as mapreduce, refine, and maprerank to handle short context windows.', 'duration': 85.794, 'highlights': ['Using retrieved documents to answer user questions, including steps and different methods such as MapReduce, Refine, and MapRerank to handle short context windows.', 'The general flow involves looking up relevant documents, passing them along with a system prompt and the human question to a language model, and getting the answer.', 'The lesson covers different methods such as MapReduce, Refine, and MapRerank for handling short context windows when passing documents to the language model.']}, {'end': 3517.948, 'start': 2905.051, 'title': 'Building a question answering chatbot', 'summary': 'Demonstrates the process of building a question-answering chatbot using retrieval qa chains and language models, covering topics such as major class topics, refining answers, and incorporating chat history for handling follow-up questions.', 'duration': 612.897, 'highlights': ['The major topic for this class is machine learning, with additional coverage of statistics and algebra in discussion sections, and extensions for the material taught in the main lectures.', 'The chatbot utilizes GPT 3.5 model with temperature set to zero for factual, reliable answers, and incorporates retrieval QA chains to answer questions backed by a vector database, achieving a high fidelity of responses.', 'The technique of refining answers sequentially through a refined documents chain improves the quality of answers by combining information from multiple documents, providing better results than the MapReduce technique.', 'The concept of chat history is introduced to allow the chatbot to consider previous conversations when answering questions, enabling it to handle follow-up questions and engage in more natural conversations.']}], 'duration': 699.851, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM2818097.jpg', 'highlights': ['The chatbot utilizes GPT 3.5 model with temperature set to zero for factual, reliable answers, and incorporates retrieval QA chains to answer questions backed by a vector database, achieving a high fidelity of responses.', 'The concept of chat history is introduced to allow the chatbot to consider previous conversations when answering questions, enabling it to handle follow-up questions and engage in more natural conversations.', 'The technique of refining answers sequentially through a refined documents chain improves the quality of answers by combining information from multiple documents, providing better results than the MapReduce technique.', 'The major topic for this class is machine learning, with additional coverage of statistics and algebra in discussion sections, and extensions for the material taught in the main lectures.', 'Using retrieved documents to answer user questions, including steps and different methods such as MapReduce, Refine, and MapRerank to handle short context windows.', 'The general flow involves looking up relevant documents, passing them along with a system prompt and the human question to a language model, and getting the answer.', 'The lesson covers different methods such as MapReduce, Refine, and MapRerank for handling short context windows when passing documents to the language model.']}, {'end': 4020.718, 'segs': [{'end': 3576.562, 'src': 'embed', 'start': 3547.686, 'weight': 0, 'content': [{'end': 3548.907, 'text': "Let's add some memory to it.", 'start': 3547.686, 'duration': 1.221}, {'end': 3551.769, 'text': "So we're going to be working with conversation buffer memory.", 'start': 3549.327, 'duration': 2.442}, {'end': 3559.015, 'text': "And what this does is it's just going to simply keep a list, a buffer of chat messages in history.", 'start': 3551.989, 'duration': 7.026}, {'end': 3562.598, 'text': "And it's going to pass those along with the question to the chat bot every time.", 'start': 3559.075, 'duration': 3.523}, {'end': 3565.954, 'text': "We're going to specify memory key, chat history.", 'start': 3563.612, 'duration': 2.342}, {'end': 3568.756, 'text': 'This is just going to line it up with an input variable in the prompt.', 'start': 3565.994, 'duration': 2.762}, {'end': 3571.638, 'text': "And then we're going to specify return messages equal true.", 'start': 3569.296, 'duration': 2.342}, {'end': 3576.562, 'text': 'This is going to return the chat history as a list of messages, as opposed to a single string.', 'start': 3572.138, 'duration': 4.424}], 'summary': 'Adding conversation buffer memory to chat bot for history tracking.', 'duration': 28.876, 'max_score': 3547.686, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM3547686.jpg'}, {'end': 3620.022, 'src': 'embed', 'start': 3592.188, 'weight': 1, 'content': [{'end': 3595.89, 'text': 'We pass in the language model, we pass in the retriever, and we pass in memory.', 'start': 3592.188, 'duration': 3.702}, {'end': 3603.133, 'text': 'The conversational retrieval chain adds a new bit on top of the retrieval QA chain, not just memory.', 'start': 3596.87, 'duration': 6.263}, {'end': 3615.179, 'text': 'Specifically, what it adds is it adds a step that takes the history and the new question and condenses it into a standalone question to pass to the vector store to look up relevant documents.', 'start': 3603.434, 'duration': 11.745}, {'end': 3620.022, 'text': "We'll take a look at this in the UI after we run it and see what effect it has.", 'start': 3616.6, 'duration': 3.422}], 'summary': 'Conversational retrieval chain adds step to condense history and question for document lookup', 'duration': 27.834, 'max_score': 3592.188, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM3592188.jpg'}, {'end': 3681.305, 'src': 'embed', 'start': 3653.218, 'weight': 5, 'content': [{'end': 3656.1, 'text': 'not getting confused with computer science as it had before.', 'start': 3653.218, 'duration': 2.882}, {'end': 3663.066, 'text': "Let's take a look at what's going on under the hood in the UI.", 'start': 3660.103, 'duration': 2.963}, {'end': 3667.289, 'text': "So here we can already see that there's a bit more complexity.", 'start': 3664.607, 'duration': 2.682}, {'end': 3673.614, 'text': 'We can see that the input to the chain now has not only the question, but also chat history.', 'start': 3667.969, 'duration': 5.645}, {'end': 3681.305, 'text': 'Chat history is coming from the memory, and this gets applied before the chain is invoked and logged in this logging system.', 'start': 3674.821, 'duration': 6.484}], 'summary': 'The ui now includes chat history as input to the chain, adding complexity.', 'duration': 28.087, 'max_score': 3653.218, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM3653218.jpg'}, {'end': 3936.921, 'src': 'embed', 'start': 3914.487, 'weight': 3, 'content': [{'end': 3923.673, 'text': 'ask it a bunch more questions, upload your own documents here and enjoy this end-to-end question answering bot complete with an amazing notebook UI.', 'start': 3914.487, 'duration': 9.186}, {'end': 3928.075, 'text': 'And that brings this class on Langchain, chat with your data to an end.', 'start': 3924.293, 'duration': 3.782}, {'end': 3929.977, 'text': 'In this class,', 'start': 3928.996, 'duration': 0.981}, {'end': 3936.921, 'text': "we've covered how to use Langchain to load data from a variety of document sources using Langchain's 80 plus different document loaders.", 'start': 3929.977, 'duration': 6.944}], 'summary': 'Langchain class covered using 80+ document loaders to load data from various sources.', 'duration': 22.434, 'max_score': 3914.487, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM3914487.jpg'}], 'start': 3520.77, 'title': 'Conversational and langchain', 'summary': 'Covers the addition of conversational buffer memory to a chat bot, retrieval of relevant documents, and the langchain class, including document loaders, semantic search, and retrieval algorithms.', 'chapters': [{'end': 3653.218, 'start': 3520.77, 'title': 'Conversational retrieval chain', 'summary': 'Discusses the addition of conversation buffer memory to a chat bot, condensing chat history into standalone questions, and the process of retrieving relevant documents, aiming to enhance the conversational retrieval chain.', 'duration': 132.448, 'highlights': ['The addition of conversation buffer memory to the chat bot involves keeping a list of chat messages in history and passing them along with the question, with the option to return the chat history as a list of messages (quantifiable: adding memory to the chat bot)', 'The conversational retrieval chain condenses chat history and the new question into a standalone question to be passed to the vector store to look up relevant documents, adding a new step on top of the retrieval QA chain (quantifiable: enhancing the retrieval process)', 'The chapter also covers asking questions without any history and the corresponding results, showcasing the use case of the conversational retrieval chain (quantifiable: demonstrating the functionality of the new chain)']}, {'end': 4020.718, 'start': 3653.218, 'title': 'Langchain: chat with your data', 'summary': 'Delves into the complexity of the ui, explaining the process of retrieving and answering questions, and concludes with a thorough walkthrough of the langchain class, covering various document loaders, semantic search, retrieval algorithms, and the creation of an end-to-end chatbot.', 'duration': 367.5, 'highlights': ['The chapter covers the process of retrieving and answering questions, showcasing the complexity of the UI and explaining the use of memory and chat history in the chain. The UI complexity is highlighted, emphasizing the process of retrieving and answering questions.', 'A thorough walkthrough of the Langchain class is provided, covering various document loaders, semantic search, retrieval algorithms, and the creation of an end-to-end chatbot. A comprehensive overview of the Langchain class is given, encompassing document loaders, semantic search, retrieval algorithms, and chatbot creation.', "The class covers the usage of Langchain's 80 plus different document loaders, the process of splitting documents into chunks, creating embeddings, and enabling semantic search. The utilization of Langchain's document loaders, document splitting, embeddings creation, and semantic search is discussed."]}], 'duration': 499.948, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/NPpRy261BuM/pics/NPpRy261BuM3520770.jpg', 'highlights': ['The addition of conversation buffer memory to the chat bot involves keeping a list of chat messages in history and passing them along with the question, with the option to return the chat history as a list of messages (quantifiable: adding memory to the chat bot)', 'The conversational retrieval chain condenses chat history and the new question into a standalone question to be passed to the vector store to look up relevant documents, adding a new step on top of the retrieval QA chain (quantifiable: enhancing the retrieval process)', 'The chapter also covers asking questions without any history and the corresponding results, showcasing the use case of the conversational retrieval chain (quantifiable: demonstrating the functionality of the new chain)', 'A thorough walkthrough of the Langchain class is provided, covering various document loaders, semantic search, retrieval algorithms, and the creation of an end-to-end chatbot. A comprehensive overview of the Langchain class is given, encompassing document loaders, semantic search, retrieval algorithms, and chatbot creation.', "The class covers the usage of Langchain's 80 plus different document loaders, the process of splitting documents into chunks, creating embeddings, and enabling semantic search. The utilization of Langchain's document loaders, document splitting, embeddings creation, and semantic search is discussed.", 'The chapter covers the process of retrieving and answering questions, showcasing the complexity of the UI and explaining the use of memory and chat history in the chain. The UI complexity is highlighted, emphasizing the process of retrieving and answering questions.']}], 'highlights': ['The chatbot utilizes GPT 3.5 model with temperature set to zero for factual, reliable answers, and incorporates retrieval QA chains to answer questions backed by a vector database, achieving a high fidelity of responses.', 'The addition of conversation buffer memory to the chat bot involves keeping a list of chat messages in history and passing them along with the question, with the option to return the chat history as a list of messages (quantifiable: adding memory to the chat bot)', 'The chapter covers using Langchain to chat with data, including details on modular components, document loading, semantic search, cases of failure, fixing them, and building a fully functioning chatbot.', 'The course will cover how to use Langchain document loaders to load data from various sources and how to split these documents into semantically meaningful chunks.', 'The chapter covers different types of text splitters in Langchain, including their methods, dimensions of variation, and use cases', 'The creation of over 200 document chunks and their utilization for creating embeddings using OpenAI, demonstrating the practical application of embeddings for efficient data processing.', 'The chapter discusses techniques in information retrieval such as using language models for filtering and searching, metadata filtering, and compression to extract the most relevant segments.', 'The chapter also covers asking questions without any history and the corresponding results, showcasing the use case of the conversational retrieval chain (quantifiable: demonstrating the functionality of the new chain)', 'The chapter covers the process of retrieving and answering questions, showcasing the complexity of the UI and explaining the use of memory and chat history in the chain.', 'The chapter illustrates the process of loading content from YouTube, converting audio into text using the YouTube Audio Loader and OpenAI Whisper Parser, enabling text-based analysis of YouTube content.']}