title
3. LangChain for LLM Application Development | Andrew Ng | DeepLearning.ai - Full Course

description
The course comes [fromhttps://learn.deeplearning.ai/langchain/lesson/1/introduction](https://learn.deeplearning.ai/langchain/lesson/1/introduction) The course will be led by Andrew Ng This video introduces LangChain, a tool for large language model application development. LangChain is an open source framework created by Harrison Chase for building LLM (Large Language Model) applications. The framework enables developers to build AI applications faster by simplifying the LLM application development process. The video covers the common components of LangChain, including models, hints, indexes, chains, and proxies. In addition, it demonstrated how to use LangChain for model hints, generate output, and parse results, and how to use different types of memory to manage session history. Get free course notes: https://t.me/NoteForYoutubeCourse

detail
{'title': '3. LangChain for LLM Application Development | Andrew Ng | DeepLearning.ai - Full Course', 'heatmap': [], 'summary': 'Introduces langchain, an open-source framework for llm applications, covering its components and functionalities, with a specific focus on language translation, chatbots, conversation optimization, sequential chains, question answering methods, and agent execution, providing valuable insights for efficient application development.', 'chapters': [{'end': 254.256, 'segs': [{'end': 34.141, 'src': 'embed', 'start': 4.983, 'weight': 3, 'content': [{'end': 9.666, 'text': 'Welcome to this short course on LanChain for large language model application development.', 'start': 4.983, 'duration': 4.683}, {'end': 18.23, 'text': 'By prompting an LLM or large language model, it is now possible to develop AI applications much faster than ever before.', 'start': 10.426, 'duration': 7.804}, {'end': 27.776, 'text': "But an application can require prompting an LLM multiple times and parsing its output, and so there's a lot of glue code that needs to be written.", 'start': 18.931, 'duration': 8.845}, {'end': 34.141, 'text': 'LanChain, created by Harrison Chase, makes this development process much easier.', 'start': 28.576, 'duration': 5.565}], 'summary': 'Lanchain simplifies llm application development for faster ai development.', 'duration': 29.158, 'max_score': 4.983, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI4983.jpg'}, {'end': 141.663, 'src': 'embed', 'start': 69.437, 'weight': 0, 'content': [{'end': 74.921, 'text': "And, in fact, as a sign of LanChain's momentum, not only does it have numerous users,", 'start': 69.437, 'duration': 5.484}, {'end': 78.623, 'text': 'but there are also many hundreds of contributors to the open source.', 'start': 74.921, 'duration': 3.702}, {'end': 82.766, 'text': 'And this has been instrumental for its rapid rate of development.', 'start': 79.144, 'duration': 3.622}, {'end': 86.048, 'text': 'This team really ships code and features at an amazing pace.', 'start': 82.806, 'duration': 3.242}, {'end': 93.453, 'text': "So hopefully, after this short course, you'll be able to quickly put together some really cool applications using LanChain.", 'start': 86.869, 'duration': 6.584}, {'end': 99.137, 'text': 'And who knows, maybe you even decide to contribute back to the open source LanChain effort.', 'start': 93.933, 'duration': 5.204}, {'end': 105.02, 'text': 'Langchain is an open source development framework for building LLM applications.', 'start': 100.377, 'duration': 4.643}, {'end': 109.262, 'text': 'We have two different packages, a Python one and a JavaScript one.', 'start': 105.62, 'duration': 3.642}, {'end': 112.563, 'text': "They're focused on composition and modularity.", 'start': 109.782, 'duration': 2.781}, {'end': 118.006, 'text': 'So they have a lot of individual components that can be used in conjunction with each other or by themselves.', 'start': 112.663, 'duration': 5.343}, {'end': 120.007, 'text': "And so that's one of the key value adds.", 'start': 118.566, 'duration': 1.441}, {'end': 123.569, 'text': 'And then the other key value add is a bunch of different use cases.', 'start': 120.127, 'duration': 3.442}, {'end': 131.978, 'text': 'So chains of ways of combining these modular components into more end-to-end applications and making it very easy to get started with those use cases.', 'start': 123.749, 'duration': 8.229}, {'end': 136.079, 'text': "In this class, we'll cover the common components of Langchain.", 'start': 133.137, 'duration': 2.942}, {'end': 137.18, 'text': "So we'll talk about models.", 'start': 136.099, 'duration': 1.081}, {'end': 141.663, 'text': "We'll talk about prompts, which are how you get models to do useful and interesting things.", 'start': 137.7, 'duration': 3.963}], 'summary': 'Lanchain has numerous users and contributors, ships code at an amazing pace, and offers open source development frameworks for llm applications with python and javascript packages.', 'duration': 72.226, 'max_score': 69.437, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI69437.jpg'}], 'start': 4.983, 'title': 'Lanchain components and development', 'summary': 'Introduces lanchain, an open-source framework for llm applications, emphasizing its ease of development, community adoption, and momentum. it covers langchain components such as models, prompts, indexes, chains, and agents, highlighting their functionalities and contributors to the course.', 'chapters': [{'end': 123.569, 'start': 4.983, 'title': 'Lanchain for llm development', 'summary': 'Introduces lanchain, an open-source framework for building llm applications, highlighting its ease of development, community adoption, and momentum, as well as the potential for quickly creating applications and contributing to the open source effort.', 'duration': 118.586, 'highlights': ['LanChain is an open-source framework for building LLM applications, with Python and JavaScript packages focused on composition and modularity, with numerous users and hundreds of open-source contributors.', 'LanChain enables faster AI application development by prompting LLMs, reducing the need for writing glue code and allowing quick application assembly.', 'The community adoption of LanChain is significant, with numerous users and many hundreds of contributors to the open source, leading to rapid development.', "The framework's focus on composition and modularity allows for the use of individual components in conjunction with each other or by themselves, offering value adds in terms of use cases."]}, {'end': 254.256, 'start': 123.749, 'title': 'Langchain components overview', 'summary': 'Covers the common components of langchain, including models, prompts, indexes, chains, and agents, highlighting their functionalities and the contributors to the course. it emphasizes its ease of use and the collaboration of the co-founders and contributors in creating the materials.', 'duration': 130.507, 'highlights': ['The chapter covers the common components of Langchain, including models, prompts, indexes, chains, and agents. It discusses the various components of Langchain, providing an overview of its modular structure and the different functionalities it offers.', 'It emphasizes its ease of use and the collaboration of the co-founders and contributors in creating the materials. The chapter highlights the ease of use of Langchain and acknowledges the contributions of co-founders and course contributors in creating the materials.', 'Models, prompts, indexes, chains, and agents are explained in detail, with their specific roles and functionalities. The chapter delves into the specifics of models, prompts, indexes, chains, and agents, outlining their individual roles and functionalities within Langchain.']}], 'duration': 249.273, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI4983.jpg', 'highlights': ['LanChain is an open-source framework for building LLM applications, with Python and JavaScript packages focused on composition and modularity, with numerous users and hundreds of open-source contributors.', 'The community adoption of LanChain is significant, with numerous users and many hundreds of contributors to the open source, leading to rapid development.', "The framework's focus on composition and modularity allows for the use of individual components in conjunction with each other or by themselves, offering value adds in terms of use cases.", 'LanChain enables faster AI application development by prompting LLMs, reducing the need for writing glue code and allowing quick application assembly.', 'The chapter covers the common components of Langchain, including models, prompts, indexes, chains, and agents. It discusses the various components of Langchain, providing an overview of its modular structure and the different functionalities it offers.', 'It emphasizes its ease of use and the collaboration of the co-founders and contributors in creating the materials. The chapter highlights the ease of use of Langchain and acknowledges the contributions of co-founders and course contributors in creating the materials.', 'Models, prompts, indexes, chains, and agents are explained in detail, with their specific roles and functionalities. The chapter delves into the specifics of models, prompts, indexes, chains, and agents, outlining their individual roles and functionalities within Langchain.']}, {'end': 1230.564, 'segs': [{'end': 280.246, 'src': 'embed', 'start': 254.676, 'weight': 2, 'content': [{'end': 263.859, 'text': 'This is actually very similar to the helper function that you might have seen in the chat GPT prompt engineering for developers course that I offered,', 'start': 254.676, 'duration': 9.183}, {'end': 266.04, 'text': "together with OpenAI's ease of forfeit.", 'start': 263.859, 'duration': 2.181}, {'end': 272.683, 'text': 'And so with this helper function you can say get to completion on what is one plus one,', 'start': 266.38, 'duration': 6.303}, {'end': 280.246, 'text': 'and this will call chat GPT or technically the model GPT 3.5 Turbo, to give you an answer back like this', 'start': 272.683, 'duration': 7.563}], 'summary': "The helper function in the chat gpt prompt engineering for developers course, with openai's ease of forfeit, allows calling gpt 3.5 turbo for answers.", 'duration': 25.57, 'max_score': 254.676, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI254676.jpg'}, {'end': 369.778, 'src': 'embed', 'start': 329.122, 'weight': 0, 'content': [{'end': 335.223, 'text': "And so, in order to actually accomplish this, if you've seen a little bit of prompting before,", 'start': 329.122, 'duration': 6.101}, {'end': 342.685, 'text': "I'm going to specify the prompt using an F string with the instructions translate the text that is delimited by triple backticks into style,", 'start': 335.223, 'duration': 7.462}, {'end': 345.386, 'text': 'that is style, and then plug in these two styles.', 'start': 342.685, 'duration': 2.701}, {'end': 348.967, 'text': 'And so this generates a prompt.', 'start': 345.946, 'duration': 3.021}, {'end': 351.911, 'text': 'that says translate the text and so on.', 'start': 349.929, 'duration': 1.982}, {'end': 359.158, 'text': 'I encourage you to pause the video and run the code and also try modifying the prompt to see if you can get a different output.', 'start': 352.071, 'duration': 7.087}, {'end': 366.725, 'text': 'You can then prompt the large language model to get a response.', 'start': 360.539, 'duration': 6.186}, {'end': 369.778, 'text': "Let's see what the response is.", 'start': 368.817, 'duration': 0.961}], 'summary': 'Using f string to specify prompt for text translation and style generation.', 'duration': 40.656, 'max_score': 329.122, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI329122.jpg'}, {'end': 757.376, 'src': 'embed', 'start': 728.995, 'weight': 3, 'content': [{'end': 733.898, 'text': 'Wrapping this in a LanChain prompt makes it easier to reuse a prompt like this.', 'start': 728.995, 'duration': 4.903}, {'end': 744.466, 'text': 'Also, you see later that LanChain provides prompts for some common operations, such as summarization or question answering,', 'start': 735.038, 'duration': 9.428}, {'end': 747.828, 'text': 'or connecting to SQL databases or connecting to different APIs.', 'start': 744.466, 'duration': 3.362}, {'end': 757.376, 'text': "And so by using some of LanChain's built-in prompts, you can quickly get an application working without needing to engineer your own prompts.", 'start': 748.349, 'duration': 9.027}], 'summary': 'Lanchain provides prompts for common operations, making it easier to reuse prompts and quickly develop applications.', 'duration': 28.381, 'max_score': 728.995, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI728995.jpg'}, {'end': 862.661, 'src': 'embed', 'start': 833.902, 'weight': 7, 'content': [{'end': 843.266, 'text': 'And so that together gives a very nice abstraction to specify the input to an LLM and then also have a parser,', 'start': 833.902, 'duration': 9.364}, {'end': 846.288, 'text': 'correctly interpret the output that the LLM gives.', 'start': 843.266, 'duration': 3.022}, {'end': 854.034, 'text': "With that, let's return to see an example of an output parser using LanChain.", 'start': 847.468, 'duration': 6.566}, {'end': 862.661, 'text': "In this example, let's take a look at how you can have an LM output JSON and use LanChain to parse that output.", 'start': 854.895, 'duration': 7.766}], 'summary': 'Abstraction to specify input for llm, parsing output with lanchain.', 'duration': 28.759, 'max_score': 833.902, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI833902.jpg'}, {'end': 1230.564, 'src': 'embed', 'start': 1203.419, 'weight': 5, 'content': [{'end': 1214.763, 'text': 'which is why I can now extract the value associated with the key gift and get true, or the value associated with delivery days and get two,', 'start': 1203.419, 'duration': 11.344}, {'end': 1219.125, 'text': 'or you can also extract the value associated with price value.', 'start': 1214.763, 'duration': 4.362}, {'end': 1230.564, 'text': 'So this is a nifty way to take your LLM output and parse it into a Python dictionary to make the output easier to use in downstream processing.', 'start': 1220.498, 'duration': 10.066}], 'summary': 'Extract values from llm output into python dictionary for easier downstream processing.', 'duration': 27.145, 'max_score': 1203.419, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI1203419.jpg'}], 'start': 254.676, 'title': 'Using langchain for language translation and parsing llm output', 'summary': 'Discusses using langchain to interact with gpt 3.5 turbo for language translation and parsing llm output, demonstrating simplification of prompt generation, translation, and extraction of values associated with keys gift, delivery days, and price value.', 'chapters': [{'end': 391.27, 'start': 254.676, 'title': 'Model prompt abstractions for language translation', 'summary': 'Discusses the use of a helper function to interact with the language model gpt 3.5 turbo for translating text and demonstrates the process of creating prompts for language translation, emphasizing the ability to modify prompts and obtain different outputs.', 'duration': 136.594, 'highlights': ['The chapter discusses the use of a helper function to interact with the language model GPT 3.5 Turbo for translating text. The transcript mentions the use of a helper function to interact with the language model GPT 3.5 Turbo for translating text.', 'Emphasizes the ability to modify prompts and obtain different outputs. The chapter encourages modifying prompts to obtain different outputs from the language model.', 'Demonstrates the process of creating prompts for language translation. The transcript demonstrates the process of creating prompts for language translation using an F string with specific instructions.']}, {'end': 1004.217, 'start': 391.27, 'title': 'Using lanchain for chatgpt api endpoint', 'summary': 'Demonstrates using lanchain to simplify the generation of prompts for the chatgpt api endpoint, including setting temperature parameter, defining and reusing prompt templates, translating customer messages, and parsing lm output json.', 'duration': 612.947, 'highlights': ['LanChain provides prompts for common operations like summarization, question answering, and connecting to different APIs, enabling quick application development without the need to engineer custom prompts.', 'Using prompt templates in LanChain is advantageous for building sophisticated applications with long and detailed prompts, allowing for easy reuse and abstraction of prompts.', 'LanChain supports output pausing and provides an example of using a parser to correctly interpret the output from the language model (LLM) based on specific keywords.', 'Demonstration of using LanChain to parse LM output JSON, extract information from a product review, and format the output in JSON format, showcasing the practical application of LanChain in data extraction and formatting.']}, {'end': 1230.564, 'start': 1005.317, 'title': 'Parsing llm output with langchain', 'summary': "Demonstrates using langchain's parser to convert llm output, initially a string, into a python dictionary, enabling the extraction of values associated with the keys gift, delivery days, and price value.", 'duration': 225.247, 'highlights': ["LangChain's parser converts LLM output, initially a string, into a Python dictionary, allowing the extraction of values associated with the keys gift, delivery days, and price value.", "By specifying schemas for gift, delivery days, and price value, LangChain's output parser provides precise instructions for the LLM, facilitating the generation of an output that the output parser can process.", 'The process involves creating a prompt from the review template, followed by the generation of messages that will be passed to the OpenAI endpoint, resulting in a response that can be parsed into an output dictionary.', 'The output parser ensures that the parsed output is of type dictionary, enabling the extraction of specific values associated with keys such as gift, delivery days, and price value.']}], 'duration': 975.888, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI254676.jpg', 'highlights': ['Demonstrates the process of creating prompts for language translation using an F string with specific instructions.', 'Emphasizes the ability to modify prompts and obtain different outputs from the language model.', 'The chapter discusses the use of a helper function to interact with the language model GPT 3.5 Turbo for translating text.', 'LanChain provides prompts for common operations like summarization, question answering, and connecting to different APIs, enabling quick application development without the need to engineer custom prompts.', 'Using prompt templates in LanChain is advantageous for building sophisticated applications with long and detailed prompts, allowing for easy reuse and abstraction of prompts.', "LangChain's parser converts LLM output, initially a string, into a Python dictionary, allowing the extraction of values associated with the keys gift, delivery days, and price value.", 'The output parser ensures that the parsed output is of type dictionary, enabling the extraction of specific values associated with keys such as gift, delivery days, and price value.', 'Demonstration of using LanChain to parse LM output JSON, extract information from a product review, and format the output in JSON format, showcasing the practical application of LanChain in data extraction and formatting.']}, {'end': 1752.657, 'segs': [{'end': 1274.068, 'src': 'embed', 'start': 1230.584, 'weight': 0, 'content': [{'end': 1238.337, 'text': "I encourage you to pause the video and run the code And so that's it for models, prompt, and parsers.", 'start': 1230.584, 'duration': 7.753}, {'end': 1239.378, 'text': 'With these tools.', 'start': 1238.478, 'duration': 0.9}, {'end': 1246.183, 'text': "hopefully you'll be able to reuse your own prompt templates easily share prompt templates with others that you're collaborating with.", 'start': 1239.378, 'duration': 6.805}, {'end': 1250.886, 'text': "even use LineChain's built-in prompt templates, which, as you just saw,", 'start': 1246.183, 'duration': 4.703}, {'end': 1265.014, 'text': 'can often be coupled with an output parser so that the input prompt to output in a specific format and then the parser pauses that output to store the data in a Python dictionary or some other data structure.', 'start': 1250.886, 'duration': 14.128}, {'end': 1267.395, 'text': 'that makes it easy for downstream processing.', 'start': 1265.014, 'duration': 2.381}, {'end': 1271.447, 'text': 'I hope you find this useful in many of your applications.', 'start': 1268.286, 'duration': 3.161}, {'end': 1274.068, 'text': "And with that let's go into the next video,", 'start': 1272.147, 'duration': 1.921}], 'summary': 'Tools enable easy reuse and sharing of prompt templates, along with parsing output for downstream processing.', 'duration': 43.484, 'max_score': 1230.584, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI1230584.jpg'}, {'end': 1669.299, 'src': 'embed', 'start': 1620.589, 'weight': 2, 'content': [{'end': 1629.154, 'text': 'As the conversation becomes long, the amount of memory needed becomes really really long and thus the cost of sending a lot of tokens to the LLM,', 'start': 1620.589, 'duration': 8.565}, {'end': 1634.918, 'text': 'which usually charges based on the number of tokens it needs to process, will also become more expensive.', 'start': 1629.154, 'duration': 5.764}, {'end': 1642.363, 'text': 'So, LanChain provides several convenient kinds of memory to store and accumulate the conversation.', 'start': 1635.558, 'duration': 6.805}, {'end': 1646.345, 'text': "So far, we've been looking at the conversation buffer memory.", 'start': 1643.604, 'duration': 2.741}, {'end': 1648.667, 'text': "Let's look at a different type of memory.", 'start': 1646.886, 'duration': 1.781}, {'end': 1654.465, 'text': "I'm going to import the conversation buffer window memory.", 'start': 1649.394, 'duration': 5.071}, {'end': 1658.309, 'text': 'that only keeps a window of memory.', 'start': 1655.526, 'duration': 2.783}, {'end': 1663.274, 'text': 'If I set memory to conversational buffer window, memory with k equals one.', 'start': 1658.689, 'duration': 4.585}, {'end': 1664.655, 'text': 'the variable k equals one.', 'start': 1663.274, 'duration': 1.381}, {'end': 1669.299, 'text': 'specifies that I wanted to remember just one conversational exchange.', 'start': 1664.655, 'duration': 4.644}], 'summary': 'Lanchain provides various memory types to store conversation, including buffer and window memory, with k=1 for one conversational exchange.', 'duration': 48.71, 'max_score': 1620.589, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI1620589.jpg'}], 'start': 1230.584, 'title': 'Model, prompt, and lanchain in chatbots', 'summary': "Introduces tools for creating and reusing prompt templates and explores lanchain's memory management options for chatbots, demonstrating how output parsers can store data and providing examples of conversation buffer memory and conversational buffer window memory.", 'chapters': [{'end': 1274.068, 'start': 1230.584, 'title': 'Model, prompt, and parsers overview', 'summary': "Introduces tools for creating and reusing prompt templates, including linechain's built-in templates, and demonstrates how output parsers can store data in a python dictionary for downstream processing.", 'duration': 43.484, 'highlights': ['The tools introduced include models, prompt, and parsers, which enable easy reuse of prompt templates and sharing with collaborators.', "LineChain's built-in prompt templates can be coupled with output parsers to format the output and store data in a Python dictionary for downstream processing.", 'This functionality is useful for various applications and makes it easier for downstream processing.']}, {'end': 1752.657, 'start': 1274.068, 'title': 'Lanchain: memory management for chatbots', 'summary': 'Explores how lanchain provides sophisticated memory management options for chatbots, enabling them to remember and use past conversation history to generate conversational flow, with examples of conversation buffer memory and conversational buffer window memory, and their impact on conversation length and cost.', 'duration': 478.589, 'highlights': ['LanChain offers multiple sophisticated options for managing memories, such as conversation buffer memory and conversational buffer window memory. LanChain provides advanced memory management options, including conversation buffer memory and conversational buffer window memory, to effectively manage conversation history and generate conversational flow.', 'The conversation buffer memory stores the entire conversation history, leading to longer memory requirements and potential increase in processing cost based on token count. The conversation buffer memory stores the entire conversation history, which can lead to longer memory requirements as the conversation becomes longer, potentially resulting in increased processing cost based on the number of tokens.', 'The conversational buffer window memory allows specifying the number of conversational exchanges to remember, preventing memory growth without limit and controlling the memory length as the conversation progresses. The conversational buffer window memory enables specifying the number of conversational exchanges to remember, preventing memory growth without limit and controlling the memory length as the conversation progresses, thus managing the cost of processing.']}], 'duration': 522.073, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI1230584.jpg', 'highlights': ['The tools introduced include models, prompt, and parsers, enabling easy reuse of prompt templates and sharing with collaborators.', "LineChain's built-in prompt templates can be coupled with output parsers to format the output and store data in a Python dictionary for downstream processing.", 'LanChain offers multiple sophisticated options for managing memories, such as conversation buffer memory and conversational buffer window memory.', 'The conversation buffer memory stores the entire conversation history, leading to longer memory requirements and potential increase in processing cost based on token count.', 'The conversational buffer window memory allows specifying the number of conversational exchanges to remember, preventing memory growth without limit and controlling the memory length as the conversation progresses.']}, {'end': 2294.637, 'segs': [{'end': 1911.95, 'src': 'embed', 'start': 1829.209, 'weight': 0, 'content': [{'end': 1837.009, 'text': 'um, what was said when, if i run this of a high token limit, It has almost a whole conversation.', 'start': 1829.209, 'duration': 7.8}, {'end': 1843.352, 'text': 'If I increase the token limit to 100, it now has a whole conversation.', 'start': 1837.029, 'duration': 6.323}, {'end': 1844.512, 'text': "It's time of AI is what?", 'start': 1843.372, 'duration': 1.14}, {'end': 1858.01, 'text': 'If I decrease it, then it chops off the earlier parts of this conversation to retain the number of tokens corresponding to the most recent exchanges,', 'start': 1845.453, 'duration': 12.557}, {'end': 1860.231, 'text': 'but subject to not exceeding the token limit.', 'start': 1858.01, 'duration': 2.221}, {'end': 1867.837, 'text': "And in case you're wondering why we needed to specify an LLM is because different LLMs use different ways of counting tokens.", 'start': 1861.092, 'duration': 6.745}, {'end': 1875.441, 'text': 'So this tells it to use the way of counting tokens that the chat openai oom uses.', 'start': 1867.977, 'duration': 7.464}, {'end': 1883.685, 'text': 'i encourage you to pause the video and run the code and also try modifying the prompt to see if you can get a different output.', 'start': 1875.441, 'duration': 8.244}, {'end': 1892.57, 'text': 'finally, this one last type of memory i want to illustrate here, which is the conversation summary buffer memory.', 'start': 1883.685, 'duration': 8.885}, {'end': 1895.115, 'text': 'And the idea is,', 'start': 1893.493, 'duration': 1.622}, {'end': 1904.123, 'text': 'instead of limiting the memory to a fixed number of tokens based on the most recent utterances or a fixed number of conversation exchanges,', 'start': 1895.115, 'duration': 9.008}, {'end': 1911.95, 'text': "let's use an LLM to write a summary of the conversation so far and let that be the memory.", 'start': 1904.123, 'duration': 7.827}], 'summary': 'Adjusting token limit impacts conversation length and memory in llm.', 'duration': 82.741, 'max_score': 1829.209, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI1829209.jpg'}, {'end': 2020.654, 'src': 'embed', 'start': 1995.417, 'weight': 3, 'content': [{'end': 2008.085, 'text': 'If I reduce the number of tokens to 100, then the conversation summary buffer memory has actually used an LLM, the open AI endpoint, in this case,', 'start': 1995.417, 'duration': 12.668}, {'end': 2013.969, 'text': "because that's where we set the LLM to to actually generate a summary of the conversation so far.", 'start': 2008.085, 'duration': 5.884}, {'end': 2017.812, 'text': 'So the summary is human AI engaged in small talk before the scheduled day schedule.', 'start': 2014.189, 'duration': 3.623}, {'end': 2020.654, 'text': 'AI informs human in the morning meeting.', 'start': 2017.812, 'duration': 2.842}], 'summary': 'Ai uses 100 tokens to generate conversation summary; human-ai engage in small talk and ai informs human in morning meeting.', 'duration': 25.237, 'max_score': 1995.417, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI1995417.jpg'}], 'start': 1755.117, 'title': 'Optimizing llm calls and conversation summary buffer memory', 'summary': 'Discusses optimizing llm calls with memory limits and the concept of conversation summary buffer memory, demonstrating impact on conversation retention and cost, and applicability to various applications.', 'chapters': [{'end': 1883.685, 'start': 1755.117, 'title': 'Optimizing llm calls with memory limit', 'summary': 'Discusses optimizing llm calls by setting memory limits, with examples showing the impact of token limits on conversation retention and cost of llm calls.', 'duration': 128.568, 'highlights': ['The conversational token buffer memory limits the number of tokens saved, directly impacting the cost of LLM calls.', 'Setting the max token limit affects the retention of conversations, with higher limits storing more exchanges and vice versa.', 'Different LLMs use different ways of counting tokens, hence the need to specify the counting method for accurate cost estimation.']}, {'end': 2294.637, 'start': 1883.685, 'title': 'Conversation summary buffer memory', 'summary': 'Illustrates the concept of conversation summary buffer memory, which utilizes an llm to write a summary of the conversation so far as memory, allowing for dynamic token limits and ai-generated summaries, applicable to various applications.', 'duration': 410.952, 'highlights': ['The conversation summary buffer memory uses an LLM to write a summary of the conversation so far and allows for dynamic token limits. The memory is not limited to a fixed number of tokens and can dynamically adjust the token limit to store and summarize conversation text.', 'Reduction of the max token limit triggers the use of an LLM to generate a summary of the conversation so far. When the token limit is reduced, the system utilizes the LLM to generate a summary of the conversation, providing a concise overview of the dialogue.', 'LanChain supports additional memory types, including vector data memory and entity memories. LanChain provides support for vector data memory to store and retrieve relevant text blocks, as well as entity memories to remember details about specific entities, offering versatile memory options for varied application needs.']}], 'duration': 539.52, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI1755117.jpg', 'highlights': ['Setting the max token limit affects the retention of conversations, with higher limits storing more exchanges and vice versa.', 'The conversation summary buffer memory uses an LLM to write a summary of the conversation so far and allows for dynamic token limits.', 'Different LLMs use different ways of counting tokens, hence the need to specify the counting method for accurate cost estimation.', 'Reduction of the max token limit triggers the use of an LLM to generate a summary of the conversation so far.']}, {'end': 3133.5, 'segs': [{'end': 2394.351, 'src': 'embed', 'start': 2365.692, 'weight': 3, 'content': [{'end': 2367.813, 'text': "if you're not familiar with pandas, don't worry about it.", 'start': 2365.692, 'duration': 2.121}, {'end': 2371.854, 'text': "the main, the main point here is that we're loading some data that we can then use later on.", 'start': 2367.813, 'duration': 4.041}, {'end': 2374.035, 'text': 'and so if we look inside this pandas data frame,', 'start': 2371.854, 'duration': 2.181}, {'end': 2381.577, 'text': 'we can see that there is a product column and then a review column and the each of these rows is a different data point that we can start passing through our chains.', 'start': 2374.035, 'duration': 7.542}, {'end': 2385.305, 'text': "So the first chain we're going to cover is the LLM chain.", 'start': 2382.984, 'duration': 2.321}, {'end': 2391.409, 'text': "And this is a simple but really powerful chain that underpins a lot of the chains that we'll go over in the future.", 'start': 2385.485, 'duration': 5.924}, {'end': 2394.351, 'text': "And so we're going to import three different things.", 'start': 2391.929, 'duration': 2.422}], 'summary': 'Introduction to using pandas data frame for data processing and covering the llm chain.', 'duration': 28.659, 'max_score': 2365.692, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI2365692.jpg'}, {'end': 2501.305, 'src': 'embed', 'start': 2463.632, 'weight': 0, 'content': [{'end': 2470.316, 'text': 'And so here would be a good time to pause and you can input any product descriptions that you would want and you can see what the chain will output as a result.', 'start': 2463.632, 'duration': 6.684}, {'end': 2476.819, 'text': "So the LLM chain is the most basic type of chain and that's gonna be used a lot in the future.", 'start': 2471.416, 'duration': 5.403}, {'end': 2481.582, 'text': 'And so we can see how this will be used in the next type of chain, which will be sequential chains.', 'start': 2477.16, 'duration': 4.422}, {'end': 2485.744, 'text': 'And so sequential chains run a sequence of chains one after another.', 'start': 2481.742, 'duration': 4.002}, {'end': 2489.967, 'text': "So to start, you're going to import the simple sequential chain.", 'start': 2486.325, 'duration': 3.642}, {'end': 2495.241, 'text': 'And this works well when we have sub chains that expect only one input and return only one output.', 'start': 2490.578, 'duration': 4.663}, {'end': 2501.305, 'text': "And so here we're going to first create one chain, which uses an LLM and a prompt.", 'start': 2496.102, 'duration': 5.203}], 'summary': 'Llm chain is the basic type used frequently. sequential chains run a sequence of chains one after another.', 'duration': 37.673, 'max_score': 2463.632, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI2463632.jpg'}, {'end': 2594.736, 'src': 'embed', 'start': 2566.474, 'weight': 2, 'content': [{'end': 2573.079, 'text': 'But what about when there are multiple inputs or multiple outputs? And so we can do this by using just the regular sequential chain.', 'start': 2566.474, 'duration': 6.605}, {'end': 2578.886, 'text': "So let's import that, and then you're going to create a bunch of chains that we're going to use one after another.", 'start': 2574.383, 'duration': 4.503}, {'end': 2582.148, 'text': "We're going to be using the data from above, which has a review.", 'start': 2579.286, 'duration': 2.862}, {'end': 2587.772, 'text': "And so the first chain, we're going to take the review and translate it into English.", 'start': 2582.749, 'duration': 5.023}, {'end': 2594.736, 'text': "With the second chain, we're going to create a summary of that review in one sentence.", 'start': 2590.374, 'duration': 4.362}], 'summary': 'Using sequential chains for multiple inputs and outputs, translating reviews into english and creating one-sentence summaries.', 'duration': 28.262, 'max_score': 2566.474, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI2566474.jpg'}], 'start': 2294.637, 'title': 'Lanchain and llm chain', 'summary': "Introduces lanchain as a key building block, utilizing llm with a prompt and pandas data frame for processing. it also covers llm chain as a foundation for future chains and discusses sequential chains' usage, functionality, and examples of running them with product descriptions and reviews.", 'chapters': [{'end': 2381.577, 'start': 2294.637, 'title': 'Understanding lanchain: the chain building block', 'summary': 'Introduces the key building block of lanchain, the chain, which combines an llm with a prompt and allows running operations on multiple inputs, utilizing a pandas data frame for processing.', 'duration': 86.94, 'highlights': ['The chain combines an LLM with a prompt and enables running operations on multiple inputs, leveraging a pandas data frame for processing.', 'Loading a pandas data frame to utilize its data structure for processing multiple elements of data.', 'The ability to run chains over many inputs at a time provides scalability for processing operations.']}, {'end': 3133.5, 'start': 2382.984, 'title': 'Llm chain and sequential chains', 'summary': 'Introduces the llm chain as a powerful foundation for future chains, then covers sequential chains for running subchains one after another, detailing their usage and functionality, along with examples of running them with product descriptions and reviews.', 'duration': 750.516, 'highlights': ['The chapter introduces the LLM chain as a powerful foundation for future chains, then covers sequential chains for running subchains one after another, detailing their usage and functionality, along with examples of running them with product descriptions and reviews. It covers the basics of the LLM chain and its combination with a prompt, as well as the usage of simple sequential chains and regular sequential chains, illustrating their functionality with examples and visual representations.', "The LLM chain is the most basic type of chain and that's gonna be used a lot in the future. The LLM chain is emphasized as a fundamental and frequently used type of chain in future applications.", 'Sequential chains run a sequence of chains one after another, importing the simple sequential chain and explaining its usage with examples. Sequential chains are introduced, emphasizing the use of the simple sequential chain and providing examples to illustrate its application in running subchains sequentially.']}], 'duration': 838.863, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI2294637.jpg', 'highlights': ['The chain combines an LLM with a prompt and enables running operations on multiple inputs, leveraging a pandas data frame for processing.', 'The chapter introduces the LLM chain as a powerful foundation for future chains, then covers sequential chains for running subchains one after another, detailing their usage and functionality, along with examples of running them with product descriptions and reviews.', 'The ability to run chains over many inputs at a time provides scalability for processing operations.', 'Loading a pandas data frame to utilize its data structure for processing multiple elements of data.', 'The LLM chain is emphasized as a fundamental and frequently used type of chain in future applications.', 'Sequential chains run a sequence of chains one after another, importing the simple sequential chain and explaining its usage with examples.']}, {'end': 3837.221, 'segs': [{'end': 3163.894, 'src': 'embed', 'start': 3133.86, 'weight': 1, 'content': [{'end': 3139.263, 'text': "This is really powerful because it starts to combine these language models with data that they weren't originally trained on.", 'start': 3133.86, 'duration': 5.403}, {'end': 3142.484, 'text': 'So it makes them much more flexible and adaptable to your use case.', 'start': 3139.723, 'duration': 2.761}, {'end': 3146.826, 'text': "It's also really exciting because we'll start to move beyond language models,", 'start': 3142.964, 'duration': 3.862}, {'end': 3154.17, 'text': 'prompts and output parsers and start introducing some more of the key components of link chain, such as embedding models and vector stores.', 'start': 3146.826, 'duration': 7.344}, {'end': 3158.472, 'text': "As Andrew mentioned, this is one of the more popular chains that we've got, so I hope you're excited.", 'start': 3155.03, 'duration': 3.442}, {'end': 3163.894, 'text': 'In fact, embeddings and vector stores are some of the most powerful modern techniques.', 'start': 3159.508, 'duration': 4.386}], 'summary': 'Combining language models with new data makes them more flexible and adaptable, introducing key components like embedding models and vector stores.', 'duration': 30.034, 'max_score': 3133.86, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI3133860.jpg'}, {'end': 3215.567, 'src': 'embed', 'start': 3184.769, 'weight': 2, 'content': [{'end': 3188.093, 'text': "We're going to import our favorite chat open AI language model.", 'start': 3184.769, 'duration': 3.324}, {'end': 3189.774, 'text': "We're going to import a document loader.", 'start': 3188.533, 'duration': 1.241}, {'end': 3194.679, 'text': "This is going to be used to load some proprietary data that we're going to combine with the language model.", 'start': 3190.135, 'duration': 4.544}, {'end': 3196.301, 'text': "In this case, it's going to be in a CSV.", 'start': 3194.719, 'duration': 1.582}, {'end': 3198.603, 'text': "So we're going to import the CSV loader.", 'start': 3196.822, 'duration': 1.781}, {'end': 3200.964, 'text': "Finally, we're going to import a vector store.", 'start': 3199.384, 'duration': 1.58}, {'end': 3206.065, 'text': "There are many different types of vector stores and we'll cover what exactly these are later on,", 'start': 3201.624, 'duration': 4.441}, {'end': 3209.326, 'text': "but we're going to get started with the docker ray in-memory search vector store.", 'start': 3206.065, 'duration': 3.261}, {'end': 3215.567, 'text': "This is really nice because it's an in-memory vector store and it doesn't require connecting to an external database of any kind,", 'start': 3209.826, 'duration': 5.741}], 'summary': 'Importing open ai language model, document loader, csv loader, and docker ray in-memory search vector store for data processing.', 'duration': 30.798, 'max_score': 3184.769, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI3184769.jpg'}, {'end': 3320.906, 'src': 'embed', 'start': 3289.534, 'weight': 7, 'content': [{'end': 3295.677, 'text': "We'll then create a response using index query and pass in this query.", 'start': 3289.534, 'duration': 6.143}, {'end': 3300.679, 'text': "Again, we'll cover what's going on under the hood down below.", 'start': 3297.337, 'duration': 3.342}, {'end': 3302.88, 'text': "For now, we'll just wait for it to respond.", 'start': 3301.359, 'duration': 1.521}, {'end': 3314.322, 'text': 'After it finishes, we can now take a look at what exactly was returned.', 'start': 3310.72, 'duration': 3.602}, {'end': 3320.906, 'text': "We've gotten back a table in Markdown with names and descriptions for all shirts with sun protection.", 'start': 3314.983, 'duration': 5.923}], 'summary': 'Create response with index query for shirts with sun protection.', 'duration': 31.372, 'max_score': 3289.534, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI3289534.jpg'}, {'end': 3583.343, 'src': 'embed', 'start': 3552.265, 'weight': 5, 'content': [{'end': 3555.967, 'text': 'If we take a look at this embedding, we can see that there are over a thousand different elements.', 'start': 3552.265, 'duration': 3.702}, {'end': 3564.613, 'text': 'Each of these elements is a different numerical value.', 'start': 3562.471, 'duration': 2.142}, {'end': 3569.456, 'text': 'Combined, this creates the overall numerical representation for this piece of text.', 'start': 3565.253, 'duration': 4.203}, {'end': 3577.882, 'text': 'We want to create embeddings for all the pieces of text that we just loaded, and then we also want to store them in a vector store.', 'start': 3571.781, 'duration': 6.101}, {'end': 3583.343, 'text': 'We can do that by using the from documents method on the vector store.', 'start': 3578.962, 'duration': 4.381}], 'summary': "Over a thousand elements in the embedding, each with a different numerical value, used to create the overall numerical representation for the text. embeddings for all the loaded text pieces will be created and stored in a vector store using the 'from documents' method.", 'duration': 31.078, 'max_score': 3552.265, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI3552265.jpg'}, {'end': 3636.748, 'src': 'embed', 'start': 3603.526, 'weight': 4, 'content': [{'end': 3609.107, 'text': 'If we use the similarity search method on the vector store and pass in a query, we will get back a list of documents.', 'start': 3603.526, 'duration': 5.581}, {'end': 3619.291, 'text': 'We can see that it returns four documents.', 'start': 3617.31, 'duration': 1.981}, {'end': 3627.033, 'text': 'And if we look at the first one, we can see that it is indeed a shirt about sunblocking.', 'start': 3619.511, 'duration': 7.522}, {'end': 3636.748, 'text': 'So how do we use this to do question answering over our own documents? First, we need to create a retriever from this vector store.', 'start': 3628.464, 'duration': 8.284}], 'summary': 'Using similarity search method on vector store returns 4 documents, enabling question answering over own documents.', 'duration': 33.222, 'max_score': 3603.526, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI3603526.jpg'}, {'end': 3738.467, 'src': 'embed', 'start': 3700.338, 'weight': 0, 'content': [{'end': 3702.7, 'text': 'So here we can create a retrieval QA chain.', 'start': 3700.338, 'duration': 2.362}, {'end': 3706.783, 'text': 'This does retrieval and then does question and answering over the retrieved documents.', 'start': 3703.38, 'duration': 3.403}, {'end': 3710.246, 'text': "To create such a chain, we'll pass in a few different things.", 'start': 3707.323, 'duration': 2.923}, {'end': 3711.867, 'text': "First, we'll pass in the language model.", 'start': 3710.506, 'duration': 1.361}, {'end': 3715.129, 'text': 'This will be used for doing the text generation at the end.', 'start': 3712.627, 'duration': 2.502}, {'end': 3717.651, 'text': "Next, we'll pass in the chain type.", 'start': 3716.21, 'duration': 1.441}, {'end': 3718.932, 'text': "We're going to use stuff.", 'start': 3718.091, 'duration': 0.841}, {'end': 3725.197, 'text': 'This is the simplest method as it just stuffs all the documents into context and makes one call to a language model.', 'start': 3719.012, 'duration': 6.185}, {'end': 3732.001, 'text': "There are a few other methods that you can use to do question answering that I'll maybe touch on at the end, but we're not going to look at in detail.", 'start': 3725.796, 'duration': 6.205}, {'end': 3734.183, 'text': "Third, we're going to pass in a retriever.", 'start': 3732.682, 'duration': 1.501}, {'end': 3738.467, 'text': 'The retriever we created above is just an interface for fetching documents.', 'start': 3734.924, 'duration': 3.543}], 'summary': 'Creating a retrieval qa chain involves passing a language model, chain type, and retriever.', 'duration': 38.129, 'max_score': 3700.338, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI3700338.jpg'}], 'start': 3133.86, 'title': 'Language models and vector stores', 'summary': 'Introduces language models, vector stores, and question answering techniques, emphasizing their power and flexibility, and covering the creation and customization of embeddings, retrievers, and index for effective information retrieval.', 'chapters': [{'end': 3265.524, 'start': 3133.86, 'title': 'Introduction to language models and vector stores', 'summary': 'Introduces the use of language models trained on external data, the integration of key components of link chain, and the importation and initialization of various tools and models, emphasizing the power and flexibility of these techniques.', 'duration': 131.664, 'highlights': ['The chapter introduces the use of language models trained on external data This approach makes language models more flexible and adaptable to specific use cases, expanding their capabilities beyond their original training data.', 'Importation and initialization of various tools and models The chapter covers the importation of retrieval QA chain, chat open AI language model, document loader, CSV loader, and a vector store, highlighting the practical steps involved in setting up the environment.', 'Emphasis on the power and flexibility of language models and vector stores The chapter stresses the significance of embeddings and vector stores as powerful modern techniques, encouraging the audience to explore and learn about these technologies for their potential applications.']}, {'end': 3837.221, 'start': 3266.464, 'title': 'Question answering with language models', 'summary': 'Covers using language models for question answering, including creating embeddings for text, storing them in a vector database, and using a retriever to fetch documents and pass them to the language model, with the ability to customize the index and embeddings.', 'duration': 570.757, 'highlights': ["We've gotten back a table in Markdown with names and descriptions for all shirts with sun protection. The response includes a table in Markdown format containing names and descriptions for all shirts with sun protection.", 'It returns four documents, and the first one is indeed a shirt about sunblocking. The similarity search method returns a list of four documents, and the first one matches the query about a shirt with sunblocking.', 'Each of these elements is a different numerical value, creating the overall numerical representation for this piece of text. Embeddings create numerical representations for pieces of text with over a thousand different elements, capturing the semantic meaning for comparison.', 'We can create a retrieval QA chain, passing in the language model, chain type, retriever, and setting verbose to true. A retrieval QA chain can be created by passing in the language model, chain type, retriever, and setting verbose to true for question answering over retrieved documents.', 'The chapter covers using language models for question answering, including creating embeddings for text, storing them in a vector database, and using a retriever to fetch documents and pass them to the language model, with the ability to customize the index and embeddings. The chapter provides comprehensive coverage of using language models for question answering, including creating and storing embeddings, using a retriever, and customizing the index and embeddings.']}], 'duration': 703.361, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI3133860.jpg', 'highlights': ['The chapter provides comprehensive coverage of using language models for question answering, including creating and storing embeddings, using a retriever, and customizing the index and embeddings.', 'The chapter introduces the use of language models trained on external data, making them more flexible and adaptable to specific use cases, expanding their capabilities beyond their original training data.', 'The chapter covers the importation of retrieval QA chain, chat open AI language model, document loader, CSV loader, and a vector store, highlighting the practical steps involved in setting up the environment.', 'The chapter stresses the significance of embeddings and vector stores as powerful modern techniques, encouraging the audience to explore and learn about these technologies for their potential applications.', 'The similarity search method returns a list of four documents, and the first one matches the query about a shirt with sunblocking.', 'Embeddings create numerical representations for pieces of text with over a thousand different elements, capturing the semantic meaning for comparison.', 'A retrieval QA chain can be created by passing in the language model, chain type, retriever, and setting verbose to true for question answering over retrieved documents.', 'The response includes a table in Markdown format containing names and descriptions for all shirts with sun protection.']}, {'end': 4224.871, 'segs': [{'end': 3881.85, 'src': 'embed', 'start': 3853.191, 'weight': 0, 'content': [{'end': 3859.134, 'text': 'So if you remember, when we fetched the documents in the notebook, we only got four documents back and they were relatively small.', 'start': 3853.191, 'duration': 5.943}, {'end': 3864.238, 'text': 'But what if you wanted to do the same type of question answering over lots of different types of chunks?', 'start': 3859.775, 'duration': 4.463}, {'end': 3866.979, 'text': 'Then there are a few different methods that we can use.', 'start': 3865.017, 'duration': 1.962}, {'end': 3868.56, 'text': 'The first is MapReduce.', 'start': 3867.519, 'duration': 1.041}, {'end': 3874.464, 'text': 'This basically takes all the chunks, passes them along with the question to a language model,', 'start': 3869.2, 'duration': 5.264}, {'end': 3881.85, 'text': 'gets back a response and then uses another language model call to summarize all of the individual responses into a final answer.', 'start': 3874.464, 'duration': 7.386}], 'summary': 'Using mapreduce to process multiple chunks for question-answering, with language models.', 'duration': 28.659, 'max_score': 3853.191, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI3853191.jpg'}, {'end': 3922.599, 'src': 'embed', 'start': 3892.261, 'weight': 2, 'content': [{'end': 3893.544, 'text': 'but it does take a lot more calls.', 'start': 3892.261, 'duration': 1.283}, {'end': 3899.055, 'text': 'And it does treat all the documents as independent, which may not always be the most desired thing.', 'start': 3894.085, 'duration': 4.97}, {'end': 3905.928, 'text': 'Refine, which is another method, is again used to loop over many documents, but it actually does it iteratively.', 'start': 3899.556, 'duration': 6.372}, {'end': 3908.65, 'text': 'It builds upon the answer from the previous document.', 'start': 3906.068, 'duration': 2.582}, {'end': 3914.133, 'text': 'So this is really good for combining information and building up an answer over time.', 'start': 3909.21, 'duration': 4.923}, {'end': 3920.078, 'text': "It will generally lead to longer answers, and it's also not as fast because now the calls aren't independent.", 'start': 3914.394, 'duration': 5.684}, {'end': 3922.599, 'text': 'They depend on the result of previous calls.', 'start': 3920.198, 'duration': 2.401}], 'summary': "Refine method builds upon previous documents iteratively, creating longer answers. it's not as fast as independent calls.", 'duration': 30.338, 'max_score': 3892.261, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI3892261.jpg'}, {'end': 3982.29, 'src': 'embed', 'start': 3952.981, 'weight': 1, 'content': [{'end': 3957.863, 'text': "Similar to MapReduce, all the calls are independent, so you can batch them, and it's relatively fast.", 'start': 3952.981, 'duration': 4.882}, {'end': 3962.484, 'text': "But again, you're making a bunch of language model calls, so it will be a bit more expensive.", 'start': 3958.523, 'duration': 3.961}, {'end': 3968.606, 'text': 'The most common of these methods is the stuff method, which we used in the notebook to combine it all into one document.', 'start': 3963.184, 'duration': 5.422}, {'end': 3974.888, 'text': 'The second most common is the MapReduce method, which takes these chunks and sends them to the language model.', 'start': 3969.666, 'duration': 5.222}, {'end': 3982.29, 'text': 'These methods here, stuff, MapReduce, refine, and re-rank, can also be used for lots of other chains besides just question answering.', 'start': 3975.968, 'duration': 6.322}], 'summary': 'Language model calls can be batched for speed, but are more expensive. methods like stuff and mapreduce can be used for various chains.', 'duration': 29.309, 'max_score': 3952.981, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI3952981.jpg'}], 'start': 3837.301, 'title': 'Question answering methods and evaluating llm-based applications', 'summary': 'Covers various question answering methods like mapreduce, refine, and map rerank, discussing their efficiency, limitations, and common use cases. it also delves into evaluating llm-based applications, providing insights on frameworks and tools, and demonstrating methods for setting up an evaluation chain and defining data points.', 'chapters': [{'end': 4006.686, 'start': 3837.301, 'title': 'Question answering methods', 'summary': 'Discusses various methods for question answering over documents, including mapreduce, refine, and map rerank, highlighting their efficiency, limitations, and common use cases.', 'duration': 169.385, 'highlights': ['MapReduce method It takes all the chunks, passes them to a language model, and operates over any number of documents, allowing for parallel processing and independent questions.', 'Refine method It iteratively combines information from multiple documents to build up an answer over time, leading to longer answers but not as fast as it depends on previous results.', 'Map Rerank method It involves a single call to the language model for each document, selects the highest score, and relies on the language model to know the score, making it relatively fast but more expensive due to multiple language model calls.']}, {'end': 4224.871, 'start': 4012.028, 'title': 'Evaluating llm-based applications', 'summary': 'Discusses evaluating llm-based applications, providing insights on frameworks and tools for evaluation, and demonstrating methods for setting up an evaluation chain and defining data points for evaluation.', 'duration': 212.843, 'highlights': ['The chapter discusses evaluating LLM-based applications, providing insights on frameworks and tools for evaluation. The video delves into frameworks for evaluating LLM-based applications and tools to aid in evaluation, addressing the challenge of assessing the performance and making improvements in LLM-based applications.', 'Demonstrating methods for setting up an evaluation chain and defining data points for evaluation. The chapter explains the process of setting up an evaluation chain and establishing data points for evaluation, emphasizing the importance of understanding the input and output of each step and providing specific examples of defining data points for evaluation.']}], 'duration': 387.57, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI3837301.jpg', 'highlights': ['MapReduce method allows parallel processing and independent questions.', 'Map Rerank method involves a single call to the language model for each document, making it relatively fast.', 'Refine method iteratively combines information from multiple documents to build up an answer over time.']}, {'end': 5116.955, 'segs': [{'end': 4272.503, 'src': 'embed', 'start': 4246.662, 'weight': 0, 'content': [{'end': 4254.448, 'text': 'So we can import the QA generation chain, and this will take in documents and it will create a question answer pair from each document.', 'start': 4246.662, 'duration': 7.786}, {'end': 4257.27, 'text': "It'll do this using a language model itself.", 'start': 4255.288, 'duration': 1.982}, {'end': 4261.973, 'text': 'So we need to create this chain by passing in the chat open AI language model.', 'start': 4257.69, 'duration': 4.283}, {'end': 4265.356, 'text': 'And then from there, we can create a bunch of examples.', 'start': 4262.714, 'duration': 2.642}, {'end': 4272.503, 'text': "And so we're going to use the apply and parse method, because this is applying an output parser to the result,", 'start': 4266.331, 'duration': 6.172}], 'summary': 'Create qa generation chain using openai language model to process documents and generate question answer pairs.', 'duration': 25.841, 'max_score': 4246.662, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI4246662.jpg'}, {'end': 4388.628, 'src': 'embed', 'start': 4358.497, 'weight': 1, 'content': [{'end': 4363.9, 'text': 'And to help with that, we have a fun little util in lane chain called lane chain debug.', 'start': 4358.497, 'duration': 5.403}, {'end': 4375.607, 'text': 'And so if we set lane chain debug equals true and we now rerun the same example as above,', 'start': 4366.982, 'duration': 8.625}, {'end': 4378.549, 'text': 'we can see that it starts printing out a lot more information.', 'start': 4375.607, 'duration': 2.942}, {'end': 4381.826, 'text': "And so if we look at what exactly it's printing out,", 'start': 4379.505, 'duration': 2.321}, {'end': 4388.628, 'text': "we can see that it's diving down first into the retrieval QA chain and then it's going down into a stuff documents chain.", 'start': 4381.826, 'duration': 6.802}], 'summary': 'The lane chain debug utility provides more information while running examples.', 'duration': 30.131, 'max_score': 4358.497, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI4358497.jpg'}, {'end': 4502.109, 'src': 'embed', 'start': 4478.021, 'weight': 2, 'content': [{'end': 4487.084, 'text': "And this can be really useful to track the tokens that you're using in your chains or calls to language models over time and keep track of the total number of tokens,", 'start': 4478.021, 'duration': 9.063}, {'end': 4489.725, 'text': 'which corresponds very closely to the total cost.', 'start': 4487.084, 'duration': 2.641}, {'end': 4492.746, 'text': 'And because this is a relatively simple chain,', 'start': 4490.665, 'duration': 2.081}, {'end': 4502.109, 'text': 'we can now see that the final response the cozy comfort pullover set stripe does have side pockets is getting bubbled up through the chains and getting returned to the user.', 'start': 4492.746, 'duration': 9.363}], 'summary': 'Tracking tokens usage over time to monitor total cost and ensuring final product meets user expectations.', 'duration': 24.088, 'max_score': 4478.021, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI4478021.jpg'}, {'end': 4800.758, 'src': 'embed', 'start': 4765.754, 'weight': 3, 'content': [{'end': 4769.518, 'text': "and we're having to invent new ones and invent new heuristics for doing so.", 'start': 4765.754, 'duration': 3.764}, {'end': 4777.225, 'text': 'And the most interesting and most popular of those heuristics at the moment is actually using a language model to do the evaluation.', 'start': 4770.058, 'duration': 7.167}, {'end': 4779.407, 'text': 'This finishes the evaluation lesson.', 'start': 4777.665, 'duration': 1.742}, {'end': 4783.09, 'text': 'But one last thing I want to show you is the link chain evaluation platform.', 'start': 4779.647, 'duration': 3.443}, {'end': 4788.996, 'text': 'This is a way to do everything that we just did in the notebook, but persist it and show it in a UI.', 'start': 4783.511, 'duration': 5.485}, {'end': 4790.638, 'text': "And so let's check it out.", 'start': 4789.436, 'duration': 1.202}, {'end': 4800.758, 'text': "Here we can see that we have a session we called it Deep Learning AI and we can see here that we've actually persisted all the runs that we ran in the notebook.", 'start': 4791.673, 'duration': 9.085}], 'summary': 'Inventing new heuristics, using language model for evaluation, and showcasing link chain evaluation platform.', 'duration': 35.004, 'max_score': 4765.754, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI4765754.jpg'}, {'end': 4969.252, 'src': 'embed', 'start': 4940.923, 'weight': 4, 'content': [{'end': 4942.784, 'text': 'And then the large language model LLM.', 'start': 4940.923, 'duration': 1.861}, {'end': 4946.788, 'text': "well, maybe use this background knowledge that's learned off the internet,", 'start': 4943.204, 'duration': 3.584}, {'end': 4954.356, 'text': 'but to use the new information you give it to help you answer questions or reason through content, or decide even what to do next.', 'start': 4946.788, 'duration': 7.568}, {'end': 4958.221, 'text': "And that's what LandChain's Agents framework helps you to do.", 'start': 4954.817, 'duration': 3.404}, {'end': 4961.91, 'text': 'Agents are probably my favorite part of Langchain.', 'start': 4959.289, 'duration': 2.621}, {'end': 4965.351, 'text': "I think they're also one of the most powerful parts, but they're also one of the newer parts.", 'start': 4961.95, 'duration': 3.401}, {'end': 4969.252, 'text': "So we're seeing a lot of stuff emerge here that's really new to everyone in the field.", 'start': 4965.911, 'duration': 3.341}], 'summary': "Llm uses background knowledge to reason and assist with new information, aided by landchain's agents framework.", 'duration': 28.329, 'max_score': 4940.923, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI4940923.jpg'}], 'start': 4224.891, 'title': 'Language models and agents', 'summary': "Covers automating question answering with language models, evaluating language models, and introduces landchain's agents framework. it demonstrates creating question-answer pairs, debugging the process with a language model, evaluating examples, and equipping agents with tools and apis.", 'chapters': [{'end': 4492.746, 'start': 4224.891, 'title': 'Automating question answering with language models', 'summary': 'Discusses automating question answering using language models, demonstrating how to create question-answer pairs from documents and debug the process using a language model.', 'duration': 267.855, 'highlights': ['Creating question-answer pairs from documents using language models The chapter demonstrates importing a QA generation chain that creates question-answer pairs from documents using a language model, saving time and enabling more efficient processes.', 'Debugging the question answering process with lane chain debug The chapter introduces the lane chain debug utility, which provides detailed information on the question answering process, including retrieval steps and language model prompts, aiding in debugging and understanding potential issues.', 'Understanding the token usage and cost in language model operations The chapter emphasizes the importance of tracking token usage and total tokens in language model operations, highlighting their close correspondence to the total cost and providing insights into optimizing resource utilization.']}, {'end': 5116.955, 'start': 4492.746, 'title': 'Language model evaluation and agent framework', 'summary': "Explores evaluating language models using a language model for evaluation, including creating predictions, evaluating examples, and using language models as a reasoning engine. it also introduces landchain's agents framework, highlighting the importance of agents and how to equip them with tools and apis.", 'duration': 624.209, 'highlights': ['The chapter explores evaluating language models using a language model for evaluation, including creating predictions, evaluating examples, and using language models as a reasoning engine. The chapter delves into the process of evaluating language models, creating predictions for examples, and utilizing language models as a reasoning engine for tasks.', "Introducing LandChain's Agents framework, highlighting the importance of agents and how to equip them with tools and APIs. The chapter introduces LandChain's Agents framework, emphasizing the significance of agents and how to equip them with various tools and APIs for effective interaction and reasoning."]}], 'duration': 892.064, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI4224891.jpg', 'highlights': ['Creating question-answer pairs from documents using language models', 'Debugging the question answering process with lane chain debug', 'Understanding the token usage and cost in language model operations', 'The chapter explores evaluating language models using a language model for evaluation', "Introducing LandChain's Agents framework, highlighting the importance of agents and how to equip them with tools and APIs"]}, {'end': 5891.287, 'segs': [{'end': 5221.37, 'src': 'embed', 'start': 5190.974, 'weight': 0, 'content': [{'end': 5192.195, 'text': 'We have the answer to the question.', 'start': 5190.974, 'duration': 1.221}, {'end': 5195.877, 'text': 'Final answer, 75.0.', 'start': 5192.635, 'duration': 3.242}, {'end': 5197.018, 'text': "And that's the output that we get.", 'start': 5195.877, 'duration': 1.141}, {'end': 5204.099, 'text': 'This is a good time to pause and try out different math problems of your own.', 'start': 5200.677, 'duration': 3.422}, {'end': 5209.943, 'text': "Next, we're going to go through an example using the Wikipedia API.", 'start': 5206.881, 'duration': 3.062}, {'end': 5216.267, 'text': "Here, we're going to ask it a question about Tom Mitchell, and we can look at the intermediate steps to see what it does.", 'start': 5211.124, 'duration': 5.143}, {'end': 5221.37, 'text': 'We can see once again that it thinks and it correctly realizes that it should use Wikipedia.', 'start': 5217.728, 'duration': 3.642}], 'summary': 'Final answer: 75.0. demonstrates using wikipedia api for information retrieval.', 'duration': 30.396, 'max_score': 5190.974, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI5190974.jpg'}, {'end': 5337.814, 'src': 'embed', 'start': 5308.043, 'weight': 1, 'content': [{'end': 5309.603, 'text': 'And we can do the same exact thing here.', 'start': 5308.043, 'duration': 1.56}, {'end': 5316.135, 'text': "So we're going to create a Python agent and we're going to use the same LLM as before.", 'start': 5311.01, 'duration': 5.125}, {'end': 5319.758, 'text': "And we're going to give it a tool, the Python REPL tool.", 'start': 5316.975, 'duration': 2.783}, {'end': 5322.861, 'text': 'A REPL is basically a way to interact with code.', 'start': 5320.418, 'duration': 2.443}, {'end': 5324.802, 'text': 'You can think of it as a Jupyter notebook.', 'start': 5322.881, 'duration': 1.921}, {'end': 5328.746, 'text': 'So the agent can execute code with this REPL.', 'start': 5325.403, 'duration': 3.343}, {'end': 5332.269, 'text': "It will then run, and then we'll get back some results.", 'start': 5329.226, 'duration': 3.043}, {'end': 5337.814, 'text': 'And those results will be passed back into the agent so it can decide what to do next.', 'start': 5332.309, 'duration': 5.505}], 'summary': 'Creating a python agent with llm, using repl tool for code execution and result processing.', 'duration': 29.771, 'max_score': 5308.043, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI5308043.jpg'}, {'end': 5518.108, 'src': 'embed', 'start': 5487.238, 'weight': 2, 'content': [{'end': 5490.639, 'text': 'Sort these customers by last name and then first name and print the output.', 'start': 5487.238, 'duration': 3.401}, {'end': 5493.8, 'text': 'From here, we call an LLM chain.', 'start': 5491.979, 'duration': 1.821}, {'end': 5497.141, 'text': 'This is the LLM chain that the agent is using.', 'start': 5494.62, 'duration': 2.521}, {'end': 5502.095, 'text': 'So the LLM chain, remember, is a combination of prompt and an LLM.', 'start': 5498.052, 'duration': 4.043}, {'end': 5506.158, 'text': "So at this point it's only got the input, an agent scratch pad.", 'start': 5502.315, 'duration': 3.843}, {'end': 5512.763, 'text': "we'll get back to that later and then some stop sequences to tell the language model when to stop doing its generations.", 'start': 5506.158, 'duration': 6.605}, {'end': 5518.108, 'text': 'At the next level, we see the exact call to the language model.', 'start': 5514.565, 'duration': 3.543}], 'summary': 'Sort customers by last name and first name, then call llm chain.', 'duration': 30.87, 'max_score': 5487.238, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI5487238.jpg'}, {'end': 5803.672, 'src': 'embed', 'start': 5780.421, 'weight': 3, 'content': [{'end': 5787.744, 'text': 'Hopefully it showed you how you can use a language model as a reasoning engine to take different actions and connect to other functions and data sources.', 'start': 5780.421, 'duration': 7.323}, {'end': 5797.203, 'text': 'In this short course, you saw a range of applications,', 'start': 5794.139, 'duration': 3.064}, {'end': 5803.672, 'text': 'including processing customer reviews and building an application to answer questions over documents,', 'start': 5797.203, 'duration': 6.469}], 'summary': 'Demonstrated using language model for reasoning, with applications in customer reviews and document queries.', 'duration': 23.251, 'max_score': 5780.421, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI5780421.jpg'}, {'end': 5851.773, 'src': 'embed', 'start': 5821.669, 'weight': 4, 'content': [{'end': 5826.212, 'text': 'But you saw in this short course how, with just a pretty reasonable number of lines of code,', 'start': 5821.669, 'duration': 4.543}, {'end': 5830.135, 'text': 'you can use Langchain to build all of these applications pretty efficiently.', 'start': 5826.212, 'duration': 3.923}, {'end': 5837.16, 'text': 'As I hope you take these ideas, maybe even take some code snippets that you saw in the Jupyter Notebooks and use them in your own applications.', 'start': 5830.675, 'duration': 6.485}, {'end': 5840.085, 'text': 'And these ideas are really just the start.', 'start': 5838.143, 'duration': 1.942}, {'end': 5843.507, 'text': "There's a lot of other applications that you can use language models for.", 'start': 5840.285, 'duration': 3.222}, {'end': 5851.773, 'text': "These models are so powerful because they're applicable to such a wide range of tasks, whether it be answering questions about CSVs,", 'start': 5843.947, 'duration': 7.826}], 'summary': 'Langchain allows building applications efficiently with a reasonable number of lines of code. it has a wide range of applications.', 'duration': 30.104, 'max_score': 5821.669, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI5821669.jpg'}], 'start': 5117.735, 'title': 'Agent execution and language model reasoning', 'summary': 'Demonstrates agent execution with various tools and explores language model reasoning in sorting names, highlighting insights into agent tasks and potential for efficient application development.', 'chapters': [{'end': 5332.269, 'start': 5117.735, 'title': 'Agent execution and tool usage', 'summary': 'Demonstrates the step-by-step process of an agent executing tasks using various tools, such as a calculator, wikipedia api, and python repl, resulting in successful outputs and instances of unreliability.', 'duration': 214.534, 'highlights': ['The agent demonstrates the step-by-step process of executing a math question using a calculator tool, obtaining the correct answer of 75.0. The agent utilizes the calculator tool to compute 25% of 300, with the result being accurately observed and recorded as 75.0.', "The chapter showcases the agent's interaction with the Wikipedia API, successfully retrieving information about Tom M. Mitchell and a book he wrote, despite encountering some unreliability issues. The agent effectively utilizes the Wikipedia API to gather information about Tom M. Mitchell and his book, 'Machine Learning,' despite encountering reliability issues during the process.", 'The demonstration includes the use of a Python REPL tool by the agent, highlighting its ability to execute code and retrieve results. The agent employs a Python REPL tool to execute code, showcasing its capability to interact with code and obtain corresponding results.']}, {'end': 5891.287, 'start': 5332.309, 'title': 'Agent-based sorting and language model reasoning', 'summary': "Demonstrates the use of an agent to sort a list of names and the language model's reasoning process in executing the agent's task, providing insights into the workings of langchain agents and the potential for building applications efficiently using language models.", 'duration': 558.978, 'highlights': ['The agent is tasked to sort a list of names by last name and then first name, and print the output, showcasing the use of Langchain agents for practical tasks. The agent is given the task to sort a list of names by last name and then first name, and then print the output, demonstrating the practical application of Langchain agents.', "The language model's reasoning process is revealed, showing the combination of prompts, LLM chains, and output parsers, highlighting the potential for using language models as reasoning engines for different actions and data sources. The transcript provides insights into the language model's reasoning process, including the use of prompts, LLM chains, and output parsers, showcasing the potential of using language models as reasoning engines for various actions and data sources.", 'Demonstration of building applications efficiently using Langchain with a reasonable number of lines of code, illustrating the potential for efficient application development using language models. The chapter illustrates the efficient development of applications using Langchain with a reasonable number of lines of code, showcasing the potential for efficient application development using language models.']}], 'duration': 773.552, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/L0VgNy3poBI/pics/L0VgNy3poBI5117735.jpg', 'highlights': ['The agent demonstrates the step-by-step process of executing a math question using a calculator tool, obtaining the correct answer of 75.0.', 'The demonstration includes the use of a Python REPL tool by the agent, highlighting its ability to execute code and retrieve results.', 'The agent showcases the use of Langchain agents for practical tasks by sorting a list of names by last name and then first name, and printing the output.', "The language model's reasoning process is revealed, showing the combination of prompts, LLM chains, and output parsers, highlighting the potential for using language models as reasoning engines for different actions and data sources.", 'Demonstration of building applications efficiently using Langchain with a reasonable number of lines of code, illustrating the potential for efficient application development using language models.']}], 'highlights': ['LanChain enables faster AI application development by prompting LLMs, reducing the need for writing glue code and allowing quick application assembly.', "The framework's focus on composition and modularity allows for the use of individual components in conjunction with each other or by themselves, offering value adds in terms of use cases.", 'The community adoption of LanChain is significant, with numerous users and many hundreds of contributors to the open source, leading to rapid development.', "LangChain's parser converts LLM output, initially a string, into a Python dictionary, allowing the extraction of values associated with the keys gift, delivery days, and price value.", 'Demonstration of using LanChain to parse LM output JSON, extract information from a product review, and format the output in JSON format, showcasing the practical application of LanChain in data extraction and formatting.', 'The conversation buffer memory stores the entire conversation history, leading to longer memory requirements and potential increase in processing cost based on token count.', 'The chain combines an LLM with a prompt and enables running operations on multiple inputs, leveraging a pandas data frame for processing.', 'The chapter introduces the LLM chain as a powerful foundation for future chains, then covers sequential chains for running subchains one after another, detailing their usage and functionality, along with examples of running them with product descriptions and reviews.', 'The chapter provides comprehensive coverage of using language models for question answering, including creating and storing embeddings, using a retriever, and customizing the index and embeddings.', 'The agent demonstrates the step-by-step process of executing a math question using a calculator tool, obtaining the correct answer of 75.0.']}