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#LangChain #NodeJS #openai 🤍 Welcome to the ultimate LangChain crash course for Node.js! In just 10 minutes, you'll discover how to harness the power of LangChain and OpenAI to engage in natural language conversations with large documents or books like Rick Rubin's "The Creative Act". This tutorial is designed for beginners and experts alike, guiding you through the essential steps to set up and integrate LangChain in your Node.js projects. Don't miss this chance to revolutionize your AI chat experiences. Join us, and let's dive into the fascinating world of natural language processing! Remember to like, share, and subscribe to stay updated with the latest AI tutorials and content. Let's get started with LangChain in Node.js today! #LangChain #NodeJS #OpenAI
Twitter: 🤍 Newsletter: 🤍 Overview about why the LangChain library is so cool In this video we're going to look at the problem with vanilla ChatGPT and how LangChain comes to the rescue. Follow me on Twitter Personal: 🤍
In this video, we're going to explore the core concepts of LangChain and understand how the framework can be used to build your own large language model applications. Code for the video is available here: 🤍 ▬▬▬▬▬▬ V I D E O C H A P T E R S & T I M E S T A M P S ▬▬▬▬▬▬ 0:00 Introduction and overview 0:38 Why Langchain? 3:40 The value proposition of Langchain 4:50 Unpacking Langchain 5:42 LLM Wrappers 6:58 Prompts and Prompt Templates 7:45 Chains 9:00 Embeddings and VectorStores 11:40 An example of a Langchain Agent
Get the free Python course 🤍 Get the code: 🤍 Sign up for the Full Stack course here and use YOUTUBE50 to get 50% off: 🤍 Hopefully you enjoyed this video. 💼 Find AWESOME ML Jobs: 🤍 Get the Code: 🤍 Links: CTC Blog Post: 🤍 Oh, and don't forget to connect with me! LinkedIn: 🤍 Facebook: 🤍 GitHub: 🤍 Patreon: 🤍 Join the Discussion on Discord: 🤍 Happy coding! Nick
#langchain #ai #chatgpt #openai #llm #embeddings - LangchainJS 🤍 - Convert video to transcript 🤍 - Retrievers post 🤍 - Supabase pgvector announcement 🤍
Build powerful AI-driven applications using LangChain. LangChain is a groundbreaking framework that combines Language Models, Agents and Tools for creating amazing AI-based apps like ChatGPT, AutoGPT, and more. In this series we will have a look at the fundamenal concepts of LangChain using the JS/TS/Node version of LangChain. USEFUL LINKS: LangChain Docs: 🤍 VSCODE: 🤍 NODE: 🤍 CHAPTERS: 00:00 - Introduction to Langchain JS 01:05 - Prerequisites and Node setup 02:08 - Getting started with Langchain 02:37 - Using the LLM Model 04:14 - Generating the OpenAI API Key 06:22 - Environment Variables 07:47 - Prompt Templates 10:36 - LLM Chains 13:04 - Agents & Tools explained 18:09 - Agent Types 20:27 - Verbose 21:28 - Memory 24:39 - Streaming Responses 26:08 - In conclusion #openai #langchain #langchainjs #gpt3 #gpt4 #autogpt #agi #babyagi #chatgpt
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In this LangChain Crash Course you will learn how to build applications powered by large language models. We go over all important features of this framework. Timeline: 00:00 - Introduction 01:09 - Installation 01:22 - LLMs 03:08 - Prompt Templates 04:58 - Chains 06:02 - Agents and Tools 09:54 - Memory 11:15 - Document Loaders 12:24 - Indexes Resources: Written guide: 🤍 Colab: 🤍 Docs: 🤍 GitHub: 🤍 Chatbot example: 🤍 Get my Free NumPy Handbook: 🤍 ✅ Write cleaner code with Sourcery, instant refactoring suggestions in VS Code & PyCharm: 🤍 * ⭐ Join Our Discord : 🤍 📓 ML Notebooks available on Patreon: 🤍 If you enjoyed this video, please subscribe to the channel: ▶️ : 🤍 ~~~~~~~~~~~~~~~ CONNECT ~~~~~~~~~~~~~~~ 🖥️ Website: 🤍 🐦 Twitter - 🤍 ✉️ Newsletter - 🤍 📸 Instagram - 🤍 🦾 Discord: 🤍 ▶️ Subscribe: 🤍 ~~~~~~~~~~~~~~ SUPPORT ME ~~~~~~~~~~~~~~ 🅿 Patreon - 🤍 #Python * This is an affiliate link. By clicking on it you will not have any additional costs, instead you will support me and my project. Thank you so much for the support! 🙏
#AutoGPT #Langchain #NodeJS #OpenAI #GPT4 Repo: 🤍 Discover the power of AutoGPT in this step-by-step tutorial! Follow along as we walk you through the process of setting up and using the experimental AutoGPT implementation in a Node.js environment. Learn how to obtain API keys from OpenAI and SERP API, install and import necessary modules, and configure your environment. We'll also show you how to use AutoGPT by attempting to create an interactive stock chart of Apple's stock price using Chart.js and stock data from Google Finance. Don't miss this opportunity to explore the capabilities of AutoGPT and enhance your development skills!
Colab: [🤍 Creating Chat Agents that can manage their memory is a big advantage of LangChain. This video goes through the various types of memory and how to implement them in a LangChain Conversation chain. My Links: Twitter - 🤍 Linkedin - 🤍 #LangChain #BuildingAppswithLLMs
Is LangChain the easiest way to interact with large language models and build applications? - integrate with various LLM providers including OpenAI, Cohere, Huggingface, and more. - create a question-answering or text summarization bot with your own document - provide OpenAI ChatGPT Retriever Plugin - deal with chat history with LangChain Memory - chain various LLMs together and use LLMs with a suite of tools like Google Search, Python REPL, and more. - integrate with ChatGPT Plugins Code in this video: 🤍 🌼 About me 🌼 Sophia Yang is a Senior Data Scientist working at a tech company. 🔔 SUBSCRIBE to my channel: 🤍 ⭐ Stay in touch ⭐ 📚 DS/ML Book Club: 🤍 ▶ YouTube: 🤍 ✍️ Medium: 🤍 🐦 Twitter: 🤍 🤝 Linkedin: 🤍 💚 #datascience
Twitter: 🤍 Or get updates to your inbox: 🤍 In this tutorial we will load a PDF book, split it up into documents, get vectors for those documents as embeddings, then ask a question. -AI Generated Description- In this tutorial, I am is discussing how to query a book using OpenAI, LangChain, and Pinecone, an external vector store, for semantic search. I'm demonstrating how to split up the book into documents, use OpenAI embeddings to change them into vectors, and then use Pinecone to store them externally. I'm then showing how to ask a question and get an answer back in natural language. This technique can be used to query books as well as internal documents or external data sets. -AI Generated Description- 0:00 - Intro 1:31 - Diagram Overview 3:33 - Code Start 5:46 - Embeddings 6:33 - Pinecone Index Create 7:45 - First Question 9:33 - Ask Questions w/ OpenAI Code: 🤍
Patreon: 🤍 (now includes Discord!) GATO Framework: 🤍 GitHub: 🤍 LinkedIn: 🤍 Twitter: 🤍 Relevant Subreddits: Artificial Sentience: 🤍 Heuristic Imperatives: 🤍 Autonomous AI: 🤍 DISCLAIMER: This video is not medical, financial, or legal advice. This is just my personal story and research findings. Always consult a licensed professional. I work to better myself and the rest of humanity.
Flowise is an open source project which will always be free for commercial and personal use. It is based on 🦜️🔗 LangChain.js and is a very advanced Graphic User Interface for developing LLM based applications. These applications are also known as Gen Apps, LLM Apps, Prompt Chaining, LLM Chains, etc.
Twitter: 🤍 Newsletter: 🤍 See how to upload your own files to Chat GPT using LangChain. In this example we are using text files, what other files types do you want to see? Google? Notion? Email? Follow me on Twitter Personal: 🤍 Github code: 🤍
Using LangChain with GPT3. I am seeing lots of cool demos based on LangChain and needed to make I covered it. It’s an easy way to take advantage of #largelanguagemodels #datascience #machinelearning #gpt3 #langchain ━━━━━━━━━━━━━━━━━━━━━━━━━ ★ Rajistics Social Media » ● Link Tree: 🤍 ● Tik Tok: 🤍 or 🤍rajistics ● Medium: 🤍 or 🤍rajistics ● Hugging Face: 🤍 or 🤍rajistics ● Twitter: 🤍 or 🤍rajistics ● Website: 🤍 ● LinkedIn: 🤍 ━━━━━━━━━━━━━━━━━━━━━━━━━
In this video we will create a chatbot that uses Nextjs and Langchain #ai #chatgpt #langchain
In part 2 of this series we use natural language (english) to query our database and CSV. LangChain integrates with GPT to convert natural language to the corresponding SQL statements, or pandas command, which is then executed to return a natural language response. LangChain is a fantastic tool for developers looking to build AI systems using the variety of LLMs (large language models, like GPT-4, Alpaca, Llama etc), as it helps unify and standardize the developer experience in text embeddings, vector stores / databases (like Chroma), and chaining it for downstream applications through agents. Mentioned in the video: - Watch PART 1 of the LangChain / LLM series: 🤍 - CSV to Sqlite in 5 lines of code: 🤍 - LangChain documentation: 🤍 All the code for the LLM (large language models) series featuring GPT-3, ChatGPT, LangChain, LlamaIndex and more are on my github repository so go and ⭐ star or 🍴 fork it. Happy Coding! 🤍
Twitter: 🤍 Newsletter: 🤍 LangChain 101 Quickstart Guide. We run through 4 examples of how to use the LangChain Library View Code: 🤍 Follow me on Twitter Personal: 🤍 * LLMs * Prompt Management * Memory * Agents For more information sign up for details at 🤍dataindependent.com
I think langchain is awesome, but the future is an easy to use UI. Think Alteryx for LLMs. Langflow is a step in the right direction. #datascience #machinelearning #largelanguagemodels #gpt4 #langchain #langflow Github: 🤍 ━━━━━━━━━━━━━━━━━━━━━━━━━ ★ Rajistics Social Media » ● Link Tree: 🤍 ● LinkedIn: 🤍 ━━━━━━━━━━━━━━━━━━━━━━━━━
Colab code Notebook: 🤍 In this we look at LangChain Agents and how they enable you to use multiple Tools and Chains in a LLM app, by allowing your LLM to decide on the next input tool to use based on the user's input. My Links: Twitter - 🤍 Linkedin - 🤍 #LangChain #BuildingAppswithLLMs
Learn how to overcome #gpt3 limitations and apply LangChain to your large language model. This is one video out of a series. Subscribe to see more videos. 🤍creativecoffeemultimedia contact: hello🤍wearecreativecoffee.com #artificialintelligence #datascienceskills #machinelearningengineer #learndatascience #llm #naturallanguageprocessing #chatgpt Limitations #openai #creativecoffeemultimedia #GenerativeAI 🤍wearecreativecoffee.com
In this video, we’re going to have a closer look at LangChain Agents and understand what this concept is all about. We will dive into what an agent is, how agents work under the hood of LangChain, and what enables businesses to do. Finally, we will build a simple custom agent that interacts with the Shopify REST API. Link to the code: 🤍 Link to ReAct paper 🤍 Link to Google Research blog post 🤍 Link to the video detailing how to extract data from Shopify: 🤍 ▬▬▬▬▬▬ V I D E O C H A P T E R S & T I M E S T A M P S ▬▬▬▬▬▬ 0:00 Introduction and overview 0:40 What is a LangChain Agent? An example use case. 2:50 LangChain Agents under the hood. 4:08 What can businesses do with LangChain Agents? 5:50 Building a simple Shopify Agent with LangChain
In this Applied NLP LLM Tutorial, We will build our Custom KnowledgeBot using GPT-Index and LangChain. LangChain for accessing OpenAI and GPT-Index for Vector Index Building and Index Querying - Information Retrieval. Timestamps: 00:00 Introduction to Q&A KnowledgeBot 02:58 OpenAI Q&A Bot Demo 04:11 Full Code Explanation 05:27 OpenAI API Key Secret Token 07:21 Input Document Text File for the ChatBot 08:15 Constructing GPT Vector Index 14:00 Constructing Ask Bot Python unction 16:40 Running KnowledgeBot Q&A 18:21 Summary and Closure 🤍 🤍 🤍 ❤️ If you want to support what we are doing ❤️ Support here: Patreon - 🤍 Ko-Fi - 🤍
LangChain is a popular framework that allows users to quickly build apps and pipelines around Large Language Models. It integrates directly with OpenAI's GPT-3 and GPT-3.5 models and Hugging Face's open-source alternatives like Google's flan-t5 models. It can be used to for chatbots, Generative Question-Anwering (GQA), summarization, and much more. The core idea of the library is that we can "chain" together different components to create more advanced use cases around LLMs. Chains may consist of multiple components from several modules. We'll explore all of this in these videos. 🌲 Article: 🤍 📌 LangChain Handbook Code: 🤍 🤖 70% Discount on the NLP With Transformers in Python course: 🤍 🎨 AI Art: 🤍 🎉 Subscribe for Article and Video Updates! 🤍 🤍 👾 Discord: 🤍 00:00 Getting LangChain 01:14 Four Components of LangChain 06:43 Using Hugging Face and OpenAI LLMs in LangChain 07:13 LangChain Hugging Face LLM 13:51 OpenAI LLMs in LangChain 18:58 Final results from GPT-3
Twitter: 🤍 Newsletter: 🤍 Cookbook Part 2: 🤍 Wild Belle - Keep You: 🤍 LangChain Cookbook: 🤍 LangChain Conceptual Docs: 🤍 Python Docs: 🤍 JS/TS Docs: 🤍 0:00 - Introduction 1:12 - Conceptual Docs 1:54 - Cookbook introduction 2:27 - What is LangChain? 5:10 - Schema (Text, Messages, Documents) 8:54 - Models (Language, Chat, Embeddings) 12:03 - Prompts (Template, Examples, Output Parse) 20:45 - Indexes (Loaders, Splitters, Retrievers, Vectorstores) 26:39 - Memory (Chat History) 28:12 - Chains (Simple, Summarize) 32:52 - Agents (Toolkits, Agents) Music by lofigenerator.com / CC BY
Are your language models ignoring previous instructions and hailing Zalgo? Do you have trouble thinking step-by-step as you implement your GPT-powered application? Check out LangChain, a new LLM application framework! In this video, Charles walks through a high-level overview of what LangChain does and runs through a demo of before interviewing LangChain creator Harrison Chase. Demo Notebook: 🤍 00:00 Intro 00:43 Why do we need LangChain? 03:46 What is LangChain? 06:18 Demo: Q&A about LangChain using LangChain 13:03 How can LangChain help me deliver value right now? 14:35 Is chat the right interface? 16:07 How can LangChain help accelerate testing and evaluation? 21:02 Audience Q: How can we combine few-shot CoT with retrieval? 24:00 Audience Q: What about multimodal modeling? 26:03 Audience Q: What's the best tooling for understanding LLMs?