Langchain Legends: Build legendary AI apps with Javascript

Explore the chat message schema, detailing system, human, and I chat messages, and see how a prototype uses the chat OpenAI model with temperature and an API key.

Learn how documents bundle text and metadata to organize vast information, using the Lang Chain document class with page content and metadata, including unique IDs and JSON.stringify for browser endpoints.

Learn how LangChain integrates a large language model like GPT-4 via OpenAI, processing input and generating text. See a live prompt example: a cappella is a song performed without music.

Explore chat models, conversational language models using prior messages and input to generate a reply, via a Lang Chain prototype delivering core-strength exercises like planks, Russian twists, bicycle crunches.

Understand text embeddings and vector embeddings that convert words into numerical coordinates for semantic similarity. Learn to embed text with OpenAI embeddings and 1536-element vectors for similarity-based search.

Learn how prompts serve as model input, built with instructions and examples to guide responses, and implement a Lang Chain OpenAI prompt using GPT-4 with an endpoint to return results.

Show how LangChain prompt templates use placeholders and variables to create dynamic prompts with OpenAI, and how to format and substitute values for retro sneakers via a web endpoint.

Explore how LangChain uses document loaders to structure data from PDFs, text files, web pages, and transcripts, enabling language models to load and index documents efficiently.

Learn to use text splitters to break long documents into 350-token chunks with 10-token overlap, using pdf and character splitters in Langchain apps.

Understand vector stores that hold embeddings and metadata, and build simple sequential chains that orchestrate LM calls with prompt templates from OpenAI, passing outputs between steps.

Break long texts into chunks with a summarization chain, summarize each chunk, then generate a final summary using Langchain with pdf loader, character text splitter, and an OpenAI model.

Explore how agents drive decisions in Langchain by selecting from a toolkit of tools, using zero-shot react, to execute dynamic, user-driven tool calls.

Organize App.js as the first page, import libraries including breakpoint provider, use useState and useEffect, then wrap component with breakpoint provider and define queries for desktop and mobile layouts.

Create a provider folder in the project root and add breakpoint js to build the breakpoint provider, then save it and move on.

Build the home page in a Next.js app by cleaning boilerplate, wiring the home component to the chat box, and centering content with a flexbox container using the @ alias.

Build a front-end and back-end chat box that handles user messages, streams OpenAI GPT responses, and auto-scrolls within a designed chat window.

Build an auto-scrolling chat area by creating a custom scroll area and scroll bar using Radix UI primitives in React, with a CDN class-merging utility for Tailwind styles.

Build a breakpoint-aware responsive chat window in JavaScript with a scroll area for auto scrolling, state and effect hooks, and rendering user and assistant messages with chat cards.

Build a chat input component wired with use state and use context, featuring a text area and a next ui button to send on enter or click.

Set up and render a text area component using the Next UI library, forwarding a ref and styling with a css prop to handle disabled state, cursor, and opacity.

Build an AI response UI by creating a card-based assistant and user chat, using React forwardRef, card subcomponents, and a Prism syntax highlighter to render code blocks from OpenAI responses.

Build the user chat ui by creating the user chat card, inverting colors, importing React and card components, and wiring a backend endpoint to receive messages and return responses.

Create a back-end node app by creating the project directory, initializing npm, installing express, and creating an index.js file as the starting point for the app.

Build a completion endpoint with LangChain and JavaScript, featuring streaming responses via Express SSE. Learn to classify prompts and generate context-aware answers using prompt templates, LM chain, and callback manager.

Polish the chat UI by updating card styling during streaming from the backend, remove margins on card title and description, and add a 1px border to the card components.

Debugs code block rendering in chat messages by fixing an import error for one dark, importing from styles/prism, and confirming the styling streams correctly.

Set up a Pinecone vector database, create an index, and upsert chunks of 1250 tokens converted to embeddings using Json loader, OpenAI embeddings, and a splitter for retrieval augmented generation.

Build a Pinecone vector store function to perform a similarity search with OpenAI embeddings and Lang Chain, then retrieve and return relevant page content as context.

Develop a modular rag function workflow that classifies user messages and routes to interactions or tutor chain using LangChain, OpenAI, and Pinecone context.

Deploy a Lang Chain chatbot by pushing frontend and backend to GitHub, configuring env files and gitignore, and deploying to Heroku and Vercel with live URL updates.