LangChain: Develop AI web-apps with JavaScript and LangChain

Initialize the chat bot with your name and favorite food to demonstrate memory in LangChain. The demo shows the chain remembers your name and that sushi is your favorite food.

Discover how buffer memory preserves chat history by extending the base chat memory class from LangChain and using the conversation chain to build your first chat bot.

Set up a chat bot UI and backend memory with a post-based API, using LangChain and OpenAI to remember prior messages and render user and bot prompts.

Instantiate a conversation chain on the first message using OpenAI, with buffer memory, and bind prompt output to the frontend. Test and return the chain's response via the UI button.

Configure keys in next config.js and log memory responses from front-end and back-end; use GPT-3.5 Turbo to improve reasoning and persist memory like the user name Sean.

Conclude by summarizing how Lang Chain uses three lines of code with the OpenAI API and GPT-3.5 to power memory in a Next.js UI, plus reusable backend templates for future modules.

Set up the frontend by configuring env keys and the streaming API, manage state, sanitize tokens, and establish an event-source connection to receive live backend tokens.

Enable real-time streaming from the back end by using server-side events and a streaming route to push OpenAI tokens from the LangChain backend to the front end.

Celebrate completing the module and recall key concepts like prompt templates, streaming, and memory within the long chain framework. Head to the next module to continue learning about long chain.

Initialize a pinecone vector database to enable high-performance vector search. Create an index with 1536 dimensions on an S1 pod, then configure API keys and env files for the backend.

Initialize pinecone instance and start the server, then use the pdf loader to upload and split the PDF, create a loader with the book path, and call load for docs.

Upload PDFs via a pdf loader, split documents into 250-word chunks with 10 overlap using a character text splitter, then reduce metadata by removing pdf fields for Pinecone search.

Build a pdf question-and-answer bot by loading a pdf into a pinecone vector store, chunking text, reducing metadata, and querying with an openai vector chain that returns source documents.

Leave a review for the LangChain and JavaScript Udemy course to improve the AI web-app learning experience, as the instructor reads every review and uses feedback to enhance the course.

Explore namespaces in Pinecone to partition a vector database, enabling uploading content to a specific namespace and querying only that subset, such as data engineers.

Add a front-end select to choose a book by ID, routing uploads to the corresponding Pinecone namespace. Initialize to 101 and send the ID in the query string for submits.

Upload differentiates books using query string params to identify the book id, maps the book path to book db, and sets the client namespace to that id as a string.

Learn to use namespaces in a vector store for querying, differentiating book IDs by a string namespace, and validating numeric IDs during testing.

Build a YouTube chat bot that fetches a video's transcript via YouTube API, embeds it with OpenAI, stores it in NSW Lib vector store, and answers queries through a chain.

Master handling follow-up questions in a LangChain AI web-app using JavaScript by managing the first message flag, updating chat history, and querying the conversational retrieval chain.

Build a Next.js and JavaScript chat bot that chats with YouTube videos by passing transcripts into a LangChain chain, persisting history with a vector store and OpenAI chat models.

Use YouTube transcript and data APIs, Serp API, and a browser tool to gather video context. Initialize a LangChain agent to generate scripts and tweets from the transcript and research.

Develop and test a front-end content generator that passes prompts and topics, extract YouTube video IDs, fetch transcripts, and wire a back-end chat chain to OpenAI for AI web-app content.

Fetches YouTube video metadata using the YouTube data API and a Google API key, extracting the video title, shortened description, and video ID via an Axios request.

Create a research agent in LangChain using the Serp API tool to perform web browsing on a topic, such as Pedro Pascal, and fetch up to date results.

Configure an agent with tools and a chain using a chat OpenAI model (GPT-3.5 Turbo) and a topic-driven init chain. Pass transcript, metadata, and topic to generate research-based YouTube scripts.

Design LLM chain prompts with system and human templates, referencing transcript, metadata, and research. Generate a lighthearted video commentary script using the prompt template and transcript.

Learn to generate follow up questions for a LangChain chat bot by triggering a new prompt, calling the chain with transcript and metadata, and updating the chat history.

Showcases a complete front-end content generator built with LangChain, leveraging YouTube transcripts and metadata, a zero-shot agent with Bing and browser tools, and prompt templates to generate content.

RoboHR demonstrates a personal assistant that summarizes resumes using document loaders, embeddings, and a summarization chain with Pinecone, then answers queries about Python skills and Meta experience with source notes.

Upload resumes to a Pinecone vector store with 1536 max dimensions for OpenAI embeddings, then use metadata-based queries and a vector store agent via backend API.

Convert resumes to embeddings by loading a directory of PDFs with Lang Chain directory loader into the vector database, then store embeddings in Pinecone using OpenAI keys.

Load PDFs with a directory loader, split into 200-word chunks using a character text splitter with 20 overlap, then reduce metadata to essential names for efficient querying and upcoming summarization.

Build a script that maps over documents, summarizes each with a MapReduce chain using an OpenAI model, then upload embeddings to Pinecone and update the vector store.

Explore Supabase as an open source firebase alternative for vector databases and OpenAI embeddings with PG vector, including creating a documents table with embeddings and vector search.

Swap Pinecone for Supabase vector store in the resume uploader, configure API keys and env vars, embed with OpenAI, and query metadata to verify the migrated setup.

Encourage learners to leave a course rating, share troubleshooting questions on Discord, and help improve updates to deployment and the new Supabase section for current and future students.

Upload resumes as embeddings to a Pinecone vector store, build a vector db chain with OpenAI embeddings, and return the top two relevant resumes plus their source documents.

Load the user prompt, initialize a Pinecone vector store for vector DB chain, apply the prompt template to input question, set K to two, and log results with source documents.

Learn how to deploy a full-stack Next.js app to Vercel, including pushing to Git, managing private repositories and env keys, and configuring environment variables for OpenAI calls.

Fix deployment issues by removing moved content generator solutions that caused an import error, then commit, push, and redeploy to resolve module errors.

Explore Pinecone as a pure vector database for embeddings and similarity searches. Set up a Pinecone index with ETA 002 embeddings and secure API keys in dot env and next.config.js.