Build RAG Applications with LlamaIndex and JavaScript [NEW]

Set up your development environment with Node.js, TypeScript, and a code editor, then obtain an OpenAI account and API key to use GPT-3 or GPT-4 with LlamaIndex.

Set up your development environment with Python and VSCode, create and securely save an OpenAI API key, and explore billing and key management to start using the API.

Explore llama index’s scalability, flexibility, and integration for efficient data retrieval, indexing, querying, and CRUD, enabling retrieval augmented generation and autonomous agents.

Explore retrieval augmented generation (rag) and how combining external knowledge retrieval with embeddings and large language models reduces hallucination, injects your data, and generates contextual answers.

Lama Index streamlines rag systems by ingesting unstructured data, parsing and indexing it into a vector store, then retrieving relevant documents through queries and autonomous agents.

Explore how LlamaIndex ingests data from SQL databases, documents, and APIs, indexes it with embeddings, and provides a query interface to return knowledge-augmented responses, enabling rapid rack system setup.

Set up a lama index retrieval augmented generation system with TypeScript. Install lama index, configure environment variables, and build a vector store with embeddings and a query engine for answers.

Explore a Rag deep dive and how LlamaIndex simplifies it with data ingestion, built-in APIs, and pre-built autonomous agents.

Share a review to help others see the course's value and support the creator. Post questions on the discussion board to receive timely answers and foster a collaborative learning environment.

Explore how LlamaIndex builds a knowledge base from data sources using data loaders and document nodes, enabling retrieval augmented generation through vector store and summary indexes.

learn how the querying stage retrieves relevant context, passes it to the large language model, and generates a knowledge-augmented response, enabling rag pipelines for q&a and chat engines.

Explore the querying stage building blocks—retriever and response synthesizer—and how query and chat engines fetch context from a vector index and generate responses from a large language model.

Build a custom rag system with llama index by loading documents, creating an index with embeddings in a vector store, and customizing prompts, service context, and a query engine.

Learn to build a rag application that extracts json from a sales call transcript using OpenAI's chat API, including summary, issues, rep and customer names, sentiment, and follow up actions.

Build a rag system by loading Berkshire's pdf with a pdf reader, creating a vector index via llama index, and querying the index for the report's main numbers and advice.

Create an express API to interact with a RAG system powered by LlamaIndex, retrieving nodes and generating responses with an LLM, enabling client queries.

Delve into llama index with loaders and query stages, explore a chat-engine workflow, and learn to customize RAC systems, build a synthesizer, and extract structured data from transcripts via API.

Explore how agents and the router query engine coordinate tools to answer complex queries, routing tasks to the appropriate engines and external tools for automated reasoning.

Build a router query engine that manages multiple query engines, automatically routing questions to the best one using engine tools and engine descriptions.

Define tools from functions, wrap them as function and query engine tools, register with a router, and assemble an OpenAI agent that uses these tools for math and weather queries.

Explore production-ready techniques for lama index, persisting data in a database, streaming chat for real-time feedback, and building a Next.js production app with the Create Lama tool.

Create a persistent storage, load Berkshire pdf data, and build a doc store, index, and vector store with LlamaIndex, enabling persistent data for fast, accurate querying.

Load the index from persisted data with a new storage context to avoid re-parsing documents, then stream responses using a chat engine with a retriever that uses chat history.

Persist data by creating a storage context, loading data, and building an index, then reuse the storage directory to initialize the index and run a streaming chat engine in production.

Set up a full-stack chat app with Next.js using the create llama command to scaffold front end and back end, delivering a ready-to-use RAG system powered by llama index.

Create a working full-stack Next.js app that chats with your data using the create Lama CLI from Lama index, generate embeddings, and run the local server.

Customize your rag app by adding your own data (e.g., Berkshire data) and chatting with it, regenerating the index with npm run generate to refresh embeddings and the document store.

Deploy a Next.js full-stack chat app powered by llama index to Vercel, handling env files and OpenAI API keys securely via Git, environment variables, and a new repository.

Wrap up and next steps summarize building rag applications with llama index in JavaScript, covering loading and ingesting documents, indexing, querying, and deploying a full-stack Next.js app on vercel.