Generative AI for Javascript Developers - LangChain, RAG

Install and verify the right node.js version for building generative ai apps with LangChain, ensuring compatibility with the latest supported node.js version 20.x.

Clone git repository, open JNI dash js project in VSCode, review src and completed folders and package.json, install dependencies with npm install, plus global TypeScript and tsx.

Create a Google Gemini API key in AI Studio and integrate it into your Lang Chain workflow to run models alongside or instead of OpenAI.

Set up a secure OpenAI API key with dotenv in a JavaScript project, load it via LangChain, run a GPT-3.5 instruct LLM, and display the response.

Learn Lang Chain basics, explore practical use cases, and build generative AI applications with Lang Chain, and adjust LMS behavior for more effective results.

Explore how LangChain enables building generative AI apps by managing prompts and chat history, outputting JSON, XML, or CSV, and assembling multi-step chains with data sources like Wikipedia and SQL.

Define a prompt template in a new lm chain.ts, using LangChain's basic prompt template with variables for course, user role, and word limit, then test formatting the prompt.

Add lm inference and the string output parser to the basic lm chain by converting code into a parameterized async function named personalized pitch.

Explore the lang chain framework's components and chains, including legacy chains, LCL, and LCL chains, for building flexible LM pipelines with providers like OpenAI.

Learn to build a legacy llm chain with LangChain's lm chain, using prompt templates and an output parser to generate course-specific, personalized responses.

Learn to build flexible llm chains with the Link Chain Expression Language (LCL) by wiring prompt templates, lm, and output parsers through pipe methods or runnable sequence in JavaScript.

Explore how language models generate output token by token and tune their behavior with temperature, top-p, and top-k to balance determinism and creativity.

Experiment with llm parameters such as temperature, top p, top k, and max tokens to control determinism, creativity, and cost in javascript demos.

Always specify the exact LLM model in your Lang Chain setup to control cost. Copy the model name or use an environment variable to swap models without changing code.

Debug LangChain apps by inspecting inputs and outputs for auditing and performance. Use console verbose logging with long_chain_verbose or Lang Smith traces for production monitoring.

Learn how LangChain makes it easy to swap language model providers with link chain, exploring OpenAI, AWS bedrock, and Anthropic cloud models.

Learn to build generative AI applications on AWS Bedrock with LangChain, explore multiple models across providers, and configure IAM access and environment setup to swap models by region.

Explore anthropic cloud models haiku, and understand why cloud 3.5 Sonnet, via LangChain, excels in reasoning and multilingual tasks for JavaScript developers.

Unlock the Rag architecture, the backbone of many generative AI apps, and learn techniques to optimize it by using embeddings, vector databases, and private enterprise data.

Learn how retrieval augmented generation (rag) adds context from a retriever to prompts, enabling language models to answer using your company data.

Explore vector embeddings and the retrieval step in rag architecture, using chunking, vectorization, and vector stores to filter large data for efficient LLM context.

Explore Rag architecture: index documents via vectorization and embeddings, then query a vector index with a retriever and prompt template to answer with an llm.

Build your first rag application by loading documents with a cheerio-based web loader, crawling LangChain documentation links, and preparing the text for splitting and embeddings.

Explore chunking strategies for building RAG applications, with emphasis on recursive character text chunking, chunk size and overlap, and handling HTML, markdown, and code to improve embeddings and answer quality.

Generate embeddings for document chunks using the OpenAI embedding model, store embeddings with their chunks in a 1536-dimensional Pinecone vector index, and verify in Pinecone.io.

Implement a Pinecone-based vector store retriever to embed questions and chunks in the same vector space, perform similarity search, and return context for retrieval augmented generation.

Build rag augmentation by creating a prompt template that combines a question with retrieved context. Use Lang Chain Hub prompts for Q&A and implement a chat prompt template in code.

Configure a chat OpenAI LLM with GPT-3.5 turbo, set a 500-token hard limit, and wire in a string output parser to complete the RAG components and prompt pipeline.

Build a LangChain retrieval chain to fetch context from a question and format results, then create a generation chain using runnable sequence to produce the final answer.

Explore building and testing a backend rag architecture with LangChain for JavaScript developers, including retrieval and generation chains, a terminal chat utility, and streaming vs. instant responses.

Explore how chat history leverages rack architecture with chunking, embeddings, and vector databases to answer questions from context, enabling a natural, conversational user experience.

Explore why chat history matters in Rag, showing how llms are stateless and lose context for follow-up questions. Learn how to add chat history to maintain context and improve responses.

Explore using git history to build a conversational lag solution that answers follow-up questions from chat history. Contextualize queries with chat history and vector embeddings to improve rag retrieval.

Create a rag with chat history by contextualizing follow-up queries, integrating chat history into the prompt and generation chain using LangChain for improved RAG responses.

Celebrate completing the course and gain confidence in building generative AI applications with Lang chain and JavaScript. Subscribe to the instructor's YouTube channel for the latest generative AI developments.