Unlock the Power of LangChain: Deploying to Production Made Easy

In this tutorial, Austin Vance, CEO and co-founder of Focused Labs, will guide you through deploying a PDF RAG with LangChain to production!

In this captivating video, we will dive deep into the process of deploying a rag to production, taking you on an informative and engaging step-by-step journey. Throughout this tutorial, we will explore the intricacies of transforming a local rag into a powerful and accessible resource on the digital ocean app platform. Don't miss out on this highly informative and exciting adventure - make sure to subscribe now to stay updated with all the latest content!
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Just to remember what happened so far:

In Part One You will Learned:

• Create a new app using LangChain's LangServe
• ingestion of PDFs using unstructuredio
• Chunking of documents via LangChain's SemanticChunker
• Embedding chunks using OpenAI's embeddings API
• Storing embedded chunks into a PGVector a vector database
• Build a LCEL Chain for LangServe that uses PGVector as a retriever
• Use the LangServe playground as a way to test our RAG
• Stream output including document sources to a future front end.

In Part 2 we will focus on:

• Creating a front end with Typescript, React, and Tailwind
• Display sources of information along with the LLM output
• Stream to the frontend with Server Sent Events

In Part 3 we will focus on:

• Deploying the Backend application to @DigitalOcean
• Deploying the frontend to @digitalocean_staff App Platform
• Use a managed Postgres Database

In Part 4 we will focus on:

• Adding Memory to the @LangChain Chain with PostgreSQL
• Add Multiquery to the chain for better breadth of search
• Add sessions to the Chat History

Github repo

https://github.com/focused-labs/pdf_rag