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
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