Retrieval Augmented Generative pipelines (RAG) are one of the most effective techniques to leveraging LLMs for production.
In this talk, Tuana Celik will walk through how you can build and customize your own RAG pipelines with Haystack, the open-source LLM framework by deepset. She will demonstrate the Haystack pipeline structure, which is built on the principle of composability, and how you may shape custom pipelines to:
- Make use of the vector databases of your choice
- Shape out how your data will interact with LLMs
- Use the latest retrieval techniques to provide LLMs with the relevant context and more.
Following that, Thomas will discuss the challenges of deploying such pipelines to production.
- You will learn how we deploy Haystack pipelines on deepset Cloud. In particular we’ll address:
- Hosting a pipeline
- Connecting your pipeline to LLMs hosted on AWS SageMaker
- What it actually means to run a RAG pipeline end-to-end covering prototyping, evaluation, inference, prompt engineering, observability and more
Senior Software Engineer at Deepset
Thomas Stadelmann is Senior Software Engineer at Deepset, where he works on Haystack, Deepset's Open Source Framework for Retrieval Augmented Generation, and on deepset Cloud, their complementary commercial product. Thomas joined Deepset in 2021. Coming from the field of Information Retrieval, he has been working on real-world search applications for over 12 years.
Developer Advocate @Deepset
Tuana works as a Developer Advocate for Haystack, the open source LLM framework by Deepset. She helps other engineers and the NLP community understand the latest approaches to build LLM applications, and in particular how they can do so with Haystack.
Session Sponsored By
deepset offers enterprise developer tools to build NLP-driven applications using LLMs.