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Tuesday, February 27 • 2:00pm - 2:50pm
[Virtual] PRO WORKSHOP: Build Your Own a Private GenAI Q&A System Using Dolly V2 and MongoDB Atlas Vector Search

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Dave Nielsen, MongoDB, Head of Community

In this talk & workshop, you will learn how to create and privately host an LLM to power a Q&A system like ChatGPT.

In this workshop, you will learn how to assemble a Generative AI stack to power an LLM for the purpose of building a Q&A system from data stored in your own database. In the workshop portion of the session, you will build your own Q&A system on top of Dolly v2 & MongoDB Atlas Vector Search in less than 30 minutes.

What we will cover:

1. Pre-processing of your data - Get it into a shape where it can be used by LLMs (For the purpose of the workshop, you will have access to pre-processed data).
2. Create chunks and embeddings.
3. Store chunks in MongoDB Atlas for fast retrieval and search.
4. Store embeddings in a vector database, such as MongoDB Vector Search.
5. Privately hosted LLM.
6. Use commercially available open-source models like Dolly v2, LLAMA v2, ChatGPT, etc. in a private VPC.

Takeaways from this session include:
1. How to assemble and operationalize a Q&A system using open-source LLMs
2. Data quality impacts the LLM response
3. How to rapidly experiment with different LLM models for your specific use case

avatar for Sujee Maniyam

Sujee Maniyam

Sujee Maniyam is a seasoned practitioner specializing in Generative AI, Machine Learning, Deep Learning, Big Data, Distributed Systems, and Cloud technologies. He has been consulting and training and mentoring other professionals across various companies large and small. Sujee is... Read More →
avatar for Dave Nielsen

Dave Nielsen

Head of Community, MongoDB
Dave Nielsen is the Head of Community at MongoDB. Prior to that Dave was Head of Community at Harness, Traceable, Redis Labs, Strikeiron & PayPal. He also co-founded CloudCamp

Tuesday February 27, 2024 2:00pm - 2:50pm PST
VIRTUAL PRO Stage A https://app.events.ringcentral.com/events/developerweek-2024/sessions