With the advent of generative AI and rapid advancement of commercial and open-source LLMs, everyone wants to create unique and compelling hyper-personalized customer experiences such as semantic search or user content driven predictions. However, transitioning these applications from prototype to production-ready solutions might be a daunting task and pose a significant challenge for developers!
To address these challenges, developers require a flexible data platform capable of adapting to ever-changing unstructured and structured data without rigid schemas. While fine-tuning remains an option, it comes with its limitations. Instead, developers need to present data as context to large models through prompts and empower generative models with long-term memory.
In this session, let’s explore how MongoDB Atlas integrates operational, analytical, and vector search data services, streamlining the seamless integration of LLMs (Large Language Models) and transformer models into your applications. This not only simplifies the application architecture but also empowers developers to build gen AI-enriched applications on a high performance, highly scalable operational database
We will broadly cover two use cases:
- How we can integrate with LLMs such as LangChain and integrating them with MongoDB Atlas which can act as a vector store
- How to set up Atlas Triggers to automatically predict the sentiment of new documents in your MongoDB database and add them as additional fields to your documents.
Key takeaways from the session:
- How to get started building gen-AI and LLM Applications
- Introduction to the complete developer data platform MongoDB Atlas and Atlas Vector Search (Preview)
- Understanding the various integrations possible with popular LLMs
- Introduction to Atlas Triggers and event driven architecture
- Live demo of LangChain integrations and sentiment prediction using Hugging Face Transformer model
