In our recent webinar, "How to Build an LLM App on Inventory, Product & Reviews Data" our panelists dove deep into the exciting new world of real-time LLM applications and their practical use cases in how to handle inventory, product and review data.
Specifically, this session showcased the increasing importance of “dynamic vector stores” in a world of ever-changing data. Let's unpack the top 10 takeaways from this enlightening discussion.
1. SingleStoreDB’s relevance to LLM apps
This makes SingleStoreDB ideally suited to providing the data context required by LLM apps in a scalable, cost-effective and performant way. Discover how SingleStoreDB works with vector data.
As a distributed, relational SQL database, SingleStoreDB has become increasingly relevant to the new class of LLM applications because it offers efficient storage and rapid querying of large datasets including structured data, unstructured data and vectors.
2. Compatibility and integration
SingleStoreDB is compatible with popular technologies like Kafka, Spark and Hadoop, giving it broader access to the world’s data.
For example, Kafka offers efficient handling of real-time data streams. Spark is a popular processing framework for large-scale analytics, and Hadoop’s framework complements SingleStoreDB’s ability to process large datasets. Taken together, this all gives SingleStoreDB access to developer’s favorite tools.
And with operations across major cloud platforms like AWS, Google Cloud and Azure, SingleStoreDB has a range of deployment options to choose from.
Learn more about SingleStoreDB and its integrations.
3. Advancements in vector support and semantic searches
The incorporation of vector support and semantic search capabilities in SingleStoreDB since 2017 has been a game changer. This feature is essential for handling modern AI and ML workloads, especially in embedding and retrieval tasks.
4. Neum AI’s role in LLM application development
Neum AI is a Y-combinator-backed platform that helps applications by synchronizing data in real time in understandable formats for LLMs. For example, Neum AI acts as an ETL (extract, transform, load) platform for LLM data that creates pipelines that stay synced, keeping context in prompts accurate and up-to-date.
Explore SingleStore’s AI capabilities.
5. The significance of Retrieval Augmented Generation (RAG) in LLMs
Neum AI supports semantic search by connecting search results to an LLM as context. This process, known as RAG, enriches LLMs with proprietary and relevant information, thereby improving the quality and relevance of the responses generated by the model
By enabling the real-time synchronization of data into vector stores, Neum AI ensures that applications like chatbots always respond with current and accurate information, avoiding outdated responses.
Learn more about Neum AI and real-time RAG
6. LLM app example using SingleStoreDB and Neum AI
One interesting demonstration of the power of real-time RAG would be this Elon Musk Twitter demo that gathers tweet data, processes them and generates vector embeddings continuously — which you can query semantically using Neum AI’s REST API or SingleStore’s API.
Twitter data makes for a fun demo, but obviously SingleStoreDB can handle vast amounts of data from other sources in various formats including relational data, JSON, geospatial, time-series, full-text search and more. These multi-model capabilities make SingleStoreDB a flexible solution for various use cases, combining the strengths of relational databases with the flexibility of NoSQL systems.
Explore how vector databases facilitate LLM management
7. What challenges are involved in embedding data for LLM applications?
In simplified terms, there are three common challenges involved in embedding data for LLM applications:
- Handling large documents. This involves navigating optimal 'chunk sizes' for breaking down extensive documents into manageable parts that fit within the LLM's limited 'token window.’
- Improving response relevance. Achieved by precisely retrieving and sometimes reranking the most relevant documents to ensure the LLM generates accurate responses.
- Model customization. Involves fine-tuning the embedding models to better suit specific needs or applications, though this can be complex and resource intensive.
8. Data synchronization and updating in LLMs
Neum AI’s emphasis on data synchronization addresses a critical aspect of LLM applications — keeping the data current and relevant. This ensures that the LLMs can provide accurate and relevant responses.
Read about Neum AI's approach to data synchronization
9. Optimization of embedding regeneration
It’s critical to update the vector embeddings in a system that uses LLMs. In dynamic data environments, where data changes frequently, it’s crucial to regularly regenerate these embeddings to ensure that they accurately reflect the current data.
Without updates, the embeddings may become outdated, leading to less accurate model predictions or responses.
Understand other challenges in developing and deploying LLMs
10. Use of metadata in enhancing retrieval efficiency
Lastly, incorporating metadata in the embedding process refines the retrieval process. This approach improves the relevance and accuracy of the information retrieved by the LLM, particularly in complex queries.
Conclusion
We believe that it will become increasingly important to keep data current in future LLM applications (which will require regularly updating vector embeddings to maintain accuracy).
As a next step, if you’re interested in exploring how SingleStoreDB and Neum AI partner for real-time data synchronization and retrieval-augmented generation in LLM apps, we recommend reading this blog post and trying the demo firsthand.
