Our current perspective has been on leveraging LLMs as part of async processes to help analyze data. This only really works when your data follows a template where I might be able to apply the analysis to a vast number of documents. Alternatively it becomes too expensive to do at a per document basis.
What types of analysis are you doing with LLMs? Have you started to integrate some of these into your existing solution?
Initial tests though are showing that summaries are affecting the quality of answers, so we'll probably remove it from the default flow and use it only for specific data types (e.g. chat logs).
There's a bunch of synthetic data scenarios we want to leverage LLMs for. Without going too much into details, sometimes "reading between the lines", and for some memory consolidation patterns (e.g. a "dream phase"), etc.
For synthetic data scenarios are you also thinking about synthetic queries over the data? (Try to predict which chunks might be more used than others)
For instance, given the user "ask" (which could be any generic message in a copilot), decide how to query one or multiple storages. Ultimately, companies and users have different storages, and a few can be indexed with vectors (and additional fine tuned models). But there's a lot of "legacy" structured data accessible only with SQL and similar languages, so a "planner" (in the SK sense of planners) could be useful to query vector indexes, text indexes and knowledge graphs, combining the result.
However, the recommended use is running it as a web service, so from a consumer perspective the language doesn't really matter.
Is anyone aware of something similar but hooked into Google Cloud infra instead of Azure?
We believe that enabling custom dependencies and logic, as well as the ability to add/remove pipeline steps, is crucial. As of now, there is no definitive answer to the best chunk size or embedding model, so our project aims to provide the flexibility to inject and replace components and pipeline behavior.
Regarding Scalability, LLM text generators and GPUs remain a limiting factor also in this area, LLMs hold great potential for analyzing input data, and I believe the focus should be less on the speed of queues and storage and more on finding the optimal way to integrate LLMs into these pipelines.