dluc

we could easily add that if there's interest, e.g. using Pub/Sub and Cloud Storage. If there are .NET libraries, should be straightforward implementing some interfaces. Similar considerations for the inference part, embedding and text generation.

Multiple reasons, some are subjective as usual in these choices. Customers, performance, existing SK community, experience, etc.

However, the recommended use is running it as a web service, so from a consumer perspective the language doesn't really matter.

yes, queries and also planning.

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.

Currently we use LLMs to generate a summary, used as an additional chunk. As you might guess, this can take time, so we postpone the summarization at the end (the current default pipeline is: extract, partition, gen embedding, save embeddings, summarize, gen embeddings (of the summary), save emb)

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.

We are also developing an open-source solution for those who would like to test it out and/or contribute, it can be consumed as a web service, or embedded into .NET apps. The project is codenamed "Semantic Memory" (available in GitHub) and offers customizable external dependencies, such as using Azure Queues, RabbitMQ, or other alternatives, and options for Azure Cognitive Search, Qdrant (with plans to include Weaviate and more). The architecture is similar, with queues and pipelines.

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.

The model doesn't run code, it generates text that happens to be code. It's up to the client calling the model API to use this text, e.g. compiling and executing (if that's your scenario) and calling the model again to fix the original code if needed.

hello there all, dluc from Semantic Kernel :wave: looking forward to attend :-)

Nice pun, it's an edge case...