"If your task can be expressed as a workflow, build a workflow". 100% true but the thing all these 'agent pattern' or 'workflow' diagrams miss is that real tasks require back-and-forth with a user, not just a Rube Goldberg machine that gets triggered in response to a _single user message_.
What you need is not 'tool use' but something like 'process use'. This is what we did at Rasa, giving you a declarative way to define multi-step processes. An LLM lets you have a fluent conversation, but the execution of the task is pre-defined and deterministic: https://rasa.com/docs/learn/concepts/calm/
The fact that every framework starts with a `while` loop around an LLM and then duct-tapes on some "guardrails" betrays a lack of imagination.
This is nice. Pulling data from an API and putting it in a SQL database should be a simple everyday task but the tools for this are 99% massive overkill. Great to see a simple library for a simple job.
Pulling from and into production databases is one of the early favourites from our dlt user base. Some reasons explained here in this MongoDB example (https://dlthub.com/docs/blog/MongoDB-dlt-Holistics)
This is a really cool project—congrats! A somewhat related project that I worked on at MongoDB is PyMongoArrow, it does some of the same transformations to take unstructured MongoDB data and convert it to tabular formats like Arrow data frames. I’m curious what the support for BSON types that do not map cleanly to JSON types looks like? One example I can think of off the top of my head is Decimal128
The Rasa Core framework has been in development for 1.5 years, so we're incredibly excited to launch it & make it accessible to everyone.
We wanted to make Machine-Learning based dialogue usable for as many developers as possible. There's so much great research in conversational AI but very little that developers can actually use to build bots and voice apps.
One of the big challenges was making Rasa Core useful on day one, when you have no training data. We came up with the interactive learning approach, where you teach the system by talking to it and providing feedback. Rasa lets you scale from nothing up to a robust dialogue system trained on a large number of real conversations.
We're so happy with the community that's developed around Rasa NLU, and so we're really, really excited to see what people build with Core.
currently there are no built-in entities like dates, times, locations etc. But really keen to set up a way for users to share models, and that would definitely include these things as well
yeah you're quite right, intents are built with a bag of words model & doesn't take order into account. Entity extraction does though. If you find a case where word order is really important for getting intents right I'd love to know about it! We could find a way to make that work.
The bot I have been working on uses bag of words and n-grams to identify intents. Useful for when someone says something that may have multiple entities of the same type. For instance a journey start and end point. Can use the "from" and "to" words match better. Also very useful when training on phrases that are very similar such as FAQ questions which often have the same words but the order and one or two keys words is super important to get the right match.
and aloo how was the search quality? We noticed delays for up to 0.5s today because of heavy traffic. When things get quieter that should improve dramatically. Cool input on the onboarding. We found it took quite a bit of work to make a really slick onboarding flow, but there's clearly still room for improvement.
