burr
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apache
Build applications that make decisions (chatbots, agents, simulations, etc...). Monitor, trace, persist, and execute on your own infrastructure.
This is tricky as async generators have no return capability. The way this will likely work is that the last `yield` is the final result: ```python @streaming_action(reads=["prompt"], writes=["prompt"]) def streaming_chat_call(state:...
This PR introduces a modular SERDE approach. 1. Singledispatch to register type serializers. 2. Any complex object deserialization, needs to be accompanied with a "type field" that will map to...
The plan here is from the docs. Problem is that we're eagerly evaluating state at every point which is not particularly efficient (TBD, however). This also helps us solve this...
[Short description explaining the high-level reason for the pull request] ## Changes ## How I tested this ## Notes ## Checklist - [ ] PR has an informative and human-readable...
Currently, exceptions will break the control flow of an action, stopping the program early. Thus, if an exception is expected, the program will stop early. We will be adding the...
Currently state is untyped. Ideally this should be able to leverage a pydantic model, and possibly a typed dict/whatnot. We'll need the proper abstractions however. Some requirements: 1. IDE-friendly --...
We have room for an example + have it in the docs. We need to think through what we want to suggest. Some ideas: 1. Time-series forecasting -- do a...
We need an ML training example, and ideally want to demonstrate the following: - [ ] Human-in-the-loop (ml training with human input) - [ ] Epoch-based training for tracking/decision making...
Follow up on #31. Next set of steps. This is in priority order: - [x] Improve UI layout https://github.com/DAGWorks-Inc/burr/issues/100 - [x] Examples for how to generate test cases - [...
All notebooks should: 1. Explain what you'll learn 1. Display/link to the code they pull in 1. Explain what they're doing 1. Explain why they use specific APIs 1. Link...