This improves the overall responsiveness of the system substantially, but does make each iteration *slightly* slower, distributing the up-front cost across the batch.
Two main changes:
1. Create BatchSessions immediately, but do not create a whole graph execution state until the batch is executed.
BatchSessions are created with a `session_id` that does not exist in sessions database.
The default state is changed to `"uninitialized"` to better represent this.
Results: Time to create 5000 batches reduced from over 30s to 2.5s
2. Use `executemany()` to retrieve lists of created sessions.
Results: time to create 5000 batches reduced from 2.5s to under 0.5s
Other changes:
- set BatchSession state to `"in_progress"` just before `invoke()` is called
- rename a few methods to accomodate the new behaviour
- remove unused `BatchProcessStorage.get_created_sessions()` method
It is `"invocation"` for invocations and `"output"` for outputs. Clients may use this to confidently and positively identify if an OpenAPI schema object is an invocation or output, instead of using a potentially fragile heuristic.
Doing this via `BaseInvocation`'s `Config.schema_extra()` means all clients get an accurate OpenAPI schema.
Shifts the responsibility of correct types to the backend, where previously it was on the client.
Doing this via these classes' `Config.schema_extra()` method makes it unintrusive and clients will get the correct types for these properties.
Shifts the responsibility of correct types to the backend, where previously it was on the client.
- move docstrings to ABC
- `start_time: int` -> `start_time: float`
- remove class attribute assignments in `StatsContext`
- add `update_mem_stats()` to ABC
- add class attributes to ABC, because they are referenced in instances of the class. if they should not be on the ABC, then maybe there needs to be some restructuring
When retrieving a graph, it is parsed through pydantic. It is possible that this graph is invalid, and an error is thrown.
Handle this by deleting the failed graph from the stats if this occurs.
The internal `BatchProcessStorage.get_session()` method throws when it finds nothing, but we were not catching any exceptions.
This caused a exception when the batch manager handles a `graph_execution_state_complete` event that did not originate from a batch.
Fixed by handling the exception.
If the values from the `session_dict` are invalid, the model instantiation will fail, or if we end up with an invalid `batch_id`, the app will not run. So I think just parsing the dict directly is equivalent.
Also the LSP analyser is pleased now - no red squigglies.
Providing a `default_factory` is enough for pydantic to know to create the attribute on instantiation if it's not already provided. We can then make make the typing just `str`.
