Conversation
Make it easier to initialize an experiment's dataset reference without calling `initDataset`
CLowbrow
changed the title
ibolmo
left a comment
ibolmo
left a comment
There was a problem hiding this comment.
i think it's just otel that needs a follow-up, but check my other comments ...
| if expected is not None: | ||
| score = 1.0 if output == expected else 0.0 | ||
|
|
||
| if trace: |
There was a problem hiding this comment.
as a user why/when would trace ever be none?
| :return: A function that can be used as a task or scorer. | ||
| """ | ||
| # Disable span cache since remote function spans won't be in the local cache | ||
| _internal_get_global_state().span_cache.disable() |
There was a problem hiding this comment.
may be too indirect. if the remote function spans are not in the cache what's the big deal in checking the cache?
| assert state.span_cache.disabled is False | ||
|
|
||
| # Call init_function | ||
| f = init_function("test-project", "test-function") |
There was a problem hiding this comment.
it is a bit odd that loading a function will cause a cache to be disabled.
| root_span.log(output=output, metadata=metadata, tags=tags) | ||
|
|
||
| # Create trace object for scorers | ||
| from braintrust.trace import LocalTrace |
There was a problem hiding this comment.
i don't think we have a circ. dep so i'd move this to the top of the file
| """ | ||
| self._otel_flush_callback = callback | ||
|
|
||
| async def flush_otel(self) -> None: |
There was a problem hiding this comment.
where is this called? you probably need to find the right spot to call this and/or register_otel_flush
aside.. we solved this well with flush async. seems like we're in a similar pattern here.
There was a problem hiding this comment.
one option is to insert quickly in mermory and on need to flush you then block to flush.
| if not line: | ||
| continue | ||
| try: | ||
| record_dict = json.loads(line) |
There was a problem hiding this comment.
maybe consider some starts_with and maybe prepending the span_id/root_id... etc to avoid too many json.loads
| "root_span_id": self._root_span_id, | ||
| } | ||
|
|
||
| async def get_spans(self, span_type: Optional[list[str]] = None) -> list[SpanData]: |
There was a problem hiding this comment.
give the option to disable the cache
| span_parents=self.span_parents, | ||
| span_attributes=serializable_partial_record.get("span_attributes"), | ||
| ) | ||
| self.state.span_cache.queue_write(self.root_span_id, self.span_id, cached_span) |
There was a problem hiding this comment.
there could a performant problem if we write a lot to filesystem. would be nice to queue an amount and do bulk inserts to files to avoid read/write to filesystem
| SpanFetchFn = Callable[[Optional[list[str]]], Awaitable[list[SpanData]]] | ||
|
|
||
|
|
||
| class CachedSpanFetcher: |
There was a problem hiding this comment.
future should we consider fetchign and saving the data locally
4 participants
No description provided.