Issue: https://github.com/langchain-ai/langchain/issues/9401
In the Async mode, SequentialChain implementation seems to run the same
callbacks over and over since it is re-using the same callbacks object.
Langchain version: 0.0.264, master
The implementation of this aysnc route differs from the sync route and
sync approach follows the right pattern of generating a new callbacks
object instead of re-using the old one and thus avoiding the cascading
run of callbacks at each step.
Async mode:
```
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
...
for i, chain in enumerate(self.chains):
_input = await chain.arun(_input, callbacks=callbacks)
...
```
Regular mode:
```
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
for i, chain in enumerate(self.chains):
_input = chain.run(_input, callbacks=_run_manager.get_child(f"step_{i+1}"))
...
```
Notice how we are reusing the callbacks object in the Async code which
will have a cascading effect as we run through the chain. It runs the
same callbacks over and over resulting in issues.
Solution:
Define the async function in the same pattern as the regular one and
added tests.
---------
Co-authored-by: vamsee_yarlagadda <vamsee.y@airbnb.com>
Ternary operators in GitHub Actions syntax are pretty ugly and hard to
read: `inputs.working-directory == '' && '.' ||
inputs.working-directory` means "if the condition is true, use `'.'` and
otherwise use the expression after the `||`".
This PR performs the ternary as few times as possible, assigning its
outcome to an env var we can then reuse as needed.
Fix spelling errors in the text: 'Therefore' and 'Retrying
I want to stress that your feedback is invaluable to us and is genuinely
cherished.
With gratitude,
@baskaryan @hwchase17
Only lint on the min and max supported Python versions.
It's extremely unlikely that there's a lint issue on any version in
between that doesn't show up on the min or max versions.
GitHub rate-limits how many jobs can be running at any one time.
Starting new jobs is also relatively slow, so linting on fewer versions
makes CI faster.
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📜
- updated the top-level descriptions to a consistent format;
- changed the format of several 100% internal functions from "name" to
"_name". So, these functions are not shown in the Top-level API
Reference page (with lists of classes/functions)
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gets announced and you'd like a mention, we'll gladly shout you out!
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submitting. Run `make format`, `make lint` and `make test` to check this
locally.
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https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
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2. an example notebook showing its use. These live is docs/extras
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Using `poetry add` to install `pydantic@2.1` was also causing poetry to
change its lockfile. This prevented dependency caching from working:
- When attempting to restore a cache, it would hash the lockfile in git
and use it as part of the cache key. Say this is a cache miss.
- Then, it would attempt to save the cache -- but the lockfile will have
changed, so the cache key would be *different* than the key in the
lookup. So the cache save would succeed, but to a key that cannot be
looked up in the next run -- meaning we never get a cache hit.
In addition to busting the cache, the lockfile update itself is also
non-trivially long, over 30s:
This PR fixes the problems by using `pip` to perform the installation,
avoiding the lockfile change.
Refactored code to ensure consistent handling of ImportError. Replaced
instances of raising ValueError with raising ImportError.
The choice of raising a ValueError here is somewhat unconventional and
might lead to confusion for anyone reading the code. Typically, when
dealing with import-related errors, the recommended approach is to raise
an ImportError with a descriptive message explaining the issue. This
provides a clearer indication that the problem is related to importing
the required module.
@hwchase17 , @baskaryan , @eyurtsev
Thanks
Aashish
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR fills in more missing type annotations on pydantic models.
It's OK if it missed some annotations, we just don't want it to get
annotations wrong at this stage.
I'll do a few more passes over the same files!
