The package is linted with mypy, so its type hints are correct and
should be exposed publicly. Without this file, the type hints remain
private and cannot be used by downstream users of the package.
Trusted Publishing is the current best practice for publishing Python
packages. Rather than long-lived secret keys, it uses OpenID Connect
(OIDC) to allow our GitHub runner to directly authenticate itself to
PyPI and get a short-lived publishing token. This locks down publishing
quite a bit:
- There's no long-lived publish key to steal anymore.
- Publishing is *only* allowed via the *specifically designated* GitHub
workflow in the designated repo.
It also is operationally easier: no keys means there's nothing that
needs to be periodically rotated, nothing to worry about leaking, and
nobody can accidentally publish a release from their laptop because they
happened to have PyPI keys set up.
After this gets merged, we'll need to configure PyPI to start expecting
trusted publishing. It's only a few clicks and should only take a
minute; instructions are here:
https://docs.pypi.org/trusted-publishers/adding-a-publisher/
More info:
- https://blog.pypi.org/posts/2023-04-20-introducing-trusted-publishers/
- https://github.com/pypa/gh-action-pypi-publish
- Description: Updated marqo integration to use tensor_fields instead of
non_tensor_fields. Upgraded marqo version to 1.2.4
- Dependencies: marqo 1.2.4
---------
Co-authored-by: Raynor Kirkson E. Chavez <raynor.chavez@192.168.254.171>
Co-authored-by: Bagatur <baskaryan@gmail.com>
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- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
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2. an example notebook showing its use. These live is docs/extras
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If no one reviews your PR within a few days, please @-mention one of
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This is safer than the prior approach, since it's safe by default: the
release workflows never get triggered for non-merged PRs, so there's no
possibility of a buggy conditional accidentally letting a workflow
proceed when it shouldn't have.
The only loss is that publishing no longer requires a `release` label on
the merged PR that bumps the version. We can add a separate CI step that
enforces that part as a condition for merging into `master`, if
desirable.
- Description: support [ERNIE
Embedding-V1](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/alj562vvu),
which is part of ERNIE ecology
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
I have discovered a bug located within `.github/workflows/_release.yml`
which is the primary cause of continuous integration (CI) errors. The
problem can be solved; therefore, I have constructed a PR to address the
issue.
## The Issue
Access the following link to view the exact errors: [Langhain Release
Workflow](https://github.com/langchain-ai/langchain/actions/workflows/langchain_release.yml)
The instances of these errors take place for **each PR** that updates
`pyproject.toml`, excluding those specifically associated with bumping
PRs.
See below for the specific error message:
```
Error: Error 422: Validation Failed: {"resource":"Release","code":"already_exists","field":"tag_name"}
```
An image of the error can be viewed here:
The `_release.yml` document contains the following if-condition:
```yaml
if: |
${{ github.event.pull_request.merged == true }}
&& ${{ contains(github.event.pull_request.labels.*.name, 'release') }}
```
## The Root Cause
The above job constantly runs as the `if-condition` is always identified
as `true`.
## The Logic
The `if-condition` can be defined as `if: ${{ b1 }} && ${{ b2 }}`, where
`b1` and `b2` are boolean values. However, in terms of condition
evaluation with GitHub Actions, `${{ false }}` is identified as a string
value, thereby rendering it as truthy as per the [official
documentation](https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idif).
I have run some tests regarding this behavior within my forked
repository. You can consult my [debug
PR](https://github.com/zawakin/langchain/pull/1) for reference.
Here is the result of the tests:
|If-Condition|Outcome|
|:--:|:--:|
|`if: true && ${{ false }}`|Execution|
|`if: ${{ false }}` |Skipped|
|`if: true && false` |Skipped|
|`if: false`|Skipped|
|`if: ${{ true && false }}` |Skipped|
In view of the first and second results, we can infer that `${{ false
}}` can only be interpreted as `true` for conditions composed of some
expressions.
It is consistent that the condition of `if: ${{ inputs.working-directory
== 'libs/langchain' }}` works.
It is surprised to be skipped for the second case but it seems the spec
of GitHub Actions 😓
Anyway, the PR would fix these errors, I believe 👍
Could you review this? @hwchase17 or @shoelsch , who is the author of
[PR](https://github.com/langchain-ai/langchain/pull/360).
Description: Updating documentation to add AmazonTextractPDFLoader
according to
[comment](https://github.com/langchain-ai/langchain/pull/8661#issuecomment-1666572992)
from [baskaryan](https://github.com/baskaryan)
Adding one notebook and instructions to the
modules/data_connection/document_loaders/pdf.mdx
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Description: Changed metadata retrieval so that it combines Vectara
doc level and part level metadata
- Tag maintainer: @rlancemartin
- Twitter handle: @ofermend
Made the notion document of how Langchain executes agents method by
method in the codebase.
Can be helpful for developers that just started working with the
Langchain codebase.
The current Collab URL returns a 404, since there is no `chatbots`
directory under `use_cases`.
<!-- Thank you for contributing to LangChain!
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
**Description**:
- Uniformed the current valid suffixes (file formats) for loading agents
from hubs and files (to better handle future additions);
- Clarified exception messages (also in unit test).
@rlancemartin The current implementation within `Geopandas.GeoDataFrame`
loader uses the python builtin `str()` function on the input geometries.
While this looks very close to WKT (Well known text), Python's str
function doesn't guarantee that.
In the interest of interop., I've changed to the of use `wkt` property
on the Shapely geometries for generating the text representation of the
geometries.
Also, included here:
- validation of the input `page_content_column` as being a GeoSeries.
- geometry `crs` (Coordinate Reference System) / bounds
(xmin/ymin/xmax/ymax) added to Document metadata. Having the CRS is
critical... having the bounds is just helpful!
I think there is a larger question of "Should the geometry live in the
`page_content`, or should the record be better summarized and tuck the
geom into metadata?" ...something for another day and another PR.
This is an extension of #8104. I updated some of the signatures so all
the tests pass.
@danhnn I couldn't commit to your PR, so I created a new one. Thanks for
your contribution!
@baskaryan Could you please merge it?
---------
Co-authored-by: Danh Nguyen <dnncntt@gmail.com>
### Summary
Fixes a bug from #7850 where post processing functions in Unstructured
loaders were not apply. Adds a assertion to the test to verify the post
processing function was applied and also updates the explanation in the
example notebook.
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>
