Async equivalent coming in future PR
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The Docugami loader was not returning the source metadata key. This was
triggering this exception when used with retrievers, per
https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/schema/prompt_template.py#L193C1-L195C41
The fix is simple and just updates the metadata key name for the
document each chunk is sourced from, from "name" to "source" as
expected.
I tested by running the python notebook that has an end to end scenario
in it.
Tagging DataLoader maintainers @rlancemartin @eyurtsev
This pull request corrects the URL links in the Async API documentation
to align with the updated project layout. The links had not been updated
despite the changes in layout.
Not obvious what the error is when you cannot index. This pr adds the
ability to log the first errors reason, to help the user diagnose the
issue.
Also added some more documentation for when you want to use the
vectorstore with an embedding model deployed in elasticsearch.
Credit: @elastic and @phoey1
- Description: a description of the change
when I set `content_format=ContentFormat.VIEW` and
`keep_markdown_format=True` on ConfluenceLoader, it shows the following
error:
```
langchain/document_loaders/confluence.py", line 459, in process_page
page["body"]["storage"]["value"], heading_style="ATX"
KeyError: 'storage'
```
The reason is because the content format was set to `view` but it was
still trying to get the content from `page["body"]["storage"]["value"]`.
Also added the other content formats which are supported by Atlassian
API
https://stackoverflow.com/questions/34353955/confluence-rest-api-expanding-page-body-when-retrieving-page-by-title/34363386#34363386
- Issue: the issue # it fixes (if applicable),
Not applicable.
- Dependencies: any dependencies required for this change,
Added optional dependency `markdownify` if anyone wants to extract in
markdown format.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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- Description: Added the capability to handles structured data from
google enterprise search,
- Issue: Retriever failed when underline search engine was integrated
with structured data,
- Dependencies: google-api-core
- Tag maintainer: @jarokaz
- Twitter handle: anifort
Please make sure you're PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
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---------
Co-authored-by: Christos Aniftos <aniftos@google.com>
Co-authored-by: Holt Skinner <13262395+holtskinner@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Updates the hub stubs to not fail when no api key is found. For
supporting singleton tenants and default values from sdk 0.1.6.
Also adds the ability to define is_public and description for backup
repo creation on push.
Currently, generation_info is not respected by only reflecting messages
in chunks. Change it to add generations so that generation chunks are
merged properly.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
With this PR:
- All lint and test jobs use the exact same Python + Poetry installation
approach, instead of lints doing it one way and tests doing it another
way.
- The Poetry installation itself is cached, which saves ~15s per run.
- We no longer pass shell commands as workflow arguments to a workflow
that just runs them in a shell. This makes our actions more resilient to
shell code injection.
If y'all like this approach, I can modify the scheduled tests workflow
and the release workflow to use this too.
Update installation instructions to only install test dependencies rather than all dependencies.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- Description: current code does not work very well on jupyter notebook,
so I changed the code so that it imports `tqdm.auto` instead.
- Issue: #9582
- Dependencies: N/A
- Tag maintainer: @hwchase17, @baskaryan
- Twitter handle: N/A
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
If another push to the same PR or branch happens while its CI is still
running, cancel the earlier run in favor of the next run.
There's no point in testing an outdated version of the code. GitHub only
allows a limited number of job runners to be active at the same time, so
it's better to cancel pointless jobs early so that more useful jobs can
run sooner.
It's possible that langchain-experimental works fine with the latest
*published* langchain, but is broken with the langchain on `master`.
Unfortunately, you can see this is currently the case — this is why this
PR also includes a minor fix for the `langchain` package itself.
We want to catch situations like that *before* releasing a new
langchain, hence this test.
The current timeouts are too long, and mean that if the GitHub cache
decides to act up, jobs get bogged down for 15min at a time. This has
happened 2-3 times already this week -- a tiny fraction of our total
workflows but really annoying when it happens to you. We can do better.
Installing deps on cache miss takes about ~4min, so it's not worth
waiting more than 4min for the deps cache. The black and mypy caches
save 1 and 2min, respectively, so wait only up to that long to download
them.
