**Description**: the "page" mode in the
AzureAIDocumentIntelligenceParser is not accessible due to a wrong
membership test. The mode argument can only be a string (also see the
assertion in the `__init__`: `assert self.mode in ["single", "page",
"object", "markdown"]`, so the check `elif self.mode == ["page"]:`
always fails.
As a result, effectively the "object" mode is used when selecting the
"page" mode, which may lead to errors.
The docstring of the `AzureAIDocumentIntelligenceLoader` also ommitted
the `mode` parameter alltogether, so I added it.
**Issue**: I could not find a related issue (this class is only 3 weeks
old anyways)
**Dependencies**: this PR does not introduce or affect dependencies.
The current demo notebook and examples are not affected because they all
use the default markdown mode.
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See preview :
https://langchain-git-fork-cbornet-astra-loader-doc-langchain.vercel.app/docs/integrations/document_loaders/astradb
- **Description:** Includes the PDF ID in the MathPix document metadata.
This is useful in case you need to re-request a processed PDF from the
MathPix API later.
- **Description:** The `error_info['id']` can be cross-referenced with
the MathPix API documentation to get very specific information about why
an error occurred.
**Description:** the MWDumpLoader implementation currently does not
support the lazy_load method, and the files are usually very large. We
are proposing refactoring the load function, extracting two private
functions with the functionality of loading the dump file and parsing a
single page, to reuse the code in the lazy_load implementation.
Added the call function get_summaries_as_docs inside of Arxivloader
- **Description:** Added a function that returns the documents from
get_summaries_as_docs, as the call signature is present in the parent
file but never used from Arxivloader, this can be used from Arxivloader
itself just like .load() as both the signatures are same.
- **Issue:** Reduces time to load papers as no pdf is processed only
metadata is pulled from Arxiv allowing users for faster load times on
bulk loads. Users can then choose one or more paper and use ID directly
with .load() to load pdf thereby loading all the contents of the paper.
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Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
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Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
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. It lives in
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fix spellings
**seperate -> separate**: found more occurrences, see
https://github.com/langchain-ai/langchain/pull/14602
**initialise -> intialize**: the latter is more common in the repo
**pre-defined > predefined**: adding a comma after a prefix is a
delicate matter, but this is a generally accepted word
also, another word that appears in the repo is "fs" (stands for
filesystem), e.g., in `libs/core/langchain_core/prompts/loading.py`
` """Unified method for loading a prompt from LangChainHub or local
fs."""`
Isn't "filesystem" better?
Addded missed docstrings. Fixed inconsistency in docstrings.
**Note** CC @efriis
There were PR errors on
`langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py`
But, I didn't touch this file in this PR! Can it be some cache problems?
I fixed this error.
