* Revert "Make the docstring bot work only on master (#2078)"
This reverts commit 649d07405770cd59696d0120107a3b2f0aafe7c2.
* Add latest docstring and tutorial changes
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* provide option to recreate es doc store on initialization
* Add latest docstring and tutorial changes
* Label expects more arguments
* Label expects also an answer
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Co-authored-by: Sara Zan <sara.zanzottera@deepset.ai>
* Remove stray requirements.txt files and update README.md
* Remove requirement files
* Add details about pip bug and link to setup.cfg
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* Fist attempt at using setup.cfg for dependency management
* Trying the new package on the CI and in Docker too
* Add composite extras_require
* Add the safe_import function for document store imports and add some try-catch statements on rest_api and ui imports
* Fix bug on class import and rephrase error message
* Introduce typing for optional modules and add type: ignore in sparse.py
* Include importlib_metadata backport for py3.7
* Add colab group to extra_requires
* Fix pillow version
* Fix grpcio
* Separate out the crawler as another extra
* Make paths relative in rest_api and ui
* Update the test matrix in the CI
* Add try catch statements around the optional imports too to account for direct imports
* Never mix direct deps with self-references and add ES deps to the base install
* Refactor several paths in tests to make them insensitive to the execution path
* Include tstadel review and re-introduce Milvus1 in the tests suite, to fix
* Wrap pdf conversion utils into safe_import
* Update some tutorials and rever Milvus1 as default for now, see #2067
* Fix mypy config
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* fix data augmentation path in finetuning notebook
* Add latest docstring and tutorial changes
* make distillation possible with other models than BERT
* use smaller dataset for distillation in finetuning tutorial
* Add latest docstring and tutorial changes
* make data augmentation in finetuning faster
* update language models forward doc strings
* fix return type of language models
* remove debug output
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* Refactored code to unify vector_dim and embedding_dim parameter in DocumentStores
* Unit test cases updated to use `embedding_dim` instead of `vector_dim`
* Unit test case update to use embedding_dim instead of vector_dim
* Add latest docstring and tutorial changes
* Put usage of `vector_dim` param in same if-block as corresponding warning
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Co-authored-by: bogdankostic <bogdankostic@web.de>
* Replace old tutorial 5 with new code based on test cases
* Add latest docstring and tutorial changes
* Use pipeline.eval() in tutorial
* Add latest docstring and tutorial changes
* Restructure notebook
* Add latest docstring and tutorial changes
* Add dataframe example
* Add latest docstring and tutorial changes
* Get eval data from doc store
* Add latest docstring and tutorial changes
* Load data from doc store
* Add latest docstring and tutorial changes
* Clear outputs
* Add latest docstring and tutorial changes
* Change example and add python script
* Add latest docstring and tutorial changes
* Fetch aggregated multilabels from doc store
* Add latest docstring and tutorial changes
* Incorporate review feedback on text comments
* Add latest docstring and tutorial changes
* Add Notebook output
* Remove queries param from pipeline.eval()
* Add latest docstring and tutorial changes
* Add output with all metrics
* Add printing of multiple metrics to script
* Add latest docstring and tutorial changes
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* Add FormRecognizerConverter
* Change signature of convert method + change return type of all converters
* Adapt preprocessing util to new return type of converters
* Parametrize number of lines used for surrounding context of table
* Change name from FormRecognizerConverter to AzureConverter
* Set version of azure-ai-formrecognizer package
* Change tutorial 8 based on new return type of converters
* Add tests
* Add latest docstring and tutorial changes
* Fix typo
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Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
* Fix link to colab notebook in tutorial 16
* Add latest docstring and tutorial changes
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* Upgrade torch to v1.10.0
* Adapt torch version for torch-scatter in TableQA tutorial
* Add latest docstring and tutorial changes
* Make torch version more flexible
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* initialize doc store with doc and label index
* change ipynb according to py for tutorial 5
* Add latest docstring and tutorial changes
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* Make Tutorial10 use print instead of logs and fix a typo in Tutoria15
* Add a type check in 'print_answers'
* Add same checks to print_documents and print_questions
* Make RAGenerator return Answers instead of dictionaries
* Fix RAGenerator tests
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* Fix yet another self.device(s) typo
* Add typing to 'initialize_device_settings' to try prevent future issues
* Fix bug in Tutorial5
* Fix the same bug in the notebook
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* Modify __str__ and __repr__ for Document and Answer
* Rename QueryClassifier in Tutorial11
* Improve the output of tutorial1
* Make the output of Tutorial8 a bit less dense
* Add a print_questions util to print the output of question generating pipelines
* Replace custom printing with the new utility in Tutorial13
* Ensure all output is printed with minimal details in Tutorial14 and add some titles
* Minor change to print_answers
* Make tutorial3's output the same as tutorial1
* Add __repr__ to Answer and fix to_dict()
* Fix a bug in the Document and Answer's __str__ method
* Improve print_answers, print_documents and print_questions
* Using print_answers in Tutorial7 and fixing typo in the utils
* Remove duplicate line in Tutorial12
* Use print_answers in Tutorial4
* Add explanation of what the documents in the output of the basic QA pipeline are
* Move the fields constant into print_answers
* Normalize all 'minimal' to 'minimum' (they were mixed up)
* Improve the sample output to include all fields from Document and Answer
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* Files moved, imports all broken
* Fix most imports and docstrings into
* Fix the paths to the modules in the API docs
* Add latest docstring and tutorial changes
* Add a few pipelines that were lost in the inports
* Fix a bunch of mypy warnings
* Add latest docstring and tutorial changes
* Create a file_classifier module
* Add docs for file_classifier
* Fixed most circular imports, now the REST API can start
* Add latest docstring and tutorial changes
* Tackling more mypy issues
* Reintroduce from FARM and fix last mypy issues hopefully
* Re-enable old-style imports
* Fix some more import from the top-level package in an attempt to sort out circular imports
* Fix some imports in tests to new-style to prevent failed class equalities from breaking tests
* Change document_store into document_stores
* Update imports in tutorials
* Add latest docstring and tutorial changes
* Probably fixes summarizer tests
* Improve the old-style import allowing module imports (should work)
* Try to fix the docs
* Remove dedicated KnowledgeGraph page from autodocs
* Remove dedicated GraphRetriever page from autodocs
* Fix generate_docstrings.sh with an updated list of yaml files to look for
* Fix some more modules in the docs
* Fix the document stores docs too
* Fix a small issue on Tutorial14
* Add latest docstring and tutorial changes
* Add deprecation warning to old-style imports
* Remove stray folder and import Dict into dense.py
* Change import path for MLFlowLogger
* Add old loggers path to the import path aliases
* Fix debug output of convert_ipynb.py
* Fix circular import on BaseRetriever
* Missed one merge block
* re-run tutorial 5
* Fix imports in tutorial 5
* Re-enable squad_to_dpr CLI from the root package and move get_batches_from_generator into document_stores.base
* Add latest docstring and tutorial changes
* Fix typo in utils __init__
* Fix a few more imports
* Fix benchmarks too
* New-style imports in test_knowledge_graph
* Rollback setup.py
* Rollback squad_to_dpr too
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* Update jobs link to personio
* Add latest docstring and tutorial changes
* Change jobs link to main website
* Add latest docstring and tutorial changes
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* Add endpoint to get documents by filter
* Add test for /documents/get_by_filter and extend the delete documents test
* Add rest_api/file-upload to .gitignore
* Make sure the document store is empty for each test
* Improve docstrings of delete_documents_by_filters and get_documents_by_filters
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* Define SAS model in notebook
* Add latest docstring and tutorial changes
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* remove not needed githab actions and reactivate docstrings and tutorial generation
* test workflow
* update pydoc version
* update python version
* update watchdog
* move to latest version pydoc-markdown
* remove version check
* Add latest docstring and tutorial changes
* remove test workflow
* test for param docstrings
* pin pydoc-markdown version
* add test workflow
* pin watchdog version
* Add latest docstring and tutorial changes
* update original workflow and delete test
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