* Properly fix MetaDocumentORM and MetaLabelORM with composite foreign key constraints
* update_document_meta() was not using index properly
* Exclude ES and Memory from the cosine_sanity_check test
* move ensure_ids_are_correct_uuids in conftest and move one test back to faiss & milvus suite
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* align document store similarity functions
* remove unnecessary imports
* undone accidental change
* stopped weaviate from pretending to support dot product similarity
* stopped weaviate from pretending to support dot product similarity
* Add latest docstring and tutorial changes
* fix fixture params for document stores
* use cosine similarity for most tests
* fix cosine similarity test
* fix faiss test
* fix weaviate test
* fix accidental deletion
* fix document_store fixture
* test fix; shouldn't be merged
* fix test_normalize_embeddings_diff_shapes
* probably a better fix
* fix for parameter combinations
* revert new pytest_generate_tests functionality
* simplify pytest_generate_tests
* normalize embeddings for test_dpr_embedding
* add to faiss doc that embeddings are normalized
* Add latest docstring and tutorial changes
* remove unnecessary parameters and add comments
* simplify two lines of memory.py into one
* test similarity scores with smaller language model
* fix test_similarity_score
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* Aliasing the join is not sufficient yet
* Update the filter query in some other functions of SQLDocumentStore - this functionality should be centralized
* Adding tests for get_all_documents, now failing
* Fix tests
* Fix typo spotted by mypy
* Added uniform normalization method to each of the DocStores (implemented), so that now Milvus and Weaviate doc stores can use cosine similarity, plus future method for making existing embeddings normaziled (empty for now).
* Fixed a typo.
* Fixed lots of stuff. Performed local tests.
* Fixed scores representation for cosine. Assuming Weavieate's rep needs no change.
* fixes as per discussion
* Trigger CI
* resolving conflicts
* small typo
* fixed a param type
* cleaned some conflicts resolving left overs
* commented out fastmath for a moment
* fixing tests
* added docstore for small vectors
* test
* fixed document_store_cosine_small
* cosine tests fixes
* fixed document_store_cosine_small
* fixed weaviate index name and lowered rtol for ES
* increased rtol
* added explicit doc_ids for weaviate, excluded ES, included Inmemory
* resolving mismatch
* fixing a typo
* flatten normalize_embedding()
* fix import for test
* standardize normalize_embeddings across doc stores
* Add latest docstring and tutorial changes
* going for the faster plain dot prod
Co-authored-by: fingoldo <fingoldo@gmail.com>
Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* Feat: Removing use of temp file while downloading archive from url along with adding CI for windows and mac platform
* Windows CI by default installing pytorch gpu hence updating CI to pick cpu version
* fixing mac cache build issue
* updating windows pip install command for torch
* another attempt
* updating ci
* Adding sudo
* fixing ls failure on windows
* another attempt to fix build issue
* Saving env variable of test files
* Adding debug log
* Github action differ on windows
* adding debug
* anohter attempt
* Windows have different ways to receive env
* fixing template
* minor fx
* Adding debug
* Removing use of json
* Adding back fromJson
* addin toJson
* removing print
* anohter attempt
* disabling parallel run at least for testing
* installing docker for mac runner
* correcting docker install command
* Linux dockers are not suported in windows
* Removing mac changes
* Upgrading pytorch
* using lts pytorch
* Separating win and ubuntu
* Install java 11
* enabling linux container env
* docker cli command
* docker cli command
* start elastic service
* List all service
* correcting service name
* Attempt to fix multiple test run
* convert to json
* another attempt to check
* Updating build cache step
* attempt
* Add tika
* Separating windows CI
* Changing CI name
* Skipping test which does not work in windows
* Skipping tests for windows
* create cleanup function in conftest
* adding skipif marker on tests
* Run windows PR on only push to master
* Addressing review comments
* Enabling windows ci for this PR
* Tika init is being called when importing tika function
* handling tika import issue
* handling tika import issue in test
* Fixing import issue
* removing tika fixure
* Removing fixture from tests
* Disable windows ci on pull request
* Add back extra pytorch install step
Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
* 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
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* Add node names validation
* Add tests
* Improve test and test that params exists before validating
* Fix the REST API
* Use minilm-uncased-squad2 instead of roberta-base-squad2
* Use roberta model for test_pipeline.yaml
* Turn off TOKENIZERS_PARALLELISM in generator tests (#1605)
* Account for non-targeted parameters
* Restore previous parameters handling in the rest api
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Julian Risch <julian.risch@deepset.ai>
* Make InMemoryDocumentStore accept and apply filters in delete_documents()
* Modify test_document_store.py to test the filtered deletion in memory, sql and milvus too
* Make FAISSDocumentStore accept and properly apply filters in delete_documents()
* Add latest docstring and tutorial changes
* Remove accidentally duplicated test
* Remove unnecessary decorators from test/test_document_store.py::test_delete_documents_with_filters
* Add embeddings count test for FAISS and Milvus; Milvus fails it.
* Fixed a bug that made Milvus not deleting embeddings
* Remove batch size parametrization in tests & update all documentstore's docstrings with a filter example
* Add latest docstring and tutorial changes
Co-authored-by: prafgup <prafulgupta6@gmail.com>
* Saves the FAISSDocumentStore init params to JSON at save() and loads them at load() if they're found. First draft, to be tested.
* Fixing issue with string/Path objects in a few string operations, thanks mypy
* Leverage self.set_config instead of saving the parameters in a separate attribute
* Modify test_faiss_and_milvus:test_faiss_index_save_and_load to test that init params are preserved
* Add assert to verify that the SQL doc count and FAISS vector count is equal. Needs to always specify the name of the SQL db for this to work
* Simplified the implementation a bit, add better comments
* Forgot a return at the end of the file
* Fixing some of the suggestions from the review
* Add a try-catch in the load method and fix the tests
* Typo
* feat: normalize embeddings for cosine sim
* WIP add test case for faiss cosine
* input to faiss normalize needs to be an array of vectors
* fix: test should compare correct result embedding to original embedding
* add sanity check for cosine sim
* fix typo
* normalize cosine score
* Update docstring
Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
* [UPDT] delete_all_documents() replaced by delete_documents()
* [UPDT] warning logs to be fixed
* [UPDT] delete_all_documents() renamed and the same method added
Co-authored-by: Ram Garg <ramgarg102@gmai.com>
