* [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>
* Removing probability field from reader and from test cases
* Add switch to FARMReader to choose score/probability
* Remove probability field from doc returned by doc store
* Relax assertion testing joined es and dpr predictions
* Use switch for confidence scores also for no_answer
* Add test that checks switching to old answer scores > 10
* Normalize score in elastic doc store and reset reader.md
* Scale weights of JoinDocuments to sum to 1 and adapt test case
* Add FARM classification node
* Add classification output to meta field of document
* Update usage example
* Add test case for FARMClassifier
* Replace FARMRanker with FARMClassifier in documentation strings
* Remove base method not implemented by any child class, etc.
* [pipeline] Allow for batch indexing when using Pipelines fix#1168
* [pipeline] Test case fixed fix#1168
* [file_converter] Path.suffix updated #1168
* [file_converter] meta can be one of these three cases:
A single dict that is applied to all files
One dict for each file being converted
None #1168
* [file_converter] mypy error fixed.
* [file_converter] mypy error fixed.
* [rest_api] batch file upload introduced in indexing API.
* [test_case] Test_api file upload parameter name updated.
* [ui] Streamlit file upload parameter updated.
* Annotation Tool: data is not persisted when using local version #853
* First version of weaviate
* First version of weaviate
* First version of weaviate
* Updated comments
* Updated comments
* ran query, get and write tests
* update embeddings, dynamic schema and filters implemented
* Initial set of tests and fixes
* Tests added for update_embeddings and delete documents
* introduced duplicate documents fix
* fixed mypy errors
* Added Weaviate to requirements
* Fix the weaviate docker env variables
* Fixing test dependencies for now
* Created weaviate test marker and fixed query
* Update docstring
* Add documentation
* Bump up weaviate version
* Bump up weaviate version in documentation
* Bump up weaviate version in documentation
* Updgrade weaviate version
Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
* Fix duplicate question in Reader.eval()
* Add duplicate question support in document store
* Support duplicate questions in retriever eval
* Update tutorial
* Rename key_tuple
* Change error message
* Add warning when more than 6 labels
* Allow for label grouping options
* Add support for aggregating by label meta
* Satisfy mypy
* Fix duplicate question in Reader.eval()
* Add duplicate question support in document store
* Support duplicate questions in retriever eval
* Update tutorial
* Rename key_tuple
* Change error message
* Add warning when more than 6 labels
* Allow for label grouping options
* Add support for aggregating by label meta
* Satisfy mypy
* Make label field flexible, add docstrings
* Satisfy mypy
* Fix failing tests
* Adjust docstring
* Fix tutorial
Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
* Adding ranker similar to retriever and reader
* Sort documents according to query-document similarity scores
* Reranking and model training runs for small example
* Added EvalRanker node
* Calculate recall@k in EvalRetriever and EvalRanker nodes
* Renaming EvalRetriever to EvalDocuments and EvalReader to EvalAnswers
* Added mean reciprocal rank as metric for EvalDocuments
* Fix bug that appeared when ranking documents with same score
* Remove commented code for unimplmented eval() of Ranker node
* Add documentation of k parameter in EvalDocuments
* Add Ranker docu and renaming top_k param
* [document_stores]Add the progressbar in update_embeddings() to track the overall documents progress closed#1037
* change 2nd level loop to docs. switch to tqdm.auto.
* [document_stores] Elasticsearch new method get_document_without_embedding_count() added.
* [test_case] Elasticsearch documentstore get_document_without_embedding_count() test case added.
* [document_stores] Add new bool arg in get_document_count() method and fixed#1082
* [document_stores] typo fixed#1082
Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
* using text hash as id to prevent document duplication. Also providing a way customize it.
* Add latest docstring and tutorial changes
* Fixing duplicate value test when text is same
* Adding test for duplicate ids in document store
* Changing exception to generic Exception type
* add exception for inmemory. update docstring Document. remove id_hash_keys from object attribute
* Add latest docstring and tutorial changes
* Add latest docstring and tutorial changes
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
* Allow filtering of duplicate answers as implemented in FARM
* Changed default behavior to filtering exact duplicates
* Change expected test result due to filtering of duplicate answers by default
* Rounding expected test results for comparison with predictions
* Add knowledge graph module
* Fix type hint
* Add graph retriver module
* Change type annotations, change return format
* Add graph retriever that executes questions as sparql queries
* Linking only those entities that are in the knowledge graph
* Added logging and using relations extracted from Knowledge graph for linking
* Preventing entity linking from linking the same token to multiple entities
* Pruning triples that have no variables for select and count queries
* Support knowledge graphs with Pipelines
* Add text2sparql
* Entity linking and relation linking consider more special cases now based on evaluation on labelled data
* Separating example code from KGQA implementation
* Add eval on combined extarctive and kg questions
* Remove references to hp-test
* Add fields sparql_query and long_answer_list to metadata
* Removing modular Question2SPARQL approach
* Removing additional classes used for modular kgqa approach
* preparing lcquad data
* change graph db
* Translating namespaces in knowledge graph queries
* Creating graphdb index and loading triples from .ttl file
* Fetching graph config files, triples and model from S3
* Fix incompatibility issues with BaseGraphRetriever and BaseComponent
* Removing unused utility functions
* Adding doc strings and tutorial header
* Adding sparqlwrapper dependency
* Moving tutorial header
* Sorting tutorials by number within name of notebook
* Add latest docstring and tutorial changes
* Creating test cases for knowledge graph
* Changing knowledge graph example to harry potter
* Add latest docstring and tutorial changes
* Adapting the tutorial notebook to harry potter example
* Add GraphDB fixture for tests
* Add latest docstring and tutorial changes
* Added GraphDB docker launch to CI
* Use correct GraphDB fixture
* Check if GraphDB instance is already running
* Renaming question/query and incorporating other feedback from Timo and Tanay
* Removed type annotation
* Add latest docstring and tutorial changes
Co-authored-by: oryx1729 <oryx1729@protonmail.com>
Co-authored-by: Timo Moeller <timo.moeller@deepset.ai>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* Adding translator with many generic input parameter support
* Making dict_key as generic
* Fixing mypy issue
* Adding pipeline and using opus models
* Add latest docstring and tutorial changes
* Adding test cases for end-to-end translation for generator, summerizer etc
* raise error join and merge nodes
* Fix test failure
* add docstrings. add usage documentation. rm skip_special_tokens param
* Add latest docstring and tutorial changes
* fix code snippets in md
* Adding few extra configuration parameters and fixing tests
* Fixingmypy issue and updating usage document
* fix for mypy issue in pipeline.py
* reverting renaming of pytest_collection_modifyitems method
* Addressing review comments
* setting skip_special_tokens to True
* removing model_max_length argument as None type is not supported to many models
* Removing padding parameter. Better to leave it as default otherwise it cause tensor size miss match error. If this option required by used then it can be added later.
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
