* 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
* 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
* Make batchwise adding of evaluation data possible
* Fix typos in docstrings
* Merge add_eval_data and add_eval_data_batchwise
* Improve import statements
* Move add_eval_data to BaseDocumentStore
* Add batch_size param to write_documents and write_labels in EsDocStore
* Adjust docstring
Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
* Adding dummy generator implementation
* Adding tutorial to try the model
* Committing current non working code
* Committing current update where we need to call generate function directly and need to convert embedding to tensor way
* Addressing review comments.
* Refactoring finder, and implementing rag_generator class.
* Refined the implementation of RAGGenerator and now it is in clean shape
* Renaming RAGGenerator to RAGenerator
* Reverting change from finder.py and addressing review comments
* Remove support for RagSequenceForGeneration
* Utilizing embed_passage function from DensePassageRetriever
* Adding sample test data to verify generator output
* Updating testing script
* Updating testing script
* Fixing bug related to top_k
* Updating latest farm dependency
* Comment out farm dependency
* Reverting changes from TransformersReader
* Adding transformers dataset to compare transformers and haystack generator implementation
* Using generator_encoder instead of question_encoder to generate context_input_ids
* Adding workaround to install FARM dependency from master branch
* Removing unnecessary changes
* Fixing generator test
* Removing transformers datasets
* Fixing generator test
* Some cleanup and updating TODO comments
* Adding tutorial notebook
* Updating tutorials with comments
* Explicitly passing token model in RAG test
* Addressing review comments
* Fixing notebook
* Refactoring tests to reduce memory footprint
* Split generator tests in separate ci step and before running it reclaim memory by terminating containers
* Moving tika dependent test to separate dir
* Remove unwanted code
* Brining reader under session scope
* Farm is now session object hence restoring changes from default value
* Updating assert for pdf converter
* Dummy commit to trigger CI flow
* REducing memory footprint required for generator tests
* Fixing mypy issues
* Marking test with tika and elasticsearch markers. Reverting changes in CI and pytest splits
* reducing changes
* Fixing CI
* changing elastic search ci
* Fixing test error
* Disabling return of embedding
* Marking generator test as well
* Refactoring tutorials
* Increasing ES memory to 750M
* Trying another fix for ES CI
* Reverting CI changes
* Splitting tests in CI
* Generator and non-generator markers split
* Adding pytest.ini to add markers and enable strict-markers option
* Reducing elastic search container memory
* Simplifying generator test by using documents with embedding directly
* Bump up farm to 0.5.0
* 1. Prevent update_embeddings function in FAISSDocumentStore to set faiss_index as None when document store does not have any docs.
2. cleaning up tests by adding fixture for retriever.
* TfidfRetriever need document store with documents during initialization as it call fit() function in constructor so fixing it by checking self.paragraphs of None
* Fix naming of retriever's fixture (embedded to embedding and tfid to tfidf)
