Refactor pipeline for better generalizability & Add TransformersReader

haystack/__init__.py

@@ -38,6 +38,7 @@ def get_answers(self, question, top_k_reader=1, top_k_retriever=10, filters=None
         :return:
         """
 
+        # 1) Optional: reduce the search space via document tags
         if filters:
             query = """
                 SELECT id FROM document WHERE id in (
@@ -61,49 +62,15 @@ def get_answers(self, question, top_k_reader=1, top_k_retriever=10, filters=None
         else:
             candidate_doc_ids = None
 
-        retrieved_scores = self.retriever.retrieve(question, top_k=top_k_retriever)
+        # 2) Apply retriever to get fast candidate paragraphs
+        paragraphs, meta_data = self.retriever.retrieve(question, top_k=top_k_retriever, candidate_doc_ids=candidate_doc_ids)
 
-        inference_dicts = self._convert_retrieved_text_to_reader_format(
-            retrieved_scores, question, candidate_doc_ids=candidate_doc_ids
-        )
-        results = self.reader.predict(inference_dicts, top_k=top_k_reader)
-        return results["results"]
+        # 3) Apply reader to get granular answer(s)
+        logger.info(f"Applying the reader now to look for the answer in detail ...")
+        results = self.reader.predict(question=question,
+                                      paragrahps=paragraphs,
+                                      meta_data_paragraphs=meta_data,
+                                      top_k=top_k_reader)
+
+        return results
 
-    def _convert_retrieved_text_to_reader_format(
-        self, retrieved_scores, question, candidate_doc_ids=None, verbose=True
-    ):
-        """
-        The reader expect the input as:
-        {
-            "text": "FARM is a home for all species of pretrained language models (e.g. BERT) that can be adapted to 
-            different domain languages or down-stream tasks. With FARM you can easily create SOTA NLP models for tasks 
-            like document classification, NER or question answering.",
-            "document_id": 127,
-            "questions" : ["What can you do with FARM?"]
-        }
-
-        :param retrieved_scores: tfidf scores as returned by the retriever
-        :param question: question string
-        :param verbose: enable verbose logging
-        """
-        df_sliced = self.retriever.df.loc[retrieved_scores.keys()]
-        if verbose:
-            logger.info(
-                f"Identified {df_sliced.shape[0]} candidates via retriever:\n {df_sliced.to_string(col_space=10, index=False)}"
-            )
-            logger.info(
-                f"Applying the reader now to look for the answer in detail ..."
-                )
-        inference_dicts = []
-        for idx, row in df_sliced.iterrows():
-            if candidate_doc_ids and row["document_id"] not in candidate_doc_ids:
-                continue
-            inference_dicts.append(
-                {
-                    "text": row["text"],
-                    "document_id": row["document_id"],
-                    "questions": [question],
-                }
-            )
-
-        return inference_dicts

haystack/indexing/io.py

@@ -9,15 +9,28 @@
 logger = logging.getLogger(__name__)
 
 
-def write_documents_to_db(document_dir, clean_func=None):
+def write_documents_to_db(document_dir, clean_func=None, only_empty_db=False):
     """
     Write all text files(.txt) in the sub-directories of the given path to the connected database.
 
     :param document_dir: path for the documents to be written to the database
-    :return:
+    :param clean_func: a custom cleaning function that gets applied to each doc (input: str, output:str)
+    :param only_empty_db: If true, docs will only be written if db is completely empty.
+                              Useful to avoid indexing the same initial docs again and again.
+    :return: None
     """
     file_paths = Path(document_dir).glob("**/*.txt")
     n_docs = 0
+
+    # check if db has already docs
+    if only_empty_db:
+        n_docs = db.session.query(Document).count()
+        if n_docs > 0:
+            logger.info(f"Skip writing documents since DB already contains {n_docs} docs ...  "
+                        "(Disable `only_empty_db`, if you want to add docs anyway.)")
+            return None
+
+    # read and add docs
     for path in file_paths:
         with open(path) as doc:
             text = doc.read()

haystack/reader/farm.py

@@ -18,6 +18,7 @@ def __init__(
         no_answer_shift=-100,
         batch_size=16,
         use_gpu=True,
+        n_best_per_passage=2
     ):
         """
         Load a saved FARM model in Inference mode.
@@ -27,56 +28,77 @@ def __init__(
         self.model = Inferencer.load(model_dir, batch_size=batch_size, gpu=use_gpu)
         self.model.model.prediction_heads[0].context_size = context_size
         self.model.model.prediction_heads[0].no_answer_shift = no_answer_shift
+        self.model.model.prediction_heads[0].n_best = n_best_per_passage
 
-    def predict(self, input_dicts, top_k=None):
+
+    def predict(self, question, paragrahps, meta_data_paragraphs=None, top_k=None, max_processes=1):
         """
-        Run inference on the loaded model for the given input dicts.
+        Use loaded QA model to find answers for a question in the supplied paragraphs.
+
+        Returns dictionaries containing answers sorted by (desc.) probability
+        Example:
+        {'question': 'Who is the father of Arya Stark?',
+        'answers': [
+                     {'answer': 'Eddard,',
+                     'context': " She travels with her father, Eddard, to King's Landing when he is ",
+                     'offset_answer_start': 147,
+                     'offset_answer_end': 154,
+                     'probability': 0.9787139466668613,
+                     'score': None,
+                     'document_id': None
+                     },
+                    ...
+                   ]
+        }
 
-        :param input_dicts: list of input dicts
+        :param question: question string
+        :param paragraphs: list of strings in which to search for the answer
+        :param meta_data_paragraphs: list of dicts containing meta data for the paragraphs.
+                                     len(paragraphs) == len(meta_data_paragraphs)
         :param top_k: the maximum number of answers to return
-        :return:
+        :param max_processes: max number of parallel processes
+        :return: dict containing question and answers
         """
-        results = self.model.inference_from_dicts(
-            dicts=input_dicts, rest_api_schema=True, use_multiprocessing=False
+
+        if meta_data_paragraphs is None:
+            meta_data_paragraphs = len(paragrahps) * [None]
+        assert len(paragrahps) == len(meta_data_paragraphs)
+
+        # convert input to FARM format
+        input_dicts = []
+        for paragraph, meta_data in zip(paragrahps, meta_data_paragraphs):
+            cur = {"text": paragraph,
+                   "questions": [question],
+                   "document_id": meta_data["document_id"]
+            }
+            input_dicts.append(cur)
+
+        # get answers from QA model (Top 5 per input paragraph)
+        predictions = self.model.inference_from_dicts(
+            dicts=input_dicts, rest_api_schema=True, max_processes=max_processes
         )
 
-        # The FARM Inferencer as of now do not support multi document QA.
-        # The QA inference is done for each text independently and the
-        # results are sorted descending by their `score`.
-
-        all_predictions = []
-        for res in results:
-            all_predictions.extend(res["predictions"])
-
-        all_answers = []
-        for pred in all_predictions:
-            answers = pred["answers"]
-            for a in answers:
-                # Two sets of offset fields are returned by FARM -- context level and document level.
-                # For the API, only context level offsets are relevant.
-                a["offset_start"] = a["offset_answer_start"] - a["offset_context_start"]
-                a["offset_end"] = a["offset_context_end"] - a["offset_answer_end"]
-            all_answers.extend(answers)
-
-        # remove all null answers (where an answers in not found in the text)
-        all_answers = [ans for ans in all_answers if ans["answer"]]
-
-        scores = np.asarray([ans["score"] for ans in all_answers])
-        probabilities = expit(scores / 8)
-        for ans, prob in zip(all_answers, probabilities):
-            ans["probability"] = prob
-
-        # sort answers by their `probability`
-        sorted_answers = sorted(
-            all_answers, key=lambda k: k["probability"], reverse=True
+        # assemble answers from all the different paragraphs & format them
+        answers = []
+        for pred in predictions:
+            for a in pred["predictions"][0]["answers"]:
+                if a["answer"]: #skip "no answer"
+                    cur = {"answer": a["answer"],
+                           "score": a["score"],
+                           "probability": expit(np.asarray([a["score"]]) / 8), #just a pseudo prob for now
+                           "context": a["context"],
+                           "offset_start": a["offset_answer_start"] - a["offset_context_start"],
+                           "offset_end": a["offset_answer_start"] - a["offset_context_start"],
+                           "document_id": a["document_id"]}
+                    answers.append(cur)
+
+        # sort answers by their `probability` and select top-k
+        answers = sorted(
+            answers, key=lambda k: k["probability"], reverse=True
         )
+        answers = answers[:top_k]
 
-        # all predictions here are for the same questions, so the the metadata from
-        # the first prediction in the list is taken.
-        if all_predictions:
-            resp = all_predictions[0]  # get the first prediction dict
-            resp["answers"] = sorted_answers[:top_k]
-        else:
-            resp = []
+        result = {"question": question,
+                   "answers": answers}
 
-        return {"results": [resp]}
+        return result

haystack/reader/transformers.py

@@ -0,0 +1,102 @@
+from transformers import pipeline
+
+
+class TransformersReader:
+    """
+    A reader using the QA Pipeline class from huggingface's Transformers.
+    Easily load any of the pretrained community QA models from here: https://huggingface.co/models
+    """
+
+    def __init__(
+        self,
+        model="distilbert-base-uncased-distilled-squad",
+        tokenizer="distilbert-base-uncased",
+        context_size=30,
+        #no_answer_shift=-100,
+        #batch_size=16,
+        use_gpu=0,
+        n_best_per_passage=2
+    ):
+        """
+        Load a QA model from Transformers.
+        Available models include:
+        - distilbert-base-uncased-distilled-squad
+        - bert-large-cased-whole-word-masking-finetuned-squad
+        - bert-large-uncased-whole-word-masking-finetuned-squad
+
+        See https://huggingface.co/models for full list of available QA models
+
+        :param model: name of the model
+        :param tokenizer: name of the tokenizer (usually the same as model)
+        :param context_size: num of chars (before and after the answer) to return as "context" for each answer.
+                            The context usually helps users to understand if the answer really makes sense.
+        :param use_gpu: < 1  -> use cpu
+                        >= 1 -> num of gpus to use
+        """
+        self.model = pipeline("question-answering", model=model, tokenizer=tokenizer, device=use_gpu)
+        self.context_size = context_size
+        self.n_best_per_passage = n_best_per_passage
+        #TODO param to modify bias for no_answer
+
+
+    def predict(self, question, paragrahps, meta_data_paragraphs=None, top_k=None):
+        """
+        Use loaded QA model to find answers for a question in the supplied paragraphs.
+
+        Returns dictionaries containing answers sorted by (desc.) probability
+        Example:
+        {'question': 'Who is the father of Arya Stark?',
+        'answers': [
+                     {'answer': 'Eddard,',
+                     'context': " She travels with her father, Eddard, to King's Landing when he is ",
+                     'offset_answer_start': 147,
+                     'offset_answer_end': 154,
+                     'probability': 0.9787139466668613,
+                     'score': None,
+                     'document_id': None
+                     },
+                    ...
+                   ]
+        }
+
+        :param question: question string
+        :param paragraphs: list of strings in which to search for the answer
+        :param meta_data_paragraphs: list of dicts containing meta data for the paragraphs.
+                                     len(paragraphs) == len(meta_data_paragraphs)
+        :param top_k: the maximum number of answers to return
+        :param max_processes: max number of parallel processes
+        :return: dict containing question and answers
+
+        """
+        #TODO pass metadata
+
+        # get top-answers for each candidate passage
+        answers = []
+        for p in paragrahps:
+            query = {"context": p, "question": question}
+            predictions = self.model(query, topk=self.n_best_per_passage)
+            # assemble and format all answers
+            for pred in predictions:
+                if pred["answer"]:
+                    context_start = max(0, pred["start"] - self.context_size)
+                    context_end = min(len(p), pred["end"] + self.context_size)
+                    answers.append({
+                        "answer": pred["answer"],
+                        "context": p[context_start:context_end],
+                        "offset_answer_start": pred["start"],
+                        "offset_answer_end": pred["end"],
+                        "probability": pred["score"],
+                        "score": None,
+                        "document_id": None
+                    })
+
+        # sort answers by their `probability` and select top-k
+        answers = sorted(
+            answers, key=lambda k: k["probability"], reverse=True
+        )
+        answers = answers[:top_k]
+
+        results = {"question": question,
+                   "answers": answers}
+
+        return results

haystack/retriever/tfidf.py

@@ -34,7 +34,7 @@ class TfidfRetriever(BaseRetriever):
     Split documents into smaller units (eg, paragraphs or pages) to reduce the 
     computations when text is passed on to a Reader for QA.
 
-    It uses sklearn TfidfVectorizer to compute a tf-idf matrix.
+    It uses sklearn's TfidfVectorizer to compute a tf-idf matrix.
     """
 
     def __init__(self):
@@ -69,15 +69,37 @@ def _get_all_paragraphs(self):
         logger.info(f"Found {len(paragraphs)} candidate paragraphs from {len(documents)} docs in DB")
         return paragraphs
 
-    def retrieve(self, query, candidate_doc_ids=None, top_k=10):
+    def _calc_scores(self, query):
         question_vector = self.vectorizer.transform([query])
 
         scores = self.tfidf_matrix.dot(question_vector.T).toarray()
         idx_scores = [(idx, score) for idx, score in enumerate(scores)]
-        top_k_scores = OrderedDict(
-            sorted(idx_scores, key=(lambda tup: tup[1]), reverse=True)[:top_k]
+        indices_and_scores = OrderedDict(
+            sorted(idx_scores, key=(lambda tup: tup[1]), reverse=True)
         )
-        return top_k_scores
+        return indices_and_scores
+
+    def retrieve(self, query, candidate_doc_ids=None, top_k=10, verbose=True):
+        # get scores
+        indices_and_scores = self._calc_scores(query)
+
+        # rank & filter paragraphs
+        df_sliced = self.df.loc[indices_and_scores.keys()]
+        if candidate_doc_ids:
+            df_sliced = df_sliced[df_sliced.document_id.isin(candidate_doc_ids)]
+        df_sliced = df_sliced[:top_k]
+
+        if verbose:
+            logger.info(
+                f"Identified {df_sliced.shape[0]} candidates via retriever:\n {df_sliced.to_string(col_space=10, index=False)}"
+            )
+
+        # get actual content for the top candidates
+        paragraphs = list(df_sliced.text.values)
+        meta_data = [{"document_id": row["document_id"], "paragraph_id": row["paragraph_id"]}
+                     for idx, row in df_sliced.iterrows()]
+
+        return paragraphs, meta_data
 
     def fit(self):
         self.df = pd.DataFrame.from_dict(self.paragraphs)

haystack/utils.py

@@ -19,7 +19,7 @@ def create_db():
 
 
 def print_answers(results, details="all"):
-    answers = results[0]["answers"]
+    answers = results["answers"]
     pp = pprint.PrettyPrinter(indent=4)
     if details != "all":
         if details == "minimal":

requirements.txt

@@ -1,4 +1,6 @@
-farm==0.3.2
+# FARM (incl. transformers 2.3.0 with pipelines)
+#farm -e git+https://github.com/deepset-ai/FARM.git@1d30237b037050ef0ac5516f427443cdd18a4d43
+-e git://github.com/deepset-ai/FARM.git@1d30237b037050ef0ac5516f427443cdd18a4d43#egg=farm
 flask
 flask_cors
 flask_restplus