Add RouteDocuments and JoinAnswers nodes

docs/_src/tutorials/tutorials/15.md

@@ -38,8 +38,9 @@ Make sure you enable the GPU runtime to experience decent speed in this tutorial
 # The TaPAs-based TableReader requires the torch-scatter library
 !pip install torch-scatter -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
 
-# If you run this notebook on Google Colab, you might need to
-# restart the runtime after installing haystack.
+# Install pygraphviz for visualization of Pipelines
+!apt install libgraphviz-dev
+!pip install pygraphviz
 ```
 
 ### Start an Elasticsearch server
@@ -94,7 +95,7 @@ Just as text passages, tables are represented as `Document` objects in Haystack.
 from haystack.utils import fetch_archive_from_http
 
 doc_dir = "data"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/ottqa_tables_sample.json.zip"
+s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/ottqa_sample.zip"
 fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
 ```
 
@@ -246,6 +247,101 @@ prediction = table_qa_pipeline.run("How many twin buildings are under constructi
 print_answers(prediction, details="minimum")
 ```
 
+# Open-Domain QA on Text and Tables
+With haystack, you not only have the possibility to do QA on texts or tables, solely, but you can also use both texts and tables as your source of information.
+
+To demonstrate this, we add 1,000 sample text passages from the OTT-QA dataset.
+
+
+```python
+# Add 1,000 text passages from OTT-QA to our document store.
+
+
+def read_ottqa_texts(filename):
+    processed_passages = []
+    with open(filename) as passages:
+        passages = json.load(passages)
+        for title, content in passages.items():
+            title = title[6:]
+            title = title.replace("_", " ")
+            document = Document(content=content, content_type="text", meta={"title": title})
+            processed_passages.append(document)
+
+    return processed_passages
+
+
+passages = read_ottqa_texts(f"{doc_dir}/ottqa_texts_sample.json")
+document_store.write_documents(passages, index=document_index)
+```
+
+
+```python
+document_store.update_embeddings(retriever=retriever, update_existing_embeddings=False)
+```
+
+## Pipeline for QA on Combination of Text and Tables
+We are using one node for retrieving both texts and tables, the `TableTextRetriever`. In order to do question-answering on the Documents coming from the `TableTextRetriever`, we need to route Documents of type `"text"` to a `FARMReader` (or alternatively `TransformersReader`) and Documents of type `"table"` to a `TableReader`.
+
+To achieve this, we make use of two additional nodes:
+- `SplitDocumentList`: Splits the List of Documents retrieved by the `TableTextRetriever` into two lists containing only Documents of type `"text"` or `"table"`, respectively.
+- `JoinAnswers`: Takes Answers coming from two different Readers (in this case `FARMReader` and `TableReader`) and joins them to a single list of Answers.
+
+
+```python
+from haystack.nodes import FARMReader, SplitDocumentList, JoinAnswers
+
+text_reader = FARMReader("deepset/roberta-base-squad2")
+# In order to get meaningful scores from the TableReader, use "deepset/tapas-large-nq-hn-reader" or
+# "deepset/tapas-large-nq-reader" as TableReader models. The disadvantage of these models is, however,
+# that they are not capable of doing aggregations over multiple table cells.
+table_reader = TableReader("deepset/tapas-large-nq-hn-reader")
+split_documents = SplitDocumentList()
+join_answers = JoinAnswers()
+```
+
+
+```python
+text_table_qa_pipeline = Pipeline()
+text_table_qa_pipeline.add_node(component=retriever, name="TableTextRetriever", inputs=["Query"])
+text_table_qa_pipeline.add_node(component=split_documents, name="SplitDocumentList", inputs=["TableTextRetriever"])
+text_table_qa_pipeline.add_node(component=text_reader, name="TextReader", inputs=["SplitDocumentList.output_1"])
+text_table_qa_pipeline.add_node(component=table_reader, name="TableReader", inputs=["SplitDocumentList.output_2"])
+text_table_qa_pipeline.add_node(component=join_answers, name="JoinAnswers", inputs=["TextReader", "TableReader"])
+```
+
+
+```python
+# Let's have a look on the structure of the combined Table an Text QA pipeline.
+from IPython import display
+
+text_table_qa_pipeline.draw()
+display.Image("pipeline.png")
+```
+
+
+```python
+# Example query whose answer resides in a text passage
+predictions = text_table_qa_pipeline.run(query="Who is Aleksandar Trifunovic?")
+```
+
+
+```python
+# We can see both text passages and tables as contexts of the predicted answers.
+print_answers(predictions, details="minimum")
+```
+
+
+```python
+# Example query whose answer resides in a table
+predictions = text_table_qa_pipeline.run(query="What is Cuba's national tree?")
+```
+
+
+```python
+# We can see both text passages and tables as contexts of the predicted answers.
+print_answers(predictions, details="minimum")
+```
+
 ## About us
 
 This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany

haystack/__init__.py

@@ -102,7 +102,7 @@ def __getattr__(self, attr):
 
 from haystack.modeling.evaluation import eval
 from haystack.modeling.logger import MLFlowLogger, StdoutLogger, TensorBoardLogger
-from haystack.nodes.other import JoinDocuments, Docs2Answers
+from haystack.nodes.other import JoinDocuments, Docs2Answers, JoinAnswers, SplitDocumentList
 from haystack.nodes.query_classifier import SklearnQueryClassifier, TransformersQueryClassifier
 from haystack.nodes.file_classifier import FileTypeClassifier
 from haystack.utils import preprocessing

haystack/nodes/__init__.py

@@ -21,7 +21,7 @@
     AzureConverter,
     ParsrConverter,
 )
-from haystack.nodes.other import Docs2Answers, JoinDocuments
+from haystack.nodes.other import Docs2Answers, JoinDocuments, SplitDocumentList, JoinAnswers
 from haystack.nodes.preprocessor import BasePreProcessor, PreProcessor
 from haystack.nodes.query_classifier import SklearnQueryClassifier, TransformersQueryClassifier
 from haystack.nodes.question_generator import QuestionGenerator

haystack/nodes/other/__init__.py

@@ -1,2 +1,4 @@
 from haystack.nodes.other.docs2answers import Docs2Answers
 from haystack.nodes.other.join_docs import JoinDocuments
+from haystack.nodes.other.split_documents import SplitDocumentList
+from haystack.nodes.other.join_answers import JoinAnswers

haystack/nodes/other/join_answers.py

@@ -0,0 +1,63 @@
+from typing import Optional, List, Dict, Tuple
+
+from haystack.schema import Answer
+from haystack.nodes import BaseComponent
+
+
+class JoinAnswers(BaseComponent):
+    """
+    A node to join `Answer`s produced by multiple `Reader` nodes.
+    """
+
+    def __init__(
+        self, join_mode: str = "concatenate", weights: Optional[List[float]] = None, top_k_join: Optional[int] = None
+    ):
+        """
+        :param join_mode: `"concatenate"` to combine documents from multiple `Reader`s. `"merge"` to aggregate scores
+        of individual `Answer`s.
+        :param weights: A node-wise list (length of list must be equal to the number of input nodes) of weights for
+            adjusting `Answer` scores when using the `"merge"` join_mode. By default, equal weight is assignef to each
+            `Reader` score. This parameter is not compatible with the `"concatenate"` join_mode.
+        :param top_k_join: Limit `Answer`s to top_k based on the resulting scored of the join.
+        """
+
+        assert join_mode in ["concatenate", "merge"], f"JoinAnswers node does not support '{join_mode}' join_mode."
+        assert not (
+            weights is not None and join_mode == "concatenate"
+        ), "Weights are not compatible with 'concatenate' join_mode"
+
+        # Save init parameters to enable export of component config as YAML
+        self.set_config(join_mode=join_mode, weights=weights, top_k_join=top_k_join)
+
+        self.join_mode = join_mode
+        self.weights = [float(i) / sum(weights) for i in weights] if weights else None
+        self.top_k_join = top_k_join
+
+    def run(self, inputs: List[Dict], top_k_join: Optional[int] = None) -> Tuple[Dict, str]:  # type: ignore
+        reader_results = [inp["answers"] for inp in inputs]
+
+        if self.join_mode == "concatenate":
+            concatenated_answers = [answer for cur_reader_result in reader_results for answer in cur_reader_result]
+            concatenated_answers = sorted(concatenated_answers, reverse=True)
+            return {"answers": concatenated_answers, "labels": inputs[0].get("labels", None)}, "output_1"
+
+        elif self.join_mode == "merge":
+            merged_answers = self._merge_answers(reader_results)
+
+            if not top_k_join:
+                top_k_join = self.top_k_join if self.top_k_join is not None else len(merged_answers)
+            merged_answers = merged_answers[:top_k_join]
+            return {"answers": merged_answers, "labels": inputs[0].get("labels", None)}, "output_1"
+
+        else:
+            raise ValueError(f"Invalid join_mode: {self.join_mode}")
+
+    def _merge_answers(self, reader_results: List[List[Answer]]) -> List[Answer]:
+        weights = self.weights if self.weights else [1 / len(reader_results)] * len(reader_results)
+
+        for result, weight in zip(reader_results, weights):
+            for answer in result:
+                if isinstance(answer.score, float):
+                    answer.score *= weight
+
+        return sorted([answer for cur_reader_result in reader_results for answer in cur_reader_result], reverse=True)

haystack/nodes/other/split_documents.py

@@ -0,0 +1,70 @@
+from typing import List, Tuple, Dict, Optional
+
+from haystack.nodes.base import BaseComponent
+from haystack.schema import Document
+
+
+class SplitDocumentList(BaseComponent):
+    """
+    A node to split a list of `Document`s by `content_type` or by the values of a metadata field.
+    """
+
+    # By default (split_by == "content_type"), the node has two outgoing edges.
+    outgoing_edges = 2
+
+    def __init__(self, split_by: str = "content_type", metadata_values: Optional[List[str]] = None):
+        """
+        :param split_by: Field to split the documents by. Either `"content_type"` or a metadata field name.
+            If this parameter is set to `"content_type"`, the list of `Document`s will be split into a list containing
+            only `Document`s of type `"text"` (will be routed to `"output_1"`) and a list containing only `Document`s of
+            type `"text"` (will be routed to `"output_2"`).
+            If this parameter is set to a metadata field name, you need to specify the parameter `metadata_values` as
+            well.
+        :param metadata_values: If the parameter `split_by` is set to a metadata field name, you need to provide a list
+            of values to group the `Document`s to. `Document`s whose metadata field is equal to the first value of the
+            provided list will be routed to `"output_1"`, `Document`s whose metadata field is equal to the second
+            value of the provided list will be routed to `"output_2"`, etc.
+        """
+
+        assert split_by == "content_type" or metadata_values is not None, (
+            "If split_by is set to the name of a metadata field, you must provide metadata_values "
+            "to group the documents to."
+        )
+
+        # Save init parameters to enable export of component config as YAML
+        self.set_config(split_by=split_by, metadata_values=metadata_values)
+
+        self.split_by = split_by
+        self.metadata_values = metadata_values
+
+        # If we split list of Documents by a metadata field, number of outgoing edges might change
+        if split_by != "content_type" and metadata_values is not None:
+            self.outgoing_edges = len(metadata_values)
+
+    def run(self, documents: List[Document]) -> Tuple[Dict, str]:  # type: ignore
+        if self.split_by == "content_type":
+            split_documents: Dict[str, List[Document]] = {"output_1": [], "output_2": []}
+
+            for doc in documents:
+                if doc.content_type == "text":
+                    split_documents["output_1"].append(doc)
+                elif doc.content_type == "table":
+                    split_documents["output_2"].append(doc)
+
+        else:
+            assert isinstance(self.metadata_values, list), "You need to provide metadata_values if you want to split" \
+                                                           " a list of Documents by a metadata field."
+            split_documents = {f"output_{i+1}": [] for i in range(len(self.metadata_values))}
+            for doc in documents:
+                current_metadata_value = doc.meta.get(self.split_by, None)
+                # Disregard current document if it does not contain the provided metadata field
+                if current_metadata_value is not None:
+                    try:
+                        index = self.metadata_values.index(current_metadata_value)
+                    except ValueError:
+                        # Disregard current document if current_metadata_value is not in the provided metadata_values
+                        continue
+
+                    split_documents[f"output_{index+1}"].append(doc)
+
+        return split_documents, "split_documents"

haystack/pipelines/base.py

@@ -645,28 +645,38 @@ def run(  # type: ignore
                         f"Exception while running node `{node_id}` with input `{node_input}`: {e}, full stack trace: {tb}"
                     )
                 queue.pop(node_id)
-                next_nodes = self.get_next_nodes(node_id, stream_id)
-                for n in next_nodes:  # add successor nodes with corresponding inputs to the queue
-                    if queue.get(n):  # concatenate inputs if it's a join node
-                        existing_input = queue[n]
-                        if "inputs" not in existing_input.keys():
-                            updated_input: dict = {"inputs": [existing_input, node_output], "params": params}
-                            if query:
-                                updated_input["query"] = query
-                            if file_paths:
-                                updated_input["file_paths"] = file_paths
-                            if labels:
-                                updated_input["labels"] = labels
-                            if documents:
-                                updated_input["documents"] = documents
-                            if meta:
-                                updated_input["meta"] = meta
+                #
+                if stream_id == "split_documents":
+                    for stream_id in [key for key in node_output.keys() if key.startswith("output_")]:
+                        current_node_output = {k: v for k, v in node_output.items() if not k.startswith("output_")}
+                        current_docs = node_output.pop(stream_id)
+                        current_node_output["documents"] = current_docs
+                        next_nodes = self.get_next_nodes(node_id, stream_id)
+                        for n in next_nodes:
+                            queue[n] = current_node_output
+                else:
+                    next_nodes = self.get_next_nodes(node_id, stream_id)
+                    for n in next_nodes:  # add successor nodes with corresponding inputs to the queue
+                        if queue.get(n):  # concatenate inputs if it's a join node
+                            existing_input = queue[n]
+                            if "inputs" not in existing_input.keys():
+                                updated_input: dict = {"inputs": [existing_input, node_output], "params": params}
+                                if query:
+                                    updated_input["query"] = query
+                                if file_paths:
+                                    updated_input["file_paths"] = file_paths
+                                if labels:
+                                    updated_input["labels"] = labels
+                                if documents:
+                                    updated_input["documents"] = documents
+                                if meta:
+                                    updated_input["meta"] = meta
+                            else:
+                                existing_input["inputs"].append(node_output)
+                                updated_input = existing_input
+                            queue[n] = updated_input
                         else:
-                            existing_input["inputs"].append(node_output)
-                            updated_input = existing_input
-                        queue[n] = updated_input
-                    else:
-                        queue[n] = node_output
+                            queue[n] = node_output
                 i = 0
             else:
                 i += 1  # attempt executing next node in the queue as current `node_id` has unprocessed predecessors