From c5542bd3fbdcd6e211e1686aa289939ed29427f4 Mon Sep 17 00:00:00 2001
From: bogdankostic <bogdankostic@web.de>
Date: Tue, 1 Mar 2022 17:42:11 +0100
Subject: [PATCH] Add `RouteDocuments` and `JoinAnswers` nodes (#2256)

* Add SplitDocumentList and JoinAnswer nodes

* Update Documentation & Code Style

* Add tests + adapt tutorial

* Update Documentation & Code Style

* Remove branch from installation path in Tutorial

* Update Documentation & Code Style

* Fix typing

* Update Documentation & Code Style

* Change name of SplitDocumentList to RouteDocuments

* Update Documentation & Code Style

* Adapt tutorials to new name

* Add test for JoinAnswers

* Update Documentation & Code Style

* Adapt name of test for JoinAnswers node

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
---
 docs/_src/tutorials/tutorials/15.md           | 102 ++-
 haystack/__init__.py                          |   2 +-
 haystack/nodes/__init__.py                    |   2 +-
 haystack/nodes/other/__init__.py              |   2 +
 haystack/nodes/other/join_answers.py          |  64 ++
 haystack/nodes/other/route_documents.py       |  72 ++
 haystack/pipelines/base.py                    |  52 +-
 .../haystack-pipeline-1.2.0.schema.json       |  92 +++
 .../haystack-pipeline-1.2.0rc0.schema.json    |  92 +++
 test/test_pipeline.py                         |  51 +-
 tutorials/Tutorial15_TableQA.ipynb            | 654 +++++++++++++++++-
 tutorials/Tutorial15_TableQA.py               |  33 +-
 12 files changed, 1160 insertions(+), 58 deletions(-)
 create mode 100644 haystack/nodes/other/join_answers.py
 create mode 100644 haystack/nodes/other/route_documents.py

diff --git a/docs/_src/tutorials/tutorials/15.md b/docs/_src/tutorials/tutorials/15.md
index c6bdb6325..b2c606c5f 100644
--- a/docs/_src/tutorials/tutorials/15.md
+++ b/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, RouteDocuments, 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")
+route_documents = RouteDocuments()
+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=route_documents, name="RouteDocuments", inputs=["TableTextRetriever"])
+text_table_qa_pipeline.add_node(component=text_reader, name="TextReader", inputs=["RouteDocuments.output_1"])
+text_table_qa_pipeline.add_node(component=table_reader, name="TableReader", inputs=["RouteDocuments.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
diff --git a/haystack/__init__.py b/haystack/__init__.py
index ae67727cf..f023dc7ef 100644
--- a/haystack/__init__.py
+++ b/haystack/__init__.py
@@ -102,7 +102,7 @@ except ImportError:
 
 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, RouteDocuments
 from haystack.nodes.query_classifier import SklearnQueryClassifier, TransformersQueryClassifier
 from haystack.nodes.file_classifier import FileTypeClassifier
 from haystack.utils import preprocessing
diff --git a/haystack/nodes/__init__.py b/haystack/nodes/__init__.py
index 52f2ae002..bc3d86239 100644
--- a/haystack/nodes/__init__.py
+++ b/haystack/nodes/__init__.py
@@ -21,7 +21,7 @@ from haystack.nodes.file_converter import (
     AzureConverter,
     ParsrConverter,
 )
-from haystack.nodes.other import Docs2Answers, JoinDocuments
+from haystack.nodes.other import Docs2Answers, JoinDocuments, RouteDocuments, JoinAnswers
 from haystack.nodes.preprocessor import BasePreProcessor, PreProcessor
 from haystack.nodes.query_classifier import SklearnQueryClassifier, TransformersQueryClassifier
 from haystack.nodes.question_generator import QuestionGenerator
diff --git a/haystack/nodes/other/__init__.py b/haystack/nodes/other/__init__.py
index 8341a135b..4cfb55cea 100644
--- a/haystack/nodes/other/__init__.py
+++ b/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.route_documents import RouteDocuments
+from haystack.nodes.other.join_answers import JoinAnswers
diff --git a/haystack/nodes/other/join_answers.py b/haystack/nodes/other/join_answers.py
new file mode 100644
index 000000000..e96652dfc
--- /dev/null
+++ b/haystack/nodes/other/join_answers.py
@@ -0,0 +1,64 @@
+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 assigned 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 not top_k_join:
+            top_k_join = self.top_k_join
+
+        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)[:top_k_join]
+            return {"answers": concatenated_answers, "labels": inputs[0].get("labels", None)}, "output_1"
+
+        elif self.join_mode == "merge":
+            merged_answers = self._merge_answers(reader_results)
+
+            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)
diff --git a/haystack/nodes/other/route_documents.py b/haystack/nodes/other/route_documents.py
new file mode 100644
index 000000000..f9ba7e3ed
--- /dev/null
+++ b/haystack/nodes/other/route_documents.py
@@ -0,0 +1,72 @@
+from typing import List, Tuple, Dict, Optional
+
+from haystack.nodes.base import BaseComponent
+from haystack.schema import Document
+
+
+class RouteDocuments(BaseComponent):
+    """
+    A node to split a list of `Document`s by `content_type` or by the values of a metadata field and route them to
+    different nodes.
+    """
+
+    # 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"
diff --git a/haystack/pipelines/base.py b/haystack/pipelines/base.py
index c9321d354..1358f7c88 100644
--- a/haystack/pipelines/base.py
+++ b/haystack/pipelines/base.py
@@ -645,28 +645,38 @@ class Pipeline(BasePipeline):
                         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
diff --git a/json-schemas/haystack-pipeline-1.2.0.schema.json b/json-schemas/haystack-pipeline-1.2.0.schema.json
index d425ee098..ac86f1de8 100644
--- a/json-schemas/haystack-pipeline-1.2.0.schema.json
+++ b/json-schemas/haystack-pipeline-1.2.0.schema.json
@@ -59,6 +59,9 @@
           {
             "$ref": "#/definitions/ImageToTextConverterComponent"
           },
+          {
+            "$ref": "#/definitions/JoinAnswersComponent"
+          },
           {
             "$ref": "#/definitions/JoinDocumentsComponent"
           },
@@ -86,6 +89,9 @@
           {
             "$ref": "#/definitions/RCIReaderComponent"
           },
+          {
+            "$ref": "#/definitions/RouteDocumentsComponent"
+          },
           {
             "$ref": "#/definitions/SentenceTransformersRankerComponent"
           },
@@ -1093,6 +1099,51 @@
       ],
       "additionalProperties": false
     },
+    "JoinAnswersComponent": {
+      "type": "object",
+      "properties": {
+        "name": {
+          "title": "Name",
+          "description": "Custom name for the component. Helpful for visualization and debugging.",
+          "type": "string"
+        },
+        "type": {
+          "title": "Type",
+          "description": "Haystack Class name for the component.",
+          "type": "string",
+          "const": "JoinAnswers"
+        },
+        "params": {
+          "title": "Parameters",
+          "type": "object",
+          "properties": {
+            "join_mode": {
+              "title": "Join Mode",
+              "default": "concatenate",
+              "type": "string"
+            },
+            "weights": {
+              "title": "Weights",
+              "type": "array",
+              "items": {
+                "type": "number"
+              }
+            },
+            "top_k_join": {
+              "title": "Top K Join",
+              "type": "integer"
+            }
+          },
+          "additionalProperties": false,
+          "description": "Each parameter can reference other components defined in the same YAML file."
+        }
+      },
+      "required": [
+        "type",
+        "name"
+      ],
+      "additionalProperties": false
+    },
     "JoinDocumentsComponent": {
       "type": "object",
       "properties": {
@@ -1646,6 +1697,47 @@
       ],
       "additionalProperties": false
     },
+    "RouteDocumentsComponent": {
+      "type": "object",
+      "properties": {
+        "name": {
+          "title": "Name",
+          "description": "Custom name for the component. Helpful for visualization and debugging.",
+          "type": "string"
+        },
+        "type": {
+          "title": "Type",
+          "description": "Haystack Class name for the component.",
+          "type": "string",
+          "const": "RouteDocuments"
+        },
+        "params": {
+          "title": "Parameters",
+          "type": "object",
+          "properties": {
+            "split_by": {
+              "title": "Split By",
+              "default": "content_type",
+              "type": "string"
+            },
+            "metadata_values": {
+              "title": "Metadata Values",
+              "type": "array",
+              "items": {
+                "type": "string"
+              }
+            }
+          },
+          "additionalProperties": false,
+          "description": "Each parameter can reference other components defined in the same YAML file."
+        }
+      },
+      "required": [
+        "type",
+        "name"
+      ],
+      "additionalProperties": false
+    },
     "SentenceTransformersRankerComponent": {
       "type": "object",
       "properties": {
diff --git a/json-schemas/haystack-pipeline-1.2.0rc0.schema.json b/json-schemas/haystack-pipeline-1.2.0rc0.schema.json
index 520d8ee0c..4387b996b 100644
--- a/json-schemas/haystack-pipeline-1.2.0rc0.schema.json
+++ b/json-schemas/haystack-pipeline-1.2.0rc0.schema.json
@@ -59,6 +59,9 @@
           {
             "$ref": "#/definitions/ImageToTextConverterComponent"
           },
+          {
+            "$ref": "#/definitions/JoinAnswersComponent"
+          },
           {
             "$ref": "#/definitions/JoinDocumentsComponent"
           },
@@ -95,6 +98,9 @@
           {
             "$ref": "#/definitions/SklearnQueryClassifierComponent"
           },
+          {
+            "$ref": "#/definitions/SplitDocumentListComponent"
+          },
           {
             "$ref": "#/definitions/TableReaderComponent"
           },
@@ -1093,6 +1099,51 @@
       ],
       "additionalProperties": false
     },
+    "JoinAnswersComponent": {
+      "type": "object",
+      "properties": {
+        "name": {
+          "title": "Name",
+          "description": "Custom name for the component. Helpful for visualization and debugging.",
+          "type": "string"
+        },
+        "type": {
+          "title": "Type",
+          "description": "Haystack Class name for the component.",
+          "type": "string",
+          "const": "JoinAnswers"
+        },
+        "params": {
+          "title": "Parameters",
+          "type": "object",
+          "properties": {
+            "join_mode": {
+              "title": "Join Mode",
+              "default": "concatenate",
+              "type": "string"
+            },
+            "weights": {
+              "title": "Weights",
+              "type": "array",
+              "items": {
+                "type": "number"
+              }
+            },
+            "top_k_join": {
+              "title": "Top K Join",
+              "type": "integer"
+            }
+          },
+          "additionalProperties": false,
+          "description": "Each parameter can reference other components defined in the same YAML file."
+        }
+      },
+      "required": [
+        "type",
+        "name"
+      ],
+      "additionalProperties": false
+    },
     "JoinDocumentsComponent": {
       "type": "object",
       "properties": {
@@ -1836,6 +1887,47 @@
       ],
       "additionalProperties": false
     },
+    "SplitDocumentListComponent": {
+      "type": "object",
+      "properties": {
+        "name": {
+          "title": "Name",
+          "description": "Custom name for the component. Helpful for visualization and debugging.",
+          "type": "string"
+        },
+        "type": {
+          "title": "Type",
+          "description": "Haystack Class name for the component.",
+          "type": "string",
+          "const": "SplitDocumentList"
+        },
+        "params": {
+          "title": "Parameters",
+          "type": "object",
+          "properties": {
+            "split_by": {
+              "title": "Split By",
+              "default": "content_type",
+              "type": "string"
+            },
+            "metadata_values": {
+              "title": "Metadata Values",
+              "type": "array",
+              "items": {
+                "type": "string"
+              }
+            }
+          },
+          "additionalProperties": false,
+          "description": "Each parameter can reference other components defined in the same YAML file."
+        }
+      },
+      "required": [
+        "type",
+        "name"
+      ],
+      "additionalProperties": false
+    },
     "TableReaderComponent": {
       "type": "object",
       "properties": {
diff --git a/test/test_pipeline.py b/test/test_pipeline.py
index 4299076c4..1b216e8f2 100644
--- a/test/test_pipeline.py
+++ b/test/test_pipeline.py
@@ -3,10 +3,12 @@ from pathlib import Path
 import os
 import json
 from unittest.mock import Mock
+
+import pandas as pd
 import pytest
 import responses
 
-from haystack import __version__
+from haystack import __version__, Document, Answer, JoinAnswers
 from haystack.document_stores.base import BaseDocumentStore
 from haystack.document_stores.deepsetcloud import DeepsetCloudDocumentStore
 from haystack.document_stores.elasticsearch import ElasticsearchDocumentStore
@@ -17,7 +19,7 @@ from haystack.nodes.retriever.base import BaseRetriever
 from haystack.nodes.retriever.sparse import ElasticsearchRetriever
 from haystack.pipelines import Pipeline, DocumentSearchPipeline, RootNode, ExtractiveQAPipeline
 from haystack.pipelines.base import _PipelineCodeGen
-from haystack.nodes import DensePassageRetriever, EmbeddingRetriever
+from haystack.nodes import DensePassageRetriever, EmbeddingRetriever, RouteDocuments
 
 from conftest import MOCK_DC, DC_API_ENDPOINT, DC_API_KEY, DC_TEST_INDEX, SAMPLES_PATH, deepset_cloud_fixture
 
@@ -1041,6 +1043,51 @@ def test_documentsearch_document_store_authentication(retriever_with_docs, docum
         assert kwargs["headers"] == auth_headers
 
 
+def test_route_documents_by_content_type():
+    # Test routing by content_type
+    docs = [
+        Document(content="text document", content_type="text"),
+        Document(
+            content=pd.DataFrame(columns=["col 1", "col 2"], data=[["row 1", "row 1"], ["row 2", "row 2"]]),
+            content_type="table",
+        ),
+    ]
+
+    route_documents = RouteDocuments()
+    result, _ = route_documents.run(documents=docs)
+    assert len(result["output_1"]) == 1
+    assert len(result["output_2"]) == 1
+    assert result["output_1"][0].content_type == "text"
+    assert result["output_2"][0].content_type == "table"
+
+
+def test_route_documents_by_metafield(test_docs_xs):
+    # Test routing by metadata field
+    docs = [Document.from_dict(doc) if isinstance(doc, dict) else doc for doc in test_docs_xs]
+    route_documents = RouteDocuments(split_by="meta_field", metadata_values=["test1", "test3", "test5"])
+    result, _ = route_documents.run(docs)
+    assert len(result["output_1"]) == 1
+    assert len(result["output_2"]) == 1
+    assert len(result["output_3"]) == 1
+    assert result["output_1"][0].meta["meta_field"] == "test1"
+    assert result["output_2"][0].meta["meta_field"] == "test3"
+    assert result["output_3"][0].meta["meta_field"] == "test5"
+
+
+@pytest.mark.parametrize("join_mode", ["concatenate", "merge"])
+def test_join_answers(join_mode):
+    inputs = [{"answers": [Answer(answer="answer 1", score=0.7)]}, {"answers": [Answer(answer="answer 2", score=0.8)]}]
+
+    join_answers = JoinAnswers(join_mode=join_mode)
+    result, _ = join_answers.run(inputs)
+    assert len(result["answers"]) == 2
+    assert result["answers"] == sorted(result["answers"], reverse=True)
+
+    result, _ = join_answers.run(inputs, top_k_join=1)
+    assert len(result["answers"]) == 1
+    assert result["answers"][0].answer == "answer 2"
+
+
 def clean_faiss_document_store():
     if Path("existing_faiss_document_store").exists():
         os.remove("existing_faiss_document_store")
diff --git a/tutorials/Tutorial15_TableQA.ipynb b/tutorials/Tutorial15_TableQA.ipynb
index b29637ea6..d448118ce 100644
--- a/tutorials/Tutorial15_TableQA.ipynb
+++ b/tutorials/Tutorial15_TableQA.ipynb
@@ -57,8 +57,9 @@
     "# The TaPAs-based TableReader requires the torch-scatter library\n",
     "!pip install torch-scatter -f https://data.pyg.org/whl/torch-1.10.0+cu113.html\n",
     "\n",
-    "# If you run this notebook on Google Colab, you might need to\n",
-    "# restart the runtime after installing haystack."
+    "# Install pygraphviz for visualization of Pipelines\n",
+    "!apt install libgraphviz-dev\n",
+    "!pip install pygraphviz"
    ]
   },
   {
@@ -87,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {
     "id": "S4PGj1A6wKWu"
    },
@@ -151,24 +152,24 @@
     "from haystack.utils import fetch_archive_from_http\n",
     "\n",
     "doc_dir = \"data\"\n",
-    "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/ottqa_tables_sample.json.zip\"\n",
+    "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/ottqa_sample.zip\"\n",
     "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "SKjw2LuXxlGh",
-    "outputId": "c24f8ca0-1a58-44ea-f01d-414db4c8f1f4"
+    "outputId": "92c67d24-d6fb-413e-8dd7-53075141d508"
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "      Result  ...            Score\n",
       "0     Winner  ...        6-1 , 6-1\n",
@@ -276,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "id": "XM-ijy6Zz11L"
    },
@@ -289,18 +290,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "YHfQWxVI0N2e",
-    "outputId": "05976ac9-bee3-4eb8-b36d-01f1db5250db"
+    "outputId": "1d8dc4d2-a184-489e-defa-d445d76c458f"
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "                             Name  ...              Status\n",
       "0                  Twin Towers II  ...         Never built\n",
@@ -364,18 +365,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "ILuAXkyN4F7x",
-    "outputId": "7bdb7190-fcf8-4296-c237-cffc78dac4aa"
+    "outputId": "4bd19dcb-df8e-4a4d-b9d2-d34650e9e5c2"
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "                             Name  ...              Status\n",
       "0                  Twin Towers II  ...         Never built\n",
@@ -412,20 +413,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "ilbsecgA4vfN",
-    "outputId": "5f4e8f0b-bc9e-485b-c933-546fcad2b411"
+    "outputId": "f845f43e-43e8-48fe-d0ef-91b17a5eff0e"
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "{   'answers': [   Answer(answer='12', type='extractive', score=1.0, context=                             Name  ...              Status\n",
+      "\n",
+      "Query: How many twin buildings are under construction?\n",
+      "Answers:\n",
+      "[   <Answer {'answer': '12', 'type': 'extractive', 'score': 1.0, 'context':                              Name  ...              Status\n",
       "0                  Twin Towers II  ...         Never built\n",
       "1              World Trade Center  ...           Destroyed\n",
       "2                Three Sixty West  ...  Under construction\n",
@@ -447,8 +451,7 @@
       "18          Oberoi Esquire Towers  ...  Under construction\n",
       "19                Bhoomi Celestia  ...  Under construction\n",
       "\n",
-      "[20 rows x 6 columns], offsets_in_document=[Span(start=12, end=13), Span(start=18, end=19), Span(start=24, end=25), Span(start=42, end=43), Span(start=48, end=49), Span(start=54, end=55), Span(start=78, end=79), Span(start=84, end=85), Span(start=90, end=91), Span(start=102, end=103), Span(start=108, end=109), Span(start=114, end=115)], offsets_in_context=[Span(start=12, end=13), Span(start=18, end=19), Span(start=24, end=25), Span(start=42, end=43), Span(start=48, end=49), Span(start=54, end=55), Span(start=78, end=79), Span(start=84, end=85), Span(start=90, end=91), Span(start=102, end=103), Span(start=108, end=109), Span(start=114, end=115)], document_id='List_of_tallest_twin_buildings_and_structures_in_the_world_1', meta={'aggregation_operator': 'COUNT', 'answer_cells': ['Three Sixty West', 'Gateway Towers', 'Rustomjee Crown', 'Lokhandwala Minerva', 'Lamar Towers', 'Indonesia One Towers', 'India Bulls Sky Forest Tower', 'Capital Towers', 'One Avighna Park', 'The Destiny ( Tower )', 'Oberoi Esquire Towers', 'Bhoomi Celestia']})],\n",
-      "    'query': 'How many twin buildings are under construction?'}\n"
+      "[20 rows x 6 columns], 'offsets_in_document': [{'start': 12, 'end': 13}, {'start': 18, 'end': 19}, {'start': 24, 'end': 25}, {'start': 42, 'end': 43}, {'start': 48, 'end': 49}, {'start': 54, 'end': 55}, {'start': 78, 'end': 79}, {'start': 84, 'end': 85}, {'start': 90, 'end': 91}, {'start': 102, 'end': 103}, {'start': 108, 'end': 109}, {'start': 114, 'end': 115}], 'offsets_in_context': [{'start': 12, 'end': 13}, {'start': 18, 'end': 19}, {'start': 24, 'end': 25}, {'start': 42, 'end': 43}, {'start': 48, 'end': 49}, {'start': 54, 'end': 55}, {'start': 78, 'end': 79}, {'start': 84, 'end': 85}, {'start': 90, 'end': 91}, {'start': 102, 'end': 103}, {'start': 108, 'end': 109}, {'start': 114, 'end': 115}], 'document_id': 'List_of_tallest_twin_buildings_and_structures_in_the_world_1', 'meta': {'aggregation_operator': 'COUNT', 'answer_cells': ['Three Sixty West', 'Gateway Towers', 'Rustomjee Crown', 'Lokhandwala Minerva', 'Lamar Towers', 'Indonesia One Towers', 'India Bulls Sky Forest Tower', 'Capital Towers', 'One Avighna Park', 'The Destiny ( Tower )', 'Oberoi Esquire Towers', 'Bhoomi Celestia']}}>]\n"
      ]
     }
    ],
@@ -472,18 +475,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "It8XYT2ZTVJs",
-    "outputId": "5bd712a0-9f22-4fc0-a4f1-b01b15cb9916"
+    "outputId": "7d31af60-e04a-485d-f0ee-f29592b03928"
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "Predicted answer: 12\n",
       "Meta field: {'aggregation_operator': 'COUNT', 'answer_cells': ['Three Sixty West', 'Gateway Towers', 'Rustomjee Crown', 'Lokhandwala Minerva', 'Lamar Towers', 'Indonesia One Towers', 'India Bulls Sky Forest Tower', 'Capital Towers', 'One Avighna Park', 'The Destiny ( Tower )', 'Oberoi Esquire Towers', 'Bhoomi Celestia']}\n"
@@ -509,7 +512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
     "id": "G-aZZvyv4-Mf"
    },
@@ -525,19 +528,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "m8evexnW6dev",
-    "outputId": "290168b1-294e-42ed-c970-e5ddfefb3396"
+    "outputId": "40514084-f516-4f13-fb48-6a55cb578366"
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
+      "\n",
+      "Query: How many twin buildings are under construction?\n",
+      "Answers:\n",
       "[   {   'answer': '12',\n",
       "        'context':                              Name  ...              Status\n",
       "0                  Twin Towers II  ...         Never built\n",
@@ -717,6 +723,596 @@
     "print_answers(prediction, details=\"minimum\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Open-Domain QA on Text and Tables\n",
+    "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.\n",
+    "\n",
+    "To demonstrate this, we add 1,000 sample text passages from the OTT-QA dataset."
+   ],
+   "metadata": {
+    "id": "8uMzl9Ml_D1B"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Add 1,000 text passages from OTT-QA to our document store.\n",
+    "\n",
+    "\n",
+    "def read_ottqa_texts(filename):\n",
+    "    processed_passages = []\n",
+    "    with open(filename) as passages:\n",
+    "        passages = json.load(passages)\n",
+    "        for title, content in passages.items():\n",
+    "            title = title[6:]\n",
+    "            title = title.replace(\"_\", \" \")\n",
+    "            document = Document(content=content, content_type=\"text\", meta={\"title\": title})\n",
+    "            processed_passages.append(document)\n",
+    "\n",
+    "    return processed_passages\n",
+    "\n",
+    "\n",
+    "passages = read_ottqa_texts(f\"{doc_dir}/ottqa_texts_sample.json\")\n",
+    "document_store.write_documents(passages, index=document_index)"
+   ],
+   "metadata": {
+    "id": "4CBcIjIq_uFx"
+   },
+   "execution_count": 18,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "document_store.update_embeddings(retriever=retriever, update_existing_embeddings=False)"
+   ],
+   "metadata": {
+    "id": "j1TaNF7SiKgH"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Pipeline for QA on Combination of Text and Tables\n",
+    "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`.\n",
+    "\n",
+    "To achieve this, we make use of two additional nodes:\n",
+    "- `SplitDocumentList`: Splits the List of Documents retrieved by the `TableTextRetriever` into two lists containing only Documents of type `\"text\"` or `\"table\"`, respectively.\n",
+    "- `JoinAnswers`: Takes Answers coming from two different Readers (in this case `FARMReader` and `TableReader`) and joins them to a single list of Answers."
+   ],
+   "metadata": {
+    "id": "c2sk_uNHj0DY"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "from haystack.nodes import FARMReader, RouteDocuments, JoinAnswers\n",
+    "\n",
+    "text_reader = FARMReader(\"deepset/roberta-base-squad2\")\n",
+    "# In order to get meaningful scores from the TableReader, use \"deepset/tapas-large-nq-hn-reader\" or\n",
+    "# \"deepset/tapas-large-nq-reader\" as TableReader models. The disadvantage of these models is, however,\n",
+    "# that they are not capable of doing aggregations over multiple table cells.\n",
+    "table_reader = TableReader(\"deepset/tapas-large-nq-hn-reader\")\n",
+    "route_documents = RouteDocuments()\n",
+    "join_answers = JoinAnswers()"
+   ],
+   "metadata": {
+    "id": "Ej_j8Q3wlxXE"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "text_table_qa_pipeline = Pipeline()\n",
+    "text_table_qa_pipeline.add_node(component=retriever, name=\"TableTextRetriever\", inputs=[\"Query\"])\n",
+    "text_table_qa_pipeline.add_node(component=route_documents, name=\"RouteDocuments\", inputs=[\"TableTextRetriever\"])\n",
+    "text_table_qa_pipeline.add_node(component=text_reader, name=\"TextReader\", inputs=[\"RouteDocuments.output_1\"])\n",
+    "text_table_qa_pipeline.add_node(component=table_reader, name=\"TableReader\", inputs=[\"RouteDocuments.output_2\"])\n",
+    "text_table_qa_pipeline.add_node(component=join_answers, name=\"JoinAnswers\", inputs=[\"TextReader\", \"TableReader\"])"
+   ],
+   "metadata": {
+    "id": "Zdq6JnF5m3aP"
+   },
+   "execution_count": 21,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Let's have a look on the structure of the combined Table an Text QA pipeline.\n",
+    "from IPython import display\n",
+    "\n",
+    "text_table_qa_pipeline.draw()\n",
+    "display.Image(\"pipeline.png\")"
+   ],
+   "metadata": {
+    "id": "K4vH1ZEnniut",
+    "outputId": "85aa17a8-227d-40e4-c8c0-5d0532faa47a",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 540
+    }
+   },
+   "execution_count": 22,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Example query whose answer resides in a text passage\n",
+    "predictions = text_table_qa_pipeline.run(query=\"Who is Aleksandar Trifunovic?\")"
+   ],
+   "metadata": {
+    "id": "strPNduPoBLe"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# We can see both text passages and tables as contexts of the predicted answers.\n",
+    "print_answers(predictions, details=\"minimum\")"
+   ],
+   "metadata": {
+    "id": "9YiK75tSoOGA",
+    "outputId": "bd52f841-3846-441f-dd6f-53b02111691e",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    }
+   },
+   "execution_count": 24,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "\n",
+      "Query: Who is Aleksandar Trifunovic?\n",
+      "Answers:\n",
+      "[   {   'answer': 'a Serbian professional basketball coach and former player',\n",
+      "        'context': 'Aleksandar Trifunović ( ; born 30 May 1967 ) is a Serbian '\n",
+      "                   'professional basketball coach and former player .'},\n",
+      "    {   'answer': 'Johnny Höglin',\n",
+      "        'context':    Rank              Athlete        Country    Time\n",
+      "0     1         Kees Verkerk    Netherlands  2:03.4\n",
+      "1     2         Ivar Eriksen         Norway  2:05.0\n",
+      "2     3           Ard Schenk    Netherlands  2:05.0\n",
+      "3     4      Magne Thomassen         Norway  2:05.1\n",
+      "4     5        Johnny Höglin         Sweden  2:05.2\n",
+      "5     5         Bjørn Tveter         Norway  2:05.2\n",
+      "6     7  Svein-Erik Stiansen         Norway  2:05.5\n",
+      "7     8    Eduard Matusevich   Soviet Union  2:06.1\n",
+      "8     9         Peter Nottet    Netherlands  2:06.3\n",
+      "9    10        Örjan Sandler         Sweden  2:07.0\n",
+      "10   11  Aleksandr Kerchenko   Soviet Union  2:07.1\n",
+      "11   12          Ants Antson   Soviet Union  2:07.2\n",
+      "12   12        Valery Kaplan   Soviet Union  2:07.2\n",
+      "13   14       Jouko Launonen        Finland  2:07.5\n",
+      "14   15         Günter Traub   West Germany  2:07.7\n",
+      "15   16             Jan Bols    Netherlands  2:07.8\n",
+      "16   16          Manne Lavås         Sweden  2:07.8\n",
+      "17   18       Kimmo Koskinen        Finland  2:07.9\n",
+      "18   19      Richard Wurster  United States  2:08.4\n",
+      "19   20        Göran Claeson         Sweden  2:08.6},\n",
+      "    {   'answer': 'Ivar Eriksen',\n",
+      "        'context':    Rank              Athlete        Country    Time\n",
+      "0     1         Kees Verkerk    Netherlands  2:03.4\n",
+      "1     2         Ivar Eriksen         Norway  2:05.0\n",
+      "2     3           Ard Schenk    Netherlands  2:05.0\n",
+      "3     4      Magne Thomassen         Norway  2:05.1\n",
+      "4     5        Johnny Höglin         Sweden  2:05.2\n",
+      "5     5         Bjørn Tveter         Norway  2:05.2\n",
+      "6     7  Svein-Erik Stiansen         Norway  2:05.5\n",
+      "7     8    Eduard Matusevich   Soviet Union  2:06.1\n",
+      "8     9         Peter Nottet    Netherlands  2:06.3\n",
+      "9    10        Örjan Sandler         Sweden  2:07.0\n",
+      "10   11  Aleksandr Kerchenko   Soviet Union  2:07.1\n",
+      "11   12          Ants Antson   Soviet Union  2:07.2\n",
+      "12   12        Valery Kaplan   Soviet Union  2:07.2\n",
+      "13   14       Jouko Launonen        Finland  2:07.5\n",
+      "14   15         Günter Traub   West Germany  2:07.7\n",
+      "15   16             Jan Bols    Netherlands  2:07.8\n",
+      "16   16          Manne Lavås         Sweden  2:07.8\n",
+      "17   18       Kimmo Koskinen        Finland  2:07.9\n",
+      "18   19      Richard Wurster  United States  2:08.4\n",
+      "19   20        Göran Claeson         Sweden  2:08.6},\n",
+      "    {   'answer': 'Magne Thomassen',\n",
+      "        'context':    Rank              Athlete        Country    Time\n",
+      "0     1         Kees Verkerk    Netherlands  2:03.4\n",
+      "1     2         Ivar Eriksen         Norway  2:05.0\n",
+      "2     3           Ard Schenk    Netherlands  2:05.0\n",
+      "3     4      Magne Thomassen         Norway  2:05.1\n",
+      "4     5        Johnny Höglin         Sweden  2:05.2\n",
+      "5     5         Bjørn Tveter         Norway  2:05.2\n",
+      "6     7  Svein-Erik Stiansen         Norway  2:05.5\n",
+      "7     8    Eduard Matusevich   Soviet Union  2:06.1\n",
+      "8     9         Peter Nottet    Netherlands  2:06.3\n",
+      "9    10        Örjan Sandler         Sweden  2:07.0\n",
+      "10   11  Aleksandr Kerchenko   Soviet Union  2:07.1\n",
+      "11   12          Ants Antson   Soviet Union  2:07.2\n",
+      "12   12        Valery Kaplan   Soviet Union  2:07.2\n",
+      "13   14       Jouko Launonen        Finland  2:07.5\n",
+      "14   15         Günter Traub   West Germany  2:07.7\n",
+      "15   16             Jan Bols    Netherlands  2:07.8\n",
+      "16   16          Manne Lavås         Sweden  2:07.8\n",
+      "17   18       Kimmo Koskinen        Finland  2:07.9\n",
+      "18   19      Richard Wurster  United States  2:08.4\n",
+      "19   20        Göran Claeson         Sweden  2:08.6},\n",
+      "    {   'answer': '5',\n",
+      "        'context':   Position   #                 Player          Moving from\n",
+      "0        F  12         Nikola Kalinić  Radnički Kragujevac\n",
+      "1       SF   6       Nemanja Dangubić          Mega Vizura\n",
+      "2        C  33            Maik Zirbes        Brose Baskets\n",
+      "3       PG   3        Marcus Williams      Lokomotiv Kuban\n",
+      "4       PG  24           Stefan Jović  Radnički Kragujevac\n",
+      "5        C  14       Đorđe Kaplanović                  FMP\n",
+      "6       SF   5        Nikola Čvorović                  FMP\n",
+      "7       SG   7  Aleksandar Aranitović    Crvena zvezda U18\n",
+      "8       SG  20         Aleksa Radanov    Crvena zvezda U18},\n",
+      "    {   'answer': 'Vasile Sărucan',\n",
+      "        'context':    Rank                Name     Nationality Result\n",
+      "0     1    Hans Baumgartner    West Germany   8.12\n",
+      "1     2  Igor Ter-Ovanesyan    Soviet Union   7.91\n",
+      "2     3      Vasile Sărucan         Romania   7.88\n",
+      "3     4       Valeriu Jurcă         Romania   7.72\n",
+      "4     5   Philippe Housiaux         Belgium   7.70\n",
+      "5     6   Andreas Gloerfeld    West Germany   7.70\n",
+      "6     7     Jan Kobuszewski          Poland   7.66\n",
+      "7     8       Jaroslav Brož  Czechoslovakia   7.66\n",
+      "8     9        Alan Lerwill   Great Britain   7.61\n",
+      "9    10     Mikhail Bariban    Soviet Union   7.58\n",
+      "10   11  Valeriy Podluzhniy    Soviet Union   7.54\n",
+      "11   12         Kari Palmen         Finland   7.51\n",
+      "12   13        Georgi Marin        Bulgaria   7.51\n",
+      "13   14      Jesper Tørring         Denmark   7.46\n",
+      "14   15    Milan Spasojević      Yugoslavia   7.23\n",
+      "15   16        Salih Mercan          Turkey   6.98\n",
+      "16   17     Henrik Kalocsai         Hungary   5.67},\n",
+      "    {   'answer': 'Belgium',\n",
+      "        'context':    Rank                Name     Nationality Result\n",
+      "0     1    Hans Baumgartner    West Germany   8.12\n",
+      "1     2  Igor Ter-Ovanesyan    Soviet Union   7.91\n",
+      "2     3      Vasile Sărucan         Romania   7.88\n",
+      "3     4       Valeriu Jurcă         Romania   7.72\n",
+      "4     5   Philippe Housiaux         Belgium   7.70\n",
+      "5     6   Andreas Gloerfeld    West Germany   7.70\n",
+      "6     7     Jan Kobuszewski          Poland   7.66\n",
+      "7     8       Jaroslav Brož  Czechoslovakia   7.66\n",
+      "8     9        Alan Lerwill   Great Britain   7.61\n",
+      "9    10     Mikhail Bariban    Soviet Union   7.58\n",
+      "10   11  Valeriy Podluzhniy    Soviet Union   7.54\n",
+      "11   12         Kari Palmen         Finland   7.51\n",
+      "12   13        Georgi Marin        Bulgaria   7.51\n",
+      "13   14      Jesper Tørring         Denmark   7.46\n",
+      "14   15    Milan Spasojević      Yugoslavia   7.23\n",
+      "15   16        Salih Mercan          Turkey   6.98\n",
+      "16   17     Henrik Kalocsai         Hungary   5.67},\n",
+      "    {   'answer': 'Poland',\n",
+      "        'context':    Rank                Name     Nationality Result\n",
+      "0     1    Hans Baumgartner    West Germany   8.12\n",
+      "1     2  Igor Ter-Ovanesyan    Soviet Union   7.91\n",
+      "2     3      Vasile Sărucan         Romania   7.88\n",
+      "3     4       Valeriu Jurcă         Romania   7.72\n",
+      "4     5   Philippe Housiaux         Belgium   7.70\n",
+      "5     6   Andreas Gloerfeld    West Germany   7.70\n",
+      "6     7     Jan Kobuszewski          Poland   7.66\n",
+      "7     8       Jaroslav Brož  Czechoslovakia   7.66\n",
+      "8     9        Alan Lerwill   Great Britain   7.61\n",
+      "9    10     Mikhail Bariban    Soviet Union   7.58\n",
+      "10   11  Valeriy Podluzhniy    Soviet Union   7.54\n",
+      "11   12         Kari Palmen         Finland   7.51\n",
+      "12   13        Georgi Marin        Bulgaria   7.51\n",
+      "13   14      Jesper Tørring         Denmark   7.46\n",
+      "14   15    Milan Spasojević      Yugoslavia   7.23\n",
+      "15   16        Salih Mercan          Turkey   6.98\n",
+      "16   17     Henrik Kalocsai         Hungary   5.67},\n",
+      "    {   'answer': 'Hafþór Júlíus Björnsson',\n",
+      "        'context':    #                     Name    Nationality   Pts\n",
+      "0  1  Hafþór Júlíus Björnsson        Iceland  31.5\n",
+      "1  2            Robert Oberst  United States    29\n",
+      "2  3               Lauri Nami        Estonia    24\n",
+      "3  4                Nick Best  United States  14.5\n",
+      "4  5        Laurence Shahlaei             UK    12\n",
+      "5  6                  Wu Long          China     6},\n",
+      "    {   'answer': 'Estonia',\n",
+      "        'context':    #                     Name    Nationality   Pts\n",
+      "0  1  Hafþór Júlíus Björnsson        Iceland  31.5\n",
+      "1  2            Robert Oberst  United States    29\n",
+      "2  3               Lauri Nami        Estonia    24\n",
+      "3  4                Nick Best  United States  14.5\n",
+      "4  5        Laurence Shahlaei             UK    12\n",
+      "5  6                  Wu Long          China     6},\n",
+      "    {   'answer': 'Iceland',\n",
+      "        'context':    #                     Name    Nationality   Pts\n",
+      "0  1  Hafþór Júlíus Björnsson        Iceland  31.5\n",
+      "1  2            Robert Oberst  United States    29\n",
+      "2  3               Lauri Nami        Estonia    24\n",
+      "3  4                Nick Best  United States  14.5\n",
+      "4  5        Laurence Shahlaei             UK    12\n",
+      "5  6                  Wu Long          China     6},\n",
+      "    {   'answer': 'Egor Antropov ( born May 8 , 1992 ) is a Russian '\n",
+      "                  'professional ice hockey defenceman',\n",
+      "        'context': 'Egor Antropov ( born May 8 , 1992 ) is a Russian '\n",
+      "                   'professional ice hockey defenceman . He is currently '\n",
+      "                   'playing with Piráti Chomutov of the Czech Extral'},\n",
+      "    {   'answer': 'Zurab Magomedovich Yevloyev ( ; born February 20 , 1980 ) '\n",
+      "                  'is a Russian professional football player',\n",
+      "        'context': 'Zurab Magomedovich Yevloyev ( ; born February 20 , 1980 ) '\n",
+      "                   'is a Russian professional football player . In 2010 , he '\n",
+      "                   'played for FC Angusht Nazran in the'}]\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Example query whose answer resides in a table\n",
+    "predictions = text_table_qa_pipeline.run(query=\"What is Cuba's national tree?\")"
+   ],
+   "metadata": {
+    "id": "QYOHDSmLpzEg"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# We can see both text passages and tables as contexts of the predicted answers.\n",
+    "print_answers(predictions, details=\"minimum\")"
+   ],
+   "metadata": {
+    "id": "4kw53uWep3zj",
+    "outputId": "b332cc17-3cb8-4e20-d79d-bb4cf656f277",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    }
+   },
+   "execution_count": 26,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "\n",
+      "Query: What is Cuba's national tree?\n",
+      "Answers:\n",
+      "[   {   'answer': 'Cuban royal palm',\n",
+      "        'context':                 Country  ...                               Scientific name\n",
+      "0           Afghanistan  ...                                              \n",
+      "1               Albania  ...                                 Olea europaea\n",
+      "2   Antigua and Barbuda  ...                                Bucida buceras\n",
+      "3             Argentina  ...  Erythrina crista-galli , Schinopsis balansae\n",
+      "4             Australia  ...                              Acacia pycnantha\n",
+      "5               Bahamas  ...                              Guaiacum sanctum\n",
+      "6            Bangladesh  ...                              Mangifera indica\n",
+      "7                Belize  ...                         Swietenia macrophylla\n",
+      "8                Bhutan  ...                         Cupressus cashmeriana\n",
+      "9                Brazil  ...                          Caesalpinia echinata\n",
+      "10             Cambodia  ...                          Borassus flabellifer\n",
+      "11               Canada  ...                                          Acer\n",
+      "12                Chile  ...                            Araucaria araucana\n",
+      "13             Colombia  ...                         Ceroxylon quindiuense\n",
+      "14           Costa Rica  ...                      Enterolobium cyclocarpum\n",
+      "15              Croatia  ...                                 Quercus robur\n",
+      "16                 Cuba  ...                               Roystonea regia\n",
+      "17               Cyprus  ...                             Quercus alnifolia\n",
+      "18       Czech Republic  ...                                 Tilia cordata\n",
+      "19              Denmark  ...                               Fagus sylvatica\n",
+      "\n",
+      "[20 rows x 3 columns]},\n",
+      "    {   'answer': 'Quercus sagraeana , the Cuban oak',\n",
+      "        'context': 'Quercus sagraeana , the Cuban oak , is a medium-sized '\n",
+      "                   'evergreen tree native to western Cuba in the Cuban pine '\n",
+      "                   'forests ecoregion .'},\n",
+      "    {   'answer': \"Glenn O'Brien\",\n",
+      "        'context':                                                                       Book title  ...                                                                            Notes\n",
+      "0                                                                            Sex  ...  The book contains erotica influenced photographs taken by Steven Meisel and ...\n",
+      "1                                                      Madonna : The Girlie Show  ...  The photographs in the book showcased behind-the-scenes of the 1993 Girlie S...\n",
+      "2                                                            The Making of Evita  ...  Featuring an introduction by Madonna , The Making of Evita chronicles the cr...\n",
+      "3   The Emperor 's New Clothes : An All-Star Retelling of the Classic Fairy Tale  ...  This fully illustrated retelling of the classic fairy tale by Hans Christian...\n",
+      "4                                                               X-Static Process  ...  In 2002 , Madonna had collaborated with photographer Steven Klein for an art...\n",
+      "5                                                                Nobody Knows Me  ...  Available for one month only via Madonna 's official website . Contained 52 ...\n",
+      "6                                                            Madonna Confessions  ...  Behind-the-scenes and on-stage pictures from Madonna 's 2006 Confessions Tou...\n",
+      "7                                                            I Am Because We Are  ...  The book contains excerpts from interviews with Malawian children , their bi...\n",
+      "8                                                       Madonna : Sticky & Sweet  ...  Behind-the-scenes and on-stage photography from Madonna 's Sticky & Sweet To...\n",
+      "9                                                                      Tom Munro  ...  Munro 's self-titled first monograph book consists of photographs taken by h...\n",
+      "10                     Mayumi 's Kitchen : Macrobiotic Cooking for Body and Soul  ...  Mayumi Nishimura worked as Madonna 's private chef for seven years , and she...\n",
+      "\n",
+      "[11 rows x 6 columns]},\n",
+      "    {   'answer': 'Guy Oseary',\n",
+      "        'context':                                                                       Book title  ...                                                                            Notes\n",
+      "0                                                                            Sex  ...  The book contains erotica influenced photographs taken by Steven Meisel and ...\n",
+      "1                                                      Madonna : The Girlie Show  ...  The photographs in the book showcased behind-the-scenes of the 1993 Girlie S...\n",
+      "2                                                            The Making of Evita  ...  Featuring an introduction by Madonna , The Making of Evita chronicles the cr...\n",
+      "3   The Emperor 's New Clothes : An All-Star Retelling of the Classic Fairy Tale  ...  This fully illustrated retelling of the classic fairy tale by Hans Christian...\n",
+      "4                                                               X-Static Process  ...  In 2002 , Madonna had collaborated with photographer Steven Klein for an art...\n",
+      "5                                                                Nobody Knows Me  ...  Available for one month only via Madonna 's official website . Contained 52 ...\n",
+      "6                                                            Madonna Confessions  ...  Behind-the-scenes and on-stage pictures from Madonna 's 2006 Confessions Tou...\n",
+      "7                                                            I Am Because We Are  ...  The book contains excerpts from interviews with Malawian children , their bi...\n",
+      "8                                                       Madonna : Sticky & Sweet  ...  Behind-the-scenes and on-stage photography from Madonna 's Sticky & Sweet To...\n",
+      "9                                                                      Tom Munro  ...  Munro 's self-titled first monograph book consists of photographs taken by h...\n",
+      "10                     Mayumi 's Kitchen : Macrobiotic Cooking for Body and Soul  ...  Mayumi Nishimura worked as Madonna 's private chef for seven years , and she...\n",
+      "\n",
+      "[11 rows x 6 columns]},\n",
+      "    {   'answer': 'Guy Oseary',\n",
+      "        'context':                                                                       Book title  ...                                                                            Notes\n",
+      "0                                                                            Sex  ...  The book contains erotica influenced photographs taken by Steven Meisel and ...\n",
+      "1                                                      Madonna : The Girlie Show  ...  The photographs in the book showcased behind-the-scenes of the 1993 Girlie S...\n",
+      "2                                                            The Making of Evita  ...  Featuring an introduction by Madonna , The Making of Evita chronicles the cr...\n",
+      "3   The Emperor 's New Clothes : An All-Star Retelling of the Classic Fairy Tale  ...  This fully illustrated retelling of the classic fairy tale by Hans Christian...\n",
+      "4                                                               X-Static Process  ...  In 2002 , Madonna had collaborated with photographer Steven Klein for an art...\n",
+      "5                                                                Nobody Knows Me  ...  Available for one month only via Madonna 's official website . Contained 52 ...\n",
+      "6                                                            Madonna Confessions  ...  Behind-the-scenes and on-stage pictures from Madonna 's 2006 Confessions Tou...\n",
+      "7                                                            I Am Because We Are  ...  The book contains excerpts from interviews with Malawian children , their bi...\n",
+      "8                                                       Madonna : Sticky & Sweet  ...  Behind-the-scenes and on-stage photography from Madonna 's Sticky & Sweet To...\n",
+      "9                                                                      Tom Munro  ...  Munro 's self-titled first monograph book consists of photographs taken by h...\n",
+      "10                     Mayumi 's Kitchen : Macrobiotic Cooking for Body and Soul  ...  Mayumi Nishimura worked as Madonna 's private chef for seven years , and she...\n",
+      "\n",
+      "[11 rows x 6 columns]},\n",
+      "    {   'answer': 'Belize',\n",
+      "        'context':                 Country  ...                               Scientific name\n",
+      "0           Afghanistan  ...                                              \n",
+      "1               Albania  ...                                 Olea europaea\n",
+      "2   Antigua and Barbuda  ...                                Bucida buceras\n",
+      "3             Argentina  ...  Erythrina crista-galli , Schinopsis balansae\n",
+      "4             Australia  ...                              Acacia pycnantha\n",
+      "5               Bahamas  ...                              Guaiacum sanctum\n",
+      "6            Bangladesh  ...                              Mangifera indica\n",
+      "7                Belize  ...                         Swietenia macrophylla\n",
+      "8                Bhutan  ...                         Cupressus cashmeriana\n",
+      "9                Brazil  ...                          Caesalpinia echinata\n",
+      "10             Cambodia  ...                          Borassus flabellifer\n",
+      "11               Canada  ...                                          Acer\n",
+      "12                Chile  ...                            Araucaria araucana\n",
+      "13             Colombia  ...                         Ceroxylon quindiuense\n",
+      "14           Costa Rica  ...                      Enterolobium cyclocarpum\n",
+      "15              Croatia  ...                                 Quercus robur\n",
+      "16                 Cuba  ...                               Roystonea regia\n",
+      "17               Cyprus  ...                             Quercus alnifolia\n",
+      "18       Czech Republic  ...                                 Tilia cordata\n",
+      "19              Denmark  ...                               Fagus sylvatica\n",
+      "\n",
+      "[20 rows x 3 columns]},\n",
+      "    {   'answer': 'Palmyra palm',\n",
+      "        'context':                 Country  ...                               Scientific name\n",
+      "0           Afghanistan  ...                                              \n",
+      "1               Albania  ...                                 Olea europaea\n",
+      "2   Antigua and Barbuda  ...                                Bucida buceras\n",
+      "3             Argentina  ...  Erythrina crista-galli , Schinopsis balansae\n",
+      "4             Australia  ...                              Acacia pycnantha\n",
+      "5               Bahamas  ...                              Guaiacum sanctum\n",
+      "6            Bangladesh  ...                              Mangifera indica\n",
+      "7                Belize  ...                         Swietenia macrophylla\n",
+      "8                Bhutan  ...                         Cupressus cashmeriana\n",
+      "9                Brazil  ...                          Caesalpinia echinata\n",
+      "10             Cambodia  ...                          Borassus flabellifer\n",
+      "11               Canada  ...                                          Acer\n",
+      "12                Chile  ...                            Araucaria araucana\n",
+      "13             Colombia  ...                         Ceroxylon quindiuense\n",
+      "14           Costa Rica  ...                      Enterolobium cyclocarpum\n",
+      "15              Croatia  ...                                 Quercus robur\n",
+      "16                 Cuba  ...                               Roystonea regia\n",
+      "17               Cyprus  ...                             Quercus alnifolia\n",
+      "18       Czech Republic  ...                                 Tilia cordata\n",
+      "19              Denmark  ...                               Fagus sylvatica\n",
+      "\n",
+      "[20 rows x 3 columns]},\n",
+      "    {   'answer': 'Guadeloupe',\n",
+      "        'context':                                State  ... Official Language ( s )\n",
+      "0                Antigua and Barbuda  ...                 English\n",
+      "1                           Dominica  ...                 English\n",
+      "2                            Grenada  ...                 English\n",
+      "3                         Montserrat  ...                 English\n",
+      "4              Saint Kitts and Nevis  ...                 English\n",
+      "5                        Saint Lucia  ...                 English\n",
+      "6   Saint Vincent and the Grenadines  ...                 English\n",
+      "7                           Anguilla  ...                 English\n",
+      "8             British Virgin Islands  ...                 English\n",
+      "9                         Guadeloupe  ...                  French\n",
+      "10                        Martinique  ...                  French\n",
+      "\n",
+      "[11 rows x 10 columns]},\n",
+      "    {   'answer': 'Basse-Terre',\n",
+      "        'context':                                State  ... Official Language ( s )\n",
+      "0                Antigua and Barbuda  ...                 English\n",
+      "1                           Dominica  ...                 English\n",
+      "2                            Grenada  ...                 English\n",
+      "3                         Montserrat  ...                 English\n",
+      "4              Saint Kitts and Nevis  ...                 English\n",
+      "5                        Saint Lucia  ...                 English\n",
+      "6   Saint Vincent and the Grenadines  ...                 English\n",
+      "7                           Anguilla  ...                 English\n",
+      "8             British Virgin Islands  ...                 English\n",
+      "9                         Guadeloupe  ...                  French\n",
+      "10                        Martinique  ...                  French\n",
+      "\n",
+      "[11 rows x 10 columns]},\n",
+      "    {   'answer': 'East Caribbean dollar',\n",
+      "        'context':                                State  ... Official Language ( s )\n",
+      "0                Antigua and Barbuda  ...                 English\n",
+      "1                           Dominica  ...                 English\n",
+      "2                            Grenada  ...                 English\n",
+      "3                         Montserrat  ...                 English\n",
+      "4              Saint Kitts and Nevis  ...                 English\n",
+      "5                        Saint Lucia  ...                 English\n",
+      "6   Saint Vincent and the Grenadines  ...                 English\n",
+      "7                           Anguilla  ...                 English\n",
+      "8             British Virgin Islands  ...                 English\n",
+      "9                         Guadeloupe  ...                  French\n",
+      "10                        Martinique  ...                  French\n",
+      "\n",
+      "[11 rows x 10 columns]},\n",
+      "    {   'answer': 'Jenkins',\n",
+      "        'context':    NRHP reference number  ...      County\n",
+      "0               72000402  ...      Wilkes\n",
+      "1                         ...  Meriwether\n",
+      "2                         ...      Bartow\n",
+      "3               71000280  ...     Jenkins\n",
+      "4                         ...     Chatham\n",
+      "5               89002015  ...      Thomas\n",
+      "6                         ...       Glynn\n",
+      "7               75000615  ...      Walton\n",
+      "8               84001156  ...      Sumter\n",
+      "9               79000713  ...        Cobb\n",
+      "10              82002491  ...      Twiggs\n",
+      "11              74000703  ...  Taliaferro\n",
+      "12              80001039  ...       Floyd\n",
+      "13              90000805  ...    Gwinnett\n",
+      "14              73000620  ...     Decatur\n",
+      "15              79000731  ...     Houston\n",
+      "16              95000741  ...       Grady\n",
+      "17              97000559  ...      Greene\n",
+      "18              74000662  ...      Brooks\n",
+      "19              75000616  ...  Washington\n",
+      "\n",
+      "[20 rows x 4 columns]},\n",
+      "    {   'answer': \"Primula farinosa , the bird's-eye primrose\",\n",
+      "        'context': \"Primula farinosa , the bird's-eye primrose , is a small \"\n",
+      "                   'perennial plant in the family Primulaceae , native to '\n",
+      "                   'Northern Europe and northern Asia , and '},\n",
+      "    {   'answer': 'Poospiza',\n",
+      "        'context': 'Poospiza is a genus of finch-like tanagers found in both '\n",
+      "                   'the South American lowlands and the Andes mountains . '\n",
+      "                   'Generally they are arboreal feeders in '},\n",
+      "    {   'answer': 'golden-crowned sparrow',\n",
+      "        'context': 'The golden-crowned sparrow ( Zonotrichia atricapilla ) is '\n",
+      "                   'a large American sparrow found in the western part of '\n",
+      "                   'North America .'},\n",
+      "    {   'answer': 'Banksia sessilis var . cordata is a variety of Banksia '\n",
+      "                  'sessilis ( Parrot Bush',\n",
+      "        'context': 'Banksia sessilis var . cordata is a variety of Banksia '\n",
+      "                   'sessilis ( Parrot Bush ) , with unusually large leaves and '\n",
+      "                   'flower heads . It is a rare variety '},\n",
+      "    {   'answer': 'rain',\n",
+      "        'context': 's and operates hotels at Machu Picchu Natural Reserve , '\n",
+      "                   'the southeastern rain forest of the Amazon in Puerto '\n",
+      "                   'Maldonado , Tambopata , the Sacred Valley'}]\n"
+     ]
+    }
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -757,5 +1353,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
-}
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/tutorials/Tutorial15_TableQA.py b/tutorials/Tutorial15_TableQA.py
index 5c282e21c..b77e719de 100644
--- a/tutorials/Tutorial15_TableQA.py
+++ b/tutorials/Tutorial15_TableQA.py
@@ -6,7 +6,7 @@ from haystack.utils import launch_es, fetch_archive_from_http, print_answers
 from haystack.document_stores import ElasticsearchDocumentStore
 from haystack import Document, Pipeline
 from haystack.nodes.retriever import TableTextRetriever
-from haystack.nodes import TableReader
+from haystack.nodes import TableReader, FARMReader, RouteDocuments, JoinAnswers
 
 
 def tutorial15_tableqa():
@@ -115,6 +115,37 @@ def tutorial15_tableqa():
     prediction = table_qa_pipeline.run("How many twin buildings are under construction?")
     print_answers(prediction, details="minimum")
 
+    ### 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.
+
+    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")
+    route_documents = RouteDocuments()
+    join_answers = JoinAnswers()
+
+    text_table_qa_pipeline = Pipeline()
+    text_table_qa_pipeline.add_node(component=retriever, name="TableTextRetriever", inputs=["Query"])
+    text_table_qa_pipeline.add_node(component=route_documents, name="RouteDocuments", inputs=["TableTextRetriever"])
+    text_table_qa_pipeline.add_node(component=text_reader, name="TextReader", inputs=["RouteDocuments.output_1"])
+    text_table_qa_pipeline.add_node(component=table_reader, name="TableReader", inputs=["RouteDocuments.output_2"])
+    text_table_qa_pipeline.add_node(component=join_answers, name="JoinAnswers", inputs=["TextReader", "TableReader"])
+
+    # Example query whose answer resides in a text passage
+    predictions = text_table_qa_pipeline.run(query="Who is Aleksandar Trifunovic?")
+    # We can see both text passages and tables as contexts of the predicted answers.
+    print_answers(predictions, details="minimum")
+
+    # Example query whose answer resides in a table
+    predictions = text_table_qa_pipeline.run(query="What is Cuba's national tree?")
+    # We can see both text passages and tables as contexts of the predicted answers.
+    print_answers(predictions, details="minimum")
+
 
 if __name__ == "__main__":
     tutorial15_tableqa()