Fix JoinAnswer/JoinNode (deepset-ai#2612)

* fix join nodes

* Update Documentation & Code Style

* fix unused import

* change arg order

* Update Documentation & Code Style

* fix kwargs check

* add warning when there is only one input node

* Update Documentation & Code Style

* fix type hint

* fix wrong import order

* Update Documentation & Code Style

* undo kwargs

* add accidentally deleted newline#

* fix type hint

* fix type hint

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

docs/_src/api/api/other.md

@@ -23,7 +23,7 @@ This ensures that your output is in a compatible format.
 ## JoinDocuments
 
 ```python
-class JoinDocuments(BaseComponent)
+class JoinDocuments(JoinNode)
 ```
 
 A node to join documents outputted by multiple retriever nodes.
@@ -61,7 +61,7 @@ to each retriever score. This param is not compatible with the `concatenate` joi
 ## JoinAnswers
 
 ```python
-class JoinAnswers(BaseComponent)
+class JoinAnswers(JoinNode)
 ```
 
 A node to join `Answer`s produced by multiple `Reader` nodes.

haystack/nodes/other/__init__.py

@@ -2,3 +2,4 @@
 from haystack.nodes.other.join_docs import JoinDocuments
 from haystack.nodes.other.route_documents import RouteDocuments
 from haystack.nodes.other.join_answers import JoinAnswers
+from haystack.nodes.other.join import JoinNode

haystack/nodes/other/join.py

@@ -0,0 +1,76 @@
+from abc import abstractmethod
+from typing import Optional, List, Tuple, Dict, Union, Any
+import warnings
+
+from haystack import MultiLabel, Document, Answer
+from haystack.nodes.base import BaseComponent
+
+
+class JoinNode(BaseComponent):
+    def run(  # type: ignore
+        self,
+        inputs: Optional[List[dict]] = None,
+        query: Optional[str] = None,
+        file_paths: Optional[List[str]] = None,
+        labels: Optional[MultiLabel] = None,
+        documents: Optional[List[Document]] = None,
+        meta: Optional[dict] = None,
+        answers: Optional[List[Answer]] = None,
+        top_k_join: Optional[int] = None,
+    ) -> Tuple[Dict, str]:
+        if inputs:
+            return self.run_accumulated(inputs, top_k_join=top_k_join)
+        warnings.warn("You are using a JoinNode with only one input. This is usually equivalent to a no-op.")
+        return self.run_accumulated(
+            inputs=[
+                {
+                    "query": query,
+                    "file_paths": file_paths,
+                    "labels": labels,
+                    "documents": documents,
+                    "meta": meta,
+                    "answers": answers,
+                }
+            ],
+            top_k_join=top_k_join,
+        )
+
+    @abstractmethod
+    def run_accumulated(self, inputs: List[dict], top_k_join: Optional[int] = None) -> Tuple[Dict, str]:
+        pass
+
+    def run_batch(  # type: ignore
+        self,
+        inputs: Optional[List[dict]] = None,
+        queries: Optional[Union[str, List[str]]] = None,
+        file_paths: Optional[List[str]] = None,
+        labels: Optional[Union[MultiLabel, List[MultiLabel]]] = None,
+        documents: Optional[Union[List[Document], List[List[Document]]]] = None,
+        meta: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
+        params: Optional[dict] = None,
+        debug: Optional[bool] = None,
+        answers: Optional[List[Answer]] = None,
+        top_k_join: Optional[int] = None,
+    ) -> Tuple[Dict, str]:
+        if inputs:
+            return self.run_batch_accumulated(inputs=inputs, top_k_join=top_k_join)
+        warnings.warn("You are using a JoinNode with only one input. This is usually equivalent to a no-op.")
+        return self.run_batch_accumulated(
+            inputs=[
+                {
+                    "queries": queries,
+                    "file_paths": file_paths,
+                    "labels": labels,
+                    "documents": documents,
+                    "meta": meta,
+                    "params": params,
+                    "debug": debug,
+                    "answers": answers,
+                }
+            ],
+            top_k_join=top_k_join
+        )
+
+    @abstractmethod
+    def run_batch_accumulated(self, inputs: List[dict], top_k_join: Optional[int] = None) -> Tuple[Dict, str]:
+        pass

haystack/nodes/other/join_answers.py

@@ -1,10 +1,10 @@
 from typing import Optional, List, Dict, Tuple
 
 from haystack.schema import Answer
-from haystack.nodes.base import BaseComponent
+from haystack.nodes.other.join import JoinNode
 
 
-class JoinAnswers(BaseComponent):
+class JoinAnswers(JoinNode):
     """
     A node to join `Answer`s produced by multiple `Reader` nodes.
     """
@@ -40,7 +40,7 @@ def __init__(
         self.top_k_join = top_k_join
         self.sort_by_score = sort_by_score
 
-    def run(self, inputs: List[Dict], top_k_join: Optional[int] = None) -> Tuple[Dict, str]:  # type: ignore
+    def run_accumulated(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:
@@ -61,7 +61,7 @@ def run(self, inputs: List[Dict], top_k_join: Optional[int] = None) -> Tuple[Dic
         else:
             raise ValueError(f"Invalid join_mode: {self.join_mode}")
 
-    def run_batch(self, inputs: List[Dict], top_k_join: Optional[int] = None) -> Tuple[Dict, str]:  # type: ignore
+    def run_batch_accumulated(self, inputs: List[Dict], top_k_join: Optional[int] = None) -> Tuple[Dict, str]:  # type: ignore
         output_ans = []
         incoming_edges = [inp["answers"] for inp in inputs]
         # At each idx, we find predicted answers for the same query from different Readers

haystack/nodes/other/join_docs.py

@@ -3,10 +3,10 @@
 from typing import Optional, List
 
 from haystack.schema import Document
-from haystack.nodes.base import BaseComponent
+from haystack.nodes.other.join import JoinNode
 
 
-class JoinDocuments(BaseComponent):
+class JoinDocuments(JoinNode):
     """
     A node to join documents outputted by multiple retriever nodes.
 
@@ -47,7 +47,7 @@ def __init__(
         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):  # type: ignore
+    def run_accumulated(self, inputs: List[dict], top_k_join: Optional[int] = None):  # type: ignore
         results = [inp["documents"] for inp in inputs]
         document_map = {doc.id: doc for result in results for doc in result}
 
@@ -77,7 +77,7 @@ def run(self, inputs: List[dict], top_k_join: Optional[int] = None):  # type: ig
 
         return output, "output_1"
 
-    def run_batch(self, inputs: List[dict], top_k_join: Optional[int] = None):  # type: ignore
+    def run_batch_accumulated(self, inputs: List[dict], top_k_join: Optional[int] = None):  # type: ignore
         # Join single document lists
         if isinstance(inputs[0]["documents"][0], Document):
             return self.run(inputs=inputs, top_k_join=top_k_join)