fix: component checks failing for components that return dataframes (#8873)

* fix: use is not to compare to sentinel value

* chore: release notes

* Update releasenotes/notes/fix-component-checks-with-ambiguous-truth-values-949c447b3702e427.yaml

Co-authored-by: David S. Batista <dsbatista@gmail.com>

* fix: another sentinel value

* test: also test base class

* add pandas as test dependency

* format

* Trigger CI

* mark test with xfail strict=False

---------

Co-authored-by: Sebastian Husch Lee <sjrl@users.noreply.github.com>
Co-authored-by: David S. Batista <dsbatista@gmail.com>
Co-authored-by: anakin87 <stefanofiorucci@gmail.com>

haystack/core/pipeline/base.py

@@ -913,7 +913,7 @@ class PipelineBase:
         greedy_inputs_to_remove = set()
         for socket_name, socket in component["input_sockets"].items():
             socket_inputs = component_inputs.get(socket_name, [])
-            socket_inputs = [sock["value"] for sock in socket_inputs if sock["value"] != _NO_OUTPUT_PRODUCED]
+            socket_inputs = [sock["value"] for sock in socket_inputs if sock["value"] is not _NO_OUTPUT_PRODUCED]
             if socket_inputs:
                 if not socket.is_variadic:
                     # We only care about the first input provided to the socket.

haystack/core/pipeline/component_checks.py

@@ -103,7 +103,7 @@ def any_socket_value_from_predecessor_received(socket_inputs: List[Dict[str, Any
     :param socket_inputs: Inputs for the component's socket.
     """
     # When sender is None, the input was provided from outside the pipeline.
-    return any(inp["value"] != _NO_OUTPUT_PRODUCED and inp["sender"] is not None for inp in socket_inputs)
+    return any(inp["value"] is not _NO_OUTPUT_PRODUCED and inp["sender"] is not None for inp in socket_inputs)
 
 
 def has_user_input(inputs: Dict) -> bool:
@@ -143,7 +143,7 @@ def any_socket_input_received(socket_inputs: List[Dict]) -> bool:
 
     :param socket_inputs: Inputs for the socket.
     """
-    return any(inp["value"] != _NO_OUTPUT_PRODUCED for inp in socket_inputs)
+    return any(inp["value"] is not _NO_OUTPUT_PRODUCED for inp in socket_inputs)
 
 
 def has_lazy_variadic_socket_received_all_inputs(socket: InputSocket, socket_inputs: List[Dict]) -> bool:
@@ -155,7 +155,9 @@ def has_lazy_variadic_socket_received_all_inputs(socket: InputSocket, socket_inp
     """
     expected_senders = set(socket.senders)
     actual_senders = {
-        sock["sender"] for sock in socket_inputs if sock["value"] != _NO_OUTPUT_PRODUCED and sock["sender"] is not None
+        sock["sender"]
+        for sock in socket_inputs
+        if sock["value"] is not _NO_OUTPUT_PRODUCED and sock["sender"] is not None
     }
 
     return expected_senders == actual_senders
@@ -182,7 +184,11 @@ def has_socket_received_all_inputs(socket: InputSocket, socket_inputs: List[Dict
         return False
 
     # The socket is greedy variadic and at least one input was produced, it is complete.
-    if socket.is_variadic and socket.is_greedy and any(sock["value"] != _NO_OUTPUT_PRODUCED for sock in socket_inputs):
+    if (
+        socket.is_variadic
+        and socket.is_greedy
+        and any(sock["value"] is not _NO_OUTPUT_PRODUCED for sock in socket_inputs)
+    ):
         return True
 
     # The socket is lazy variadic and all expected inputs were produced.
@@ -190,7 +196,7 @@ def has_socket_received_all_inputs(socket: InputSocket, socket_inputs: List[Dict
         return True
 
     # The socket is not variadic and the only expected input is complete.
-    return not socket.is_variadic and socket_inputs[0]["value"] != _NO_OUTPUT_PRODUCED
+    return not socket.is_variadic and socket_inputs[0]["value"] is not _NO_OUTPUT_PRODUCED
 
 
 def all_predecessors_executed(component: Dict, inputs: Dict) -> bool:

pyproject.toml

@@ -93,6 +93,7 @@ extra-dependencies = [
   "langdetect",                                # TextLanguageRouter and DocumentLanguageClassifier
   "openai-whisper>=20231106",                  # LocalWhisperTranscriber
   "arrow>=1.3.0",                              # Jinja2TimeExtension
+  "pandas",                                    # Needed for pipeline tests with components that return dataframes
 
   # NamedEntityExtractor
   "spacy>=3.8,<3.9",

releasenotes/notes/fix-component-checks-with-ambiguous-truth-values-949c447b3702e427.yaml

@@ -0,0 +1,5 @@
+---
+fixes:
+  - |
+    Pipelines with components that return plain pandas dataframes failed.
+    The comparison of socket values is now 'is not' instead of '!=' to avoid errors with dataframes.

test/components/generators/chat/test_hugging_face_api.py

@@ -583,7 +583,9 @@ class TestHuggingFaceAPIChatGenerator:
         not os.environ.get("HF_API_TOKEN", None),
         reason="Export an env var called HF_API_TOKEN containing the Hugging Face token to run this test.",
     )
-    @pytest.mark.flaky(reruns=3, reruns_delay=10)
+    @pytest.mark.xfail(
+        reason="The Hugging Face API can be unstable and this test may fail intermittently", strict=False
+    )
     def test_live_run_with_tools(self, tools):
         """
         We test the round trip: generate tool call, pass tool message, generate response.

test/core/pipeline/features/conftest.py

@@ -4,6 +4,7 @@ from pathlib import Path
 import re
 import pytest
 import asyncio
+import pandas as pd
 
 from pytest_bdd import when, then, parsers
 
@@ -142,15 +143,39 @@ def draw_pipeline(pipeline_data: Tuple[Pipeline, List[PipelineRunData]], request
 @then("it should return the expected result")
 def check_pipeline_result(pipeline_result: List[Tuple[_PipelineResult, PipelineRunData]]):
     for res, data in pipeline_result:
-        assert res.outputs == data.expected_outputs
+        compare_outputs_with_dataframes(res.outputs, data.expected_outputs)
 
 
 @then("components are called with the expected inputs")
 def check_component_calls(pipeline_result: List[Tuple[_PipelineResult, PipelineRunData]]):
     for res, data in pipeline_result:
-        assert res.component_calls == data.expected_component_calls
+        assert compare_outputs_with_dataframes(res.component_calls, data.expected_component_calls)
 
 
 @then(parsers.parse("it must have raised {exception_class_name}"))
 def check_pipeline_raised(pipeline_result: Exception, exception_class_name: str):
     assert pipeline_result.__class__.__name__ == exception_class_name
+
+
+def compare_outputs_with_dataframes(actual: Dict, expected: Dict) -> bool:
+    """
+    Compare two component_calls or pipeline outputs dictionaries where values may contain DataFrames.
+    """
+    assert actual.keys() == expected.keys()
+
+    for key in actual:
+        actual_data = actual[key]
+        expected_data = expected[key]
+
+        assert actual_data.keys() == expected_data.keys()
+
+        for data_key in actual_data:
+            actual_value = actual_data[data_key]
+            expected_value = expected_data[data_key]
+
+            if isinstance(actual_value, pd.DataFrame) and isinstance(expected_value, pd.DataFrame):
+                assert actual_value.equals(expected_value)
+            else:
+                assert actual_value == expected_value
+
+    return True

test/core/pipeline/features/pipeline_run.feature

@@ -52,6 +52,7 @@ Feature: Pipeline running
         | with a component that has dynamic default inputs |
         | with a component that has variadic dynamic default inputs |
         | that is a file conversion pipeline with two joiners |
+        | that has components returning dataframes |
 
     Scenario Outline: Running a bad Pipeline
         Given a pipeline <kind>

test/core/pipeline/features/test_run.py

@@ -4,6 +4,7 @@ import re
 
 from pytest_bdd import scenarios, given
 import pytest
+import pandas as pd
 
 from haystack import Document, component
 from haystack.document_stores.types import DuplicatePolicy
@@ -5079,3 +5080,33 @@ some,header,row
             )
         ],
     )
+
+
+@given("a pipeline that has components returning dataframes", target_fixture="pipeline_data")
+def pipeline_has_components_returning_dataframes(pipeline_class):
+    def get_df():
+        return pd.DataFrame({"a": [1, 2], "b": [1, 2]})
+
+    @component
+    class DataFramer:
+        @component.output_types(dataframe=pd.DataFrame)
+        def run(self, dataframe: pd.DataFrame) -> Dict[str, Any]:
+            return {"dataframe": get_df()}
+
+    pp = pipeline_class(max_runs_per_component=1)
+
+    pp.add_component("df_1", DataFramer())
+    pp.add_component("df_2", DataFramer())
+
+    pp.connect("df_1", "df_2")
+
+    return (
+        pp,
+        [
+            PipelineRunData(
+                inputs={"df_1": {"dataframe": get_df()}},
+                expected_outputs={"df_2": {"dataframe": get_df()}},
+                expected_component_calls={("df_1", 1): {"dataframe": get_df()}, ("df_2", 1): {"dataframe": get_df()}},
+            )
+        ],
+    )

test/core/pipeline/test_component_checks.py

@@ -9,6 +9,9 @@ from haystack.core.pipeline.component_checks import _NO_OUTPUT_PRODUCED
 from haystack.core.component.types import InputSocket, OutputSocket, Variadic, GreedyVariadic
 
 
+import pandas as pd
+
+
 @pytest.fixture
 def basic_component():
     """Basic component with one mandatory and one optional input."""
@@ -130,6 +133,26 @@ class TestCanComponentRun:
         inputs = {"optional_input": [{"sender": "previous_component", "value": "test"}]}
         assert can_component_run(basic_component, inputs) is False
 
+    # We added these tests because a component that returned a pandas dataframe caused the pipeline to fail.
+    # Previously, we compared the value of the socket using '!=' which leads to an error with dataframes.
+    # Instead, we use 'is not' to compare with the sentinel value.
+    def test_sockets_with_ambiguous_truth_value(self, basic_component, greedy_variadic_socket, regular_socket):
+        inputs = {
+            "mandatory_input": [{"sender": "previous_component", "value": pd.DataFrame.from_dict([{"value": 42}])}]
+        }
+
+        assert are_all_sockets_ready(basic_component, inputs, only_check_mandatory=True) is True
+        assert any_socket_value_from_predecessor_received(inputs["mandatory_input"]) is True
+        assert any_socket_input_received(inputs["mandatory_input"]) is True
+        assert (
+            has_lazy_variadic_socket_received_all_inputs(
+                basic_component["input_sockets"]["mandatory_input"], inputs["mandatory_input"]
+            )
+            is True
+        )
+        assert has_socket_received_all_inputs(greedy_variadic_socket, inputs["mandatory_input"]) is True
+        assert has_socket_received_all_inputs(regular_socket, inputs["mandatory_input"]) is True
+
     def test_component_with_no_trigger_but_all_inputs(self, basic_component):
         """
         Test case where all mandatory inputs are present with valid values,

test/core/pipeline/test_pipeline_base.py

@@ -8,6 +8,8 @@ from unittest.mock import patch
 
 import pytest
 
+import pandas as pd
+
 from haystack import Document
 from haystack.core.component import component
 from haystack.core.component.types import InputSocket, OutputSocket, Variadic, GreedyVariadic, _empty
@@ -1625,3 +1627,13 @@ class TestPipelineBase:
         # Verify
         assert consumed == expected_consumed
         assert inputs["test_component"] == expected_remaining
+
+    def test__consume_component_inputs_with_df(self, regular_input_socket):
+        component = {"input_sockets": {"input1": regular_input_socket}}
+        inputs = {
+            "test_component": {"input1": [{"sender": "sender1", "value": pd.DataFrame({"a": [1, 2], "b": [1, 2]})}]}
+        }
+
+        consumed = PipelineBase._consume_component_inputs("test_component", component, inputs)
+
+        assert consumed["input1"].equals(pd.DataFrame({"a": [1, 2], "b": [1, 2]}))