fix: SingleChunkQa crashing on specific prompt ordering

CHANGELOG.md

@@ -10,7 +10,9 @@
 ...
 
 ### Fixes
-- Serialization and Deserialization of `Union[SpanAttributes, TaskSpanAttributes]` is working now.
+- Serialization and deserialization of `ExportedSpan` and its `attributes` now works as expected.
+- `PromptTemplate.to_rich_prompt` now always returns an empty list for prompt ranges that are empty.
+- `SingleChunkQa` no longer crashes if given an empty input and a specific prompt template. This did not affect users who used models provided in `core`.
 
 ### Deprecations 
 ...

src/intelligence_layer/core/prompt_template.py

@@ -394,9 +394,19 @@ def valid_range_for(
                 yield new_prompt_item(placeholder_prompt_item)
             else:
                 if range_starts.get(placeholder):
-                    placeholder_ranges[placeholder].extend(
-                        valid_range_for(placeholder, end_cursor())
-                    )
+                    test_range = list(valid_range_for(placeholder, end_cursor()))
+                    # the test_range is empty if there was never any text and the range is empty
+                    # However, if there has been text previously, an empty text range will have
+                    # a range consisting of start==end. This check is there to filter these as well.
+                    if len(test_range) == 0 or (
+                        test_range[0].start.item == test_range[0].end.item
+                        and isinstance(test_range[0].end, TextCursor)
+                        and isinstance(test_range[0].start, TextCursor)
+                        and test_range[0].end.position == test_range[0].start.position
+                    ):
+                        placeholder_ranges[placeholder].extend([])
+                    else:
+                        placeholder_ranges[placeholder].extend(test_range)
                 else:
                     range_starts[placeholder] = initial_start_text_cursor()
             last_to = placeholder_to

src/intelligence_layer/examples/qa/single_chunk_qa.py

@@ -93,7 +93,7 @@ class SingleChunkQa(Task[SingleChunkQaInput, SingleChunkQaOutput]):
         model: The model used throughout the task for model related API calls.
         text_highlight: The task that is used for highlighting that parts of the input that are
             relevant for the answer. Defaults to :class:`TextHighlight` .
-        instruction_config: defines instructions for different langaugaes.
+        instruction_config: defines instructions for different languages.
         maximum_token: the maximal number of tokens to be generated for an answer.
 
     Attributes:
@@ -176,6 +176,8 @@ def _shift_highlight_ranges_to_input(
         self, prompt: RichPrompt, raw_highlights: Sequence[ScoredTextHighlight]
     ) -> Sequence[ScoredTextHighlight]:
         # This only works with models that have an 'input' range, e.g. control models.
+        if "input" not in prompt.ranges or len(prompt.ranges["input"]) == 0:
+            return raw_highlights
         input_cursor = prompt.ranges["input"][0].start
         assert isinstance(input_cursor, TextCursor)
         input_offset = input_cursor.position

tests/core/test_prompt_template.py

@@ -193,7 +193,7 @@ def test_to_prompt_resets_template(prompt_image: Image) -> None:
     assert prompt_with_reset_template != prompt
 
 
-def test_to_prompt_data_returns_ranges(prompt_image: Image) -> None:
+def test_to_rich_prompt_returns_ranges(prompt_image: Image) -> None:
     embedded_text = "Embedded"
     prefix_items: list[PromptItem] = [
         Text.from_text("Prefix Text Item"),
@@ -229,7 +229,7 @@ def test_to_prompt_data_returns_ranges(prompt_image: Image) -> None:
     ]
 
 
-def test_to_prompt_data_returns_ranges_for_image_only_prompt(
+def test_to_rich_prompt_returns_ranges_for_image_only_prompt(
     prompt_image: Image,
 ) -> None:
     template = PromptTemplate(
@@ -250,13 +250,18 @@ def test_to_prompt_data_returns_ranges_for_image_only_prompt(
     assert r1 == prompt_data.ranges.get("r2")
 
 
-def test_to_prompt_data_returns_no_range_with_empty_template() -> None:
+def test_to_rich_prompt_returns_no_range_with_empty_template() -> None:
     template = PromptTemplate("{% promptrange r1 %}{% endpromptrange %}")
 
     assert template.to_rich_prompt().ranges.get("r1") == []
 
 
-def test_to_prompt_data_returns_no_empty_ranges(prompt_image: Image) -> None:
+def test_to_rich_prompt_returns_no_range_with_range_with_other_ranges() -> None:
+    template = PromptTemplate("""text{% promptrange r1 %}{% endpromptrange %}""")
+    assert template.to_rich_prompt().ranges.get("r1") == []
+
+
+def test_to_rich_prompt_returns_no_empty_ranges(prompt_image: Image) -> None:
     template = PromptTemplate(
         "{{image}}{% promptrange r1 %}{% endpromptrange %} Some Text"
     )
@@ -276,7 +281,7 @@ def test_prompt_template_raises_liquid_error_on_illeagal_range() -> None:
         )
 
 
-def test_to_prompt_data_returns_multiple_text_ranges_in_for_loop() -> None:
+def test_to_rich_prompt_returns_multiple_text_ranges_in_for_loop() -> None:
     embedded = "Some Content"
     template = PromptTemplate(
         "{% for i in (1..4) %}{% promptrange r1 %}{{embedded}}{% endpromptrange %}{% endfor %}"
@@ -293,7 +298,7 @@ def test_to_prompt_data_returns_multiple_text_ranges_in_for_loop() -> None:
     ]
 
 
-def test_to_prompt_data_returns_multiple_imgae_ranges_in_for_loop(
+def test_to_rich_prompt_returns_multiple_image_ranges_in_for_loop(
     prompt_image: Image,
 ) -> None:
     template = PromptTemplate(

tests/examples/qa/test_single_chunk_qa.py

@@ -119,3 +119,16 @@ def test_qa_highlights_will_not_become_out_of_bounds(
     for highlight in output.highlights:
         assert highlight.start >= 0
         assert 0 < highlight.end <= len(input_text)
+
+
+def test_qa_single_chunk_does_not_crash_when_input_is_empty(
+    single_chunk_qa: SingleChunkQa,
+) -> None:
+    input_text = ""
+    input = SingleChunkQaInput(
+        chunk=TextChunk(input_text),
+        question="something",
+        language=Language("de"),
+    )
+    res = single_chunk_qa.run(input, NoOpTracer())
+    assert res.answer is None