removed PromptWithMetadata in favor of RichPrompt which extends Prompt

src/examples/classification.ipynb

@@ -443,7 +443,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

src/intelligence_layer/core/__init__.py

@@ -33,7 +33,7 @@
 from .prompt_template import PromptItemCursor as PromptItemCursor
 from .prompt_template import PromptRange as PromptRange
 from .prompt_template import PromptTemplate as PromptTemplate
-from .prompt_template import PromptWithMetadata as PromptWithMetadata
+from .prompt_template import RichPrompt as RichPrompt
 from .prompt_template import TextCursor as TextCursor
 from .task import Input as Input
 from .task import Output as Output

src/intelligence_layer/core/complete.py

@@ -6,7 +6,7 @@
 from intelligence_layer.connectors.limited_concurrency_client import (
     AlephAlphaClientProtocol,
 )
-from intelligence_layer.core.prompt_template import PromptTemplate, PromptWithMetadata
+from intelligence_layer.core.prompt_template import PromptTemplate, RichPrompt
 from intelligence_layer.core.task import Task
 from intelligence_layer.core.tracer import TaskSpan
 
@@ -90,14 +90,14 @@ class PromptOutput(CompleteOutput):
 
     Attributes:
         response: The generated response to the instruction.
-        prompt_with_metadata: To handle the instruction, a `PromptTemplate` is used.
+        rich_prompt: To handle the instruction, a `PromptTemplate` is used.
             The template defines two `PromptRange` instances:
             - "instruction": covering the instruction text.
             - "input": covering the input text.
             These can for example be used for downstream `TextHighlight` tasks.
     """
 
-    prompt_with_metadata: PromptWithMetadata
+    rich_prompt: RichPrompt
 
 
 class Instruct(Task[InstructInput, PromptOutput]):
@@ -147,22 +147,18 @@ def __init__(self, client: AlephAlphaClientProtocol, model: str) -> None:
         self._model = model
 
     def do_run(self, input: InstructInput, task_span: TaskSpan) -> PromptOutput:
-        prompt_with_metadata = PromptTemplate(
-            self.INSTRUCTION_PROMPT_TEMPLATE
-        ).to_prompt_with_metadata(
+        prompt = PromptTemplate(self.INSTRUCTION_PROMPT_TEMPLATE).to_rich_prompt(
             input=input.input,
             instruction=input.instruction,
             response_prefix=input.response_prefix,
         )
         response = self._complete(
-            prompt_with_metadata.prompt,
+            prompt,
             input.maximum_response_tokens,
             self._model,
             task_span,
         )
-        return PromptOutput(
-            response=response, prompt_with_metadata=prompt_with_metadata
-        )
+        return PromptOutput(response=response, rich_prompt=prompt)
 
     def _complete(
         self, prompt: Prompt, maximum_tokens: int, model: str, task_span: TaskSpan
@@ -274,9 +270,7 @@ def __init__(self, client: AlephAlphaClientProtocol, model: str) -> None:
         self._model = model
 
     def do_run(self, input: FewShotInput, task_span: TaskSpan) -> PromptOutput:
-        prompt_with_metadata = PromptTemplate(
-            self.FEW_SHOT_PROMPT_TEMPLATE
-        ).to_prompt_with_metadata(
+        prompt = PromptTemplate(self.FEW_SHOT_PROMPT_TEMPLATE).to_rich_prompt(
             instruction=input.few_shot_config.instruction,
             input=input.input,
             few_shot_examples=[
@@ -286,15 +280,13 @@ def do_run(self, input: FewShotInput, task_span: TaskSpan) -> PromptOutput:
             response_prefix=input.few_shot_config.response_prefix,
         )
         response = self._complete(
-            prompt_with_metadata.prompt,
+            prompt,
             input.maximum_response_tokens,
             input.few_shot_config.additional_stop_sequences,
             self._model,
             task_span,
         )
-        return PromptOutput(
-            response=response, prompt_with_metadata=prompt_with_metadata
-        )
+        return PromptOutput(response=response, rich_prompt=prompt)
 
     def _complete(
         self,

src/intelligence_layer/core/echo.py

@@ -88,7 +88,7 @@ def do_run(self, input: EchoInput, task_span: TaskSpan) -> EchoOutput:
             input.expected_completion, input.model
         )
         prompt_template = PromptTemplate(self.PROMPT_TEMPLATE_STR)
-        prompt = prompt_template.to_prompt(
+        prompt = prompt_template.to_rich_prompt(
             prompt=prompt_template.embed_prompt(input.prompt),
             expected_completion=prompt_template.placeholder(
                 Tokens.from_token_ids(

src/intelligence_layer/core/prompt_template.py

@@ -76,16 +76,14 @@ class PromptRange:
     end: Cursor
 
 
-@dataclass(frozen=True)
-class PromptWithMetadata:
+@dataclass
+class RichPrompt(Prompt):
     """The `Prompt` along with some metadata generated when a `PromptTemplate` is turned into a `Prompt`.
 
     Args:
-      prompt: The actual `Prompt`.
       ranges: A mapping of range name to a `Sequence` of corresponding `PromptRange` instances.
     """
 
-    prompt: Prompt
     ranges: Mapping[str, Sequence[PromptRange]]
 
 
@@ -278,7 +276,7 @@ def embed_prompt(self, prompt: Prompt) -> str:
             last_item = item
         return prompt_text
 
-    def to_prompt_with_metadata(self, **kwargs: Any) -> PromptWithMetadata:
+    def to_rich_prompt(self, **kwargs: Any) -> RichPrompt:
         """Creates a `Prompt` along with metadata from the template string and the given parameters.
 
         Currently the only metadata returned is information about ranges that are marked in the template.
@@ -303,17 +301,10 @@ def to_prompt_with_metadata(self, **kwargs: Any) -> PromptWithMetadata:
             placeholder_indices, context.placeholder_range_names(), liquid_prompt
         )
 
-        result = PromptWithMetadata(Prompt(modalities), placeholder_ranges)
+        result = RichPrompt(modalities, placeholder_ranges)
         self._reset_placeholder_state()
         return result
 
-    def to_prompt(self, **kwargs: Any) -> Prompt:
-        """Creates a `Prompt` from the template string and the given parameters.
-
-        Provided parameters are passed to `liquid.Template.render`.
-        """
-        return self.to_prompt_with_metadata(**kwargs).prompt
-
     def _reset_placeholder_state(self) -> None:
         self._prompt_item_placeholders = {}
 

src/intelligence_layer/core/text_highlight.py

@@ -18,7 +18,7 @@
 from intelligence_layer.core.prompt_template import (
     Cursor,
     PromptRange,
-    PromptWithMetadata,
+    RichPrompt,
     TextCursor,
 )
 from intelligence_layer.core.task import Task
@@ -38,7 +38,7 @@ class TextHighlightInput(BaseModel):
             If this set is empty highlights of the entire prompt are returned.
     """
 
-    prompt_with_metadata: PromptWithMetadata
+    rich_prompt: RichPrompt
     target: str
     model: str
     focus_ranges: frozenset[str] = frozenset()
@@ -70,7 +70,7 @@ class TextHighlight(Task[TextHighlightInput, TextHighlightOutput]):
     """Generates text highlights given a prompt and completion.
 
     For a given prompt and target (completion), extracts the parts of the prompt responsible for generation.
-    A range can be provided in the input 'PromptWithMetadata' via use of the liquid language (see the example).
+    A range can be provided via use of the liquid language (see the example).
     In this case, the highlights will only refer to text within this range.
 
     Args:
@@ -117,19 +117,19 @@ def do_run(
     ) -> TextHighlightOutput:
         self._raise_on_invalid_focus_range(input)
         explanation = self._explain(
-            prompt=input.prompt_with_metadata.prompt,
+            prompt=input.rich_prompt,
             target=input.target,
             model=input.model,
             task_span=task_span,
         )
         prompt_ranges = self._flatten_prompt_ranges(
             range
-            for name, range in input.prompt_with_metadata.ranges.items()
+            for name, range in input.rich_prompt.ranges.items()
             if name in input.focus_ranges
         )
         text_prompt_item_explanations_and_indices = (
             self._extract_text_prompt_item_explanations_and_item_index(
-                input.prompt_with_metadata.prompt, explanation
+                input.rich_prompt, explanation
             )
         )
         highlights = self._to_highlights(
@@ -140,9 +140,7 @@ def do_run(
         return TextHighlightOutput(highlights=highlights)
 
     def _raise_on_invalid_focus_range(self, input: TextHighlightInput) -> None:
-        unknown_focus_ranges = input.focus_ranges - set(
-            input.prompt_with_metadata.ranges.keys()
-        )
+        unknown_focus_ranges = input.focus_ranges - set(input.rich_prompt.ranges.keys())
         if unknown_focus_ranges:
             raise ValueError(f"Unknown focus ranges: {', '.join(unknown_focus_ranges)}")
 

src/intelligence_layer/use_cases/qa/single_chunk_qa.py

@@ -9,7 +9,7 @@
 from intelligence_layer.core.chunk import Chunk
 from intelligence_layer.core.complete import Instruct, InstructInput, PromptOutput
 from intelligence_layer.core.detect_language import Language, language_config
-from intelligence_layer.core.prompt_template import PromptWithMetadata
+from intelligence_layer.core.prompt_template import RichPrompt
 from intelligence_layer.core.task import Task
 from intelligence_layer.core.text_highlight import TextHighlight, TextHighlightInput
 from intelligence_layer.core.tracer import TaskSpan
@@ -132,7 +132,7 @@ def do_run(
         answer = self._no_answer_to_none(output.completion.strip())
         highlights = (
             self._get_highlights(
-                output.prompt_with_metadata,
+                output.rich_prompt,
                 output.completion,
                 task_span,
             )
@@ -158,12 +158,12 @@ def _generate_answer(
 
     def _get_highlights(
         self,
-        prompt_with_metadata: PromptWithMetadata,
+        prompt_with_metadata: RichPrompt,
         completion: str,
         task_span: TaskSpan,
     ) -> Sequence[str]:
         highlight_input = TextHighlightInput(
-            prompt_with_metadata=prompt_with_metadata,
+            rich_prompt=prompt_with_metadata,
             target=completion,
             model=self._model,
             focus_ranges=frozenset({"input"}),

tests/core/test_complete.py

@@ -33,7 +33,7 @@ def test_instruct_without_input(instruct: Instruct, no_op_tracer: NoOpTracer) ->
     output = instruct.run(input, no_op_tracer)
 
     assert "Berlin" in output.completion
-    prompt_text_item = output.prompt_with_metadata.prompt.items[0]
+    prompt_text_item = output.rich_prompt.items[0]
     assert isinstance(prompt_text_item, Text)
     assert "Input" not in prompt_text_item.text