Google Generators: change answers to replies

integrations/google_ai/src/haystack_integrations/components/generators/google_ai/gemini.py

@@ -26,7 +26,7 @@ class GoogleAIGeminiGenerator:
 
     gemini = GoogleAIGeminiGenerator(model="gemini-pro", api_key=Secret.from_token("<MY_API_KEY>"))
     res = gemini.run(parts = ["What is the most interesting thing you know?"])
-    for answer in res["answers"]:
+    for answer in res["replies"]:
         print(answer)
     ```
 
@@ -55,7 +55,7 @@ class GoogleAIGeminiGenerator:
 
     gemini = GoogleAIGeminiGenerator(model="gemini-pro-vision", api_key=Secret.from_token("<MY_API_KEY>"))
     result = gemini.run(parts = ["What can you tell me about this robots?", *images])
-    for answer in result["answers"]:
+    for answer in result["replies"]:
         print(answer)
     ```
     """
@@ -173,7 +173,7 @@ def _convert_part(self, part: Union[str, ByteStream, Part]) -> Part:
             msg = f"Unsupported type {type(part)} for part {part}"
             raise ValueError(msg)
 
-    @component.output_types(answers=List[Union[str, Dict[str, str]]])
+    @component.output_types(replies=List[Union[str, Dict[str, str]]])
     def run(self, parts: Variadic[Union[str, ByteStream, Part]]):
         """
         Generates text based on the given input parts.
@@ -182,7 +182,7 @@ def run(self, parts: Variadic[Union[str, ByteStream, Part]]):
             A heterogeneous list of strings, `ByteStream` or `Part` objects.
         :returns:
             A dictionary containing the following key:
-            - `answers`: A list of strings or dictionaries with function calls.
+            - `replies`: A list of strings or dictionaries with function calls.
         """
 
         converted_parts = [self._convert_part(p) for p in parts]
@@ -194,16 +194,16 @@ def run(self, parts: Variadic[Union[str, ByteStream, Part]]):
             safety_settings=self._safety_settings,
         )
         self._model.start_chat()
-        answers = []
+        replies = []
         for candidate in res.candidates:
             for part in candidate.content.parts:
                 if part.text != "":
-                    answers.append(part.text)
+                    replies.append(part.text)
                 elif part.function_call is not None:
                     function_call = {
                         "name": part.function_call.name,
                         "args": dict(part.function_call.args.items()),
                     }
-                    answers.append(function_call)
+                    replies.append(function_call)
 
-        return {"answers": answers}
+        return {"replies": replies}

integrations/google_ai/tests/generators/test_gemini.py

@@ -188,4 +188,4 @@ def test_from_dict(monkeypatch):
 def test_run():
     gemini = GoogleAIGeminiGenerator(model="gemini-pro")
     res = gemini.run("Tell me something cool")
-    assert len(res["answers"]) > 0
+    assert len(res["replies"]) > 0

integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/code_generator.py

@@ -24,7 +24,7 @@ class VertexAICodeGenerator:
 
         result = generator.run(prefix="def to_json(data):")
 
-        for answer in result["answers"]:
+        for answer in result["replies"]:
             print(answer)
 
         >>> ```python
@@ -92,17 +92,17 @@ def from_dict(cls, data: Dict[str, Any]) -> "VertexAICodeGenerator":
         """
         return default_from_dict(cls, data)
 
-    @component.output_types(answers=List[str])
+    @component.output_types(replies=List[str])
     def run(self, prefix: str, suffix: Optional[str] = None):
         """
         Generate code using a Google Vertex AI model.
 
         :param prefix: Code before the current point.
         :param suffix: Code after the current point.
         :returns: A dictionary with the following keys:
-            - `answers`: A list of generated code snippets.
+            - `replies`: A list of generated code snippets.
         """
         res = self._model.predict(prefix=prefix, suffix=suffix, **self._kwargs)
         # Handle the case where the model returns multiple candidates
-        answers = [c.text for c in res.candidates] if hasattr(res, "candidates") else [res.text]
-        return {"answers": answers}
+        replies = [c.text for c in res.candidates] if hasattr(res, "candidates") else [res.text]
+        return {"replies": replies}

integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/gemini.py

@@ -35,7 +35,7 @@ class VertexAIGeminiGenerator:
 
     gemini = VertexAIGeminiGenerator(project_id=project_id)
     result = gemini.run(parts = ["What is the most interesting thing you know?"])
-    for answer in result["answers"]:
+    for answer in result["replies"]:
         print(answer)
 
     >>> 1. **The Origin of Life:** How and where did life begin? The answers to this ...
@@ -175,14 +175,14 @@ def _convert_part(self, part: Union[str, ByteStream, Part]) -> Part:
             msg = f"Unsupported type {type(part)} for part {part}"
             raise ValueError(msg)
 
-    @component.output_types(answers=List[Union[str, Dict[str, str]]])
+    @component.output_types(replies=List[Union[str, Dict[str, str]]])
     def run(self, parts: Variadic[Union[str, ByteStream, Part]]):
         """
         Generates content using the Gemini model.
 
         :param parts: Prompt for the model.
         :returns: A dictionary with the following keys:
-            - `answers`: A list of generated content.
+            - `replies`: A list of generated content.
         """
         converted_parts = [self._convert_part(p) for p in parts]
 
@@ -194,16 +194,16 @@ def run(self, parts: Variadic[Union[str, ByteStream, Part]]):
             tools=self._tools,
         )
         self._model.start_chat()
-        answers = []
+        replies = []
         for candidate in res.candidates:
             for part in candidate.content.parts:
                 if part._raw_part.text != "":
-                    answers.append(part.text)
+                    replies.append(part.text)
                 elif part.function_call is not None:
                     function_call = {
                         "name": part.function_call.name,
                         "args": dict(part.function_call.args.items()),
                     }
-                    answers.append(function_call)
+                    replies.append(function_call)
 
-        return {"answers": answers}
+        return {"replies": replies}

integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/question_answering.py

@@ -29,7 +29,7 @@ class VertexAIImageQA:
 
     res = qa.run(image=image, question="What color is this dog")
 
-    print(res["answers"][0])
+    print(res["replies"][0])
 
     >>> white
     ```
@@ -82,14 +82,14 @@ def from_dict(cls, data: Dict[str, Any]) -> "VertexAIImageQA":
         """
         return default_from_dict(cls, data)
 
-    @component.output_types(answers=List[str])
+    @component.output_types(replies=List[str])
     def run(self, image: ByteStream, question: str):
         """Prompts model to answer a question about an image.
 
         :param image: The image to ask the question about.
         :param question: The question to ask.
         :returns: A dictionary with the following keys:
-            - `answers`: A list of answers to the question.
+            - `replies`: A list of answers to the question.
         """
-        answers = self._model.ask_question(image=Image(image.data), question=question, **self._kwargs)
-        return {"answers": answers}
+        replies = self._model.ask_question(image=Image(image.data), question=question, **self._kwargs)
+        return {"replies": replies}

integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/text_generator.py

@@ -28,7 +28,7 @@ class VertexAITextGenerator:
         generator = VertexAITextGenerator(project_id=project_id)
         res = generator.run("Tell me a good interview question for a software engineer.")
 
-        print(res["answers"][0])
+        print(res["replies"][0])
 
         >>> **Question:**
         >>> You are given a list of integers and a target sum.
@@ -109,26 +109,26 @@ def from_dict(cls, data: Dict[str, Any]) -> "VertexAITextGenerator":
             )
         return default_from_dict(cls, data)
 
-    @component.output_types(answers=List[str], safety_attributes=Dict[str, float], citations=List[Dict[str, Any]])
+    @component.output_types(replies=List[str], safety_attributes=Dict[str, float], citations=List[Dict[str, Any]])
     def run(self, prompt: str):
         """Prompts the model to generate text.
 
         :param prompt: The prompt to use for text generation.
         :returns:  A dictionary with the following keys:
-            - `answers`: A list of generated answers.
+            - `replies`: A list of generated replies.
             - `safety_attributes`: A dictionary with the [safety scores](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/responsible-ai#safety_attribute_descriptions)
               of each answer.
             - `citations`: A list of citations for each answer.
         """
         res = self._model.predict(prompt=prompt, **self._kwargs)
 
-        answers = []
+        replies = []
         safety_attributes = []
         citations = []
 
         for prediction in res.raw_prediction_response.predictions:
-            answers.append(prediction["content"])
+            replies.append(prediction["content"])
             safety_attributes.append(prediction["safetyAttributes"])
             citations.append(prediction["citationMetadata"]["citations"])
 
-        return {"answers": answers, "safety_attributes": safety_attributes, "citations": citations}
+        return {"replies": replies, "safety_attributes": safety_attributes, "citations": citations}