fix:moderize_signatures

ovos_solver_openai_persona/__init__.py

@@ -1,3 +1,5 @@
+from typing import Optional
+
 from ovos_solver_openai_persona.engines import OpenAIChatCompletionsSolver
 
 
@@ -24,10 +26,21 @@ def get_chat_history(self, persona=None):
         return messages
 
     # officially exported Solver methods
-    def get_spoken_answer(self, query, context=None):
-        context = context or {}
-        persona = context.get("persona") or self.default_persona
-        messages = self.get_prompt(query, persona)
+    def get_spoken_answer(self, query: str,
+                          lang: Optional[str] = None,
+                          units: Optional[str] = None) -> Optional[str]:
+        """
+        Obtain the spoken answer for a given query.
+
+        Args:
+            query (str): The query text.
+            lang (Optional[str]): Optional language code. Defaults to None.
+            units (Optional[str]): Optional units for the query. Defaults to None.
+
+        Returns:
+            str: The spoken answer as a text response.
+        """
+        messages = self.get_prompt(query, self.default_persona)
         response = self._do_api_request(messages)
         answer = response.strip()
         if not answer or not answer.strip("?") or not answer.strip("_"):
@@ -46,7 +59,6 @@ def get_spoken_answer(self, query, context=None):
         #  Think of it like playing with toy building blocks that represent particles.
         #  Instead of rigid structures, these particles can be in different energy levels or "states." Quantum mechanics helps scientists understand and predict these states, making it crucial for many fields like chemistry, materials science, and engineering.
 
-
     # Quantum mechanics is a branch of physics that deals with the behavior of particles on a very small scale, such as atoms and subatomic particles. It explores the idea that particles can exist in multiple states at once and that their behavior is not predictable in the traditional sense.
-    print(bot.spoken_answer("Quem encontrou o caminho maritimo para o Brazil", {"lang": "pt-pt"}))
-    # Explorador português Pedro Álvares Cabral é creditado com a descoberta do Brasil em 1500
+    print(bot.spoken_answer("Quem encontrou o caminho maritimo para o Brazil", lang="pt-pt"))
+    # O português Pedro Álvares Cabral encontrou o caminho marítimo para o Brasil em 1500. Ele foi o responsável por descobrir o litoral brasileiro, embora Cristóvão Colombo tenha chegado à América do Sul em 1498, cinco anos antes. Cabral desembarcou na atual costa de Alagoas, no Nordeste do Brasil.

ovos_solver_openai_persona/dialog_transformers.py

@@ -24,4 +24,4 @@ def transform(self, dialog: str, context: dict = None) -> Tuple[str, dict]:
         prompt = context.get("prompt") or self.config.get("rewrite_prompt")
         if not prompt:
             return dialog, context
-        return self.solver.get_spoken_answer(f"{prompt} : {dialog}"), context
+        return self.solver.get_spoken_answer(f"{prompt} : {dialog}", lang=context.get("lang")), context

ovos_solver_openai_persona/engines.py

@@ -1,4 +1,5 @@
 import json
+from typing import Optional, Iterable
 
 import requests
 from ovos_plugin_manager.templates.solvers import QuestionSolver
@@ -47,7 +48,20 @@ def _do_api_request(self, prompt):
         return response["choices"][0]["text"]
 
     # officially exported Solver methods
-    def get_spoken_answer(self, query, context=None):
+    def get_spoken_answer(self, query: str,
+                          lang: Optional[str] = None,
+                          units: Optional[str] = None) -> Optional[str]:
+        """
+        Obtain the spoken answer for a given query.
+
+        Args:
+            query (str): The query text.
+            lang (Optional[str]): Optional language code. Defaults to None.
+            units (Optional[str]): Optional units for the query. Defaults to None.
+
+        Returns:
+            str: The spoken answer as a text response.
+        """
         response = self._do_api_request(query)
         answer = response.strip()
         if not answer or not answer.strip("?") or not answer.strip("_"):
@@ -154,20 +168,46 @@ def get_prompt(self, utt, initial_prompt=None):
         messages.append({"role": "user", "content": utt})
         return messages
 
-    # officially exported Solver methods
-    def stream_utterances(self, query):
+    # asbtract Solver methods
+    def stream_utterances(self, query: str,
+                          lang: Optional[str] = None,
+                          units: Optional[str] = None) -> Iterable[str]:
+        """
+        Stream utterances for the given query as they become available.
+
+        Args:
+            query (str): The query text.
+            lang (Optional[str]): Optional language code. Defaults to None.
+            units (Optional[str]): Optional units for the query. Defaults to None.
+
+        Returns:
+            Iterable[str]: An iterable of utterances.
+        """
         messages = self.get_prompt(query)
         answer = ""
         for chunk in self._do_streaming_api_request(messages):
             answer += chunk
             if any(chunk.endswith(p) for p in [".", "!", "?", "\n", ":"]):
                 if len(chunk) >= 2 and chunk[-2].isdigit() and chunk[-1] == ".":
-                    continue # dont split numbers
+                    continue  # dont split numbers
                 if answer.strip():
                     yield answer
                 answer = ""
 
-    def get_spoken_answer(self, query, context=None):
+    def get_spoken_answer(self, query: str,
+                          lang: Optional[str] = None,
+                          units: Optional[str] = None) -> Optional[str]:
+        """
+        Obtain the spoken answer for a given query.
+
+        Args:
+            query (str): The query text.
+            lang (Optional[str]): Optional language code. Defaults to None.
+            units (Optional[str]): Optional units for the query. Defaults to None.
+
+        Returns:
+            str: The spoken answer as a text response.
+        """
         messages = self.get_prompt(query)
         response = self._do_api_request(messages)
         answer = response.strip()
@@ -176,69 +216,3 @@ def get_spoken_answer(self, query, context=None):
         if self.memory:
             self.qa_pairs.append((query, answer))
         return answer
-
-
-# Base models
-class GPT35Turbo(OpenAIChatCompletionsSolver):
-    def __init__(self, config=None):
-        config = config or {}
-        config["model"] = "gpt-3.5-turbo"
-        super().__init__(config=config)
-
-
-class AdaSolver(OpenAICompletionsSolver):
-    def __init__(self, config=None):
-        config = config or {}
-        config["model"] = "ada"
-        super().__init__(config=config)
-
-
-class BabbageSolver(OpenAICompletionsSolver):
-    def __init__(self, config=None):
-        config = config or {}
-        config["model"] = "babbage"
-        super().__init__(config=config)
-
-
-class CurieSolver(OpenAICompletionsSolver):
-    def __init__(self, config=None):
-        config = config or {}
-        config["model"] = "curie"
-        super().__init__(config=config)
-
-
-class DavinciSolver(OpenAICompletionsSolver):
-    def __init__(self, config=None):
-        config = config or {}
-        config["model"] = "davinci"
-        super().__init__(config=config)
-
-
-class Davinci2Solver(OpenAICompletionsSolver):
-    def __init__(self, config=None):
-        config = config or {}
-        config["model"] = "text-davinci-02"
-        super().__init__(config=config)
-
-
-class Davinci3Solver(OpenAICompletionsSolver):
-    def __init__(self, config=None):
-        config = config or {}
-        config["model"] = "text-davinci-03"
-        super().__init__(config=config)
-
-
-# Code completion
-class DavinciCodeSolver(OpenAICompletionsSolver):
-    def __init__(self, config=None):
-        config = config or {}
-        config["model"] = "code-davinci-002"
-        super().__init__(config=config)
-
-
-class CushmanCodeSolver(OpenAICompletionsSolver):
-    def __init__(self, config=None):
-        config = config or {}
-        config["model"] = "code-cushman-001"
-        super().__init__(config=config)
-