update base class to use the new ChatMessageSolver api (#15)

* semver

* update parent class

* fix

Author: JarbasAl

.github/workflows/build_tests.yml

@@ -13,7 +13,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v1
         with:
-          python-version: 3.8
+          python-version: "3.11"
       - name: Install Build Tools
         run: |
           python -m pip install build wheel

.github/workflows/license_tests.yml

@@ -16,7 +16,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v1
         with:
-          python-version: 3.8
+          python-version: "3.11"
       - name: Install Build Tools
         run: |
           python -m pip install build wheel

.github/workflows/publish_stable.yml

@@ -26,7 +26,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v1
         with:
-          python-version: 3.8
+          python-version: "3.11"
       - name: Install Build Tools
         run: |
           python -m pip install build wheel

.github/workflows/release_workflow.yml

@@ -46,7 +46,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v1
         with:
-          python-version: 3.8
+          python-version: "3.11"
       - name: Install Build Tools
         run: |
           python -m pip install build wheel

ovos_solver_openai_persona/__init__.py

@@ -12,18 +12,11 @@ def __init__(self, config=None):
         self.default_persona = config.get("persona") or "helpful, creative, clever, and very friendly."
 
     def get_chat_history(self, persona=None):
-        qa = self.qa_pairs[-1 * self.max_utts:]
         persona = persona or self.default_persona
         initial_prompt = f"You are a helpful assistant. " \
                          f"You give short and factual answers. " \
                          f"You are {persona}"
-        messages = [
-            {"role": "system", "content": initial_prompt},
-        ]
-        for q, a in qa:
-            messages.append({"role": "user", "content": q})
-            messages.append({"role": "assistant", "content": a})
-        return messages
+        return super().get_chat_history(initial_prompt)
 
     # officially exported Solver methods
     def get_spoken_answer(self, query: str,
@@ -40,13 +33,9 @@ def get_spoken_answer(self, query: str,
         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()
+        answer = super().get_spoken_answer(query, lang, units)
         if not answer or not answer.strip("?") or not answer.strip("_"):
             return None
-        if self.memory:
-            self.qa_pairs.append((query, answer))
         return answer
 
 
@@ -64,14 +53,14 @@ def get_spoken_answer(self, query: str,
 
 
 if __name__ == "__main__":
-    bot = OpenAIPersonaSolver({"key": "sk-xxxx", "api_url": "https://llama.smartgic.io/v1"})
-    for utt in bot.stream_utterances("describe quantum mechanics in simple terms"):
-        print(utt)
+    bot = OpenAIPersonaSolver(LLAMA_DEMO["ovos-solver-openai-persona-plugin"])
+    #for utt in bot.stream_utterances("describe quantum mechanics in simple terms"):
+    #    print(utt)
         #  Quantum mechanics is a branch of physics that studies the behavior of atoms and particles at the smallest scales.
         #  It describes how these particles interact with each other, move, and change energy levels.
         #  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"))
+    print(bot.spoken_answer("what is the definition of computer", lang="en-US"))
     # 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/engines.py

@@ -1,10 +1,13 @@
 import json
-from typing import Optional, Iterable
+from typing import Optional, Iterable, List, Dict
 
 import requests
 from ovos_plugin_manager.templates.solvers import QuestionSolver
 from ovos_utils.log import LOG
 
+from ovos_plugin_manager.templates.solvers import ChatMessageSolver
+
+MessageList = List[Dict[str, str]]  # for typing
 
 class OpenAICompletionsSolver(QuestionSolver):
     enable_tx = False
@@ -69,14 +72,14 @@ def get_spoken_answer(self, query: str,
         return answer
 
 
-class OpenAIChatCompletionsSolver(QuestionSolver):
+class OpenAIChatCompletionsSolver(ChatMessageSolver):
     enable_tx = False
     priority = 25
 
     def __init__(self, config=None):
         super().__init__(config)
         self.api_url = f"{self.config.get('api_url', 'https://api.openai.com/v1')}/chat/completions"
-        self.engine = self.config.get("model", "gpt-3.5-turbo")  # "ada" cheaper and faster, "davinci" better
+        self.engine = self.config.get("model", "gpt-4o-mini")  # "ada" cheaper and faster, "davinci" better
         self.stop_token = "<|im_end|>"
         self.key = self.config.get("key")
         if not self.key:
@@ -154,7 +157,7 @@ def _do_streaming_api_request(self, messages):
 
     def get_chat_history(self, initial_prompt=None):
         qa = self.qa_pairs[-1 * self.max_utts:]
-        initial_prompt = self.initial_prompt or "You are a helpful assistant."
+        initial_prompt = initial_prompt or self.initial_prompt or "You are a helpful assistant."
         messages = [
             {"role": "system", "content": initial_prompt},
         ]
@@ -163,37 +166,82 @@ def get_chat_history(self, initial_prompt=None):
             messages.append({"role": "assistant", "content": a})
         return messages
 
-    def get_prompt(self, utt, initial_prompt=None):
+    def get_messages(self, utt, initial_prompt=None) -> MessageList:
         messages = self.get_chat_history(initial_prompt)
         messages.append({"role": "user", "content": utt})
         return messages
 
     # asbtract Solver methods
-    def stream_utterances(self, query: str,
-                          lang: Optional[str] = None,
-                          units: Optional[str] = None) -> Iterable[str]:
+    def continue_chat(self, messages: MessageList,
+                      lang: Optional[str],
+                      units: Optional[str] = None) -> Optional[str]:
+        """Generate a response based on the chat history.
+
+        Args:
+            messages (List[Dict[str, str]]): List of chat messages, each containing 'role' and 'content'.
+            lang (Optional[str]): The language code for the response. If None, will be auto-detected.
+            units (Optional[str]): Optional unit system for numerical values.
+
+        Returns:
+            Optional[str]: The generated response or None if no response could be generated.
         """
-        Stream utterances for the given query as they become available.
+        response = self._do_api_request(messages)
+        answer = response.strip()
+        if not answer or not answer.strip("?") or not answer.strip("_"):
+            return None
+        if self.memory:
+            query = messages[-1]["content"]
+            self.qa_pairs.append((query, answer))
+        return answer
+
+    def stream_chat_utterances(self, messages: List[Dict[str, str]],
+                               lang: Optional[str] = None,
+                               units: Optional[str] = None) -> Iterable[str]:
+        """
+        Stream utterances for the given chat history as they become available.
 
         Args:
-            query (str): The query text.
+            messages: The chat messages.
             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 = ""
+        query = messages[-1]["content"]
+        if self.memory:
+            self.qa_pairs.append((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
                 if answer.strip():
+                    if self.memory:
+                        full_ans = f"{self.qa_pairs[-1][-1]}\n{answer}".strip()
+                        self.qa_pairs[-1] = (query, full_ans)
                     yield answer
                 answer = ""
 
+    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_messages(query)
+        yield from self.stream_chat_utterances(messages, lang, units)
+
     def get_spoken_answer(self, query: str,
                           lang: Optional[str] = None,
                           units: Optional[str] = None) -> Optional[str]:
@@ -208,11 +256,7 @@ def get_spoken_answer(self, query: str,
         Returns:
             str: The spoken answer as a text response.
         """
-        messages = self.get_prompt(query)
-        response = self._do_api_request(messages)
-        answer = response.strip()
-        if not answer or not answer.strip("?") or not answer.strip("_"):
-            return None
-        if self.memory:
-            self.qa_pairs.append((query, answer))
-        return answer
+        messages = self.get_messages(query)
+        # just for api compat since it's a subclass, shouldn't be directly used
+        return self.continue_chat(messages=messages, lang=lang, units=units)
+