Retry refactor

edsl/agents/Invigilator.py

@@ -2,14 +2,13 @@
 
 from typing import Dict, Any, Optional
 
-from edsl.exceptions import AgentRespondedWithBadJSONError
 from edsl.prompts.Prompt import Prompt
 from edsl.utilities.decorators import sync_wrapper, jupyter_nb_handler
 from edsl.prompts.registry import get_classes as prompt_lookup
 from edsl.exceptions.questions import QuestionAnswerValidationError
-from edsl.agents.PromptConstructionMixin import PromptConstructorMixin
 from edsl.agents.InvigilatorBase import InvigilatorBase
 from edsl.data_transfer_models import AgentResponseDict, EDSLResultObjectInput
+from edsl.agents.PromptConstructor import PromptConstructor
 
 
 class NotApplicable(str):
@@ -19,9 +18,13 @@ def __new__(cls):
         return instance
 
 
-class InvigilatorAI(PromptConstructorMixin, InvigilatorBase):
+class InvigilatorAI(InvigilatorBase):
     """An invigilator that uses an AI model to answer questions."""
 
+    def get_prompts(self) -> Dict[str, Prompt]:
+        """Return the prompts used."""
+        return self.prompt_constructor.get_prompts()
+
     async def async_answer_question(self) -> AgentResponseDict:
         """Answer a question using the AI model.
 

edsl/agents/InvigilatorBase.py

@@ -14,6 +14,7 @@
 from edsl.language_models.LanguageModel import LanguageModel
 
 from edsl.data_transfer_models import EDSLResultObjectInput
+from edsl.agents.PromptConstructor import PromptConstructor
 
 
 class InvigilatorBase(ABC):
@@ -27,16 +28,7 @@ class InvigilatorBase(ABC):
 
     This returns an empty prompt because there is no memory the agent needs to have at q0.
 
-    >>> InvigilatorBase.example().create_memory_prompt("q0")
-    Prompt(text=\"""\""")
 
-    >>> i = InvigilatorBase.example()
-    >>> i.current_answers = {"q0": "Prior answer"}
-    >>> i.memory_plan.add_single_memory("q1", "q0")
-    >>> i.create_memory_prompt("q1")
-    Prompt(text=\"""
-            Before the question you are now answering, you already answered the following question(s):
-    ...
     """
 
     def __init__(
@@ -72,6 +64,11 @@ def __init__(
             None  # placeholder for the raw response from the model
         )
 
+    @property
+    def prompt_constructor(self) -> PromptConstructor:
+        """Return the prompt constructor."""
+        return PromptConstructor(self)
+
     def to_dict(self):
         attributes = [
             "agent",
@@ -207,22 +204,6 @@ async def main():
 
         return main()
 
-    def create_memory_prompt(self, question_name: str) -> Prompt:
-        """Create a memory for the agent.
-
-        The returns a memory prompt for the agent.
-
-        >>> i = InvigilatorBase.example()
-        >>> i.current_answers = {"q0": "Prior answer"}
-        >>> i.memory_plan.add_single_memory("q1", "q0")
-        >>> p = i.create_memory_prompt("q1")
-        >>> p.text.strip().replace("\\n", " ").replace("\\t", " ")
-        'Before the question you are now answering, you already answered the following question(s):          Question: Do you like school?  Answer: Prior answer'
-        """
-        return self.memory_plan.get_memory_prompt_fragment(
-            question_name, self.current_answers
-        )
-
     @classmethod
     def example(
         cls, throw_an_exception=False, question=None, scenario=None, survey=None
@@ -285,9 +266,9 @@ def example(
 
         memory_plan = MemoryPlan(survey=survey)
         current_answers = None
-        from edsl.agents.PromptConstructionMixin import PromptConstructorMixin
+        from edsl.agents.PromptConstructor import PromptConstructor
 
-        class InvigilatorExample(PromptConstructorMixin, InvigilatorBase):
+        class InvigilatorExample(InvigilatorBase):
             """An example invigilator."""
 
             async def async_answer_question(self):

edsl/agents/PromptConstructionMixin.py → edsl/agents/PromptConstructor.py

@@ -137,7 +137,7 @@ def get_prompts(self, **kwargs) -> Dict[str, Prompt]:
         }
 
 
-class PromptConstructorMixin:
+class PromptConstructor:
     """Mixin for constructing prompts for the LLM call.
 
     The pieces of a prompt are:
@@ -149,14 +149,25 @@ class PromptConstructorMixin:
     This is mixed into the Invigilator class.
     """
 
-    prompt_plan = PromptPlan()
+    def __init__(self, invigilator):
+        self.invigilator = invigilator
+        self.agent = invigilator.agent
+        self.question = invigilator.question
+        self.scenario = invigilator.scenario
+        self.survey = invigilator.survey
+        self.model = invigilator.model
+        self.current_answers = invigilator.current_answers
+        self.memory_plan = invigilator.memory_plan
+        self.prompt_plan = PromptPlan()  # Assuming PromptPlan is defined elsewhere
+
+        # prompt_plan = PromptPlan()
 
     @property
     def agent_instructions_prompt(self) -> Prompt:
         """
         >>> from edsl.agents.InvigilatorBase import InvigilatorBase
         >>> i = InvigilatorBase.example()
-        >>> i.agent_instructions_prompt
+        >>> i.prompt_constructor.agent_instructions_prompt
         Prompt(text=\"""You are answering questions as if you were a human. Do not break character.\""")
         """
         if not hasattr(self, "_agent_instructions_prompt"):
@@ -176,7 +187,7 @@ def agent_persona_prompt(self) -> Prompt:
         """
         >>> from edsl.agents.InvigilatorBase import InvigilatorBase
         >>> i = InvigilatorBase.example()
-        >>> i.agent_persona_prompt
+        >>> i.prompt_constructor.agent_persona_prompt
         Prompt(text=\"""You are an agent with the following persona:
         {'age': 22, 'hair': 'brown', 'height': 5.5}\""")
 
@@ -231,7 +242,7 @@ def question_instructions_prompt(self) -> Prompt:
         """
         >>> from edsl.agents.InvigilatorBase import InvigilatorBase
         >>> i = InvigilatorBase.example()
-        >>> i.question_instructions_prompt
+        >>> i.prompt_constructor.question_instructions_prompt
         Prompt(text=\"""...
         ...
         """
@@ -329,6 +340,23 @@ def prior_question_memory_prompt(self) -> Prompt:
             self._prior_question_memory_prompt = memory_prompt
         return self._prior_question_memory_prompt
 
+    def create_memory_prompt(self, question_name: str) -> Prompt:
+        """Create a memory for the agent.
+
+        The returns a memory prompt for the agent.
+
+        >>> from edsl.agents.InvigilatorBase import InvigilatorBase
+        >>> i = InvigilatorBase.example()
+        >>> i.current_answers = {"q0": "Prior answer"}
+        >>> i.memory_plan.add_single_memory("q1", "q0")
+        >>> p = i.prompt_constructor.create_memory_prompt("q1")
+        >>> p.text.strip().replace("\\n", " ").replace("\\t", " ")
+        'Before the question you are now answering, you already answered the following question(s):          Question: Do you like school?  Answer: Prior answer'
+        """
+        return self.memory_plan.get_memory_prompt_fragment(
+            question_name, self.current_answers
+        )
+
     def construct_system_prompt(self) -> Prompt:
         """Construct the system prompt for the LLM call."""
         import warnings

edsl/inference_services/TestService.py

@@ -7,6 +7,7 @@
 from edsl.utilities.utilities import fix_partial_correct_response
 
 from edsl.enums import InferenceServiceType
+import random
 
 
 class TestService(InferenceServiceABC):
@@ -60,22 +61,16 @@ async def async_execute_model_call(
                 await asyncio.sleep(0.1)
                 # return {"message": """{"answer": "Hello, world"}"""}
                 if hasattr(self, "throw_exception") and self.throw_exception:
-                    raise Exception("This is a test error")
+                    if hasattr(self, "exception_probability"):
+                        p = self.exception_probability
+                    else:
+                        p = 1
+
+                    if random.random() < p:
+                        raise Exception("This is a test error")
                 return {
                     "message": [{"text": f"{self._canned_response}"}],
                     "usage": {"prompt_tokens": 1, "completion_tokens": 1},
                 }
 
         return TestServiceLanguageModel
-
-    # _inference_service_ = "openai"
-    # _env_key_name_ = "OPENAI_API_KEY"
-    # _base_url_ = None
-
-    # _sync_client_ = openai.OpenAI
-    # _async_client_ = openai.AsyncOpenAI
-
-    # _sync_client_instance = None
-    # _async_client_instance = None
-
-    # key_sequence = ["choices", 0, "message", "content"]