prompt generator overhaul

activities/tool_activities.py

@@ -3,7 +3,6 @@ from temporalio import activity
 from temporalio.exceptions import ApplicationError
 from ollama import chat, ChatResponse
 import json
-from models.tool_definitions import ToolsData
 from typing import Sequence
 from temporalio.common import RawValue
 
@@ -16,7 +15,7 @@ class ToolPromptInput:
 
 class ToolActivities:
     @activity.defn
-    def prompt_llm(self, input: ToolPromptInput) -> str:
+    def prompt_llm(self, input: ToolPromptInput) -> dict:
         model_name = "qwen2.5:14b"
         messages = [
             {
@@ -32,62 +31,14 @@ class ToolActivities:
         ]
 
         response: ChatResponse = chat(model=model_name, messages=messages)
-        return response.message.content
 
-    @activity.defn
-    def parse_tool_data(self, json_str: str) -> dict:
-        """
-        Parses a JSON string into a dictionary.
-        Raises a ValueError if the JSON is invalid.
-        """
         try:
-            data = json.loads(json_str)
+            data = json.loads(response.message.content)
         except json.JSONDecodeError as e:
             raise ApplicationError(f"Invalid JSON: {e}")
 
         return data
 
-    @activity.defn
-    def validate_and_parse_json(
-        self,
-        response_prechecked: str,
-        tools_data: ToolsData,
-        conversation_history: str,
-    ) -> dict:
-        """
-        1) Build JSON validation instructions
-        2) Call LLM with those instructions
-        3) Parse the result
-        4) If parsing fails, raise exception -> triggers retry
-        """
-
-        # 1) Build validation instructions
-        #   (Generate the validation prompt exactly as you do in your workflow.)
-        from prompts.agent_prompt_generators import (
-            generate_json_validation_prompt_from_tools_data,
-        )
-
-        validation_prompt = generate_json_validation_prompt_from_tools_data(
-            tools_data, conversation_history, response_prechecked
-        )
-
-        # 2) Call LLM
-        prompt_input = ToolPromptInput(
-            prompt=response_prechecked,
-            context_instructions=validation_prompt,
-        )
-        validated_response = self.prompt_llm(prompt_input)
-
-        # 3) Parse
-        #    If parse fails, we raise ApplicationError -> triggers retry
-        try:
-            parsed = self.parse_tool_data(validated_response)
-        except Exception as e:
-            raise ApplicationError(f"Failed to parse validated JSON: {e}")
-
-        # 4) If we get here, parse succeeded
-        return parsed
-
 
 def get_current_date_human_readable():
     """