refactor, date context

README.md

@@ -11,6 +11,8 @@ This demo shows a multi-turn conversation with an AI agent running inside a Temp
 
 ## Running the example
 
+From the /scripts directory:
+
 1. Run the worker: `poetry run python run_worker.py`
 2. In another terminal run the client with a prompt.
 

activities/tool_activities.py

@@ -1,24 +1,26 @@
 from dataclasses import dataclass
 from temporalio import activity
+from temporalio.exceptions import ApplicationError
 from ollama import chat, ChatResponse
 import json
-from temporalio.exceptions import ApplicationError
 
 
 @dataclass
-class OllamaPromptInput:
+class ToolPromptInput:
     prompt: str
     context_instructions: str
 
 
-class OllamaActivities:
+class ToolActivities:
     @activity.defn
-    def prompt_ollama(self, input: OllamaPromptInput) -> str:
+    def prompt_llm(self, input: ToolPromptInput) -> str:
         model_name = "qwen2.5:14b"
         messages = [
             {
                 "role": "system",
-                "content": input.context_instructions,
+                "content": input.context_instructions
+                + ". The current date is "
+                + get_current_date_human_readable(),
             },
             {
                 "role": "user",
@@ -41,3 +43,14 @@ class OllamaActivities:
             raise ApplicationError(f"Invalid JSON: {e}")
 
         return data
+
+
+def get_current_date_human_readable():
+    """
+    Returns the current date in a human-readable format.
+
+    Example: Wednesday, January 1, 2025
+    """
+    from datetime import datetime
+
+    return datetime.now().strftime("%A, %B %d, %Y")

models/data_types.py

@@ -0,0 +1,15 @@
+from dataclasses import dataclass
+from typing import Optional, Deque
+from models.tool_definitions import ToolsData
+
+
+@dataclass
+class ToolWorkflowParams:
+    conversation_summary: Optional[str] = None
+    prompt_queue: Optional[Deque[str]] = None
+
+
+@dataclass
+class CombinedInput:
+    tool_params: ToolWorkflowParams
+    tools_data: ToolsData

models/tool_definitions.py

@@ -0,0 +1,21 @@
+from dataclasses import dataclass
+from typing import List
+
+
+@dataclass
+class ToolArgument:
+    name: str
+    type: str
+    description: str
+
+
+@dataclass
+class ToolDefinition:
+    name: str
+    description: str
+    arguments: List[ToolArgument]
+
+
+@dataclass
+class ToolsData:
+    tools: List[ToolDefinition]

prompts/agent_prompt_generators.py

@@ -1,4 +1,4 @@
-from workflows import ToolsData
+from models.tool_definitions import ToolsData
 
 
 def generate_genai_prompt_from_tools_data(
@@ -51,7 +51,7 @@ def generate_genai_prompt_from_tools_data(
         '- If you have enough information and are confirming, use "next": "confirm". This is the final step once you have filled all args.'
     )
     prompt_lines.append(
-        '- Example of a good answer: {"response": "It seems we have all the information needed to search for flights. You will be flying from <city> to <city> from <date> to <date>. Is this correct?", "args":{"origin": "Seattle", "destination": "San Francisco", "dateFrom": "2025-01-04", "dateTo": "2025-01-08"}, "next": "confirm", "tool": "<toolName>" }'
+        '- Example of a good answer: {"response": "It seems we have all the information needed to search for flights. You will be flying from <city> to <city> from <date> to <date>. Is this correct?", "args":{"origin": "Seattle", "destination": "San Francisco", "dateDepart": "2025-01-04", "dateReturn": "2025-01-08"}, "next": "confirm", "tool": "<toolName>" }'
     )
     prompt_lines.append("- Return valid JSON without special characters.")
     prompt_lines.append("")

pyproject.toml

@@ -1,7 +1,7 @@
 [tool.poetry]
-name = "temporal-ollama-agent"
+name = "temporal-AI-agent"
 version = "0.1.0"
-description = "Temporal Ollama Agent"
+description = "Temporal AI Agent"
 license = "MIT"
 authors = ["Steve Androulakis <steve.androulakis@temporal.io>"]
 readme = "README.md"

scripts/end_chat.py

@@ -1,8 +1,7 @@
 import asyncio
-import sys
 
 from temporalio.client import Client
-from workflows import EntityOllamaWorkflow
+from workflows.tool_workflow import ToolWorkflow
 
 
 async def main():
@@ -11,10 +10,10 @@ async def main():
 
     workflow_id = "ollama-agent"
 
-    handle = client.get_workflow_handle_for(EntityOllamaWorkflow.run, workflow_id)
+    handle = client.get_workflow_handle_for(ToolWorkflow.run, workflow_id)
 
     # Sends a signal to the workflow
-    await handle.signal(EntityOllamaWorkflow.end_chat)
+    await handle.signal(ToolWorkflow.end_chat)
 
 
 if __name__ == "__main__":

scripts/get_history.py

@@ -1,7 +1,7 @@
 import asyncio
 
 from temporalio.client import Client
-from workflows import EntityOllamaWorkflow
+from workflows import ToolWorkflow
 
 
 async def main():
@@ -12,7 +12,7 @@ async def main():
     handle = client.get_workflow_handle(workflow_id)
 
     # Queries the workflow for the conversation history
-    history = await handle.query(EntityOllamaWorkflow.get_conversation_history)
+    history = await handle.query(ToolWorkflow.get_conversation_history)
 
     print("Conversation History")
     print(
@@ -20,7 +20,7 @@ async def main():
     )
 
     # Queries the workflow for the conversation summary
-    summary = await handle.query(EntityOllamaWorkflow.get_summary_from_history)
+    summary = await handle.query(ToolWorkflow.get_summary_from_history)
 
     if summary is not None:
         print("Conversation Summary:")

scripts/run_worker.py

@@ -4,23 +4,24 @@ import logging
 
 from temporalio.client import Client
 from temporalio.worker import Worker
-from workflows import EntityOllamaWorkflow
 
-from activities import OllamaActivities
+from activities.tool_activities import ToolActivities
+from workflows.tool_workflow import ToolWorkflow
+from workflows.parent_workflow import ParentWorkflow
 
 
 async def main():
     # Create client connected to server at the given address
     client = await Client.connect("localhost:7233")
-    activities = OllamaActivities()
+    activities = ToolActivities()
 
     # Run the worker
     with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor:
         worker = Worker(
             client,
             task_queue="ollama-task-queue",
-            workflows=[EntityOllamaWorkflow],
+            workflows=[ToolWorkflow, ParentWorkflow],
-            activities=[activities.prompt_ollama, activities.parse_tool_data],
+            activities=[activities.prompt_llm, activities.parse_tool_data],
             activity_executor=activity_executor,
         )
         await worker.run()

scripts/send_message.py

@@ -3,22 +3,16 @@ import sys
 
 from temporalio.client import Client
 
-# Import your dataclasses/types
+from models.data_types import CombinedInput, ToolsData, ToolWorkflowParams
-from workflows import (
+from models.tool_definitions import ToolDefinition, ToolArgument
-    OllamaParams,
+from workflows.tool_workflow import ToolWorkflow
-    EntityOllamaWorkflow,
-    ToolsData,
-    ToolDefinition,
-    ToolArgument,
-    CombinedInput,
-)
 
 
 async def main(prompt):
     # Construct your tool definitions in code
     search_flights_tool = ToolDefinition(
         name="SearchFlights",
-        description="Search for flights from an origin to a destination within a date range",
+        description="Search for return flights from an origin to a destination within a date range",
         arguments=[
             ToolArgument(
                 name="origin",
@@ -31,12 +25,12 @@ async def main(prompt):
                 description="Airport or city code for arrival (infer airport code from city)",
             ),
             ToolArgument(
-                name="dateFrom",
+                name="dateDepart",
                 type="ISO8601",
                 description="Start of date range in human readable format",
             ),
             ToolArgument(
-                name="dateTo",
+                name="dateReturn",
                 type="ISO8601",
                 description="End of date range in human readable format",
             ),
@@ -47,7 +41,7 @@ async def main(prompt):
     tools_data = ToolsData(tools=[search_flights_tool])
 
     combined_input = CombinedInput(
-        ollama_params=OllamaParams(None, None), tools_data=tools_data
+        tool_params=ToolWorkflowParams(None, None), tools_data=tools_data
     )
 
     # Create client connected to Temporal server
@@ -57,7 +51,7 @@ async def main(prompt):
 
     # Start or signal the workflow, passing OllamaParams and tools_data
     await client.start_workflow(
-        EntityOllamaWorkflow.run,
+        ToolWorkflow.run,
         combined_input,  # or pass custom summary/prompt_queue
         id=workflow_id,
         task_queue="ollama-task-queue",

workflows/parent_workflow.py

@@ -0,0 +1,14 @@
+from temporalio import workflow
+from .tool_workflow import ToolWorkflow, CombinedInput, ToolWorkflowParams
+
+
+@workflow.defn
+class ParentWorkflow:
+    @workflow.run
+    async def run(self, some_input: dict) -> dict:
+        combined_input = CombinedInput(
+            tool_params=ToolWorkflowParams(None, None), tools_data=some_input
+        )
+        child = workflow.start_child_workflow(ToolWorkflow.run, combined_input)
+        result = await child
+        return result

workflows/tool_workflow.py

@@ -1,67 +1,36 @@
-import yaml
 from collections import deque
-from dataclasses import dataclass
 from datetime import timedelta
 from typing import Deque, List, Optional, Tuple
 
 from temporalio import workflow
-
+from prompts.agent_prompt_generators import (
-with workflow.unsafe.imports_passed_through():
-    # Import the updated OllamaActivities and the new dataclass
-    from activities import OllamaActivities, OllamaPromptInput
-
-
-@dataclass
-class ToolArgument:
-    name: str
-    type: str
-    description: str
-
-
-@dataclass
-class ToolDefinition:
-    name: str
-    description: str
-    arguments: List[ToolArgument]
-
-
-@dataclass
-class ToolsData:
-    tools: List[ToolDefinition]
-
-
-@dataclass
-class OllamaParams:
-    conversation_summary: Optional[str] = None
-    prompt_queue: Optional[Deque[str]] = None
-
-
-@dataclass
-class CombinedInput:
-    ollama_params: OllamaParams
-    tools_data: ToolsData
-
-
-from agent_prompt_generators import (
     generate_genai_prompt_from_tools_data,
     generate_json_validation_prompt_from_tools_data,
 )
 
+with workflow.unsafe.imports_passed_through():
+    from activities.tool_activities import ToolActivities, ToolPromptInput
+    from prompts.agent_prompt_generators import (
+        generate_genai_prompt_from_tools_data,
+        generate_json_validation_prompt_from_tools_data,
+    )
+    from models.data_types import CombinedInput, ToolWorkflowParams
+
 
 @workflow.defn
-class EntityOllamaWorkflow:
+class ToolWorkflow:
     def __init__(self) -> None:
         self.conversation_history: List[Tuple[str, str]] = []
         self.prompt_queue: Deque[str] = deque()
         self.conversation_summary: Optional[str] = None
-        self.continue_as_new_per_turns: int = 250
         self.chat_ended: bool = False
         self.tool_data = None
+        self.max_turns_before_continue: int = 250
 
     @workflow.run
     async def run(self, combined_input: CombinedInput) -> str:
 
-        params = combined_input.ollama_params
+        params = combined_input.tool_params
         tools_data = combined_input.tools_data
 
         if params and params.conversation_summary:
@@ -92,14 +61,14 @@ class EntityOllamaWorkflow:
                 workflow.logger.info("Prompt: " + prompt)
 
                 # Pass a single input object
-                prompt_input = OllamaPromptInput(
+                prompt_input = ToolPromptInput(
                     prompt=prompt,
                     context_instructions=context_instructions,
                 )
 
                 # Call activity with one argument
                 responsePrechecked = await workflow.execute_activity_method(
-                    OllamaActivities.prompt_ollama,
+                    ToolActivities.prompt_llm,
                     prompt_input,
                     schedule_to_close_timeout=timedelta(seconds=20),
                 )
@@ -113,14 +82,14 @@ class EntityOllamaWorkflow:
                 workflow.logger.info("Prompt: " + prompt)
 
                 # Pass a single input object
-                prompt_input = OllamaPromptInput(
+                prompt_input = ToolPromptInput(
                     prompt=responsePrechecked,
                     context_instructions=json_validation_instructions,
                 )
 
                 # Call activity with one argument
                 response = await workflow.execute_activity_method(
-                    OllamaActivities.prompt_ollama,
+                    ToolActivities.prompt_llm,
                     prompt_input,
                     schedule_to_close_timeout=timedelta(seconds=20),
                 )
@@ -130,7 +99,7 @@ class EntityOllamaWorkflow:
 
                 # Call activity with one argument
                 tool_data = await workflow.execute_activity_method(
-                    OllamaActivities.parse_tool_data,
+                    ToolActivities.parse_tool_data,
                     response,
                     schedule_to_close_timeout=timedelta(seconds=1),
                 )
@@ -141,29 +110,29 @@ class EntityOllamaWorkflow:
                     return self.tool_data
 
                 # Continue as new after X turns
-                if len(self.conversation_history) >= self.continue_as_new_per_turns:
+                if len(self.conversation_history) >= self.max_turns_before_continue:
                     # Summarize conversation
                     summary_context, summary_prompt = self.prompt_summary_with_history()
-                    summary_input = OllamaPromptInput(
+                    summary_input = ToolPromptInput(
                         prompt=summary_prompt,
                         context_instructions=summary_context,
                     )
 
                     self.conversation_summary = await workflow.start_activity_method(
-                        OllamaActivities.prompt_ollama,
+                        ToolActivities.prompt_llm,
                         summary_input,
                         schedule_to_close_timeout=timedelta(seconds=20),
                     )
 
                     workflow.logger.info(
                         "Continuing as new after %i turns."
-                        % self.continue_as_new_per_turns,
+                        % self.max_turns_before_continue,
                     )
 
                     workflow.continue_as_new(
                         args=[
                             CombinedInput(
-                                ollama_params=OllamaParams(
+                                tool_params=ToolWorkflowParams(
                                     conversation_summary=self.conversation_summary,
                                     prompt_queue=self.prompt_queue,
                                 ),
@@ -179,13 +148,13 @@ class EntityOllamaWorkflow:
                 if len(self.conversation_history) > 1:
                     # Summarize conversation
                     summary_context, summary_prompt = self.prompt_summary_with_history()
-                    summary_input = OllamaPromptInput(
+                    summary_input = ToolPromptInput(
                         prompt=summary_prompt,
                         context_instructions=summary_context,
                     )
 
                     self.conversation_summary = await workflow.start_activity_method(
-                        OllamaActivities.prompt_ollama,
+                        ToolActivities.prompt_llm,
                         summary_input,
                         schedule_to_close_timeout=timedelta(seconds=20),
                     )