Merge branch 'development' into main

pyproject.toml

@@ -1,5 +1,5 @@
 [tool.poetry]
-name = "temporal-AI-agent"
+name = "temporal_AI_agent"
 version = "0.1.0"
 description = "Temporal AI Agent"
 license = "MIT"
@@ -13,7 +13,7 @@ packages = [
 ]
 
 [tool.poetry.urls]
-"Bug Tracker" = "https://github.com/temporalio/samples-python/issues"
+"Bug Tracker" = "https://github.com/temporal-community/temporal-ai-agent/issues"
 
 [tool.poe.tasks]
 format = [{cmd = "black ."}, {cmd = "isort ."}]
@@ -43,9 +43,16 @@ gtfs-kit = "^10.1.1"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.3"
+pytest-asyncio = "^0.18.3"
 black = "^23.7"
 isort = "^5.12"
 
 [build-system]
 requires = ["poetry-core>=1.4.0"]
 build-backend = "poetry.core.masonry.api"
+
+[tool.pytest.ini_options]
+asyncio_mode = "auto"
+log_cli = true
+log_cli_level = "INFO"
+log_cli_format = "%(asctime)s [%(levelname)8s] %(message)s (%(filename)s:%(lineno)s)"

todo.md

@@ -10,11 +10,14 @@
 [ ] financial advise - args being freeform customer input about their financial situation, goals
     [ ] tool is maybe a new tool asking the LLM to advise
 
+
 [ ] LLM failure->autoswitch: <br />
     - detect failure in the activity using failurecount <br />
     - activity switches to secondary LLM defined in .env
     - activity reports switch to workflow
 
+[ ] for demo simulate failure  - add utilities/simulated failures from pipeline demo <br />
+
 [ ] ask the ai agent how it did at the end of the conversation, was it efficient? successful? insert a search attribute to document that before return <br />
 - Insight into the agent’s performance <br />
 [ ] non-retry the api key error - "Invalid API Key provided: sk_test_**J..." and "AuthenticationError" <br />

workflows/agent_goal_workflow.py

@@ -108,7 +108,7 @@ class AgentGoalWorkflow:
                         conversation_history=self.conversation_history,
                         agent_goal=self.goal,
                     )
-                    validation_result = await workflow.execute_activity(
+                    validation_result = await workflow.execute_activity_method(
                         ToolActivities.agent_validatePrompt,
                         args=[validation_input],
                         schedule_to_close_timeout=LLM_ACTIVITY_SCHEDULE_TO_CLOSE_TIMEOUT,
@@ -134,7 +134,7 @@ class AgentGoalWorkflow:
                 prompt_input = ToolPromptInput(prompt=prompt, context_instructions=context_instructions)
 
                 # connect to LLM and execute to get next steps
-                tool_data = await workflow.execute_activity(
+                tool_data = await workflow.execute_activity_method(
                     ToolActivities.agent_toolPlanner,
                     prompt_input,
                     schedule_to_close_timeout=LLM_ACTIVITY_SCHEDULE_TO_CLOSE_TIMEOUT,
@@ -316,8 +316,9 @@ class AgentGoalWorkflow:
     async def lookup_wf_env_settings(self, combined_input: CombinedInput)->None:
         env_lookup_input = EnvLookupInput(
             show_confirm_env_var_name = "SHOW_CONFIRM", 
-            show_confirm_default = True)
+            show_confirm_default = True,
-        env_output:EnvLookupOutput = await workflow.execute_activity(
+        )
+        env_output:EnvLookupOutput = await workflow.execute_activity_method(
             ToolActivities.get_wf_env_vars, 
             env_lookup_input,
             start_to_close_timeout=LLM_ACTIVITY_START_TO_CLOSE_TIMEOUT,