Use large github action machine to run e2e tests (#230)

demos/hr_agent/arch_config.yaml

@@ -8,7 +8,7 @@ listener:
 llm_providers:
   - name: OpenAI
     provider: openai
-    access_key: OPENAI_API_KEY
+    access_key: $OPENAI_API_KEY
     model: gpt-4o-mini
     default: true
 
@@ -24,7 +24,9 @@ endpoints:
 
 # default system prompt used by all prompt targets
 system_prompt: |
-  You are a Workforce assistant that helps on workforce planning and HR decision makers with reporting and workfoce planning. NOTHING ELSE. When you get data in json format, offer some summary but don't be too verbose.
+  You are a Workforce assistant that helps on workforce planning and HR decision makers with reporting and workforce planning. Use following rules when responding,
+  - when you get data in json format, offer some summary but don't be too verbose
+  - be concise, to the point and do not over analyze the data
 
 prompt_targets:
     - name: workforce

demos/hr_agent/main.py

@@ -30,7 +30,7 @@ with open("workforce_data.json") as file:
 
 
 # Define the request model
-class WorkforceRequset(BaseModel):
+class WorkforceRequest(BaseModel):
     region: str
     staffing_type: str
     data_snapshot_days_ago: Optional[int] = None
@@ -74,7 +74,7 @@ def send_slack_message(request: SlackRequest):
 
 # Post method for device summary
 @app.post("/agent/workforce")
-def get_workforce(request: WorkforceRequset):
+def get_workforce(request: WorkforceRequest):
     """
     Endpoint to workforce data by region, staffing type at a given point in time.
     """
@@ -90,7 +90,7 @@ def get_workforce(request: WorkforceRequset):
         "region": region,
         "staffing_type": f"Staffing agency: {staffing_type}",
         "headcount": f"Headcount: {int(workforce_data_df[(workforce_data_df['region']==region) & (workforce_data_df['data_snapshot_days_ago']==data_snapshot_days_ago)][staffing_type].values[0])}",
-        "satisfaction": f"Satisifaction: {float(workforce_data_df[(workforce_data_df['region']==region) & (workforce_data_df['data_snapshot_days_ago']==data_snapshot_days_ago)]['satisfaction'].values[0])}",
+        "satisfaction": f"Satisfaction: {float(workforce_data_df[(workforce_data_df['region']==region) & (workforce_data_df['data_snapshot_days_ago']==data_snapshot_days_ago)]['satisfaction'].values[0])}",
     }
     return response
 

demos/insurance_agent/arch_config.yaml

@@ -10,7 +10,7 @@ system_prompt: |
 llm_providers:
   - name: OpenAI
     provider: openai
-    access_key: OPENAI_API_KEY
+    access_key: $OPENAI_API_KEY
     model: gpt-4o
     default: true
 

demos/network_agent/arch_config.yaml

@@ -8,7 +8,7 @@ listener:
 llm_providers:
   - name: OpenAI
     provider: openai
-    access_key: OPENAI_API_KEY
+    access_key: $OPENAI_API_KEY
     model: gpt-3.5-turbo
     default: true