fixes to orchestration - smoke tested

Author: marktayl1

src/backend/sql_agents/agent_base.py

@@ -28,7 +28,6 @@ def __init__(
         self,
         agent_type: AgentType,
         config: AgentBaseConfig,
-        deployment_name: None,
         temperature: float = 0.0,
     ):
         """Initialize the base SQL agent.
@@ -41,7 +40,6 @@ def __init__(
         """
         self.agent_type = agent_type
         self.config = config
-        self.deployment_name = deployment_name
         self.temperature = temperature
         self.agent: AzureAIAgent = None
 
@@ -60,6 +58,15 @@ def num_candidates(self) -> Optional[int]:
         """
         return None
 
+    @property
+    def deployment_name(self) -> Optional[str]:
+        """Get the name of the model to be used for this agent.
+
+        Returns:
+            The model name, or None if not applicable.
+        """
+        return None
+
     @property
     def plugins(self) -> Optional[List[Union[str, Any]]]:
         """Get the plugins for this agent.

src/backend/sql_agents/agent_config.py

@@ -22,7 +22,7 @@ class AgentBaseConfig:
 
     def __init__(self, project_client: AIProjectClient, sql_from: str, sql_to: str):
 
-        self.ai_project_client: AIProjectClient = project_client
+        self.ai_project_client = project_client
         self.sql_from = sql_from
         self.sql_to = sql_to
 

src/backend/sql_agents/fixer/agent.py

@@ -2,9 +2,8 @@
 
 import logging
 
-from sql_agents.agent_base import (
-    BaseSQLAgent,
-)  # Ensure this import is correct and the module exists
+from common.models.api import AgentType
+from sql_agents.agent_base import BaseSQLAgent
 from sql_agents.fixer.response import FixerResponse
 
 logger = logging.getLogger(__name__)
@@ -18,3 +17,8 @@ class FixerAgent(BaseSQLAgent[FixerResponse]):
     def response_schema(self) -> type:
         """Get the response schema for the fixer agent."""
         return FixerResponse
+
+    @property
+    def deployment_name(self) -> str:
+        """Get the name of the model to use for the picker agent."""
+        return self.config.model_type[AgentType.FIXER]

src/backend/sql_agents/migrator/agent.py

@@ -2,6 +2,7 @@
 
 import logging
 
+from common.models.api import AgentType
 from sql_agents.agent_base import BaseSQLAgent
 from sql_agents.migrator.response import MigratorResponse
 
@@ -21,3 +22,8 @@ def response_schema(self) -> type:
     def num_candidates(self) -> int:
         """Get the number of candidates for the migrator agent."""
         return 3
+
+    @property
+    def deployment_name(self) -> str:
+        """Get the name of the model to use for the picker agent."""
+        return self.config.model_type[AgentType.MIGRATOR]

src/backend/sql_agents/picker/agent.py

@@ -2,6 +2,7 @@
 
 import logging
 
+from common.models.api import AgentType
 from sql_agents.agent_base import BaseSQLAgent
 from sql_agents.picker.response import PickerResponse
 
@@ -21,3 +22,8 @@ def response_schema(self) -> type:
     def num_candidates(self) -> int:
         """Get the number of candidates for the picker agent."""
         return 3
+
+    @property
+    def deployment_name(self) -> str:
+        """Get the name of the model to use for the picker agent."""
+        return self.config.model_type[AgentType.PICKER]

src/backend/sql_agents/picker/prompt.txt

@@ -18,15 +18,7 @@
 # Output structure description
 Your final answer should **strictly** adhere to the following JSON structure:
 {
-    "source_summary": "Here, you should provide a summary of the logic in the source query.",
-    "candidate_summaries": [
-        {
-            "candidate_index": "The index of the candidate in the list of candidates.",
-            "summary": "Here, you should provide a summary of the logic in this candidate query."
-        },
-        <more candidate summaries>
-    ],
     "conclusion": "A brief reasoning of which candidate you picked and why."
-    "summary": "A one sentence description about your activities."
     "picked_query": "The picked candidate query."
+    "summary": "A one sentence description about your activities."
 }

src/backend/sql_agents/picker/response.py

@@ -1,18 +1,11 @@
 from semantic_kernel.kernel_pydantic import KernelBaseModel
 
 
-class PickerCandidateSummary(KernelBaseModel):
-    candidate_index: int
-    candidate_summary: str
-
-
 class PickerResponse(KernelBaseModel):
     """
     The response of the picker agent.
     """
 
-    source_summary: str
-    candidate_summaries: list[PickerCandidateSummary]
     conclusion: str
     picked_query: str
     summary: str | None

src/backend/sql_agents/semantic_verifier/agent.py

@@ -44,6 +44,11 @@ def response_schema(self) -> type:
         """Get the response schema for the semantic verifier agent."""
         return SemanticVerifierResponse
 
+    @property
+    def deployment_name(self) -> str:
+        """Get the name of the model to use for the picker agent."""
+        return self.config.model_type[AgentType.SEMANTIC_VERIFIER]
+
     def get_kernel_arguments(self) -> Dict[str, Any]:
         """Get the kernel arguments for this agent.
 

src/backend/sql_agents/semantic_verifier/prompt.txt

@@ -9,7 +9,7 @@
 - You are allowed to make common sense assumptions about the data and return types.
 - Your final answer should be a JSON with the following fields: 'analysis', 'judgement', 'differences'.
 - If the scripts are not semantically equivalent, judgement would be 'Semantically Not Equivalent' and list the differences in the 'differences' field.
-- If the scripts are semantically equivalent, judgement would be 'Semantically Equivalent' and skip the differences field.
+- If the scripts are semantically equivalent, judgement would be 'Semantically Equivalent' and the differences filed would be an empty list.
 
 # Output structure description
 Your final answer should **strictly** adhere to the following JSON structure:

src/backend/sql_agents/semantic_verifier/setup.py

@@ -18,8 +18,9 @@ async def setup_semantic_verifier_agent(
 ) -> AzureAIAgent:
     """Setup the semantic verifier agent using the factory."""
     return await SQLAgentFactory.create_agent(
-        AgentType.SEMANTIC_VERIFIER,
-        config,
+        agent_type=AgentType.SEMANTIC_VERIFIER,
+        config=config,
+        temperature=0.0,
         source_query=source_query,
         target_query=target_query,
     )