MAIN-2806: add structured sections to AI output

src/robusta/core/model/base_params.py

@@ -99,6 +99,7 @@ class AIInvestigateParams(HolmesParams):
     :var runbooks: List of human readable recommended runbooks that holmes can use for the investigation.
     :var ask: Override question to ask holmes
     :var context: Additional information that can assist with the investigation
+    :var sections: Sections that Holmes should return. Dictionary where the key is the section's title and the value a description for Holmes (LLM)
 
     :example ask: What are all the issues in my cluster right now?
     :example runbooks: ["Try to get the pod logs and find errors", "get the pod yaml and check if there are finalizers"]
@@ -109,6 +110,7 @@ class AIInvestigateParams(HolmesParams):
     runbooks: Optional[List[str]]
     ask: Optional[str]
     context: Optional[Dict[str, Any]]
+    sections: Optional[Dict[str, str]] = None
 
 
 class HolmesToolsResult(BaseModel):

src/robusta/core/playbooks/internal/ai_integration.py

@@ -58,6 +58,7 @@ def ask_holmes(event: ExecutionBaseEvent, params: AIInvestigateParams):
             context=params.context if params.context else {},
             include_tool_calls=True,
             include_tool_call_results=True,
+            sections=params.sections
         )
         result = requests.post(f"{holmes_url}/api/investigate", data=holmes_req.json())
         result.raise_for_status()
@@ -123,7 +124,7 @@ def holmes_workload_health(event: ExecutionBaseEvent, params: HolmesWorkloadHeal
             analysis = json.loads(holmes_result.analysis)
             healthy = analysis.get("workload_healthy")
         except Exception:
-            logging.exception("Error in holmes response format, analysis did not return the expecrted json format.")
+            logging.exception("Error in holmes response format, analysis did not return the expected json format.")
             pass
 
         if params.silent_healthy and healthy:

src/robusta/core/reporting/holmes.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
 from pydantic import BaseModel
 
@@ -19,6 +19,7 @@ class HolmesRequest(BaseModel):
     context: Dict[str, Any]
     include_tool_calls: bool = False
     include_tool_call_results: bool = False
+    sections: Optional[Dict[str, str]] = None
 
 
 class HolmesConversationRequest(BaseModel):
@@ -50,6 +51,7 @@ class ToolCallResult(BaseModel):
 class HolmesResult(BaseModel):
     tool_calls: Optional[List[ToolCallResult]] = None
     analysis: Optional[str] = None
+    sections: Optional[Dict[str, Union[str, None]]] = None
     instructions: List[str] = []
 
 

src/robusta/core/sinks/robusta/dal/model_conversion.py

@@ -95,6 +95,7 @@ def append_to_structured_data_tool_calls(tool_calls: List[ToolCallResult], struc
             data_obj["metadata"] = {"description": tool_call.description, "tool_name": tool_call.tool_name}
             structured_data.append(data_obj)
 
+
     @staticmethod
     def add_ai_chat_data(structured_data: List[Dict], block: HolmesChatResultsBlock):
         structured_data.append(
@@ -117,6 +118,8 @@ def add_ai_chat_data(structured_data: List[Dict], block: HolmesChatResultsBlock)
 
     @staticmethod
     def add_ai_analysis_data(structured_data: List[Dict], block: HolmesResultsBlock):
+        if not block.holmes_result:
+            return
         structured_data.append(
             {
                 "type": "markdown",
@@ -125,6 +128,19 @@ def add_ai_analysis_data(structured_data: List[Dict], block: HolmesResultsBlock)
             }
         )
         ModelConversion.append_to_structured_data_tool_calls(block.holmes_result.tool_calls, structured_data)
+        if block.holmes_result.sections and len(block.holmes_result.sections) > 0:
+            transformed_sections = {}
+            for section_title, section_content in block.holmes_result.sections.items():
+                if section_content:
+                    transformed_sections[section_title] = Transformer.to_github_markdown(section_content)
+
+            structured_data.append(
+                {
+                    "type": "markdown",
+                    "metadata": {"type": "ai_investigation_sections", "createdAt": datetime_to_db_str(datetime.now())},
+                    "data": transformed_sections,
+                }
+            )
 
         structured_data.append({"type": "list", "data": block.holmes_result.instructions})