chat worked

backend/src/plugins/llm_analysis/plugin.py

@@ -23,6 +23,11 @@ from .service import ScreenshotService, LLMClient
 from .models import LLMProviderType, ValidationStatus, ValidationResult, DetectedIssue
 from ...models.llm import ValidationRecord
 from ...core.task_manager.context import TaskContext
+from ...services.llm_prompt_templates import (
+    DEFAULT_LLM_PROMPTS,
+    normalize_llm_settings,
+    render_prompt,
+)
 
 # [DEF:DashboardValidationPlugin:Class]
 # @PURPOSE: Plugin for automated dashboard health analysis using LLMs.
@@ -181,7 +186,16 @@ class DashboardValidationPlugin(PluginBase):
                 )
                 
                 llm_log.info(f"Analyzing dashboard {dashboard_id} with LLM")
-                analysis = await llm_client.analyze_dashboard(screenshot_path, logs)
+                llm_settings = normalize_llm_settings(config_mgr.get_config().settings.llm)
+                dashboard_prompt = llm_settings["prompts"].get(
+                    "dashboard_validation_prompt",
+                    DEFAULT_LLM_PROMPTS["dashboard_validation_prompt"],
+                )
+                analysis = await llm_client.analyze_dashboard(
+                    screenshot_path,
+                    logs,
+                    prompt_template=dashboard_prompt,
+                )
                 
                 # Log analysis summary to task logs for better visibility
                 llm_log.info(f"[ANALYSIS_SUMMARY] Status: {analysis['status']}")
@@ -341,22 +355,18 @@ class DocumentationPlugin(PluginBase):
                     default_model=db_provider.default_model
                 )
                 
-                prompt = f"""
-                Generate professional documentation for the following dataset and its columns.
-                Dataset: {dataset.get('table_name')}
-                Columns: {columns_data}
-                
-                Provide the documentation in JSON format:
-                {{
-                    "dataset_description": "General description of the dataset",
-                    "column_descriptions": [
-                        {{
-                            "name": "column_name",
-                            "description": "Generated description"
-                        }}
-                    ]
-                }}
-                """
+                llm_settings = normalize_llm_settings(config_mgr.get_config().settings.llm)
+                documentation_prompt = llm_settings["prompts"].get(
+                    "documentation_prompt",
+                    DEFAULT_LLM_PROMPTS["documentation_prompt"],
+                )
+                prompt = render_prompt(
+                    documentation_prompt,
+                    {
+                        "dataset_name": dataset.get("table_name") or "",
+                        "columns_json": json.dumps(columns_data, ensure_ascii=False),
+                    },
+                )
                 
                 # Using a generic chat completion for text-only US2
                 llm_log.info(f"Generating documentation for dataset {dataset_id}")

backend/src/plugins/llm_analysis/service.py

@@ -20,6 +20,7 @@ from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_excep
 from .models import LLMProviderType
 from ...core.logger import belief_scope, logger
 from ...core.config_models import Environment
+from ...services.llm_prompt_templates import DEFAULT_LLM_PROMPTS, render_prompt
 
 # [DEF:ScreenshotService:Class]
 # @PURPOSE: Handles capturing screenshots of Superset dashboards.
@@ -548,7 +549,12 @@ class LLMClient:
     # @PRE: screenshot_path exists, logs is a list of strings.
     # @POST: Returns a structured analysis dictionary (status, summary, issues).
     # @SIDE_EFFECT: Reads screenshot file and calls external LLM API.
-    async def analyze_dashboard(self, screenshot_path: str, logs: List[str]) -> Dict[str, Any]:
+    async def analyze_dashboard(
+        self,
+        screenshot_path: str,
+        logs: List[str],
+        prompt_template: str = DEFAULT_LLM_PROMPTS["dashboard_validation_prompt"],
+    ) -> Dict[str, Any]:
         with belief_scope("analyze_dashboard"):
             # Optimize image to reduce token count (US1 / T023)
             # Gemini/Gemma models have limits on input tokens, and large images contribute significantly.
@@ -582,25 +588,7 @@ class LLMClient:
                     base_64_image = base64.b64encode(image_file.read()).decode('utf-8')
 
             log_text = "\n".join(logs)
-            prompt = f"""
-            Analyze the attached dashboard screenshot and the following execution logs for health and visual issues.
-            
-            Logs:
-            {log_text}
-            
-            Provide the analysis in JSON format with the following structure:
-            {{
-                "status": "PASS" | "WARN" | "FAIL",
-                "summary": "Short summary of findings",
-                "issues": [
-                    {{
-                        "severity": "WARN" | "FAIL",
-                        "message": "Description of the issue",
-                        "location": "Optional location info (e.g. chart name)"
-                    }}
-                ]
-            }}
-            """
+            prompt = render_prompt(prompt_template, {"logs": log_text})
             
             messages = [
                 {