Вроде работает
This commit is contained in:
272
backend/src/plugins/llm_analysis/plugin.py
Normal file
272
backend/src/plugins/llm_analysis/plugin.py
Normal file
@@ -0,0 +1,272 @@
|
||||
# [DEF:backend.src.plugins.llm_analysis.plugin:Module]
|
||||
# @TIER: STANDARD
|
||||
# @SEMANTICS: plugin, llm, analysis, documentation
|
||||
# @PURPOSE: Implements DashboardValidationPlugin and DocumentationPlugin.
|
||||
# @LAYER: Domain
|
||||
# @RELATION: INHERITS_FROM -> backend.src.core.plugin_base.PluginBase
|
||||
|
||||
from typing import Dict, Any, Optional, List
|
||||
import os
|
||||
from datetime import datetime, timedelta
|
||||
from ...core.plugin_base import PluginBase
|
||||
from ...core.logger import belief_scope, logger
|
||||
from ...core.database import SessionLocal
|
||||
from ...core.config_manager import ConfigManager
|
||||
from ...services.llm_provider import LLMProviderService
|
||||
from .service import ScreenshotService, LLMClient
|
||||
from .models import LLMProviderType, ValidationStatus, ValidationResult, DetectedIssue
|
||||
from ...models.llm import ValidationRecord
|
||||
|
||||
# [DEF:DashboardValidationPlugin:Class]
|
||||
# @PURPOSE: Plugin for automated dashboard health analysis using LLMs.
|
||||
class DashboardValidationPlugin(PluginBase):
|
||||
@property
|
||||
def id(self) -> str:
|
||||
return "llm_dashboard_validation"
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
return "Dashboard LLM Validation"
|
||||
|
||||
@property
|
||||
def description(self) -> str:
|
||||
return "Automated dashboard health analysis using multimodal LLMs."
|
||||
|
||||
@property
|
||||
def version(self) -> str:
|
||||
return "1.0.0"
|
||||
|
||||
def get_schema(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"dashboard_id": {"type": "string", "title": "Dashboard ID"},
|
||||
"environment_id": {"type": "string", "title": "Environment ID"},
|
||||
"provider_id": {"type": "string", "title": "LLM Provider ID"}
|
||||
},
|
||||
"required": ["dashboard_id", "environment_id", "provider_id"]
|
||||
}
|
||||
|
||||
async def execute(self, params: Dict[str, Any]):
|
||||
with belief_scope("execute", f"plugin_id={self.id}"):
|
||||
logger.info(f"Executing {self.name} with params: {params}")
|
||||
|
||||
dashboard_id = params.get("dashboard_id")
|
||||
env_id = params.get("environment_id")
|
||||
provider_id = params.get("provider_id")
|
||||
task_id = params.get("_task_id")
|
||||
|
||||
db = SessionLocal()
|
||||
try:
|
||||
# 1. Get Environment
|
||||
from ...dependencies import get_config_manager
|
||||
config_mgr = get_config_manager()
|
||||
env = config_mgr.get_environment(env_id)
|
||||
if not env:
|
||||
raise ValueError(f"Environment {env_id} not found")
|
||||
|
||||
# 2. Get LLM Provider
|
||||
llm_service = LLMProviderService(db)
|
||||
db_provider = llm_service.get_provider(provider_id)
|
||||
if not db_provider:
|
||||
raise ValueError(f"LLM Provider {provider_id} not found")
|
||||
|
||||
api_key = llm_service.get_decrypted_api_key(provider_id)
|
||||
|
||||
# 3. Capture Screenshot
|
||||
screenshot_service = ScreenshotService(env)
|
||||
os.makedirs("ss-tools-storage/screenshots", exist_ok=True)
|
||||
screenshot_path = f"ss-tools-storage/screenshots/{dashboard_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png"
|
||||
|
||||
await screenshot_service.capture_dashboard(dashboard_id, screenshot_path)
|
||||
|
||||
# 4. Fetch Logs (Last 100 lines from backend.log)
|
||||
logs = []
|
||||
log_file = "backend.log"
|
||||
if os.path.exists(log_file):
|
||||
with open(log_file, "r") as f:
|
||||
# Read last 100 lines
|
||||
all_lines = f.readlines()
|
||||
logs = all_lines[-100:]
|
||||
|
||||
if not logs:
|
||||
logs = ["No logs found in backend.log"]
|
||||
|
||||
# 5. Analyze with LLM
|
||||
llm_client = LLMClient(
|
||||
provider_type=LLMProviderType(db_provider.provider_type),
|
||||
api_key=api_key,
|
||||
base_url=db_provider.base_url,
|
||||
default_model=db_provider.default_model
|
||||
)
|
||||
|
||||
analysis = await llm_client.analyze_dashboard(screenshot_path, logs)
|
||||
|
||||
# 6. Persist Result
|
||||
validation_result = ValidationResult(
|
||||
dashboard_id=dashboard_id,
|
||||
status=ValidationStatus(analysis["status"]),
|
||||
summary=analysis["summary"],
|
||||
issues=[DetectedIssue(**issue) for issue in analysis["issues"]],
|
||||
screenshot_path=screenshot_path,
|
||||
raw_response=str(analysis)
|
||||
)
|
||||
|
||||
db_record = ValidationRecord(
|
||||
dashboard_id=validation_result.dashboard_id,
|
||||
status=validation_result.status.value,
|
||||
summary=validation_result.summary,
|
||||
issues=[issue.dict() for issue in validation_result.issues],
|
||||
screenshot_path=validation_result.screenshot_path,
|
||||
raw_response=validation_result.raw_response
|
||||
)
|
||||
db.add(db_record)
|
||||
db.commit()
|
||||
|
||||
# 7. Notification on failure (US1 / FR-015)
|
||||
if validation_result.status == ValidationStatus.FAIL:
|
||||
logger.warning(f"Dashboard {dashboard_id} validation FAILED. Summary: {validation_result.summary}")
|
||||
# Placeholder for Email/Pulse notification dispatch
|
||||
# In a real implementation, we would call a NotificationService here
|
||||
# with a payload containing the summary and a link to the report.
|
||||
|
||||
return validation_result.dict()
|
||||
|
||||
finally:
|
||||
db.close()
|
||||
# [/DEF:DashboardValidationPlugin:Class]
|
||||
|
||||
# [DEF:DocumentationPlugin:Class]
|
||||
# @PURPOSE: Plugin for automated dataset documentation using LLMs.
|
||||
class DocumentationPlugin(PluginBase):
|
||||
@property
|
||||
def id(self) -> str:
|
||||
return "llm_documentation"
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
return "Dataset LLM Documentation"
|
||||
|
||||
@property
|
||||
def description(self) -> str:
|
||||
return "Automated dataset and column documentation using LLMs."
|
||||
|
||||
@property
|
||||
def version(self) -> str:
|
||||
return "1.0.0"
|
||||
|
||||
def get_schema(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"dataset_id": {"type": "string", "title": "Dataset ID"},
|
||||
"environment_id": {"type": "string", "title": "Environment ID"},
|
||||
"provider_id": {"type": "string", "title": "LLM Provider ID"}
|
||||
},
|
||||
"required": ["dataset_id", "environment_id", "provider_id"]
|
||||
}
|
||||
|
||||
async def execute(self, params: Dict[str, Any]):
|
||||
with belief_scope("execute", f"plugin_id={self.id}"):
|
||||
logger.info(f"Executing {self.name} with params: {params}")
|
||||
|
||||
dataset_id = params.get("dataset_id")
|
||||
env_id = params.get("environment_id")
|
||||
provider_id = params.get("provider_id")
|
||||
|
||||
db = SessionLocal()
|
||||
try:
|
||||
# 1. Get Environment
|
||||
from ...dependencies import get_config_manager
|
||||
config_mgr = get_config_manager()
|
||||
env = config_mgr.get_environment(env_id)
|
||||
if not env:
|
||||
raise ValueError(f"Environment {env_id} not found")
|
||||
|
||||
# 2. Get LLM Provider
|
||||
llm_service = LLMProviderService(db)
|
||||
db_provider = llm_service.get_provider(provider_id)
|
||||
if not db_provider:
|
||||
raise ValueError(f"LLM Provider {provider_id} not found")
|
||||
|
||||
api_key = llm_service.get_decrypted_api_key(provider_id)
|
||||
|
||||
# 3. Fetch Metadata (US2 / T024)
|
||||
from ...core.superset_client import SupersetClient
|
||||
client = SupersetClient(env)
|
||||
|
||||
# Optimistic locking check (T045)
|
||||
dataset = client.get_dataset(int(dataset_id))
|
||||
# dataset structure might vary, ensure we get the right field
|
||||
original_changed_on = dataset.get("changed_on_utc") or dataset.get("result", {}).get("changed_on_utc")
|
||||
|
||||
# Extract columns and existing descriptions
|
||||
columns_data = []
|
||||
for col in dataset.get("columns", []):
|
||||
columns_data.append({
|
||||
"name": col.get("column_name"),
|
||||
"type": col.get("type"),
|
||||
"description": col.get("description")
|
||||
})
|
||||
|
||||
# 4. Construct Prompt & Analyze (US2 / T025)
|
||||
llm_client = LLMClient(
|
||||
provider_type=LLMProviderType(db_provider.provider_type),
|
||||
api_key=api_key,
|
||||
base_url=db_provider.base_url,
|
||||
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"
|
||||
}}
|
||||
]
|
||||
}}
|
||||
"""
|
||||
|
||||
# Using a generic chat completion for text-only US2
|
||||
response = await llm_client.client.chat.completions.create(
|
||||
model=db_provider.default_model,
|
||||
messages=[{"role": "user", "content": prompt}],
|
||||
response_format={"type": "json_object"}
|
||||
)
|
||||
|
||||
import json
|
||||
doc_result = json.loads(response.choices[0].message.content)
|
||||
|
||||
# 5. Update Metadata (US2 / T026)
|
||||
# This part normally goes to mapping_service, but we implement the logic here for the plugin flow
|
||||
# We'll update the dataset in Superset
|
||||
update_payload = {
|
||||
"description": doc_result["dataset_description"],
|
||||
"columns": []
|
||||
}
|
||||
|
||||
# Map generated descriptions back to column IDs
|
||||
for col_doc in doc_result["column_descriptions"]:
|
||||
for col in dataset.get("columns", []):
|
||||
if col.get("column_name") == col_doc["name"]:
|
||||
update_payload["columns"].append({
|
||||
"id": col.get("id"),
|
||||
"description": col_doc["description"]
|
||||
})
|
||||
|
||||
client.update_dataset(int(dataset_id), update_payload)
|
||||
|
||||
return doc_result
|
||||
|
||||
finally:
|
||||
db.close()
|
||||
# [/DEF:DocumentationPlugin:Class]
|
||||
|
||||
# [/DEF:backend.src.plugins.llm_analysis.plugin:Module]
|
||||
Reference in New Issue
Block a user