Refactor monitoring module: modularized EndpointMonitor and SchemaRegistry into separate submodules under endpoint and schema respectively. Centralized endpoint definitions, improved structure, and updated imports accordingly.
This commit is contained in:
Yûki VACHOT
parent
aad2bc93ea
commit
80631f6f44
11 changed files with 315 additions and 245 deletions
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"""
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Dynamic Schema Detection and Management.
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This module automatically detects, tracks, and adapts to changes in API response formats.
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It maintains a versioned history of schemas and provides tools for the LLM to understand
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the current data structure.
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"""
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import hashlib
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import json
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import logging
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from dataclasses import dataclass, field
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from datetime import datetime, UTC
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from pathlib import Path
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from typing import Any, Optional
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from ..config import settings
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logger = logging.getLogger(__name__)
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@dataclass
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class SchemaField:
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"""Represents a single field in a schema."""
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name: str
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field_type: str
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nullable: bool = False
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nested_schema: Optional[dict] = None
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example_value: Any = None
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description: str = ""
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@dataclass
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class EndpointSchema:
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"""Schema definition for an API endpoint."""
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endpoint: str
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method: str
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fields: dict[str, SchemaField] = field(default_factory=dict)
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last_updated: datetime = field(default_factory=lambda: datetime.now(UTC))
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schema_hash: str = ""
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response_code: int = 200
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is_available: bool = True
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error_message: Optional[str] = None
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sample_response: Optional[dict] = None
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def to_dict(self) -> dict:
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"""Convert to dictionary for serialization."""
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return {
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"endpoint": self.endpoint,
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"method": self.method,
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"fields": {
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name: {
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"name": f.name,
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"field_type": f.field_type,
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"nullable": f.nullable,
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"nested_schema": f.nested_schema,
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"example_value": self._serialize_example(f.example_value),
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"description": f.description,
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}
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for name, f in self.fields.items()
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},
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"last_updated": self.last_updated.isoformat(),
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"schema_hash": self.schema_hash,
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"response_code": self.response_code,
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"is_available": self.is_available,
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"error_message": self.error_message,
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"sample_response": self.sample_response,
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}
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@staticmethod
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def _serialize_example(value: Any) -> Any:
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"""Safely serialize example values."""
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if isinstance(value, (str, int, float, bool, type(None))):
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return value
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if isinstance(value, (list, dict)):
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return str(value)[:100] + "..." if len(str(value)) > 100 else value
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return str(value)
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@classmethod
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def from_dict(cls, data: dict) -> "EndpointSchema":
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"""Create from dictionary."""
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fields = {}
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for name, f_data in data.get("fields", {}).items():
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fields[name] = SchemaField(
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name=f_data["name"],
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field_type=f_data["field_type"],
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nullable=f_data.get("nullable", False),
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nested_schema=f_data.get("nested_schema"),
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example_value=f_data.get("example_value"),
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description=f_data.get("description", ""),
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)
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last_updated = data.get("last_updated")
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if isinstance(last_updated, str):
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last_updated = datetime.fromisoformat(last_updated)
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else:
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last_updated = datetime.now(UTC)
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return cls(
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endpoint=data["endpoint"],
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method=data.get("method", "GET"),
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fields=fields,
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last_updated=last_updated,
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schema_hash=data.get("schema_hash", ""),
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response_code=data.get("response_code", 200),
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is_available=data.get("is_available", True),
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error_message=data.get("error_message"),
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sample_response=data.get("sample_response"),
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)
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class SchemaDetector:
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"""Detects and analyzes JSON response schemas dynamically."""
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@staticmethod
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def detect_type(value: Any) -> str:
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"""Detect the type of value."""
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if value is None:
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return "null"
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if isinstance(value, bool):
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return "boolean"
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if isinstance(value, int):
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return "integer"
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if isinstance(value, float):
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return "number"
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if isinstance(value, str):
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# Try to detect special string types
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if SchemaDetector._is_iso_datetime(value):
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return "datetime"
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if SchemaDetector._is_uuid(value):
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return "uuid"
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if SchemaDetector._is_url(value):
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return "url"
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return "string"
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if isinstance(value, list):
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return "array"
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if isinstance(value, dict):
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return "object"
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return "unknown"
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@staticmethod
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def _is_iso_datetime(value: str) -> bool:
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"""Check if string is ISO datetime format."""
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try:
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datetime.fromisoformat(value.replace("Z", "+00:00"))
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return True
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except (ValueError, AttributeError):
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return False
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@staticmethod
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def _is_uuid(value: str) -> bool:
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"""Check if string is UUID format."""
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import re
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uuid_pattern = r'^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$'
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return bool(re.match(uuid_pattern, value.lower()))
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@staticmethod
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def _is_url(value: str) -> bool:
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"""Check if string is URL format."""
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return value.startswith(("http://", "https://"))
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def analyze_response(self, data: Any, max_depth: int = 5) -> dict[str, SchemaField]:
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"""
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Analyze a JSON response and extract its schema.
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Args:
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data: The JSON response data
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max_depth: Maximum depth for nested object analysis
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Returns:
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Dictionary mapping field names to SchemaField objects
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"""
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if not isinstance(data, dict):
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return {}
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fields = {}
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self._analyze_object(data, fields, "", max_depth)
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return fields
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def _analyze_object(
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self,
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obj: dict,
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fields: dict,
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prefix: str,
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remaining_depth: int
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) -> None:
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"""Recursively analyze an object and extract field information."""
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if remaining_depth <= 0:
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return
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for key, value in obj.items():
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field_name = f"{prefix}.{key}" if prefix else key
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field_type = self.detect_type(value)
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nested_schema = None
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if field_type == "object" and isinstance(value, dict):
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nested_schema = {}
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self._analyze_object(value, nested_schema, "", remaining_depth - 1)
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elif field_type == "array" and value and isinstance(value[0], dict):
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nested_schema = {}
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self._analyze_object(value[0], nested_schema, "", remaining_depth - 1)
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fields[field_name] = SchemaField(
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name=field_name,
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field_type=field_type,
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nullable=value is None,
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nested_schema=nested_schema if nested_schema else None,
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example_value=value,
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)
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@staticmethod
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def compute_schema_hash(fields: dict[str, SchemaField]) -> str:
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"""Compute a hash of the schema for change detection."""
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schema_repr = json.dumps(
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{name: (f.field_type, f.nullable) for name, f in sorted(fields.items())},
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sort_keys=True
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)
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return hashlib.sha256(schema_repr.encode()).hexdigest()[:16]
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class SchemaRegistry:
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"""
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Manages schema storage, versioning, and change detection.
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Schemas are persisted to disk and loaded on startup, allowing the system
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to track changes over time and adapt automatically.
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"""
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def __init__(self, cache_dir: Optional[str] = None):
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self.cache_dir = Path(cache_dir or settings.SCHEMA_CACHE_DIR)
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self.cache_dir.mkdir(parents=True, exist_ok=True)
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self.schemas: dict[str, EndpointSchema] = {}
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self.schema_history: dict[str, list[EndpointSchema]] = {}
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self.detector = SchemaDetector()
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self._load_cached_schemas()
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def _get_schema_file(self) -> Path:
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"""Get the path to the schema cache file."""
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return self.cache_dir / "schemas.json"
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def _get_history_file(self) -> Path:
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"""Get the path to the schema history file."""
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return self.cache_dir / "schema_history.json"
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def _load_cached_schemas(self) -> None:
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"""Load schemas from disk cache."""
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schema_file = self._get_schema_file()
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if schema_file.exists():
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try:
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with open(schema_file) as f:
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data = json.load(f)
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for endpoint, schema_data in data.items():
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self.schemas[endpoint] = EndpointSchema.from_dict(schema_data)
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logger.info(f"Loaded {len(self.schemas)} cached schemas")
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except Exception as e:
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logger.warning(f"Failed to load cached schemas: {e}")
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history_file = self._get_history_file()
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if history_file.exists():
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try:
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with open(history_file) as f:
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data = json.load(f)
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for endpoint, history in data.items():
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self.schema_history[endpoint] = [
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EndpointSchema.from_dict(h) for h in history
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]
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except Exception as e:
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logger.warning(f"Failed to load schema history: {e}")
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def _save_schemas(self) -> None:
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"""Save schemas to disk cache."""
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try:
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with open(self._get_schema_file(), "w") as f:
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json.dump(
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{ep: schema.to_dict() for ep, schema in self.schemas.items()},
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f,
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indent=2
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)
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with open(self._get_history_file(), "w") as f:
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json.dump(
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{
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ep: [h.to_dict() for h in history[-10:]] # Keep last 10 versions
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for ep, history in self.schema_history.items()
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},
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f,
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indent=2
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)
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except Exception as e:
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logger.error(f"Failed to save schemas: {e}")
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def update_schema(
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self,
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endpoint: str,
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response_data: Any,
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response_code: int = 200,
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method: str = "GET"
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) -> tuple[EndpointSchema, bool]:
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"""
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Update schema for an endpoint based on response data.
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Args:
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endpoint: The API endpoint
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response_data: The JSON response data
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response_code: HTTP response code
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method: HTTP method
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Returns:
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Tuple of (updated schema, whether schema changed)
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"""
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fields = self.detector.analyze_response(response_data)
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new_hash = self.detector.compute_schema_hash(fields)
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existing_schema = self.schemas.get(endpoint)
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schema_changed = existing_schema is None or existing_schema.schema_hash != new_hash
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new_schema = EndpointSchema(
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endpoint=endpoint,
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method=method,
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fields=fields,
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last_updated=datetime.now(UTC),
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schema_hash=new_hash,
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response_code=response_code,
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is_available=True,
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sample_response=self._truncate_sample(response_data),
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)
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if schema_changed:
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if endpoint not in self.schema_history:
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self.schema_history[endpoint] = []
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if existing_schema:
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self.schema_history[endpoint].append(existing_schema)
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logger.info(f"Schema changed for {endpoint}: {new_hash}")
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self.schemas[endpoint] = new_schema
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self._save_schemas()
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return new_schema, schema_changed
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def mark_unavailable(
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self,
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endpoint: str,
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error_message: str,
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response_code: int = 0
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) -> None:
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"""Mark an endpoint as unavailable."""
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if endpoint in self.schemas:
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self.schemas[endpoint].is_available = False
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self.schemas[endpoint].error_message = error_message
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self.schemas[endpoint].response_code = response_code
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self.schemas[endpoint].last_updated = datetime.now(UTC)
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else:
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self.schemas[endpoint] = EndpointSchema(
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endpoint=endpoint,
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method="GET",
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is_available=False,
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error_message=error_message,
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response_code=response_code,
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)
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self._save_schemas()
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def get_schema(self, endpoint: str) -> Optional[EndpointSchema]:
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"""Get the current schema for an endpoint."""
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return self.schemas.get(endpoint)
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def get_all_schemas(self) -> dict[str, EndpointSchema]:
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"""Get all registered schemas."""
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return self.schemas.copy()
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def get_available_endpoints(self) -> list[str]:
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"""Get list of currently available endpoints."""
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return [ep for ep, schema in self.schemas.items() if schema.is_available]
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def get_schema_summary(self) -> dict:
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"""Get a summary of all schemas for LLM context."""
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return {
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"total_endpoints": len(self.schemas),
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"available_endpoints": len(self.get_available_endpoints()),
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"endpoints": {
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endpoint: {
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"available": schema.is_available,
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"last_updated": schema.last_updated.isoformat(),
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"field_count": len(schema.fields),
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"fields": list(schema.fields.keys())[:20], # Limit for context
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"response_code": schema.response_code,
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}
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for endpoint, schema in self.schemas.items()
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}
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}
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def generate_dynamic_description(self, endpoint: str) -> str:
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"""
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Generate a dynamic description of an endpoint's response format.
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This is used to provide context to the LLM about what data is available.
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"""
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schema = self.get_schema(endpoint)
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if not schema:
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return f"No schema information available for {endpoint}"
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if not schema.is_available:
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return f"Endpoint {endpoint} is currently unavailable: {schema.error_message}"
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lines = [
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f"Endpoint: {endpoint}",
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f"Method: {schema.method}",
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f"Last Updated: {schema.last_updated.isoformat()}",
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f"Status: {'Available' if schema.is_available else 'Unavailable'}",
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"",
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"Response Fields:",
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]
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for name, item in sorted(schema.fields.items()):
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nullable_str = " (nullable)" if item.nullable else ""
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lines.append(f" - {name}: {item.field_type}{nullable_str}")
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if item.nested_schema:
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lines.append(f" Nested fields: {list(item.nested_schema.keys())}")
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return "\n".join(lines)
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@staticmethod
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def _truncate_sample(data: Any, max_items: int = 3) -> Any:
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"""Truncate sample response for storage."""
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if isinstance(data, dict):
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return {
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k: SchemaRegistry._truncate_sample(v, max_items)
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for k, v in list(data.items())[:20]
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}
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if isinstance(data, list):
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return [
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SchemaRegistry._truncate_sample(item, max_items)
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for item in data[:max_items]
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]
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if isinstance(data, str) and len(data) > 200:
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return data[:200] + "..."
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return data
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# Global registry instance
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schema_registry = SchemaRegistry()
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