forked from sagnik/Project_Velocity
fix: Oracle Canvas JSON Component Generation planning and orchestration logic
This commit is contained in:
Sagnik
382
backend/oracle/visualization_planner.py
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382
backend/oracle/visualization_planner.py
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"""
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oracle/visualization_planner.py
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Pick Oracle canvas renderer types from actual result shape.
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"""
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from __future__ import annotations
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import re
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from dataclasses import dataclass
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from typing import Any
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@dataclass
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class ColumnProfile:
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name: str
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is_numeric: bool
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is_string: bool
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is_datetime: bool
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is_boolean: bool
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null_rate: float
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sample_values: list[Any]
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@dataclass
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class VisualizationDecision:
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component_type: str
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x_axis: str | None
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y_axis: str | None
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series_cols: list[str]
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dimension_cols: list[str]
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measure_cols: list[str]
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title: str
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width_mode: str
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min_height_px: int
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skeleton_variant: str
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viz_params: dict[str, Any]
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data_bindings: dict[str, Any]
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confidence: float
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reasoning: str
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def _looks_like_timestamp(value: str) -> bool:
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return bool(re.match(r"\d{4}-\d{2}-\d{2}", value))
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def _profile_columns(rows: list[dict[str, Any]], columns: list[str]) -> list[ColumnProfile]:
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if not rows:
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return [ColumnProfile(column, False, False, False, False, 1.0, []) for column in columns]
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sample_size = min(len(rows), 20)
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profiles: list[ColumnProfile] = []
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for column in columns:
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values = [rows[index].get(column) for index in range(sample_size)]
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non_null = [value for value in values if value is not None]
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null_rate = 1.0 - len(non_null) / sample_size if sample_size else 1.0
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profiles.append(
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ColumnProfile(
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name=column,
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is_numeric=any(isinstance(value, (int, float)) for value in non_null),
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is_string=any(isinstance(value, str) and not _looks_like_timestamp(value) for value in non_null[:5]),
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is_datetime=any(isinstance(value, str) and _looks_like_timestamp(value) for value in non_null[:5]),
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is_boolean=any(isinstance(value, bool) for value in non_null),
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null_rate=null_rate,
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sample_values=non_null[:3],
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)
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)
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return profiles
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_DIMENSION_HINTS = {
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"name", "full_name", "project_name", "developer_name", "agent_name",
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"broker_company", "category", "label", "stage", "channel", "type",
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"micro_market", "district", "status", "persona", "nationality",
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}
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_MEASURE_HINTS = {
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"count", "total", "sum", "avg", "average", "value", "score", "rate",
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"current_value", "qd_score", "probability", "interest_count", "visit_count",
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"interaction_count", "days", "amount", "revenue",
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}
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_TIMESTAMP_HINTS = {"at", "date", "time", "when", "timestamp"}
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_PREFERRED_X = [
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"project_name", "developer_name", "category", "stage", "channel",
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"micro_market", "broker_company", "agent_name", "name", "full_name",
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"label", "status", "type",
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]
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_PREFERRED_Y = [
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"count", "total", "interested_clients", "interest_count", "client_count",
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"current_value", "qd_score", "value", "probability", "interaction_count",
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"visit_count", "days_since_last_contact",
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]
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_TABLE_COLUMN_PRESETS: dict[str, list[str]] = {
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"crm_people": ["full_name", "primary_phone", "primary_email", "persona_labels"],
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"intel_qd_scores": ["full_name", "current_value", "score_type", "computed_at"],
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"crm_leads": ["full_name", "stage", "status", "budget_band", "urgency"],
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"intel_interactions": ["full_name", "channel", "interaction_type", "happened_at", "summary"],
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"read_last_contacted": ["full_name", "last_contacted_at", "last_channel", "days_since_last_contact", "staleness_label"],
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"crm_property_interests": ["full_name", "project_name", "interest_level", "configuration_preference"],
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"intel_call_objections": ["full_name", "objection_type", "intensity", "was_resolved", "raised_at"],
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"intel_extracted_facts": ["full_name", "fact_type", "fact_text", "confidence", "effective_date"],
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"read_next_best_action": ["full_name", "action_label", "urgency", "recommended_channel", "execute_within_hours"],
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}
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def _pick_axis(candidates: list[str], preferred: list[str]) -> str | None:
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for candidate in preferred:
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if candidate in candidates:
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return candidate
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return candidates[0] if candidates else None
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def _title_from_prompt(prompt: str) -> str:
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words = re.sub(r"\s+", " ", prompt.strip()).strip(" ?.!")[:72]
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return (words[:1].upper() + words[1:]) if words else "Oracle Query Result"
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class VisualizationPlanner:
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def plan(
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self,
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*,
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rows: list[dict[str, Any]],
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columns: list[str],
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prompt: str,
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source_tables: list[str],
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profile_suggested_type: str | None = None,
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title_from_planner: str | None = None,
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) -> VisualizationDecision:
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profiles = _profile_columns(rows, columns)
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classifications = {profile.name: self._classify_column(profile) for profile in profiles}
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dimensions = [column for column, kind in classifications.items() if kind == "dimension"]
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measures = [column for column, kind in classifications.items() if kind == "measure"]
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timestamps = [column for column, kind in classifications.items() if kind == "timestamp"]
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row_count = len(rows)
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prompt_lower = prompt.lower()
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if profile_suggested_type:
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return self._build_decision(
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component_type=profile_suggested_type,
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dimensions=dimensions,
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measures=measures,
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timestamps=timestamps,
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columns=columns,
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rows=rows,
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row_count=row_count,
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prompt=prompt,
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source_tables=source_tables,
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title=title_from_planner,
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reasoning=f"Execution profiler suggested {profile_suggested_type}",
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confidence=0.9,
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)
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timeline_terms = ("timeline", "history", "activity", "message", "call log", "whatsapp", "email", "conversation", "transcript", "interaction")
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if any(term in prompt_lower for term in timeline_terms) and timestamps:
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return self._build_decision(
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component_type="activityStream",
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dimensions=dimensions,
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measures=measures,
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timestamps=timestamps,
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columns=columns,
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rows=rows,
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row_count=row_count,
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prompt=prompt,
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source_tables=source_tables,
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title=title_from_planner,
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reasoning="Activity-like prompt plus timestamped result.",
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confidence=0.88,
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)
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if row_count == 1 and measures and not dimensions:
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return self._build_decision(
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component_type="kpiTile",
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dimensions=dimensions,
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measures=measures,
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timestamps=timestamps,
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columns=columns,
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rows=rows,
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row_count=row_count,
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prompt=prompt,
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source_tables=source_tables,
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title=title_from_planner,
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reasoning="Single numeric row.",
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confidence=0.92,
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)
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if timestamps and measures and any(term in prompt_lower for term in ("trend", "over time", "monthly", "weekly", "growth", "timeseries")):
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return self._build_decision(
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component_type="lineChart",
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dimensions=dimensions,
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measures=measures,
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timestamps=timestamps,
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columns=columns,
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rows=rows,
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row_count=row_count,
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prompt=prompt,
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source_tables=source_tables,
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title=title_from_planner,
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reasoning="Temporal series plus measure.",
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confidence=0.87,
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)
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if ("stage" in columns or "pipeline" in prompt_lower) and any(term in prompt_lower for term in ("pipeline", "funnel", "stage", "kanban", "deal")):
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return self._build_decision(
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component_type="pipelineBoard",
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dimensions=dimensions,
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measures=measures,
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timestamps=timestamps,
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columns=columns,
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rows=rows,
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row_count=row_count,
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prompt=prompt,
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source_tables=source_tables,
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title=title_from_planner,
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reasoning="Pipeline-like prompt and stage-like data.",
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confidence=0.85,
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)
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if dimensions and measures and row_count <= 30 and not timestamps:
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return self._build_decision(
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component_type="barChart",
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dimensions=dimensions,
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measures=measures,
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timestamps=timestamps,
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columns=columns,
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rows=rows,
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row_count=row_count,
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prompt=prompt,
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source_tables=source_tables,
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title=title_from_planner,
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reasoning="Categorical dimension plus measure.",
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confidence=0.8,
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)
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return self._build_decision(
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component_type="table",
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dimensions=dimensions,
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measures=measures,
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timestamps=timestamps,
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columns=columns,
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rows=rows,
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row_count=row_count,
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prompt=prompt,
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source_tables=source_tables,
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title=title_from_planner,
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reasoning="Default structured table.",
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confidence=0.7,
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)
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@staticmethod
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def _classify_column(profile: ColumnProfile) -> str:
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lower = profile.name.lower()
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if lower.endswith("_id"):
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return "identity"
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if profile.is_datetime or any(token in lower for token in _TIMESTAMP_HINTS):
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return "timestamp"
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if lower in _DIMENSION_HINTS or (profile.is_string and not profile.is_numeric):
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return "dimension"
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if profile.is_numeric or any(token in lower for token in _MEASURE_HINTS):
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return "measure"
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return "other"
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def _build_decision(
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self,
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*,
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component_type: str,
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dimensions: list[str],
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measures: list[str],
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timestamps: list[str],
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columns: list[str],
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rows: list[dict[str, Any]],
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row_count: int,
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prompt: str,
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source_tables: list[str],
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title: str | None,
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reasoning: str,
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confidence: float,
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) -> VisualizationDecision:
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x_axis = _pick_axis(dimensions + timestamps, _PREFERRED_X + list(timestamps))
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y_axis = _pick_axis(measures, _PREFERRED_Y)
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if component_type == "table":
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display_columns = self._table_columns(columns, source_tables)
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else:
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display_columns = columns
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viz_params = self._build_viz_params(
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component_type=component_type,
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x_axis=x_axis,
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y_axis=y_axis,
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display_columns=display_columns,
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row_count=row_count,
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)
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data_bindings = {
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"dimensions": dimensions[:2] if dimensions else (timestamps[:1] if timestamps else []),
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"measures": measures[:3],
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"series": [],
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"filters": [],
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}
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width_mode = "full" if component_type in {"table", "activityStream", "pipelineBoard"} else "half"
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height_map = {
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"kpiTile": 140,
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"barChart": 320,
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"lineChart": 320,
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"activityStream": 380,
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"table": 300,
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"pipelineBoard": 400,
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}
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skeleton_map = {
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"kpiTile": "kpi",
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"barChart": "chart",
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"lineChart": "chart",
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"activityStream": "table",
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"table": "table",
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"pipelineBoard": "pipeline",
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}
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return VisualizationDecision(
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component_type=component_type,
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x_axis=x_axis,
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y_axis=y_axis,
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series_cols=[],
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dimension_cols=dimensions,
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measure_cols=measures,
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title=title or _title_from_prompt(prompt),
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width_mode=width_mode,
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min_height_px=height_map.get(component_type, 300),
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skeleton_variant=skeleton_map.get(component_type, "generic"),
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viz_params=viz_params,
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data_bindings=data_bindings,
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confidence=confidence,
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reasoning=reasoning,
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)
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@staticmethod
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def _table_columns(all_columns: list[str], source_tables: list[str]) -> list[str]:
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for table in source_tables:
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preset = _TABLE_COLUMN_PRESETS.get(table)
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if preset:
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matched = [column for column in preset if column in all_columns]
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if matched:
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return matched
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return [column for column in all_columns if not column.endswith("_id") or column == "person_id"][:8]
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@staticmethod
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def _build_viz_params(
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*,
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component_type: str,
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x_axis: str | None,
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y_axis: str | None,
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display_columns: list[str],
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row_count: int,
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) -> dict[str, Any]:
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del row_count
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if component_type == "barChart":
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return {
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"xAxis": x_axis or "category",
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"yAxis": y_axis or "value",
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"sort": "desc",
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"showLabels": True,
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"legend": False,
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}
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if component_type == "lineChart":
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return {"showPoints": True, "smooth": True}
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if component_type == "kpiTile":
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return {"label": "Result", "trend": "", "comparisonLabel": ""}
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if component_type == "table":
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return {
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"columns": display_columns,
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"emptyStateTitle": "No matching records found",
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"emptyStateDescription": "The query ran successfully but returned no rows for this prompt.",
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"rankBy": y_axis,
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"showTopBadge": False,
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}
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if component_type == "activityStream":
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return {"showUrgencyIndicator": True}
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if component_type == "pipelineBoard":
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return {"showValue": True, "colorByStage": True}
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return {}
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visualization_planner = VisualizationPlanner()
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