forked from sagnik/Project_Velocity
sagnik
6cdc366718
Co-authored-by: Sagnik <sagnik7896@gmail.com> Reviewed-on: sagnik/Project_Velocity#33
980 lines
40 KiB
Python
980 lines
40 KiB
Python
"""
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oracle/prompt_orchestrator.py
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Accepts a user prompt, assembles context, calls the Nemoclaw model runtime
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(or uses a deterministic fallback), validates the generated plan via policy,
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triggers the data access gateway, and produces a PromptExecution.
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"""
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from __future__ import annotations
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import logging
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import os
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import uuid
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import json
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from datetime import datetime, timezone
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from typing import Any
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from .policy_service import PolicyContext, PolicyService
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from .canvas_service import canvas_service
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from .data_access_gateway import data_access_gateway
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from .persona_service import persona_service
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from .codebook_service import codebook_service, CodebookExample
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from backend.services.runtime_llm_service import runtime_llm_service
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from backend.services.nemoclaw_runtime import nemoclaw_runtime
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try:
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import asyncpg # type: ignore
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except Exception: # pragma: no cover
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asyncpg = None # type: ignore
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logger = logging.getLogger(__name__)
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_DB_URL = os.getenv("DATABASE_URL", "")
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policy_svc = PolicyService()
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def _now() -> datetime:
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return datetime.now(timezone.utc)
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def _iso(value: datetime | None) -> str | None:
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if value is None:
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return None
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return value.isoformat()
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def _coerce_datetime(value: datetime | str | None) -> datetime | None:
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if value is None or isinstance(value, datetime):
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return value
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if isinstance(value, str) and value.strip():
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try:
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return datetime.fromisoformat(value)
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except ValueError:
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return None
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return None
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# ── Execution store ───────────────────────────────────────────────────────────
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def _json_safe(value: Any) -> Any:
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if isinstance(value, datetime):
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return value.isoformat()
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if isinstance(value, dict):
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return {str(key): _json_safe(val) for key, val in value.items()}
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if isinstance(value, list):
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return [_json_safe(item) for item in value]
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if isinstance(value, tuple):
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return [_json_safe(item) for item in value]
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return value
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_DEMO_EXECUTIONS: dict[str, dict[str, Any]] = {}
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def _db_ready() -> bool:
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return bool(_DB_URL and not _DB_URL.startswith("PLACEHOLDER") and asyncpg is not None)
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# ── Semantic intent detection (simplified) ────────────────────────────────────
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_INTENT_KEYWORDS: dict[str, list[str]] = {
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"pipeline_board": ["pipeline", "stage", "kanban", "deal", "funnel"],
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"bar_chart": ["bar", "compare", "source", "channel", "distribution", "ranked", "lead", "whale"],
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"geo_map": ["map", "geographic", "location", "district", "region", "area", "dubai"],
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"table": ["table", "list", "broker", "performance", "leaderboard", "rank", "top", "contact", "client", "account", "crm"],
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"line_chart": ["trend", "time", "monthly", "weekly", "absorption", "forecast"],
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"kpi_tile": ["kpi", "total", "summary", "attainment", "quota", "how many"],
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"activity_stream": ["timeline", "activity", "history", "follow-up", "queue", "contact", "interaction", "message", "call", "email"],
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}
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def _detect_component_types(prompt: str) -> list[str]:
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lower = prompt.lower()
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types: list[str] = []
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for comp_type, keywords in _INTENT_KEYWORDS.items():
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if any(k in lower for k in keywords):
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types.append(comp_type)
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return types or ["bar_chart"]
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def _build_demo_retrieval_plan(
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prompt: str,
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tenant_id: str,
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actor_role: str,
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) -> dict[str, Any]:
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"""
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Deterministic plan builder for demo mode.
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Produces a valid retrieval plan that passes policy validation.
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"""
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component_types = _detect_component_types(prompt)
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row_limit = 50 if actor_role in ("senior_broker", "junior_broker") else 200
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return {
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"planId": str(uuid.uuid4()),
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"components": [
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{
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"suggestedType": ct,
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"dataset": _DATASET_MAP.get(ct, "aggregated_results"),
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"privacyTier": "standard",
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"rowLimit": row_limit,
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"joins": [],
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"queryTemplate": f"SELECT * FROM {_DATASET_MAP.get(ct, 'aggregated_results')} WHERE tenant_id = :tenant_id LIMIT :limit",
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"queryParameters": {"tenant_id": tenant_id, "limit": row_limit},
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}
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for ct in component_types
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],
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"semanticModelVersion": "oracle_semantic_v2026_04_08_01",
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"intentClass": "analytical",
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}
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_DATASET_MAP: dict[str, str] = {
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"pipeline_board": "crm_opportunity_pipeline",
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"bar_chart": "crm_property_interest_rollup",
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"geo_map": "lead_geo_interest_rollup",
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"table": "crm_contacts_overview",
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"line_chart": "crm_property_interest_rollup",
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"kpi_tile": "oracle_aggregated_metric",
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"activity_stream": "crm_interaction_timeline",
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}
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_CODEBOOK_COMPONENT_MAP: dict[str, str] = {
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"summary_card": "kpi_tile",
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"summary_strip": "kpi_tile",
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"metric_card_group": "kpi_tile",
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"compact_alert_card": "kpi_tile",
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"gauge_stack": "kpi_tile",
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"lead_profile_card": "table",
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"property_card": "table",
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"data_table": "table",
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"leaderboard_table": "table",
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"matrix_grid": "table",
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"interaction_timeline": "activity_stream",
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"message_thread_summary": "activity_stream",
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"timeline": "activity_stream",
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"bar_chart": "bar_chart",
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"line_chart": "line_chart",
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"heatmap": "geo_map",
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"geo_map": "geo_map",
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"pipeline_board": "pipeline_board",
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}
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def _component_plan_type_from_codebook(example: CodebookExample) -> str:
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return _CODEBOOK_COMPONENT_MAP.get(example.component_type, "table")
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def _dataset_for_codebook(example: CodebookExample, prompt: str, component_plan_type: str | None = None) -> str:
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chapter = example.chapter_name.lower()
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subchapter = example.subchapter_name.lower()
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component_plan_type = component_plan_type or _component_plan_type_from_codebook(example)
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lowered_prompt = prompt.lower()
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if component_plan_type == "activity_stream":
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return "crm_interaction_timeline"
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if component_plan_type == "pipeline_board":
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return "crm_opportunity_pipeline"
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if component_plan_type == "table" and any(term in lowered_prompt for term in ("last interacted", "last interaction", "recently contacted", "recent interaction")):
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return "crm_last_interacted_clients"
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if component_plan_type == "table" and any(term in lowered_prompt for term in ("interest", "interested", "project", "property", "properties")) and any(term in lowered_prompt for term in ("client", "clients", "contact", "contacts")):
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return "crm_top_interested_clients"
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if component_plan_type == "line_chart" and any(term in lowered_prompt for term in ("trend", "time", "history", "growth")):
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return "crm_property_interest_rollup"
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if any(term in lowered_prompt for term in ("contact", "client 360", "crm", "account", "lead")):
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if "timeline" in lowered_prompt or "message" in lowered_prompt or "call" in lowered_prompt or "email" in lowered_prompt:
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return "crm_interaction_timeline"
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if "pipeline" in lowered_prompt or "opportunit" in lowered_prompt:
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return "crm_opportunity_pipeline"
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if ("interest" in lowered_prompt or "project" in lowered_prompt or "property" in lowered_prompt) and ("client" in lowered_prompt or "contact" in lowered_prompt):
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return "crm_top_interested_clients"
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if "interest" in lowered_prompt or "project" in lowered_prompt or "property" in lowered_prompt:
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return "crm_property_interest_rollup"
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if "last interacted" in lowered_prompt or "recently contacted" in lowered_prompt or "recent interaction" in lowered_prompt:
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return "crm_last_interacted_clients"
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return "crm_contacts_overview"
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if "client" in chapter or "client" in subchapter or "contact" in subchapter:
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return "crm_contacts_overview"
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if "opportun" in chapter or "pipeline" in subchapter:
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return "crm_opportunity_pipeline"
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if "interaction" in chapter or "communication" in chapter or "timeline" in subchapter:
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return "crm_interaction_timeline"
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if "property" in chapter or "inventory" in chapter or "interest" in subchapter:
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return "crm_property_interest_rollup"
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return _DATASET_MAP.get(component_plan_type, "oracle_aggregated_metric")
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def _build_codebook_retrieval_plan(
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prompt: str,
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tenant_id: str,
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actor_role: str,
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matches: list[CodebookExample],
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) -> dict[str, Any]:
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row_limit = 50 if actor_role in ("senior_broker", "junior_broker") else 200
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desired_types = _detect_component_types(prompt)
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if not desired_types:
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desired_types = [_component_plan_type_from_codebook(matches[0])] if matches else ["table"]
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title_hints: dict[str, str] = {}
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for example in matches:
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mapped = _component_plan_type_from_codebook(example)
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title_hints.setdefault(mapped, example.title)
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components: list[dict[str, Any]] = []
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exemplar = matches[0]
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for component_plan_type in desired_types[:4]:
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dataset = _dataset_for_codebook(exemplar, prompt, component_plan_type)
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title_hint = _title_for_dataset(dataset, component_plan_type, prompt) or title_hints.get(component_plan_type, exemplar.title)
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components.append(
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{
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"suggestedType": component_plan_type,
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"dataset": dataset,
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"privacyTier": "standard",
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"rowLimit": row_limit,
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"joins": [],
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"queryTemplate": f"SELECT * FROM {dataset} WHERE tenant_id = :tenant_id LIMIT :limit",
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"queryParameters": {"tenant_id": tenant_id, "limit": row_limit},
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"templateRef": {
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"exampleId": exemplar.example_id,
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"templateName": exemplar.template_name,
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"componentType": exemplar.component_type,
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"chapterName": exemplar.chapter_name,
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"subchapterName": exemplar.subchapter_name,
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"sourcePack": exemplar.source_pack,
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},
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"titleHint": title_hint,
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}
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)
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return {
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"planId": str(uuid.uuid4()),
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"components": components,
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"semanticModelVersion": "oracle_codebook_v2026_04_19_01",
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"intentClass": "analytical",
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"planner": "codebook_retrieval",
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}
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def _title_for_dataset(dataset: str, component_plan_type: str, prompt: str) -> str | None:
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lowered_prompt = prompt.lower()
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dataset_titles = {
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"crm_contacts_overview": "CRM Contacts Overview",
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"crm_opportunity_pipeline": "Opportunity Pipeline",
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"crm_property_interest_rollup": "Property Interest Rollup",
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"crm_interaction_timeline": "Client Interaction Timeline",
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"crm_last_interacted_clients": "Last Interacted Clients",
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"crm_top_interested_clients": "Top Interested Clients",
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"broker_performance": "Broker Performance",
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}
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if dataset == "crm_top_interested_clients" and "top" in lowered_prompt:
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return "Top Interested Clients"
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if dataset == "crm_last_interacted_clients" and ("top" in lowered_prompt or "last" in lowered_prompt):
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return "Last Interacted Clients"
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return dataset_titles.get(dataset)
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_RUNTIME_ALLOWED_DATASETS = {
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"deals",
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"lead_daily_snapshot",
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"lead_geo_interest_rollup",
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"broker_performance",
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"inventory_absorption",
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"oracle_aggregated_metric",
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"lead_activity_log",
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"crm_contacts_overview",
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"crm_opportunity_pipeline",
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"crm_property_interest_rollup",
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"crm_interaction_timeline",
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"crm_last_interacted_clients",
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"crm_top_interested_clients",
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}
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class PromptOrchestrator:
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"""
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Orchestrates the full prompt-to-canvas pipeline:
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1. Intent classification
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2. Retrieval plan construction (Nemoclaw or fallback)
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3. Policy validation
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4. Component plan construction
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5. Execution record persistence
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"""
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async def execute(
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self,
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*,
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tenant_id: str,
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page_id: str,
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branch_id: str,
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actor_id: str,
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actor_role: str,
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prompt: str,
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conversation_context: list[dict[str, str]] | None = None,
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client_request_id: str,
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placement_mode: str = "append_after_last_visible_component",
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) -> dict[str, Any]:
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"""
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Full orchestration flow. Returns a PromptExecution dict.
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"""
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execution_id = str(uuid.uuid4())
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now = _now()
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warnings: list[str] = []
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ctx = PolicyContext(
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tenant_id=tenant_id,
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actor_id=actor_id,
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actor_role=actor_role,
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)
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execution: dict[str, Any] = {
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"executionId": execution_id,
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"tenantId": tenant_id,
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"pageId": page_id,
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"branchId": branch_id,
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"actorId": actor_id,
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"prompt": prompt,
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"intentClass": "analytical",
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"status": "planning",
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"modelRuntime": "runtime_llm" if runtime_llm_service._provider_catalog() else "deterministic_fallback",
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"semanticModelVersion": "oracle_semantic_v2026_04_08_01",
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"warnings": warnings,
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"componentsCreated": [],
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"clientRequestId": client_request_id,
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"createdAt": now,
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"codebookMatches": [],
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}
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_DEMO_EXECUTIONS[execution_id] = execution
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await self._persist_execution(execution)
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# ── Step 1: Build retrieval plan ──────────────────────────────────────
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codebook_matches = codebook_service.search_examples(prompt, limit=4)
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execution["codebookMatches"] = [
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{
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"exampleId": match.example_id,
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"templateName": match.template_name,
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"componentType": match.component_type,
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"chapterName": match.chapter_name,
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"subchapterName": match.subchapter_name,
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"sourcePack": match.source_pack,
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}
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for match in codebook_matches
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]
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if codebook_matches:
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retrieval_plan = _build_codebook_retrieval_plan(prompt, tenant_id, actor_role, codebook_matches)
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execution["status"] = "validated"
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elif runtime_llm_service._provider_catalog():
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try:
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retrieval_plan = await self._call_nemoclaw(prompt, conversation_context or [], ctx)
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execution["status"] = "validated"
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except Exception as exc:
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logger.warning("ORCH Nemoclaw call failed, using fallback: %s", exc)
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warnings.append(f"Model runtime unavailable ({exc}); using deterministic fallback.")
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retrieval_plan = _build_demo_retrieval_plan(prompt, tenant_id, actor_role)
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else:
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retrieval_plan = _build_demo_retrieval_plan(prompt, tenant_id, actor_role)
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execution["retrievalPlan"] = retrieval_plan
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persona_plan = await persona_service.plan_for_prompt(
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prompt=prompt,
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tenant_id=tenant_id,
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actor_role=actor_role,
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)
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execution["personaPlan"] = persona_plan
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execution["workflowDispatch"] = nemoclaw_runtime.build_workflow_dispatch(
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prompt=prompt,
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tenant_id=tenant_id,
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actor_role=actor_role,
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component_templates=persona_plan["recommendedTemplates"],
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)
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# ── Step 2: Policy validation ─────────────────────────────────────────
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policy_errors = []
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for component_plan in retrieval_plan.get("components", []):
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result = policy_svc.validate_retrieval_plan(component_plan, ctx)
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if not result.passed:
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policy_errors.extend(result.errors)
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if result.warnings:
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warnings.extend(result.warnings)
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if policy_errors:
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execution["status"] = "failed"
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execution["warnings"] = warnings + policy_errors
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execution["completedAt"] = _now()
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logger.warning(
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"ORCH policy_denial execution_id=%s actor=%s errors=%s",
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execution_id, actor_id, policy_errors,
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)
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return execution
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execution["status"] = "executing"
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await self._persist_execution(execution)
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page = await canvas_service.get_page(page_id, tenant_id)
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existing_comps = page.get("components", []) if page else []
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next_order_base = self._next_order_base(existing_comps)
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section_id = f"sec_prompt_generated_{execution_id.replace('-', '')[:12]}"
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# ── Step 3: Build visualization plan (component descriptors) ──────────
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viz_plan = await self._build_visualization_plan(
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retrieval_plan=retrieval_plan,
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prompt=prompt,
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execution_id=execution_id,
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actor_id=actor_id,
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tenant_id=tenant_id,
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branch_id=branch_id,
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placement_mode=placement_mode,
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ctx=ctx,
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persona_plan=persona_plan,
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base_order=next_order_base,
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section_id=section_id,
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)
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execution["visualizationPlan"] = viz_plan
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# ── Step 4: Commit revision ───────────────────────────────────────────
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component_ids = [c["componentId"] for c in viz_plan.get("components", [])]
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execution["componentsCreated"] = component_ids
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# Commit a revision bump with the new components
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try:
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if page:
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new_comps = existing_comps + viz_plan.get("components", [])
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revision = await canvas_service.commit_revision(
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page_id=page_id,
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tenant_id=tenant_id,
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actor_id=actor_id,
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commit_kind="prompt",
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commit_summary=f"Oracle: {prompt[:80]}",
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components=new_comps,
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execution_id=execution_id,
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idempotency_key=client_request_id,
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)
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execution["headRevision"] = revision["revisionNumber"]
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except Exception as exc:
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logger.warning("ORCH revision_commit failed (non-fatal): %s", exc)
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warnings.append("Revision commit deferred — will retry on next sync.")
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execution["status"] = "completed"
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|
execution["summary"] = self._generate_summary(prompt, viz_plan)
|
|
execution["completedAt"] = _now()
|
|
execution["warnings"] = warnings
|
|
await self._persist_execution(execution)
|
|
return execution
|
|
|
|
async def _build_visualization_plan(
|
|
self,
|
|
*,
|
|
retrieval_plan: dict[str, Any],
|
|
prompt: str,
|
|
execution_id: str,
|
|
actor_id: str,
|
|
tenant_id: str,
|
|
branch_id: str,
|
|
placement_mode: str,
|
|
ctx: PolicyContext,
|
|
persona_plan: dict[str, Any],
|
|
base_order: int,
|
|
section_id: str,
|
|
) -> dict[str, Any]:
|
|
"""Converts a retrieval plan into a list of CanvasComponent descriptors."""
|
|
components = [
|
|
self._persona_text_canvas(
|
|
execution_id=execution_id,
|
|
actor_id=actor_id,
|
|
branch_id=branch_id,
|
|
prompt=prompt,
|
|
persona_plan=persona_plan,
|
|
order_index=base_order + 100,
|
|
section_id=section_id,
|
|
)
|
|
]
|
|
|
|
component_plans = retrieval_plan.get("components", [])
|
|
for i, plan in enumerate(component_plans):
|
|
ctype = plan["suggestedType"]
|
|
dataset = plan["dataset"]
|
|
component_id = str(uuid.uuid4())
|
|
query_result = await data_access_gateway.execute_component_plan(plan, ctx, prompt)
|
|
component_warnings = query_result.warnings
|
|
mapped_type = self._map_type(ctype)
|
|
data_rows = query_result.rows
|
|
|
|
comp: dict[str, Any] = {
|
|
"componentId": component_id,
|
|
"type": mapped_type,
|
|
"title": str(plan.get("titleHint") or self._generate_title(prompt, ctype)),
|
|
"description": f"Generated from: \"{prompt[:80]}\"",
|
|
"dataSourceDescriptor": {
|
|
"descriptorId": str(uuid.uuid4()),
|
|
"sourceType": "postgres",
|
|
"connectorId": "velocity-core-postgres",
|
|
"dataset": dataset,
|
|
"authContextRef": f"authctx_{actor_id}_scope",
|
|
"queryTemplate": plan.get("queryTemplate", f"SELECT * FROM {dataset} WHERE tenant_id = :tenant_id"),
|
|
"queryParameters": plan.get("queryParameters", {"tenant_id": tenant_id}),
|
|
"rowLimit": plan.get("rowLimit", 50),
|
|
"privacyTier": plan.get("privacyTier", "standard"),
|
|
"cachePolicy": {"mode": "ttl", "ttlSeconds": 120},
|
|
},
|
|
"visualizationParameters": self._default_viz_params(ctype, dataset, data_rows),
|
|
"dataBindings": self._default_bindings(ctype),
|
|
"version": 1,
|
|
"lifecycleState": "active",
|
|
"provenance": {
|
|
"originType": "prompt_generated",
|
|
"promptExecutionId": execution_id,
|
|
"sourceBranchId": branch_id,
|
|
"createdBy": actor_id,
|
|
"createdAt": _iso(_now()),
|
|
},
|
|
"renderingHints": self._rendering_hints(ctype),
|
|
"layout": {
|
|
"orderIndex": base_order + (i + 1) * 100,
|
|
"sectionId": section_id,
|
|
"widthMode": "full" if ctype in ("pipeline_board", "table", "geo_map") else "half",
|
|
"minHeightPx": 300,
|
|
"stickyHeader": False,
|
|
},
|
|
"accessControls": {
|
|
"visibilityScope": "private",
|
|
"allowedRoles": ["senior_broker", "sales_director", "marketing_operator", "data_steward", "compliance_reviewer", "platform_admin"],
|
|
"redactionPolicy": "none",
|
|
},
|
|
"styleSignature": {
|
|
"theme": "velocity_glass",
|
|
"paletteToken": "ocean_signal",
|
|
"motionProfile": "calm_reveal",
|
|
"density": "comfortable",
|
|
"radiusScale": "lg",
|
|
"typographyScale": "balanced",
|
|
},
|
|
"validationState": {
|
|
"schema": "pass",
|
|
"policy": "pass",
|
|
"a11y": "pass",
|
|
"performance": "pass",
|
|
"status": "validated",
|
|
},
|
|
"auditLog": [f"aud_{execution_id}_create"],
|
|
"dataRows": data_rows,
|
|
}
|
|
if component_warnings and not data_rows:
|
|
comp = self._error_component(
|
|
component_id=component_id,
|
|
execution_id=execution_id,
|
|
actor_id=actor_id,
|
|
branch_id=branch_id,
|
|
dataset=dataset,
|
|
warnings=component_warnings,
|
|
order_index=base_order + (i + 1) * 100,
|
|
section_id=section_id,
|
|
)
|
|
components.append(comp)
|
|
|
|
if len(components) > 1:
|
|
planning_component = components.pop(0)
|
|
planning_component["layout"]["orderIndex"] = base_order + (len(component_plans) + 1) * 100
|
|
components.append(planning_component)
|
|
|
|
return {"components": components}
|
|
|
|
@staticmethod
|
|
def _next_order_base(existing_components: list[dict[str, Any]]) -> int:
|
|
max_existing = 0
|
|
for component in existing_components:
|
|
try:
|
|
order_index = int((component.get("layout") or {}).get("orderIndex", 0))
|
|
except (TypeError, ValueError):
|
|
order_index = 0
|
|
if order_index > max_existing:
|
|
max_existing = order_index
|
|
return ((max_existing // 100) + 1) * 100
|
|
|
|
@staticmethod
|
|
def _persona_text_canvas(
|
|
*,
|
|
execution_id: str,
|
|
actor_id: str,
|
|
branch_id: str,
|
|
prompt: str,
|
|
persona_plan: dict[str, Any],
|
|
order_index: int,
|
|
section_id: str,
|
|
) -> dict[str, Any]:
|
|
content = (
|
|
f"Oracle received: {prompt}\n\n"
|
|
"Execution policy: query live CRM data first, pick the strongest-fitting canvas components, "
|
|
"and synthesize any missing UI blocks before rendering the result."
|
|
)
|
|
return {
|
|
"componentId": str(uuid.uuid4()),
|
|
"type": "textCanvas",
|
|
"title": "Oracle Planning Notes",
|
|
"description": "Persona-driven guidance generated before data-bound components.",
|
|
"dataSourceDescriptor": {
|
|
"descriptorId": str(uuid.uuid4()),
|
|
"sourceType": "inline",
|
|
"connectorId": "oracle-persona",
|
|
"dataset": "oracle_persona_plan",
|
|
"authContextRef": f"authctx_{actor_id}_scope",
|
|
"queryTemplate": "",
|
|
"queryParameters": {},
|
|
"rowLimit": 1,
|
|
"privacyTier": "standard",
|
|
},
|
|
"visualizationParameters": {
|
|
"content": content,
|
|
"widthMode": "full",
|
|
"adjustableHeight": True,
|
|
},
|
|
"dataBindings": {"dimensions": [], "measures": [], "series": [], "filters": []},
|
|
"version": 1,
|
|
"lifecycleState": "active",
|
|
"provenance": {
|
|
"originType": "prompt_generated",
|
|
"promptExecutionId": execution_id,
|
|
"sourceBranchId": branch_id,
|
|
"createdBy": actor_id,
|
|
"createdAt": _iso(_now()),
|
|
},
|
|
"renderingHints": {"estimatedHeightPx": 180, "skeletonVariant": "text", "virtualizationPriority": 4},
|
|
"layout": {
|
|
"orderIndex": order_index,
|
|
"sectionId": section_id,
|
|
"widthMode": "full",
|
|
"minHeightPx": 180,
|
|
"stickyHeader": False,
|
|
},
|
|
"accessControls": {
|
|
"visibilityScope": "private",
|
|
"allowedRoles": ["senior_broker", "sales_director", "marketing_operator", "data_steward", "compliance_reviewer", "platform_admin"],
|
|
"redactionPolicy": "none",
|
|
},
|
|
"styleSignature": {
|
|
"theme": "velocity_glass",
|
|
"paletteToken": "ocean_signal",
|
|
"motionProfile": "calm_reveal",
|
|
"density": "comfortable",
|
|
"radiusScale": "lg",
|
|
"typographyScale": "balanced",
|
|
},
|
|
"validationState": {
|
|
"schema": "pass",
|
|
"policy": "pass",
|
|
"a11y": "pass",
|
|
"performance": "pass",
|
|
"status": "validated",
|
|
},
|
|
"auditLog": [f"aud_{execution_id}_persona"],
|
|
"dataRows": [],
|
|
}
|
|
|
|
@staticmethod
|
|
def _map_type(plan_type: str) -> str:
|
|
mapping = {
|
|
"pipeline_board": "pipelineBoard",
|
|
"bar_chart": "barChart",
|
|
"geo_map": "geoMap",
|
|
"table": "table",
|
|
"line_chart": "lineChart",
|
|
"kpi_tile": "kpiTile",
|
|
"activity_stream": "activityStream",
|
|
}
|
|
return mapping.get(plan_type, "barChart")
|
|
|
|
@staticmethod
|
|
def _generate_title(prompt: str, comp_type: str) -> str:
|
|
labels = {
|
|
"pipeline_board": "Pipeline View",
|
|
"bar_chart": "Comparative Analysis",
|
|
"geo_map": "Geographic Distribution",
|
|
"table": "Performance Table",
|
|
"line_chart": "Trend Analysis",
|
|
"kpi_tile": "Key Metric",
|
|
"activity_stream": "Activity Stream",
|
|
}
|
|
return labels.get(comp_type, "Oracle Canvas Component")
|
|
|
|
@staticmethod
|
|
def _default_viz_params(comp_type: str, dataset: str, rows: list[dict[str, Any]]) -> dict[str, Any]:
|
|
first_row = rows[0] if rows else {}
|
|
inferred_columns = [key for key in first_row.keys() if key not in {"avatar"}] or ["name", "status"]
|
|
table_columns_by_dataset: dict[str, list[str]] = {
|
|
"broker_performance": ["name", "deals_closed", "revenue_generated"],
|
|
"crm_contacts_overview": ["name", "email", "phone", "city", "buyer_type", "qd_score"],
|
|
"crm_last_interacted_clients": ["name", "email", "phone", "last_interaction_at", "interaction_count", "qd_score"],
|
|
"crm_top_interested_clients": ["name", "email", "phone", "interest_count", "projects", "qd_score"],
|
|
}
|
|
defaults: dict[str, dict[str, Any]] = {
|
|
"bar_chart": {"xAxis": "category", "yAxis": "value", "sort": "desc", "showLabels": True, "legend": False},
|
|
"line_chart": {"showPoints": True, "smooth": True},
|
|
"kpi_tile": {
|
|
"label": first_row.get("metric_label", "Result"),
|
|
"trend": str(first_row.get("trend_value", "")),
|
|
"comparisonLabel": first_row.get("comparison_label", ""),
|
|
},
|
|
"geo_map": {"mapStyle": "dubai_district_heat", "intensityField": "lead_count", "interactive": True, "tooltipFields": ["district", "lead_count", "avg_qd_score"]},
|
|
"table": {
|
|
"rankBy": "revenue_generated",
|
|
"showTopBadge": True,
|
|
"columns": table_columns_by_dataset.get(
|
|
dataset,
|
|
inferred_columns,
|
|
),
|
|
"emptyStateTitle": "No matching records found",
|
|
"emptyStateDescription": "The query ran successfully but returned no rows for this prompt.",
|
|
},
|
|
"pipeline_board": {"showValue": True, "colorByStage": True},
|
|
"activity_stream": {"showUrgencyIndicator": True},
|
|
}
|
|
return defaults.get(comp_type, {})
|
|
|
|
@staticmethod
|
|
def _default_bindings(comp_type: str) -> dict[str, Any]:
|
|
return {"dimensions": [], "measures": [], "series": [], "filters": []}
|
|
|
|
@staticmethod
|
|
def _rendering_hints(comp_type: str) -> dict[str, Any]:
|
|
priority_map = {
|
|
"pipeline_board": ("pipeline", 9), "bar_chart": ("chart", 8),
|
|
"geo_map": ("map", 9), "table": ("table", 7),
|
|
"line_chart": ("chart", 8), "kpi_tile": ("kpi", 6),
|
|
"activity_stream": ("table", 8),
|
|
}
|
|
skeleton, priority = priority_map.get(comp_type, ("chart", 7))
|
|
height_map = {
|
|
"pipeline_board": 400, "bar_chart": 320, "geo_map": 420,
|
|
"table": 300, "line_chart": 320, "kpi_tile": 140, "activity_stream": 360,
|
|
}
|
|
return {
|
|
"estimatedHeightPx": height_map.get(comp_type, 300),
|
|
"skeletonVariant": skeleton,
|
|
"virtualizationPriority": priority,
|
|
}
|
|
|
|
@staticmethod
|
|
def _generate_summary(prompt: str, viz_plan: dict[str, Any]) -> str:
|
|
count = len(viz_plan.get("components", []))
|
|
short_prompt = prompt[:60] + ("…" if len(prompt) > 60 else "")
|
|
data_component_count = max(count - 1, 0)
|
|
return f'Generated {data_component_count} component{"s" if data_component_count != 1 else ""} for: "{short_prompt}"'
|
|
|
|
@staticmethod
|
|
def _error_component(
|
|
*,
|
|
component_id: str,
|
|
execution_id: str,
|
|
actor_id: str,
|
|
branch_id: str,
|
|
dataset: str,
|
|
warnings: list[str],
|
|
order_index: int,
|
|
section_id: str,
|
|
) -> dict[str, Any]:
|
|
return {
|
|
"componentId": component_id,
|
|
"type": "errorNotice",
|
|
"title": f"{dataset} unavailable",
|
|
"description": "Oracle could not render live data for this component.",
|
|
"dataSourceDescriptor": {
|
|
"descriptorId": str(uuid.uuid4()),
|
|
"sourceType": "postgres",
|
|
"connectorId": "velocity-core-postgres",
|
|
"dataset": dataset,
|
|
"authContextRef": f"authctx_{actor_id}_scope",
|
|
"queryTemplate": "",
|
|
"queryParameters": {},
|
|
"rowLimit": 0,
|
|
"privacyTier": "standard",
|
|
},
|
|
"visualizationParameters": {
|
|
"errorCode": "oracle_live_query_failed",
|
|
"message": " | ".join(warnings[:2]),
|
|
"severity": "warning",
|
|
"retryable": True,
|
|
},
|
|
"dataBindings": {"dimensions": [], "measures": [], "series": [], "filters": []},
|
|
"version": 1,
|
|
"lifecycleState": "active",
|
|
"provenance": {
|
|
"originType": "prompt_generated",
|
|
"promptExecutionId": execution_id,
|
|
"sourceBranchId": branch_id,
|
|
"createdBy": actor_id,
|
|
"createdAt": _iso(_now()),
|
|
},
|
|
"renderingHints": {"estimatedHeightPx": 140, "skeletonVariant": "generic", "virtualizationPriority": 5},
|
|
"layout": {
|
|
"orderIndex": order_index,
|
|
"sectionId": section_id,
|
|
"widthMode": "full",
|
|
"minHeightPx": 140,
|
|
"stickyHeader": False,
|
|
},
|
|
"accessControls": {
|
|
"visibilityScope": "private",
|
|
"allowedRoles": ["senior_broker", "sales_director", "marketing_operator", "data_steward", "compliance_reviewer", "platform_admin"],
|
|
"redactionPolicy": "none",
|
|
},
|
|
"styleSignature": {
|
|
"theme": "velocity_glass",
|
|
"paletteToken": "ocean_signal",
|
|
"motionProfile": "calm_reveal",
|
|
"density": "comfortable",
|
|
"radiusScale": "lg",
|
|
"typographyScale": "balanced",
|
|
},
|
|
"validationState": {
|
|
"schema": "pass",
|
|
"policy": "pass",
|
|
"a11y": "pass",
|
|
"performance": "pass",
|
|
"status": "validated",
|
|
},
|
|
"auditLog": [f"aud_{execution_id}_error"],
|
|
"dataRows": [],
|
|
}
|
|
|
|
async def _call_nemoclaw(
|
|
self,
|
|
prompt: str,
|
|
context: list[dict[str, str]],
|
|
ctx: PolicyContext,
|
|
) -> dict[str, Any]:
|
|
"""
|
|
Uses the shared runtime LLM service to propose a retrieval plan.
|
|
Raises on malformed output so the orchestrator can fall back safely.
|
|
"""
|
|
row_limit = 50 if ctx.actor_role in ("senior_broker", "junior_broker") else 200
|
|
system_prompt = (
|
|
"You are the Oracle planner for Project Velocity. "
|
|
"Return JSON only. "
|
|
"Choose up to 4 analytical components for the prompt. "
|
|
"Allowed component types: pipeline_board, bar_chart, geo_map, table, line_chart, kpi_tile, activity_stream. "
|
|
"Allowed datasets: deals, lead_daily_snapshot, lead_geo_interest_rollup, broker_performance, inventory_absorption, "
|
|
"oracle_aggregated_metric, lead_activity_log, crm_contacts_overview, crm_opportunity_pipeline, "
|
|
"crm_property_interest_rollup, crm_interaction_timeline. "
|
|
"Return an object with keys semanticModelVersion, intentClass, components. "
|
|
"Each component must include suggestedType, dataset, and titleHint. "
|
|
"Do not emit SQL. Do not invent datasets outside the allowlist."
|
|
)
|
|
response = await runtime_llm_service.chat(
|
|
provider_id=None,
|
|
model=None,
|
|
system_prompt=system_prompt,
|
|
messages=[
|
|
*context,
|
|
{
|
|
"role": "user",
|
|
"content": json.dumps(
|
|
{
|
|
"prompt": prompt,
|
|
"tenantId": ctx.tenant_id,
|
|
"actorRole": ctx.actor_role,
|
|
"rowLimit": row_limit,
|
|
}
|
|
),
|
|
},
|
|
],
|
|
temperature=0.1,
|
|
response_format="json",
|
|
metadata={"planner": "oracle_canvas"},
|
|
)
|
|
payload = response.get("message", {}).get("parsedJson") or {}
|
|
components_payload = payload.get("components")
|
|
if not isinstance(components_payload, list) or not components_payload:
|
|
raise ValueError("Runtime LLM planner returned no components.")
|
|
|
|
normalized_components: list[dict[str, Any]] = []
|
|
for raw_component in components_payload[:4]:
|
|
if not isinstance(raw_component, dict):
|
|
continue
|
|
suggested_type = str(raw_component.get("suggestedType", "")).strip()
|
|
dataset = str(raw_component.get("dataset", "")).strip()
|
|
if suggested_type not in _DATASET_MAP or dataset not in _RUNTIME_ALLOWED_DATASETS:
|
|
continue
|
|
normalized_components.append(
|
|
{
|
|
"suggestedType": suggested_type,
|
|
"dataset": dataset,
|
|
"privacyTier": "standard",
|
|
"rowLimit": row_limit,
|
|
"joins": [],
|
|
"queryTemplate": f"SELECT * FROM {dataset} WHERE tenant_id = :tenant_id LIMIT :limit",
|
|
"queryParameters": {"tenant_id": ctx.tenant_id, "limit": row_limit},
|
|
"titleHint": str(raw_component.get("titleHint", "")).strip() or self._generate_title(prompt, suggested_type),
|
|
}
|
|
)
|
|
|
|
if not normalized_components:
|
|
raise ValueError("Runtime LLM planner returned no valid whitelisted components.")
|
|
|
|
return {
|
|
"planId": str(uuid.uuid4()),
|
|
"components": normalized_components,
|
|
"semanticModelVersion": str(payload.get("semanticModelVersion") or "oracle_runtime_llm_v2026_04_19_01"),
|
|
"intentClass": str(payload.get("intentClass") or "analytical"),
|
|
"planner": "runtime_llm",
|
|
}
|
|
|
|
async def get_execution(self, execution_id: str) -> dict[str, Any] | None:
|
|
return _DEMO_EXECUTIONS.get(execution_id)
|
|
|
|
async def _persist_execution(self, execution: dict[str, Any]) -> None:
|
|
_DEMO_EXECUTIONS[execution["executionId"]] = execution
|
|
if not _db_ready():
|
|
return
|
|
assert asyncpg is not None
|
|
conn = await asyncpg.connect(_DB_URL)
|
|
try:
|
|
await conn.execute(
|
|
"""
|
|
INSERT INTO oracle_prompt_executions (
|
|
execution_id, tenant_id, page_id, branch_id, actor_id, prompt, intent_class,
|
|
status, model_runtime, semantic_model_version, retrieval_plan, visualization_plan,
|
|
warnings, summary, components_created, client_request_id, created_at, completed_at
|
|
)
|
|
VALUES (
|
|
$1::uuid, $2, $3::uuid, $4, $5, $6, $7,
|
|
$8, $9, $10, $11::jsonb, $12::jsonb,
|
|
$13::text[], $14, $15::text[], $16, $17::timestamptz, $18::timestamptz
|
|
)
|
|
ON CONFLICT (execution_id)
|
|
DO UPDATE SET
|
|
status = EXCLUDED.status,
|
|
retrieval_plan = EXCLUDED.retrieval_plan,
|
|
visualization_plan = EXCLUDED.visualization_plan,
|
|
warnings = EXCLUDED.warnings,
|
|
summary = EXCLUDED.summary,
|
|
components_created = EXCLUDED.components_created,
|
|
completed_at = EXCLUDED.completed_at
|
|
""",
|
|
execution["executionId"],
|
|
execution["tenantId"],
|
|
execution["pageId"],
|
|
execution["branchId"],
|
|
execution["actorId"],
|
|
execution["prompt"],
|
|
execution["intentClass"],
|
|
execution["status"],
|
|
execution["modelRuntime"],
|
|
execution["semanticModelVersion"],
|
|
json.dumps(_json_safe(execution.get("retrievalPlan") or {})),
|
|
json.dumps(_json_safe(execution.get("visualizationPlan") or {})),
|
|
execution.get("warnings", []),
|
|
execution.get("summary"),
|
|
execution.get("componentsCreated", []),
|
|
execution.get("clientRequestId"),
|
|
_coerce_datetime(execution["createdAt"]),
|
|
_coerce_datetime(execution.get("completedAt")),
|
|
)
|
|
finally:
|
|
await conn.close()
|
|
|
|
|
|
# ── Singleton ─────────────────────────────────────────────────────────────────
|
|
|
|
prompt_orchestrator = PromptOrchestrator()
|