feat: Oracle Canvas, Revision History and Canvas Sharing (#33)

Co-authored-by: Sagnik <sagnik7896@gmail.com> Reviewed-on: sagnik/Project_Velocity#33
2026-04-23 01:20:21 +05:30
parent e519339cc9
commit 6cdc366718
58 changed files with 3187 additions and 705 deletions
--- a/backend/oracle/prompt_orchestrator.py
+++ b/backend/oracle/prompt_orchestrator.py
@@ -56,6 +56,18 @@ def _coerce_datetime(value: datetime | str | None) -> datetime | None:

 # ── Execution store ───────────────────────────────────────────────────────────

+def _json_safe(value: Any) -> Any:
+    if isinstance(value, datetime):
+        return value.isoformat()
+    if isinstance(value, dict):
+        return {str(key): _json_safe(val) for key, val in value.items()}
+    if isinstance(value, list):
+        return [_json_safe(item) for item in value]
+    if isinstance(value, tuple):
+        return [_json_safe(item) for item in value]
+    return value
+
+
 _DEMO_EXECUTIONS: dict[str, dict[str, Any]] = {}


@@ -117,13 +129,13 @@ def _build_demo_retrieval_plan(


 _DATASET_MAP: dict[str, str] = {
-    "pipeline_board": "deals",
-    "bar_chart": "lead_daily_snapshot",
+    "pipeline_board": "crm_opportunity_pipeline",
+    "bar_chart": "crm_property_interest_rollup",
    "geo_map": "lead_geo_interest_rollup",
-    "table": "broker_performance",
-    "line_chart": "inventory_absorption",
+    "table": "crm_contacts_overview",
+    "line_chart": "crm_property_interest_rollup",
    "kpi_tile": "oracle_aggregated_metric",
-    "activity_stream": "lead_activity_log",
+    "activity_stream": "crm_interaction_timeline",
 }

 _CODEBOOK_COMPONENT_MAP: dict[str, str] = {
@@ -162,6 +174,10 @@ def _dataset_for_codebook(example: CodebookExample, prompt: str, component_plan_
        return "crm_interaction_timeline"
    if component_plan_type == "pipeline_board":
        return "crm_opportunity_pipeline"
+    if component_plan_type == "table" and any(term in lowered_prompt for term in ("last interacted", "last interaction", "recently contacted", "recent interaction")):
+        return "crm_last_interacted_clients"
+    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")):
+        return "crm_top_interested_clients"
    if component_plan_type == "line_chart" and any(term in lowered_prompt for term in ("trend", "time", "history", "growth")):
        return "crm_property_interest_rollup"

@@ -170,8 +186,12 @@ def _dataset_for_codebook(example: CodebookExample, prompt: str, component_plan_
            return "crm_interaction_timeline"
        if "pipeline" in lowered_prompt or "opportunit" in lowered_prompt:
            return "crm_opportunity_pipeline"
+        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):
+            return "crm_top_interested_clients"
        if "interest" in lowered_prompt or "project" in lowered_prompt or "property" in lowered_prompt:
            return "crm_property_interest_rollup"
+        if "last interacted" in lowered_prompt or "recently contacted" in lowered_prompt or "recent interaction" in lowered_prompt:
+            return "crm_last_interacted_clients"
        return "crm_contacts_overview"

    if "client" in chapter or "client" in subchapter or "contact" in subchapter:
@@ -205,6 +225,7 @@ def _build_codebook_retrieval_plan(
    exemplar = matches[0]
    for component_plan_type in desired_types[:4]:
        dataset = _dataset_for_codebook(exemplar, prompt, component_plan_type)
+        title_hint = _title_for_dataset(dataset, component_plan_type, prompt) or title_hints.get(component_plan_type, exemplar.title)
        components.append(
            {
                "suggestedType": component_plan_type,
@@ -222,7 +243,7 @@ def _build_codebook_retrieval_plan(
                    "subchapterName": exemplar.subchapter_name,
                    "sourcePack": exemplar.source_pack,
                },
-                "titleHint": title_hints.get(component_plan_type, exemplar.title),
+                "titleHint": title_hint,
            }
        )

@@ -235,6 +256,24 @@ def _build_codebook_retrieval_plan(
    }


+def _title_for_dataset(dataset: str, component_plan_type: str, prompt: str) -> str | None:
+    lowered_prompt = prompt.lower()
+    dataset_titles = {
+        "crm_contacts_overview": "CRM Contacts Overview",
+        "crm_opportunity_pipeline": "Opportunity Pipeline",
+        "crm_property_interest_rollup": "Property Interest Rollup",
+        "crm_interaction_timeline": "Client Interaction Timeline",
+        "crm_last_interacted_clients": "Last Interacted Clients",
+        "crm_top_interested_clients": "Top Interested Clients",
+        "broker_performance": "Broker Performance",
+    }
+    if dataset == "crm_top_interested_clients" and "top" in lowered_prompt:
+        return "Top Interested Clients"
+    if dataset == "crm_last_interacted_clients" and ("top" in lowered_prompt or "last" in lowered_prompt):
+        return "Last Interacted Clients"
+    return dataset_titles.get(dataset)
+
+
 _RUNTIME_ALLOWED_DATASETS = {
    "deals",
    "lead_daily_snapshot",
@@ -247,6 +286,8 @@ _RUNTIME_ALLOWED_DATASETS = {
    "crm_opportunity_pipeline",
    "crm_property_interest_rollup",
    "crm_interaction_timeline",
+    "crm_last_interacted_clients",
+    "crm_top_interested_clients",
 }


@@ -371,6 +412,11 @@ class PromptOrchestrator:
        execution["status"] = "executing"
        await self._persist_execution(execution)

+        page = await canvas_service.get_page(page_id, tenant_id)
+        existing_comps = page.get("components", []) if page else []
+        next_order_base = self._next_order_base(existing_comps)
+        section_id = f"sec_prompt_generated_{execution_id.replace('-', '')[:12]}"
+
        # ── Step 3: Build visualization plan (component descriptors) ──────────
        viz_plan = await self._build_visualization_plan(
            retrieval_plan=retrieval_plan,
@@ -382,6 +428,8 @@ class PromptOrchestrator:
            placement_mode=placement_mode,
            ctx=ctx,
            persona_plan=persona_plan,
+            base_order=next_order_base,
+            section_id=section_id,
        )
        execution["visualizationPlan"] = viz_plan

@@ -391,9 +439,7 @@ class PromptOrchestrator:

        # Commit a revision bump with the new components
        try:
-            page = await canvas_service.get_page(page_id, tenant_id)
            if page:
-                existing_comps = page.get("components", [])
                new_comps = existing_comps + viz_plan.get("components", [])
                revision = await canvas_service.commit_revision(
                    page_id=page_id,
@@ -429,6 +475,8 @@ class PromptOrchestrator:
        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 = [
@@ -438,9 +486,10 @@ class PromptOrchestrator:
                branch_id=branch_id,
                prompt=prompt,
                persona_plan=persona_plan,
+                order_index=base_order + 100,
+                section_id=section_id,
            )
        ]
-        base_order = 900  # Append after existing components

        component_plans = retrieval_plan.get("components", [])
        for i, plan in enumerate(component_plans):
@@ -469,7 +518,7 @@ class PromptOrchestrator:
                    "privacyTier": plan.get("privacyTier", "standard"),
                    "cachePolicy": {"mode": "ttl", "ttlSeconds": 120},
                },
-                "visualizationParameters": self._default_viz_params(ctype, data_rows),
+                "visualizationParameters": self._default_viz_params(ctype, dataset, data_rows),
                "dataBindings": self._default_bindings(ctype),
                "version": 1,
                "lifecycleState": "active",
@@ -483,7 +532,7 @@ class PromptOrchestrator:
                "renderingHints": self._rendering_hints(ctype),
                "layout": {
                    "orderIndex": base_order + (i + 1) * 100,
-                    "sectionId": "sec_prompt_generated",
+                    "sectionId": section_id,
                    "widthMode": "full" if ctype in ("pipeline_board", "table", "geo_map") else "half",
                    "minHeightPx": 300,
                    "stickyHeader": False,
@@ -520,11 +569,29 @@ class PromptOrchestrator:
                    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(
        *,
@@ -533,13 +600,13 @@ class PromptOrchestrator:
        branch_id: str,
        prompt: str,
        persona_plan: dict[str, Any],
+        order_index: int,
+        section_id: str,
    ) -> dict[str, Any]:
-        recommended = ", ".join(persona_plan.get("recommendedTemplates", [])) or "no direct template matches"
        content = (
            f"Oracle received: {prompt}\n\n"
-            f"Reusable templates: {recommended}\n\n"
-            "Execution policy: query live CRM data first, reuse matching templates, "
-            "synthesize missing UI blocks, then dispatch the required ComfyUI-backed workflow."
+            "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()),
@@ -574,8 +641,8 @@ class PromptOrchestrator:
            },
            "renderingHints": {"estimatedHeightPx": 180, "skeletonVariant": "text", "virtualizationPriority": 4},
            "layout": {
-                "orderIndex": 910,
-                "sectionId": "sec_prompt_generated",
+                "orderIndex": order_index,
+                "sectionId": section_id,
                "widthMode": "full",
                "minHeightPx": 180,
                "stickyHeader": False,
@@ -631,17 +698,34 @@ class PromptOrchestrator:
        return labels.get(comp_type, "Oracle Canvas Component")

    @staticmethod
-    def _default_viz_params(comp_type: str, rows: list[dict[str, Any]]) -> dict[str, Any]:
+    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": rows[0].get("metric_label", "Result") if rows else "Result",
-                "trend": str(rows[0].get("trend_value", "")) if rows else "",
-                "comparisonLabel": rows[0].get("comparison_label", "") if rows else "",
+                "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": ["name", "deals_closed", "revenue_generated"]},
+            "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},
        }
@@ -674,7 +758,8 @@ class PromptOrchestrator:
    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 "")
-        return f'Generated {count} component{"s" if count != 1 else ""} for: "{short_prompt}"'
+        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(
@@ -686,6 +771,7 @@ class PromptOrchestrator:
        dataset: str,
        warnings: list[str],
        order_index: int,
+        section_id: str,
    ) -> dict[str, Any]:
        return {
            "componentId": component_id,
@@ -722,7 +808,7 @@ class PromptOrchestrator:
            "renderingHints": {"estimatedHeightPx": 140, "skeletonVariant": "generic", "virtualizationPriority": 5},
            "layout": {
                "orderIndex": order_index,
-                "sectionId": "sec_prompt_generated",
+                "sectionId": section_id,
                "widthMode": "full",
                "minHeightPx": 140,
                "stickyHeader": False,
@@ -875,8 +961,8 @@ class PromptOrchestrator:
                execution["status"],
                execution["modelRuntime"],
                execution["semanticModelVersion"],
-                json.dumps(execution.get("retrievalPlan") or {}),
-                json.dumps(execution.get("visualizationPlan") or {}),
+                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", []),