"""BLCE full engine workflow — 22-phase business logic pipeline.

Reuses:
    - ``src.plugins.ce.blce.orchestrator.BLCEOrchestrator.run_full_engine()``

IKE integration (optional):
    - P3 Debate Engine: Resolve ambiguous logic classifications
    - P1 Knowledge Base: Ingest extracted logic into institutional memory
"""

from __future__ import annotations

from typing import Any, Dict, List, Optional

from databridge_core.sdk.core.context import SDKContext
from databridge_core.sdk.core.exceptions import NotImplementedStubError
from databridge_core.sdk.core.types import CompiledArtifact, Result
from databridge_core.sdk.workflows.base import BaseWorkflow
from databridge_core.sdk.workflows.result import WorkflowResult
from databridge_core.sdk.workflows.step import workflow_step


class BLCEFullEngineWorkflow(BaseWorkflow):
    """Run the full 22-phase BLCE pipeline.

    Processes source artifacts through parsing, reconciliation,
    normalization, hierarchy attachment, model generation, and
    deployment phases.

    When IKE is available, ambiguous logic classifications are
    resolved through IKE's P3 debate engine (Proposer-Challenger-
    Arbiter), and extracted logic is ingested into IKE's P1 KB.

    Reuses:
        - ``src.plugins.ce.blce.orchestrator.BLCEOrchestrator``
        - ``src.plugins.ce.blce.orchestrator.BLCEOrchestrator.run_full_engine()``
    """

    name = "blce.full_engine"
    category = "blce"
    description = "Run 22-phase BLCE business logic extraction pipeline"
    required_params = []
    optional_params = {
        "source_paths": None,
        "client_id": "",
        "mode": "analyze_only",
        "skip_phases": None,
        "ike_debate": True,
        "ike_ingest": True,
    }

    def validate(self, params: Dict[str, Any]) -> Dict[str, Any]:
        return self._apply_defaults(params)

    def execute(
        self, params: Dict[str, Any], ctx: Optional[SDKContext] = None
    ) -> Result:
        steps = [self._run_engine(params, ctx)]
        if ctx and ctx.ike.available:
            # IKE P3: Debate ambiguous classifications
            if params.get("ike_debate"):
                steps.append(self._ike_debate_ambiguous(params, ctx))
            # IKE P1: Ingest extracted logic into KB
            if params.get("ike_ingest"):
                steps.append(self._ike_ingest_logic(params, ctx))
        # Graph integration
        if ctx and ctx.graphs.available:
            steps.append(self._graph_record_lineage(params, ctx))
            steps.append(self._graph_record_evidence(params, ctx))
            steps.append(self._graph_index_vectors(params, ctx))
            steps.append(self._graph_ingest_kb(params, ctx))
            steps.append(self._graph_update_entities(params, ctx))
        return WorkflowResult.from_steps(self.name, steps)

    def compile_to_snowflake(
        self, params: Dict[str, Any], ctx: Optional[SDKContext] = None
    ) -> List[CompiledArtifact]:
        raise NotImplementedStubError(
            self.name,
            "compile_to_snowflake",
            "Use the Wright pipeline compiler on BLCE model outputs; "
            "BLCE itself produces model proposals that feed into "
            "WrightPipelineWorkflow.compile_to_snowflake()",
        )

    @workflow_step("run_engine", "Run 22-phase BLCE engine")
    def _run_engine(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # REUSE: BLCEOrchestrator(config).run_full_engine(
        #     source_paths=params.get("source_paths"),
        #     client_id=params.get("client_id", ""),
        #     mode=params.get("mode", "analyze_only"),
        #     skip_phases=params.get("skip_phases"),
        # )
        raise NotImplementedStubError(
            self.name,
            "run_engine",
            "src.plugins.ce.blce.orchestrator.BLCEOrchestrator.run_full_engine("
            "source_paths, client_id, mode, skip_phases)",
        )

    @workflow_step("ike_debate_ambiguous", "Debate ambiguous logic via IKE P3")
    def _ike_debate_ambiguous(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # IKE P3: For each ambiguous classification from the BLCE engine,
        # run a 3-agent debate (Proposer-Challenger-Arbiter) to resolve.
        #
        # For each ambiguous finding:
        #   ctx.ike.run_debate(
        #       topic=f"Should we classify {logic_item} as {proposed_type}?",
        #       finding={"logic": logic_item, "proposed": proposed_type,
        #                "alternatives": alt_types},
        #       context={"client_id": params.get("client_id")},
        #   )
        raise NotImplementedStubError(
            self.name,
            "ike_debate_ambiguous",
            "ctx.ike.run_debate(topic, finding, context) — IKE P3 Proposer-Challenger-Arbiter",
        )

    @workflow_step("ike_ingest_logic", "Ingest extracted logic into IKE KB")
    def _ike_ingest_logic(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # IKE P1: Ingest confirmed business logic extractions into
        # the institutional knowledge base for future reference.
        #
        # For each confirmed logic extraction:
        #   ctx.ike.ingest_knowledge(
        #       content=logic_summary,
        #       source_type="run_result",
        #       tags=["blce", params.get("client_id", "")],
        #   )
        raise NotImplementedStubError(
            self.name,
            "ike_ingest_logic",
            "ctx.ike.ingest_knowledge(content, source_type='run_result') — IKE P1 KB ingest",
        )

    # --- Graph integration ---

    @workflow_step("graph_record_lineage", "Record RULE, BUSINESS_TERM, EXPORT nodes in lineage")
    def _graph_record_lineage(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # ctx.graphs.record_lineage_node("sdk", rule_id, "EXTERNAL", description=rule_desc)
        # ctx.graphs.record_lineage_node("sdk", term_id, "EXTERNAL", description=term_def)
        # ctx.graphs.record_lineage_edge("sdk", rule_id, export_id, "DERIVED")
        raise NotImplementedStubError(
            self.name,
            "graph_record_lineage",
            "ctx.graphs.record_lineage_node/edge — RULE + BUSINESS_TERM + EXPORT nodes",
        )

    @workflow_step("graph_record_evidence", "Record debate verdicts in evidence trail")
    def _graph_record_evidence(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # ctx.graphs.record_evidence(
        #     account_id=logic_item_id, hierarchy_node_id=classification,
        #     method="blce_debate", confidence=verdict_confidence,
        #     evidence_sources=[{"content": verdict_summary, ...}],
        # )
        raise NotImplementedStubError(
            self.name,
            "graph_record_evidence",
            "ctx.graphs.record_evidence — debate verdicts as evidence trail",
        )

    @workflow_step("graph_index_vectors", "Index extracted logic in vector store")
    def _graph_index_vectors(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # ctx.graphs.index_content(
        #     doc_id=f"blce_{rule_id}", content=logic_summary,
        #     source_type="blce_logic", metadata={...},
        # )
        raise NotImplementedStubError(
            self.name,
            "graph_index_vectors",
            "ctx.graphs.index_content(doc_id, content, 'blce_logic', metadata)",
        )

    @workflow_step("graph_ingest_kb", "Ingest rules and terms into KB graph")
    def _graph_ingest_kb(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # ctx.graphs.ingest_kb_nodes(
        #     nodes=[{"id": rule_id, "type": "RULE", "properties": {...}},
        #            {"id": term_id, "type": "BUSINESS_TERM", "properties": {...}}],
        #     edges=[{"source": rule_id, "target": term_id, "type": "APPLIES_TO"}],
        # )
        raise NotImplementedStubError(
            self.name,
            "graph_ingest_kb",
            "ctx.graphs.ingest_kb_nodes(nodes, edges) — rules and terms as KB graph",
        )

    @workflow_step("graph_update_entities", "Update entity clusters via entity linker")
    def _graph_update_entities(self, params: Dict[str, Any], ctx: Optional[SDKContext] = None):
        # ctx.graphs.link_entities_incremental(logic_dna_path)
        # ctx.graphs.publish_entity_clusters()
        raise NotImplementedStubError(
            self.name,
            "graph_update_entities",
            "ctx.graphs.link_entities_incremental(logic_dna_path) + publish_entity_clusters()",
        )
