"""Structured context workspace and capsule contracts for multi-turn QA."""

from __future__ import annotations

from typing import Any, Literal

from pydantic import BaseModel, ConfigDict, Field, field_validator


def _clean_text(value: Any, *, limit: int = 240) -> str:
    return " ".join(str(value or "").strip().split())[:limit]


class ResearchCandidateItem(BaseModel):
    model_config = ConfigDict(extra="ignore")

    candidate_ref: str = ""
    display_name: str = ""
    summary: str = ""
    confidence: Literal["high", "medium", "low", "unknown"] = "unknown"
    status: Literal["direct_competitor", "adjacent", "weak_candidate", "unknown"] = "unknown"
    source_labels: list[str] = Field(default_factory=list)
    source_urls: list[str] = Field(default_factory=list)

    @field_validator("candidate_ref", "display_name", "summary", mode="before")
    @classmethod
    def _normalize_text(cls, value: Any) -> str:
        return _clean_text(value, limit=400)

    @field_validator("source_labels", "source_urls", mode="before")
    @classmethod
    def _normalize_list(cls, value: Any) -> list[str]:
        rows = value if isinstance(value, list) else []
        out: list[str] = []
        for item in rows:
            normalized = _clean_text(item, limit=240)
            if normalized and normalized not in out:
                out.append(normalized)
        return out[:8]


class ResearchWorkspace(BaseModel):
    model_config = ConfigDict(extra="ignore")

    workspace_version: str = "research_workspace.v1"
    workspace_id: str = ""
    workspace_kind: Literal["external_research", "hybrid_research", "internal_research", "new_project_ideation"] = "external_research"
    topic: str = ""
    primary_object_ref: str | None = None
    primary_object_id: int | None = None
    primary_object_title: str | None = None
    candidate_set_binding: str | None = None
    candidate_items: list[ResearchCandidateItem] = Field(default_factory=list)
    selected_candidate_refs: list[str] = Field(default_factory=list)
    research_goal: str | None = None
    analysis_dimensions: list[str] = Field(default_factory=list)
    evidence_refs: list[str] = Field(default_factory=list)
    confidence_state: Literal["high", "medium", "low", "unknown"] = "unknown"
    collected_inputs: dict[str, Any] = Field(default_factory=dict)
    open_questions: list[str] = Field(default_factory=list)
    next_recommended_actions: list[str] = Field(default_factory=list)
    status: Literal["active", "paused", "done"] = "active"
    pmflow_ai_thread_workspace: dict[str, Any] = Field(default_factory=dict)

    @field_validator(
        "workspace_id",
        "topic",
        "primary_object_ref",
        "primary_object_title",
        "candidate_set_binding",
        "research_goal",
        mode="before",
    )
    @classmethod
    def _normalize_field(cls, value: Any) -> str | None:
        if value is None:
            return None
        return _clean_text(value, limit=400)

    @field_validator(
        "selected_candidate_refs",
        "analysis_dimensions",
        "evidence_refs",
        "open_questions",
        "next_recommended_actions",
        mode="before",
    )
    @classmethod
    def _normalize_text_list(cls, value: Any) -> list[str]:
        rows = value if isinstance(value, list) else []
        out: list[str] = []
        for item in rows:
            normalized = _clean_text(item, limit=240)
            if normalized and normalized not in out:
                out.append(normalized)
        return out[:12]

    @field_validator("collected_inputs", "pmflow_ai_thread_workspace", mode="before")
    @classmethod
    def _normalize_dict(cls, value: Any) -> dict[str, Any]:
        return dict(value) if isinstance(value, dict) else {}


class ContextCapsule(BaseModel):
    model_config = ConfigDict(extra="ignore")

    capsule_version: str = "context_capsule.v1"
    workspace_id: str = ""
    workspace_kind: str = ""
    topic: str = ""
    primary_object_ref: str | None = None
    candidate_set_binding: str | None = None
    summary: str = ""
    open_questions: list[str] = Field(default_factory=list)
    next_action: str | None = None

    @field_validator(
        "workspace_id",
        "workspace_kind",
        "topic",
        "primary_object_ref",
        "candidate_set_binding",
        "summary",
        "next_action",
        mode="before",
    )
    @classmethod
    def _normalize_capsule_text(cls, value: Any) -> str | None:
        if value is None:
            return None
        return _clean_text(value, limit=400)

    @field_validator("open_questions", mode="before")
    @classmethod
    def _normalize_open_questions(cls, value: Any) -> list[str]:
        rows = value if isinstance(value, list) else []
        out: list[str] = []
        for item in rows:
            normalized = _clean_text(item, limit=240)
            if normalized and normalized not in out:
                out.append(normalized)
        return out[:8]


def normalize_research_workspace_payload(value: Any) -> dict[str, Any]:
    if not isinstance(value, dict) or not value:
        return {}
    try:
        return ResearchWorkspace.model_validate(value).model_dump()
    except Exception:
        return {}


def normalize_context_capsules_payload(value: Any) -> list[dict[str, Any]]:
    rows = value if isinstance(value, list) else []
    out: list[dict[str, Any]] = []
    for item in rows:
        if not isinstance(item, dict):
            continue
        try:
            out.append(ContextCapsule.model_validate(item).model_dump())
        except Exception:
            continue
    return out[:8]