"""Strict LLM contract for extracting local graph memory."""

from __future__ import annotations

import json
from typing import Any

from pydantic import BaseModel, ConfigDict, Field, ValidationError


MAX_EPISODE_CHARS = 20_000
MAX_CONTEXT_CHARS = 12_000


class _StrictModel(BaseModel):
    model_config = ConfigDict(extra="forbid")


class ExtractedEntity(_StrictModel):
    mention: str = Field(min_length=1, max_length=2_000)
    canonical_name: str = Field(min_length=1, max_length=512)
    labels: list[str] = Field(default_factory=list, max_length=32)
    aliases: list[str] = Field(default_factory=list, max_length=32)
    attributes: dict[str, Any] = Field(default_factory=dict)
    summary: str = Field(default="", max_length=4_000)
    confidence: float = Field(default=0.0, ge=0.0, le=1.0)


class ExtractedEdge(_StrictModel):
    source_entity_ref: str = Field(min_length=1, max_length=512)
    target_entity_ref: str = Field(min_length=1, max_length=512)
    relation: str = Field(min_length=1, max_length=128)
    fact: str = Field(min_length=1, max_length=4_000)
    attributes: dict[str, Any] = Field(default_factory=dict)
    valid_at: str | None = Field(default=None, max_length=128)
    invalid_at: str | None = Field(default=None, max_length=128)
    expired_at: str | None = Field(default=None, max_length=128)
    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
    evidence: list[str] = Field(default_factory=list, max_length=32)


class MemoryExtractionResult(_StrictModel):
    entities: list[ExtractedEntity] = Field(default_factory=list, max_length=500)
    edges: list[ExtractedEdge] = Field(default_factory=list, max_length=1_000)
    episode_summary: str = Field(default="", max_length=8_000)
    unresolved_mentions: list[str] = Field(default_factory=list, max_length=200)


def parse_extraction_response(raw: str | dict[str, Any]) -> MemoryExtractionResult:
    """Parse and validate an LLM response without accepting extra fields."""
    if isinstance(raw, str):
        text = raw.strip()
        if text.startswith("```"):
            lines = text.splitlines()
            if lines and lines[0].strip().startswith("```"):
                lines = lines[1:]
            if lines and lines[-1].strip() == "```":
                lines = lines[:-1]
            text = "\n".join(lines).strip()
        try:
            raw = json.loads(text)
        except json.JSONDecodeError as exc:
            raise ValueError("invalid_memory_json") from exc
    if not isinstance(raw, dict):
        raise ValueError("invalid_memory_payload")
    return MemoryExtractionResult.model_validate(raw)


def _language_instruction(language: str) -> str:
    if language == "en":
        return "IMPORTANT: Write summaries and facts in English. Return JSON only."
    return "IMPORTANT: Write summaries and facts in Thai. Return JSON only."


def build_extraction_prompt(
    *,
    language: str,
    ontology: dict[str, Any],
    episode_text: str,
    context: str = "",
) -> str:
    if not isinstance(episode_text, str) or len(episode_text) > MAX_EPISODE_CHARS:
        raise ValueError("episode_too_large")
    if not isinstance(context, str) or len(context) > MAX_CONTEXT_CHARS:
        raise ValueError("context_too_large")
    if not isinstance(ontology, dict):
        raise ValueError("invalid_ontology")

    ontology_json = json.dumps(ontology, ensure_ascii=False, sort_keys=True)
    context_block = context if context else "(none)"
    return f"""{_language_instruction(language)}

You extract evidence-grounded graph memory from one episode.
Never invent facts. Use only labels and relations allowed by the ontology.
Use stable entity_refs inside this response; never guess database IDs.
Preserve temporal fields as null when the evidence does not support a date.
Keep confidence between 0 and 1 and keep evidence references when available.

Return JSON only with this shape:
{{
  "entities": [{{
    "mention": "text span",
    "canonical_name": "stable name",
    "labels": ["Person"],
    "aliases": [],
    "attributes": {{}},
    "summary": "short evidence-grounded summary",
    "confidence": 0.0
  }}],
  "edges": [{{
    "source_entity_ref": "entity-ref",
    "target_entity_ref": "entity-ref",
    "relation": "RELATION_NAME",
    "fact": "evidence-grounded fact",
    "attributes": {{}},
    "valid_at": null,
    "invalid_at": null,
    "expired_at": null,
    "confidence": 0.0,
    "evidence": ["episode reference or span"]
  }}],
  "episode_summary": "short summary",
  "unresolved_mentions": []
}}

Ontology:
{ontology_json}

Additional context:
{context_block}

Episode:
{episode_text}
"""


__all__ = [
    "ExtractedEdge",
    "ExtractedEntity",
    "MemoryExtractionResult",
    "build_extraction_prompt",
    "parse_extraction_response",
]