"""Prompt and formatting helpers for per-thread memory."""

from __future__ import annotations

import json
from typing import Any

from deerflow.agents.memory.prompt import _coerce_confidence, _count_tokens, format_conversation_for_update

THREAD_MEMORY_UPDATE_PROMPT = """You are a user profile memory system.

Current per-thread memory:
<existing_memory>
{existing_memory}
</existing_memory>

Conversation:
<conversation>
{conversation}
</conversation>

Return JSON only with this schema:
{{
  "profile": {{
    "name": string|null,
    "role": string|null,
    "expertise": string[],
    "language": "zh-CN"|"en-US"|null,
    "context": string|null
  }},
  "preferences": {{
    "tone": "casual"|"formal"|"technical"|"friendly"|null,
    "verbosity": "concise"|"detailed"|null,
    "codeStyle": string|null,
    "other": string|null
  }},
  "facts": [
    {{
      "content": string,
      "category": "tech_stack"|"preference"|"personal"|"context"|"goal",
      "confidence": number
    }}
  ]
}}

Rules:
- Keep only stable and useful user profile facts.
- Do not store sensitive personal data (phone/email/address/password/token/id/bank).
- Deduplicate and keep high-confidence facts.
- Return valid JSON only.
"""


def create_empty_thread_memory() -> dict[str, Any]:
    return {
        "profile": {"name": None, "role": None, "expertise": [], "language": None, "context": None},
        "preferences": {"tone": None, "verbosity": None, "codeStyle": None, "other": None},
        "facts": [],
    }


def format_thread_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
    if not memory_data:
        return ""

    profile = memory_data.get("profile") or {}
    preferences = memory_data.get("preferences") or {}
    facts = memory_data.get("facts") or []

    profile_lines: list[str] = []
    for key, label in (("name", "Name"), ("role", "Role"), ("language", "Language"), ("context", "Context")):
        value = profile.get(key)
        if isinstance(value, str) and value.strip():
            profile_lines.append(f"- {label}: {value.strip()}")
    expertise = profile.get("expertise")
    if isinstance(expertise, list):
        cleaned = [str(item).strip() for item in expertise if str(item).strip()]
        if cleaned:
            profile_lines.append(f"- Expertise: {', '.join(cleaned)}")

    pref_lines: list[str] = []
    for key, label in (("tone", "Tone"), ("verbosity", "Verbosity"), ("codeStyle", "Code Style"), ("other", "Other")):
        value = preferences.get(key)
        if isinstance(value, str) and value.strip():
            pref_lines.append(f"- {label}: {value.strip()}")

    sections: list[str] = []
    if profile_lines:
        sections.append("Profile:\n" + "\n".join(profile_lines))
    if pref_lines:
        sections.append("Preferences:\n" + "\n".join(pref_lines))

    # Facts are lowest priority: include by confidence/recency and trim by token budget.
    ranked_facts = sorted(
        (
            f
            for f in facts
            if isinstance(f, dict) and isinstance(f.get("content"), str) and f.get("content", "").strip()
        ),
        key=lambda f: (_coerce_confidence(f.get("confidence"), default=0.0), str(f.get("createdAt", ""))),
        reverse=True,
    )
    base = "\n\n".join(sections)
    running = _count_tokens(base) if base else 0
    fact_lines: list[str] = []
    if ranked_facts:
        running += _count_tokens("\n\nFacts:\n" if base else "Facts:\n")
    for fact in ranked_facts:
        line = (
            f"- [{str(fact.get('category', 'context')).strip() or 'context'} | "
            f"{_coerce_confidence(fact.get('confidence'), default=0.0):.2f}] {fact.get('content').strip()}"
        )
        candidate = ("\n" + line) if fact_lines else line
        cost = _count_tokens(candidate)
        if running + cost > max_tokens:
            break
        fact_lines.append(line)
        running += cost
    if fact_lines:
        sections.append("Facts:\n" + "\n".join(fact_lines))

    return "\n\n".join(sections)


def build_thread_memory_prompt(existing_memory: dict[str, Any], messages: list[Any]) -> str:
    return THREAD_MEMORY_UPDATE_PROMPT.format(
        existing_memory=json.dumps(existing_memory, ensure_ascii=False, indent=2),
        conversation=format_conversation_for_update(messages),
    )