"""ChatbotAgent — final answer formation. Phase 2 chatbot.

Receives one of:
  - a `QueryResult` (structured query path),
  - a list of document chunks (unstructured path), or
  - nothing (chat-only path: greeting, farewell, meta question).

Streams the answer token-by-token so the chat handler can wrap each token
into an SSE event. Conversation history is supported.
"""

from __future__ import annotations

from collections.abc import AsyncIterator
from dataclasses import dataclass
from pathlib import Path
from typing import Any

from langchain_core.messages import BaseMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import Runnable
from langchain_openai import AzureChatOpenAI

from src.middlewares.logging import get_logger

from ..query.executor.base import QueryResult

logger = get_logger("chatbot")


_PROMPT_DIR = Path(__file__).resolve().parent.parent / "config" / "prompts"
_SYSTEM_PROMPT_PATH = _PROMPT_DIR / "chatbot_system.md"
_GUARDRAILS_PATH = _PROMPT_DIR / "guardrails.md"


@dataclass
class DocumentChunk:
    """One retrieved document chunk for the unstructured path."""

    content: str
    filename: str | None = None
    page_label: str | None = None


def _load_system_prompt() -> str:
    """Compose system prompt = chatbot_system.md + guardrails.md.

    Guardrails appended last so they take precedence in conflict (matches
    the docstring at the top of guardrails.md).
    """
    chatbot = _SYSTEM_PROMPT_PATH.read_text(encoding="utf-8")
    guardrails = _GUARDRAILS_PATH.read_text(encoding="utf-8")
    return f"{chatbot}\n\n{guardrails}"


def _format_query_result(qr: QueryResult) -> str:
    """Render a QueryResult as a compact context block for the LLM."""
    source_label = qr.source_name or "(unknown source)"
    table_label = qr.table_name or "(unknown table)"
    if qr.error:
        return (
            f"[Query result — FAILED]\n"
            f"source: {source_label}\n"
            f"table: {table_label}\n"
            f"error: {qr.error}"
        )
    lines: list[str] = [
        "[Query result]",
        f"source: {source_label}",
        f"table: {table_label}",
        f"backend: {qr.backend}",
        f"row_count: {qr.row_count}"
        + (" (truncated)" if qr.truncated else ""),
        f"elapsed_ms: {qr.elapsed_ms}",
    ]
    if qr.rows:
        # Cap rendering at 25 rows; the LLM doesn't need the full set
        cap = min(len(qr.rows), 25)
        columns = list(qr.rows[0].keys())
        lines.append("columns: " + ", ".join(columns))
        lines.append("rows:")
        for row in qr.rows[:cap]:
            lines.append("  " + ", ".join(f"{k}={row[k]!r}" for k in columns))
        if cap < len(qr.rows):
            lines.append(f"  ... (+{len(qr.rows) - cap} more rows omitted from prompt)")
    return "\n".join(lines)


def _format_document_chunks(chunks: list[DocumentChunk]) -> str:
    if not chunks:
        return ""
    blocks: list[str] = []
    for c in chunks:
        label_parts = [p for p in (c.filename, c.page_label) if p]
        label = ", ".join(label_parts) if label_parts else "Unknown source"
        blocks.append(f"[Source: {label}]\n{c.content}")
    return "\n\n".join(blocks)


def _build_context_block(
    query_result: QueryResult | None,
    chunks: list[DocumentChunk] | None,
) -> str:
    parts: list[str] = []
    if query_result is not None:
        parts.append(_format_query_result(query_result))
    if chunks:
        parts.append(_format_document_chunks(chunks))
    return "\n\n".join(parts) if parts else "(no data context — answer conversationally)"


def _build_default_chain() -> Runnable:
    from src.config.settings import settings

    llm = AzureChatOpenAI(
        azure_deployment=settings.azureai_deployment_name_4o,
        openai_api_version=settings.azureai_api_version_4o,
        azure_endpoint=settings.azureai_endpoint_url_4o,
        api_key=settings.azureai_api_key_4o,
        temperature=0.3,
    )
    prompt = ChatPromptTemplate.from_messages(
        [
            ("system", _load_system_prompt()),
            MessagesPlaceholder(variable_name="history", optional=True),
            ("human", "{message}"),
            ("system", "Data context for this turn:\n\n{context}"),
        ]
    )
    return prompt | llm | StrOutputParser()


class ChatbotAgent:
    """Formats and streams the final user-facing answer.

    `chain` is injectable: tests pass a fake that yields canned tokens.
    Default constructs the production Azure OpenAI streaming chain on
    first use.
    """

    def __init__(self, chain: Runnable | None = None) -> None:
        self._chain = chain

    def _ensure_chain(self) -> Runnable:
        if self._chain is None:
            self._chain = _build_default_chain()
        return self._chain

    async def astream(
        self,
        message: str,
        history: list[BaseMessage] | None = None,
        query_result: QueryResult | None = None,
        chunks: list[DocumentChunk] | None = None,
    ) -> AsyncIterator[str]:
        """Stream tokens of the final answer.

        Caller wraps each token into the SSE format. Empty `history` and
        no context = pure chat reply.
        """
        chain = self._ensure_chain()
        payload: dict[str, Any] = {
            "message": message,
            "history": history or [],
            "context": _build_context_block(query_result, chunks),
        }
        async for token in chain.astream(payload):
            yield token