"""System prompts for RAG-based codebase Q&A."""

from app.embeddings.retriever import RetrievedChunk
from app.memory.conversation import Message

# For codebase questions WITH retrieved context
CODEBASE_SYSTEM_PROMPT = """You answer questions about the Tyndale trading system using ONLY the provided YAML artifacts.

HARD CONSTRAINTS:
- Do NOT assume access to source code
- Do NOT invent implementation details not in the artifacts
- Do NOT speculate about code mechanics beyond what artifacts describe
- If artifacts do not contain enough information, say so explicitly

RESPONSE STYLE:
- Prefer architectural and behavioral explanations over mechanics
- Reference source files by path (e.g., ./trader.py)
- Explain trading concepts for developers without finance background
- Keep responses focused and concise"""

# For general questions (no RAG context)
GENERAL_SYSTEM_PROMPT = """You are an assistant for the Tyndale trading system documentation.
You can answer general questions, but for specific codebase questions, you need artifact context.
If the user asks about specific code without context, ask them to rephrase or be more specific."""

# For clarification/follow-ups (uses conversation history)
CLARIFICATION_SYSTEM_PROMPT = """You are continuing a conversation about the Tyndale trading system.
Use the conversation history to answer follow-up questions.
If you need to look up new information, ask the user to rephrase as a standalone question."""


def select_system_prompt(intent: str, has_context: bool) -> str:
    """Select appropriate system prompt based on intent and context.

    Args:
        intent: Classified intent (codebase, general, clarification)
        has_context: Whether RAG context was retrieved

    Returns:
        System prompt string
    """
    if intent == "codebase" and has_context:
        return CODEBASE_SYSTEM_PROMPT
    elif intent == "codebase" and not has_context:
        return CODEBASE_SYSTEM_PROMPT + "\n\nNOTE: No relevant artifacts were found for this question. Acknowledge this limitation in your response."
    elif intent == "clarification":
        return CLARIFICATION_SYSTEM_PROMPT
    else:
        return GENERAL_SYSTEM_PROMPT


def format_context(chunks: list[RetrievedChunk]) -> str:
    """Format retrieved chunks as context for the LLM.

    Args:
        chunks: List of retrieved chunks

    Returns:
        Formatted context string
    """
    if not chunks:
        return ""

    context_parts = ["## Retrieved Artifact Context\n"]

    for i, chunk in enumerate(chunks, 1):
        context_parts.append(f"### Source {i}: {chunk.source_file} ({chunk.chunk_type})")
        context_parts.append(chunk.content)
        context_parts.append("")  # Empty line separator

    return "\n".join(context_parts)


def format_history(history: list[Message], max_messages: int = 10) -> str:
    """Format conversation history for the LLM.

    Args:
        history: List of conversation messages
        max_messages: Maximum messages to include

    Returns:
        Formatted history string
    """
    if not history:
        return ""

    # Take most recent messages
    recent = history[-max_messages:] if len(history) > max_messages else history

    history_parts = ["## Conversation History\n"]

    for msg in recent:
        role = "User" if msg.role == "user" else "Assistant"
        history_parts.append(f"**{role}**: {msg.content}")
        history_parts.append("")

    return "\n".join(history_parts)


def build_rag_prompt(
    user_message: str,
    intent: str,
    chunks: list[RetrievedChunk],
    history: list[Message],
) -> tuple[str, str]:
    """Build complete RAG prompt with system message and user content.

    Args:
        user_message: Current user message
        intent: Classified intent
        chunks: Retrieved context chunks
        history: Conversation history

    Returns:
        Tuple of (system_prompt, user_content)
    """
    # Select system prompt
    system_prompt = select_system_prompt(intent, bool(chunks))

    # Build user content
    parts = []

    # Add context if available
    if chunks:
        parts.append(format_context(chunks))

    # Add history for clarification intent
    if intent == "clarification" and history:
        parts.append(format_history(history))

    # Add current question
    parts.append(f"## Current Question\n{user_message}")

    user_content = "\n\n".join(parts)

    return system_prompt, user_content