CheddahBot/cheddahbot/router.py

"""Message formatting and system prompt construction."""

from __future__ import annotations

import json
from pathlib import Path


def build_system_prompt(
    identity_dir: Path,
    memory_context: str = "",
    tools_description: str = "",
    skills_context: str = "",
) -> str:
    """Build the system prompt from identity files + memory + skills + tools."""
    parts = []

    # 1. Identity: SOUL.md
    soul_path = identity_dir / "SOUL.md"
    if soul_path.exists():
        parts.append(soul_path.read_text(encoding="utf-8").strip())

    # 2. Identity: USER.md
    user_path = identity_dir / "USER.md"
    if user_path.exists():
        parts.append(user_path.read_text(encoding="utf-8").strip())

    # 3. Memory context (injected by memory system)
    if memory_context:
        parts.append(f"# Relevant Memory\n{memory_context}")

    # 4. Skills context (injected by skill registry)
    if skills_context:
        parts.append(skills_context)

    # 5. Available tools
    if tools_description:
        parts.append(f"# Available Tools\n{tools_description}")

    # 5. Core instructions
    parts.append(
        "# Instructions\n"
        "- Use tools when they would help answer the user's request.\n"
        "- If you learn something important about the user, save it to memory.\n"
        "- Be concise but thorough. Don't pad responses unnecessarily.\n"
        "- When uncertain, ask for clarification.\n"
        "- Reference memories naturally when relevant.\n"
        "- IMPORTANT: Do NOT call the same tool twice with the same arguments. "
        "If a tool already returned a result, use that result — do not re-call it.\n"
        "- After using tools, always respond to the user with a final answer. "
        "Do not end your turn with only tool calls and no text.\n"
        "- For tasks requiring shell commands, file edits, or system access, "
        "use the delegate_task tool instead of trying to do it yourself.\n"
        "- EXCEPTION: If a specialized tool exists for the request (e.g. write_press_releases), "
        "call that tool directly — do NOT delegate it. Specialized tools have their own "
        "orchestration and skill prompts built in."
    )

    return "\n\n---\n\n".join(parts)


def format_messages_for_llm(
    system_prompt: str,
    history: list[dict],
    max_messages: int = 50,
) -> list[dict]:
    """Format conversation history into LLM message format.

    Handles three message types from the DB:
    - user: passed through as role=user
    - assistant: reconstructed with tool_calls in OpenAI format when present;
      skipped if empty content AND no tool_calls
    - tool: kept as role=tool with a tool_call_id linking back to the
      assistant message that requested it
    """
    messages = [{"role": "system", "content": system_prompt}]

    recent = history[-max_messages:] if len(history) > max_messages else history

    for msg in recent:
        role = msg.get("role", "user")
        content = msg.get("content", "")
        tool_calls = msg.get("tool_calls")  # list or None

        if role == "user":
            messages.append({"role": "user", "content": content})

        elif role == "assistant":
            # Skip completely empty assistant messages (no text, no tool_calls)
            if not content and not tool_calls:
                continue

            entry: dict = {"role": "assistant", "content": content or None}

            if tool_calls:
                openai_tcs = []
                for i, tc in enumerate(tool_calls):
                    tc_id = tc.get("id") or f"call_{tc.get('name', 'unknown')}_{i}"
                    openai_tcs.append(
                        {
                            "id": tc_id,
                            "type": "function",
                            "function": {
                                "name": tc.get("name", "unknown"),
                                "arguments": json.dumps(tc.get("input", {})),
                            },
                        }
                    )
                entry["tool_calls"] = openai_tcs

            messages.append(entry)

        elif role == "tool":
            tool_name = msg.get("tool_result", "unknown")
            tc_id = _find_tool_call_id(messages, tool_name)
            messages.append(
                {
                    "role": "tool",
                    "tool_call_id": tc_id,
                    "content": content,
                }
            )

    return _merge_consecutive(messages)


def _find_tool_call_id(messages: list[dict], tool_name: str) -> str:
    """Walk backwards through messages to find the tool_call_id for a tool result."""
    for msg in reversed(messages):
        if msg.get("role") != "assistant" or "tool_calls" not in msg:
            continue
        for tc in msg["tool_calls"]:
            fn = tc.get("function", {})
            if fn.get("name") == tool_name:
                return tc["id"]
    # Fallback: generate a deterministic ID so the API doesn't reject it
    return f"call_{tool_name}_0"


def _merge_consecutive(messages: list[dict]) -> list[dict]:
    """Merge back-to-back messages with the same role to avoid API rejection.

    Only merges user or assistant messages (not system or tool).
    """
    if not messages:
        return messages

    merged: list[dict] = [messages[0]]
    for msg in messages[1:]:
        prev = merged[-1]
        if (
            msg["role"] == prev["role"]
            and msg["role"] in ("user", "assistant")
            and "tool_calls" not in prev
            and "tool_calls" not in msg
        ):
            # Merge text content
            prev_text = prev.get("content") or ""
            new_text = msg.get("content") or ""
            prev["content"] = f"{prev_text}\n\n{new_text}".strip()
        else:
            merged.append(msg)
    return merged