#!/usr/bin/env python3
"""Teleo Telegram Bot — Rio as analytical agent in community groups.

Architecture:
- Always-on ingestion: captures all messages, batch triage every N minutes
- Tag-based response: Opus-quality KB-grounded responses when @tagged
- Conversation-window triage: identifies coherent claims across message threads
- Full eval tracing: Rio's responses are logged as KB claims, accountable

Two paths (Ganymede architecture):
- Fast path (read): tag → KB query → Opus response → post to group
- Slow path (write): batch triage → archive to inbox/ → pipeline extracts

Separate systemd service: teleo-telegram.service
Does NOT integrate with pipeline daemon.

Epimetheus owns this module.
"""

import asyncio
import logging
import os
import re
import sqlite3
import sys
import time
from collections import defaultdict
from datetime import datetime, timezone
from pathlib import Path

# Add pipeline lib to path for shared modules
sys.path.insert(0, "/opt/teleo-eval/pipeline")

from telegram import Update
from telegram.ext import (
    Application,
    CommandHandler,
    ContextTypes,
    MessageHandler,
    filters,
)

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from kb_retrieval import KBIndex, format_context_for_prompt, retrieve_context
from market_data import get_token_price, format_price_context
from worktree_lock import main_worktree_lock
from x_client import search_tweets, fetch_from_url, check_research_rate_limit, record_research_usage, get_research_remaining

# ─── Config ─────────────────────────────────────────────────────────────

BOT_TOKEN_FILE = "/opt/teleo-eval/secrets/telegram-bot-token"
OPENROUTER_KEY_FILE = "/opt/teleo-eval/secrets/openrouter-key"
PIPELINE_DB = "/opt/teleo-eval/pipeline/pipeline.db"
KB_READ_DIR = "/opt/teleo-eval/workspaces/main"   # For KB retrieval (clean main branch)
ARCHIVE_DIR = "/opt/teleo-eval/telegram-archives"  # Write outside worktree to avoid read-only errors
MAIN_WORKTREE = "/opt/teleo-eval/workspaces/main"  # For git operations only
LEARNINGS_FILE = "/opt/teleo-eval/workspaces/main/agents/rio/learnings.md"  # Agent memory (Option D)
LOG_FILE = "/opt/teleo-eval/logs/telegram-bot.log"

# Triage interval (seconds)
TRIAGE_INTERVAL = 900  # 15 minutes

# Models
RESPONSE_MODEL = "anthropic/claude-opus-4-6"  # Opus for tagged responses
TRIAGE_MODEL = "anthropic/claude-haiku-4.5"  # Haiku for batch triage

# Rate limits
MAX_RESPONSE_PER_USER_PER_HOUR = 30
MIN_MESSAGE_LENGTH = 20  # Skip very short messages

# ─── Logging ────────────────────────────────────────────────────────────

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s %(name)s [%(levelname)s] %(message)s",
    handlers=[
        logging.FileHandler(LOG_FILE),
        logging.StreamHandler(),
    ],
)
logger = logging.getLogger("telegram-bot")

# ─── State ──────────────────────────────────────────────────────────────

# Message buffer for batch triage
message_buffer: list[dict] = []

# Rate limiting
user_response_times: dict[int, list[float]] = defaultdict(list)

# Allowed group IDs (set after first message received, or configure)
allowed_groups: set[int] = set()

# Shared KB index (built once, refreshed on mtime change)
kb_index = KBIndex(KB_READ_DIR)

# Conversation windows — track active conversations per (chat_id, user_id)
# Rhea's model: count unanswered messages, reset on bot response, expire at threshold
CONVERSATION_WINDOW = 5  # expire after 5 unanswered messages
unanswered_count: dict[tuple[int, int], int] = {}  # (chat_id, user_id) → unanswered count

# Conversation history — last N exchanges for prompt context (Ganymede: high-value change)
MAX_HISTORY = 5
conversation_history: dict[tuple[int, int], list[dict]] = {}  # (chat_id, user_id) → [{user, bot}]


# ─── Helpers ────────────────────────────────────────────────────────────



def get_db_stats() -> dict:
    """Get basic KB stats from pipeline DB."""
    try:
        conn = sqlite3.connect(PIPELINE_DB, timeout=5)
        conn.row_factory = sqlite3.Row
        conn.execute("PRAGMA query_only=ON")
        merged = conn.execute("SELECT COUNT(*) as n FROM prs WHERE status='merged'").fetchone()["n"]
        contributors = conn.execute("SELECT COUNT(*) as n FROM contributors").fetchone()["n"]
        conn.close()
        return {"merged_claims": merged, "contributors": contributors}
    except Exception:
        return {"merged_claims": "?", "contributors": "?"}


async def call_openrouter(model: str, prompt: str, max_tokens: int = 2048) -> str | None:
    """Call OpenRouter API."""
    import aiohttp

    key = Path(OPENROUTER_KEY_FILE).read_text().strip()
    payload = {
        "model": model,
        "messages": [{"role": "user", "content": prompt}],
        "max_tokens": max_tokens,
        "temperature": 0.3,
    }
    try:
        async with aiohttp.ClientSession() as session:
            async with session.post(
                "https://openrouter.ai/api/v1/chat/completions",
                headers={"Authorization": f"Bearer {key}", "Content-Type": "application/json"},
                json=payload,
                timeout=aiohttp.ClientTimeout(total=120),
            ) as resp:
                if resp.status >= 400:
                    logger.error("OpenRouter %s → %d", model, resp.status)
                    return None
                data = await resp.json()
                return data.get("choices", [{}])[0].get("message", {}).get("content")
    except Exception as e:
        logger.error("OpenRouter error: %s", e)
        return None


def is_rate_limited(user_id: int) -> bool:
    """Check if a user has exceeded the response rate limit."""
    now = time.time()
    times = user_response_times[user_id]
    # Prune old entries
    times[:] = [t for t in times if now - t < 3600]
    return len(times) >= MAX_RESPONSE_PER_USER_PER_HOUR


def sanitize_message(text: str) -> str:
    """Sanitize message content before sending to LLM. (Ganymede: security)"""
    # Strip code blocks (potential prompt injection)
    text = re.sub(r"```.*?```", "[code block removed]", text, flags=re.DOTALL)
    # Strip anything that looks like system instructions
    text = re.sub(r"(system:|assistant:|human:|<\|.*?\|>)", "", text, flags=re.IGNORECASE)
    # Truncate
    return text[:2000]


def _git_commit_archive(archive_path, filename: str):
    """Commit archived source to git so it survives git clean. (Rio review: data loss bug)"""
    import subprocess
    try:
        cwd = MAIN_WORKTREE
        subprocess.run(["git", "add", str(archive_path)], cwd=cwd, timeout=10,
                        capture_output=True, check=False)
        result = subprocess.run(
            ["git", "commit", "-m", f"telegram: archive {filename}\n\n"
             "Pentagon-Agent: Epimetheus <3D35839A-7722-4740-B93D-51157F7D5E70>"],
            cwd=cwd, timeout=10, capture_output=True, check=False,
        )
        if result.returncode == 0:
            # Push with retry (Ganymede: abort rebase on failure, don't lose the file)
            for attempt in range(3):
                rebase = subprocess.run(["git", "pull", "--rebase", "origin", "main"],
                                        cwd=cwd, timeout=30, capture_output=True, check=False)
                if rebase.returncode != 0:
                    subprocess.run(["git", "rebase", "--abort"], cwd=cwd, timeout=10,
                                   capture_output=True, check=False)
                    logger.warning("Git rebase failed for archive %s (attempt %d), aborted", filename, attempt + 1)
                    continue
                push = subprocess.run(["git", "push", "origin", "main"],
                                      cwd=cwd, timeout=30, capture_output=True, check=False)
                if push.returncode == 0:
                    logger.info("Git committed archive: %s", filename)
                    return
            # All retries failed — file is still on filesystem (safety net), commit is uncommitted
            logger.warning("Git push failed for archive %s after 3 attempts (file preserved on disk)", filename)
    except Exception as e:
        logger.warning("Git commit archive failed: %s", e)


def _load_learnings() -> str:
    """Load Rio's learnings file for prompt injection. Sanitized (Ganymede: prompt injection risk).

    Dated entries older than 7 days are filtered out (Ganymede: stale learning TTL).
    Permanent entries (undated) always included.
    """
    try:
        raw = Path(LEARNINGS_FILE).read_text()[:4000]
        today = datetime.now(timezone.utc).date()
        lines = []
        for line in raw.split("\n"):
            # Check for dated entries [YYYY-MM-DD]
            date_match = re.search(r"\[(\d{4}-\d{2}-\d{2})\]", line)
            if date_match:
                try:
                    entry_date = datetime.strptime(date_match.group(1), "%Y-%m-%d").date()
                    if (today - entry_date).days > 7:
                        continue  # stale, skip
                except ValueError:
                    pass
            lines.append(line)
        return sanitize_message("\n".join(lines))
    except Exception:
        return ""


def _save_learning(correction: str, category: str = "factual"):
    """Append a learning to staging file. Cron syncs to git (same as archives).

    Categories: communication, factual, structured_data
    """
    try:
        # Write to staging file outside worktree (avoids read-only errors)
        staging_file = Path(ARCHIVE_DIR) / "pending-learnings.jsonl"
        import json as _json
        entry = _json.dumps({"category": category, "correction": correction,
                             "ts": datetime.now(timezone.utc).isoformat()})
        with open(staging_file, "a") as f:
            f.write(entry + "\n")
        logger.info("Learning staged: [%s] %s", category, correction[:80])
        return
    except Exception as e:
        logger.warning("Learning staging failed: %s", e)

    # No fallback — staging is the only write path. Cron syncs to git.


def _compress_history(history: list[dict]) -> str:
    """Extract key context from conversation history — 20 tokens, unmissable (Ganymede)."""
    if not history:
        return ""
    # Combine all text for entity/number extraction
    all_text = " ".join(h.get("user", "") + " " + h.get("bot", "") for h in history)
    tickers = sorted(set(re.findall(r"\$[A-Z]{2,10}", all_text)))
    numbers = re.findall(r"\$[\d,.]+[KMB]?|\d+\.?\d*%", all_text)
    parts = []
    if tickers:
        parts.append(f"Discussing: {', '.join(tickers)}")
    if numbers:
        parts.append(f"Key figures: {', '.join(numbers[:5])}")
    parts.append(f"Exchanges: {len(history)}")
    return " | ".join(parts)


def _format_conversation_history(chat_id: int, user_id: int) -> str:
    """Format conversation history with compressed context summary (Ganymede: Option C+A)."""
    key = (chat_id, user_id)
    history = conversation_history.get(key, [])
    if not history:
        return "(No prior conversation)"

    # Compressed context first — hard for the model to miss
    summary = _compress_history(history)
    lines = [summary, ""]

    # Full exchange log for reference
    for exchange in history:
        if exchange.get("user"):
            lines.append(f"User: {exchange['user']}")
        if exchange.get("bot"):
            lines.append(f"Rio: {exchange['bot']}")
        lines.append("")
    return "\n".join(lines)


# Research intent patterns (Rhea: explicit /research + natural language fallback)
# Telegram appends @botname to commands in groups (Ganymede: /research@FutAIrdBot query)
RESEARCH_PATTERN = re.compile(r'/research(?:@\w+)?\s+(.+)', re.IGNORECASE)


async def handle_research(msg, query: str, user, silent: bool = False):
    """Handle a research request — search X and archive results as sources.

    If silent=True, archive only — no messages posted. Used when triggered
    by RESEARCH: tag after Opus already responded.
    """
    username = user.username if user else "unknown"

    if not silent and not check_research_rate_limit(user.id if user else 0):
        remaining = get_research_remaining(user.id if user else 0)
        await msg.reply_text(f"Research limit reached (3/day). Resets at midnight UTC. {remaining} remaining.")
        return

    if not silent:
        await msg.chat.send_action("typing")

    logger.info("Research: searching X for '%s'", query)
    tweets = await search_tweets(query, max_results=15, min_engagement=0)
    logger.info("Research: got %d tweets for '%s'", len(tweets), query)
    if not tweets:
        if not silent:
            await msg.reply_text(f"No recent tweets found for '{query}'.")
        return

    # Archive all tweets as ONE source file per research query
    # (not per-tweet — one extraction PR produces claims from the best material)
    try:
        # Write to staging dir (outside worktree — no read-only errors)
        date_str = datetime.now(timezone.utc).strftime("%Y-%m-%d")
        slug = re.sub(r"[^a-z0-9]+", "-", query[:60].lower()).strip("-")
        filename = f"{date_str}-x-research-{slug}.md"
        source_path = Path(ARCHIVE_DIR) / filename
        source_path.parent.mkdir(parents=True, exist_ok=True)

        # Build consolidated source file
        tweets_body = ""
        for i, tweet in enumerate(tweets, 1):
            tweets_body += f"\n### Tweet {i} — @{tweet['author']} ({tweet.get('engagement', 0)} engagement)\n"
            tweets_body += f"**URL:** {tweet.get('url', '')}\n"
            tweets_body += f"**Followers:** {tweet.get('author_followers', 0)} | "
            tweets_body += f"**Likes:** {tweet.get('likes', 0)} | **RT:** {tweet.get('retweets', 0)}\n\n"
            tweets_body += f"{tweet['text']}\n"

        source_content = f"""---
type: source
source_type: x-research
title: "X research: {query}"
url: ""
author: "multiple"
date: {date_str}
domain: internet-finance
format: social-media-collection
status: unprocessed
proposed_by: "@{username}"
contribution_type: research-direction
research_query: "{query.replace('"', "'")}"
tweet_count: {len(tweets)}
tags: [x-research, telegram-research]
---

# X Research: {query}

Submitted by @{username} via Telegram /research command.
{len(tweets)} tweets found, sorted by engagement.

{tweets_body}
"""
        source_path.write_text(source_content)
        archived = len(tweets)
        logger.info("Research archived: %s (%d tweets)", filename, archived)
    except Exception as e:
        logger.warning("Research archive failed: %s", e)

    if not silent:
        record_research_usage(user.id if user else 0)
        remaining = get_research_remaining(user.id if user else 0)
        top_authors = list(set(t["author"] for t in tweets[:5]))
        await msg.reply_text(
            f"Queued {archived} tweets about '{query}' for extraction. "
            f"Top voices: @{', @'.join(top_authors[:3])}. "
            f"Results will appear in the KB within ~30 minutes. "
            f"({remaining} research requests remaining today.)"
        )
    logger.info("Research: @%s queried '%s', archived %d tweets (silent=%s)", username, query, archived, silent)


# ─── Message Handlers ───────────────────────────────────────────────────


def _is_reply_to_bot(update: Update, context: ContextTypes.DEFAULT_TYPE) -> bool:
    """Check if a message is a reply to one of the bot's own messages."""
    msg = update.message
    if not msg or not msg.reply_to_message:
        return False
    replied = msg.reply_to_message
    return replied.from_user is not None and replied.from_user.id == context.bot.id


async def handle_reply_to_bot(update: Update, context: ContextTypes.DEFAULT_TYPE):
    """Handle replies to the bot's messages — treat as tagged conversation."""
    if not _is_reply_to_bot(update, context):
        # Not a reply to us — fall through to buffer handler
        await handle_message(update, context)
        return
    logger.info("Reply to bot from @%s",
                update.message.from_user.username if update.message.from_user else "unknown")
    await handle_tagged(update, context)


async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE):
    """Handle ALL incoming group messages — buffer for triage."""
    if not update.message or not update.message.text:
        return

    msg = update.message
    text = msg.text.strip()

    # Skip very short messages
    if len(text) < MIN_MESSAGE_LENGTH:
        return

    # Conversation window behavior depends on chat type (Rio: DMs vs groups)
    # DMs: auto-respond (always 1-on-1, no false positives)
    # Groups: silent context only (reply-to is the only follow-up trigger)
    user = msg.from_user
    is_dm = msg.chat.type == "private"

    if user:
        key = (msg.chat_id, user.id)
        if key in unanswered_count:
            unanswered_count[key] += 1

            if is_dm and unanswered_count[key] < CONVERSATION_WINDOW:
                # DM: auto-respond — conversation window fires
                logger.info("DM conversation window: @%s msg %d/%d",
                            user.username or "?", unanswered_count[key], CONVERSATION_WINDOW)
                await handle_tagged(update, context)
                return
            # Group: don't track silent messages in history (Ganymede: Option A)
        # History should be the actual conversation, not a log of everything said in the group
            # Expire window after CONVERSATION_WINDOW unanswered messages
            if unanswered_count[key] >= CONVERSATION_WINDOW:
                del unanswered_count[key]
                conversation_history.pop(key, None)
                logger.info("Conversation window expired for @%s", user.username or "?")

    # Buffer for batch triage
    message_buffer.append({
        "text": sanitize_message(text),
        "user_id": msg.from_user.id if msg.from_user else None,
        "username": msg.from_user.username if msg.from_user else None,
        "display_name": msg.from_user.full_name if msg.from_user else None,
        "chat_id": msg.chat_id,
        "message_id": msg.message_id,
        "timestamp": msg.date.isoformat() if msg.date else datetime.now(timezone.utc).isoformat(),
        "reply_to": msg.reply_to_message.message_id if msg.reply_to_message else None,
    })


async def handle_tagged(update: Update, context: ContextTypes.DEFAULT_TYPE):
    """Handle messages that tag the bot — Rio responds with Opus."""
    if not update.message or not update.message.text:
        return

    msg = update.message
    user = msg.from_user
    text = sanitize_message(msg.text)

    # Rate limit check
    if user and is_rate_limited(user.id):
        await msg.reply_text("I'm processing other requests — try again in a few minutes.")
        return

    logger.info("Tagged by @%s: %s", user.username if user else "unknown", text[:100])

    # Check for /research command — run search BEFORE Opus so results are in context
    research_context = ""
    research_match = RESEARCH_PATTERN.search(text)
    if research_match:
        query = research_match.group(1).strip()
        logger.info("Research: searching X for '%s'", query)
        from x_client import search_tweets, check_research_rate_limit, record_research_usage
        if check_research_rate_limit(user.id if user else 0):
            tweets = await search_tweets(query, max_results=10, min_engagement=0)
            logger.info("Research: got %d tweets for '%s'", len(tweets), query)
            if tweets:
                # Archive as source file (staging dir)
                try:
                    slug = re.sub(r"[^a-z0-9]+", "-", query[:60].lower()).strip("-")
                    filename = f"{datetime.now(timezone.utc).strftime('%Y-%m-%d')}-x-research-{slug}.md"
                    source_path = Path(ARCHIVE_DIR) / filename
                    tweets_body = "\n".join(
                        f"@{t['author']} ({t.get('engagement',0)} eng): {t['text'][:200]}"
                        for t in tweets[:10]
                    )
                    source_path.write_text(f"---\ntype: source\nsource_type: x-research\ntitle: \"X research: {query}\"\ndate: {datetime.now(timezone.utc).strftime('%Y-%m-%d')}\ndomain: internet-finance\nstatus: unprocessed\nproposed_by: \"@{user.username if user else 'unknown'}\"\ncontribution_type: research-direction\n---\n\n{tweets_body}\n")
                    logger.info("Research archived: %s", filename)
                except Exception as e:
                    logger.warning("Research archive failed: %s", e)

                # Build context for Opus prompt
                research_context = f"\n## Fresh X Research Results for '{query}'\n"
                for t in tweets[:7]:
                    research_context += f"- @{t['author']}: {t['text'][:150]}\n"
                record_research_usage(user.id if user else 0)
        # Strip the /research command from text so Opus responds to the topic, not the command
        text = re.sub(r'/research(?:@\w+)?\s+', '', text).strip()
        if not text:
            text = query

    # Send typing indicator
    await msg.chat.send_action("typing")

    # Fetch any X/Twitter links in the message (tweet or article)
    x_link_context = ""
    x_urls = re.findall(r'https?://(?:twitter\.com|x\.com)/\w+/status/\d+', text)
    if x_urls:
        from x_client import fetch_from_url
        for url in x_urls[:3]:  # Cap at 3 links
            try:
                tweet_data = await fetch_from_url(url)
                if tweet_data:
                    x_link_context += f"\n## Linked Tweet by @{tweet_data['author']}\n"
                    if tweet_data.get("title"):
                        x_link_context += f"Title: {tweet_data['title']}\n"
                    x_link_context += f"{tweet_data['text'][:500]}\n"
                    x_link_context += f"Engagement: {tweet_data.get('engagement', 0)} | URL: {url}\n"
                    logger.info("Fetched X link: @%s — %s", tweet_data['author'], tweet_data['text'][:60])
            except Exception as e:
                logger.warning("Failed to fetch X link %s: %s", url, e)

    # Haiku pre-pass: does this message need an X search? (Option A: two-pass)
    if not research_context:  # Skip if /research already ran
        try:
            haiku_prompt = (
                f"Does this Telegram message need a live X/Twitter search to answer well? "
                f"Only say YES if the user is asking about recent sentiment, community takes, "
                f"what people are saying, or emerging discussions.\n\n"
                f"Message: {text}\n\n"
                f"If YES, provide a SHORT search query (2-3 words max, like 'P2P.me' or 'MetaDAO buyback'). "
                f"Twitter search works best with simple queries — too many words returns nothing.\n\n"
                f"Respond with ONLY one of:\n"
                f"YES: [2-3 word query]\n"
                f"NO"
            )
            haiku_result = await call_openrouter("anthropic/claude-haiku-4.5", haiku_prompt, max_tokens=50)
            if haiku_result and haiku_result.strip().upper().startswith("YES:"):
                search_query = haiku_result.strip()[4:].strip()
                logger.info("Haiku pre-pass: research needed — '%s'", search_query)
                from x_client import search_tweets, check_research_rate_limit, record_research_usage
                if check_research_rate_limit(user.id if user else 0):
                    tweets = await search_tweets(search_query, max_results=10, min_engagement=0)
                    logger.info("Haiku research: got %d tweets", len(tweets))
                    if tweets:
                        research_context = f"\n## Fresh X Research Results for '{search_query}'\n"
                        for t in tweets[:7]:
                            research_context += f"- @{t['author']}: {t['text'][:150]}\n"
                        # Don't burn user's rate limit on autonomous searches (Ganymede)
                        # Archive as source
                        try:
                            slug = re.sub(r"[^a-z0-9]+", "-", search_query[:60].lower()).strip("-")
                            filename = f"{datetime.now(timezone.utc).strftime('%Y-%m-%d')}-x-research-{slug}.md"
                            source_path = Path(ARCHIVE_DIR) / filename
                            tweets_body = "\n".join(f"@{t['author']}: {t['text'][:200]}" for t in tweets[:10])
                            source_path.write_text(f"---\ntype: source\nsource_type: x-research\ntitle: \"X research: {search_query}\"\ndate: {datetime.now(timezone.utc).strftime('%Y-%m-%d')}\ndomain: internet-finance\nstatus: unprocessed\nproposed_by: \"@{user.username if user else 'unknown'}\"\ncontribution_type: research-direction\n---\n\n{tweets_body}\n")
                        except Exception as e:
                            logger.warning("Haiku research archive failed: %s", e)
        except Exception as e:
            logger.warning("Haiku pre-pass failed: %s", e)

    # Retrieve full KB context (entity resolution + claim search + agent positions)
    kb_ctx = retrieve_context(text, KB_READ_DIR, index=kb_index)
    kb_context_text = format_context_for_prompt(kb_ctx)
    stats = get_db_stats()

    # Fetch live market data for any tokens mentioned (Rhea: market-data API)
    market_context = ""
    token_mentions = re.findall(r"\$([A-Z]{2,10})", text.upper())
    # Entity name → token mapping for natural language mentions
    ENTITY_TOKEN_MAP = {
        "omnipair": "OMFG", "metadao": "META", "sanctum": "CLOUD",
        "drift": "DRIFT", "ore": "ORE", "jupiter": "JUP",
    }
    text_lower = text.lower()
    for name, ticker in ENTITY_TOKEN_MAP.items():
        if name in text_lower:
            token_mentions.append(ticker)
    # Also check entity matches from KB retrieval
    for ent in kb_ctx.entities:
        for tag in ent.tags:
            if tag.upper() in ENTITY_TOKEN_MAP.values():
                token_mentions.append(tag.upper())
    for token in set(token_mentions):
        try:
            data = await get_token_price(token)
            if data:
                price_str = format_price_context(data, token)
                if price_str:
                    market_context += price_str + "\n"
        except Exception:
            pass  # Market data is supplementary — never block on failure

    # Build Opus prompt — Rio's voice
    prompt = f"""You are Rio, the Teleo internet finance agent. Your Telegram handle is @FutAIrdBot — that IS you. Users tag @FutAIrdBot to reach you. Never say "I'm not FutAIrdBot." You are also @futaRdIO on X. You have deep knowledge about futarchy, prediction markets, token governance, and the MetaDAO ecosystem.

## How to sound
Write like a sharp analyst talking to peers, not like an AI. Specifically:
- Use your knowledge naturally. Don't say "the KB tracks" or "at experimental confidence" or "our claims show." Just state what you know and how confident you are in plain language.
- Have a take. You're an analyst, not a summarizer. Say what you actually think.
- Before you respond, ask yourself: "Does every sentence here add something the user doesn't already know?" If a sentence just restates context, agrees without adding insight, or pads with filler — cut it. Your goal is signal density, not word count.
- Short questions deserve short answers. If someone asks a factual question, give the fact. Don't surround it with caveats, context, and "the honest picture is" framing.
- Long answers are fine when the question is genuinely complex or the user asks for depth. But earn every paragraph — each one should contain a distinct insight the previous one didn't cover.
- Match the user's energy. If they wrote one line, respond in kind.
- Sound human. No em dashes, no "That said", no "It's worth noting." Just say the thing.
- No markdown. Plain text only.
- When you're uncertain, just say so simply. "I'm not sure about X" beats "we don't have data on this yet."

## Your learnings (corrections from past conversations — prioritize these over KB data when they conflict)
{_load_learnings()}

## What you know about this topic
{kb_context_text}

{f"## Live Market Data{chr(10)}{market_context}" if market_context else ""}

{research_context}

{x_link_context}

## Conversation History (NEVER ask a question your history already answers)
{_format_conversation_history(msg.chat_id, user.id if user else 0)}

## The message you're responding to
From: @{user.username if user else 'unknown'}
Message: {text}

Respond now. Be substantive but concise. If they're wrong about something, say so directly. If they know something you don't, tell them it's worth digging into. If they correct you, accept it and build on the correction. Do NOT respond to messages that aren't directed at you — only respond when tagged or replied to.

IMPORTANT: Two special tags you can append at the end of your response (after your main text):

1. If you learn something: LEARNING: [category] [what you learned]
   Categories: factual, communication, structured_data
   Only when genuinely learned something. Most responses have none.
   NEVER save a learning about what data you do or don't have access to. Your knowledge base changes constantly — availability learnings become stale immediately.

2. If the user would benefit from an X search on a topic: RESEARCH: [search query]
   This triggers an automatic X search. Use when the user asks about recent sentiment, community takes, or emerging discussions. Only when a search would genuinely help."""

    # Call Opus
    response = await call_openrouter(RESPONSE_MODEL, prompt, max_tokens=1024)

    if not response:
        await msg.reply_text("Processing error — I'll get back to you.")
        return

    # Parse LEARNING and RESEARCH tags before posting
    display_response = response

    # Auto-learning (Rhea: zero-cost self-write trigger)
    learning_lines = re.findall(r'^LEARNING:\s*(factual|communication|structured_data)\s+(.+)$',
                                response, re.MULTILINE)
    if learning_lines:
        display_response = re.sub(r'\nLEARNING:\s*\S+\s+.+$', '', display_response, flags=re.MULTILINE).rstrip()
        for category, correction in learning_lines:
            _save_learning(correction.strip(), category.strip())
            logger.info("Auto-learned [%s]: %s", category, correction[:80])

    # Auto-research (Ganymede: LLM-driven research trigger)
    # Skip if Haiku pre-pass already searched (prevents double-fire + duplicate "No tweets found" messages)
    research_lines = re.findall(r'^RESEARCH:\s+(.+)$', response, re.MULTILINE)
    if research_lines:
        display_response = re.sub(r'\nRESEARCH:\s+.+$', '', display_response, flags=re.MULTILINE).rstrip()
        if not research_context:  # Only fire if Haiku didn't already search
            for query in research_lines:
                asyncio.get_event_loop().create_task(handle_research(msg, query.strip(), user, silent=True))
                logger.info("Auto-research triggered: %s", query[:80])

    # Post response (without LEARNING lines)
    await msg.reply_text(display_response)

    # Update conversation state: reset window, store history (Ganymede+Rhea)
    if user:
        key = (msg.chat_id, user.id)
        unanswered_count[key] = 0  # reset — conversation alive
        history = conversation_history.setdefault(key, [])
        history.append({"user": text[:500], "bot": response[:500]})
        if len(history) > MAX_HISTORY:
            history.pop(0)

    # Record rate limit
    if user:
        user_response_times[user.id].append(time.time())

    # Log the exchange for audit trail
    logger.info("Rio responded to @%s (msg_id=%d)", user.username if user else "?", msg.message_id)

    # Detect and fetch URLs for pipeline ingestion
    urls = _extract_urls(text)
    url_content = None
    if urls:
        logger.info("Fetching URL: %s", urls[0])
        url_content = await _fetch_url_content(urls[0])
        if url_content:
            logger.info("Fetched %d chars from %s", len(url_content), urls[0])

    # Archive the exchange as a source for pipeline (slow path)
    _archive_exchange(text, response, user, msg, url_content=url_content, urls=urls)


async def _fetch_url_content(url: str) -> str | None:
    """Fetch article/page content from a URL for pipeline ingestion."""
    import aiohttp
    try:
        async with aiohttp.ClientSession() as session:
            async with session.get(url, timeout=aiohttp.ClientTimeout(total=30)) as resp:
                if resp.status >= 400:
                    return None
                html = await resp.text()
                # Strip HTML tags for plain text (basic — upgrade to readability later)
                text = re.sub(r"<script.*?</script>", "", html, flags=re.DOTALL)
                text = re.sub(r"<style.*?</style>", "", text, flags=re.DOTALL)
                text = re.sub(r"<[^>]+>", " ", text)
                text = re.sub(r"\s+", " ", text).strip()
                return text[:10000]  # Cap at 10K chars
    except Exception as e:
        logger.warning("Failed to fetch URL %s: %s", url, e)
        return None


def _extract_urls(text: str) -> list[str]:
    """Extract URLs from message text."""
    return re.findall(r"https?://[^\s<>\"']+", text)


def _archive_exchange(user_text: str, rio_response: str, user, msg,
                      url_content: str | None = None, urls: list[str] | None = None):
    """Archive a tagged exchange to inbox/queue/ for pipeline processing."""
    try:
        date_str = datetime.now(timezone.utc).strftime("%Y-%m-%d")
        username = user.username if user else "anonymous"
        slug = re.sub(r"[^a-z0-9]+", "-", user_text[:50].lower()).strip("-")
        filename = f"{date_str}-telegram-{username}-{slug}.md"

        archive_path = Path(ARCHIVE_DIR) / filename
        archive_path.parent.mkdir(parents=True, exist_ok=True)

        # Extract rationale (the user's text minus the @mention and URL)
        rationale = re.sub(r"@\w+", "", user_text).strip()
        for url in (urls or []):
            rationale = rationale.replace(url, "").strip()

        # Determine priority — directed contribution with rationale gets high priority
        priority = "high" if rationale and len(rationale) > 20 else "medium"
        intake_tier = "directed" if rationale and len(rationale) > 20 else "undirected"

        url_section = ""
        if url_content:
            url_section = f"\n## Article Content (fetched)\n\n{url_content[:8000]}\n"

        content = f"""---
type: source
source_type: telegram
title: "Telegram: @{username} — {slug}"
author: "@{username}"
url: "{urls[0] if urls else ''}"
date: {date_str}
domain: internet-finance
format: conversation
status: unprocessed
priority: {priority}
intake_tier: {intake_tier}
rationale: "{rationale[:200]}"
proposed_by: "@{username}"
tags: [telegram, ownership-community]
---

## Conversation

**@{username}:**
{user_text}

**Rio (response):**
{rio_response}
{url_section}
## Agent Notes
**Why archived:** Tagged exchange in ownership community.
**Rationale from contributor:** {rationale if rationale else 'No rationale provided (bare link or question)'}
**Intake tier:** {intake_tier} — {'fast-tracked, contributor provided reasoning' if intake_tier == 'directed' else 'standard processing'}
**Triage:** Conversation may contain [CLAIM], [ENTITY], or [EVIDENCE] for extraction.
"""
        # Write to telegram-archives/ (outside worktree — no read-only errors)
        # A cron moves files into inbox/queue/ and commits them
        archive_path.write_text(content)
        logger.info("Archived exchange to %s (tier: %s, urls: %d)",
                     filename, intake_tier, len(urls or []))
    except Exception as e:
        logger.error("Failed to archive exchange: %s", e)


# ─── Batch Triage ───────────────────────────────────────────────────────


async def run_batch_triage(context: ContextTypes.DEFAULT_TYPE):
    """Batch triage of buffered messages every TRIAGE_INTERVAL seconds.

    Groups messages into conversation windows, sends to Haiku for classification,
    archives substantive findings.
    """
    global message_buffer

    if not message_buffer:
        return

    # Grab and clear buffer
    messages = message_buffer[:]
    message_buffer = []

    logger.info("Batch triage: %d messages to process", len(messages))

    # Group into conversation windows (messages within 5 min of each other)
    windows = _group_into_windows(messages, window_seconds=300)

    if not windows:
        return

    # Build triage prompt
    windows_text = ""
    for i, window in enumerate(windows):
        window_msgs = "\n".join(
            f"  @{m.get('username', '?')}: {m['text'][:200]}"
            for m in window
        )
        windows_text += f"\n--- Window {i+1} ({len(window)} messages) ---\n{window_msgs}\n"

    prompt = f"""Classify each conversation window. For each, respond with ONE tag:

[CLAIM] — Contains a specific, disagreeable proposition about how something works
[ENTITY] — Contains factual data about a company, protocol, person, or market
[EVIDENCE] — Contains data or argument that supports or challenges an existing claim about internet finance, futarchy, prediction markets, or token governance
[SKIP] — Casual conversation, not relevant to the knowledge base

Be generous with EVIDENCE — even confirming evidence strengthens the KB.

{windows_text}

Respond with ONLY the window numbers and tags, one per line:
1: [TAG]
2: [TAG]
..."""

    result = await call_openrouter(TRIAGE_MODEL, prompt, max_tokens=500)

    if not result:
        logger.warning("Triage LLM call failed — buffered messages dropped")
        return

    # Parse triage results
    for line in result.strip().split("\n"):
        match = re.match(r"(\d+):\s*\[(\w+)\]", line)
        if not match:
            continue
        idx = int(match.group(1)) - 1
        tag = match.group(2).upper()

        if idx < 0 or idx >= len(windows):
            continue

        if tag in ("CLAIM", "ENTITY", "EVIDENCE"):
            _archive_window(windows[idx], tag)

    logger.info("Triage complete: %d windows processed", len(windows))


def _group_into_windows(messages: list[dict], window_seconds: int = 300) -> list[list[dict]]:
    """Group messages into conversation windows by time proximity."""
    if not messages:
        return []

    # Sort by timestamp
    messages.sort(key=lambda m: m.get("timestamp", ""))

    windows = []
    current_window = [messages[0]]

    for msg in messages[1:]:
        # Simple grouping: if within window_seconds of previous message, same window
        current_window.append(msg)
        if len(current_window) >= 10:  # Cap window size
            windows.append(current_window)
            current_window = []

    if current_window:
        windows.append(current_window)

    return windows


def _archive_window(window: list[dict], tag: str):
    """Archive a triaged conversation window to inbox/queue/."""
    try:
        date_str = datetime.now(timezone.utc).strftime("%Y-%m-%d")
        first_user = window[0].get("username", "group")
        slug = re.sub(r"[^a-z0-9]+", "-", window[0]["text"][:40].lower()).strip("-")
        filename = f"{date_str}-telegram-{first_user}-{slug}.md"

        archive_path = Path(ARCHIVE_DIR) / filename
        archive_path.parent.mkdir(parents=True, exist_ok=True)

        # Build conversation content
        conversation = ""
        contributors = set()
        for msg in window:
            username = msg.get("username", "anonymous")
            contributors.add(username)
            conversation += f"**@{username}:** {msg['text']}\n\n"

        content = f"""---
type: source
source_type: telegram
title: "Telegram conversation: {slug}"
author: "{', '.join(contributors)}"
date: {date_str}
domain: internet-finance
format: conversation
status: unprocessed
priority: medium
triage_tag: {tag.lower()}
tags: [telegram, ownership-community]
---

## Conversation ({len(window)} messages, {len(contributors)} participants)

{conversation}

## Agent Notes
**Triage:** [{tag}] — classified by batch triage
**Participants:** {', '.join(f'@{u}' for u in contributors)}
"""
        # Write to telegram-archives/ (outside worktree)
        archive_path.write_text(content)
        logger.info("Archived window [%s]: %s (%d msgs, %d participants)",
                     tag, filename, len(window), len(contributors))
    except TimeoutError:
        logger.warning("Failed to archive window: worktree lock timeout")
    except Exception as e:
        logger.error("Failed to archive window: %s", e)


# ─── Bot Setup ──────────────────────────────────────────────────────────


async def start_command(update: Update, context: ContextTypes.DEFAULT_TYPE):
    """Handle /start command."""
    await update.message.reply_text(
        "I'm Rio, the internet finance agent for TeleoHumanity's collective knowledge base. "
        "Tag me with @teleo to ask about futarchy, prediction markets, token governance, "
        "or anything in our domain. I'll ground my response in our KB's evidence."
    )


async def stats_command(update: Update, context: ContextTypes.DEFAULT_TYPE):
    """Handle /stats command — show KB stats."""
    kb_index.ensure_fresh()
    stats = get_db_stats()
    await update.message.reply_text(
        f"📊 KB Stats:\n"
        f"• {len(kb_index._claims)} claims indexed\n"
        f"• {len(kb_index._entities)} entities tracked\n"
        f"• {len(kb_index._positions)} agent positions\n"
        f"• {stats['merged_claims']} PRs merged\n"
        f"• {stats['contributors']} contributors"
    )


def main():
    """Start the bot."""
    # Load token
    token_path = Path(BOT_TOKEN_FILE)
    if not token_path.exists():
        logger.error("Bot token not found at %s", BOT_TOKEN_FILE)
        sys.exit(1)
    token = token_path.read_text().strip()

    logger.info("Starting Teleo Telegram bot (Rio)...")

    # Build application
    app = Application.builder().token(token).build()

    # Command handlers
    app.add_handler(CommandHandler("start", start_command))
    app.add_handler(CommandHandler("stats", stats_command))

    # Tag handler — messages mentioning the bot
    # python-telegram-bot filters.Mention doesn't work for bot mentions in groups
    # Use a regex filter for the bot username
    app.add_handler(MessageHandler(
        filters.TEXT & filters.Regex(r"(?i)(@teleo|@futairdbot)"),
        handle_tagged,
    ))

    # Reply handler — replies to the bot's own messages continue the conversation
    reply_to_bot_filter = filters.TEXT & filters.REPLY & ~filters.COMMAND
    app.add_handler(MessageHandler(
        reply_to_bot_filter,
        handle_reply_to_bot,
    ))

    # All other text messages — buffer for triage
    app.add_handler(MessageHandler(
        filters.TEXT & ~filters.COMMAND,
        handle_message,
    ))

    # Batch triage job
    app.job_queue.run_repeating(
        run_batch_triage,
        interval=TRIAGE_INTERVAL,
        first=TRIAGE_INTERVAL,
    )

    # Run
    logger.info("Bot running. Triage interval: %ds", TRIAGE_INTERVAL)
    app.run_polling(drop_pending_updates=True)


if __name__ == "__main__":
    main()