hermes-agent/agent/model_metadata.py

"""Model metadata, context lengths, and token estimation utilities.

Pure utility functions with no AIAgent dependency. Used by ContextCompressor
and run_agent.py for pre-flight context checks.
"""

import logging
import os
import re
import time
from pathlib import Path
from typing import Any, Dict, List, Optional

import requests
import yaml

from hermes_constants import OPENROUTER_MODELS_URL

logger = logging.getLogger(__name__)

_model_metadata_cache: Dict[str, Dict[str, Any]] = {}
_model_metadata_cache_time: float = 0
_MODEL_CACHE_TTL = 3600

# Descending tiers for context length probing when the model is unknown.
# We start high and step down on context-length errors until one works.
CONTEXT_PROBE_TIERS = [
    2_000_000,
    1_000_000,
    512_000,
    200_000,
    128_000,
    64_000,
    32_000,
]

DEFAULT_CONTEXT_LENGTHS = {
    "anthropic/claude-opus-4": 200000,
    "anthropic/claude-opus-4.5": 200000,
    "anthropic/claude-opus-4.6": 200000,
    "anthropic/claude-sonnet-4": 200000,
    "anthropic/claude-sonnet-4-20250514": 200000,
    "anthropic/claude-sonnet-4.5": 200000,
    "anthropic/claude-sonnet-4.6": 200000,
    "anthropic/claude-haiku-4.5": 200000,
    # Bare Anthropic model IDs (for native API provider)
    "claude-opus-4-6": 200000,
    "claude-sonnet-4-6": 200000,
    "claude-opus-4-5-20251101": 200000,
    "claude-sonnet-4-5-20250929": 200000,
    "claude-opus-4-1-20250805": 200000,
    "claude-opus-4-20250514": 200000,
    "claude-sonnet-4-20250514": 200000,
    "claude-haiku-4-5-20251001": 200000,
    "openai/gpt-5": 128000,
    "openai/gpt-4.1": 1047576,
    "openai/gpt-4.1-mini": 1047576,
    "openai/gpt-4o": 128000,
    "openai/gpt-4-turbo": 128000,
    "openai/gpt-4o-mini": 128000,
    "google/gemini-3-pro-preview": 1048576,
    "google/gemini-3-flash": 1048576,
    "google/gemini-2.5-flash": 1048576,
    "google/gemini-2.0-flash": 1048576,
    "google/gemini-2.5-pro": 1048576,
    "deepseek/deepseek-v3.2": 65536,
    "meta-llama/llama-3.3-70b-instruct": 131072,
    "deepseek/deepseek-chat-v3": 65536,
    "qwen/qwen-2.5-72b-instruct": 32768,
    "glm-4.7": 202752,
    "glm-5": 202752,
    "glm-4.5": 131072,
    "glm-4.5-flash": 131072,
    "kimi-for-coding": 262144,
    "kimi-k2.5": 262144,
    "kimi-k2-thinking": 262144,
    "kimi-k2-thinking-turbo": 262144,
    "kimi-k2-turbo-preview": 262144,
    "kimi-k2-0905-preview": 131072,
    "MiniMax-M2.5": 204800,
    "MiniMax-M2.5-highspeed": 204800,
    "MiniMax-M2.1": 204800,
}


def fetch_model_metadata(force_refresh: bool = False) -> Dict[str, Dict[str, Any]]:
    """Fetch model metadata from OpenRouter (cached for 1 hour)."""
    global _model_metadata_cache, _model_metadata_cache_time

    if not force_refresh and _model_metadata_cache and (time.time() - _model_metadata_cache_time) < _MODEL_CACHE_TTL:
        return _model_metadata_cache

    try:
        response = requests.get(OPENROUTER_MODELS_URL, timeout=10)
        response.raise_for_status()
        data = response.json()

        cache = {}
        for model in data.get("data", []):
            model_id = model.get("id", "")
            cache[model_id] = {
                "context_length": model.get("context_length", 128000),
                "max_completion_tokens": model.get("top_provider", {}).get("max_completion_tokens", 4096),
                "name": model.get("name", model_id),
                "pricing": model.get("pricing", {}),
            }
            canonical = model.get("canonical_slug", "")
            if canonical and canonical != model_id:
                cache[canonical] = cache[model_id]

        _model_metadata_cache = cache
        _model_metadata_cache_time = time.time()
        logger.debug("Fetched metadata for %s models from OpenRouter", len(cache))
        return cache

    except Exception as e:
        logging.warning(f"Failed to fetch model metadata from OpenRouter: {e}")
        return _model_metadata_cache or {}


def _get_context_cache_path() -> Path:
    """Return path to the persistent context length cache file."""
    hermes_home = Path(os.environ.get("HERMES_HOME", Path.home() / ".hermes"))
    return hermes_home / "context_length_cache.yaml"


def _load_context_cache() -> Dict[str, int]:
    """Load the model+provider → context_length cache from disk."""
    path = _get_context_cache_path()
    if not path.exists():
        return {}
    try:
        with open(path) as f:
            data = yaml.safe_load(f) or {}
        return data.get("context_lengths", {})
    except Exception as e:
        logger.debug("Failed to load context length cache: %s", e)
        return {}


def save_context_length(model: str, base_url: str, length: int) -> None:
    """Persist a discovered context length for a model+provider combo.

    Cache key is ``model@base_url`` so the same model name served from
    different providers can have different limits.
    """
    key = f"{model}@{base_url}"
    cache = _load_context_cache()
    if cache.get(key) == length:
        return  # already stored
    cache[key] = length
    path = _get_context_cache_path()
    try:
        path.parent.mkdir(parents=True, exist_ok=True)
        with open(path, "w") as f:
            yaml.dump({"context_lengths": cache}, f, default_flow_style=False)
        logger.info("Cached context length %s → %s tokens", key, f"{length:,}")
    except Exception as e:
        logger.debug("Failed to save context length cache: %s", e)


def get_cached_context_length(model: str, base_url: str) -> Optional[int]:
    """Look up a previously discovered context length for model+provider."""
    key = f"{model}@{base_url}"
    cache = _load_context_cache()
    return cache.get(key)


def get_next_probe_tier(current_length: int) -> Optional[int]:
    """Return the next lower probe tier, or None if already at minimum."""
    for tier in CONTEXT_PROBE_TIERS:
        if tier < current_length:
            return tier
    return None


def parse_context_limit_from_error(error_msg: str) -> Optional[int]:
    """Try to extract the actual context limit from an API error message.

    Many providers include the limit in their error text, e.g.:
      - "maximum context length is 32768 tokens"
      - "context_length_exceeded: 131072"
      - "Maximum context size 32768 exceeded"
      - "model's max context length is 65536"
    """
    error_lower = error_msg.lower()
    # Pattern: look for numbers near context-related keywords
    patterns = [
        r'(?:max(?:imum)?|limit)\s*(?:context\s*)?(?:length|size|window)?\s*(?:is|of|:)?\s*(\d{4,})',
        r'context\s*(?:length|size|window)\s*(?:is|of|:)?\s*(\d{4,})',
        r'(\d{4,})\s*(?:token)?\s*(?:context|limit)',
        r'>\s*(\d{4,})\s*(?:max|limit|token)',  # "250000 tokens > 200000 maximum"
        r'(\d{4,})\s*(?:max(?:imum)?)\b',  # "200000 maximum"
    ]
    for pattern in patterns:
        match = re.search(pattern, error_lower)
        if match:
            limit = int(match.group(1))
            # Sanity check: must be a reasonable context length
            if 1024 <= limit <= 10_000_000:
                return limit
    return None


def get_model_context_length(model: str, base_url: str = "") -> int:
    """Get the context length for a model.

    Resolution order:
    1. Persistent cache (previously discovered via probing)
    2. OpenRouter API metadata
    3. Hardcoded DEFAULT_CONTEXT_LENGTHS (fuzzy match)
    4. First probe tier (2M) — will be narrowed on first context error
    """
    # 1. Check persistent cache (model+provider)
    if base_url:
        cached = get_cached_context_length(model, base_url)
        if cached is not None:
            return cached

    # 2. OpenRouter API metadata
    metadata = fetch_model_metadata()
    if model in metadata:
        return metadata[model].get("context_length", 128000)

    # 3. Hardcoded defaults (fuzzy match)
    for default_model, length in DEFAULT_CONTEXT_LENGTHS.items():
        if default_model in model or model in default_model:
            return length

    # 4. Unknown model — start at highest probe tier
    return CONTEXT_PROBE_TIERS[0]


def estimate_tokens_rough(text: str) -> int:
    """Rough token estimate (~4 chars/token) for pre-flight checks."""
    if not text:
        return 0
    return len(text) // 4


def estimate_messages_tokens_rough(messages: List[Dict[str, Any]]) -> int:
    """Rough token estimate for a message list (pre-flight only)."""
    total_chars = sum(len(str(msg)) for msg in messages)
    return total_chars // 4