agent/models_dev.py

"""Models.dev registry integration — primary database for providers and models.

Fetches from https://models.dev/api.json — a community-maintained database
of 4000+ models across 109+ providers.  Provides:

- **Provider metadata**: name, base URL, env vars, documentation link
- **Model metadata**: context window, max output, cost/M tokens, capabilities
  (reasoning, tools, vision, PDF, audio), modalities, knowledge cutoff,
  open-weights flag, family grouping, deprecation status

Data resolution order (like TypeScript OpenCode):
  1. Bundled snapshot (ships with the package — offline-first)
  2. Disk cache (~/.hermes/models_dev_cache.json)
  3. Network fetch (https://models.dev/api.json)
  4. Background refresh every 60 minutes

Other modules should import the dataclasses and query functions from here
rather than parsing the raw JSON themselves.
"""

import difflib
import json
import logging
import os
import time
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple

from utils import atomic_json_write

import requests

logger = logging.getLogger(__name__)

MODELS_DEV_URL = "https://models.dev/api.json"
_MODELS_DEV_CACHE_TTL = 3600  # 1 hour in-memory

# In-memory cache
_models_dev_cache: Dict[str, Any] = {}
_models_dev_cache_time: float = 0


# ---------------------------------------------------------------------------
# Dataclasses — rich metadata for providers and models
# ---------------------------------------------------------------------------

@dataclass
class ModelInfo:
    """Full metadata for a single model from models.dev."""

    id: str
    name: str
    family: str
    provider_id: str        # models.dev provider ID (e.g. "anthropic")

    # Capabilities
    reasoning: bool = False
    tool_call: bool = False
    attachment: bool = False       # supports image/file attachments (vision)
    temperature: bool = False
    structured_output: bool = False
    open_weights: bool = False

    # Modalities
    input_modalities: Tuple[str, ...] = ()    # ("text", "image", "pdf", ...)
    output_modalities: Tuple[str, ...] = ()

    # Limits
    context_window: int = 0
    max_output: int = 0
    max_input: Optional[int] = None

    # Cost (per million tokens, USD)
    cost_input: float = 0.0
    cost_output: float = 0.0
    cost_cache_read: Optional[float] = None
    cost_cache_write: Optional[float] = None

    # Metadata
    knowledge_cutoff: str = ""
    release_date: str = ""
    status: str = ""          # "alpha", "beta", "deprecated", or ""
    interleaved: Any = False  # True or {"field": "reasoning_content"}

    def has_cost_data(self) -> bool:
        return self.cost_input > 0 or self.cost_output > 0

    def supports_vision(self) -> bool:
        return self.attachment or "image" in self.input_modalities

    def supports_pdf(self) -> bool:
        return "pdf" in self.input_modalities

    def supports_audio_input(self) -> bool:
        return "audio" in self.input_modalities

    def format_cost(self) -> str:
        """Human-readable cost string, e.g. '$3.00/M in, $15.00/M out'."""
        if not self.has_cost_data():
            return "unknown"
        parts = [f"${self.cost_input:.2f}/M in", f"${self.cost_output:.2f}/M out"]
        if self.cost_cache_read is not None:
            parts.append(f"cache read ${self.cost_cache_read:.2f}/M")
        return ", ".join(parts)

    def format_capabilities(self) -> str:
        """Human-readable capabilities, e.g. 'reasoning, tools, vision, PDF'."""
        caps = []
        if self.reasoning:
            caps.append("reasoning")
        if self.tool_call:
            caps.append("tools")
        if self.supports_vision():
            caps.append("vision")
        if self.supports_pdf():
            caps.append("PDF")
        if self.supports_audio_input():
            caps.append("audio")
        if self.structured_output:
            caps.append("structured output")
        if self.open_weights:
            caps.append("open weights")
        return ", ".join(caps) if caps else "basic"


@dataclass
class ProviderInfo:
    """Full metadata for a provider from models.dev."""

    id: str                         # models.dev provider ID
    name: str                       # display name
    env: Tuple[str, ...]            # env var names for API key
    api: str                        # base URL
    doc: str = ""                   # documentation URL
    model_count: int = 0


# ---------------------------------------------------------------------------
# Provider ID mapping: Hermes ↔ models.dev
# ---------------------------------------------------------------------------

# Hermes provider names → models.dev provider IDs
PROVIDER_TO_MODELS_DEV: Dict[str, str] = {
    "openrouter": "openrouter",
    "anthropic": "anthropic",
    "openai": "openai",
    "openai-codex": "openai",
    "zai": "zai",
    "kimi-coding": "kimi-for-coding",
    "kimi-coding-cn": "kimi-for-coding",
    "minimax": "minimax",
    "minimax-cn": "minimax-cn",
    "deepseek": "deepseek",
    "alibaba": "alibaba",
    "qwen-oauth": "alibaba",
    "copilot": "github-copilot",
    "ai-gateway": "vercel",
    "opencode-zen": "opencode",
    "opencode-go": "opencode-go",
    "kilocode": "kilo",
    "fireworks": "fireworks-ai",
    "huggingface": "huggingface",
    "gemini": "google",
    "google": "google",
    "xai": "xai",
    "xiaomi": "xiaomi",
    "nvidia": "nvidia",
    "groq": "groq",
    "mistral": "mistral",
    "togetherai": "togetherai",
    "perplexity": "perplexity",
    "cohere": "cohere",
}

# Reverse mapping: models.dev → Hermes (built lazily)
_MODELS_DEV_TO_PROVIDER: Optional[Dict[str, str]] = None


def _get_reverse_mapping() -> Dict[str, str]:
    """Return models.dev ID → Hermes provider ID mapping."""
    global _MODELS_DEV_TO_PROVIDER
    if _MODELS_DEV_TO_PROVIDER is None:
        _MODELS_DEV_TO_PROVIDER = {v: k for k, v in PROVIDER_TO_MODELS_DEV.items()}
    return _MODELS_DEV_TO_PROVIDER


def _get_cache_path() -> Path:
    """Return path to disk cache file."""
    from hermes_constants import get_hermes_home
    return get_hermes_home() / "models_dev_cache.json"


def _load_disk_cache() -> Dict[str, Any]:
    """Load models.dev data from disk cache."""
    try:
        cache_path = _get_cache_path()
        if cache_path.exists():
            with open(cache_path, encoding="utf-8") as f:
                return json.load(f)
    except Exception as e:
        logger.debug("Failed to load models.dev disk cache: %s", e)
    return {}


def _save_disk_cache(data: Dict[str, Any]) -> None:
    """Save models.dev data to disk cache atomically."""
    try:
        cache_path = _get_cache_path()
        atomic_json_write(cache_path, data, indent=None, separators=(",", ":"))
    except Exception as e:
        logger.debug("Failed to save models.dev disk cache: %s", e)


def fetch_models_dev(force_refresh: bool = False) -> Dict[str, Any]:
    """Fetch models.dev registry. In-memory cache (1hr) + disk fallback.

    Returns the full registry dict keyed by provider ID, or empty dict on failure.
    """
    global _models_dev_cache, _models_dev_cache_time

    # Check in-memory cache
    if (
        not force_refresh
        and _models_dev_cache
        and (time.time() - _models_dev_cache_time) < _MODELS_DEV_CACHE_TTL
    ):
        return _models_dev_cache

    # Try network fetch
    try:
        response = requests.get(MODELS_DEV_URL, timeout=15)
        response.raise_for_status()
        data = response.json()
        if isinstance(data, dict) and data:
            _models_dev_cache = data
            _models_dev_cache_time = time.time()
            _save_disk_cache(data)
            logger.debug(
                "Fetched models.dev registry: %d providers, %d total models",
                len(data),
                sum(len(p.get("models", {})) for p in data.values() if isinstance(p, dict)),
            )
            return data
    except Exception as e:
        logger.debug("Failed to fetch models.dev: %s", e)

    # Fall back to disk cache — use a short TTL (5 min) so we retry
    # the network fetch soon instead of serving stale data for a full hour.
    if not _models_dev_cache:
        _models_dev_cache = _load_disk_cache()
        if _models_dev_cache:
            _models_dev_cache_time = time.time() - _MODELS_DEV_CACHE_TTL + 300
            logger.debug("Loaded models.dev from disk cache (%d providers)", len(_models_dev_cache))

    return _models_dev_cache


def lookup_models_dev_context(provider: str, model: str) -> Optional[int]:
    """Look up context_length for a provider+model combo in models.dev.

    Returns the context window in tokens, or None if not found.
    Handles case-insensitive matching and filters out context=0 entries.
    """
    mdev_provider_id = PROVIDER_TO_MODELS_DEV.get(provider)
    if not mdev_provider_id:
        return None

    data = fetch_models_dev()
    provider_data = data.get(mdev_provider_id)
    if not isinstance(provider_data, dict):
        return None

    models = provider_data.get("models", {})
    if not isinstance(models, dict):
        return None

    # Exact match
    entry = models.get(model)
    if entry:
        ctx = _extract_context(entry)
        if ctx:
            return ctx

    # Case-insensitive match
    model_lower = model.lower()
    for mid, mdata in models.items():
        if mid.lower() == model_lower:
            ctx = _extract_context(mdata)
            if ctx:
                return ctx

    return None


def _extract_context(entry: Dict[str, Any]) -> Optional[int]:
    """Extract context_length from a models.dev model entry.

    Returns None for invalid/zero values (some audio/image models have context=0).
    """
    if not isinstance(entry, dict):
        return None
    limit = entry.get("limit")
    if not isinstance(limit, dict):
        return None
    ctx = limit.get("context")
    if isinstance(ctx, (int, float)) and ctx > 0:
        return int(ctx)
    return None


# ---------------------------------------------------------------------------
# Model capability metadata
# ---------------------------------------------------------------------------


@dataclass
class ModelCapabilities:
    """Structured capability metadata for a model from models.dev."""

    supports_tools: bool = True
    supports_vision: bool = False
    supports_reasoning: bool = False
    context_window: int = 200000
    max_output_tokens: int = 8192
    model_family: str = ""


def _get_provider_models(provider: str) -> Optional[Dict[str, Any]]:
    """Resolve a Hermes provider ID to its models dict from models.dev.

    Returns the models dict or None if the provider is unknown or has no data.
    """
    mdev_provider_id = PROVIDER_TO_MODELS_DEV.get(provider)
    if not mdev_provider_id:
        return None

    data = fetch_models_dev()
    provider_data = data.get(mdev_provider_id)
    if not isinstance(provider_data, dict):
        return None

    models = provider_data.get("models", {})
    if not isinstance(models, dict):
        return None

    return models


def _find_model_entry(models: Dict[str, Any], model: str) -> Optional[Dict[str, Any]]:
    """Find a model entry by exact match, then case-insensitive fallback."""
    # Exact match
    entry = models.get(model)
    if isinstance(entry, dict):
        return entry

    # Case-insensitive match
    model_lower = model.lower()
    for mid, mdata in models.items():
        if mid.lower() == model_lower and isinstance(mdata, dict):
            return mdata

    return None


def get_model_capabilities(provider: str, model: str) -> Optional[ModelCapabilities]:
    """Look up full capability metadata from models.dev cache.

    Uses the existing fetch_models_dev() and PROVIDER_TO_MODELS_DEV mapping.
    Returns None if model not found.

    Extracts from model entry fields:
      - reasoning  (bool)  → supports_reasoning
      - tool_call  (bool)  → supports_tools
      - attachment (bool)  → supports_vision
      - limit.context (int) → context_window
      - limit.output  (int) → max_output_tokens
      - family     (str)   → model_family
    """
    models = _get_provider_models(provider)
    if models is None:
        return None

    entry = _find_model_entry(models, model)
    if entry is None:
        return None

    # Extract capability flags (default to False if missing)
    supports_tools = bool(entry.get("tool_call", False))
    # Vision: check both the `attachment` flag and `modalities.input` for "image".
    # Some models (e.g. gemma-4) list image in input modalities but not attachment.
    input_mods = entry.get("modalities", {})
    if isinstance(input_mods, dict):
        input_mods = input_mods.get("input", [])
    else:
        input_mods = []
    supports_vision = bool(entry.get("attachment", False)) or "image" in input_mods
    supports_reasoning = bool(entry.get("reasoning", False))

    # Extract limits
    limit = entry.get("limit", {})
    if not isinstance(limit, dict):
        limit = {}

    ctx = limit.get("context")
    context_window = int(ctx) if isinstance(ctx, (int, float)) and ctx > 0 else 200000

    out = limit.get("output")
    max_output_tokens = int(out) if isinstance(out, (int, float)) and out > 0 else 8192

    model_family = entry.get("family", "") or ""

    return ModelCapabilities(
        supports_tools=supports_tools,
        supports_vision=supports_vision,
        supports_reasoning=supports_reasoning,
        context_window=context_window,
        max_output_tokens=max_output_tokens,
        model_family=model_family,
    )


def list_provider_models(provider: str) -> List[str]:
    """Return all model IDs for a provider from models.dev.

    Returns an empty list if the provider is unknown or has no data.
    """
    models = _get_provider_models(provider)
    if models is None:
        return []
    return list(models.keys())


# Patterns that indicate non-agentic or noise models (TTS, embedding,
# dated preview snapshots, live/streaming-only, image-only).
import re
_NOISE_PATTERNS: re.Pattern = re.compile(
    r"-tts\b|embedding|live-|-(preview|exp)-\d{2,4}[-_]|"
    r"-image\b|-image-preview\b|-customtools\b",
    re.IGNORECASE,
)


def list_agentic_models(provider: str) -> List[str]:
    """Return model IDs suitable for agentic use from models.dev.

    Filters for tool_call=True and excludes noise (TTS, embedding,
    dated preview snapshots, live/streaming, image-only models).
    Returns an empty list on any failure.
    """
    models = _get_provider_models(provider)
    if models is None:
        return []

    result = []
    for mid, entry in models.items():
        if not isinstance(entry, dict):
            continue
        if not entry.get("tool_call", False):
            continue
        if _NOISE_PATTERNS.search(mid):
            continue
        result.append(mid)
    return result


def search_models_dev(
    query: str, provider: str = None, limit: int = 5
) -> List[Dict[str, Any]]:
    """Fuzzy search across models.dev catalog. Returns matching model entries.

    Args:
        query: Search string to match against model IDs.
        provider: Optional Hermes provider ID to restrict search scope.
                  If None, searches across all providers in PROVIDER_TO_MODELS_DEV.
        limit: Maximum number of results to return.

    Returns:
        List of dicts, each containing 'provider', 'model_id', and the full
        model 'entry' from models.dev.
    """
    data = fetch_models_dev()
    if not data:
        return []

    # Build list of (provider_id, model_id, entry) candidates
    candidates: List[tuple] = []

    if provider is not None:
        # Search only the specified provider
        mdev_provider_id = PROVIDER_TO_MODELS_DEV.get(provider)
        if not mdev_provider_id:
            return []
        provider_data = data.get(mdev_provider_id, {})
        if isinstance(provider_data, dict):
            models = provider_data.get("models", {})
            if isinstance(models, dict):
                for mid, mdata in models.items():
                    candidates.append((provider, mid, mdata))
    else:
        # Search across all mapped providers
        for hermes_prov, mdev_prov in PROVIDER_TO_MODELS_DEV.items():
            provider_data = data.get(mdev_prov, {})
            if isinstance(provider_data, dict):
                models = provider_data.get("models", {})
                if isinstance(models, dict):
                    for mid, mdata in models.items():
                        candidates.append((hermes_prov, mid, mdata))

    if not candidates:
        return []

    # Use difflib for fuzzy matching — case-insensitive comparison
    model_ids_lower = [c[1].lower() for c in candidates]
    query_lower = query.lower()

    # First try exact substring matches (more intuitive than pure edit-distance)
    substring_matches = []
    for prov, mid, mdata in candidates:
        if query_lower in mid.lower():
            substring_matches.append({"provider": prov, "model_id": mid, "entry": mdata})

    # Then add difflib fuzzy matches for any remaining slots
    fuzzy_ids = difflib.get_close_matches(
        query_lower, model_ids_lower, n=limit * 2, cutoff=0.4
    )

    seen_ids: set = set()
    results: List[Dict[str, Any]] = []

    # Prioritize substring matches
    for match in substring_matches:
        key = (match["provider"], match["model_id"])
        if key not in seen_ids:
            seen_ids.add(key)
            results.append(match)
            if len(results) >= limit:
                return results

    # Add fuzzy matches
    for fid in fuzzy_ids:
        # Find original-case candidates matching this lowered ID
        for prov, mid, mdata in candidates:
            if mid.lower() == fid:
                key = (prov, mid)
                if key not in seen_ids:
                    seen_ids.add(key)
                    results.append({"provider": prov, "model_id": mid, "entry": mdata})
                    if len(results) >= limit:
                        return results

    return results


# ---------------------------------------------------------------------------
# Rich dataclass constructors — parse raw models.dev JSON into dataclasses
# ---------------------------------------------------------------------------

def _parse_model_info(model_id: str, raw: Dict[str, Any], provider_id: str) -> ModelInfo:
    """Convert a raw models.dev model entry dict into a ModelInfo dataclass."""
    limit = raw.get("limit") or {}
    if not isinstance(limit, dict):
        limit = {}

    cost = raw.get("cost") or {}
    if not isinstance(cost, dict):
        cost = {}

    modalities = raw.get("modalities") or {}
    if not isinstance(modalities, dict):
        modalities = {}

    input_mods = modalities.get("input") or []
    output_mods = modalities.get("output") or []

    ctx = limit.get("context")
    ctx_int = int(ctx) if isinstance(ctx, (int, float)) and ctx > 0 else 0
    out = limit.get("output")
    out_int = int(out) if isinstance(out, (int, float)) and out > 0 else 0
    inp = limit.get("input")
    inp_int = int(inp) if isinstance(inp, (int, float)) and inp > 0 else None

    return ModelInfo(
        id=model_id,
        name=raw.get("name", "") or model_id,
        family=raw.get("family", "") or "",
        provider_id=provider_id,
        reasoning=bool(raw.get("reasoning", False)),
        tool_call=bool(raw.get("tool_call", False)),
        attachment=bool(raw.get("attachment", False)),
        temperature=bool(raw.get("temperature", False)),
        structured_output=bool(raw.get("structured_output", False)),
        open_weights=bool(raw.get("open_weights", False)),
        input_modalities=tuple(input_mods) if isinstance(input_mods, list) else (),
        output_modalities=tuple(output_mods) if isinstance(output_mods, list) else (),
        context_window=ctx_int,
        max_output=out_int,
        max_input=inp_int,
        cost_input=float(cost.get("input", 0) or 0),
        cost_output=float(cost.get("output", 0) or 0),
        cost_cache_read=float(cost["cache_read"]) if "cache_read" in cost and cost["cache_read"] is not None else None,
        cost_cache_write=float(cost["cache_write"]) if "cache_write" in cost and cost["cache_write"] is not None else None,
        knowledge_cutoff=raw.get("knowledge", "") or "",
        release_date=raw.get("release_date", "") or "",
        status=raw.get("status", "") or "",
        interleaved=raw.get("interleaved", False),
    )


def _parse_provider_info(provider_id: str, raw: Dict[str, Any]) -> ProviderInfo:
    """Convert a raw models.dev provider entry dict into a ProviderInfo."""
    env = raw.get("env") or []
    models = raw.get("models") or {}
    return ProviderInfo(
        id=provider_id,
        name=raw.get("name", "") or provider_id,
        env=tuple(env) if isinstance(env, list) else (),
        api=raw.get("api", "") or "",
        doc=raw.get("doc", "") or "",
        model_count=len(models) if isinstance(models, dict) else 0,
    )


# ---------------------------------------------------------------------------
# Provider-level queries
# ---------------------------------------------------------------------------

def get_provider_info(provider_id: str) -> Optional[ProviderInfo]:
    """Get full provider metadata from models.dev.

    Accepts either a Hermes provider ID (e.g. "kilocode") or a models.dev
    ID (e.g. "kilo").  Returns None if the provider is not in the catalog.
    """
    # Resolve Hermes ID → models.dev ID
    mdev_id = PROVIDER_TO_MODELS_DEV.get(provider_id, provider_id)

    data = fetch_models_dev()
    raw = data.get(mdev_id)
    if not isinstance(raw, dict):
        return None

    return _parse_provider_info(mdev_id, raw)


# ---------------------------------------------------------------------------
# Model-level queries (rich ModelInfo)
# ---------------------------------------------------------------------------

def get_model_info(
    provider_id: str, model_id: str
) -> Optional[ModelInfo]:
    """Get full model metadata from models.dev.

    Accepts Hermes or models.dev provider ID.  Tries exact match then
    case-insensitive fallback.  Returns None if not found.
    """
    mdev_id = PROVIDER_TO_MODELS_DEV.get(provider_id, provider_id)

    data = fetch_models_dev()
    pdata = data.get(mdev_id)
    if not isinstance(pdata, dict):
        return None

    models = pdata.get("models", {})
    if not isinstance(models, dict):
        return None

    # Exact match
    raw = models.get(model_id)
    if isinstance(raw, dict):
        return _parse_model_info(model_id, raw, mdev_id)

    # Case-insensitive fallback
    model_lower = model_id.lower()
    for mid, mdata in models.items():
        if mid.lower() == model_lower and isinstance(mdata, dict):
            return _parse_model_info(mid, mdata, mdev_id)

    return None