src/agents/bookmarks_agent.py

from typing import List, Dict, Any
from smolagents import CodeAgent, InferenceClientModel, tool
import os
import json
import sys
from pathlib import Path
from datetime import datetime


def find_chrome_bookmarks_file():
    """
    Automatically detects the Chrome bookmarks file path based on the OS.
    Returns the absolute path string.
    """
    home = Path.home()

    if sys.platform.startswith("win"):
        # Windows
        base = home / "AppData" / "Local" / "Google" / "Chrome" / "User Data" / "Default"
    elif sys.platform.startswith("darwin"):
        # macOS
        base = home / "Library" / "Application Support" / "Google" / "Chrome" / "Default"
    else:
        # Linux
        base = home / ".config" / "google-chrome" / "Default"

    bookmark_file = base / "Bookmarks"
    if not bookmark_file.exists():
        raise FileNotFoundError(f"Cannot find Chrome Bookmarks file at: {bookmark_file}")
    return str(bookmark_file)


def find_folder_by_name(node, target_name):
    """
    Recursively searches for a folder with the given name.
    """
    if not isinstance(node, dict):
        return None
    if node.get("type") == "folder" and node.get("name") == target_name:
        return node
    # Search in children
    for child in node.get("children", []):
        found = find_folder_by_name(child, target_name)
        if found:
            return found
    return None


def extract_bookmarks_from_folder(node, bookmark_list):
    """
    Recursively extracts all bookmarks from a folder node.
    """
    if not isinstance(node, dict):
        return

    if node.get("type") == "url":
        # Individual bookmark
        bookmark_data = {
            "title": node.get("name", ""),
            "url": node.get("url", ""),
            "date_added": node.get("date_added", ""),
            "date_modified": node.get("date_modified", ""),
            "id": node.get("id", ""),
        }
        bookmark_list.append(bookmark_data)
    elif node.get("type") == "folder":
        # Folder, process children
        children = node.get("children", [])
        for child in children:
            extract_bookmarks_from_folder(child, bookmark_list)


def get_cache_file_path():
    """Returns the path for the bookmark cache file."""
    # Create data folder in the root repository
    data_dir = Path("data")
    data_dir.mkdir(exist_ok=True)
    return str(data_dir / "ai_bookmarks_cache.json")


def load_cache():
    """Loads the bookmark cache from JSON file."""
    cache_file = get_cache_file_path()
    if os.path.exists(cache_file):
        try:
            with open(cache_file, "r", encoding="utf-8") as f:
                return json.load(f)
        except Exception as e:
            print(f"Error loading cache: {e}")
    return {"bookmarks": [], "last_updated": None}


def save_cache(cache_data):
    """Saves the bookmark cache to JSON file."""
    cache_file = get_cache_file_path()
    try:
        with open(cache_file, "w", encoding="utf-8") as f:
            json.dump(cache_data, f, indent=2, ensure_ascii=False)
        return True
    except Exception as e:
        print(f"Error saving cache: {e}")
        return False


@tool
def update_ai_bookmarks_cache() -> Dict[str, Any]:
    """
    Extracts bookmarks from Chrome's 'AI ressources' folder and saves them to the data/ai_bookmarks_cache.json file.
    This creates a local cache that avoids direct interaction with Chrome's raw JSON file for subsequent operations.

    Returns:
        Dictionary with update status and bookmark count.
    """
    try:
        # Find Chrome bookmarks file
        bookmarks_file = find_chrome_bookmarks_file()

        # Load Chrome bookmarks
        with open(bookmarks_file, "r", encoding="utf-8") as f:
            data = json.load(f)

        # Find the 'AI ressources' folder
        ai_folder = None
        roots = data.get("roots", {})
        for key in ("bookmark_bar", "other", "synced"):
            if key in roots:
                ai_folder = find_folder_by_name(roots[key], "AI ressources")
                if ai_folder:
                    break

        if not ai_folder:
            return {"status": "error", "message": "AI ressources folder not found in bookmarks"}

        # Extract bookmarks from AI ressources folder
        bookmarks = []
        extract_bookmarks_from_folder(ai_folder, bookmarks)

        # Create cache data with metadata
        cache_data = {
            "bookmarks": bookmarks,
            "last_updated": datetime.now().isoformat(),
            "folder_name": "AI ressources",
            "total_count": len(bookmarks),
        }

        # Save to cache
        if save_cache(cache_data):
            return {
                "status": "success",
                "message": f"Successfully updated cache with {len(bookmarks)} bookmarks",
                "count": len(bookmarks),
            }
        else:
            return {"status": "error", "message": "Failed to save cache"}

    except Exception as e:
        return {"status": "error", "message": f"Error updating cache: {str(e)}"}


@tool
def get_latest_ai_bookmarks(n: int = 10) -> List[Dict[str, Any]]:
    """
    Gets the n latest bookmarks from the AI ressources cache.

    Args:
        n: Number of latest bookmarks to return (default: 10)

    Returns:
        List of the latest bookmarks with metadata.
    """
    cache = load_cache()
    bookmarks = cache.get("bookmarks", [])

    if not bookmarks:
        return []

    # Sort by date_added (newest first) if available
    try:
        sorted_bookmarks = sorted(bookmarks, key=lambda x: int(x.get("date_added", "0")), reverse=True)
    except (ValueError, TypeError):
        # If sorting fails, return as is
        sorted_bookmarks = bookmarks

    return sorted_bookmarks[:n]


@tool
def search_ai_bookmarks(query: str) -> List[Dict[str, Any]]:
    """
    Search AI ressources bookmarks for entries matching a query.

    Args:
        query: Search term to find in bookmark titles or URLs.

    Returns:
        List of matching bookmarks.
    """
    cache = load_cache()
    bookmarks = cache.get("bookmarks", [])

    if not bookmarks:
        return []

    query_lower = query.lower()
    matching_bookmarks = []

    for bookmark in bookmarks:
        title = bookmark.get("title", "").lower()
        url = bookmark.get("url", "").lower()

        if query_lower in title or query_lower in url:
            matching_bookmarks.append(bookmark)

    return matching_bookmarks


@tool
def get_bookmark_statistics() -> Dict[str, Any]:
    """
    Gets statistics about the AI ressources bookmarks cache.

    Returns:
        Dictionary with various statistics about the bookmarks.
    """
    cache = load_cache()
    bookmarks = cache.get("bookmarks", [])

    if not bookmarks:
        return {"total_count": 0, "last_updated": None}

    # Calculate statistics
    total_count = len(bookmarks)
    domains = {}

    for bookmark in bookmarks:
        url = bookmark.get("url", "")
        try:
            from urllib.parse import urlparse

            domain = urlparse(url).netloc
            domains[domain] = domains.get(domain, 0) + 1
        except (ValueError, AttributeError):
            pass

    # Get top domains
    top_domains = sorted(domains.items(), key=lambda x: x[1], reverse=True)[:5]

    return {
        "total_count": total_count,
        "last_updated": cache.get("last_updated"),
        "top_domains": top_domains,
        "unique_domains": len(domains),
    }


@tool
def get_all_ai_bookmarks() -> List[Dict[str, Any]]:
    """
    Gets all bookmarks from the AI ressources cache.

    Returns:
        List of all cached bookmarks.
    """
    cache = load_cache()
    return cache.get("bookmarks", [])


@tool
def filter_bookmarks_by_domain(domain: str) -> List[Dict[str, Any]]:
    """
    Filters AI ressources bookmarks by domain.

    Args:
        domain: Domain name to filter by (e.g., 'github.com')

    Returns:
        List of bookmarks from the specified domain.
    """
    cache = load_cache()
    bookmarks = cache.get("bookmarks", [])

    if not bookmarks:
        return []

    domain_lower = domain.lower()
    filtered_bookmarks = []

    for bookmark in bookmarks:
        url = bookmark.get("url", "")
        try:
            from urllib.parse import urlparse

            bookmark_domain = urlparse(url).netloc.lower()
            if domain_lower in bookmark_domain:
                filtered_bookmarks.append(bookmark)
        except (ValueError, AttributeError):
            pass

    return filtered_bookmarks


@tool
def get_cache_info() -> Dict[str, Any]:
    """
    Gets information about the bookmark cache file.

    Returns:
        Dictionary with cache file information.
    """
    cache_file = get_cache_file_path()
    cache = load_cache()

    info = {
        "cache_file_path": cache_file,
        "cache_exists": os.path.exists(cache_file),
        "last_updated": cache.get("last_updated"),
        "bookmark_count": len(cache.get("bookmarks", [])),
        "folder_name": cache.get("folder_name", "Unknown"),
    }

    if os.path.exists(cache_file):
        stat = os.stat(cache_file)
        info["file_size_bytes"] = stat.st_size
        info["file_modified"] = datetime.fromtimestamp(stat.st_mtime).isoformat()

    return info


# Instantiate the Bookmarks CodeAgent with enhanced tools
bookmarks_agent = CodeAgent(
    model=InferenceClientModel(
        provider="nebius",
        token=os.environ["HF_TOKEN"],
    ),
    tools=[
        update_ai_bookmarks_cache,
        get_latest_ai_bookmarks,
        search_ai_bookmarks,
        get_bookmark_statistics,
        get_all_ai_bookmarks,
        filter_bookmarks_by_domain,
        get_cache_info,
    ],
    name="bookmarks_agent",
    description="Specialized agent for Chrome bookmarks operations, focusing on AI ressources folder. Extracts bookmarks from Chrome and caches them in data/ai_bookmarks_cache.json to avoid direct interaction with Chrome's raw JSON. Provides search, filtering, statistics, and cache management for AI-related bookmarks.",
    max_steps=10,
    additional_authorized_imports=["json", "datetime", "urllib.parse", "pathlib"],
    # Reduce verbosity
    stream_outputs=False,
    max_print_outputs_length=300,
)