"""
Memory Vector Search CLI
========================
Search memories using semantic similarity.

Usage:
    python search_memories.py "your query here"
    python search_memories.py --interactive
"""

import sys
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

import requests
from memory_vector import search_memories

OLLAMA_URL = "http://localhost:11434"
EMBED_MODEL = "nomic-embed-text"


def get_embedding(text: str) -> list:
    """Generate embedding for query text."""
    response = requests.post(
        f"{OLLAMA_URL}/api/embeddings",
        json={"model": EMBED_MODEL, "prompt": text},
        timeout=30
    )
    response.raise_for_status()
    return response.json()["embedding"]


def search(query: str, k: int = 5):
    """Search memories by query."""
    print(f"Searching: '{query}'\n")
    
    # Generate embedding for query
    print("Generating embedding...")
    query_embedding = get_embedding(query)
    
    # Search database
    print(f"Searching {k} nearest neighbors...\n")
    results = search_memories(query_embedding, k=k)
    
    if not results:
        print("No results found.")
        return
    
    print("=" * 60)
    print("RESULTS")
    print("=" * 60)
    
    for i, (path, content, distance) in enumerate(results, 1):
        # Clean up unicode for console
        path_clean = path.encode('ascii', 'ignore').decode() if path else "Unknown"
        content_clean = content.encode('ascii', 'ignore').decode() if content else ""
        print(f"\n{i}. [{path_clean}]")
        print(f"   Distance: {distance:.4f}")
        print(f"   Content: {content_clean[:150]}...")
    
    print("\n" + "=" * 60)
    print(f"Found {len(results)} result(s)")


def interactive():
    """Interactive search mode."""
    print("Memory Vector Search")
    print("Type 'quit' to exit\n")
    
    while True:
        query = input("Search: ").strip()
        if query.lower() in ('quit', 'exit', 'q'):
            break
        if not query:
            continue
        
        try:
            search(query)
        except Exception as e:
            print(f"Error: {e}")
        print()


def main():
    import argparse
    
    parser = argparse.ArgumentParser(description='Search memories with vector similarity')
    parser.add_argument('query', nargs='?', help='Search query')
    parser.add_argument('-n', '--num', type=int, default=5, help='Number of results (default: 5)')
    parser.add_argument('-i', '--interactive', action='store_true', help='Interactive mode')
    
    args = parser.parse_args()
    
    if args.interactive or not args.query:
        interactive()
    else:
        search(args.query, k=args.num)


if __name__ == "__main__":
    main()