import json
import requests
import sys


def get_embedding(text, prefix="search_query: "):
    url = "http://localhost:11434/api/embeddings"
    payload = {"model": "nomic-embed-text-v2-moe:latest", "prompt": f"{prefix}{text}"}
    response = requests.post(url, json=payload)
    if response.status_code != 200:
        print(f"Error: {response.status_code} - {response.text}")
        return None
    data = response.json()
    return data["embedding"]


def search_qdrant(vector, limit=10):
    url = "http://127.0.0.1:6333/collections/momentry_rule1/points/search"
    headers = {
        "Content-Type": "application/json",
        "api-key": "Test3200Test3200Test3200",
    }
    payload = {"vector": vector, "limit": limit, "with_payload": True}
    response = requests.post(url, json=payload, headers=headers)
    if response.status_code != 200:
        print(f"Qdrant error: {response.status_code} - {response.text}")
        return None
    return response.json()


if __name__ == "__main__":
    # Test Chinese text
    text = "檔案傳輸"
    print(f"Testing embedding for: '{text}'")
    vector = get_embedding(text)
    if vector:
        print(f"Vector length: {len(vector)}")
        print(f"First 5 values: {vector[:5]}")

        # Search Qdrant
        print("\nSearching Qdrant...")
        results = search_qdrant(vector, limit=5)
        if results:
            print(f"Found {len(results['result'])} results")
            for i, r in enumerate(results["result"]):
                payload = r.get("payload", {})
                text = payload.get("text", "No text")
                chunk_id = payload.get("chunk_id", "N/A")
                uuid = payload.get("uuid", "N/A")
                score = r.get("score", 0)
                print(f"{i + 1}. Score: {score:.4f}, UUID: {uuid}, Chunk: {chunk_id}")
                print(f"   Text: {text[:100]}...")
        else:
            print("No results")
    else:
        print("Failed to get embedding")