from qdrant_client import QdrantClient
from fastembed import TextEmbedding
from qdrant_client.models import Distance, VectorParams, PointStruct
from qdrant_mcp.config import Settings

settings = Settings()
embedding_model = TextEmbedding()
client = QdrantClient(url=settings.url, api_key=settings.api_key)

documents: list[str] = [
    "Rick es el mas guapo",
    "Los pulpos tienen tres corazones y sangre azul",
    "Las cucarachas pueden vivir hasta una semana sin cabeza",
    "Los koalas tienen huellas dactilares casi idénticas a las humanas",
    "La miel nunca se echa a perder, incluso después de miles de años"
]
embeddings = list(embedding_model.embed(documents))
size = len(embeddings[0])

_ = client.recreate_collection(
    collection_name="dummy_collection",
    vectors_config=VectorParams(
        distance=Distance.COSINE,
        size=size
    )
)

for idx, (emb, document) in enumerate(zip(embeddings, documents)):
    _ = client.upsert(
        collection_name="dummy_collection",
        points=[
            PointStruct(
                id=idx,
                vector=emb,
                payload={
                    "text": document
                }
            )
        ]
    )