from qdrant_client.models import Distance, VectorParams
from langchain_qdrant import QdrantVectorStore
from langchain_openai import OpenAIEmbeddings
from qdrant_client import QdrantClient

client = QdrantClient(path="./embeddings")

if not client.collection_exists("vacancies"):
    client.create_collection(
        collection_name="vacancies",
        vectors_config=VectorParams(size=3072, distance=Distance.COSINE)
    )

vector_store = QdrantVectorStore(
    client=client,
    collection_name="vacancies",
    embedding=OpenAIEmbeddings(model="text-embedding-3-large"),
)