from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from qdrant_client.http.models import (
    Distance,
    VectorParams,
)

from config import (
    COLLECTION_NAME,
    OPENAI_API_KEY,
    OPENAI_BASE_URL,
    QDRANT_API_KEY,
    QDRANT_URL,
)

EMBEDDING = OpenAIEmbeddings(
    openai_api_key=OPENAI_API_KEY,
    openai_api_base=OPENAI_BASE_URL,
    model="Qwen/Qwen3-Embedding-8B",
    check_embedding_ctx_length=False,
)

QDRANT_CLIENT = QdrantClient(
    url=QDRANT_URL,
    api_key=QDRANT_API_KEY,
    port=443,
    https=True,
)

if not QDRANT_CLIENT.collection_exists(COLLECTION_NAME):
    QDRANT_CLIENT.create_collection(
        collection_name=COLLECTION_NAME,
        vectors_config=VectorParams(
            size=4096,
            distance=Distance.COSINE,
        ),
    )

VECTOR_STORE = QdrantVectorStore(
    client=QDRANT_CLIENT,
    collection_name=COLLECTION_NAME,
    embedding=EMBEDDING,
)

LLM = ChatOpenAI(
    openai_api_key=OPENAI_API_KEY,
    openai_api_base=OPENAI_BASE_URL,
    model="openai/gpt-oss-120b",
    temperature=0.3,
    streaming=True,
)