# Create a list of dictionary objects, each having the document and its embedding
documents = [
    {"id": i, "text": doc.page_content, "embedding": embedding}
    for i, (doc, embedding) in enumerate(zip(split, embeddings))
]

qdrant_client.upload_collection(
    collection_name=collection_name,
    vectors=[doc["embedding"] for doc in documents],  # The embedding vectors
    payload=[{"page_content": doc["text"]} for doc in documents],  # Original text payload
    ids=[doc["id"] for doc in documents],  # Unique ID for each document
    batch_size=50  # Adjust batch size as necessary
)
# LangChain Qdrant vector store
vector_store = Qdrant(client=qdrant_client, collection_name=collection_name, embeddings=embed_model)

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