Yes, of course! But it doesn't show how I query the indices stored in the database, after. Let me describe the use case:
I'm working on an app allowing users to load their custom data and then query them. I'm building index (exactly as shown in this example), then save to the qdrant. As I should provide a unified code for every single user, at them moment they will query their own index, I have no idea which index they will query.
I assume I can create a separate collection for each index (say, based on the user's dataset's ID or so) and then I will have to find this collection (what is easy) and query it. So, how to do that?
I found this example for Qdrant:
response = openai.Embedding.create(
input="What is starship?",
model="text-embedding-ada-002"
)
embeddings = response['data'][0]['embedding']
search_result = qdrant_client.search(
collection_name="mycollection",
query_vector=embeddings,
limit=5
)
I suppose LlamIndex should have something like that?