Qdrant Reader

%pip install llama-index-readers-qdrant

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

!pip install llama-index

from llama_index.readers.qdrant import QdrantReader

reader = QdrantReader(host="localhost")

# the query_vector is an embedding representation of your query_vector
# Example query vector:
#   query_vector=[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]

query_vector = [n1, n2, n3, ...]

# NOTE: Required args are collection_name, query_vector.
# See the Python client: https://github.com/qdrant/qdrant_client
# for more details.
documents = reader.load_data(
    collection_name="demo", query_vector=query_vector, limit=5
)

Create index

index = SummaryIndex.from_documents(documents)

# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query("<query_text>")

display(Markdown(f"<b>{response}</b>"))