Qdrant Reader¶

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%pip install llama-index-readers-qdrant

%pip install llama-index-readers-qdrant

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import logging
import sys

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

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 🦙.

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!pip install llama-index

!pip install llama-index

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from llama_index.readers.qdrant import QdrantReader

from llama_index.readers.qdrant import QdrantReader

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reader = QdrantReader(host="localhost")

reader = QdrantReader(host="localhost")

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# 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, ...]

# 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, ...]

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# 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
)

# 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¶

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index = SummaryIndex.from_documents(documents)

index = SummaryIndex.from_documents(documents)

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

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

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

display(Markdown(f"{response}"))