LlamaIndex Readers Integration: Qdrant

Overview

The Qdrant Reader allows you to retrieve documents from existing Qdrant collections. Qdrant is a similarity search engine that helps you efficiently search and retrieve similar items from large datasets based on vector embeddings.

For more detailed information about Qdrant, visit Qdrant

Installation

You can install the Qdrant Reader via pip:

bash

pip install llama-index-readers-qdrant

Usage

python

from llama_index.readers.qdrant import QdrantReader

# Initialize QdrantReader
reader = QdrantReader(
    location="<Qdrant Location>",
    url="<Qdrant URL>",
    port="<Port>",
    grpc_port="<gRPC Port>",
    prefer_grpc="<Prefer gRPC>",
    https="<Use HTTPS>",
    api_key="<API Key>",
    prefix="<URL Prefix>",
    timeout="<Timeout>",
    host="<Host>",
)

# Load data from Qdrant
documents = reader.load_data(
    collection_name="<Collection Name>",
    query_vector=[0.1, 0.2, 0.3],
    should_search_mapping={"text_field": "text"},
    must_search_mapping={"text_field": "text"},
    must_not_search_mapping={"text_field": "text"},
    rang_search_mapping={"text_field": {"gte": 0.1, "lte": 0.2}},
    limit=10,
)

This loader is designed to be used as a way to load data into LlamaIndex and/or subsequently used as a Tool in a LangChain Agent.