Llama Index acts as an interface between your external data and Large Language Models. So you can bring your private data and augment LLMs with it. LlamaIndex simplifies data ingestion and indexing, integrating Qdrant as a vector index.

Installing Llama Index is straightforward if we use pip as a package manager. Qdrant is not installed by default, so we need to install it separately. The integration of both tools also comes as another package.

pip install llama-index llama-index-vector-stores-qdrant

Llama Index requires providing an instance of QdrantClient, so it can interact with Qdrant server.

from llama_index.core.indices.vector_store.base import VectorStoreIndex
from llama_index.vector_stores.qdrant import QdrantVectorStore

import qdrant_client

client = qdrant_client.QdrantClient(
    api_key="<qdrant-api-key>", # For Qdrant Cloud, None for local instance

vector_store = QdrantVectorStore(client=client, collection_name="documents")
index = VectorStoreIndex.from_vector_store(vector_store=vector_store)

Further Reading