LlamaIndex (GPT Index)

LlamaIndex (formerly GPT 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 LlamaIndex is straightforward if we use pip as a package manager. Qdrant is not installed by default, so we need to install it separately:

pip install llama-index qdrant-client

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

from llama_index.vector_stores.qdrant import QdrantVectorStore

import qdrant_client

client = qdrant_client.QdrantClient(
    "<qdrant-url>",
    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)

The library comes with a notebook that shows an end-to-end example of how to use Qdrant within LlamaIndex.