landing_page/qdrant-landing/content/documentation/frameworks/llama-index.md

title, short_description, description, aliases

title	short_description	description	aliases
LlamaIndex	Index and retrieve private data in LlamaIndex with Qdrant as the vector store, augmenting LLMs with grounded context from your documents.	Use Qdrant as the vector store in LlamaIndex to ingest private data and augment LLM apps with semantic retrieval and retrieval-augmented generation.	../integrations/llama-index/ /documentation/overview/integrations/llama-index/

LlamaIndex

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(
    "<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)

Further Reading

• LlamaIndex Documentation
• Example Notebook
• Source Code