---
title: "Use Qdrant with LlamaIndex"
description: "Use a TIR Vector Database as the vector store for LlamaIndex, to index your documents and query them."
content_type: "how-to"
product: "vector-database"
platform: "TIR"
url: "/tir/vector-database/how-to/use-qdrant-with-llamaindex/"
topic: ["integrations"]
---

# Use Qdrant with LlamaIndex

<!-- cspell:ignore llamaindex -->

LlamaIndex connects your private data to language models. Use your Qdrant database as its vector store
to index documents and query them.

## Before you begin

You need a **Running** database with its **Endpoint URL** and **API Key**; see
[Connect to a vector database](/tir/vector-database/how-to/connect-to-a-vector-database/). You need
Python 3.8 or later and a folder of documents.

```bash
pip install llama-index llama-index-vector-stores-qdrant qdrant-client
```

## Index documents and query

`<your-endpoint-url>` is the **Endpoint URL** and `<your-api-key>` is your API key.
`<your-data-dir>` is the folder of documents. `<collection_name>` is the collection that stores them.
`<your-query>` is the question to ask.

```python
import qdrant_client
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext, Settings
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.embeddings.fastembed import FastEmbedEmbedding

# Configure the embedding model
Settings.embed_model = FastEmbedEmbedding(model_name="BAAI/bge-base-en-v1.5")
Settings.llm = None

# Load documents
documents = SimpleDirectoryReader("<your-data-dir>").load_data()

# Connect and insert into Qdrant
client = qdrant_client.QdrantClient(
    host="<your-endpoint-url>", port=6333, api_key="<your-api-key>"
)
vector_store = QdrantVectorStore(client=client, collection_name="<collection_name>")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("<your-query>")
```

## What to do next

For more, see the [Qdrant LlamaIndex documentation](https://qdrant.tech/documentation/frameworks/llamaindex/).
To create the embeddings with the TIR GenAI API, see
[Generate vector embeddings](/tir/genai-api/how-to/generate-vector-embeddings/). If a connection
fails, see [Troubleshooting Vector Database](/tir/vector-database/troubleshooting/vector-database/).