mirror of
https://github.com/qdrant/landing_page.git
synced 2026-09-29 07:58:31 +02:00
34 lines
1.1 KiB
Markdown
34 lines
1.1 KiB
Markdown
---
|
|
title: LlamaIndex
|
|
weight: 200
|
|
---
|
|
|
|
# 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:
|
|
|
|
```bash
|
|
pip install llama-index qdrant-client
|
|
```
|
|
|
|
LlamaIndex requires providing an instance of `QdrantClient`, so it can interact with Qdrant server.
|
|
|
|
```python
|
|
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
|
|
)
|
|
|
|
index = QdrantVectorStore(client=client, collection_name="documents")
|
|
```
|
|
|
|
The library [comes with a notebook](https://github.com/jerryjliu/llama_index/blob/main/docs/examples/vector_stores/QdrantIndexDemo.ipynb)
|
|
that shows an end-to-end example of how to use Qdrant within LlamaIndex. |