krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-09-27 15:08:30 +02:00
Code Issues Packages Projects Releases Wiki Activity

Improved Nested Docs and Link Grouping Support (#147)

Browse Source 
* added a table of content

* wide layout for docs, styles for the table of content

* fixes for docs layout

* wide footer at the docs section

* added support for nested docs, added toggling groups of links, delimiters, external links

* external link icon

* added active state for nested links, styles for the external link icon

* styles fix

* remove doc sync

* update directory structure and doc titles

* fix outstanding links

* fix more links for merge

* Revert "fix more links for merge"

This reverts commit 46c9ccaf1b7765f2cda8dc85d625fa6b4e3f5436.

* Revert "fix outstanding links"

This reverts commit 28e6380b74f1ab74690c8184551f186656d6d4e9.

* fix remaining broken links

* move how-to tutorials in the different page

* split tutorials

* fix link

* upd github edit link

* skip empty index pages

---------

Co-authored-by: Andrey Vasnetsov <andrey@vasnetsov.com>
Co-authored-by: David Sertic <62056091+davidmyriel@users.noreply.github.com>
This commit is contained in:
trean
2023-05-29 15:11:40 +02:00
committed by GitHub
co-authored by Andrey Vasnetsov David Sertic
parent b9874e5c56
commit 3215985d1d
60 changed files with 734 additions and 421 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/documentation/integrations/llama-index.md
+33
View File
@@ -0,0 +1,33 @@
---
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:
```bash
pip install llama-index
```
LlamaIndex requires providing an instance of `QdrantClient`, so it can interact with Qdrant server.
```python
from llama_index import GPTQdrantIndex
import qdrant_client
client = qdrant_client.QdrantClient(
"<qdrant-url>",
api_key="<qdrant-api-key>", # For Qdrant Cloud, None for local instance
)
index = GPTQdrantIndex.from_documents(documents, 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.