krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-10-01 00:48:32 +02:00
Code Issues Packages Projects Releases Wiki Activity

docs auto-sync

Browse Source
This commit is contained in:
qdrant
2023-05-25 13:35:46 +00:00
parent c02a3881bc
commit 03e273fa9b
3 changed files with 3 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/documentation/collections.md
+2 -2
View File
@@ -8,7 +8,7 @@ weight: 30
A collection is a named set of points (vectors with a payload) among which you can search. A collection is a named set of points (vectors with a payload) among which you can search.
Vectors within the same collection must have the same dimensionality and be compared by a single metric. Vectors within the same collection must have the same dimensionality and be compared by a single metric.
Distance metrics used to measure similarities among vectors. Distance metrics are used to measure similarities among vectors.
The choice of metric depends on the way vectors obtaining and, in particular, on the method of neural network encoder training. The choice of metric depends on the way vectors obtaining and, in particular, on the method of neural network encoder training.
Qdrant supports these most popular types of metrics: Qdrant supports these most popular types of metrics:
@@ -385,7 +385,7 @@ from qdrant_client import QdrantClient
client = QdrantClient("localhost", port=6333) client = QdrantClient("localhost", port=6333)
client.list_aliases() client.get_aliases()
``` ```
### List all collections ### List all collections
qdrant-landing/content/documentation/integrations.md
+1 -1
View File
@@ -81,7 +81,7 @@ client = qdrant_client.QdrantClient(
index = GPTQdrantIndex.from_documents(documents, client=client, collection_name="documents") index = GPTQdrantIndex.from_documents(documents, client=client, collection_name="documents")
``` ```
The library [comes with a notebook](https://github.com/jerryjliu/llama_index/blob/main/examples/vector_indices/QdrantIndexDemo.ipynb) 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. that shows an end-to-end example of how to use Qdrant within LlamaIndex.
## DocArray ## DocArray
qdrant-landing/content/documentation/security.md
-4
View File
@@ -5,10 +5,6 @@ weight: 165
There are various ways to secure your own Qdrant instance. There are various ways to secure your own Qdrant instance.
For authentication on Qdrant cloud refer to its
[Authentication](https://qdrant.tech/documentation/cloud/cloud-quick-start/#authentication)
section.
## Authentication ## Authentication
*Available as of v1.2.0* *Available as of v1.2.0*