krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-09-28 23:48:31 +02:00
Code Issues Packages Projects Releases Wiki Activity

fix bash examples (#605)

Browse Source
This commit is contained in:
Andrey Vasnetsov
2024-02-13 01:38:41 +01:00
committed by GitHub
parent 069f4d85e8
commit 7a4349cd83
1 changed files with 187 additions and 211 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/documentation/concepts/collections.md
+187 -211
View File
@@ -38,16 +38,6 @@ specify collections named `test_collection1` through `test_collection4`.
## Create a collection
```bash
curl -X PUT http://localhost:6333/collections/test_collection1 \
-H 'Content-Type: application/json' \
--data-raw '{
"vectors": {
"size": 300,
"distance": "Cosine"
}
}'
```
```http
PUT /collections/{collection_name}
@@ -59,6 +49,17 @@ PUT /collections/{collection_name}
}
```
```bash
curl -X PUT http://localhost:6333/collections/test_collection1 \
-H 'Content-Type: application/json' \
--data-raw '{
"vectors": {
"size": 300,
"distance": "Cosine"
}
}'
```
```python
from qdrant_client import QdrantClient
from qdrant_client.http import models
@@ -150,19 +151,6 @@ This might be useful for experimenting quickly with different configurations for
Make sure the vectors have the same `size` and `distance` function when setting up the vectors configuration in the new collection. If you used the previous sample
code, `"size": 300` and `"distance": "Cosine"`.
```bash
curl -X PUT http://localhost:6333/collections/test_collection2 \
-H 'Content-Type: application/json' \
--data-raw '{
"vectors": {
"size": 300,
"distance": "Cosine"
},
"init_from": {
"collection": "test_collection1"
}
}'
```
```http
PUT /collections/{collection_name}
@@ -177,6 +165,20 @@ PUT /collections/{collection_name}
}
```
```bash
curl -X PUT http://localhost:6333/collections/test_collection2 \
-H 'Content-Type: application/json' \
--data-raw '{
"vectors": {
"size": 300,
"distance": "Cosine"
},
"init_from": {
"collection": "test_collection1"
}
}'
```
```python
from qdrant_client import QdrantClient
from qdrant_client.http import models
@@ -261,22 +263,6 @@ This feature allows for multiple vector storages per collection.
To distinguish vectors in one record, they should have a unique name defined when creating the collection.
Each named vector in this mode has its distance and size:
```bash
curl -X PUT http://localhost:6333/collections/test_collection3 \
-H 'Content-Type: application/json' \
--data-raw '{
"vectors": {
"image": {
"size": 4,
"distance": "Dot"
},
"text": {
"size": 8,
"distance": "Cosine"
}
}
}'
```
```http
PUT /collections/{collection_name}
@@ -294,6 +280,23 @@ PUT /collections/{collection_name}
}
```
```bash
curl -X PUT http://localhost:6333/collections/test_collection3 \
-H 'Content-Type: application/json' \
--data-raw '{
"vectors": {
"image": {
"size": 4,
"distance": "Dot"
},
"text": {
"size": 8,
"distance": "Cosine"
}
}
}'
```
```python
from qdrant_client import QdrantClient
from qdrant_client.http import models
@@ -417,6 +420,15 @@ Collections can contain sparse vectors as additional [named vectors](#collection
Unlike dense vectors, sparse vectors must be named.
And additionally, sparse vectors and dense vectors must have different names within a collection.
```http
PUT /collections/{collection_name}
{
"sparse_vectors": {
"text": { },
}
}
```
```bash
curl -X PUT http://localhost:6333/collections/test_collection4 \
-H 'Content-Type: application/json' \
@@ -427,14 +439,6 @@ curl -X PUT http://localhost:6333/collections/test_collection4 \
}'
```
```http
PUT /collections/{collection_name}
{
"sparse_vectors": {
"text": { },
}
}
```
```python
from qdrant_client import QdrantClient
@@ -520,14 +524,14 @@ However, there are optional parameters to tune the underlying [sparse vector ind
### Delete collection
```bash
curl -X DELETE http://localhost:6333/collections/test_collection4
```
```http
DELETE http://localhost:6333/collections/test_collection4
```
```bash
curl -X DELETE http://localhost:6333/collections/test_collection4
```
```python
client.delete_collection(collection_name="{collection_name}")
```
@@ -558,15 +562,6 @@ As a result, you will not waste extra computation resources on rebuilding the in
The following command enables indexing for segments that have more than 10000 kB of vectors stored:
```bash
curl -X PATCH http://localhost:6333/collections/test_collection1 \
-H 'Content-Type: application/json' \
--data-raw '{
"optimizers_config": {
"indexing_threshold": 10000
}
}'
```
```http
PATCH /collections/{collection_name}
@@ -577,6 +572,16 @@ PATCH /collections/{collection_name}
}
```
```bash
curl -X PATCH http://localhost:6333/collections/test_collection1 \
-H 'Content-Type: application/json' \
--data-raw '{
"optimizers_config": {
"indexing_threshold": 10000
}
}'
```
```python
client.update_collection(
collection_name="{collection_name}",
@@ -651,17 +656,6 @@ automatically be rebuilt in the background to match updated parameters.
To put vector data on disk for a collection that **does not have** named vectors,
use `""` as name:
```bash
curl -X PATCH http://localhost:6333/collections/test_collection1 \
-H 'Content-Type: application/json' \
--data-raw '{
"vectors": {
"": {
"on_disk": true
}
}
}'
```
```http
PATCH /collections/{collection_name}
@@ -674,10 +668,35 @@ PATCH /collections/{collection_name}
}
```
```bash
curl -X PATCH http://localhost:6333/collections/test_collection1 \
-H 'Content-Type: application/json' \
--data-raw '{
"vectors": {
"": {
"on_disk": true
}
}
}'
```
To put vector data on disk for a collection that **does have** named vectors:
Note: To create a vector name, follow the procedure from our [Points](/documentation/concepts/points/#create-vector-name).
```http
PATCH /collections/{collection_name}
{
"vectors": {
"my_vector": {
"on_disk": true
}
}
}
```
```bash
curl -X PATCH http://localhost:6333/collections/test_collection1 \
-H 'Content-Type: application/json' \
@@ -690,20 +709,41 @@ curl -X PATCH http://localhost:6333/collections/test_collection1 \
}'
```
In the following example the HNSW index and quantization parameters are updated,
both for the whole collection, and for `my_vector` specifically:
```http
PATCH /collections/{collection_name}
{
"vectors": {
"my_vector": {
"hnsw_config": {
"m": 32,
"ef_construct": 123
},
"quantization_config": {
"product": {
"compression": "x32",
"always_ram": true
}
},
"on_disk": true
}
},
"hnsw_config": {
"ef_construct": 123
},
"quantization_config": {
"scalar": {
"type": "int8",
"quantile": 0.8,
"always_ram": false
}
}
}
```
In the following example the HNSW index and quantization parameters are updated,
both for the whole collection, and for `my_vector` specifically:
```bash
curl -X PATCH http://localhost:6333/collections/test_collection1 \
-H 'Content-Type: application/json' \
@@ -736,37 +776,6 @@ curl -X PATCH http://localhost:6333/collections/test_collection1 \
}'
```
```http
PATCH /collections/{collection_name}
{
"vectors": {
"my_vector": {
"hnsw_config": {
"m": 32,
"ef_construct": 123
},
"quantization_config": {
"product": {
"compression": "x32",
"always_ram": true
}
},
"on_disk": true
}
},
"hnsw_config": {
"ef_construct": 123
},
"quantization_config": {
"scalar": {
"type": "int8",
"quantile": 0.8,
"always_ram": false
}
}
}
```
```python
client.update_collection(
collection_name="{collection_name}",
@@ -920,58 +929,34 @@ client
Qdrant allows determining the configuration parameters of an existing collection to better understand how the points are
distributed and indexed.
```bash
curl -X GET http://localhost:6333/collections/test_collection1 \
-H 'Content-Type: application/json' \
--data-raw '{
"result": {
"status": "green",
"optimizer_status": "ok",
"vectors_count": 1068786,
"indexed_vectors_count": 1024232,
"points_count": 1068786,
"segments_count": 31,
"config": {
"params": {
"vectors": {
"size": 384,
"distance": "Cosine"
},
"shard_number": 1,
"replication_factor": 1,
"write_consistency_factor": 1,
"on_disk_payload": false
},
"hnsw_config": {
"m": 16,
"ef_construct": 100,
"full_scan_threshold": 10000,
"max_indexing_threads": 0
},
"optimizer_config": {
"deleted_threshold": 0.2,
"vacuum_min_vector_number": 1000,
"default_segment_number": 0,
"max_segment_size": null,
"memmap_threshold": null,
"indexing_threshold": 20000,
"flush_interval_sec": 5,
"max_optimization_threads": 1
},
"wal_config": {
"wal_capacity_mb": 32,
"wal_segments_ahead": 0
}
},
"payload_schema": {}
},
"status": "ok",
"time": 0.00010143
}'
```
```http
GET /collections/test_collection1
```
```bash
curl -X GET http://localhost:6333/collections/test_collection1
```
```python
client.get_collection(collection_name="{collection_name}")
```
```typescript
client.getCollection("{collection_name}");
```
```rust
client.collection_info("{collection_name}").await?;
```
```java
client.getCollectionInfoAsync("{collection_name}").get();
```
<details>
<summary>Expected result</summary>
```json
{
"result": {
"status": "green",
@@ -1019,21 +1004,10 @@ GET /collections/test_collection1
}
```
```python
client.get_collection(collection_name="{collection_name}")
```
</details>
<br/>
```typescript
client.getCollection("{collection_name}");
```
```rust
client.collection_info("{collection_name}").await?;
```
```java
client.getCollectionInfoAsync("{collection_name}").get();
```
If you insert the vectors into the collection, the `status` field may become
`yellow` whilst it is optimizing. It will become `green` once all the points are
@@ -1097,6 +1071,20 @@ Since all changes of aliases happen atomically, no concurrent requests will be a
### Create alias
```http
POST /collections/aliases
{
"actions": [
{
"create_alias": {
"collection_name": "test_collection1",
"alias_name": "production_collection"
}
}
]
}
```
```bash
curl -X POST http://localhost:6333/collections/aliases \
-H 'Content-Type: application/json' \
@@ -1112,20 +1100,6 @@ curl -X POST http://localhost:6333/collections/aliases \
}'
```
```http
POST /collections/aliases
{
"actions": [
{
"create_alias": {
"collection_name": "test_collection1",
"alias_name": "production_collection"
}
}
]
}
```
```python
client.update_collection_aliases(
change_aliases_operations=[
@@ -1224,6 +1198,25 @@ client.deleteAliasAsync("production_collection").get();
Multiple alias actions are performed atomically.
For example, you can switch underlying collection with the following command:
```http
POST /collections/aliases
{
"actions": [
{
"delete_alias": {
"alias_name": "production_collection"
}
},
{
"create_alias": {
"collection_name": "test_collection2",
"alias_name": "production_collection"
}
}
]
}
```
```bash
curl -X POST http://localhost:6333/collections/aliases \
-H 'Content-Type: application/json' \
@@ -1244,25 +1237,6 @@ curl -X POST http://localhost:6333/collections/aliases \
}'
```
```http
POST /collections/aliases
{
"actions": [
{
"delete_alias": {
"alias_name": "production_collection"
}
},
{
"create_alias": {
"collection_name": "test_collection2",
"alias_name": "production_collection"
}
}
]
}
```
```python
client.update_collection_aliases(
change_aliases_operations=[
@@ -1308,14 +1282,14 @@ client.createAliasAsync("production_collection", "example_collection").get();
### List collection aliases
```bash
curl -X GET http://localhost:6333/collections/test_collection2/aliases
```
```http
GET /collections/test_collection2/aliases
```
```bash
curl -X GET http://localhost:6333/collections/test_collection2/aliases
```
```python
from qdrant_client import QdrantClient
@@ -1352,13 +1326,14 @@ client.listCollectionAliasesAsync("{collection_name}").get();
### List all aliases
```http
GET /aliases
```
```bash
curl -X GET http://localhost:6333/aliases
```
```http
GET /aliases
```
```python
from qdrant_client import QdrantClient
@@ -1396,13 +1371,14 @@ client.listAliasesAsync().get();
### List all collections
```http
GET /collections
```
```bash
curl -X GET http://localhost:6333/collections
```
```http
GET /collections
```
```python
from qdrant_client import QdrantClient