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328 lines
7.2 KiB
Markdown
328 lines
7.2 KiB
Markdown
---
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title: Payload
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weight: 25
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---
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One of the significant features of Qdrant is the ability to store additional information along with vectors.
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This information is called `payload` in Qdrant terminology.
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Qdrant allows you to store any information that can be represented using JSON.
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Here is an example of a typical payload:
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```json
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{
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"name": "jacket",
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"colors": ["red", "blue"],
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"count": 10,
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"price": 11.99,
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"locations": [
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{
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"lon": 52.5200,
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"lat": 13.4050
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}
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],
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"reviews": [
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{
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"user": "alice",
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"score": 4
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},
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{
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"user": "bob",
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"score": 5
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}
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]
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}
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```
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## Payload types
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In addition to storing payloads, Qdrant also allows you search based on certain kinds of values.
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This feature is implemented as additional filters during the search and will enable you to incorporate custom logic on top of semantic similarity.
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During the filtering, Qdrant will check the conditions over those values that match the type of the filtering condition. If the stored value type does not fit the filtering condition - it will be considered not satisfied.
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For example, you will get an empty output if you apply the [range condition](../filtering/#range) on the string data.
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Qdrant also allows multiple values of the same type to be stored and applied to the filter at once.
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The condition will be considered satisfied if at least one value meets the condition.
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The filtering process is discussed in detail in the section [Filtering](../filtering).
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Let's look at the data types that Qdrant supports for searching:
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### Integer
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`integer` - 64-bit integer in the range from `-9223372036854775808` to `9223372036854775807`.
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Example of single and multiple `integer` values:
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```json
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{
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"count": 10,
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"sizes": [35, 36, 38]
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}
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```
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### Float
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`float` - 64-bit floating point number.
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Example of single and multiple `float` values:
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```json
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{
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"price": 11.99,
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"ratings": [9.1, 9.2, 9.4]
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}
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```
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### Bool
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Bool - is binary value equals to `true` or `false`.
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Example of single and multiple `bool` values:
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```json
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{
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"is_delivered": true,
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"responses": [false, false, true, false]
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}
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```
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### Keyword
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`keyword` - string value.
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Example of single and multiple `keyword` values:
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```json
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{
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"name": "Alice",
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"friends": [
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"bob",
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"eva",
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"jack"
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]
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}
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```
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### Geo
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`geo` is used to represent geographical coordinates.
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Example of single and multiple `geo` values:
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```json
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{
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"location": {
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"lon": 52.5200,
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"lat": 13.4050
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},
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"cities": [
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{
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"lon": 51.5072,
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"lat": 0.1276
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},
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{
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"lon": 40.7128,
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"lat": 74.0060
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}
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]
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}
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```
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Coordinate should be described as an object containing two fields: `lon` - for longitude, and `lat` - for latitude.
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## Create point with payload
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```http
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PUT /collections/{collection_name}/points
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{
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"points": [
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{
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"id": 1,
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"vector": [0.05, 0.61, 0.76, 0.74],
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"payload": {"city": "Berlin", "price": 1.99}
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},
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{
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"id": 2,
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"vector": [0.19, 0.81, 0.75, 0.11],
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"payload": {"city": ["Berlin", "London"], "price": 1.99}
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},
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{
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"id": 3,
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"vector": [0.36, 0.55, 0.47, 0.94],
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"payload": {"city": ["Berlin", "Moscow"], "price": [1.99, 2.99]}
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}
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]
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}
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```
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```python
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from qdrant_client import QdrantClient
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from qdrant_client.http import models
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client = QdrantClient(host="localhost", port=6333)
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client.upsert(
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collection_name="{collection_name}",
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points=[
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models.PointStruct(
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id=1,
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vector=[0.05, 0.61, 0.76, 0.74],
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payload={
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"city": "Berlin",
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"price": 1.99,
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},
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),
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models.PointStruct(
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id=2,
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vector=[0.19, 0.81, 0.75, 0.11],
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payload={
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"city": ["Berlin", "London"],
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"price": 1.99,
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},
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),
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models.PointStruct(
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id=3,
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vector=[0.36, 0.55, 0.47, 0.94],
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payload={
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"city": ["Berlin", "Moscow"],
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"price": [1.99, 2.99],
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},
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),
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]
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)
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```
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## Update payload
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### Set payload
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REST API ([Schema](https://qdrant.github.io/qdrant/redoc/index.html#operation/set_payload)):
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```http
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POST /collections/{collection_name}/points/payload
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{
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"payload": {
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"property1": "string",
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"property2": "string"
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},
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"points": [
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0, 3, 100
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]
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}
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```
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```python
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client.set_payload(
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collection_name="{collection_name}",
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payload={
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"property1": "string",
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"property2": "string",
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},
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points=[0, 3, 10],
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)
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```
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### Delete payload
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This method removes specified payload keys from specified points
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REST API ([Schema](https://qdrant.github.io/qdrant/redoc/index.html#operation/delete_payload)):
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```http
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POST /collections/{collection_name}/points/payload/delete
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{
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"keys": ["color", "price"],
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"points": [0, 3, 100]
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}
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```
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```python
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client.delete_payload(
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collection_name="{collection_name}",
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keys=["color", "price"],
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points=[0, 3, 100],
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)
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```
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### Clear payload
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This method removes all payload keys from specified points
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REST API ([Schema](https://qdrant.github.io/qdrant/redoc/index.html#operation/clear_payload)):
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```http
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POST /collections/{collection_name}/points/payload/clear
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{
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"points": [0, 3, 100]
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}
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```
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```python
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client.clear_payload(
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collection_name="{collection_name}",
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points_selector=models.PointIdsList(
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points=[0, 3, 100],
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)
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)
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```
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<aside role="status">You can also use `models.FilterSelector` to remove the points matching given filter criteria, instead of providing the ids.</aside>
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## Payload indexing
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To search more efficiently with filters, Qdrant allows you to specify payload fields as indexed.
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For marked fields will Qdrant will build an index for the corresponding types of queries.
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The indexed fields also affect the vector index. See [Indexing](../indexing) for details.
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In practice, we recommend creating an index on those fields that could potentially constrain the results the most.
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For example, building an index for the object ID (if it is used in the filter) will be much more efficient than an index by its color, which has only a few possible values.
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In compound queries involving multiple fields, Qdrant will attempt to use the most restrictive index first.
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To create index for the field, you can use the following:
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REST API
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```http
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PUT /collections/{collection_name}/index
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{
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"field_name": "name_of_the_field_to_index",
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"field_schema": "keyword"
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}
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```
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```python
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client.create_payload_index(
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collection_name="{collection_name}",
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field_name="name_of_the_field_to_index",
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field_schema="keyword",
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)
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```
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The index usage flag is displayed in the payload schema with the [collection info API](https://qdrant.github.io/qdrant/redoc/index.html#operation/get_collection).
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Payload schema example:
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```json
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{
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"payload_schema": {
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"property1": {
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"data_type": "keyword"
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},
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"property2": {
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"data_type": "integer"
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}
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}
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}
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```
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