Payload
One of the significant features of Qdrant is the ability to store additional information along with vectors.
This information is called payload in Qdrant terminology.
Qdrant allows you to store any information that can be represented using JSON.
Here is an example of a typical payload:
{
"name": "jacket",
"colors": ["red", "blue"],
"count": 10,
"price": 11.99,
"locations": [
{
"lon": 52.5200,
"lat": 13.4050
}
],
"reviews": [
{
"user": "alice",
"score": 4
},
{
"user": "bob",
"score": 5
}
]
}
Payload types
In addition to storing payloads, Qdrant also allows you search based on certain kinds of values. This feature is implemented as additional filters during the search and will enable you to incorporate custom logic on top of semantic similarity.
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.
For example, you will get an empty output if you apply the range condition on the string data.
However, arrays (multiple values of the same type) are treated a little bit different. When we apply a filter to an array, it will succeed if at least one of the values inside the array meets the condition.
The filtering process is discussed in detail in the section Filtering.
Let’s look at the data types that Qdrant supports for searching:
Integer
integer - 64-bit integer in the range from -9223372036854775808 to 9223372036854775807.
Example of single and multiple integer values:
{
"count": 10,
"sizes": [35, 36, 38]
}
Float
float - 64-bit floating point number.
Example of single and multiple float values:
{
"price": 11.99,
"ratings": [9.1, 9.2, 9.4]
}
Bool
Bool - binary value. Equals to true or false.
Example of single and multiple bool values:
{
"is_delivered": true,
"responses": [false, false, true, false]
}
Keyword
keyword - string value.
Example of single and multiple keyword values:
{
"name": "Alice",
"friends": [
"bob",
"eva",
"jack"
]
}
Geo
geo is used to represent geographical coordinates.
Example of single and multiple geo values:
{
"location": {
"lon": 52.5200,
"lat": 13.4050
},
"cities": [
{
"lon": 51.5072,
"lat": 0.1276
},
{
"lon": 40.7128,
"lat": 74.0060
}
]
}
Coordinate should be described as an object containing two fields: lon - for longitude, and lat - for latitude.
Create point with payload
REST API (Schema)
PUT /collections/{collection_name}/points
{
"points": [
{
"id": 1,
"vector": [0.05, 0.61, 0.76, 0.74],
"payload": {"city": "Berlin", "price": 1.99}
},
{
"id": 2,
"vector": [0.19, 0.81, 0.75, 0.11],
"payload": {"city": ["Berlin", "London"], "price": 1.99}
},
{
"id": 3,
"vector": [0.36, 0.55, 0.47, 0.94],
"payload": {"city": ["Berlin", "Moscow"], "price": [1.99, 2.99]}
}
]
}
from qdrant_client import QdrantClient
from qdrant_client.http import models
client = QdrantClient(host="localhost", port=6333)
client.upsert(
collection_name="{collection_name}",
points=[
models.PointStruct(
id=1,
vector=[0.05, 0.61, 0.76, 0.74],
payload={
"city": "Berlin",
"price": 1.99,
},
),
models.PointStruct(
id=2,
vector=[0.19, 0.81, 0.75, 0.11],
payload={
"city": ["Berlin", "London"],
"price": 1.99,
},
),
models.PointStruct(
id=3,
vector=[0.36, 0.55, 0.47, 0.94],
payload={
"city": ["Berlin", "Moscow"],
"price": [1.99, 2.99],
},
),
]
)
Update payload
Set payload
REST API (Schema):
POST /collections/{collection_name}/points/payload
{
"payload": {
"property1": "string",
"property2": "string"
},
"points": [
0, 3, 100
]
}
client.set_payload(
collection_name="{collection_name}",
payload={
"property1": "string",
"property2": "string",
},
points=[0, 3, 10],
)
Delete payload
This method removes specified payload keys from specified points
REST API (Schema):
POST /collections/{collection_name}/points/payload/delete
{
"keys": ["color", "price"],
"points": [0, 3, 100]
}
client.delete_payload(
collection_name="{collection_name}",
keys=["color", "price"],
points=[0, 3, 100],
)
Clear payload
This method removes all payload keys from specified points
REST API (Schema):
POST /collections/{collection_name}/points/payload/clear
{
"points": [0, 3, 100]
}
client.clear_payload(
collection_name="{collection_name}",
points_selector=models.PointIdsList(
points=[0, 3, 100],
)
)
Payload indexing
To search more efficiently with filters, Qdrant allows you to create indexes for payload fields by specifying the name and type of field it is intended to be.
The indexed fields also affect the vector index. See Indexing for details.
In practice, we recommend creating an index on those fields that could potentially constrain the results the most. For example, using an index for the object ID will be much more efficient, being unique for each record, than an index by its color, which has only a few possible values.
In compound queries involving multiple fields, Qdrant will attempt to use the most restrictive index first.
To create index for the field, you can use the following:
REST API (Schema)
PUT /collections/{collection_name}/index
{
"field_name": "name_of_the_field_to_index",
"field_schema": "keyword"
}
client.create_payload_index(
collection_name="{collection_name}",
field_name="name_of_the_field_to_index",
field_schema="keyword",
)
The index usage flag is displayed in the payload schema with the collection info API.
Payload schema example:
{
"payload_schema": {
"property1": {
"data_type": "keyword"
},
"property2": {
"data_type": "integer"
}
}
}