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Description:

qstash 2.0.0

Upstash Python QStash SDK

[!NOTE]
This project is in GA Stage.
The Upstash Professional Support fully covers this project. It receives regular updates, and bug fixes.
The Upstash team is committed to maintaining and improving its functionality.

QStash is an HTTP based messaging and scheduling solution for serverless and edge runtimes.
QStash Documentation
Install
pip install qstash

Usage
You can get your QStash token from the Upstash Console.
Publish a JSON message
from qstash import QStash

client = QStash("<QSTASH_TOKEN>")

res = client.message.publish_json(
url="https://example.com",
body={"hello": "world"},
headers={
"test-header": "test-value",
},
)

print(res.message_id)

Create a scheduled message
from qstash import QStash

client = QStash("<QSTASH_TOKEN>")

schedule_id = client.schedule.create(
destination="https://example.com",
cron="*/5 * * * *",
)

print(schedule_id)

Receiving messages
from qstash import Receiver

# Keys available from the QStash console
receiver = Receiver(
current_signing_key="CURRENT_SIGNING_KEY",
next_signing_key="NEXT_SIGNING_KEY",
)

# ... in your request handler

signature, body = req.headers["Upstash-Signature"], req.body

receiver.verify(
body=body,
signature=signature,
url="https://example.com", # Optional
)

Create Chat Completions
from qstash import QStash
from qstash.chat import upstash

client = QStash("<QSTASH_TOKEN>")

res = client.chat.create(
model="meta-llama/Meta-Llama-3-8B-Instruct",
provider=upstash(),
messages=[
{
"role": "user",
"content": "What is the capital of Turkey?",
}
],
)

print(res.choices[0].message.content)

Create Chat Completions Using Custom Providers
from qstash import QStash
from qstash.chat import openai

client = QStash("<QSTASH_TOKEN>")

res = client.chat.create(
model="gpt-3.5-turbo",
provider=openai("<OPENAI_API_KEY>"),
messages=[
{
"role": "user",
"content": "What is the capital of Turkey?",
}
],
)

print(res.choices[0].message.content)

Publish a JSON message to LLM
from qstash import QStash
from qstash.chat import upstash

client = QStash("<QSTASH_TOKEN>")

res = client.message.publish_json(
api={"name": "llm", "provider": upstash()},
body={
"model": "meta-llama/Meta-Llama-3-8B-Instruct",
"messages": [
{
"role": "user",
"content": "What is the capital of Turkey?",
}
],
},
callback="https://example-cb.com",
)

print(res.message_id)

Publish a JSON message to LLM Using Custom Providers
from qstash import QStash
from qstash.chat import openai

client = QStash("<QSTASH_TOKEN>")

res = client.message.publish_json(
api={"name": "llm", "provider": openai("<OPENAI_API_KEY>")},
body={
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "What is the capital of Turkey?",
}
],
},
callback="https://example-cb.com",
)

print(res.message_id)

Additional configuration
from qstash import QStash

# Create a client with a custom retry configuration. This is
# for sending messages to QStash, not for sending messages to
# your endpoints.
# The default configuration is:
# {
# "retries": 5,
# "backoff": lambda retry_count: math.exp(retry_count) * 50,
# }
client = QStash(
token="<QSTASH_TOKEN>",
retry={
"retries": 1,
"backoff": lambda retry_count: (2 ** retry_count) * 20,
},
)

# Publish to URL
client.message.publish_json(
url="https://example.com",
body={"key": "value"},
# Retry sending message to API 3 times
# https://upstash.com/docs/qstash/features/retry
retries=3,
# Schedule message to be sent 4 seconds from now
delay="4s",
# When message is sent, send a request to this URL
# https://upstash.com/docs/qstash/features/callbacks
callback="https://example.com/callback",
# When message fails to send, send a request to this URL
failure_callback="https://example.com/failure_callback",
# Headers to forward to the endpoint
headers={
"test-header": "test-value",
},
# Enable content-based deduplication
# https://upstash.com/docs/qstash/features/deduplication#content-based-deduplication
content_based_deduplication=True,
)

Additional methods are available for managing url groups, schedules, and messages. See the examples folder for more.
Development

Clone the repository
Install Poetry
Install dependencies with poetry install
Create a .env file with cp .env.example .env and fill in the QSTASH_TOKEN
Run tests with poetry run pytest
Format with poetry run ruff format .