Build AI Applications from Playground to Production
The orq.ai Python library enables easy orq.ai REST API integration in Python 3.7+ apps, offering typed request/response elements and httpx-based sync/async clients
The REST API documentation can be found on docs.orq.ai.
# install from PyPI
pip install orq-ai-sdk
You can get your workspace API key from the settings section in your orq.ai workspace. https://my.orq.ai/<workspace>/settings/developers
import os
from orq_ai_sdk import OrqAI
client = OrqAI(
api_key=os.environ.get("ORQ_API_KEY", "__API_KEY__"),
environment="production"
)
generation = client.deployments.invoke(
key="customer_service",
context={"environments": "production", "country": "NLD"},
inputs={"firstname": "John", "city": "New York"},
metadata={"customer_id": "Qwtqwty90281"},
)
Simply import AsyncOrqAI instead of OrqAI and use await with each API call:
import os
import asyncio
from orq_ai_sdk import AsyncOrqAI
client = AsyncOrqAI(
api_key=os.environ.get("ORQ_API_KEY", "__API_KEY__"),
environment="production"
)
async def main() -> None:
generation = await client.deployments.invoke(
key="customer_service",
context={"environments": "production", "country": "NLD"},
inputs={"firstname": "John", "city": "New York"},
metadata={"customer_id": "Qwtqwty90281"},
)
print(generation.choices[0].message.content)
asyncio.run(main())
The Deployments API delivers text outputs, images or tool calls based on the configuration established within Orq for your deployments. Additionally, this API supports streaming. To ensure ease of use and minimize errors, using the code snippets from the Orq Admin panel is highly recommended.
deployment = await client.deployments.invoke(
key="customer_service",
context={"environments": "production", "country": "NLD"},
inputs={"firstname": "John", "city": "New York"},
metadata={"customer_id": "Qwtqwty90281"},
)
print(deployment.choices[0].message.content)
stream = client.deployments.invoke_with_stream(
key="customer_service",
context={"environments": "production", "country": "NLD"},
inputs={"firstname": "John", "city": "New York"},
metadata={"customer_id": "Qwtqwty90281"},
)
await for chunk in stream:
if chunk.is_final:
print("Stream is finished")
print(chunk.choices[0].message.content or "", end="")
If you are using the invoke
method, you can include messages
in your request to the model. The messages
property
allows you to combine chat_history
with the prompt configuration in Orq, or to directly send messages
to the
model if you are managing the prompt in your code.
generation = client.deployments.invoke(
key="customer_service",
context={
"language": [],
"environments": []
},
metadata={
"custom-field-name": "custom-metadata-value"
},
inputs={"firstname": "John", "city": "New York"},
messages=[{
"role": "user",
"content": "A customer is asking about the latest software update features. Generate a detailed and informative response highlighting the key new features and improvements in the latest update.",
}]
)
After invoking, streaming or getting the configuration of a deployment, you can use the add_metrics
method to add
information to the deployment.
await generation.add_metrics(
chain_id="c4a75b53-62fa-401b-8e97-493f3d299316",
conversation_id="ee7b0c8c-eeb2-43cf-83e9-a4a49f8f13ea",
user_id="e3a202a6-461b-447c-abe2-018ba4d04cd0",
feedback={"score": 100},
metadata={
"custom": "custom_metadata",
"chain_id": "ad1231xsdaABw",
},
messages=[{
"role": "user",
"content": "A customer is asking about the latest software update features. Generate a detailed and informative response highlighting the key new features and improvements in the latest update.",
}]
)
prompt_config = client.deployments.get_config(
key="customer_service",
context={"environments": "production", "country": "NLD"},
inputs={"firstname": "John", "city": "New York"},
metadata={"customer_id": "Qwtqwty90281"},
)
print(prompt_config.to_dict())
After invoking, streaming or getting the configuration of a deployment, you can use the add_metrics
method to add
information to the deployment.
prompt_config.add_metrics(
chain_id="c4a75b53-62fa-401b-8e97-493f3d299316",
conversation_id="ee7b0c8c-eeb2-43cf-83e9-a4a49f8f13ea",
user_id="e3a202a6-461b-447c-abe2-018ba4d04cd0",
feedback={"score": 100},
metadata={
"custom": "custom_metadata",
"chain_id": "ad1231xsdaABw",
},
usage={
"prompt_tokens": 100,
"completion_tokens": 900,
"total_tokens": 1000,
},
performance={
"latency": 9000,
"time_to_first_token": 250,
},
)
Whether you use the get_config
or invoke
, you can log the model generations to the deployment. Here are some
examples of how to do it.
generation.add_metrics(
choices=[
{
"index": 0,
"finish_reason": "assistant",
"message": {
"role": "assistant",
"content": "Dear customer: Thank you for your interest in our latest software update! We're excited to share with you the new features and improvements we've rolled out. Here's what you can look forward to in this update",
},
},
]
)
You can save the images generated by the model in Orq. If the image format is base64
we always store it as
a png
.
generation.add_metrics(
choices=[
{
"index": 0,
"finish_reason": 'stop',
"message": {
"role": "assistant",
"url": "<image_url>"
},
},
],
)
generation.add_metrics(
choices=[
{
"index": 0,
"message": {
"role": "assistant",
"content": None,
"tool_calls": [
{
"type": "function",
"id": "call_pDBPMMacPXOtoWhTWibW1D94",
"function": {
"name": "get_weather",
"arguments": '{"location":"San Francisco, CA"}',
},
},
],
},
"finish_reason": 'tool_calls',
}
]
)