orq-ai-sdk 2.11.0

Integrate and operate your products with the power of Large Language Models from a single collaboration platform. Conduct prompt engineering, experimentation, operations and monitoring across models, with full transparency on quality and costs.

Build AI Applications from Playground to Production

orq.ai Python SDK

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

Documentation

The REST API documentation can be found on docs.orq.ai.

Installation

# install from PyPI
pip install orq-ai-sdk

Usage

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"},
)

Async usage

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())

Deployments

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.

Invoke a deployment

invoke()

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)

invoke_with_stream()

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="")

Adding messages as part of your request

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.",
    }]
)

Logging metrics to the deployment configuration

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.",
    }]
)

Get deployment configuration

get_config()

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())

Logging metrics to the deployment configuration

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,
    },
)

Logging LLM responses

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.

Logging the completion choices the model generated for the input prompt

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",
            },
        },
    ]
)

Logging the completion choices the model generated for the input prompt

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>"
            },
        },
    ],
)

Logging the output of the tool calls

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',
    }
  ]
)

API documentation

Feedback API