Prompt management tutorial

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Configuration Management

• You manage the calls to LLM Models end-to-end, this lets you keep control over the integration and manage its lifecycle, ensuring data stays within your infrastructure before reaching LLM backends.
• You still benefit from the configuration management on Orq.ai side and can fetch at runtime the latest configuration from your Deployment.
• You still benefit from Deployment Routing, ensuring your users reach the model you desire, using dynamic Context Attributes.

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Using get_config

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Example Call

import os
from orq_ai_sdk import Orq

client = Orq(api_key=os.environ.get("ORQ_API_KEY"))

config = client.deployments.get_config(
    key="Deployment-configuration",
    context={"environments": ["production"], "locale": ["en"]},
    inputs={"country": "Netherlands"},
    metadata={"custom-field-name": "custom-metadata-value"},
)

print(config.to_dict())

import { Orq } from "@orq-ai/node";

const client = new Orq({ apiKey: process.env.ORQ_API_KEY });

const deploymentConfig = await client.deployments.getConfig({
   key: "Deployment-configuration",
   context: { environments: ["production"], locale: ["en"] },
   inputs: { country: "Netherlands" },
   metadata: { "custom-field-name": "custom-metadata-value" }
});

key: The deployment to invoke. inputs: The key-value pair of variables to replace in your prompts. Default variables are used if not provided. context: This key-value pair matches your data model and fields declared in your Variant Routing Configuration matrix. metadata: This key-value pairs that you want to attach to the log generated by this request.

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Configuration Caching

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