orquesta-sdk 1.11.1

_LLM Operations and Integration Platform_

LLM Operations and Integration Platform

Orquesta Python SDK

Contents

• Installation
• Create a client instance
• Usage
• Reference

Installation

pip install orquesta-sdk

Creating a client instance

You can get your workspace API key from the settings section in your Orquesta workspace. https://my.orquesta.dev/<workspace>/settings/developers

Initialize the Orquesta client with your API key:

import os

from orquesta_sdk import OrquestaClient, OrquestaClientOptions

api_key = os.environ.get("ORQUESTA_API_KEY", "__API_KEY__")

options = OrquestaClientOptions(
    api_key=api_key,
    ttl=3600,
    environment="production"
)

client = OrquestaClient(options)

When creating a client instance, the following connection settings can be adjusted using the OrquestaClientOptions class:

OrquestaClientOptions

• api_key: str - your workspace API key to use for authentication.
• environment: Optional[str] - the environment to use for the client. Not required but recommended to use so it"s added to the evaluation context automatically.
• ttl: Optional[int] - the time to live in seconds for the local cache. Default is 3600 seconds (1 hour).

Usage - Endpoints

Use the Endpoints API to query or stream your endpoints from Orquesta.

Using endpoints to generate a LLM response based on your use case with Orquesta provides a low-latency, secure connection to the Endpoints API online prediction service. Getting out of the box metrics and logging for your LLMs.

Endpoints API support streaming and querying. The endpoint response type is OrquestaEndpointResponse. We recommend to use the code snippets provided in the Orquesta Admin panel to reduce risk of errors and improve ease of use.

Example: Querying an endpoint

from orquesta_sdk.endpoints import OrquestaEndpointRequest

request = OrquestaEndpointRequest(
    key="customer_service",
    context=context: { environments: "production", country: "NLD" },,
    variables: { firstname: "John", city: "New York" },
    metadata: { customer_id: "Qwtqwty90281" },
)

endpointRef = client.endpoints.query(
    request
)

print(endpointRef.content)

Example: Streaming your endpoints

request = OrquestaEndpointRequest(
    key="customer_service",
    context=context: { environments: "production", country: "NLD" },,
    variables: { firstname: "John", city: "New York" },
    metadata: { customer_id: "Qwtqwty90281" },
)

endpointRef = None

def handle_next(chunk):
    endpointRef = chunk
    print(f"Received {chunk.content}")


def handle_error(e):
    print(f"Error Occurred: {e}")


def handle_completed():
    print("Stream completed!")


const stream = client.endpoints.stream(
    request
).subscribe(
    on_next=handle_next,
    on_error=handle_error,
    on_completed=handle_completed,
)

Logging score and metadata for endpoints

After every query, Orquesta will generate a log with the result of the evaluation. You can add metadata and score to the endpoint by using the addMetrics method.

If you need to cancel a stream, you can call stream.unsubscribe() method.

metrics = OrquestaEndpointMetrics(
    score=85,
    metadata={
        "custom": "custom_metadata",
        "chain_id": "ad1231xsdaABw",
    },
)

endpointRef.addMetrics(metrics);

Usage - Prompts

Use the Prompts API to query your prompts from Orquesta.

You can use Orquesta in prompt management mode by consuming our Prompts API. The prompt value type is OrquestaPrompt. We recommend to use the code snippets provided in the Orquesta Admin panel to reduce risk of errors and improve ease of use.

We support an unified data model structure for all our prompts and provide helper functions that map the returned value from Orquesta to the specific provider.

The query method receives an object of type OrquestaPromptRequest as parameter.

Example: Querying a prompt

from orquesta_sdk.helpers import orquesta_openai_parameters_mapper

prompt = client.prompts.query(
    key="prompt_key",
    context={"environments": "production", "workspaceId": "soql1odAABC2"},
    variables={"firstname": "John", "city": "New York"},
    metadata={"chain_id": "ad1231xsdaABw"},
)

openai_api_parameters = orquesta_openai_parameters_mapper(prompt.value)

Helper functions per LLM provider

We provide helper functions that map the returned value from Orquesta to a dict following the definitions of the specific provider, so it"s easy for you to forward the Prompt to your different LLM providers.

Provider	Helper
Anthropic	orquesta_anthropic_parameters_mapper
Cohere	orquesta_cohere_parameters_mapper
Google	orquesta_google_parameters_mapper
Hugging Face	orquesta_huggingface_parameters_mapper
OpenAI	⚠️ Work in progres
Replicate	⚠️ Work in progres

Logging metrics and metadata for prompts

After every query, Orquesta will generate a log with the result of the evaluation. You can add metadata and information about the interaction with the LLM to the log by using the add_metrics method.

The properties score, latency, llm_response and economics are reserved and used to generate your real-time dashboards. metadata is a set of key-value pairs that you can use to add custom information to the log.

Example: Add metrics to your request log

from orquesta_sdk.prompts import OrquestaPromptMetricsEconomics, OrquestaPromptMetrics

economics = OrquestaPromptMetricsEconomics(
    prompt_tokens=1200,
    completion_tokens=750,
    total_tokens=1950,
)

metrics = OrquestaPromptMetrics(
    score=100,
    latency=40,
    llm_response="Orquesta is awesome!",
    economics=economics,
    metadata={
        "custom": "custom_metadata",
        "chain_id": "ad1231xsdaABw",
        "total_interactions": 200,
    }
)

prompt.add_metrics(metrics)

Usage - Remote Configurations

Orquesta also comes with a powerful Remote Configurations API that allows you to dynamically configure and run all your environments and services remotely.

Orquesta has a powerful Remote Configurations API that allows you to configure and run all your environments and services remotely dynamically. Orquesta supports different Class of remote configurations, and we recommend always typing the query method to help Classcript infer the correct type.

Supported Class: bool, float, str, dict, list

Example: Querying a configuration of type boolean

config = client.remoteconfigs.query(
    key="boolean_config",
    default_value=False,
    context={"environments": "production", "role": "admin"},
    metadata={"user_id": 450}
)

Example: Querying a configuration of type str

config = client.remoteconfigs.query(
    key="str_config",
    default_value="str_value",
    context={"environments": "production", "country": "NL"},
    metadata={"timestamp": 1623345600}
)

Example: Querying a configuration of type int

config = client.remoteconfigs.query(
    key="int_config",
    default_value=1990,
    context={"environments": "production", "market": "US" },
    metadata={"domain": "ecommerce"}
)

Example: Querying a configuration of type array

config = client.remoteconfigs.query(
    key="list_config",
    default_value=["USA", "NL"],
    context={"environments": "acceptance", "is_enable": True},
    metadata={"domain": "ecommerce"}
)

Example: Querying a configuration of type JSON

config = client.remoteconfigs.query(
    key="json_config",
    default_value=dict,
    contenxt={"environments": "develop", "platform": "mobile"},
)

Additional metadata logging

After every query, Orquesta will generate a log with data about the request. You can add metadata to the log using the add_metrics method anytime.

metadata is a set of key-value pairs that you can use to add custom information to the log.

Example: Add metrics to your request log

from orquesta_sdk.remoteconfigs import OrquestaRemoteConfigMetrics

metrics = OrquestaRemoteConfigMetrics(
    metadata={
        "custom": "custom_metadata",
        "user_clicks": 20,
        "selected_option": "option1"
    }
)

config.add_metrics(metrics)

Orquesta API

Endpoints API

Class:

• OrquestaEndpoint
• OrquestaEndpointMetrics
• OrquestaEndpointRequest

Methods:

• client.endpoints.query({ ...params }) -> OrquestaEndpoint
• client.endpoints.stream({ ...params }) -> Observable[OrquestaEndpoint]

Prompts API

Class:

• OrquestaPrompt
• OrquestaPromptMetrics
• OrquestaPromptMetricsEconomics
• OrquestaPromptRequest

Methods:

• client.prompts.query({ ...params }) -> OrquestaPrompt

RemoteConfigs API

Class:

• OrquestaRemoteConfigKind
• OrquestaRemoteConfig
• OrquestaRemoteConfigMetrics
• OrquestaRemoteConfigRequest

Methods:

• client.remoteconfigs.query({ ...params }) -> OrquestaRemoteConfig