orca-sdk 0.1.22

SDK for interacting with Orca Services

OrcaSDK

OrcaSDK is a Python library for building and using retrieval-augmented models with OrcaCloud. It enables you to create, deploy, and maintain models that can adapt to changing circumstances without retraining by accessing external data called "memories."

Documentation

You can find the documentation for all things Orca at docs.orcadb.ai. This includes tutorials, how-to guides, and the full interface reference for OrcaSDK.

Features

• Labeled Memorysets: Store and manage labeled examples that your models can use to guide predictions
• Classification Models: Build retrieval-augmented classification models that adapt to new data without retraining
• Embedding Models: Use pre-trained or finetuned embedding models to represent your data
• Telemetry: Collect feedback and monitor memory usage to optimize model performance
• Datasources: Easily ingest data from various sources into your memorysets

Installation

OrcaSDK is compatible with Python 3.10 or higher and is available on PyPI. You can install it with your favorite python package manager:

• Pip: pip install orca_sdk
• Conda: conda install orca_sdk
• Poetry: poetry add orca_sdk

Quick Start

from dotenv import load_dotenv
from orca_sdk import OrcaCredentials, LabeledMemoryset, ClassificationModel

# Load your API key from environment variables
load_dotenv()
assert OrcaCredentials.is_authenticated()

# Create a labeled memoryset
memoryset = LabeledMemoryset.from_disk("my_memoryset", "./data.jsonl")

# Create a classification model using the memoryset
model = ClassificationModel("my_model", memoryset)

# Make predictions
prediction = model.predict("my input")

# Get Action Recommendation
action, rationale = prediction.recommend_action()
print(f"Recommended action: {action}")
print(f"Rationale: {rationale}")

# Generate and add synthetic memory suggestions
if action == "add_memories":
    suggestions = prediction.generate_memory_suggestions(num_memories=3)

    # Review suggestions
    for suggestion in suggestions:
        print(f"Suggested: '{suggestion['value']}' -> {suggestion['label']}")

    # Add suggestions to memoryset
    model.memoryset.insert(suggestions)
    print(f"Added {len(suggestions)} new memories to improve model performance!")

For a more detailed walkthrough, check out our Quick Start Guide.

Support

If you have any questions, please reach out to us at [e-mail].