get-systems 0.2.31

Get Systems Prefect Blocks - Enterprise LLM and HTTP operations

get_systems - Get Systems Prefect Blocks

Enterprise-grade Prefect blocks for LLM operations, HTTP requests, and data models.

🐍 Available on PyPI — install it with pip install get-systems

Features

LLM Module (get_systems.llm)

• ✅ OpenAI & Azure OpenAI Support - Seamlessly switch between providers
• ✅ Environment Variables - Automatic fallback to env vars
• ✅ Retry Logic - Exponential backoff with jitter
• ✅ Caching - Optional in-memory response caching
• ✅ Safe Logging - No API key leaks
• ✅ Flexible Parameters - Pass any OpenAI API parameter via **kwargs
• ✅ Function Calling - Full support for tools

HTTP Module (get_systems.http)

• ✅ Multiple Auth Types - None, Basic, Token, Bearer
• ✅ Async HTTP Client - Built on httpx
• ✅ Prefect Integration - First-class block support

Models Module (get_systems.models)

• ✅ Pydantic Models - Type-safe data models with validation
• ✅ Address Parsing - German address format support with normalization
• ✅ Contact Management - Client, Debtor, and Contact models
• ✅ GDPR Compliance - Data protection and source tracking fields
• ✅ Bank Accounts - IBAN/BIC validation via python-stdnum
• ✅ Event Handling - Case events and interest calculations

Azure Content Understanding Module (get_systems.azure_cu)

• ✅ Async Azure SDK Client - URL analysis through Azure AI Content Understanding
• ✅ Prefect Block Config - Store endpoint, key, and optional API version
• ✅ Usage Metadata - Returns raw analysis data plus token usage metadata when available
• ✅ LangChain Tool Block - Optional separate block for langchain-azure-ai

Langfuse Module (get_systems.langfuse)

• ✅ Prefect Credentials Block - Store Langfuse keys and base URL in Prefect
• ✅ Client Factory - Create an authenticated client with get_client()

Installation

Install Everything (Recommended)

pip install "get_systems[all]"

Install Specific Modules

Install only what you need:

# For LLM operations only
pip install "get_systems[llm]"

# For HTTP operations only  
pip install "get_systems[http]"

# For Azure Content Understanding only
pip install "get_systems[azure_cu]"

# For Azure Content Understanding LangChain tools
pip install "get_systems[azure_cu_langchain]"

# For Langfuse credentials and client
pip install "get_systems[langfuse]"

# Install several modules
pip install "get_systems[llm,http,azure_cu]"

From Azure Artifacts

# All modules
pip install "get_systems[all]" --extra-index-url https://pkget_systems.dev.azure.com/get-systems/_packaging/get-systems/pypi/simple/

# Specific modules
pip install "get_systems[llm]" --extra-index-url https://pkget_systems.dev.azure.com/get-systems/_packaging/get-systems/pypi/simple/
pip install "get_systems[http]" --extra-index-url https://pkget_systems.dev.azure.com/get-systems/_packaging/get-systems/pypi/simple/

What Gets Installed

Installation	Dependencies
pip install get_systems	prefect, pydantic (base only)
pip install "get_systems[llm]"	Base + openai
pip install "get_systems[http]"	Base + httpx
pip install "get_systems[azure_cu]"	Base + Azure Content Understanding SDK
pip install "get_systems[azure_cu_langchain]"	Base + langchain-azure-ai
pip install "get_systems[langfuse]"	Base + Langfuse Python SDK
pip install "get_systems[all]"	Base + all optional integrations

Quick Start

LLM Operations

from get_systems.llm import GptCompletionBlock, GptAuth, LlmRuntime
from prefect import flow

# Configure auth
auth = GptAuth(
    api_key="sk-...",
    model="gpt-4o-mini",
    is_azure=False
)

# Create completion block
block = GptCompletionBlock(
    auth=auth,
    prompt="What is Prefect?",
    temperature=0.7
)

@flow
async def my_llm_flow():
    result = await block.run()
    print(result.content)

HTTP Operations

from get_systems.http import HttpAuth, HttpBlock
from prefect import flow

# Configure HTTP auth
auth = HttpAuth(
    auth_type="bearer",
    token="your-token"
)

# Create HTTP block
http_block = HttpBlock(
    auth=auth,
    url="https://api.example.com"
)

@flow
async def my_http_flow():
    response = await http_block.request("GET", "/users")
    print(response.json())

Azure Content Understanding

from get_systems.azure_cu import AzureAIContentBlock
from prefect import flow

cu_block = AzureAIContentBlock(
    endpoint="https://your-resource.services.ai.azure.com",
    key="your-key",
)

@flow
async def analyze_document():
    result = await cu_block.analyze_url(
        analyzer_id="auftrag",
        document_url="https://example.com/document.pdf",
    )
    print(result["raw"])
    print(result["usage"])

Azure Content Understanding LangChain Tool

from get_systems.azure_cu import AzureAIContentLangChainBlock

config_block = await AzureAIContentLangChainBlock.load("azure-cu-langchain-config")
tool = config_block.get_tool(analyzer_id="auftrag")

Data Models

from get_systems.models import Address, Contact, Debtor, BankAccount

# Parse German address from string
address = Address.from_raw("Zimmerstraße 456 c/o Amtsgericht, 63225 Langen, DE")
print(address.street)  # "Zimmerstraße 456 c/o Amtsgericht"
print(address.zip_code)  # "63225"
print(address.city)  # "Langen"

# Create address with validation
address = Address(
    street="Musterstraße 321a",
    zip_code="12345",
    city="Berlin",
    country_code="DE",
    validity="G"  # Gültig (Valid)
)

# GDPR-compliant data tracking
address_with_source = Address(
    street="Hauptstraße 10",
    zip_code="10115",
    city="Berlin",
    country_code="DE",
    source_contact_designation="Auskunftei",
    source_contact_id="12345",
    source_date="2026-06-01"
)

# Create debtor with contact info
debtor = Debtor(
    first_name="Max",
    last_name="Mustermann",
    addresses=[address],
    person_type="NP"  # Natural Person
)

Langfuse Credentials

from get_systems.langfuse import LangfuseCredentials

credentials = await LangfuseCredentials.load("langfuse-dev")
client = credentials.get_client()

Model Data Masking

Models derived from MaskedBaseModel or WithExtraFields can mark sensitive fields declaratively:

from pydantic import Field
from get_systems.models import MaskedBaseModel

class DebtorTrace(MaskedBaseModel):
    name: str | None = Field(
        default=None,
        json_schema_extra={"mask": True},
    )
    email: str | None = Field(
        default=None,
        json_schema_extra={"mask": True},
    )

masked = DebtorTrace(
    name="Max Mustermann",
    email="[e-mail]",
).masked_dump()

masked_dump() recursively processes nested masked models, dictionaries, and collections. Sensitive strings are rendered as their first three characters, ..., and their last character, for example Max...n. Short values are rendered as ***. The original model remains unchanged.

Register Blocks in Prefect

# Register all blocks
prefect block register -m get_systems.llm.gpt_blocks
prefect block register -m get_systems.http
prefect block register -m get_systems.azure_cu
prefect block register -m get_systems.langfuse

# View registered blocks
prefect block ls

Import Styles

All import styles are supported:

# Submodule imports (recommended)
from get_systems.llm import GptCompletionBlock, GptAuth
from get_systems.http import HttpAuth, HttpBlock
from get_systems.azure_cu import AzureAIContentBlock, AzureAIContentLangChainBlock
from get_systems.langfuse import LangfuseCredentials

# Direct module imports
from get_systems.llm.gpt_blocks import GptCompletionBlock
from get_systems.http.http_block import HttpAuth
from get_systems.azure_cu.blocks import AzureAIContentBlock
from get_systems.langfuse.credentials import LangfuseCredentials

# Package-level imports
from get_systems import GptCompletionBlock, HttpAuth, AzureAIContentBlock, LangfuseCredentials

Environment Variables

OpenAI Configuration:

OPENAI_API_KEY=sk-...
OPENAI_MODEL=gpt-4o-mini
OPENAI_BASE_URL=https://api.openai.com/v1  # optional

Azure OpenAI Configuration:

OPENAI_API_KEY=your-azure-key
OPENAI_BASE_URL=https://your-resource.openai.azure.com
OPENAI_MODEL=your-deployment-name
OPENAI_API_VERSION=2024-02-15-preview
OPENAI_IS_AZURE=true

Azure Content Understanding Configuration:

CONTENT_UNDERSTANDING_ENDPOINT=https://your-resource.services.ai.azure.com
CONTENT_UNDERSTANDING_KEY=your-key
CONTENT_UNDERSTANDING_API_VERSION=2025-11-01  # optional; SDK default is used when omitted

Documentation

• docs/QUICKSTART-GS.md - Quick start and usage examples
• docs/MODELS.md - Complete models documentation (Address, Contact, BankAccount, etc.)
• docs/MIGRATION.md - Migration guide from old packages
• LLM Module: Full OpenAI/Azure OpenAI integration with enterprise features
• HTTP Module: Flexible HTTP client with multiple authentication types
• Models Module: Pydantic models for addresses, contacts, bank accounts with validation
• Prefect Integration: Native Prefect block support for LLM and HTTP modules

License

Proprietary - Get Systems