aissemble-inference-sumy 1.5.0

sumy model family support for aiSSEMBLE Inference

aiSSEMBLE Inference Sumy Module

Sumy library integration for text summarization support in the aiSSEMBLE Inference library.

Supported Algorithms

This module supports multiple summarization algorithms through the sumy library:

Algorithm	Description
TextRank	Graph-based ranking algorithm (default)
LSA	Latent Semantic Analysis
LexRank	Graph-based ranking with cosine similarity

Installation

pip install aissemble-inference-sumy

This will also install the required dependencies:

• aissemble-inference-core
• sumy (text summarization library)
• nltk (natural language processing toolkit)

Usage

With MLServer

Create a model-settings.json:

{
    "name": "sumy",
    "implementation": "aissemble_inference_sumy.SumyRuntime",
    "parameters": {
        "algorithm": "textrank",
        "sentences_count": 3,
        "language": "english"
    }
}

Start MLServer:

mlserver start /path/to/models

With OIP Client

from aissemble_inference_core.client import InferenceClient
from aissemble_inference_core.client.registry import ModuleRegistry

# The module auto-registers via entry points
print(ModuleRegistry.instance().list_available())
# {'runtimes': ['sumy', ...], 'translators': ['sumy', ...], ...}

# Use with InferenceClient
client = InferenceClient(adapter, endpoint)
result = client.summarize("sumy").text("Long article text...").run()
print(result.summary)

Components

SumyRuntime

MLServer-compatible runtime that wraps sumy summarization algorithms:

• Accepts text strings via OIP protocol
• Returns summarized text
• Configurable algorithm, sentence count, and language via parameters

SumyTranslator

Translator for sumy outputs:

• Inherits from DefaultSummarizationTranslator
• Can be extended with sumy-specific optimizations

Configuration Parameters

Parameter	Type	Default	Description
algorithm	string	"textrank"	Summarization algorithm (textrank, lsa, lexrank)
sentences_count	int	3	Number of sentences in the summary
language	string	"english"	Language for text tokenization

Note: The max_length and min_length parameters from SummarizationBuilder are not currently supported by this module. This is planned for a future enhancement.

Entry Points

This module registers the following entry points:

Group	Name	Class
inference.runtimes	sumy	SumyRuntime
inference.translators	sumy	SumyTranslator

Language Support

The sumy library supports multiple languages for tokenization and stemming. Specify the language using the language parameter in model-settings.json. Common languages include:

• english
• spanish
• french
• german
• italian
• portuguese
• russian
• czech
• slovak

Refer to the sumy documentation for a complete list of supported languages.

Testing

This module includes comprehensive tests covering:

Unit Tests

• Module registration and discovery
• Runtime loading with different algorithms
• Direct inference via SumyRuntime
• Translator functionality
• Error handling and validation

Integration Tests (tagged with @integration)

• End-to-end OIP communication via MLServer
• Real HTTP requests using InferenceClient
• Algorithm switching (TextRank, LSA, LexRank)
• Parameter configuration validation

Run tests:

# Run all tests (unit + integration)
mvn clean install

# Run only unit tests (excluding @integration tag)
behave --tags=-integration

The integration tests automatically:

1. Start MLServer with configured models
2. Execute real inference requests via OIP protocol
3. Validate responses and summaries
4. Clean up resources

See tests/features/ for test scenarios and tests/features/environment.py for MLServer test infrastructure.

Example

For a complete working example, see the aissemble-summarization-example in the examples directory:

cd aissemble-inference-examples/aissemble-summarization-example
uv sync --group test
uv run behave

The example demonstrates:

• MLServer configuration for multiple summarization algorithms
• Integration with InferenceClient and HttpOipAdapter
• BDD test patterns for OIP-based applications
• Sample article data for testing