aissemble-oip-core 1.5.0rc2

Core aiSSEMBLE Open Inference Protocol (OIP) functionality

aiSSEMBLE Open Inference Protocol (OIP) Client

Provides a modular Python library for standardized ML model inference, promoting interoperability across diverse runtimes and platforms. This client allows invocation of OIP endpoints in a more natural manner by ensuring that inference runtime details (like tensor-specific input/output structures) as not leaked from the model implementation into the client.

Features

Fluent Builder API

InferenceBuilder (abstract base class) offers a task-specific, chainable interface for configuring inference requests. Supports model selection, adapter/translator registration, custom parameters, and streaming responses. Extensible via subclasses implementing build_predictor() to produce a Predictor instance.

Task-Specific Inference

The client provides specialized builders for common ML tasks with natural, task-oriented APIs:

Object Detection

• client.detect_object(model_name) - Returns ObjectDetectionBuilder
• Fluent methods: .image(), .confidence(), .labels()
• Returns ObjectDetectionResult with bounding boxes, labels, scores
• Supports PIL images, numpy arrays, file paths, and bytes
• Example:

  result = client.detect_object("yolov8") \
      .image("photo.jpg") \
      .confidence(0.6) \
      .run()
  for detection in result.detections:
      print(f"{detection.label}: {detection.confidence}")
  

Text Summarization

• client.summarize(model_name) - Returns SummarizationBuilder
• Fluent methods: .text(), .max_length(), .min_length()
• Returns SummarizationResult with summary text and compression metrics
• Example:

  result = client.summarize("bart-large") \
      .text("Very long article text here...") \
      .max_length(100) \
      .run()
  print(result.summary)
  print(f"Compressed {result.compression_ratio:.1f}x")
  

Raw Inference Support

RawInferenceBuilder provides a low-level, non-fluent API for direct tensor inputs and parameters, ideal for custom or performance-critical scenarios.

Streaming Capable

Builders support iterable responses for continuous inference outputs.