Principle:Neuml Txtai Pipeline Definition
| Knowledge Sources | |
|---|---|
| Domains | Data_Processing, Workflow, Pipeline |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
Pipeline Definition is the foundational principle behind txtai's approach to composable NLP processing. A pipeline in txtai is a callable object that encapsulates a specific natural language processing operation -- text extraction, summarization, translation, or language generation -- behind a uniform construction and invocation interface. Each pipeline is instantiated with model configuration parameters and, once created, acts as a reusable function that transforms input data through its designated NLP operation.
Description
txtai pipelines follow a consistent design pattern: each pipeline class constructor accepts configuration parameters (model path, quantization flags, GPU settings, and operation-specific options) and produces a callable instance. When invoked, the instance accepts text (as a string or list of strings) and returns the transformed output. This uniformity is critical to the larger workflow chaining architecture because it allows pipelines to be treated interchangeably as actions inside Task and Workflow containers.
The key pipeline types in the workflow chaining context are:
- Textractor -- Extracts text content from files (PDF, DOCX, HTML, URLs) and converts it to structured Markdown. Inherits from the Segmentation base class, adding file-to-HTML conversion and HTML-to-Markdown rendering. Configuration controls whether output is segmented by sentences, lines, paragraphs, or sections.
- Summary -- Produces abstractive summaries of input text using Hugging Face summarization models. Wraps the HFPipeline base class and delegates to a transformers summarization pipeline. Supports configurable minimum and maximum output lengths.
- Translation -- Translates text between languages with automatic source language detection. Manages a cache of Helsinki-NLP translation models and falls back to the multilingual M2M100 model when a direct source-to-target model is not available.
- LLM -- Runs large language models for text generation. Supports local Hugging Face Transformers, llama.cpp, remote LiteLLM API backends, and custom generation implementations via the GenerationFactory.
All four pipeline types share the property that they are instantiated once and called many times, making them ideal building blocks for reusable data processing chains.
Usage
Use Pipeline Definition when you need to:
- Encapsulate a single NLP operation as a portable, reusable object that can be passed to tasks and workflows.
- Standardize model loading so that GPU placement, quantization, and model selection happen at construction time and do not need to be repeated at each invocation.
- Build modular processing chains where each step in the chain is a well-defined, independently testable pipeline.
- Support heterogeneous input formats -- Textractor handles file paths and URLs; Summary and Translation handle strings and lists; LLM handles strings, lists, and chat-format dictionaries.
Theoretical Basis
The Pipeline Definition principle draws on two established software design patterns:
Strategy Pattern: Each pipeline class implements a common callable interface (__call__) while encapsulating a different algorithm (extraction, summarization, translation, generation). Consumers of the pipeline do not need to know which specific operation is being performed -- they simply call the object with input data and receive output data.
Factory Initialization: Heavy resources (model weights, tokenizers, language detectors) are loaded once during construction. Subsequent calls reuse these resources, amortizing initialization cost across many invocations. This is especially important for GPU-backed models where loading and device placement are expensive.
Uniform Callable Contract: By ensuring that every pipeline is callable with a list of data elements and returns a list of results, txtai enables pipelines to be composed into higher-order structures (Tasks and Workflows) without adapter code. This composability is the foundation of the Pipeline Workflow Chaining pattern.