sieves
Plug-and-play, zero-shot document processing pipelines.
Stars: 101
sieves is a library for zero- and few-shot NLP tasks with structured generation, enabling rapid prototyping of NLP applications without the need for training. It simplifies NLP prototyping by bundling capabilities into a single library, providing zero- and few-shot model support, a unified interface for structured generation, built-in tasks for common NLP operations, easy extendability, document-based pipeline architecture, caching to prevent redundant model calls, and more. The tool draws inspiration from spaCy and spacy-llm, offering features like immediate inference, observable pipelines, integrated tools for document parsing and text chunking, ready-to-use tasks such as classification, summarization, translation, and more, persistence for saving and loading pipelines, distillation for specialized model creation, and caching to optimize performance.
README:
sieves is a library for zero- and few-shot NLP tasks with structured generation. Build production-ready NLP prototypes quickly, with guaranteed output formats and no training required.
Read our documentation here. An automatically generated version (courtesy of Devin via DeepWiki) is available here.
Install sieves with pip install sieves. If you want to install all optional dependencies right away, install with
pip install sieves[engines,distill]. You can also choose to install individual dependencies as you see fit.
Even in the era of generative AI, structured outputs and observability remain crucial.
Many real-world scenarios require rapid prototyping with minimal data. Generative language models excel here, but
producing clean, structured output can be challenging. Various tools address this need for structured/guided language
model output, including outlines, dspy,
ollama, and others. Each has different design patterns, pros and cons. sieves wraps these tools and provides
a unified interface for input, processing, and output.
Developing NLP prototypes often involves repetitive steps: parsing and chunking documents, exporting results for
model fine-tuning, and experimenting with different prompting techniques. All these needs are addressed by existing
libraries in the NLP ecosystem address (e.g. docling for file parsing, or datasets for transforming
data into a unified format for model training).
sieves simplifies NLP prototyping by bundling these capabilities into a single library, allowing you to quickly
build modern NLP applications. It provides:
- Zero- and few-shot model support for immediate inference
- A bundle of utilities addressing common requirements in NLP applications
- A unified interface for structured generation across multiple libraries
- Built-in tasks for common NLP operations
- Easy extendability
- A document-based pipeline architecture for easy observability and debugging
- Caching - pipelines cache processed documents to prevent costly redundant model calls
sieves draws a lot of inspiration from spaCy and particularly spacy-llm.
- 🎯 Zero Training Required: Immediate inference using zero-/few-shot models
- 🤖 Unified Generation Interface: Seamlessly use multiple libraries
▶️ Observable Pipelines: Easy debugging and monitoring- 🛠️ Integrated Tools:
- Document parsing:
docling,unstructured,marker - Text chunking:
chonkie
- Document parsing:
- 🏷️ Ready-to-Use Tasks:
- Multi-label classification
- Information extraction
- Summarization
- Translation
- Multi-question answering
- Aspect-based sentiment analysis
- PII (personally identifiable information) anonymization
- Named entity recognition
- Coming soon: entity linking, knowledge graph creation, ...
- 💾 Persistence: Save and load pipelines with configurations
- 🧑🏫 Distillation: Distill local, specialized models using your zero-shot model results automatically.
Export your results as HuggingFace
Datasetif you want to run your own training routine. - ♻️ Caching to avoid unnecessary model calls
Here's a simple classification example using outlines:
from sieves import Pipeline, Engine, tasks, Doc
# 1. Define documents by text or URI.
docs = [Doc(text="Special relativity applies to all physical phenomena in the absence of gravity.")]
# 2. Create pipeline with tasks.
pipe = Pipeline(
# 3. Add classification task to pipeline.
# By default Engine uses Outlines with HuggingFaceTB/SmolLM-360M-Instruct. This is a pretty small model, you
# might want to consider upgrading to a different model for better results.
tasks.Classification(labels=["science", "politics"], engine=Engine())
)
# 4. Run pipe and output results.
for doc in pipe(docs):
print(doc.results)Advanced Example
This example demonstrates PDF parsing, text chunking, and classification:
import pickle
import gliner.multitask
import chonkie
import tokenizers
import docling.document_converter
from sieves import Pipeline, Engine, tasks, Doc
# 1. Define documents by text or URI.
docs = [Doc(uri="https://arxiv.org/pdf/2408.09869")]
# 2. Create engine responsible for generating structured output.
model_name = 'knowledgator/gliner-multitask-v1.0'
engine = Engine(model=gliner.GLiNER.from_pretrained(model_name))
# 3. Create chunker object.
chunker = chonkie.TokenChunker(tokenizers.Tokenizer.from_pretrained(model_name))
# 3. Create pipeline with tasks.
pipe = Pipeline(
[
# 4. Add document parsing task.
tasks.Ingestion(export_format="markdown"),
# 5. Add chunking task to ensure we don't exceed our model's context window.
tasks.Chunking(chunker),
# 6. Add classification task to pipeline.
tasks.Classification(task_id="classifier", labels=["science", "politics"], engine=engine),
]
)
# 7. Run pipe and output results.
docs = list(pipe(docs))
for doc in docs:
print(doc.results["classifier"])
# 8. Serialize pipeline and docs.
pipe.dump("pipeline.yml")
with open("docs.pkl", "wb") as f:
pickle.dump(docs, f)
# 9. Load pipeline and docs from disk. Note: we don't serialize complex third-party objects, so you'll have
# to pass those in at load time.
loaded_pipe = Pipeline.load(
"pipeline.yml",
(
{"converter": docling.document_converter.DocumentConverter(), "export_format": "markdown"},
{"chunker": chunker},
{"engine": {"model": engine.model}},
),
)
with open("docs.pkl", "rb") as f:
loaded_docs = pickle.load(f)sieves is built on five key abstractions.
Orchestrates task execution with features for.
- Task configuration and sequencing
- Pipeline execution
- Configuration management and serialization
Represents a document in the pipeline.
- Contains text content and metadata
- Tracks document URI and processing results
- Passes information between pipeline tasks
Encapsulates a single processing step in a pipeline.
- Defines input arguments
- Wraps and initializes
Bridgeinstances handling task-engine-specific logic - Implements task-specific dataset export
Provides a unified interface to structured generation libraries.
- Manages model interactions
- Handles prompt execution
- Standardizes output formats
Connects Task with Engine.
- Implements engine-specific prompt templates
- Manages output type specifications
- Ensures compatibility between tasks and engine
Show FAQs
sieves was originally motivated by the want to use generative models for structured information extraction. Coming
from this angle, there are two ways to explain why we settled on this name (pick the one you like better):
- An analogy to gold panning: run your raw data through a sieve to obtain structured, refined "gold."
- An acronym - "sieves" can be read as "Structured Information Extraction and VErification System" (but that's a mouthful).
Asked differently: what are the benefits of using sieves over directly interacting with an LLM?
- Validated, structured data output - also for LLMs that don't offer structured outputs natively. Zero-/few-shot language models can be finicky without guardrails or parsing.
- A step-by-step pipeline, making it easier to debug and track each stage.
- The flexibility to switch between different models and ways to ensure structured and validated output.
- A bunch of useful utilities for pre- and post-processing you might need.
- An array of useful tasks you can right of the bat without having to roll your own.
Which library makes the most sense to you depends strongly on your use-case. outlines provides structured generation
abilities, but not the pipeline system, utilities and pre-built tasks that sieves has to offer (and of course not the
flexibility to switch between different structured generation libraries). Then again, maybe you don't need all that -
in which case we recommend using outlines (or any other structured generation libray) directly.
Similarly, maybe you already have an existing tech stack in your project that uses exclusively ollama, langchain, or
dspy? All of these libraries (and more) are supported by sieves - but they are not just structured generation
libraries, they come with a plethora of features that are out of scope for sieves. If your application deeply
integrates with a framework like LangChain or DSPy, it may be reasonable to stick to those libraries directly.
As many things in engineering, this is a trade-off. The way we see it: the less tightly coupled your existing
application is with a particular language model framework, the more mileage you'll get out of sieves. This means that
it's ideal for prototyping (there's no reason you can't use it in production too, of course).
Source for
sievesicon: Sieve icons created by Freepik - Flaticon.
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