From ea0691307175c1ef4f5190694359b163fd8fd8e1 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Tue, 30 Dec 2025 22:40:37 +0100 Subject: [PATCH 01/29] docs: Update readme, agents.md. --- AGENTS.md | 2 +- README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/AGENTS.md b/AGENTS.md index fc40997..a255448 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1,6 +1,6 @@ # AGENTS.md -This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. +This file provides guidance to LLM agents when working with code in this repository. --- diff --git a/README.md b/README.md index 73203db..06c403e 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,7 @@
-## A Unified Interface for Document AI. +## A Unified Interface for Structured Document AI. `sieves` provides a **framework-agnostic abstraction for building document AI pipelines**. From cd3686091bb81038762bc2cb107e0ad3cad3cc33 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Tue, 30 Dec 2025 22:40:58 +0100 Subject: [PATCH 02/29] docs: Add first draft of blog post. --- blog_post.md | 87 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 87 insertions(+) create mode 100644 blog_post.md diff --git a/blog_post.md b/blog_post.md new file mode 100644 index 0000000..2aeb22f --- /dev/null +++ b/blog_post.md @@ -0,0 +1,87 @@ +# `sieves`: A Unified Interface for Structured Document AI + +Document AI is the process of turning unstructured documents - PDFs, emails, social media feeds - into structured data. Files are ingested, texts chunked, structured entities extracted, and perhaps a specialized model distilled for more efficient processing. + +The tools for these steps exist. They are excellent, and they are built with different paradigms in mind. **[Outlines](https://github.com/dottxt-ai/outlines)** provides robust constrained generation. **[DSPy](https://github.com/stanfordnlp/dspy)** focuses on declarative prompt optimization. **[LangChain](https://github.com/langchain-ai/langchain)** has broad API support and a huge ecosystem. **[GLiNER](https://github.com/fastino-ai/GLiNER2)** and **[Transformers](https://github.com/huggingface/transformers)** zero-shot classification pipelines offer specialized, high-performance inference. + +Choosing one creates path dependency. If you build your application logic around LangChain's abstractions, switching to a specialized model like GLiNER for efficiency is painful. You must migrate prompt signatures, integrations, and orchestration logic. + +A significant amount of developer time can be spent due to this friction. You piece together disparate tools for ingestion, chunking, and prediction. You reinvent the same boilerplate for every project. This is what we at Mantis experienced when working on document AI problems, at least. + +To mitigate this, we built `sieves`. `sieves` is a framework-agnostic abstraction for building these pipelines. It replaces imperative glue code and boilerplate with declarative design to shorten the turnaround time from specification to working document AI pipeline. + +## Declarative Document AI + +`sieves` decouples the business logic - the "what" - from the execution framework - the "how." + +You focus on the data you need. The framework handles the execution. Because `sieves` provides a unified interface over multiple backends for structured genreation, you can swap execution engines by changing a single parameter. The rest of your pipeline remains untouched. + +`sieves`, in this perspective, acts as vertical stack for the entire document AI lifecycle: + +1. **Ingestion**: Standardized parsing of PDFs, images, and Office docs via **[docling](https://github.com/DS4SD/docling)**. +2. **Preprocessing**: Built-in text chunking and windowing via **[chonkie](https://github.com/chonkie-inc/chonkie)**. +3. **Task Library**: A collection of ready-to-use tasks like NER, classification, and summarization. Skip the prompt engineering and focus on the schema. +4. **Prediction**: Structured generation across `outlines`, `dspy`, `langchain`, `gliner2`, and `transformers` zero-shot classification pipelines. +5. **Persistence**: Save and load pipelines with their configurations to ensure reproducibility across environments. + +## Evaluation and Optimization + +A production pipeline is not static. You need to know if it works and how to make it better. `sieves` builds these needs into the core workflow. + +**Evaluation** is a first-class citizen. You can measure pipeline performance against ground-truth data using deterministic metrics or LLM-based judging. This allows you to track regression as you update models or prompts. + +**Optimization** is integrated via DSPy's MIPROv2. If your extraction precision is low, `sieves` can automatically optimize your prompts and few-shot examples. + +**Distillation** completes the cycle. Once you have a high-performing pipeline using a large LLM, you can distill that logic into a specialized local model using **SetFit** or **Model2Vec**. This reduces costs and latency without a total rewrite of your application. + +## Small Abstractions + +We built `sieves` around three objects: + +- **Doc**: The atomic unit of data. It holds text, metadata, and the pipeline's history. +- **Task**: A reusable unit of work. It encapsulates logic and schema validation. +- **Pipeline**: A sequence of tasks. It manages execution, caching, and conditional logic. + +Tasks are portable. A sentiment analysis task defined for a prototype with GPT-4 will work with a local Llama model. The schema is the contract. + +## Case Study: Filtering the Noise + +In our **[Crisis Tweet Case Study](https://sieves.ai/demos/crisis_tweets)**, we used the CrisisNLP dataset to solve a common engineering problem: noise. Social media text is informal and often irrelevant to emergency response. + +Running complex extraction on every noisy tweet is a waste of money, time and a source of errors. +To address this, we built a multi-stage pipeline. A classifier identifies if a tweet is crisis-related. A "gatekeeper" condition determines if the expensive extraction tasks should run. + +```python +def related_to_crisis(doc: Doc) -> bool: + result = doc.results.get("crisis_label_classifier") + return result and result.label != 'irrelevant' + +pipeline = ( + crisis_label_classifier + + tasks.InformationExtraction( + task_id="location_extractor", + entity_type=Country, + model=model, + condition=related_to_crisis + ) +) +``` + +Filtering the noise before later extraction stage can significantly improve the reliability of your pipeline. Stop asking your model to extract entities from irrelevant text, and you'll end up with a more resilient, accurate, and cheaper system. + +## What v1.0 Means + +We are releasing v1.0 to signal API stability. We have used `sieves` in production for complex document processing. The abstractions are robust enough to handle the rapid evolution of the underlying LLM ecosystem. + +We are committed to stability. The underlying models and frameworks will change. Your `sieves` pipelines should remain a stable part of your infrastructure. + +## When to Use sieves + +`sieves` is for teams building document-centric NLP pipelines. + +- **Good fits**: Structured data extraction, multi-stage processing, and moving from prototypes to production without backend lock-in. +- **Poor fits**: Chatbots, RAGs or simple one-off LLM calls where a single prompt suffices. + +*** + +*`sieves` is open-source under a MIT license and available on **[GitHub](https://github.com/MantisAI/sieves)**. Read the documentation and see the full case study at **[sieves.ai](https://sieves.ai)**.* From b8771344b1fb0649bb33f9955be00a9bac816072 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Tue, 30 Dec 2025 23:12:54 +0100 Subject: [PATCH 03/29] docs: Second draft of blog post. --- blog_post.md | 26 ++++++++++++++++---------- 1 file changed, 16 insertions(+), 10 deletions(-) diff --git a/blog_post.md b/blog_post.md index 2aeb22f..4f29b5e 100644 --- a/blog_post.md +++ b/blog_post.md @@ -1,22 +1,26 @@ # `sieves`: A Unified Interface for Structured Document AI -Document AI is the process of turning unstructured documents - PDFs, emails, social media feeds - into structured data. Files are ingested, texts chunked, structured entities extracted, and perhaps a specialized model distilled for more efficient processing. +Document AI is the process of turning unstructured documents - PDFs, emails, social media feeds - into structured data. Files are ingested, texts chunked, structured entities extracted, and perhaps a specialized model distilled for more efficient processing. Think invoices, support emails, or tweets at scale. -The tools for these steps exist. They are excellent, and they are built with different paradigms in mind. **[Outlines](https://github.com/dottxt-ai/outlines)** provides robust constrained generation. **[DSPy](https://github.com/stanfordnlp/dspy)** focuses on declarative prompt optimization. **[LangChain](https://github.com/langchain-ai/langchain)** has broad API support and a huge ecosystem. **[GLiNER](https://github.com/fastino-ai/GLiNER2)** and **[Transformers](https://github.com/huggingface/transformers)** zero-shot classification pipelines offer specialized, high-performance inference. +## Isn't this solved already? -Choosing one creates path dependency. If you build your application logic around LangChain's abstractions, switching to a specialized model like GLiNER for efficiency is painful. You must migrate prompt signatures, integrations, and orchestration logic. +The tools for these steps exist. They are excellent, and they are built with different paradigms and goals in mind. **[Outlines](https://github.com/dottxt-ai/outlines)** provides robust constrained generation. **[DSPy](https://github.com/stanfordnlp/dspy)** focuses on declarative prompt optimization. **[LangChain](https://github.com/langchain-ai/langchain)** has broad API support and a large ecosystem. **[GLiNER](https://github.com/fastino-ai/GLiNER2)** and **[Transformers](https://github.com/huggingface/transformers)** zero-shot classification pipelines offer specialized, high-performance inference. -A significant amount of developer time can be spent due to this friction. You piece together disparate tools for ingestion, chunking, and prediction. You reinvent the same boilerplate for every project. This is what we at Mantis experienced when working on document AI problems, at least. +Choosing one creates path dependency early in the application design. If you build your application logic around LangChain's abstractions, switching to a specialized model like GLiNER for efficiency is painful. You must migrate prompt signatures, integrations, and orchestration logic. -To mitigate this, we built `sieves`. `sieves` is a framework-agnostic abstraction for building these pipelines. It replaces imperative glue code and boilerplate with declarative design to shorten the turnaround time from specification to working document AI pipeline. +Too much developer time is lost to this friction, largely on integration and rework. You piece together disparate tools for ingestion, chunking, and prediction. You reinvent the same boilerplate for every project. This is a pattern we repeatedly encountered while working on document AI problems. + +The missing piece was a stable, schema-first layer that sits above these tools and allows them to be composed without entangling application logic with a specific backend or execution framework. + +To mitigate this, we built `sieves`. `sieves` is a framework-agnostic abstraction for building these workflows. It replaces imperative glue code with declarative design. Its modularity allows complex pipelines to be built from simple, reusable tasks. ## Declarative Document AI `sieves` decouples the business logic - the "what" - from the execution framework - the "how." -You focus on the data you need. The framework handles the execution. Because `sieves` provides a unified interface over multiple backends for structured genreation, you can swap execution engines by changing a single parameter. The rest of your pipeline remains untouched. +You focus on the data you need and the **structure it should conform to**, while the framework handles execution. Because `sieves` provides a unified interface over multiple backends for structured generation, you can swap execution engines by changing a single parameter. The rest of your pipeline remains untouched. -`sieves`, in this perspective, acts as vertical stack for the entire document AI lifecycle: +`sieves` acts as a cohesive layer for the entire document AI lifecycle: 1. **Ingestion**: Standardized parsing of PDFs, images, and Office docs via **[docling](https://github.com/DS4SD/docling)**. 2. **Preprocessing**: Built-in text chunking and windowing via **[chonkie](https://github.com/chonkie-inc/chonkie)**. @@ -48,9 +52,11 @@ Tasks are portable. A sentiment analysis task defined for a prototype with GPT-4 In our **[Crisis Tweet Case Study](https://sieves.ai/demos/crisis_tweets)**, we used the CrisisNLP dataset to solve a common engineering problem: noise. Social media text is informal and often irrelevant to emergency response. -Running complex extraction on every noisy tweet is a waste of money, time and a source of errors. +Running complex extraction on every noisy tweet wastes money, time, and introduces avoidable errors. To address this, we built a multi-stage pipeline. A classifier identifies if a tweet is crisis-related. A "gatekeeper" condition determines if the expensive extraction tasks should run. +This kind of conditional execution is straightforward only when classification, extraction, and orchestration are expressed as first-class pipeline components. Without explicit task boundaries and structured results, this logic quickly devolves into ad-hoc control flow and duplicated checks scattered across the codebase. + ```python def related_to_crisis(doc: Doc) -> bool: result = doc.results.get("crisis_label_classifier") @@ -67,11 +73,11 @@ pipeline = ( ) ``` -Filtering the noise before later extraction stage can significantly improve the reliability of your pipeline. Stop asking your model to extract entities from irrelevant text, and you'll end up with a more resilient, accurate, and cheaper system. +Filtering the noise before the later extraction stage can significantly improve the reliability of your pipeline. Don't ask your model to extract entities from irrelevant text, and you'll end up with a more resilient, accurate, and cheaper system. ## What v1.0 Means -We are releasing v1.0 to signal API stability. We have used `sieves` in production for complex document processing. The abstractions are robust enough to handle the rapid evolution of the underlying LLM ecosystem. +We are releasing v1.0 to signal API stability. We have used `sieves` in production for complex, multi-stage document processing. The abstractions are robust enough to handle the rapid evolution of the underlying LLM ecosystem. We are committed to stability. The underlying models and frameworks will change. Your `sieves` pipelines should remain a stable part of your infrastructure. From c5bf807bc4950f25aaccffcc320e500bef51d5e8 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Tue, 30 Dec 2025 23:37:21 +0100 Subject: [PATCH 04/29] docs: Include PyData link in blog post. --- blog_post.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/blog_post.md b/blog_post.md index 4f29b5e..50065f7 100644 --- a/blog_post.md +++ b/blog_post.md @@ -81,6 +81,8 @@ We are releasing v1.0 to signal API stability. We have used `sieves` in producti We are committed to stability. The underlying models and frameworks will change. Your `sieves` pipelines should remain a stable part of your infrastructure. +We also presented an earlier version of `sieves` at PyData Amsterdam 2025, where we walked through the design rationale, core abstractions, and examples. The recording is available [here](https://www.youtube.com/watch?v=5i8tEvrYEyQ). + ## When to Use sieves `sieves` is for teams building document-centric NLP pipelines. From 745845c6a875b1e806f940f8b46871f3497339d1 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Wed, 31 Dec 2025 18:33:40 +0100 Subject: [PATCH 05/29] chore: Stylize as `sieves` in header elements. --- mkdocs.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mkdocs.yml b/mkdocs.yml index a2f5e71..53b7e1c 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,8 +1,8 @@ -site_name: sieves +site_name: sieves site_url: https://sieves.ai/docs docs_dir: docs repo_url: https://github.com/mantisai/sieves -repo_name: "sieves on GitHub" +repo_name: "sieves on GitHub" theme: name: material From 0af7ae2b6df12d4c36e74d52a1756ec673ad9c83 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Wed, 31 Dec 2025 18:34:23 +0100 Subject: [PATCH 06/29] chore: Stylize as Sieves in header elements. --- mkdocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mkdocs.yml b/mkdocs.yml index 53b7e1c..ea54b18 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,4 +1,4 @@ -site_name: sieves +site_name: Sieves site_url: https://sieves.ai/docs docs_dir: docs repo_url: https://github.com/mantisai/sieves From 489c153acaf194e173d8f677847fcc3b1391f345 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Wed, 31 Dec 2025 18:43:19 +0100 Subject: [PATCH 07/29] docs: Embed crisis tweet example in docs. --- docs/examples/crisis_tweets.md | 3 +++ .../case_study.html => docs/examples/crisis_tweets_raw.html | 0 mkdocs.yml | 2 ++ 3 files changed, 5 insertions(+) create mode 100644 docs/examples/crisis_tweets.md rename demos/crisis_tweets/case_study.html => docs/examples/crisis_tweets_raw.html (100%) diff --git a/docs/examples/crisis_tweets.md b/docs/examples/crisis_tweets.md new file mode 100644 index 0000000..1081338 --- /dev/null +++ b/docs/examples/crisis_tweets.md @@ -0,0 +1,3 @@ +# Crisis Tweet Analysis + + diff --git a/demos/crisis_tweets/case_study.html b/docs/examples/crisis_tweets_raw.html similarity index 100% rename from demos/crisis_tweets/case_study.html rename to docs/examples/crisis_tweets_raw.html diff --git a/mkdocs.yml b/mkdocs.yml index ea54b18..c29bf9c 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -93,3 +93,5 @@ nav: - guides/optimization.md - guides/distillation.md - guides/observability.md + - Examples: + - examples/crisis_tweets.md From e18246afb88a8b327075078bf4e8e02e00db9fd6 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Thu, 1 Jan 2026 23:28:36 +0100 Subject: [PATCH 08/29] chore: Extend official Python support to >=3.13,<3.14. --- AGENTS.md | 3 +- README.md | 1 - pyproject.toml | 5 +- uv.lock | 1711 ++++++++++++++++++++++++++++++++++++++++++++++-- 4 files changed, 1649 insertions(+), 71 deletions(-) diff --git a/AGENTS.md b/AGENTS.md index a255448..3d0b8b3 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -49,7 +49,7 @@ AGENTS.md # This file (Claude Code guidelines) ## Installation & Setup -**Python requirement:** 3.12 (exact version required) +**Python requirement:** 3.12 or 3.13 (3.14 not supported yet due to dependency wheels missing) ### Using `uv` (preferred) @@ -377,7 +377,6 @@ Before proposing changes, ensure: - **All model libraries** (Outlines, DSPy, LangChain, Transformers, GLiNER2) are **core dependencies** - Serialization excludes complex third-party objects (models, converters); must pass at load time - Ingestion tasks may require system packages (Tesseract for OCR, etc.) -- Python 3.12 exact version required (not 3.12+) --- diff --git a/README.md b/README.md index 06c403e..9327469 100644 --- a/README.md +++ b/README.md @@ -89,7 +89,6 @@ to skip having to define tasks from scratch. ```bash pip install sieves ``` -*Requires Python 3.12 (due to dependency constraints in `docling` and `pyarrow`).* **2. Basic: text classification with a small local model** diff --git a/pyproject.toml b/pyproject.toml index 062efd0..9722962 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,7 +15,7 @@ authors = [ { name = "Angelo Di Gianvito", email = "hi@mantisnlp.com" }, ] dynamic = ["version"] -requires-python = "==3.12.*" +requires-python = ">=3.12,<3.14" dependencies = [ "chonkie>=1,<2", "datasets>=3,<4", @@ -45,7 +45,8 @@ classifiers = [ "Topic :: Software Development :: Libraries", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.12" + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13" ] [project.urls] diff --git a/uv.lock b/uv.lock index 0f7c10e..610528d 100644 --- a/uv.lock +++ b/uv.lock @@ -1,10 +1,16 @@ version = 1 revision = 2 -requires-python = "==3.12.*" +requires-python = ">=3.12" resolution-markers = [ - "sys_platform == 'darwin'", - "platform_machine == 'aarch64' and 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insertions(+), 0 deletions(-) rename blog_post.md => docs/blog_post.md (100%) diff --git a/blog_post.md b/docs/blog_post.md similarity index 100% rename from blog_post.md rename to docs/blog_post.md From 6466aa7e795122e92218e943910a84a8b454845d Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Fri, 2 Jan 2026 10:12:39 +0100 Subject: [PATCH 10/29] test: Relax test thresholds. --- sieves/tests/tasks/predictive/test_summarization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sieves/tests/tasks/predictive/test_summarization.py b/sieves/tests/tasks/predictive/test_summarization.py index 4ef9b38..a6ac1c9 100644 --- a/sieves/tests/tasks/predictive/test_summarization.py +++ b/sieves/tests/tasks/predictive/test_summarization.py @@ -174,4 +174,4 @@ def test_evaluation(batch_runtime) -> None: doc_partial.results["sum"] = Result(summary="A fox jumps.", score=1.0) doc_partial.gold["sum"] = Result(summary="The quick brown fox jumps over the lazy dog.", score=1.0) report_partial = task.evaluate([doc_partial], judge=batch_runtime.model) - assert 0.2 <= report_partial.metrics[task.metric] <= 0.8 + assert 0.4 <= report_partial.metrics[task.metric] <= 0.9 From 83481cecce0c6499fe199a46dad982f58ed95870 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Sat, 17 Jan 2026 15:49:56 +0100 Subject: [PATCH 11/29] feat: Allow overwriting track_usage flag for dspy.configure. --- sieves/model_wrappers/dspy_.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sieves/model_wrappers/dspy_.py b/sieves/model_wrappers/dspy_.py index fd29b08..73ccfb7 100644 --- a/sieves/model_wrappers/dspy_.py +++ b/sieves/model_wrappers/dspy_.py @@ -54,7 +54,7 @@ def __init__(self, model: Model, model_settings: ModelSettings): """ super().__init__(model, model_settings) cfg = model_settings.config_kwargs or {} - dspy.configure(lm=model, track_usage=True, **cfg) + dspy.configure(lm=model, **({"track_usage": True} | cfg)) # Disable noisy LiteLLM logging. dspy.disable_litellm_logging() From 1bde4b5ddfd494b1c9a35740c55aced5bce4f81d Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Sat, 17 Jan 2026 15:50:49 +0100 Subject: [PATCH 12/29] docs: Remove top language badge, add Python version badge. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9327469..2bc4053 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ [![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/mantisai/sieves/test.yml)](https://github.com/mantisai/sieves/actions/workflows/test.yml) -![GitHub top language](https://img.shields.io/github/languages/top/mantisai/sieves) +![PyPI - Python Version](https://img.shields.io/pypi/pyversions/sieves) [![Version](https://img.shields.io/github/v/release/mantisai/sieves?include_prereleases&label=version)](https://github.com/mantisai/sieves/releases) ![Status](https://img.shields.io/badge/status-beta-blue) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/ambv/black) From 3ed8e66edb4b47424a0de35d4a8a0856b1e60bf1 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Sat, 17 Jan 2026 16:43:27 +0100 Subject: [PATCH 13/29] docs: Add images to blog post. --- docs/blog_post.md | 53 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 53 insertions(+) diff --git a/docs/blog_post.md b/docs/blog_post.md index 50065f7..ce4dd17 100644 --- a/docs/blog_post.md +++ b/docs/blog_post.md @@ -1,9 +1,14 @@ +from sieves.tasks import DistillationFramework + # `sieves`: A Unified Interface for Structured Document AI Document AI is the process of turning unstructured documents - PDFs, emails, social media feeds - into structured data. Files are ingested, texts chunked, structured entities extracted, and perhaps a specialized model distilled for more efficient processing. Think invoices, support emails, or tweets at scale. ## Isn't this solved already? +![xkcd #927 – Standards](https://imgs.xkcd.com/comics/standards_2x.png) +_There's an xkcd for everything, including the current language model tooling landscape._ + The tools for these steps exist. They are excellent, and they are built with different paradigms and goals in mind. **[Outlines](https://github.com/dottxt-ai/outlines)** provides robust constrained generation. **[DSPy](https://github.com/stanfordnlp/dspy)** focuses on declarative prompt optimization. **[LangChain](https://github.com/langchain-ai/langchain)** has broad API support and a large ecosystem. **[GLiNER](https://github.com/fastino-ai/GLiNER2)** and **[Transformers](https://github.com/huggingface/transformers)** zero-shot classification pipelines offer specialized, high-performance inference. Choosing one creates path dependency early in the application design. If you build your application logic around LangChain's abstractions, switching to a specialized model like GLiNER for efficiency is painful. You must migrate prompt signatures, integrations, and orchestration logic. @@ -14,6 +19,22 @@ The missing piece was a stable, schema-first layer that sits above these tools a To mitigate this, we built `sieves`. `sieves` is a framework-agnostic abstraction for building these workflows. It replaces imperative glue code with declarative design. Its modularity allows complex pipelines to be built from simple, reusable tasks. +It allows you to mix and match language model frameworks, so you can build a pipeline like this: +```mermaid +%%{init: {'themeVariables': { + 'nodeBorder': 'transparent', + 'primaryColor': 'transparent' +}}}%% + +flowchart LR + Ingestion@{ img: "https://repository-images.githubusercontent.com/826168160/d3c8a8f9-af99-449f-856b-4ab9c897cce2", label: "**Ingestion**:\nDocling", pos: "t", w: 100, h: 100, constraint: "on" } + Chunking@{ img: "https://avatars.githubusercontent.com/u/205278415?s=280&v=4", label: "**Chunking**:\nChonkie", pos: "t", w: 0, h: 100, constraint: "on" } + Classification@{ img: "data:image/png;base64,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", label: "**Classification**:\nTransformers", pos: "t", w: 250, h: 100, constraint: "on" } + NER@{ img: "https://avatars.githubusercontent.com/u/161639825?s=200&v=4", label: "**NER**:\nGliNER2", pos: "t", w: 0, h: 100, constraint: "on" } + InformationExtraction@{ img: "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcShzp30XASXzPGrU2z1yjrI5WUriI-Iz2N1jw&s", label: "**Information extraction**:\n LangChain", pos: "t", h: 100, constraint: "on" } + + Ingestion --> Chunking --> Classification --> NER --> InformationExtraction +``` ## Declarative Document AI `sieves` decouples the business logic - the "what" - from the execution framework - the "how." @@ -34,10 +55,42 @@ A production pipeline is not static. You need to know if it works and how to mak **Evaluation** is a first-class citizen. You can measure pipeline performance against ground-truth data using deterministic metrics or LLM-based judging. This allows you to track regression as you update models or prompts. +If your documents contain ground truth/gold data, you can evaluate your pipeline like so: +```python +results = list(pipeline(docs)) +eval_report = pipeline.evaluate(results) +# Inspect the evaluation results for pipeline components: +for task_id in eval_report.reports: + print(eval_report[task_id].summary()) +# In the case of our case study (see below) this prints something like +# 'Task: crisis_label_classifier | Metrics: F1 (Macro): 0.4874 | Failures: 48' +# 'Task: crisis_type_classifier | Metrics: F1 (Macro): 0.9563 | Failures: 16' +# 'Task: location_extractor | Metrics: Accuracy: 0.1900 | Failures: 89' +``` + **Optimization** is integrated via DSPy's MIPROv2. If your extraction precision is low, `sieves` can automatically optimize your prompts and few-shot examples. +In order to optimize your tasks to have to +- provide your task with fewshot examples +- initialize an optimizer +- run `task.optimize()` + +Use it like this: +```python +task = Classification(..., fewshot_examples=...) +optimizer = Optimizer(...) +best_prompt, best_examples = task.optimize(optimizer) +``` + **Distillation** completes the cycle. Once you have a high-performing pipeline using a large LLM, you can distill that logic into a specialized local model using **SetFit** or **Model2Vec**. This reduces costs and latency without a total rewrite of your application. +In order to e.g. distill a classification task with `setfit`, you call the tasks `.distill()` method: +```python +task = Classification(...) +# This distills a `setfit` model from the results in `docs` and stores it at `output_path`. +task.distill(..., data=docs, framework=DistillationFramework.setfit, output_path=...) +``` + ## Small Abstractions We built `sieves` around three objects: From ccb06dd06d151ec2606b0ff1ae3b8797202ac2c3 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Sat, 17 Jan 2026 16:44:33 +0100 Subject: [PATCH 14/29] docs: Fix Mermaid diagram. --- docs/blog_post.md | 5 ----- 1 file changed, 5 deletions(-) diff --git a/docs/blog_post.md b/docs/blog_post.md index ce4dd17..763ff6c 100644 --- a/docs/blog_post.md +++ b/docs/blog_post.md @@ -21,11 +21,6 @@ To mitigate this, we built `sieves`. `sieves` is a framework-agnostic abstractio It allows you to mix and match language model frameworks, so you can build a pipeline like this: ```mermaid -%%{init: {'themeVariables': { - 'nodeBorder': 'transparent', - 'primaryColor': 'transparent' -}}}%% - flowchart LR Ingestion@{ img: "https://repository-images.githubusercontent.com/826168160/d3c8a8f9-af99-449f-856b-4ab9c897cce2", label: "**Ingestion**:\nDocling", pos: "t", w: 100, h: 100, constraint: "on" } Chunking@{ img: "https://avatars.githubusercontent.com/u/205278415?s=280&v=4", label: "**Chunking**:\nChonkie", pos: "t", w: 0, h: 100, constraint: "on" } From 2e837d8909bc4a448adf77b1c72caa1949946776 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Sat, 17 Jan 2026 16:55:31 +0100 Subject: [PATCH 15/29] docs: Fix Mermaid diagram. --- docs/blog_post.md | 8 ++++++++ docs/mermaid-diagram-2026-01-17-164531.svg | 3 +++ docs/mermaid-diagram-2026-01-17-164538.png | Bin 0 -> 102070 bytes docs/mermaid-diagram-2026-01-17-164540.png | Bin 0 -> 102070 bytes docs/mermaid-diagram-2026-01-17T15-47-25.png | Bin 0 -> 173345 bytes docs/mermaid-diagram-2026-01-17T15-47-59.png | Bin 0 -> 173345 bytes 6 files changed, 11 insertions(+) create mode 100644 docs/mermaid-diagram-2026-01-17-164531.svg create mode 100644 docs/mermaid-diagram-2026-01-17-164538.png create mode 100644 docs/mermaid-diagram-2026-01-17-164540.png create mode 100644 docs/mermaid-diagram-2026-01-17T15-47-25.png create mode 100644 docs/mermaid-diagram-2026-01-17T15-47-59.png diff --git a/docs/blog_post.md b/docs/blog_post.md index 763ff6c..30ab50f 100644 --- a/docs/blog_post.md +++ b/docs/blog_post.md @@ -20,7 +20,15 @@ The missing piece was a stable, schema-first layer that sits above these tools a To mitigate this, we built `sieves`. `sieves` is a framework-agnostic abstraction for building these workflows. It replaces imperative glue code with declarative design. Its modularity allows complex pipelines to be built from simple, reusable tasks. It allows you to mix and match language model frameworks, so you can build a pipeline like this: + +[![](https://mermaid.ink/img/pako:eNqlWGmPo0qy_SslS3daulR1si_1dGfGNjbGmMWYzai-JIvNvoOBVv_3oaretHTv7ac3S36AzCAy4kRkJsTh28ovg3D1uvrll29xEXevT9--dFGYhxZsYuhlYftlEb0VT09fikVxUzZB2CyiL10Di7aCTVh0X54_nldNnMNm2pZZ-SeNt-L79--__PJWvBW3rHz4EWy6p5P-Pu_pSSzuYdvFZfH3b09xfn99eltFXVe1rwA0YVW2cVc208tifFH7eo-7qPf6Nmz8sugW21_9MgcsTmM0i9EoCAifheyNe4E3jnshSe72wlK090JCj_NZjvH9EH9bPT9l0Auzd1e__vrD_6-_vr69FXzpZ3Fxf1danL-rdO_9x-sThqLPT9H_3hf37RJiXHTvKmXxtnr6_hnPNuqLdLHw53DgADvY_F9B9ABHKZxhSYz6W_sbzqJ_GX4j_4D1n7Y_oW6jchmFP4H6rwHNYNvGt9iHv89-sKB8_cg3qIr7_3iwDWnyObY2qv5AJeFerpemXMxoZ96XnuUvl62xXcsfchKW7HsHYBvZ2jkAgPchD-aLAebJAcV56W-1B6ttzexoXHElaWhkkGd5FC-7iA6LuqylJKNCE2gzEhQuLifiDMbLAZSbGyjvPM4NcGobMREnDXT6DGq9AOMkDqcJADRW0OCgn809Dyo9JjrmBvqR9A-bMbra-xrmzebhHwABb1U_uY6F-aTKJ6DdDOBhLXC1A8COBmC5GdBsae43OnadDTCRHmADD0zFDEjGAU08gGFvgN4sQMcTAKdVLJ2I-TE5Zj2l8JiMh3OSukee2BaXtjjx6FrgaSVI1CyaGDDm7OmQoJPC9008sUAxsFnl2VROxt1xxxN6mihn1NioqSVfkhua8wdgmg4IktkVhc170gV8l-3Olk4WKg63WOhdNrEgmut0jybb43lJZ3HUz7SE2TXeYY1beU5INMXAAYbLXIZ0d_k0c_ZUQ3Bh5zm1LgIUJl973AzZ1M-79X_etlokad2e0o6uypFItw5Cs5I5lEIJW30ME3LTHH0o7rNXRHSewn2V97tdUdA04BbdaNFVNpGRtn6VeI9BxQ9MJxpXrZJ4WjfvOtwjfb_InISi7XBfY1NrwvLAIoBD0DjTHe063KB163p1eJCqNgoSWWBUIuCNDqvGgbZo2Ug9k2A9QC4RBusaZw4jxHpA1U6_7gcD1kxgh5xTsLnT6juYdIU66bY6c8wOKF2p3ozGpenJ3kC-i3MNS6rTWrFD7NIRGU5gFMZpF7Xbj_58yq7N4BAEYqCEd4iNa5NOLFOPSJ7VdddQKCQRKWMavpGvBYN7NyfS7gmQ3YirHoIb-qGNsfsSN6y-shP8xgC_rSfsUNo5PPH9srS7oaaibkIqva7rK2-ZAR67nCPsMTaR8GWrS_h8dLeeGiM83jjh1TrZLdIOqUuJO1YOnMxbvKpnTNVODRNwFFfttnfBDYrjKU5VpxoJz3nAOqsfA10FplXFLu9RM8I-Ck86GGXI2qc8G6KJlsGpNsedfTk_cP3aXBkhao51qx_QVHqwZbIeZgERdyKTAuog0JWl12W1TzG_OAJHvcTeljZH6Xghu2RnR6fUoxzpcOOlB4LDzEGgSA6DXN9qzNuVTrMGyMMeXI8JXSsgoqs3ZjM4xmzUsOrGE4045NGBsOSSw3auSaVE28Cjr5Z7xBCb4gFSlfSI2uIfUebjZulyo33zkhzVxvM9S8-aQeSmFz88x-sKQkHuW0tCO62NPa1EZljfDjydY5samIKNnJGrokfG3OeJYdumcReZqM_JnPSTS4FvsJY9F1KvnhjPEfLOvkADozEF5-Iqu5xmjvU6og3azt6aHVOyu72zMxDNY70WlXwmC4C8JsimmyYjYh9wPOPZ1FAtm3rCOZv47qTidiEdaMzZYSK1JzGU8rQOjeqrqCU0cO9ouHYbhW5K4V6n_YgPLNC0WUFuwtxbWsEimM2g9jGd87EVm31wT5txeYOVOFtqy4HjGpIQid4LIWawfidfdMp1PGU6cces66VauepOXGDo9UKjsKGnrtmSJL9hMYdlWacl85LNWSJsM35dMOSVKLvtw-1C9wJxBS_Nc6EOR2EvO3FHbdXxbIcplp7lx_BoZIieDic9bRTWcqN9ENISr6kSn132ys5L3FjBHW8NNISHhAjdCJNZRSwp_FRjCOIccGp5w8qFdzajAG9ZMblDGsRcpFX1Fu5uvWW1NznVc0UOK0y74jYKxSS6mMQoHk5GTaO9utmEru0LiB8m4RiiYowsi7VWepr0nBtF326Ex4UcyPYnh_e6CNVsddtbfdibUYowmVHZCO0QTeZgcewC7c7wqNrOy4G6ip24lCyYtOzpLLfstT14p74TA7TC57Ci2ttVlahrOJFX_SKwLgcci7uBEXW9fks6UDMDj9jqQn_EaIGMQzxz4jY3gkHJq3U0-NRAudfryAFiGmTL9fKaubT7ZrTnEWE3IcUTrkpcsVKTg1C8J3sVl4OTGXvOOvEu_WN2N1UMEALfHi0KdgLZiOYJH4ZbNRWaZD5I69iAB2T3hhrN9RCSg9Pfk0zkDiih9jkvQ7uvKW3aU4zePx4xFKII7gVdoO-qlftRyrT0koFWPjcHQuZ9yIGcCUnGv0xRIamSh0vpfvamVutPFlk91lQVhVdn0KRGzikYWCbeF4uNADPjZY_Qeq1epnYQkQujV1Vl9xUdU067qzmkqKwKyjTnlQWpZmxt2seaWjdT5YbXukRPTp6KXM6RluntiVMoNkDdO8Xpds-u6Xyc5PnsXbe8fdUTaGGGSEipqYLpLBeyKGM5myqBlCdCdRRzqGZn5oQCW7LNrt5D2z_yjjS3anosT_FdCG3HFUYVzRTtjs4qMieN5VFUG9inpcZZEzubiA1lRh60zOGDiY59O8pNeFFY6dIun0HIbrPU326IPJ25MTgTim8ptF3f94LRk_JVCaYhLKiBmRoAMhJ0LXUAebdpd0kEjxmckzafrcxkD2KMKvde2q21x8FIT8pWOV6Yyjkycl2ssfoMW68jR7OlrJhVd7xmM3ab03AjUQzP8j5qtoBHxrRh-8licget0takj4xKTJaEX3d65eaFQW-wwKW4bV8oJ4XwcoS8GdA3ELexp4vQgnhzwIn-EHE9NS4nuT34cn1e_9eNXDPUZuapG-cdQ9_efgh32d5IL_05327_WFn_rhj-rK-NdwpzK5s8bNqfFNk49S-V2cpO__epwPJhoAmOxT-oAPozKrCY_UQpZPHSx_9TFiAW7xF-RL0bFxX_51QsLPzlrHRh8NJ5Bfr13nbLFP8D7Sc1-1v92_Lkda0EnOBforkiUGd9cWZNaEx8xqakESnbbGLxRZxxBUsef2n_RMR-IHkKf0D5DPLpBIv7NloC-GOc_3-Ef6CZTy8vf_1B0j4Hv1v7D9GS0Y_7T7Ozel7dmzhYvXZNHz6vlu2Rw_fh6oMpL7jeGfTb6pNLNenbauG_y5wKFm5Z5v-c1pT9PVq93mDWLqO-WmhXyMfw3sD8h3Rhzwvh3pZ90a1eCZJhP6ysXr-txtXrC0bSX2mMJVCM4Bbyi1H082paveIk95XhWJLGOIxEaZz6_ryaPxxjX1EOJygOpVmUonCKxZ9XYfBOsOXP3wAffwO-_wOtfTs1?type=png)](https://mermaid.live/edit#pako:eNqlWGmPo0qy_SslS3daulR1si_1dGfGNjbGmMWYzai-JIvNvoOBVv_3oaretHTv7ac3S36AzCAy4kRkJsTh28ovg3D1uvrll29xEXevT9--dFGYhxZsYuhlYftlEb0VT09fikVxUzZB2CyiL10Di7aCTVh0X54_nldNnMNm2pZZ-SeNt-L79--__PJWvBW3rHz4EWy6p5P-Pu_pSSzuYdvFZfH3b09xfn99eltFXVe1rwA0YVW2cVc208tifFH7eo-7qPf6Nmz8sugW21_9MgcsTmM0i9EoCAifheyNe4E3jnshSe72wlK090JCj_NZjvH9EH9bPT9l0Auzd1e__vrD_6-_vr69FXzpZ3Fxf1danL-rdO_9x-sThqLPT9H_3hf37RJiXHTvKmXxtnr6_hnPNuqLdLHw53DgADvY_F9B9ABHKZxhSYz6W_sbzqJ_GX4j_4D1n7Y_oW6jchmFP4H6rwHNYNvGt9iHv89-sKB8_cg3qIr7_3iwDWnyObY2qv5AJeFerpemXMxoZ96XnuUvl62xXcsfchKW7HsHYBvZ2jkAgPchD-aLAebJAcV56W-1B6ttzexoXHElaWhkkGd5FC-7iA6LuqylJKNCE2gzEhQuLifiDMbLAZSbGyjvPM4NcGobMREnDXT6DGq9AOMkDqcJADRW0OCgn809Dyo9JjrmBvqR9A-bMbra-xrmzebhHwABb1U_uY6F-aTKJ6DdDOBhLXC1A8COBmC5GdBsae43OnadDTCRHmADD0zFDEjGAU08gGFvgN4sQMcTAKdVLJ2I-TE5Zj2l8JiMh3OSukee2BaXtjjx6FrgaSVI1CyaGDDm7OmQoJPC9008sUAxsFnl2VROxt1xxxN6mihn1NioqSVfkhua8wdgmg4IktkVhc170gV8l-3Olk4WKg63WOhdNrEgmut0jybb43lJZ3HUz7SE2TXeYY1beU5INMXAAYbLXIZ0d_k0c_ZUQ3Bh5zm1LgIUJl973AzZ1M-79X_etlokad2e0o6uypFItw5Cs5I5lEIJW30ME3LTHH0o7rNXRHSewn2V97tdUdA04BbdaNFVNpGRtn6VeI9BxQ9MJxpXrZJ4WjfvOtwjfb_InISi7XBfY1NrwvLAIoBD0DjTHe063KB163p1eJCqNgoSWWBUIuCNDqvGgbZo2Ug9k2A9QC4RBusaZw4jxHpA1U6_7gcD1kxgh5xTsLnT6juYdIU66bY6c8wOKF2p3ozGpenJ3kC-i3MNS6rTWrFD7NIRGU5gFM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+ ```mermaid +%%{init: {'themeVariables': { + 'nodeBorder': 'transparent', + 'primaryColor': 'transparent' +}}}%% + flowchart LR Ingestion@{ img: "https://repository-images.githubusercontent.com/826168160/d3c8a8f9-af99-449f-856b-4ab9c897cce2", label: "**Ingestion**:\nDocling", pos: "t", w: 100, h: 100, constraint: "on" } Chunking@{ img: "https://avatars.githubusercontent.com/u/205278415?s=280&v=4", label: "**Chunking**:\nChonkie", pos: "t", w: 0, h: 100, constraint: "on" } diff --git a/docs/mermaid-diagram-2026-01-17-164531.svg b/docs/mermaid-diagram-2026-01-17-164531.svg new file mode 100644 index 0000000..e15345a --- /dev/null +++ b/docs/mermaid-diagram-2026-01-17-164531.svg @@ -0,0 +1,3 @@ + + +

Ingestion:
Docling

Chunking:
Chonkie

Classification:
Transformers

NER:
GliNER2

Information extraction:
LangChain

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z1Q43$P>^Vgvq-CI&2H#syj-LDayUMHP{d>X0&*-f73*`M=`+EQRT+^g=pA28gbq?Y zZA=(8HAPd0fPX@hT~6G&SUA4^3MN)@I+IbNx^gDBIJ#!{GUdL<)t+w;idz4s5GmsD z0>LQv0D?buGi8%FmgoiJopd~D3&v9`j?uNx5xy8^Pr!|6 zhqI5Y!3-PCc%`n`kdu#yhbI4HXjut!dK-a<-VC_@IVyi>r4^>0{yK(0!Lz3`@`Q3g zz}MG8ot7i~hGRA1qQ@N3LF&x}m%SsA++u`6x)-^SdC>Y!zAG!;E3Rja77 zZ#M-$e3{H=h%Kv<7#lZnHu>DOC}2yo{a^+c?d3%%Tl`g9VKvycW~L>qb}S55_o3W<25EAyz-I(;r1F# zq+KOAR&$~xG9yGeHWap39LnDL1 zf@eM*2~HnBavQYjB5#AcX52SP-YpKKcj=}7Ez2)Oh5;==b)PpF31s?Oq}^{IKUm$L8$)7TwxD*r5Ibabn$Rx zWoq%}-5U*Kj!Bo+(^QWB-}{CjU?Pi!i^-{NWzUI+(^QWB(+C>t6-DV9!W~r zBqeN;N=Qj1q@{@<#s;a3cvfl5isl zHz#zgA3%K1%daqL2S|HT=IGD> Date: Sat, 17 Jan 2026 17:04:50 +0100 Subject: [PATCH 16/29] docs: Fix Mermaid diagram. --- docs/blog_post.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/blog_post.md b/docs/blog_post.md index 30ab50f..cc949ab 100644 --- a/docs/blog_post.md +++ b/docs/blog_post.md @@ -21,9 +21,9 @@ To mitigate this, we built `sieves`. `sieves` is a framework-agnostic abstractio It allows you to mix and match language model frameworks, so you can build a pipeline like this: 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-```mermaid + Chunking --> Classification --> NER --> InformationExtraction -``` +--> + ## Declarative Document AI `sieves` decouples the business logic - the "what" - from the execution framework - the "how." From a66c3c62c60a3a4ad0b96870c48ffc3894bde94a Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Sat, 17 Jan 2026 17:06:49 +0100 Subject: [PATCH 17/29] docs: Fix Mermaid diagram. --- docs/blog_post.md | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/docs/blog_post.md b/docs/blog_post.md index cc949ab..382dcb2 100644 --- a/docs/blog_post.md +++ b/docs/blog_post.md @@ -24,19 +24,25 @@ It allows you to mix and match language model frameworks, so you can build a pip [![](https://mermaid.ink/img/pako:eNqlWAmP47iV_isFA5MBRtVN3UcFs4lt2bIs67Csy0IBAXXYum_Jkhr931dVNZn0dHp3kyxhWOTj4-P7yEfpffyy8ssgXL2sfvrpS1zE3cuX1-Lp6ecuCvPQgk0MvSxsf355ehcvHVUT57CZtmVWNov4566BRVvBJiy6n59_0ykWi5uyCcL_XSO9f9_91vv1tfj69aefXovX4paVDz-CTfd00j8G-hlsWz68PX0zalOOT7c4y16-kT23XVOm4beiP79ZfLMhFvew7eKy-OuXpzi_vzy9rqKuq9oXAJqwKtu4K5vp04JyUft8j7uo9_o2bPyy6BYzn_0yByxOYzSL0SgICJ-F7I37BG8c94kkudsnlqK9TyT0OJ_lGN8P8dfV81MGvTB7m-qXX36f_5dfXl5fC770s7i4vyktk7-pdG_1x8sThqLPT9Fvz2X6BROMi-5NpSxeV28r9YZnG_VFulj4ZzhwgB1s_icQPcBRCmdYEqP-0v6Ks-ifhl_J73z9u-0PV7dRubTCH7j6rzn6tnnxLfbhH1c_WLx8eV9vUBX3P3uwDWnyObY2qv5AJeFerpeiXMxoZ96XmuUvf1tju5bf5SQs2bcKwDaytXMAAG9NHswXA8yTA4rzUt9qD1bbmtnRuOJK0tDIIM_yKF52ER0WdVlLSUaFJtBmJChcXE7EGYyXAyg3N1DeeZwb4NQ2YiJOGuj0GdR6AcZJHE4TAGisoMFBP5t7HlR6THTMDfQj6R82Y3S19zXMm83DPwAC3qp-ch0L80mVT0C7GcDDWtzVDgA7GoDlZkCzpbnf6Nh1NsBEeoANPDAVMyAZBzTxAIa9AXqzAB1PAJxWsXQi5sfkmPWUwmMyHs5J6h55Yltc2uLEo2uBp5UgUbNoYsCYs6dDgk4K3zfxxALFwGaVZ1M5GXfHHU_oaaKcUWOjppZ8SW5ozh-AaTogSGZXFDZviy7gu2x3tnSyUHG4xULvsokF0VynezTZHs_LchZH_UxLmF3jHda4leeERFMMHGC4zGVId5dPM2dPNQQXdp5T6yJAYfK1x82QTf28W__nZatFktbtKe3oqhyJdOsgNCuZQymUsNXHMCE3zdGH4j57RUTnKdxXeb_bFQVNA27RjRZdZRMZaetXifcYVPzAdKJx1SqJp3XzrsM90veLzEko2g73NTa1JiwPLAI4BI0z3dGuww1at65XhwepaqMgkQVGJQLe6LBqHGiLlo3UMwnWA-QSYbCuceYwQqwHVO30634wYM0Edsg5BZs7rb6DSVeok26rM8fsgNKV6s1oXJqe7A3kuzjXsKQ6rRU7xC4dkeEERmGcdlG7_ejPp-zaDA5BIAZKeIfYuDbpxDL1iORZXXcNhUISkTKm4Rv5WjC4d3Mi7Z4A2Y246iG4oR_aGLsvccPqKzvBbwzw23rCDqWdwxPfL1u7G2oq6iak0uu6vvKWGeCxyznCHmMTCV9CXcLno7v11Bjh8cYJr9bJbpF2SF1K3LFy4GTeMqt6xlTt1DABR3HVbnsX3KA4nuJUdaqR8JwHrLP6MdBVYFpV7PIeNSPso_Ckg1GGrH3KsyGaaBmcanPc2ZfzA9evzZURouZYt_oBTaUHWybrYRYQcScyKaAOAl1Zel1W-xTziyNw1EvsbWlzlI4Xskt2dnRKPcqRDjdeeiA4zBwEiuQwyPWtxrxd6TRrgDzswfWY0LUCIrp6YzaDY8xGDatuPNGIQx4dCEsuOWznmlRKtA08-mq5RwyxKR4gVUmPqC3-EWU-bpYuN9o3L8lRbTzfs_SsGURuevHDc7yuIBTkvrUktNPa2NNKZIb17cDTObapgSnYyBm5KnpkzH2eGLZtGneRifqczEk_uRT4BmvZcyH16onxHCHv7As0MBpTcC6usstp5livI9qg7eyt2TElu9s7OwPRPNZrUclnsgDIa4JsumkyIvYBxzOeTQ3VsqknnLOJ704qbhfSgcacHSZSexJDKU_r0Ki-ilpCA_eOhmu3UeimFO512o_4wAJNmxXkJsy9pRUsgtkMah_TOR9bsdkH97QZlzdYibOlthw4riEJkei9EGIG63fyRadcx1OmE3fMul6qlavuxAWGXi80Cht66potSfIbFnNYlnVaMi_ZnCXCNuPXBUNeibLbPtwudC8QV_DSPBfqcBT2shN31FYdz3aYYulZfgyPRobo6XDS00ZhLTfaByEt8Zoq8dllr-y8xI0V3PHWQEN4SIjQjTCZVcSSwk81hiDOAaeWN6xceGczCvCWFZM7pEHMRVpVb-Hu1ltWe5NTPVfksMK0K26jUEyii0mM4uFk1DTaq5tN6Nq-gPhhEo4hKsbIsllrpadJz7lR9O1GeFzIgWx_cnivi1DNVre91Ye9GaUIkxmVjdAO0WQOFscu0O4Mj6rtvByoq9iJS8qCSUtMZ7llr-3BO_WdGKAVPocV1d6uqkRdw4m86heBdTngWNwNjKjr9VvSgZoZeMRWF_ojRgtkHOKZE7e5EQxKXq2jwacGyr1eRw4Q0yBbrpfXzKXdN6M9jwi7CSmecFXiipWaHITiPdmruByczNhz1ol36R-zu6ligBD49mhRsBPIRjRP-DDcqqnQJPNBWscGPCC7N9RoroeQHJz-nmQid0AJtc95Gdp9TWnTnmL0_vGIoRBFcC_oAn1XrdyPUqallxVo5XNzIGTehxzImZBk_MsUFZIqebiU7mdvarX-ZJHVY01VUXh1Bk1q5JyCgWXifbHYCDAzXmKE1mv1MrWDiFwYvaoqu6_omHLaXc0hRWVVUKY5ryxINWNr0z7W1LqZKje81iV6cvJU5HKOtExvT5xCsQHq3ilOt3t2TefjJM9n77rl7aueQAszREJKTRVMZ7mQRRnL2VQJpDwRqqOYQzU7MycU2JJtdvUe2v6Rd6S5VdNjeYrvQmg7rjCqaKZod3RWkTlpLI-i2sA-LTnOmtjZRGwoM_KgZQ4fTHTs21FuwovCSpd2-QxCdpul_nZD5OnMjcGZUHxLoe36vheMnpSvSjANYUENzNQAkJGga6kDyLtNu0sieMzgnLT5bGUmexBjVLn30m6tPQ5GelK2yvHCVM6RketijdVn2HodOZotZcWsuuM1m7HbnIYbiWJ4lvdRswU8MqYN208WkztolbYmfWRUYrIk_LrTKzcvDHqDBS7FbftCOSmElyPkzYC-gbiNPV2EFsSbA070h4jrqXE5ye3Bl-vz-v9dyDVDbWaeunHeMfTt7btwl-2N9NKf8-32-8z6D8nwR35tvLGVW9nkYdP-IMnGqX8pzVZ2-r9PBZYPA01wLP5OBdAfUYHF7IeXQhYvdfw_ZQFi8YbwHfVuXFT8H1OxsPCXs9KFwafOK9DP97Zbhvjv3n5Qs7_Uvy49L2sl4AT_Es0VgTrrizNrQmPiMzYljUjZZhOLn8QZV7Dk8af2n4jY7548hb-78gHy6QSL-zZaAHyP8_9GyMcLvcsy-GNgHzvwDkReCHcbnaAH8oUPZ_gQ-qCBD5AvVsESHmHX_gb2Hwp_y8p7-bn6IIzfgPl20t8gyO9jrND_9yF8x5SfPn36r9955kfjD-H7LlqC4v35ww1-7_nWx9Xz6t7EwWqh6H34vFpCfkG9NFfvFw2Lo28XEK-rD37YpK-r1-LrMqaChVuW-d-HNWV_j1YvN5i1S6uvFioZ8jG8N_AfKmERhM227Itu9UK9W1i9fFmNqxcMYz-zS8wTHEZQDIlj3PNqWsQc_RlHOZYlaZKgKJL5-rya3-dEP3M0iS-_5aRgBIMzzPMqDN7uC-SPO5T3q5Sv_w0kkagn?type=png)](https://mermaid.live/edit#pako:eNqlWAmP47iV_isFA5MBRtVN3UcFs4lt2bIs67Csy0IBAXXYum_Jkhr931dVNZn0dHp3kyxhWOTj4-P7yEfpffyy8ssgXL2sfvrpS1zE3cuX1-Lp6ecuCvPQgk0MvSxsf355ehcvHVUT57CZtmVWNov4566BRVvBJiy6n59_0ykWi5uyCcL_XSO9f9_91vv1tfj69aefXovX4paVDz-CTfd00j8G-hlsWz68PX0zalOOT7c4y16-kT23XVOm4beiP79ZfLMhFvew7eKy-OuXpzi_vzy9rqKuq9oXAJqwKtu4K5vp04JyUft8j7uo9_o2bPyy6BYzn_0yByxOYzSL0SgICJ-F7I37BG8c94kkudsnlqK9TyT0OJ_lGN8P8dfV81MGvTB7m-qXX36f_5dfXl5fC770s7i4vyktk7-pdG_1x8sThqLPT9Fvz2X6BROMi-5NpSxeV28r9YZnG_VFulj4ZzhwgB1s_icQPcBRCmdYEqP-0v6Ks-ifhl_J73z9u-0PV7dRubTCH7j6rzn6tnnxLfbhH1c_WLx8eV9vUBX3P3uwDWnyObY2qv5AJeFerpeiXMxoZ96XmuUvf1tju5bf5SQs2bcKwDaytXMAAG9NHswXA8yTA4rzUt9qD1bbmtnRuOJK0tDIIM_yKF52ER0WdVlLSUaFJtBmJChcXE7EGYyXAyg3N1DeeZwb4NQ2YiJOGuj0GdR6AcZJHE4TAGisoMFBP5t7HlR6THTMDfQj6R82Y3S19zXMm83DPwAC3qp-ch0L80mVT0C7GcDDWtzVDgA7GoDlZkCzpbnf6Nh1NsBEeoANPDAVMyAZBzTxAIa9AXqzAB1PAJxWsXQi5sfkmPWUwmMyHs5J6h55Yltc2uLEo2uBp5UgUbNoYsCYs6dDgk4K3zfxxALFwGaVZ1M5GXfHHU_oaaKcUWOjppZ8SW5ozh-AaTogSGZXFDZviy7gu2x3tnSyUHG4xULvsokF0VynezTZHs_LchZH_UxLmF3jHda4leeERFMMHGC4zGVId5dPM2dPNQQXdp5T6yJAYfK1x82QTf28W__nZatFktbtKe3oqhyJdOsgNCuZQymUsNXHMCE3zdGH4j57RUTnKdxXeb_bFQV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Chunking --> Classification --> NER --> InformationExtraction + Ingestion -> Chunking -> Classification -> NER -> InformationExtraction -> Distillation --> ## Declarative Document AI From 691091b8f4c5f8a9bd8922c5555f1bd26a34d498 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Sat, 17 Jan 2026 17:20:24 +0100 Subject: [PATCH 18/29] docs: Rephrase mix-and-match description. --- docs/blog_post.md | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/docs/blog_post.md b/docs/blog_post.md index 382dcb2..26545ab 100644 --- a/docs/blog_post.md +++ b/docs/blog_post.md @@ -1,5 +1,3 @@ -from sieves.tasks import DistillationFramework - # `sieves`: A Unified Interface for Structured Document AI Document AI is the process of turning unstructured documents - PDFs, emails, social media feeds - into structured data. Files are ingested, texts chunked, structured entities extracted, and perhaps a specialized model distilled for more efficient processing. Think invoices, support emails, or tweets at scale. @@ -19,11 +17,11 @@ The missing piece was a stable, schema-first layer that sits above these tools a To mitigate this, we built `sieves`. `sieves` is a framework-agnostic abstraction for building these workflows. It replaces imperative glue code with declarative design. Its modularity allows complex pipelines to be built from simple, reusable tasks. -It allows you to mix and match language model frameworks, so you can build a pipeline like this: - -[![](https://mermaid.ink/img/pako:eNqlWAmP47iV_isFA5MBRtVN3UcFs4lt2bIs67Csy0IBAXXYum_Jkhr931dVNZn0dHp3kyxhWOTj4-P7yEfpffyy8ssgXL2sfvrpS1zE3cuX1-Lp6ecuCvPQgk0MvSxsf355ehcvHVUT57CZtmVWNov4566BRVvBJiy6n59_0ykWi5uyCcL_XSO9f9_91vv1tfj69aefXovX4paVDz-CTfd00j8G-hlsWz68PX0zalOOT7c4y16-kT23XVOm4beiP79ZfLMhFvew7eKy-OuXpzi_vzy9rqKuq9oXAJqwKtu4K5vp04JyUft8j7uo9_o2bPyy6BYzn_0yByxOYzSL0SgICJ-F7I37BG8c94kkudsnlqK9TyT0OJ_lGN8P8dfV81MGvTB7m-qXX36f_5dfXl5fC770s7i4vyktk7-pdG_1x8sThqLPT9Fvz2X6BROMi-5NpSxeV28r9YZnG_VFulj4ZzhwgB1s_icQPcBRCmdYEqP-0v6Ks-ifhl_J73z9u-0PV7dRubTCH7j6rzn6tnnxLfbhH1c_WLx8eV9vUBX3P3uwDWnyObY2qv5AJeFerpeiXMxoZ96XmuUvf1tju5bf5SQs2bcKwDaytXMAAG9NHswXA8yTA4rzUt9qD1bbmtnRuOJK0tDIIM_yKF52ER0WdVlLSUaFJtBmJChcXE7EGYyXAyg3N1DeeZwb4NQ2YiJOGuj0GdR6AcZJHE4TAGisoMFBP5t7HlR6THTMDfQj6R82Y3S19zXMm83DPwAC3qp-ch0L80mVT0C7GcDDWtzVDgA7GoDlZkCzpbnf6Nh1NsBEeoANPDAVMyAZBzTxAIa9AXqzAB1PAJxWsXQi5sfkmPWUwmMyHs5J6h55Yltc2uLEo2uBp5UgUbNoYsCYs6dDgk4K3zfxxALFwGaVZ1M5GXfHHU_oaaKcUWOjppZ8SW5ozh-AaTogSGZXFDZviy7gu2x3tnSyUHG4xULvsokF0VynezTZHs_LchZH_UxLmF3jHda4leeERFMMHGC4zGVId5dPM2dPNQQXdp5T6yJAYfK1x82QTf28W__nZatFktbtKe3oqhyJdOsgNCuZQymUsNXHMCE3zdGH4j57RUTnKdxXeb_bFQVNA27RjRZdZRMZaetXifcYVPzAdKJx1SqJp3XzrsM90veLzEko2g73NTa1JiwPLAI4BI0z3dGuww1at65XhwepaqMgkQVGJQLe6LBqHGiLlo3UMwnWA-QSYbCuceYwQqwHVO30634wYM0Edsg5BZs7rb6DSVeok26rM8fsgNKV6s1oXJqe7A3kuzjXsKQ6rRU7xC4dkeEERmGcdlG7_ejPp-zaDA5BIAZKeIfYuDbpxDL1iORZXXcNhUISkTKm4Rv5WjC4d3Mi7Z4A2Y246iG4oR_aGLsvccPqKzvBbwzw23rCDqWdwxPfL1u7G2oq6iak0uu6vvKWGeCxyznCHmMTCV9CXcLno7v11Bjh8cYJr9bJbpF2SF1K3LFy4GTeMqt6xlTt1DABR3HVbnsX3KA4nuJUdaqR8JwHrLP6MdBVYFpV7PIeNSPso_Ckg1GGrH3KsyGaaBmcanPc2ZfzA9evzZURouZYt_oBTaUHWybrYRYQcScyKaAOAl1Zel1W-xTziyNw1EvsbWlzlI4Xskt2dnRKPcqRDjdeeiA4zBwEiuQwyPWtxrxd6TRrgDzswfWY0LUCIrp6YzaDY8xGDatuPNGIQx4dCEsuOWznmlRKtA08-mq5RwyxKR4gVUmPqC3-EWU-bpYuN9o3L8lRbTzfs_SsGURuevHDc7yuIBTkvrUktNPa2NNKZIb17cDTObapgSnYyBm5KnpkzH2eGLZtGneRifqczEk_uRT4BmvZcyH16onxHCHv7As0MBpTcC6usstp5livI9qg7eyt2TElu9s7OwPRPNZrUclnsgDIa4JsumkyIvYBxzOeTQ3VsqknnLOJ704qbhfSgcacHSZSexJDKU_r0Ki-ilpCA_eOhmu3UeimFO512o_4wAJNmxXkJsy9pRUsgtkMah_TOR9bsdkH97QZlzdYibOlthw4riEJkei9EGIG63fyRadcx1OmE3fMul6qlavuxAWGXi80Cht66potSfIbFnNYlnVaMi_ZnCXCNuPXBUNeibLbPtwudC8QV_DSPBfqcBT2shN31FYdz3aYYulZfgyPRobo6XDS00ZhLTfaByEt8Zoq8dllr-y8xI0V3PHWQEN4SIjQjTCZVcSSwk81hiDOAaeWN6xceGczCvCWFZM7pEHMRVpVb-Hu1ltWe5NTPVfksMK0K26jUEyii0mM4uFk1DTaq5tN6Nq-gPhhEo4hKsbIsllrpadJz7lR9O1GeFzIgWx_cnivi1DNVre91Ye9GaUIkxmVjdAO0WQOFscu0O4Mj6rtvByoq9iJS8qCSUtMZ7llr-3BO_WdGKAVPocV1d6uqkRdw4m86heBdTngWNwNjKjr9VvSgZoZeMRWF_ojRgtkHOKZE7e5EQxKXq2jwacGyr1eRw4Q0yBbrpfXzKXdN6M9jwi7CSmecFXiipWaHITiPdmruByczNhz1ol36R-zu6ligBD49mhRsBPIRjRP-DDcqqnQJPNBWscGPCC7N9RoroeQHJz-nmQid0AJtc95Gdp9TWnTnmL0_vGIoRBFcC_oAn1XrdyPUqallxVo5XNzIGTehxzImZBk_MsUFZIqebiU7mdvarX-ZJHVY01VUXh1Bk1q5JyCgWXifbHYCDAzXmKE1mv1MrWDiFwYvaoqu6_omHLaXc0hRWVVUKY5ryxINWNr0z7W1LqZKje81iV6cvJU5HKOtExvT5xCsQHq3ilOt3t2TefjJM9n77rl7aueQAszREJKTRVMZ7mQRRnL2VQJpDwRqqOYQzU7MycU2JJtdvUe2v6Rd6S5VdNjeYrvQmg7rjCqaKZod3RWkTlpLI-i2sA-LTnOmtjZRGwoM_KgZQ4fTHTs21FuwovCSpd2-QxCdpul_nZD5OnMjcGZUHxLoe36vheMnpSvSjANYUENzNQAkJGga6kDyLtNu0sieMzgnLT5bGUmexBjVLn30m6tPQ5GelK2yvHCVM6RketijdVn2HodOZotZcWsuuM1m7HbnIYbiWJ4lvdRswU8MqYN208WkztolbYmfWRUYrIk_LrTKzcvDHqDBS7FbftCOSmElyPkzYC-gbiNPV2EFsSbA070h4jrqXE5ye3Bl-vz-v9dyDVDbWaeunHeMfTt7btwl-2N9NKf8-32-8z6D8nwR35tvLGVW9nkYdP-IMnGqX8pzVZ2-r9PBZYPA01wLP5OBdAfUYHF7IeXQhYvdfw_ZQFi8YbwHfVuXFT8H1OxsPCXs9KFwafOK9DP97Zbhvjv3n5Qs7_Uvy49L2sl4AT_Es0VgTrrizNrQmPiMzYljUjZZhOLn8QZV7Dk8af2n4jY7548hb-78gHy6QSL-zZaAHyP8_9GyMcLvcsy-GNgHzvwDkReCHcbnaAH8oUPZ_gQ-qCBD5AvVsESHmHX_gb2Hwp_y8p7-bn6IIzfgPl20t8gyO9jrND_9yF8x5SfPn36r9955kfjD-H7LlqC4v35ww1-7_nWx9Xz6t7EwWqh6H34vFpCfkG9NFfvFw2Lo28XEK-rD37YpK-r1-LrMqaChVuW-d-HNWV_j1YvN5i1S6uvFioZ8jG8N_AfKmERhM227Itu9UK9W1i9fFmNqxcMYz-zS8wTHEZQDIlj3PNqWsQc_RlHOZYlaZKgKJL5-rya3-dEP3M0iS-_5aRgBIMzzPMqDN7uC-SPO5T3q5Sv_w0kkagn?type=png)](https://mermaid.live/edit#pako:eNqlWAmP47iV_isFA5MBRtVN3UcFs4lt2bIs67Csy0IBAXXYum_Jkhr931dVNZn0dHp3kyxhWOTj4-P7yEfpffyy8ssgXL2sfvrpS1zE3cuX1-Lp6ecuCvPQgk0MvSxsf355ehcvHVUT57CZtmVWNov4566BRVvBJiy6n59_0ykWi5uyCcL_XSO9f9_91vv1tfj69aefXovX4paVDz-CTfd00j8G-hlsWz68PX0zalOOT7c4y16-kT23XVOm4beiP79ZfLMhFvew7eKy-OuXpzi_vzy9rqKuq9oXAJqwKtu4K5vp04JyUft8j7uo9_o2bPyy6BYzn_0yByxOYzSL0SgICJ-F7I37BG8c94kkudsnlqK9TyT0OJ_lGN8P8dfV81MGvTB7m-qXX36f_5dfXl5fC770s7i4vyktk7-pdG_1x8sThqLPT9Fvz2X6BROMi-5NpSxeV28r9YZnG_VFulj4ZzhwgB1s_icQPcBRCmdYEqP-0v6Ks-ifhl_J73z9u-0PV7dRubTCH7j6rzn6tnnxLfbhH1c_WLx8eV9vUBX3P3uwDWnyObY2qv5AJeFerpeiXMxoZ96XmuUvf1tju5bf5SQs2bcKwDaytXMAAG9NHswXA8yTA4rzUt9qD1bbmtnRuOJK0tDIIM_yKF52ER0WdVlLSUaFJtBmJChcXE7EGYyXAyg3N1DeeZwb4NQ2YiJOGuj0GdR6AcZJHE4TAGisoMFBP5t7HlR6THTMDfQj6R82Y3S19zXMm83DPwAC3qp-ch0L80mVT0C7GcDDWtzVDgA7GoDlZkCzpbnf6Nh1NsBEeoANPDAVMyAZBzTxAIa9AXqzAB1PAJxWsXQi5sfkmPWUwmMyHs5J6h55Yltc2uLEo2uBp5UgUbNoYsCYs6dDgk4K3zfxxALFwGaVZ1M5GXfHHU_oaaKcUWOjppZ8SW5ozh-AaTogSGZXFDZviy7gu2x3tnSyUHG4xULvsokF0VynezTZHs_LchZH_UxLmF3jHda4leeERFMMHGC4zGVId5dPM2dPNQQXdp5T6yJAYfK1x82QTf28W__nZatFktbtKe3oqhyJdOsgNCuZQymUsNXHMCE3zdGH4j57RUTnKdxXeb_bFQ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+`sieves` lets you mix and match language model frameworks within a single pipeline. That means you can assign the best tool for each task instead of being locked into one framework. For example, you might use a fast local model for document classification, a lightweight frame like `gliner2` for NER, and a more expressive LLM framework like `langchain` for challenging information extraction. With `sieves`, you can define and orchestrate this heterogeneous pipeline without rewriting your business logic: +[![](https://mermaid.ink/img/pako:eNqlWHmvo0iS_ypPlnpaal5Vch9v1DtrGxtjzGHMZfT-SQ6b-wYDpfrui1_NlLprandnZ5FlMiIj48pIiB9fVn4ZhKu31S-_fImLuHv78l68vPzaRWEeWrCJoZeF7a9vLx_sZaJq4hw207bMymZh_9o1sGgr2IRF9-vr32WKReOmbILwf5ZI7z9OP2e_vhdfv_7yy3vxXtyy8uFHsOleTvq3hX4G25YPby9_WLUpx5dbnGVvf-C9tl1TpuEfWX99anzqEIt72HZxWfznl5c4v7-9vK-irqvaNwCasCrbuCub6dMS5SL2-R53Ue_1bdj4ZdEtaj77ZQ5YnMZoFqNREBA-C9kb9wneOO4TSXK3TyxFe59I6HE-yzG-H-Lvq9eXDHph9jT122_f7f_229v7e8GXfhYX96fQYvwp0j3Hj7cXDEVfX6K_3xfzS0wwLrqnSFm8r56ZesazjfoiXTT8czhwgB1s_rsgeoCjFM6wJEb9rf0dZ9G_DL-TP_j6D93fXN1G5UKFP3H1X3P0uXnxLfbhn7MfLF6-feQbVMX9rx5sQ5p8ja2Nqj9QSbiX6-VSLma0M-_LyPKXv62xXcsffBKW7HMAsI1s7RwAwJPkwXwxwDw5oDgv4632YLWtmR2NK64kDY0M8iyP4mUX0WFRl7WUZFRoAm1GgsLF5UScwXg5gHJzA-Wdx7kBTm0jJuKkgU6fQa0XYJzE4TQBgMYKGhz0s7nnQaXHRMfcQD-S_mEzRld7X8O82Tz8AyDgreon17Ewn1T5BLSbATysxV3tALCjAVhuBjRbmvuNjl1nA0ykB9jAA1MxA5JxQBMPYNgboDcL0PEEwGkVSydifkyOWU8pPCbj4Zyk7pEntsWlLU48uhZ4WgkSNYsmBow5ezok6KTwfRNPLFAMbFZ5NpWTcXfc8YSeJsoZNTZqasmX5Ibm_AGYpgOCZHZFYfNMuoDvst3Z0slCxeEWC73LJhZEc53u0WR7PC_pLI76mZYwu8Y7rHErzwmJphg4wHCZy5DuLp9mzp5qCC7sPKfWRYDC5GuPmyGb-nm3_vevrRZJWrentKOrciTSrYPQrGQOpVDCVh_DhNw0Rx-K--wVEZ2ncF_l_W5XFDQNuEU2WmSVTWSkrV8l3mNQ8QPTicZVqySe1s27DvdI3y88J6FoO9zX2NSasDywCOAQNM50R7sON2jdul4dHqSqjYJEFhiVCHijw6pxoC1aNlLPJFgPkEuEwbrGmcMIsR5QtdOv-8GANRPYIecUbO60-g4mXaFOuq3OHLMDSleqN6NxaXqyN5Dv4lzDkuq0VuwQu3REhhMYhXHaRe32oz-fsmszOASBGCjhHWLj2qQTy9Qjkmd13TUUCklEypiGb-RrweDezYm0ewJkN-Kqh-CGfmhj7L7EDauv7AS_McBv6wk7lHYOT3y_bO1uqKmom5BKr-v6yltmgMcu5wh7jE0kfCl1CZ-P7tZTY4THGye8Wie7RdohdSlxx8qBk3mLVfWMqdqpYQKO4qrd9i64QXE8xanqVCPhOQ9YZ_VjoKvAtKrY5T1qRthH4UkHowxZ-5RnQzTRMjjV5rizL-cHrl-bKyNEzbFu9QOaSg-2TNbDLCDiTmRSQB0EurL0uqz2KeYXR-Col9jb0uYoHS9kl-zs6JR6lCMdbrz0QHCYOQgUyWGQ61uNebvSadYAediD6zGhawVEdPXGbAbHmI0aVt14ohGHPDoQllxy2M41qZRoG3j01XKPGGJTPECqkh5RW_wjynzcLF1utG9ekqPaeL5n6VkziNz04ofneF1BKMh9a0lop7Wxp5XIDOvbgadzbFMDU7CRM3JV9MiY-zwxbNs07iIT9TmZk35yKfAN1rLnQurVE-M5Qt7ZF2hgNKbgXFxll9PMsV5HtEHb2VuzY0p2t3d2BqJ5rNeiks9kAZDXBNl002RE7AOOZzybGqplU084ZxPfnVTcLqQDjTk7TKT2JIZSntahUX0VtYQG7h0N126j0E0p3Ou0H_GBBZo2K8hNmHtLK1gEsxnUPqZzPrZisw_uaTMuT7ASZ0ttOXBcQxIi0XshxAzW7-SLTrmOp0wn7ph1vVQrV92JCwy9XmgUNvTUNVuS5Dcs5rAs67RkXrI5S4Rtxq8LhrwSZbd9uF3oXiCu4KV5LtThKOxlJ-6orTqe7TDF0rP8GB6NDNHT4aSnjcJabrQPQlriNVXis8te2XmJGyu4462BhvCQEKEbYTKriCWFn2oMQZwDTi1PWLnwzmYU4C0rJndIg5iLtKrewt2tt6z2Jqd6rshhhWlX3EahmEQXkxjFw8moabRXN5vQtX0B8cMkHENUjJFls9ZKT5Oec6Po243wuJAD2f7k8F4XoZqtbnurD3szShEmMyoboR2iyRwsjl2g3RkeVdt5OVBXsROXlgWTlprOcste24N36jsxQCt8DiuqvV1VibqGE3nVLwLrcsCxuBsYUdfrt6QDNTPwiK0u9EeMFsg4xDMnbnMjGJS8WkeDTw2Ue72OHCCmQbZcL6-ZS7tvRnseEXYTUjzhqsQVKzU5CMV7sldxOTiZseesE-_SP2Z3U8UAIfDt0aJgJ5CNaJ7wYbhVU6FJ5oO0jg14QHZvqNFcDyE5OP09yUTugBJqn_MytPua0qY9xej94xFDIYrgXtAF-q5auR-lTEsvGWjlc3MgZN6HHMiZkGT8yxQVkip5uJTuZ29qtf5kkdVjTVVReHUGTWrknIKBZeJ9segIMDNeaoTWa_UytYOIXBi9qiq7r-iYctpdzSFFZVVQpjmvLEg1Y2vTPtbUupkqN7zWJXpy8lTkco60TG9PnEKxAereKU63e3ZN5-Mkz2fvuuXtq55ACzNEQkpNFUxnuZBFGcvZVAmkPBGqo5hDNTszJxTYkm129R7a_pF3pLlV02N5iu9CaDuuMKpopmh3dFaROWksj6LawD4tPc6a2NlEbCgz8qBlDh9MdOzbUW7Ci8JKl3Z5DUJ2m6X-dkPk6cyNwZlQfEuh7fq-F4yelK9KMA1hQQ3M1ACQkaBrqQPIu027SyJ4zOCctPlsZSZ7EGNUuffSbq09DkZ6UrbK8cJUzpGR62KN1WfYeh05mi1lxay64zWbsduchhuJYniW91GzBTwypg3bTxaTO2iVtiZ9ZFRisiT8utMrNy8MeoMFLsVt-0I5KYSXI-TNgL6BuI09XYQWxJsDTvSHiOupcTnJ7cGX6_P6_32Ra4bazDx147xj6NvbD-Yu2xvppT_n2-2PnfWfmuFv_bXxRCu3ssnDpv1Jk41T_1Kbrez0_zsUWF4MNMGx-AcUQH8GBRa137wUsngZ4_8uChCLZ4QfUe_GRcT_ORQLC385K10YfOq8Av18b7tlif_h7Tdo9rf692Xmba0EnOBforkiUGd9cWZNaEx8xqakESnbbGLxkzjjCpY8_tL-ExD77slL-N2Vb0G-nGBx30ZLAD_G-b9H-APMfPn06T--g7RvxJ_2_oO1ZPTj_tPsrF5X9yYOVguc7cPX1VIeOXySqw9Qvvj1BOvvq29YqknfVwuEXtZUsHDLMv_Hsqbs79Hq7QazdqH6aoFdIR_DewPz79wFKC-ofVv2Rbd6w5gPHau3L6txoTD2M7tUCMFhBMWQOMa9rqaFzdGfcZRjWZImCYoima-vq_nDKvqZo0l8-eFLaREsSWCvqzB4omv52xeHjw8PX_8LJ_Fgyg?type=png)](https://mermaid.live/edit#pako:eNqlWHmvo0iS_ypPlnpaal5Vch9v1DtrGxtjzGHMZfT-SQ6b-wYDpfrui1_NlLprandnZ5FlMiIj48pIiB9fVn4ZhKu31S-_fImLuHv78l68vPzaRWEeWrCJoZeF7a9vLx_sZaJq4hw207bMymZh_9o1sGgr2IRF9-vr32WKReOmbILwf5ZI7z9OP2e_vhdfv_7yy3vxXtyy8uFHsOleTvq3hX4G25YPby9_WLUpx5dbnGVvf-C9tl1TpuEfWX99anzqEIt72HZxWfznl5c4v7-9vK-irqvaNwCasCrbuCub6dMS5SL2-R53Ue_1bdj4ZdEtaj77ZQ5YnMZoFqNREBA-C9kb9wneOO4TSXK3TyxFe59I6HE-yzG-H-Lvq9eXDHph9jT122_f7f_229v7e8GXfhYX96fQYvwp0j3Hj7cXDEVfX6K_3xfzS0wwLrqnSFm8r56ZesazjfoiXTT8czhwgB1s_rsgeoCjFM6wJEb9rf0dZ9G_DL-TP_j6D93fXN1G5UKFP3H1X3P0uXnxLfbhn7MfLF6-feQbVMX9rx5sQ5p8ja2Nqj9QSbiX6-VSLma0M-_LyPKXv62xXcsffBKW7HMAsI1s7RwAwJPkwXwxwDw5oDgv4632YLWtmR2NK64kDY0M8iyP4mUX0WFRl7WUZFRoAm1GgsLF5UScwXg5gHJzA-Wdx7kBTm0jJuKkgU6fQa0XYJzE4TQBgMYKGhz0s7nnQaXHRMfcQD-S_mEzRld7X8O82Tz8AyDgreon17Ewn1T5BLSbATysxV3tALCjAVhuBjRbmvuNjl1nA0ykB9jAA1MxA5JxQBMPYNgboDcL0PEEwGkVSydifkyOWU8pPCbj4Zyk7pEntsWlLU48uhZ4WgkSNYsmBow5ezok6KTwfRNPLFAMbFZ5NpWTcXfc8YSeJsoZNTZqasmX5Ibm_AGYpgOCZHZFYfNMuoDvst3Z0slCxeEWC73LJhZEc53u0WR7PC_pLI76mZYwu8Y7rHErzwmJphg4wHCZy5DuLp9mzp5qCC7sPKfWRYDC5GuPmyGb-nm3_vevrRZJWrentKOrciTSrYPQrGQOpVDCVh_DhNw0Rx-K--wVEZ2ncF_l_W5XFDQNuEU2WmSVTWSkrV8l3mNQ8QPTicZVqySe1s27DvdI3y88J6FoO9zX2NSasDywCOAQNM50R7sON2jdul4dHqSqjYJEFhiVCHijw6pxoC1a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## Declarative Document AI From 09c9f6b2637f72a71c44f88717c4cc0fae913b4a Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Thu, 22 Jan 2026 17:25:23 +0100 Subject: [PATCH 21/29] docs: Update blog draft. --- docs/blog_post.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/blog_post.md b/docs/blog_post.md index a8ad80f..6c89113 100644 --- a/docs/blog_post.md +++ b/docs/blog_post.md @@ -19,6 +19,7 @@ To mitigate this, we built `sieves`. `sieves` is a framework-agnostic abstractio `sieves` lets you mix and match language model frameworks within a single pipeline. That means you can assign the best tool for each task instead of being locked into one framework. For example, you might use a fast local model for document classification, and a more expressive LLM framework like `langchain` for challenging information extraction. With `sieves`, you can define and orchestrate this heterogeneous pipeline without rewriting your business logic. `sieves` gives you control over how you how you want to execute your pipeline. [![](https://mermaid.ink/img/pako:eNrtmgmP60ZygP_KQIFtwJyZ5n3MwnEkUQclUaQOHmImMJqHeF_NQyQf3n9fasZ-a_u99TobIMgGjwI07OpidVWxWlOfqA8TJ3e9ycvkm28-hFlYv3x4zR4evqsDL_V0iEJoJ1713cvDm3icKFCYQtTP8yRHo_i7GsGsKiDysvq7x591stHiLEeu98casf_76fvsx9fs48dvvnnNXrNrkt-cAKL6YXd8v9BJYFWJ3vXhV1fN8u7hGibJy69kj1WN8tj7tegvd4t3G1Lme1Ud5tl_fHgIU__l4XUS1HVRvQCAvCKvwjpH_dMY5aj27Id10NhN5SEnz-rRzLOTp4AnWYLlCRYHLuXwkL8KT_AqCE80LVyfeIa1n2hoCw4vcI7jka-Tx4cE2l5yX-r77z-t__33L6-vmZg7SZj5d6Vx8btKfT-_vTwQOP74EPz8d1x-jAmGWX1XybPXyT1T93jmQZPFo4XPw4EtrCH6e0E0gMQZkuNpgvmx-oHk8W_bH-jf-fqL7XdX50E-jrwvuPrnHL3fvPAaOvC32XdHL1_e8g2KzP-LDSuPpR9DfaYcb_h25efT8diftGCh-eOZ7oxv8_N8Kr_JaZjz9xNAzGR9YQIA7kMRDKczGHoTZIfxfK7eeHWuJZvzhdxHiMVaeZA76bQIWC8r83IbJYynAXXA3Mwi5UgaQHdag3x2BbkvkkIL-wpJkdSroD4OoDxmoOuldtcDgId73F0fD9pSBMUxpGruCpqOdtazLrgYyxKmaHZz1oCC16LpLVMnHFoRI1DNWnDTR3fVNSA2Z8ALA2D5XFvOjsRlOIOetgHv2qDPBkBzJkBhC9rlGTRaBmqRAiSrEHFPDbfe1Mo-hpuoWx-i2NqI1Dw7VdlOxKcrkd27kZIEPQe6lN-tI7zfiw0Kex7sz8SgiHwsR91isxCpYxztD_h5psS6fIqueCqugaaZwI0GS1rN7klfkYtkcdCPdKaQcE549mkWriRtGi_xaL45jOnMNscDuyWMkqwJZBW26VEoawXACYnF0dYi7QfB6EsITvwwxPppBVe9o96uZ1k7HhbTf_6Yq8FWrZeMurEUgcbqqetphSzgDE4Zyq3tsatqHtvMH-wsYNMYLou0WSyyjGWBMOoGo-5-Fpzjyiki-9Yq5JqrpfNFLbYie9T8I1xiTTPKzIhhDW9ZEn2lwXzNY0DA8DA5muqlvUL9WjdKe6MVtVtt6YxgohWJjrBAJjQk3cDKgQbTFgrRqtUvYWJyq_DoMqXZTJv2DEvONTzBzPjUrI4LGNWZ0h8NZRC4BdjXuXI9I4tle2MGxTpMVSIqdtO94RGnmkpIimAIQT0p9bJzhl1yQa1JUdgZp-x1eL6guOe5ssPSpCxrxOCQxrYJh0QkXzKOtK9moPoRkK1AKG4ry3M8g-CXOXnWm8KIyCsHnKrsiXVupHAnNuOtXbQlE9Q9VhzLsryIuuaSoSWYqyXBR1tyLPUtOWysua2EmEgi07voO6PCqja2GGnBy66Z2OOqyoFQ1B3iXIERisXcX1luttmFsWIWHWWbN1gm5a1lC1fTi9ASbWbA-Ftmb9fn3OONXZq0Qc_KYFdq3cI4HW7k8YIu3CpAm7I6rvF4e-PzaNoOK0xaSFwMmPWKLfRjmRfLmHCyDTCVU2jPWa3bbk50HS2MYBfbjLldX8XtDSNhYmJQottWLq8lYS9yE00BdjNay-Y8S3ep4GJ3yQA2IR8gXpnZ0jn0RLyldDkXiIWlMTFVIbhxlHyJnSWU3UCs0DZV6uItSBxSyy2hM652lOJqd_CT-KCeqVSzw5tt2nVG7TF_rm_xWq1CW82xAZbXtcimxKwE2srADthlfwzOQ5NGZ8PQzr7EBU1Kp7QTnTJyRlT8Ids2yo6zzVVaGyd4JlhiTwphkZx2g8DbNVW5VW3MtZrL-cXSXJwx1ebtCt86XOICeUrRqO77c8DfYHcgkx4xFR_bq0PSi_VOIY1su2YJc0FIzJImcMZWazwoL5IascDycW9qoT2L8pVfxk1HtjxQ1WGPXVdDo6sZjxEGhxubeEi7SkJL149RN36C5SSfq-OGExBNSVRje5A4804tn46MZdr7fidskrrZlvvL0QwzAr-cWBwitq_RnKbFGU-YPM-bFZ3mfMpTXpWI04yjL1Rez29W7VknSO7JXDtkSrtZLWUzrJm50h0MLybig3xrb0iG-G69O8Zoz-tWsHQ9diuqylZMTsv9wo6scE-a9hSomAgpCVoBIfN7KWfIXUlgmLkmmfETVs7sgxa4ZMVLkQ9ZEAqBWpRzuLg2ul5d5fiY7mWvINQLaeBQioKTRnXSencuWbxRZjPPMpwV5niR13m4FGLjzZruG5a2zSvDXq-ULXgCSJY7U7TrAFcNZd7ojddoQYxxybkwMNakUGISYWgB1edEXKmGcUNdpFoaWxZiO9Z0kurG1GjtXVNLLl6Qg1cw1fWibJmL19OX42nFWwIwdeEKOtyymzltQlVzbWp-XDUbgl3RoUcmZlilZ7fdp8U0aB2mZazLpRMA1beybtlpyZ2qJeqMocP4mceIlKVQFyJXZdeT_GipkLK700LbnEb2qbkN1qwIAUaR843OwHpFI0nbkW17LfpM3Wo3Wt8gcIP88qwEQ9l6dGs2fpRIwhqnlCYVZWg0JaP2S4Y7NrdbCFdBAJer44r1FT11gpir2DEDlXxAa0oWHSiAlPNozjn1QbZVtja5jZeD3Vdqs9Pp4jZlisC7mK26RXLKQFfXyCYbbbiEFo41wh5L5dRXrYSduGNRFEZTsCFjVotSwLJCL6DMCnae0UrCl5qxKZkp6gvLu5Q5vjPTWBJSgdY1e0ntPAkBZWlmu6ufXOJh08vDwb7MReNyjKBOnCVqG2sK6A9yJksykfLx3t2m0arYSClUkgO3w4GxNbS6XELD2YjmdqiUeJPvQn_lGaa16hQ82as-PijYECHdZpjKNXZjjzOlFgYVnvcDdmNlgWw1vGuqTkbeac9vT9X4bxDy8yR25jMqjQehcw_U3tH3rFH6y9W5oeXL3u1bL2NarkcAJDSoK2YN0npWLaIAbhI4RFU66InGr6UQ3_vNdjFVb-tzvNvP95sTV5gbTi6zKVEeYGXXdKdVjB7yykJUDc6oUhbOtgwn8qKDaxUQsS5GfNPrXGriRVxp7IZTqF7fkpfFsbDS7MzOCNdihHmT7Xd7yk4x-nqGzhmzkNGfVhUIZ2uSataB0DDduJOrtSOXh-n_-KCnHDMbROYq2BvPMeZvwkWyPMen5pDO57_vrH_TDL_31-c7rVxzlHqo-kKTTTJ_qs2WsruJN7OLblRxvsw6XuaMxVh77lNtZ_izX9XjJc4bGbyzz4_lD-PMy3TvCivnFAwFhZvTkzmoK6SRA9FHSGIMDYXSkzSQeyK6fVt9RjqfPHnwPrnyHuvDDmb-PBgD-H2kfxzhe4xVY_sIFsE48dtEvk7eFe6HGyLvbcVP-PhOIdVPq5G4PPR5Tq4Ijrn_PSi9pwMkU_1w8Zk2UXp7jXRic2XLIiN4JtGeR2758Ra6dfADQVDkt4EX-kE9nhPsl7iO-cP794uTnxL0uZ9NkeTQfb6FcZh6bgifc-SD-6i4j8DodDqaByP1pTZgwcipn4z9tMv9_LlqfUCMyFd0T5_PvEVD4mO5sQQ5vgiG-kIUFP6nohBPav95AG5V9M8wBO9FN2bYfxP9lNxdKL5Iw39yvfXyK1h-BcuvYPkVLL-C5Vew_AqWX8HyK1h-Bcv_72D5z_XKSlPfQaj6vD9_f2L0Rj9uXtdd_TQ26_nP6gDBG0hHw-OcU4ERv7y6-kRJYwf_5EIUP6W5691x4t_84G-CpzxL-v-Ox7_4--65l7lfQMAvEu_r5D9_zZ9_m_ivf0SI0uL_PiCOPv7r8-EYxP8mHo7L_StU_GeV_umB8cPT079_etz7PvjNlx9voi9uhsnjxEehO3mpUeM9TsYaHqMZh5O3x_qjU_fH_a-Td2hG8f1blI_jNQXMrDxPf7kM5Y0fTF6uMKnGUVOMfO2JIRy3YfpJikbHPTTPm6yevJAETr1Zmbx8mHSTF4JjnnGKYXBuLB-C5mn6cdKPajT3TNECw_E8w9EkwfMfHyfD28L4M4_zLM9TDEWNzEXixONkrOE6R_L7zxbefr3w8a8Kfbjw?type=png)](https://mermaid.live/edit#pako:eNrtmgmP60ZygP_KQIFtwJyZ5n3MwnEkUQclUaQOHmImMJqHeF_NQyQf3n9fasZ-a_u99TobIMgGjwI07OpidVWxWlOfqA8TJ3e9ycvkm28-hFlYv3x4zR4evqsDL_V0iEJoJ1713cvDm3icKFCYQtTP8yRHo_i7GsGsKiDysvq7x591stHiLEeu98casf_76fvsx9fs48dvvnnNXrNrkt-cAKL6YXd8v9BJYFWJ3vXhV1fN8u7hGibJy69kj1WN8tj7tegvd4t3G1Lme1Ud5tl_fHgIU__l4XUS1HVRvQCAvCKvwjpH_dMY5aj27Id10NhN5SEnz-rRzLOTp4AnWYLlCRYHLuXwkL8KT_AqCE80LVyfeIa1n2hoCw4vcI7jka-Tx4cE2l5yX-r77z-t__33L6-vmZg7SZj5d6Vx8btKfT-_vTwQOP74EPz8d1x-jAmGWX1XybPXyT1T93jmQZPFo4XPw4EtrCH6e0E0gMQZkuNpgvmx-oHk8W_bH-jf-fqL7XdX50E-jrwvuPrnHL3fvPAaOvC32XdHL1_e8g2KzP-LDSuPpR9DfaYcb_h25efT8diftGCh-eOZ7oxv8_N8Kr_JaZjz9xNAzGR9YQIA7kMRDKczGHoTZIfxfK7eeHWuJZvzhdxHiMVaeZA76bQIWC8r83IbJYynAXXA3Mwi5UgaQHdag3x2BbkvkkIL-wpJkdSroD4OoDxmoOuldtcDgId73F0fD9pSBMUxpGruCpqOdtazLrgYyxK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+ + ## Declarative Document AI `sieves` decouples the business logic - the "what" - from the execution framework - the "how." From fc84050f7b26c13cecabeb10cbd1bc87b02a18ec Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Fri, 23 Jan 2026 12:53:10 +0100 Subject: [PATCH 23/29] docs: Update blog draft. --- docs/blog_post.md | 26 ++++++++++---------------- 1 file changed, 10 insertions(+), 16 deletions(-) diff --git a/docs/blog_post.md b/docs/blog_post.md index 19fcb59..fd4e4fc 100644 --- a/docs/blog_post.md +++ b/docs/blog_post.md @@ -17,23 +17,16 @@ The missing piece was a stable, schema-first layer that sits above these tools a To mitigate this, we built `sieves`. `sieves` is a framework-agnostic abstraction for building these workflows. It replaces imperative glue code with declarative design. Its modularity allows complex pipelines to be built from simple, reusable tasks. -`sieves` lets you mix and match language model frameworks within a single pipeline. That means you can assign the best tool for each task instead of being locked into one framework. For example, you might use a fast local model for document classification, and a more expressive LLM framework like `langchain` for challenging information extraction. With `sieves`, you can define and orchestrate this heterogeneous pipeline without rewriting your business logic. You can control how to execute your pipeline. 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- +`sieves` lets you mix and match language model frameworks within a single pipeline. That means you can assign the best +tool for each task instead of being locked into one framework. +For example, you might use a fast local model for document classification, and a more expressive LLM framework like +`langchain` for challenging information extraction. +With `sieves`, you can define and orchestrate this heterogeneous pipeline without rewriting your business logic. +You control how to execute your pipeline. 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Chunking:
Chonkie

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With `sieves`, you can define and orchestrate this heterogeneous pipeline without rewriting your business logic. You control how to execute your pipeline. + +![Pipeline diagram](assets/mermaid-diagram-2026-01-23-125601.svg) +![Pipeline diagram](assets/mermaid-diagram-2026-01-23-125939.png) + 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Information Extraction

Classification

Ingestion:
Docling

Chunking:
Chonkie

 diff --git a/docs/blog_post.md b/docs/blog_post.md index deb047e..56a84f7 100644 --- a/docs/blog_post.md +++ b/docs/blog_post.md @@ -24,8 +24,7 @@ For example, you might use a fast local model for document classification, and a With `sieves`, you can define and orchestrate this heterogeneous pipeline without rewriting your business logic. You control how to execute your pipeline. -![Pipeline diagram](assets/mermaid-diagram-2026-01-23-125601.svg) -![Pipeline diagram](assets/mermaid-diagram-2026-01-23-125939.png) +![Pipeline diagram](assets/pipeline.png) 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zMFf(F?EH9a+W*}JZLj|+YP1&M?+vc7FF_nQSd9`^@0}WtVduUxHV>%!7?KhH;~bZi zVor{B%x0$mowLd9$5|Wy7)f_R5cesJ1e|+WN#Ey0=ST@skydFv5yC(N^S%TekeaT4 zEP*Xd zj*_Zz2vgX=iuEdz6TXsL&$zi9!Xfu=2tR+)$YCnOVFpmj_Dk0*OB&C6k|S`3(m$DN zG`N&NXU?54$+ty%PHDe|twZd>VhZN57ewxW*$d$sI1zTBlXUizk)~xsDC@) zZwLHMgTHC;Hxmse9}Mm{6Af4Hw>$XT9gLv2Zy*bSV1Em;>cnIl{p_$w(@rlMod40Z zt>pK?n_9+0rzgL%o{*VTj&eFd{Z!QW2|+Fg+*w|i^A&VNYN>Oi;Vz&*4IBD_uK?V) ze>k!oH@_*FkIltx>V9z#Y@Dz>s4LJg7WZeK+U zun^7?PPwWk^+uB)@@z9o>2!Ll&TZ*`4!1lq230(PD;``9@PyTUX zo9z!l^#(%*cXZ|>(e_w)ZoWZ*4YOEC2;g&F;$0{^$~;Fs}svu){H>`(t6Jn&U& From fe444d815b83553a6be68ab7404269a6428fca36 Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Fri, 23 Jan 2026 16:54:27 +0100 Subject: [PATCH 27/29] docs: Update blog draft. --- docs/blog_post.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/blog_post.md b/docs/blog_post.md index 56a84f7..23692cd 100644 --- a/docs/blog_post.md +++ b/docs/blog_post.md @@ -19,8 +19,9 @@ To mitigate this, we built `sieves`. `sieves` is a framework-agnostic abstractio `sieves` lets you mix and match language model frameworks within a single pipeline. That means you can assign the best tool for each task instead of being locked into one framework. -For example, you might use a fast local model for document classification, and a more expressive LLM framework like -`langchain` for challenging information extraction. +For example, you might use a fast local `transformers` model for document classification, and a frontier LLM for challenging information extraction. +You might also want to experiment with different models and model types to see which work best for you. + With `sieves`, you can define and orchestrate this heterogeneous pipeline without rewriting your business logic. You control how to execute your pipeline. From 78419656d4ac3b0615d08c04fa855c4a796a6b4c Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Fri, 23 Jan 2026 16:55:56 +0100 Subject: [PATCH 28/29] docs: Update blog draft. --- docs/blog_post.md | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/docs/blog_post.md b/docs/blog_post.md index 23692cd..84999ec 100644 --- a/docs/blog_post.md +++ b/docs/blog_post.md @@ -17,13 +17,12 @@ The missing piece was a stable, schema-first layer that sits above these tools a To mitigate this, we built `sieves`. `sieves` is a framework-agnostic abstraction for building these workflows. It replaces imperative glue code with declarative design. Its modularity allows complex pipelines to be built from simple, reusable tasks. -`sieves` lets you mix and match language model frameworks within a single pipeline. That means you can assign the best -tool for each task instead of being locked into one framework. -For example, you might use a fast local `transformers` model for document classification, and a frontier LLM for challenging information extraction. -You might also want to experiment with different models and model types to see which work best for you. +`sieves` makes it easy to mix and match language model frameworks in one pipeline. You can pick the best tool for each +task instead of being locked into a single one. Use a fast local `transformers` model for classification, a frontier +LLM for complex extraction, or swap in different models to see what works best. -With `sieves`, you can define and orchestrate this heterogeneous pipeline without rewriting your business logic. -You control how to execute your pipeline. +With `sieves`, you define and orchestrate these heterogeneous workflows without rewriting your business logic. You stay +in control of how your pipeline executes. ![Pipeline diagram](assets/pipeline.png) From a07764bc615e2a6c60650ec18ec26b15ef09bb5a Mon Sep 17 00:00:00 2001 From: Raphael Mitsch Date: Fri, 6 Feb 2026 23:10:52 +0100 Subject: [PATCH 29/29] fix: Use --clear for venv creation. --- .github/workflows/test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 78f76be..379ebcb 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -44,7 +44,7 @@ jobs: - name: Install Python dependencies run: | - uv venv .venv --python 3.12 + uv venv .venv --python 3.12 --clear uv build source .venv/bin/activate uv pip install ./dist/sieves-*.whl