REANIMATOR-VLM Technical Documentation & API Reference
=====================================================

Welcome to the official technical documentation for **REANIMATOR-VLM** — a Python toolkit for multi-modal document parsing, visual layout grounding, disk-backed workspace management, and synthetic relevance assessment.

.. toctree::
   :maxdepth: 2
   :caption: API Reference

   reference/index
   reference/models
   reference/pipeline
   reference/collection
   reference/relevance
   reference/backends
   reference/visualization

1. Overview & Architecture
--------------------------

REANIMATOR-VLM revitalizes scientific and complex document collections for Information Retrieval (IR) evaluation and Retrieval-Augmented Generation (RAG).

2. Quick Installation
---------------------

.. code-block:: bash

   pip install reanimator-vlm
   pip install "reanimator-vlm[all]"

3. Quickstart Example
---------------------

.. code-block:: python

   from reanimator import (
       ReanimatorVLM,
       ProjectCollection,
       OpenAIVisionBackend,
       CachedBackend,
       RelevanceEvaluator,
   )

   backend = CachedBackend(
       OpenAIVisionBackend(
           model="rednote-hilab/dots.mocr",
           base_url="http://139.6.160.244:6543/v1",
           api_key="not-needed",
           use_structured_layout=True,
       ),
       cache_dir="./.md_cache",
   )
   pipeline = ReanimatorVLM(backend=backend)

   project = ProjectCollection(
       project_dir="./project_data/my_collection",
       arxiv_ids=["2504.07584"],
       topics=[{"topic_id": "101", "title": "Table parsing performance"}]
   )

   project.process(pipeline)

   evaluator = RelevanceEvaluator(backend=backend, model_name="dots.mocr")
   project.run_relevance_assessment(evaluator, modality="tables")