fix: address PR review feedback from anakin87

- Replace Datadog nightly failure notification with Slack via
  deepset-ai/notify-slack-action
- Remove virtualenv version pin from hatch install (fixed upstream)
- Trim README.md to match standard integration format (chroma)
- Remove kreuzberg.md (auto-generated on release)
- Remove "unit" pytest marker (not used in this repo)

Author: v-tan

.github/workflows/kreuzberg.yml

@@ -47,7 +47,7 @@ jobs:
           python-version: ${{ matrix.python-version }}
 
       - name: Install Hatch
-        run: pip install hatch "virtualenv<21.0.0"
+        run: pip install hatch
 
       - name: Lint
         if: matrix.python-version == '3.10' && runner.os == 'Linux'
@@ -69,10 +69,12 @@ jobs:
           hatch -e test env run -- uv pip install git+https://github.com/deepset-ai/haystack.git@main
           hatch run test:unit
 
-      - name: Send event to Datadog for nightly failures
-        if: failure() && github.event_name == 'schedule'
-        uses: ./.github/actions/send_failure
+
+  notify-slack-on-failure:
+    needs: run
+    if: failure() && github.event_name == 'schedule'
+    runs-on: ubuntu-slim
+    steps:
+      - uses: deepset-ai/notify-slack-action@v1
         with:
-          title: |
-            Core integrations nightly tests failure: ${{ github.workflow }}
-          api-key: ${{ secrets.CORE_DATADOG_API_KEY }}
+          slack-webhook-url: ${{ secrets.SLACK_WEBHOOK_URL_NOTIFICATIONS }}

integrations/kreuzberg/README.md

@@ -7,229 +7,8 @@
 
 ---
 
-## Overview
-
-A [Haystack](https://haystack.deepset.ai/) integration for [Kreuzberg](https://docs.kreuzberg.dev/), a document intelligence framework that extracts text from PDFs, Office documents, images, and 75+ other file formats. All processing is performed locally — no external API calls.
-
-## Installation
-
-```console
-pip install kreuzberg-haystack
-```
-
-Kreuzberg requires system OCR libraries for image-based extraction. See the [Kreuzberg installation docs](https://docs.kreuzberg.dev/) for platform-specific setup (e.g. Tesseract, EasyOCR).
-
-## Quick Start
-
-```python
-from haystack_integrations.components.converters.kreuzberg import KreuzbergConverter
-
-converter = KreuzbergConverter()
-result = converter.run(sources=["document.pdf", "report.docx"])
-
-for doc in result["documents"]:
-    print(doc.content[:200])
-```
-
-## Features
-
-- **75+ file formats** — PDF, DOCX, PPTX, XLSX, HTML, images, email, ebooks, and more
-- **Local processing** — No external API calls; fully offline capable
-- **Batch extraction** — Parallel processing via Rust rayon thread pool
-- **Per-page splitting** — One Document per page for fine-grained retrieval
-- **Built-in chunking** — Server-side chunking via kreuzberg's `ChunkingConfig`
-- **Multiple output formats** — Plain text, Markdown, or HTML
-- **OCR backends** — Tesseract and EasyOCR with configurable language and preprocessing
-- **Token reduction** — Reduce output size for LLM consumption (5 levels)
-- **Rich metadata** — Tables, images, annotations, keywords, quality scores, detected languages
-- **Configuration files** — Load settings from TOML, YAML, or JSON files
-
-## Usage Examples
-
-### Basic Conversion
-
-```python
-from haystack_integrations.components.converters.kreuzberg import KreuzbergConverter
-
-converter = KreuzbergConverter()
-result = converter.run(sources=["report.pdf"])
-documents = result["documents"]
-```
-
-### Markdown Output with OCR
-
-```python
-from kreuzberg import ExtractionConfig, OcrConfig
-
-converter = KreuzbergConverter(
-    config=ExtractionConfig(
-        output_format="markdown",
-        ocr=OcrConfig(backend="tesseract", language="eng"),
-    ),
-)
-result = converter.run(sources=["scanned_document.pdf"])
-```
-
-### Per-Page Extraction
-
-```python
-from kreuzberg import ExtractionConfig, PageConfig
-
-converter = KreuzbergConverter(
-    config=ExtractionConfig(
-        pages=PageConfig(extract_pages=True),
-    ),
-)
-result = converter.run(sources=["multi_page.pdf"])
-# One Document per page, with page_number in metadata
-```
-
-### Token Reduction for LLMs
-
-```python
-from kreuzberg import ExtractionConfig, TokenReductionConfig
-
-converter = KreuzbergConverter(
-    config=ExtractionConfig(
-        token_reduction=TokenReductionConfig(mode="moderate"),
-    ),
-)
-```
-
-### Directory Input
-
-```python
-converter = KreuzbergConverter()
-# Expands to all files in the directory (non-recursive, sorted)
-result = converter.run(sources=["./documents/"])
-```
-
-### In a Haystack Pipeline
-
-```python
-from haystack import Pipeline
-from haystack.components.preprocessors import DocumentCleaner
-from haystack.components.writers import DocumentWriter
-from haystack.document_stores.in_memory import InMemoryDocumentStore
-from haystack_integrations.components.converters.kreuzberg import KreuzbergConverter
-
-document_store = InMemoryDocumentStore()
-
-pipeline = Pipeline()
-pipeline.add_component("converter", KreuzbergConverter())
-pipeline.add_component("cleaner", DocumentCleaner())
-pipeline.add_component("writer", DocumentWriter(document_store=document_store))
-
-pipeline.connect("converter.documents", "cleaner")
-pipeline.connect("cleaner", "writer")
-
-pipeline.run({"converter": {"sources": ["report.pdf", "notes.docx"]}})
-```
-
-### Configuration File
-
-```python
-converter = KreuzbergConverter(config_path="kreuzberg.toml")
-```
-
-Where `kreuzberg.toml` might contain:
-
-```toml
-output_format = "markdown"
-
-[ocr]
-backend = "tesseract"
-language = "eng+deu"
-```
-
-## API Reference
-
-### `KreuzbergConverter.__init__`
-
-| Parameter | Type | Default | Description |
-|---|---|---|---|
-| `config` | `ExtractionConfig \| None` | `None` | Kreuzberg extraction configuration object. Controls output format, OCR, chunking, keyword extraction, and more. |
-| `config_path` | `str \| Path \| None` | `None` | Path to a kreuzberg config file (TOML/YAML/JSON). When both `config` and `config_path` are given, `config` takes precedence. |
-| `store_full_path` | `bool` | `False` | If `True`, store full file paths in metadata. If `False`, store only the file name. |
-| `batch` | `bool` | `True` | Use kreuzberg's batch APIs for parallel extraction via Rust rayon thread pool. |
-| `easyocr_kwargs` | `dict \| None` | `None` | Extra keyword arguments for EasyOCR (GPU, beam width, model storage, etc.). |
-
-### `KreuzbergConverter.run`
-
-| Parameter | Type | Default | Description |
-|---|---|---|---|
-| `sources` | `list[str \| Path \| ByteStream]` | *(required)* | File paths, directory paths, or ByteStream objects to convert. |
-| `meta` | `dict \| list[dict] \| None` | `None` | Metadata to attach to Documents. A single dict applies to all; a list is zipped with sources. |
-
-**Returns** a dict with:
-- `documents` — `list[Document]`: Converted documents with content and metadata.
-- `raw_extraction` — `list[dict]`: Serialized kreuzberg `ExtractionResult` for each source (useful for debugging).
-
-## Metadata Fields
-
-Each Document's `meta` dict may include the following fields (depending on source format and configuration):
-
-| Field | Type | Description |
-|---|---|---|
-| `file_path` | `str` | Source file name (or full path if `store_full_path=True`) |
-| `mime_type` | `str` | Detected MIME type of the source |
-| `file_extensions` | `list[str]` | Known extensions for the MIME type |
-| `output_format` | `str` | Output format used (plain/markdown/html) |
-| `result_format` | `str` | Result format from kreuzberg |
-| `quality_score` | `float` | Extraction quality score (0.0–1.0) |
-| `detected_languages` | `list[str]` | Languages detected in the content |
-| `extracted_keywords` | `list[dict]` | Keywords with text, score, and algorithm |
-| `table_count` | `int` | Number of tables extracted |
-| `tables` | `list[dict]` | Table data (cells, markdown, page_number) |
-| `image_count` | `int` | Number of images found |
-| `images` | `list[dict]` | Image metadata (format, dimensions, page, description) |
-| `annotations` | `list[dict]` | PDF annotations (type, content, page_number) |
-| `processing_warnings` | `list[dict]` | Warnings from extraction (source, message) |
-| `page_number` | `int` | Page number (per-page mode only) |
-| `is_blank` | `bool` | Whether the page is blank (per-page mode only) |
-| `chunk_index` | `int` | Chunk index (chunking mode only) |
-| `total_chunks` | `int` | Total chunks (chunking mode only) |
-
-Format-specific metadata from kreuzberg (e.g. PDF title, author, page count) is also flattened into `meta`.
-
-## Supported Formats
-
-Kreuzberg supports 75+ file formats. You can query the available extractors at runtime:
-
-```python
-KreuzbergConverter.supported_extractors()
-KreuzbergConverter.supported_ocr_backends()
-```
-
-Common supported formats include:
-
-| Category | Formats |
-|---|---|
-| Documents | PDF, DOCX, DOC, ODT, RTF, EPUB |
-| Spreadsheets | XLSX, XLS, ODS, CSV, TSV |
-| Presentations | PPTX, PPT, ODP |
-| Images | PNG, JPEG, TIFF, BMP, WebP (via OCR) |
-| Web | HTML, XHTML, XML, Markdown |
-| Email | EML, MSG |
-| Code | Plain text, source code files |
-| Archives | Extracts from contained documents |
-
 ## Contributing
 
 Refer to the general [Contribution Guidelines](https://github.com/deepset-ai/haystack-core-integrations/blob/main/CONTRIBUTING.md).
 
-To run tests locally:
-
-```console
-# Install the integration in development mode
-pip install -e ".[dev]"
-
-# Run unit tests
-pytest tests/
-```
-
 No external services or API keys are required — kreuzberg processes everything locally. For OCR tests, ensure Tesseract is installed on your system.
-
-## License
-
-`kreuzberg-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.