Refinery components for the Sayou Data Platform
Project description
sayou-refinery
The Universal Data Cleaning & Normalization Engine for Sayou Fabric.
sayou-refinery acts as the "Cleaning Plant" in your data pipeline. It transforms heterogeneous raw data (JSON Documents, HTML, DB Records) into a standardized stream of SayouBlocks.
It ensures that downstream components (like Chunkers or LLMs) receive clean, uniform data regardless of whether the source was a messy web scrape or a structured database row.
1. Architecture & Role
Refinery operates in two distinct stages to guarantee data quality: Normalization (Shape Shifting) and Processing (Hygiene).
graph LR
Raw[Raw Input] --> Pipeline[Refinery Pipeline]
subgraph Stage1 [Normalization]
Doc[Doc Normalizer]
Html[Html Normalizer]
Json[Json Normalizer]
end
subgraph Stage2 [Processing Chain]
Space[Whitespace]
PII[PII Masker]
Link[Link Extractor]
end
Pipeline --> Stage1
Stage1 --> Stage2
Stage2 --> Blocks[Clean SayouBlocks]
1.1. Core Features
- Normalization: Flattens complex structures (Nested JSON, HTML Trees) into a linear list of blocks.
- Hygiene: Removes invisible characters, normalizes Unicode, and fixes broken encoding.
- Safety: Automatically masks sensitive information (PII) like emails or phone numbers before they reach the LLM.
2. Available Strategies
sayou-refinery provides strategies tailored to specific input formats.
| Strategy Key | Target Format | Description |
|---|---|---|
standard_doc |
Sayou Document | [Default] Converts parsed document dictionaries into Markdown blocks. Applies standard text cleaning. |
html |
Web Pages | Strips HTML tags, extracts links, and converts the DOM tree into readable text blocks. |
json |
API/DB Records | Flattens JSON objects into key-value pairs or text representations. |
3. Installation
pip install sayou-refinery
4. Usage
The RefineryPipeline orchestrates the normalization and processing chain.
Case A: Document Cleaning (Standard)
Cleans messy OCR output or parsed document text.
from sayou.refinery import RefineryPipeline
raw_doc = {
"metadata": {"title": "Test Doc"},
"pages": [{
"elements": [
{"type": "text", "text": "Contact: admin@sayou.ai "},
{"type": "text", "text": "Generic Whitespace Error"}
]
}]
}
blocks = RefineryPipeline.process(
data=raw_doc,
strategy="standard_doc"
)
for block in blocks:
print(f"[{block.type}] {block.content}")
# Output: [text] Contact: [EMAIL]
# Output: [text] Generic Whitespace Error
Case B: HTML Processing
Converts web content into clean text while preserving hyperlinks.
from sayou.refinery import RefineryPipeline
raw_html = """
<html>
<body>
<h1>Welcome</h1>
<p>Click <a href='https://sayou.ai'>here</a>.</p>
</body>
</html>
"""
blocks = RefineryPipeline.process(
data=raw_html,
strategy="html"
)
# Result:
# [heading] Welcome
# [text] Click here (Link: https://sayou.ai)
5. Configuration Keys
Customize the cleaning processors via the config dictionary.
mask_pii: (bool) Mask emails, phone numbers, and IP addresses.normalize_whitespace: (bool) Collapse multiple spaces and trim lines.extract_links: (bool) Extract<a>tags or markdown links into metadata.remove_stopwords: (bool) Filter out common stopwords (optional).
6. License
Apache 2.0 License © 2026 Sayouzone
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