High-performance document preparation toolkit for LLM and RAG pipelines.

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Project description

preembed

Clean, chunk, deduplicate, and score documents before embedding.

preembed is a high-performance toolkit that sits between your raw documents and your vector database. It turns messy HTML, markdown, logs, and exports into high-quality, LLM-ready chunks — so your RAG pipeline retrieves better context and hallucinates less.

Why

Most RAG pipelines embed documents directly — noise, duplicates, and all. The result: wasted tokens, bloated vector stores, and retrieval that returns boilerplate instead of answers.

preembed fixes the data before it enters the pipeline:

raw documents → clean → chunk → dedupe → score → embed
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                        preembed handles this

Install

pip install preembed

For development (requires Rust toolchain):

pip install -e ".[dev]"
maturin develop --release

Quickstart

Pipeline (recommended)

from preembed import Pipeline

result = Pipeline(chunk_size=512, overlap=64).run("path/to/documents/")
result.save_report("report.json")

print(f"{result.stats.total_chunks} chunks, {result.stats.duplicate_ratio:.0%} duplicates removed")

Individual primitives

from preembed import clean_text, chunk_text, dedupe_chunks, score_chunks

text = clean_text("<html><script>tracking()</script><p>Actual content here.</p></html>")
# → "Actual content here."

chunks = chunk_text(text, chunk_size=512, overlap=64)
chunks = dedupe_chunks(chunks)
scored = score_chunks(chunks)
# Each chunk gets a 0-1 quality score + warning labels

Streaming (memory-efficient)

for batch in Pipeline().stream("huge_corpus/", batch_size=50):
    upload_to_vector_db(batch.scored_chunks)

Cross-batch deduplication included. Processes one batch at a time — constant memory regardless of corpus size.

Custom scoring

from preembed import register_scorer

@register_scorer("domain_relevance", weight=0.15)
def domain_scorer(chunk: str) -> float:
    return 1.0 if "patient" in chunk.lower() else 0.3

Blends with the built-in scoring engine. Errors default to neutral (0.5) — won't crash your pipeline.

How it works

Stage	What it does
Clean	Strip HTML, scripts, styles. Normalize whitespace and unicode.
Chunk	Structure-aware splitting. Preserves headings, code blocks, paragraphs. Configurable size + overlap.
Dedupe	Exact, normalized, and near-duplicate removal via fingerprint similarity.
Score	Quality score (0–1) based on length, density, structure, duplicates. Warning labels for boilerplate, too-short/long, low-density.
Report	JSON, Markdown, or HTML diagnostics with per-chunk scores and recommendations.

API

# Core primitives
clean_text(text: str) -> str
chunk_text(text: str, chunk_size=512, overlap=64, mode="word", tokenizer=None) -> list[str]
dedupe_chunks(chunks, return_stats=False, near_duplicate_threshold=0.9) -> list[str] | DedupeResult
score_chunks(chunks) -> list[Chunk]

# Pipeline
Pipeline(chunk_size=512, overlap=64, dedupe=True, score=True)
Pipeline.run(source, progress=None) -> PipelineResult
Pipeline.stream(source, batch_size=50, progress=None) -> Iterator[PipelineResult]

# Extensibility
register_scorer(name, weight=0.1)(fn)
clear_scorers()

Development

python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest

License

MIT

Project details

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Release history Release notifications | RSS feed

This version

0.1.1

Apr 30, 2026

0.1.0

Apr 30, 2026

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