vecclean 1.0.0.1

Production-ready text cleaning, deduplication, and vectorization pipeline with C++ acceleration

VecClean Documentation

VecClean is an ultra-low latency text cleaning, deduplication, and vectorization pipeline designed for production-ready document processing at scale. It combines the flexibility of Python with the performance of C++ to deliver maximum throughput for RAG pipelines.

Installation

From PyPI (Development Version)

pip install vecclean

Quick Start

Basic Text Processing

import asyncio
from vecclean import Pipeline

async def main():
    # Initialize the pipeline with default configuration
    pipeline = Pipeline()
    
    # Process a simple text
    sample_text = """
    This is a sample document for testing.
    It contains multiple sentences that need processing.
    
    Some sentences might have    extra   whitespace.
    Others might contain HTML tags like <b>bold text</b>.
    """
    
    # Process the text
    result = await pipeline.process_text(sample_text)
    
    # Access the processed chunks
    for i, chunk in enumerate(result.chunks):
        print(f"Chunk {i}: {chunk.text}")
        print(f"Hash: {chunk.text_hash}")
        print(f"Word count: {chunk.word_count}")
        print(f"Embedding shape: {chunk.embedding.shape if chunk.embedding is not None else 'None'}")
        print("---")

# Run the async function
asyncio.run(main())

Processing Files

import asyncio
from vecclean import Pipeline
from pathlib import Path

async def process_files():
    pipeline = Pipeline()
    
    # Process multiple files
    files = [
        "document1.pdf",
        "document2.docx", 
        "document3.txt"
    ]
    
    result = await pipeline.process_files(files)
    
    print(f"Processed {len(result.chunks)} chunks from {len(files)} files")
    print(f"Processing time: {result.stats.total_processing_time:.2f} seconds")
    
    # Save results to JSON
    import json
    with open("processed_chunks.json", "w") as f:
        json.dump(result.to_dict(), f, indent=2)

asyncio.run(process_files())

Configuration

Custom Configuration

from vecclean import Pipeline, Config

# Create custom configuration
config = Config(
    chunking={
        "chunk_size": 512,
        "chunk_overlap": 50,
        "strategy": "sentence"
    },
    cleaning={
        "normalize_whitespace": True,
        "strip_html_tags": True,
        "remove_stopwords": True
    },
    dedup={
        "sentence_dedup": True,
        "chunk_dedup": True,
        "similarity_threshold": 0.85
    },
    embedding={
        "model_name": "all-MiniLM-L6-v2",
        "device": "auto"
    }
)

# Initialize pipeline with custom config
pipeline = Pipeline(config)

Configuration Options

Chunking Configuration

• chunk_size: Maximum size of each chunk (default: 512)
• chunk_overlap: Overlap between chunks (default: 50)
• strategy: Chunking strategy - "sentence", "token", "recursive" (default: "sentence")
• min_chunk_size: Minimum chunk size (default: 100)
• max_chunk_size: Maximum chunk size (default: 1000)

Cleaning Configuration

• normalize_unicode: Unicode normalization form (default: "NFC")
• normalize_whitespace: Normalize whitespace (default: True)
• standardize_punctuation: Standardize punctuation (default: True)
• strip_html_tags: Remove HTML tags (default: True)
• remove_stopwords: Remove stopwords (default: True)
• min_text_length: Minimum text length (default: 10)

Deduplication Configuration

• sentence_dedup: Enable sentence-level deduplication (default: True)
• chunk_dedup: Enable chunk-level deduplication (default: True)
• similarity_threshold: Similarity threshold for deduplication (default: 0.85)
• hash_algorithm: Hash algorithm for deduplication (default: "xxhash")

Embedding Configuration

• model_name: Embedding model name (default: "all-MiniLM-L6-v2")
• device: Device for embedding generation (default: "auto")
• batch_size: Batch size for embedding generation (default: 32)
• cache_embeddings: Cache embeddings (default: True)

API Reference

Pipeline Class

The main entry point for text processing.

Methods

process_text(text: str) -> ProcessingResult

Process a single text string.

result = await pipeline.process_text("Your text here")

process_files(files: List[str]) -> ProcessingResult

Process multiple files.

result = await pipeline.process_files(["file1.pdf", "file2.txt"])

process_documents(documents: List[Document]) -> ProcessingResult

Process a list of document objects.

from vecclean.core.types import Document

documents = [
    Document(content="Text 1", metadata={"source": "file1.txt"}),
    Document(content="Text 2", metadata={"source": "file2.txt"})
]
result = await pipeline.process_documents(documents)

ProcessingResult

The result object containing processed chunks and metadata.

Attributes

• chunks: List of CleanedChunk objects
• stats: Processing statistics
• status: Processing status (COMPLETED, FAILED, etc.)
• errors: List of error messages
• warnings: List of warning messages

Methods

• to_dict(): Convert to dictionary for serialization
• to_json(): Convert to JSON string

CleanedChunk

Represents a processed text chunk.

Attributes

• text: The cleaned text content
• text_hash: Hash of the text for deduplication
• embedding: Vector embedding (numpy array)
• chunk_index: Index of the chunk in the document
• start_char: Starting character position
• end_char: Ending character position
• word_count: Number of words in the chunk
• char_count: Number of characters in the chunk

Supported File Formats

VecClean supports processing various file formats:

• PDF (.pdf) - Text extraction with metadata
• Word Documents (.docx) - Text and formatting
• PowerPoint (.pptx) - Text from slides
• Text Files (.txt) - Plain text
• HTML (.html, .htm) - Web content
• Email (.eml) - Email messages

Performance Features

C++ Acceleration

VecClean includes C++ optimizations for high-performance text processing:

• SIMD-optimized text cleaning
• Parallel processing with work-stealing thread pools
• Memory-efficient streaming for large files

Async Processing

All operations are asynchronous for better performance:

# Process multiple texts concurrently
tasks = [
    pipeline.process_text(text1),
    pipeline.process_text(text2),
    pipeline.process_text(text3)
]
results = await asyncio.gather(*tasks)

Error Handling

try:
    result = await pipeline.process_text(text)
    if result.status == ProcessingStatus.COMPLETED:
        print(f"Successfully processed {len(result.chunks)} chunks")
    else:
        print(f"Processing failed: {result.errors}")
except Exception as e:
    print(f"Error during processing: {e}")

Integration Examples

With FastAPI

from fastapi import FastAPI, UploadFile, File
from vecclean import Pipeline

app = FastAPI()
pipeline = Pipeline()

@app.post("/process-text")
async def process_text(text: str):
    result = await pipeline.process_text(text)
    return {
        "chunks": [chunk.to_dict() for chunk in result.chunks],
        "stats": result.stats.to_dict()
    }

@app.post("/process-file")
async def process_file(file: UploadFile = File(...)):
    content = await file.read()
    result = await pipeline.process_text(content.decode())
    return {"chunks": len(result.chunks)}

With LangChain

from langchain.text_splitter import TextSplitter
from vecclean import Pipeline

class VecCleanTextSplitter(TextSplitter):
    def __init__(self, pipeline: Pipeline):
        self.pipeline = pipeline
    
    async def split_text(self, text: str):
        result = await self.pipeline.process_text(text)
        return [chunk.text for chunk in result.chunks]

# Usage
pipeline = Pipeline()
splitter = VecCleanTextSplitter(pipeline)
chunks = await splitter.split_text("Your long text here")

Best Practices

1. Batch Processing: Process multiple documents together for better performance
2. Memory Management: Use streaming for very large files
3. Configuration: Tune chunking parameters based on your use case
4. Error Handling: Always check the processing status and handle errors
5. Caching: Enable embedding caching for repeated processing

Troubleshooting

Common Issues

1. C++ Backend Not Available: VecClean will fall back to Python implementation
2. Memory Issues: Reduce batch size or use streaming for large files
3. Slow Processing: Check if C++ backend is enabled and consider using GPU for embeddings

Debug Mode

Enable debug logging to see detailed processing information:

import logging
logging.basicConfig(level=logging.DEBUG)

pipeline = Pipeline()
# Processing will now show detailed logs

License

VecClean is licensed under the MIT License. See the LICENSE file for details.