chunk-embed 0.1.0

Chunk Embed Store - Chunk & Embed Knowledge Base

Chunk Embed Store

A powerful tool for chunking, embedding, and storing documents in a vector database for efficient retrieval and semantic search capabilities.

🔍 Overview

Chunk Embed Store is a Python tool designed to process, chunk, and embed text documents into a vector database. It analyzes your code base or documentation, breaks it into manageable chunks, generates semantic embeddings, and stores them in a ChromaDB vector database for later retrieval.

✨ Features

• Intelligent Document Processing: Supports a wide variety of file types (code files, documentation, text files, etc.)
• Smart Chunking: Breaks documents into optimal chunks while preserving context
• High-Quality Embeddings: Uses Sentence Transformers to generate semantic embeddings
• Efficient Storage: Stores document chunks and embeddings in ChromaDB for fast retrieval
• Memory-Aware Processing: Dynamically adjusts batch size based on available memory
• Resilient Operation: Includes retry mechanisms and error handling
• Progress Tracking: Shows real-time progress with helpful statistics

📋 Prerequisites

• Python 3.10 or higher
• A pre-trained Sentence Transformer model
• Sufficient disk space for the vector database
• Sufficient RAM for processing (8GB recommended)

🔧 Installation

From PyPI

pip install chunk-embed

From Source

git clone https://github.com/yourusername/chunk-embed-store.git
cd chunk-embed-store
pip install -e .

💻 Usage

Basic Usage

chunk-embed \
  --base_dir "/path/to/your/documents" \
  --collection_name "your-collection" \
  --embedding_model_name "/path/to/embedding/model" \
  --persist_dir "/path/to/store/database"

With UVX

You can also run the tool with UVX for an enhanced experience:

uvx chunk-embed \
  --base_dir "/path/to/your/documents" \
  --collection_name "your-collection" \
  --embedding_model_name "/path/to/embedding/model" \
  --persist_dir "/path/to/store/database"

All Options

Parameter	Required	Default	Description
--base_dir	Yes	-	Directory containing documents to process
--collection_name	Yes	-	Name for the ChromaDB collection
--embedding_model_name	Yes	-	Path to Sentence Transformer model
--persist_dir	Yes	-	Directory to store the vector database
--chunk_size	No	500	Size of text chunks
--chunk_overlap	No	50	Overlap between text chunks
--batch_size	No	1000	Batch size for processing
--memory_threshold_mb	No	8000	Memory threshold in MB
--max_retries	No	3	Maximum retries for embedding generation

📊 Example

Process a code repository and store embeddings in a local database:

chunk-embed \
  --base_dir "/Users/username/projects/my-repo" \
  --collection_name "my-repo-knowledge" \
  --embedding_model_name "/Users/username/models/all-MiniLM-L6-v2" \
  --persist_dir "/Users/username/vector-db/my-repo-db" \
  --chunk_size 600 \
  --chunk_overlap 75

📁 Supported File Types

The tool processes a wide variety of file types including:

• Code files: .py, .js, .java, .cpp, .go, etc.
• Documentation: .md, .txt, .html, etc.
• Configuration: .yml, .json, .toml, etc.
• And many more (over 100 file extensions supported)

🔄 How It Works

1. Loading: The tool recursively explores your specified directory and loads files with supported extensions.
2. Chunking: Each document is split into smaller chunks with configurable size and overlap, trying to respect natural boundaries like line breaks.
3. Embedding: The chunks are processed through a Sentence Transformer model to generate vector embeddings.
4. Storage: The chunks and their embeddings are stored in a ChromaDB vector database for efficient retrieval.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

• Aditya Mishra ([e-mail])

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📚 Advanced Usage

Customizing Embedding Model

The embedding model significantly impacts the quality of your vector database. By default, the tool works well with Sentence Transformer models. If you want to customize:

1. Download or train your preferred model
2. Specify the path using --embedding_model_name

Memory Optimization

For large document collections, memory usage can be a concern. The tool includes automatic memory management:

• Monitors memory usage during processing
• Dynamically reduces batch size if memory threshold is exceeded
• Override the default threshold with --memory_threshold_mb

Integration with Retrieval Systems

The ChromaDB collections created by this tool can be easily integrated with retrieval augmented generation systems. You can load the collection in your application:

import chromadb

client = chromadb.PersistentClient(path="/path/to/your/database")
collection = client.get_collection(name="your-collection-name")

# Query for similar documents
results = collection.query(
    query_texts=["Your query text here"],
    n_results=5
)

📌 Development Roadmap

• Support for more embedding models
• Parallel processing for faster execution
• Incremental updates to existing collections
• Improved chunking strategies for code files
• Web UI for database exploration