embeddingframework 1.0.2

A high-performance, asynchronous, and extensible Python package for processing files, generating embeddings, and storing them in various vector databases with optional cloud storage integration.

🚀 EmbeddingFramework

Modular • Extensible • Production-Ready
A Python framework for embeddings, vector databases, and cloud storage providers.

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A modular, extensible, and production-ready Python framework for working with embeddings, vector databases, and cloud storage providers.
Designed for AI, NLP, and semantic search applications, EmbeddingFramework provides a unified API to process, store, and query embeddings across multiple backends.

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✨ Features

🔹 Multi-Vector Database Support

• ChromaDB – Local and persistent vector storage.
• Milvus – High-performance distributed vector database.
• Pinecone – Fully managed vector database service.
• Weaviate – Open-source vector search engine.

🔹 Cloud Storage Integrations

• AWS S3 – Store and retrieve embeddings or documents.
• Google Cloud Storage (GCS) – Scalable object storage.
• Azure Blob Storage – Enterprise-grade cloud storage.

🔹 Embedding Providers

• OpenAI Embeddings – State-of-the-art embedding generation.
• Easily extendable to other providers.

🔹 File Processing & Preprocessing

• Automatic file type detection.
• Text extraction from multiple formats.
• Preprocessing utilities for cleaning and normalizing text.
• Intelligent text splitting for optimal embedding performance.

🔹 Utilities

• Retry logic for robust API calls.
• File utilities for safe and efficient I/O.
• Modular architecture for easy extension.

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📦 Installation & Setup

# Basic installation
pip install embeddingframework

# With development dependencies
pip install embeddingframework[dev]

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⚡ Quick Start Example

from embeddingframework.adapters.openai_embedding_adapter import OpenAIEmbeddingAdapter
from embeddingframework.adapters.vector_dbs import ChromaDBAdapter

# Initialize embedding provider
embedding_provider = OpenAIEmbeddingAdapter(api_key="YOUR_OPENAI_API_KEY")

# Initialize vector database
vector_db = ChromaDBAdapter(persist_directory="./chroma_store")

# Generate embeddings
embeddings = embedding_provider.embed_texts(["Hello world", "EmbeddingFramework is awesome!"])

# Store embeddings
vector_db.add_texts(["Hello world", "EmbeddingFramework is awesome!"], embeddings)

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📂 Project Structure

embeddingframework/
│
├── adapters/                # Vector DB & storage adapters
│   ├── base.py
│   ├── chromadb_adapter.py
│   ├── milvus_adapter.py
│   ├── pinecone_adapter.py
│   ├── weaviate_adapter.py
│   ├── storage/             # Cloud storage adapters
│
├── processors/              # File processing logic
├── utils/                    # Helper utilities
└── tests/                    # Test suite

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🧪 Testing

pytest --maxfail=1 --disable-warnings -q

With coverage:

pytest --cov=embeddingframework --cov-report=term-missing

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🔄 CI/CD

This project includes a GitHub Actions workflow (.github/workflows/python-package.yml) for:

• Automated testing with coverage.
• Version bumping & changelog generation.
• PyPI publishing.
• GitHub release creation.

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📜 License

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

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🤝 Contributing

Contributions, issues, and feature requests are welcome!
Feel free to check the issues page.

1. Fork the repository.
2. Create a new branch (feature/my-feature).
3. Commit your changes.
4. Push to your branch.
5. Open a Pull Request.

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🌟 Why EmbeddingFramework?

• Unified API – Work with multiple vector DBs and storage providers seamlessly.
• Extensible – Add new adapters with minimal effort.
• Production-Ready – Built with scalability and reliability in mind.
• Developer-Friendly – Clean, modular, and well-documented codebase.