📚 EmbeddingFramework Documentation

Complete Guide to Installation, Configuration & Usage
For embedding generation, storage, and retrieval.

🌟 Highlights at a Glance¶

📚 What You Will Learn¶

This documentation will guide you through: - Installing and configuring EmbeddingFramework - Understanding its architecture - Using built-in adapters for vector DBs and cloud storage - Processing files for embedding - Writing your own adapters and utilities - Running tests and contributing

Getting Started
Installation
Quick Start
Features
Vector Databases
Cloud Storage
Embedding Providers
File Processing
Utilities
Modules
Adapters
Processors
Utils
Examples
Basic Usage
Advanced Usage
Contributing
License

📌 Pro Tips¶

💡 Tip: Use the search bar in the top right to quickly find topics.
🛠 Tip: Check the Examples section for ready-to-use code snippets.
📦 Tip: Use pip install embeddingframework[dev] for development dependencies.

Welcome to the EmbeddingFramework documentation.
This site provides a complete guide to installing, configuring, and using the framework for embedding generation, storage, and retrieval.

📖 Overview¶

EmbeddingFramework is a modular, extensible, and production-ready Python framework for: - Generating embeddings from multiple providers. - Storing and querying embeddings in various vector databases. - Integrating with cloud storage providers. - Processing and preparing data for embedding.

✨ Key Features¶

Multi-Vector Database Support: ChromaDB, Milvus, Pinecone, Weaviate.
Cloud Storage Integrations: AWS S3, Google Cloud Storage, Azure Blob Storage.
Embedding Providers: OpenAI and easily extendable to others.
File Processing: Automatic type detection, text extraction, preprocessing, and splitting.
Utilities: Retry logic, file utilities, modular architecture.

🗂 Documentation Structure¶

Getting Started – Installation and quick start guide.
Features – Detailed explanation of all features.
Modules – API reference for adapters, processors, and utilities.
Examples – Practical usage examples.
Contributing – How to contribute to the project.

🚀 Quick Links¶

Installation • Quick Start • Features • Examples

🛠 Source Code¶

The source code is available on GitHub.