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🌟 Features Overview

Comprehensive capabilities for embeddings, vector databases, and cloud storage

EmbeddingFramework offers a comprehensive set of features for working with embeddings, vector databases, and cloud storage.

🔹 Multi-Vector Database Support

Easily switch between different vector database backends without changing your application logic.

Supported databases: - 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

Store and retrieve embeddings or documents from major cloud providers: - AWS S3 - Google Cloud Storage (GCS) - Azure Blob Storage

🔹 Embedding Providers

Generate embeddings from multiple 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.

📚 Next Steps

Vector Databases • Cloud Storage • Embedding Providers • File Processing • Utilities