v5.4.0

This release adds prompt templates, conversational task chaining and Hugging Face Hub integration

📃 Prompt templates dynamically generate text using workflow task inputs. This enables chaining multiple prompts and models together.

🤗 Embeddings now integrate with the Hugging Face Hub! Easily share and load embeddings indexes. There is a full embeddings index available for English Wikipedia.

See below for full details on the new features, improvements and bug fixes.

New Features

• Add translation pipeline parameter to return selected models and detected language - Thank you @saucam! (#383, #424)
• Add sample parameter to Faiss ANN (#427)
• Add support for instruction-based embeddings (#428)
• Add Hugging Face Hub integration (#430)
• Add cloud object storage support for uncompressed embeddings indexes (#431)
• Add support for custom cloud providers (#432)
• Add support for storing embeddings config as JSON (#433)
• Add notebook for syncing embeddings with the cloud (#434)
• Add terms method to embeddings (#445)
• Add extractor reference output format (#446)
• Add template task (#448)
• Add prompt template and task chaining example notebook (#451)

Improvements

• Mention the default storage engine - Thank you @hsm207! (#422)
• Refactor archive module into separate package (#429)
• Resolve application references in pipelines (#441)
• Extractor pipeline improvements (#443)
• Allow task action arguments to be dictionaries in addition to tuples (#447)
• Automatically mark embeddings index files for lfs tracking with Hugging Face Hub (#450)

Bug Fixes

• Pin onnxruntime for macOS in build script (#425)