v0.5.0

This release adds new dedicated RAG examples showcasing state of the art usecases, switches to the latest API catalog endpoints from NVIDIA and also refactors the API interface of chain-server. This release also improves the developer experience by adding github pages based documentation and streamlining the example deployment flow using dedicated compose files.

Added

• Github pages based documentation.
• New examples showcasing
  • Multi-turn RAG
  • Multi-modal RAG
  • Structured data CSV RAG
• Support for delete and list APIs in chain-server component
• Streamlined RAG example deployment
  • Dedicated new docker compose files for every examples.
  • Dedicated docker compose files for launching vector DB solutions.
• New configurations to control top k and confidence score of retrieval pipeline.
• Added a notebook which covers how to train SLMs with various techniques using NeMo Framework.
• Added more experimental examples showcasing new usecases.
  • NVIDIA ORAN chatbot multimodal Assistant
  • NVIDIA Retrieval Customization
  • NVIDIA RAG Streaming Document Ingestion Pipeline
  • NVIDIA Live FM Radio ASR RAG
• New dedicated notebook showcasing a RAG pipeline using web pages.

Changed

• Switched from NVIDIA AI Foundation to NVIDIA API Catalog endpoints for accessing cloud hosted LLM models.
• Refactored API schema of chain-server component to support runtime allocation of llm parameters like temperature, max tokens, chat history etc.
• Renamed llm-playground service in compose files to rag-playground.
• Switched base containers for all components to ubuntu instead of pytorch and optimized container build time as well as container size.
• Deprecated yaml based configuration to avoid confusion, all configurations are now environment variable based.
• Removed requirement of hardcoding NVIDIA_API_KEY in compose.env file.
• Upgraded all python dependencies for chain-server and rag-playground services.

Fixed

• Fixed a bug causing hallucinated answer when retriever fails to return any documents.
• Fixed some accuracy issues for all the examples.