v0.6.0

This release adds ability to switch between API Catalog models and on-prem models using NIM-LLM and adds documentation on how to build an RAG application from scratch. It also releases a containerized end to end RAG evaluation application integrated with RAG chain-server APIs.

Added

• Ability to switch between API Catalog models to on-prem models using NIM-LLM.
• New API endpoint
  • /health - Provides a health check for the chain server.
• Containerized evaluation application for RAG pipeline accuracy measurement.
• Observability support for langchain based examples.
• New Notebooks
  • Added Chat with NVIDIA financial data notebook.
  • Added notebook showcasing langgraph agent handling.
• A simple rag example template showcasing how to build an example from scratch.

Changed

• Renamed example csv_rag to structured_data_rag
• Model Engine name update
  • nv-ai-foundation and nv-api-catalog llm engine are renamed to nvidia-ai-endpoints
  • nv-ai-foundation embedding engine is renamed to nvidia-ai-endpoints
• Embedding model update
  • developer_rag example uses UAE-Large-V1 embedding model.
  • Using ai-embed-qa-4 for api catalog examples instead of nvolveqa_40k as embedding model
• Ingested data now persists across multiple sessions.
• Updated langchain-nvidia-endpoints to version 0.0.11, enabling support for models like llama3.
• File extension based validation to throw error for unsupported files.
• The default output token length in the UI has been increased from 250 to 1024 for more comprehensive responses.
• Stricter chain-server API validation support to enhance API security
• Updated version of llama-index, pymilvus.
• Updated pgvector container to pgvector/pgvector:pg16
• LLM Model Updates
  • Multiturn Chatbot now uses ai-mixtral-8x7b-instruct model for response generation.
  • Structured data rag now uses ai-llama3-70b for response and code generation.