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v0.9.0 - RAG Improvements | Model Directory | Odds and Ends

Due by July 19, 2024 0% complete

Users should be able to:

  • [RAG] View annotations in UI to see sources when using RAG for "chat with your docs"
  • [RAG] Upload new document types, including Word, PowerPoint, and Excel (images ignored)
  • [MD] Select from LLMs available in Model Directory via UI to use with an Assistant

MUST HAVE (Non-negotiable product needs that are mandatory for the team)

  • […

Users should be able to:

  • [RAG] View annotations in UI to see sources when using RAG for "chat with your docs"
  • [RAG] Upload new document types, including Word, PowerPoint, and Excel (images ignored)
  • [MD] Select from LLMs available in Model Directory via UI to use with an Assistant

MUST HAVE (Non-negotiable product needs that are mandatory for the team)

  • [RAG] UI lets users upload Word, PowerPoint, and Excel files (images are ignored)
    • The RAG system chunks data appropriately according to file type
  • [RAG] Messages include file_citation and/or file_path annotations
  • [RAG] UI shows users which document(s) were used in a RAG response
    • Optional (if easy): Users can select an annotation to view the passage that was used
    • Optional (if easy): Users can select an annotation to view/download the original file
  • [MD] Models are bundled separately from packages (no longer "baked in") for UDS deployment

SHOULD HAVE (Important initiatives that are not vital, but add significant value)

  • [RAG] File uploads are queued for initial processing/ingestion
  • [MD] Deploy multiple models as separate containers
  • [MD] UI lets users select from one or more LLMs in the Model Directory to use with an Assistant
  • [Other] Users can request long-lived API keys available via API
  • [Other] transcriptions and translations endpoints are implemented according to OpenAI API spec

COULD HAVE (Nice to have initiatives that will have a small impact if left out)

  • [RAG] Initial Set of Model Evals
    • Establish a list of models to evaluate, both for LLM and Embeddings
    • Create a testing dataset for RAG and question/answer/ground-truth data
    • Formalize a set of metrics for evaluation
    • Evaluate a subset of models and present for mission hero interpretation
  • [RAG] Integrate RAG eval tools into our repository & connect with LeapfrogAI
  • [RAG] Implement OCR/image-analysis to extract data from embedded images in file types listed above (e.g., PowerPoint)
  • [Other] UI lets users generate long-lived API keys (dependent on above work)

WILL NOT HAVE (Initiatives that are not a priority for this specific time frame)

  • UI implements workflow for transcription/translation/summarization