v0.4.26

This add support for Qdrant vector store allowing the app to run fully local.

Thanks @Anush008

v0.4.25

Rebuild of the groq_models.py module, now including tool calls

• Added support for "openai/gpt-oss-120b" and "moonshotai/kimi-k2-instruct" from the Groq API
• Added support for "gpt-5"and "gpt-5-mini" (The OpenAI verification requirement/process is annoying!)

v0.4.24

Date pickers for SweatStack window selection (#47)

• Changes default port in docker compose to 5001 because MacOS uses 5000 already

• Date pickers for SweatStack date selection and better button loading message

• Adds back lap metric for SweatStack data selection

@AartGoossens

v0.4.22

Add prompt enhancements for better dataset comprehension

• Added dataset summary/describe in addition to df.head. This is required for composite datasets eg. SweatStack multi athlete.
• Ontology rebuild to match SweatStack/Garmin datasets
• Better management of analyst_selector context window
• Some styling improvements

@AartGoossens

v0.4.21

Added export to PDF feature

• When the analysis is complete, user now has an ability to export the summary and the visualisations as PDF
• Updated the LLM_CONFIG.json with the new Gemini pro and Flash models

v0.4.20

Added feature to generate and explore ideas

• A new feature that allows generation and exploration of ideas. When the analysis is complete the user can request the variation of the original task to explore and execute the varied versions. User has an ability to set the degree of variation on scale 1-5.
• Updated workflow map to indicate forking, as related to the previous point
• Theme toggle moved under settings
• A few bug fixes

v0.4.19

A major refactor of Messages management, and some bug fixes
Thanks @rnYulianto

0.4.18

Integration with SweatStack

SweatStack is a Backend-as-a-Service (BaaS) for sport data applications. More information about SweatStack can be found at sweatstack.no.

Thanks @AartGoossens

v0.4.17

• Bug fixes
• Custom code exec fix
• Better way to handle text highlighting
• Cleaned up OutputManager class and system messages from QA module

v0.4.15

Adds manual Episodic memory search

• This allows user to manually search for previous interactions stored in episodic memory
• The feature requires vector_db to be enabled, and index initialised
• Returns top_k=10 results, filtered for score > 0.2