Tasks

Tasks

• It shouldn't require to reconnect every time that you open the app, it should
  load ConnectDeviceWidget and in there listen/reconnect to the device.
• Device disconnected, display dialog to user asking to reconnect, or take the user back
  to find_devices page.
• Settings bottom sheet, improve way of handling *** blurring of api keys, as if you save it
  while is blurred with *, it sets the key to that value, and you have to set them again
• [iOS] memories and chat page on the bottom do not have the blurred colors pattern, but plain
  primary color
• Improve structured memory results performance by sending n previous memories as part of the
  structuring but as context, not as part of the structure, so that if there's some reference to a
  person, and then you use a pronoun, the LLM understands what you are referring to.
• Migrate MemoryRecord from SharedPreferences to sqlite
• Implement similarity search locally
  • Use from the AppStandalone _ragContext function as a baseline for creating the query
    embedding.
  • When a memory is created, compute the vector embedding and store it locally.
  • When the user sends a question in the chat, extract from the AppStandalone
    the function_calling that determines if the message requires context, if that's the case,
    retrieve the top 10 most similar vectors ~~ For an initial version we can read all memories
    from sqlite or SharedPreferences, and compute the formula between the query and each vector.
  • Use that as context, and ask to the LLM. Retrieve the prompt from the AppStandalone.
  • Improve function call way of parsing the text sent to the RAG, GPT should format the input better for RAG to retrieve better context.
• Settings Deepgram + openAI key are forced to be set
• In case an API key fails, either Deepgram WebSocket connection fails, or GPT requests, let the user know the error message, either has no more credits, api key is invalid, etc.
• Improve connected device page UI, including transcription text, and when memory creates after
  30 seconds, let the user know
• Structure the memory asking JSON output {"title", "summary"}, in that way we can have better parsed data.
• Test/Implement speaker diarization to
  recognize multiple speakers in transcription, use that for better context when creating the
  structured memory.
• Better AppWithWerable folders structure.
• Define flutter code style rules.
• Include documentation on how to run AppWithWearable.
• If only 1 speaker, set memory prompt creation, explain those are your thoughts, not a conversation, also, remove Speaker $i in transcript.
• Allow users who don't have a GCP bucket to store their recordings locally.
• Improve recordings audio player.

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• Multilanguage option, implement settings selector, and use that for the deepgram websocket
  creation

• Option for storing your transcripts somewhere in the cloud, user inputs their own GCP storage
  bucket + auth key, and the files are uploaded there + a reference is stored in the MemoryRecord
  object.

  • createWavFile remove empty sounds without words, and saves that fixed file.

• ~~ (Idea) Detect a keyword or special order e.g. "Hey Friend" (but not so generic) and
  triggers a prompt execution + response. This would require a few hardware updates (could also be a
  button on the device), and it's way bigger than it seems.

• ~~ (Idea) Store the location at which the memory was created, and have saved places, like " at
  Home you were chatting about x and y"

• ~~ (Idea) Speaker detection, use something like the python
  library librosa, so that friend recognizes when is you the
  one speaking and creates memories better considering that. Maybe even later learns to recognize
  other people.