TODO

v0 - Generate periodic summary from journal entry

Components

1. Chunking 1.1 No chunking 1.2 Linewise 1.3 Rule-based division - smalltalk, specific to an issue e.g. major new event, reference to past event, follow up on a plan

2. Summarization - 2.1 No prompt - as a default - summarize this/frontend button 2.2 System generated prompt - role based summary - 2.3 e.g. with focus on one issue from above, with o/p format as given in i/p - see HowWeFeel app format - emotions wheel, reason, event details 2.3.1 Prompt engg 2.3.2 Instruction base fine tuning

3. Database - 3.1 Metadata - postgres(mongo in V1) 3.2 Vector db - for embeddings - pgvector (qdrant/FAIS/elastic in v1) 3.3 Jobs - Redis/Kafka - async msg queue - to ingest new text embeddings and push out to the db with proper pacing 3.4 Results - Precomputed Store for previous results

4. Text Context for RAG querying - txt/pdf upload as context/prescription plan

5. UI - v1, No UI - v0 5.1 Postman Curl Collection 5.2 Data upload - POST API

v1 - Interactive AI-assisted journalling with user

1. Flow prompting - prompt user for diary entry 1.1 Fixed structure of diary entry 1.2 Current context-based - full chat vs current conv 1.3 Past context-base 1.4 Action taking outside world - book aptmt for the diet plan/gym plan follow up

2. Self-selective routing on which one to pick from above 3 types 2.1 rule-based - listen vs speak - if user wants to discontinue the journaling, prompt them to stick 2.2 some other - 2.3 Temperature/entropy

3. Output - 3.1 concatenated as a chat form 3.2 lossless summarization/ paraphrasing in 1st person 3.3 thematic segmentation - segmenting the whole conv as per separate themes

API Endpoints

1. Authentication endpoints 1.1 Register User 1.2 Login User 1.3 Logout User 1.4 View/Edit Profile 1.5 extra - end-to-end encryption

2. Data - CRUD 2.1 Upload previous - txt/image for ocr 2.2 Get by ID of orig conversation/entry 2.3 Search - by metadata/date 2.4 Delete orig convo and all its generated chunks and summarizations recursively

3. Generation - text-to-vector and back 3.1 extra - Create chunks 3.2 Generate Embeddings 3.3 Query Embeddings

4. Processes - LLM calls - text-to-text 4.1 Segmentation 4.2 Summarization 4.3 extra - rule-based routing based on params like temperature, window size